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In open-ended continuous environments, robots need to learn multiple
parameterised control tasks in hierarchical reinforcement learning. We
hypothesise that the most complex tasks can be learned more easily by
transferring knowledge from simpler tasks, and faster by adapting the
complexity of the actions to the task. We propose a task-oriented
representation of complex actions, called procedures, to learn online task
relationships and unbounded sequences of action primitives to control the
different observables of the environment. Combining both goal-babbling with
imitation learning, and active learning with transfer of knowledge based on
intrinsic motivation, our algorithm self-organises its learning process. It
chooses at any given time a task to focus on; and what, how, when and from whom
to transfer knowledge. We show with a simulation and a real industrial robot
arm, in cross-task and cross-learner transfer settings, that task composition
is key to tackle highly complex tasks. Task decomposition is also efficiently
transferred across different embodied learners and by active imitation, where
the robot requests just a small amount of demonstrations and the adequate type
of information. The robot learns and exploits task dependencies so as to learn
tasks of every complexity. | [
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] |
In this paper I explore the scaffolding of normative assumptions that
supports Sabina Leonelli's implicit appeal to the values of epistemic integrity
and the global public good that conjointly animate the ethos of responsible and
sustainable data work in the context of COVID-19. Drawing primarily on the
writings of sociologist Robert K. Merton, the thinkers of the Vienna Circle,
and Charles Sanders Peirce, I make some of these assumptions explicit by
telling a longer story about the evolution of social thinking about the
normative structure of science from Merton's articulation of his well-known
norms (those of universalism, communism, organized skepticism, and
disinterestedness) to the present. I show that while Merton's norms and his
intertwinement of these with the underlying mechanisms of democratic order
provide us with an especially good starting point to explore and clarify the
commitments and values of science, Leonelli's broader, more context-responsive,
and more holistic vision of the epistemic integrity of data scientific
understanding, and her discernment of the global and biospheric scope of its
moral-practical reach, move beyond Merton's schema in ways that effectively
draw upon important critiques. Stepping past Merton, I argue that a combination
of situated universalism, methodological pluralism, strong objectivity, and
unbounded communalism must guide the responsible and sustainable data work of
the future. | [
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] |
Hazard detection is critical for enabling autonomous landing on planetary
surfaces. Current state-of-the-art methods leverage traditional computer vision
approaches to automate the identification of safe terrain from input digital
elevation models (DEMs). However, performance for these methods can degrade for
input DEMs with increased sensor noise. In the last decade, deep learning
techniques have been developed for various applications. Nevertheless, their
applicability to safety-critical space missions has often been limited due to
concerns regarding their outputs' reliability. In response to these
limitations, this paper proposes an application of the Bayesian deep-learning
segmentation method for hazard detection. The developed approach enables
reliable, safe landing site detection by: (i) generating simultaneously a
safety prediction map and its uncertainty map via Bayesian deep learning and
semantic segmentation; and (ii) using the uncertainty map to filter out the
uncertain pixels in the prediction map so that the safe site identification is
performed only based on the certain pixels (i.e., pixels for which the model is
certain about its safety prediction). Experiments are presented with simulated
data based on a Mars HiRISE digital terrain model by varying uncertainty
threshold and noise levels to demonstrate the performance of the proposed
approach. | [
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] |
The distance protection relays are used to determine the impedance based
fault location according to the current and voltage magnitudes in the
transmission lines. However, the fault location cannot be correctly detected in
mixed transmission lines due to different characteristic impedance per unit
length because the characteristic impedance of high voltage cable line is
significantly different from overhead line. Thus, determinations of the fault
section and location with the distance protection relays are difficult in the
mixed transmission lines. In this study, 154 kV overhead transmission line and
underground cable line are examined as the mixed transmission line for the
distance protection relays. Phase to ground faults are created in the mixed
transmission line. overhead line section and underground cable section are
simulated by using PSCAD-EMTDC.The short circuit fault images are generated in
the distance protection relay for the overhead transmission line and
underground cable transmission line faults. The images include the R-X
impedance diagram of the fault, and the R-X impedance diagram have been
detected by applying image processing steps. Artificial neural network (ANN)
and the regression methods are used for prediction of the fault location, and
the results of image processing are used as the input parameters for the
training process of ANN and the regression methods. The results of ANN and
regression methods are compared to select the most suitable method at the end
of this study for forecasting of the fault location in transmission lines. | [
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] |
Despite remarkable success in a variety of applications, it is well-known
that deep learning can fail catastrophically when presented with
out-of-distribution data. Toward addressing this challenge, we consider the
domain generalization problem, wherein predictors are trained using data drawn
from a family of related training domains and then evaluated on a distinct and
unseen test domain. We show that under a natural model of data generation and a
concomitant invariance condition, the domain generalization problem is
equivalent to an infinite-dimensional constrained statistical learning problem;
this problem forms the basis of our approach, which we call Model-Based Domain
Generalization. Due to the inherent challenges in solving constrained
optimization problems in deep learning, we exploit nonconvex duality theory to
develop unconstrained relaxations of this statistical problem with tight bounds
on the duality gap. Based on this theoretical motivation, we propose a novel
domain generalization algorithm with convergence guarantees. In our
experiments, we report improvements of up to 30 percentage points over
state-of-the-art domain generalization baselines on several benchmarks
including ColoredMNIST, Camelyon17-WILDS, FMoW-WILDS, and PACS. | [
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] |
An important problem in causal inference is to break down the total effect of
a treatment on an outcome into different causal pathways and to quantify the
causal effect in each pathway. For instance, in causal fairness, the total
effect of being a male employee (i.e., treatment) constitutes its direct effect
on annual income (i.e., outcome) and the indirect effect via the employee's
occupation (i.e., mediator). Causal mediation analysis (CMA) is a formal
statistical framework commonly used to reveal such underlying causal
mechanisms. One major challenge of CMA in observational studies is handling
confounders, variables that cause spurious causal relationships among
treatment, mediator, and outcome. Conventional methods assume sequential
ignorability that implies all confounders can be measured, which is often
unverifiable in practice. This work aims to circumvent the stringent sequential
ignorability assumptions and consider hidden confounders. Drawing upon proxy
strategies and recent advances in deep learning, we propose to simultaneously
uncover the latent variables that characterize hidden confounders and estimate
the causal effects. Empirical evaluations using both synthetic and
semi-synthetic datasets validate the effectiveness of the proposed method. We
further show the potentials of our approach for causal fairness analysis. | [
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] |
Deep learning applications are drastically progressing in seismic processing
and interpretation tasks. However, the majority of approaches subsample data
volumes and restrict model sizes to minimise computational requirements.
Subsampling the data risks losing vital spatio-temporal information which could
aid training whilst restricting model sizes can impact model performance, or in
some extreme cases, renders more complicated tasks such as segmentation
impossible. This paper illustrates how to tackle the two main issues of
training of large neural networks: memory limitations and impracticably large
training times. Typically, training data is preloaded into memory prior to
training, a particular challenge for seismic applications where data is
typically four times larger than that used for standard image processing tasks
(float32 vs. uint8). Using a microseismic use case, we illustrate how over
750GB of data can be used to train a model by using a data generator approach
which only stores in memory the data required for that training batch.
Furthermore, efficient training over large models is illustrated through the
training of a 7-layer UNet with input data dimensions of 4096X4096. Through a
batch-splitting distributed training approach, training times are reduced by a
factor of four. The combination of data generators and distributed training
removes any necessity of data 1 subsampling or restriction of neural network
sizes, offering the opportunity of utilisation of larger networks,
higher-resolution input data or moving from 2D to 3D problem spaces. | [
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] |
Energy harvesting offers an attractive and promising mechanism to power
low-energy devices. However, it alone is insufficient to enable an
energy-neutral operation, which can eliminate tedious battery charging and
replacement requirements. Achieving an energy-neutral operation is challenging
since the uncertainties in harvested energy undermine the quality of service
requirements. To address this challenge, we present a runtime energy-allocation
framework that optimizes the utility of the target device under energy
constraints using a rollout algorithm, which is a sequential approach to solve
dynamic optimization problems. The proposed framework uses an efficient
iterative algorithm to compute initial energy allocations at the beginning of a
day. The initial allocations are then corrected at every interval to compensate
for the deviations from the expected energy harvesting pattern. We evaluate
this framework using solar and motion energy harvesting modalities and American
Time Use Survey data from 4772 different users. Compared to prior techniques,
the proposed framework achieves up to 35% higher utility even under
energy-limited scenarios. Moreover, measurements on a wearable device prototype
show that the proposed framework has 1000x smaller energy overhead than
iterative approaches with a negligible loss in utility. | [
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Automatic program repair (APR) is crucial to improve software reliability.
Recently, neural machine translation (NMT) techniques have been used to fix
software bugs automatically. While promising, these approaches have two major
limitations. Their search space often does not contain the correct fix, and
their search strategy ignores software knowledge such as strict code syntax.
Due to these limitations, existing NMT-based techniques underperform the best
template-based approaches.
We propose CURE, a new NMT-based APR technique with three major novelties.
First, CURE pre-trains a programming language (PL) model on a large software
codebase to learn developer-like source code before the APR task. Second, CURE
designs a new code-aware search strategy that finds more correct fixes by
focusing on compilable patches and patches that are close in length to the
buggy code. Finally, CURE uses a subword tokenization technique to generate a
smaller search space that contains more correct fixes.
Our evaluation on two widely-used benchmarks shows that CURE correctly fixes
57 Defects4J bugs and 26 QuixBugs bugs, outperforming all existing APR
techniques on both benchmarks. | [
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] |
This paper presents an Expert Decision Support System for the identification
of time-invariant, aeroacoustic source types. The system comprises two steps:
first, acoustic properties are calculated based on spectral and spatial
information. Second, clustering is performed based on these properties. The
clustering aims at helping and guiding an expert for quick identification of
different source types, providing an understanding of how sources differ. This
supports the expert in determining similar or atypical behavior. A variety of
features are proposed for capturing the characteristics of the sources. These
features represent aeroacoustic properties that can be interpreted by both the
machine and by experts. The features are independent of the absolute Mach
number which enables the proposed method to cluster data measured at different
flow configurations. The method is evaluated on deconvolved beamforming data
from two scaled airframe half-model measurements. For this exemplary data, the
proposed support system method results in clusters that mostly correspond to
the source types identified by the authors. The clustering also provides the
mean feature values and the cluster hierarchy for each cluster and for each
cluster member a clustering confidence. This additional information makes the
results transparent and allows the expert to understand the clustering choices. | [
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] |
Recommender models are commonly used to suggest relevant items to a user for
e-commerce and online advertisement-based applications. These models use
massive embedding tables to store numerical representation of items' and users'
categorical variables (memory intensive) and employ neural networks (compute
intensive) to generate final recommendations. Training these large-scale
recommendation models is evolving to require increasing data and compute
resources. The highly parallel neural networks portion of these models can
benefit from GPU acceleration however, large embedding tables often cannot fit
in the limited-capacity GPU device memory. Hence, this paper deep dives into
the semantics of training data and obtains insights about the feature access,
transfer, and usage patterns of these models. We observe that, due to the
popularity of certain inputs, the accesses to the embeddings are highly skewed
with a few embedding entries being accessed up to 10000x more. This paper
leverages this asymmetrical access pattern to offer a framework, called FAE,
and proposes a hot-embedding aware data layout for training recommender models.
This layout utilizes the scarce GPU memory for storing the highly accessed
embeddings, thus reduces the data transfers from CPU to GPU. At the same time,
FAE engages the GPU to accelerate the executions of these hot embedding
entries. Experiments on production-scale recommendation models with real
datasets show that FAE reduces the overall training time by 2.3x and 1.52x in
comparison to XDL CPU-only and XDL CPU-GPU execution while maintaining baseline
accuracy | [
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] |
Autonomous ultrasound (US) acquisition is an important yet challenging task,
as it involves interpretation of the highly complex and variable images and
their spatial relationships. In this work, we propose a deep reinforcement
learning framework to autonomously control the 6-D pose of a virtual US probe
based on real-time image feedback to navigate towards the standard scan planes
under the restrictions in real-world US scans. Furthermore, we propose a
confidence-based approach to encode the optimization of image quality in the
learning process. We validate our method in a simulation environment built with
real-world data collected in the US imaging of the spine. Experimental results
demonstrate that our method can perform reproducible US probe navigation
towards the standard scan plane with an accuracy of $4.91mm/4.65^\circ$ in the
intra-patient setting, and accomplish the task in the intra- and inter-patient
settings with a success rate of $92\%$ and $46\%$, respectively. The results
also show that the introduction of image quality optimization in our method can
effectively improve the navigation performance. | [
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] |
Visual environments are structured, consisting of distinct objects or
entities. These entities have properties -- both visible and latent -- that
determine the manner in which they interact with one another. To partition
images into entities, deep-learning researchers have proposed structural
inductive biases such as slot-based architectures. To model interactions among
entities, equivariant graph neural nets (GNNs) are used, but these are not
particularly well suited to the task for two reasons. First, GNNs do not
predispose interactions to be sparse, as relationships among independent
entities are likely to be. Second, GNNs do not factorize knowledge about
interactions in an entity-conditional manner. As an alternative, we take
inspiration from cognitive science and resurrect a classic approach, production
systems, which consist of a set of rule templates that are applied by binding
placeholder variables in the rules to specific entities. Rules are scored on
their match to entities, and the best fitting rules are applied to update
entity properties. In a series of experiments, we demonstrate that this
architecture achieves a flexible, dynamic flow of control and serves to
factorize entity-specific and rule-based information. This disentangling of
knowledge achieves robust future-state prediction in rich visual environments,
outperforming state-of-the-art methods using GNNs, and allows for the
extrapolation from simple (few object) environments to more complex
environments. | [
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] |
Data privacy concerns often prevent the use of cloud-based machine learning
services for sensitive personal data. While homomorphic encryption (HE) offers
a potential solution by enabling computations on encrypted data, the challenge
is to obtain accurate machine learning models that work within the
multiplicative depth constraints of a leveled HE scheme. Existing approaches
for encrypted inference either make ad-hoc simplifications to a pre-trained
model (e.g., replace hard comparisons in a decision tree with soft comparators)
at the cost of accuracy or directly train a new depth-constrained model using
the original training set. In this work, we propose a framework to transfer
knowledge extracted by complex decision tree ensembles to shallow neural
networks (referred to as DTNets) that are highly conducive to encrypted
inference. Our approach minimizes the accuracy loss by searching for the best
DTNet architecture that operates within the given depth constraints and
training this DTNet using only synthetic data sampled from the training data
distribution. Extensive experiments on real-world datasets demonstrate that
these characteristics are critical in ensuring that DTNet accuracy approaches
that of the original tree ensemble. Our system is highly scalable and can
perform efficient inference on batched encrypted (134 bits of security) data
with amortized time in milliseconds. This is approximately three orders of
magnitude faster than the standard approach of applying soft comparison at the
internal nodes of the ensemble trees. | [
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] |
One of the lessons from the COVID-19 pandemic is the importance of social
distancing, even in challenging circumstances such as pre-hurricane evacuation.
To explore the implications of integrating social distancing with evacuation
operations, we describe this evacuation process as a Capacitated Vehicle
Routing Problem (CVRP) and solve it using a DNN (Deep Neural Network)-based
solution (Deep Reinforcement Learning) and a non-DNN solution (Sweep
Algorithm). A central question is whether Deep Reinforcement Learning provides
sufficient extra routing efficiency to accommodate increased social distancing
in a time-constrained evacuation operation. We found that, in comparison to the
Sweep Algorithm, Deep Reinforcement Learning can provide decision-makers with
more efficient routing. However, the evacuation time saved by Deep
Reinforcement Learning does not come close to compensating for the extra time
required for social distancing, and its advantage disappears as the emergency
vehicle capacity approaches the number of people per household. | [
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] |
Deep deterministic policy gradient (DDPG)-based car-following strategy can
break through the constraints of the differential equation model due to the
ability of exploration on complex environments. However, the car-following
performance of DDPG is usually degraded by unreasonable reward function design,
insufficient training, and low sampling efficiency. In order to solve this kind
of problem, a hybrid car-following strategy based on DDPG and cooperative
adaptive cruise control (CACC) is proposed. First, the car-following process is
modeled as the Markov decision process to calculate CACC and DDPG
simultaneously at each frame. Given a current state, two actions are obtained
from CACC and DDPG, respectively. Then, an optimal action, corresponding to the
one offering a larger reward, is chosen as the output of the hybrid strategy.
Meanwhile, a rule is designed to ensure that the change rate of acceleration is
smaller than the desired value. Therefore, the proposed strategy not only
guarantees the basic performance of car-following through CACC but also makes
full use of the advantages of exploration on complex environments via DDPG.
Finally, simulation results show that the car-following performance of the
proposed strategy is improved compared with that of DDPG and CACC. | [
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] |
Artificial Intelligence (AI) and Machine Learning (ML) are pervasive in the
current computer science landscape. Yet, there still exists a lack of software
engineering experience and best practices in this field. One such best
practice, static code analysis, can be used to find code smells, i.e.,
(potential) defects in the source code, refactoring opportunities, and
violations of common coding standards. Our research set out to discover the
most prevalent code smells in ML projects. We gathered a dataset of 74
open-source ML projects, installed their dependencies and ran Pylint on them.
This resulted in a top 20 of all detected code smells, per category. Manual
analysis of these smells mainly showed that code duplication is widespread and
that the PEP8 convention for identifier naming style may not always be
applicable to ML code due to its resemblance with mathematical notation. More
interestingly, however, we found several major obstructions to the
maintainability and reproducibility of ML projects, primarily related to the
dependency management of Python projects. We also found that Pylint cannot
reliably check for correct usage of imported dependencies, including prominent
ML libraries such as PyTorch. | [
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] |
Unobserved confounding is one of the main challenges when estimating causal
effects. We propose a causal reduction method that, given a causal model,
replaces an arbitrary number of possibly high-dimensional latent confounders
with a single latent confounder that takes values in the same space as the
treatment variable, without changing the observational and interventional
distributions the causal model entails. This allows us to estimate the causal
effect in a principled way from combined data without relying on the common but
often unrealistic assumption that all confounders have been observed. We apply
our causal reduction in three different settings. In the first setting, we
assume the treatment and outcome to be discrete. The causal reduction then
implies bounds between the observational and interventional distributions that
can be exploited for estimation purposes. In certain cases with highly
unbalanced observational samples, the accuracy of the causal effect estimate
can be improved by incorporating observational data. Second, for continuous
variables and assuming a linear-Gaussian model, we derive equality constraints
for the parameters of the observational and interventional distributions.
Third, for the general continuous setting (possibly nonlinear and
non-Gaussian), we parameterize the reduced causal model using normalizing
flows, a flexible class of easily invertible nonlinear transformations. We
perform a series of experiments on synthetic data and find that in several
cases the number of interventional samples can be reduced when adding
observational training samples without sacrificing accuracy. | [
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] |
Collaborative Filtering (CF) based recommendation methods have been widely
studied, which can be generally categorized into two types, i.e.,
representation learning-based CF methods and matching function learning-based
CF methods. Representation learning tries to learn a common low dimensional
space for the representations of users and items. In this case, a user and item
match better if they have higher similarity in that common space. Matching
function learning tries to directly learn the complex matching function that
maps user-item pairs to matching scores. Although both methods are well
developed, they suffer from two fundamental flaws, i.e., the representation
learning resorts to applying a dot product which has limited expressiveness on
the latent features of users and items, while the matching function learning
has weakness in capturing low-rank relations. To overcome such flaws, we
propose a novel recommendation model named Balanced Collaborative Filtering
Network (BCFNet), which has the strengths of the two types of methods. In
addition, an attention mechanism is designed to better capture the hidden
information within implicit feedback and strengthen the learning ability of the
neural network. Furthermore, a balance module is designed to alleviate the
over-fitting issue in DNNs. Extensive experiments on eight real-world datasets
demonstrate the effectiveness of the proposed model. | [
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] |
Quantum machine learning (QML) can complement the growing trend of using
learned models for a myriad of classification tasks, from image recognition to
natural speech processing. A quantum advantage arises due to the intractability
of quantum operations on a classical computer. Many datasets used in machine
learning are crowd sourced or contain some private information. To the best of
our knowledge, no current QML models are equipped with privacy-preserving
features, which raises concerns as it is paramount that models do not expose
sensitive information. Thus, privacy-preserving algorithms need to be
implemented with QML. One solution is to make the machine learning algorithm
differentially private, meaning the effect of a single data point on the
training dataset is minimized. Differentially private machine learning models
have been investigated, but differential privacy has yet to be studied in the
context of QML. In this study, we develop a hybrid quantum-classical model that
is trained to preserve privacy using differentially private optimization
algorithm. This marks the first proof-of-principle demonstration of
privacy-preserving QML. The experiments demonstrate that differentially private
QML can protect user-sensitive information without diminishing model accuracy.
Although the quantum model is simulated and tested on a classical computer, it
demonstrates potential to be efficiently implemented on near-term quantum
devices (noisy intermediate-scale quantum [NISQ]). The approach's success is
illustrated via the classification of spatially classed two-dimensional
datasets and a binary MNIST classification. This implementation of
privacy-preserving QML will ensure confidentiality and accurate learning on
NISQ technology. | [
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] |
Use of Deep Learning (DL) in commercial applications such as image
classification, sentiment analysis and speech recognition is increasing. When
training DL models with large number of parameters and/or large datasets, cost
and speed of training can become prohibitive. Distributed DL training solutions
that split a training job into subtasks and execute them over multiple nodes
can decrease training time. However, the cost of current solutions, built
predominantly for cluster computing systems, can still be an issue. In contrast
to cluster computing systems, Volunteer Computing (VC) systems can lower the
cost of computing, but applications running on VC systems have to handle fault
tolerance, variable network latency and heterogeneity of compute nodes, and the
current solutions are not designed to do so. We design a distributed solution
that can run DL training on a VC system by using a data parallel approach. We
implement a novel asynchronous SGD scheme called VC-ASGD suited for VC systems.
In contrast to traditional VC systems that lower cost by using untrustworthy
volunteer devices, we lower cost by leveraging preemptible computing instances
on commercial cloud platforms. By using preemptible instances that require
applications to be fault tolerant, we lower cost by 70-90% and improve data
security. | [
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] |
The ability to recognize human partners is an important social skill to build
personalized and long-term human-robot interactions, especially in scenarios
like education, care-giving, and rehabilitation. Faces and voices constitute
two important sources of information to enable artificial systems to reliably
recognize individuals. Deep learning networks have achieved state-of-the-art
results and demonstrated to be suitable tools to address such a task. However,
when those networks are applied to different and unprecedented scenarios not
included in the training set, they can suffer a drop in performance. For
example, with robotic platforms in ever-changing and realistic environments,
where always new sensory evidence is acquired, the performance of those models
degrades. One solution is to make robots learn from their first-hand sensory
data with self-supervision. This allows coping with the inherent variability of
the data gathered in realistic and interactive contexts. To this aim, we
propose a cognitive architecture integrating low-level perceptual processes
with a spatial working memory mechanism. The architecture autonomously
organizes the robot's sensory experience into a structured dataset suitable for
human recognition. Our results demonstrate the effectiveness of our
architecture and show that it is a promising solution in the quest of making
robots more autonomous in their learning process. | [
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] |
There is growing interest in data-driven weather prediction (DDWP), for
example using convolutional neural networks such as U-NETs that are trained on
data from models or reanalysis. Here, we propose 3 components to integrate with
commonly used DDWP models in order to improve their physical consistency and
forecast accuracy. These components are 1) a deep spatial transformer added to
the latent space of the U-NETs to preserve a property called equivariance,
which is related to correctly capturing rotations and scalings of features in
spatio-temporal data, 2) a data-assimilation (DA) algorithm to ingest noisy
observations and improve the initial conditions for next forecasts, and 3) a
multi-time-step algorithm, which combines forecasts from DDWP models with
different time steps through DA, improving the accuracy of forecasts at short
intervals. To show the benefit/feasibility of each component, we use
geopotential height at 500~hPa (Z500) from ERA5 reanalysis and examine the
short-term forecast accuracy of specific setups of the DDWP framework. Results
show that the equivariance-preserving networks (U-STNs) clearly outperform the
U-NETs, for example improving the forecast skill by $45\%$. Using a sigma-point
ensemble Kalman (SPEnKF) algorithm for DA and U-STN as the forward model, we
show that stable, accurate DA cycles are achieved even with high observation
noise. The DDWP+DA framework substantially benefits from large ($O(1000)$)
ensembles that are inexpensively generated with the data-driven forward model
in each DA cycle. The multi-time-step DDWP+DA framework also shows promises,
e.g., it reduces the average error by factors of 2-3. | [
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] |
In this study, we report the successful execution of in-air knotting of rope
using a dual-arm two-finger robot based on deep learning. Owing to its
flexibility, the state of the rope was in constant flux during the operation of
the robot. This required the robot control system to dynamically correspond to
the state of the object at all times. However, a manual description of
appropriate robot motions corresponding to all object states is difficult to be
prepared in advance. To resolve this issue, we constructed a model that
instructed the robot to perform bowknots and overhand knots based on two deep
neural networks trained using the data gathered from its sensorimotor,
including visual and proximity sensors. The resultant model was verified to be
capable of predicting the appropriate robot motions based on the sensory
information available online. In addition, we designed certain task motions
based on the Ian knot method using the dual-arm two-fingers robot. The designed
knotting motions do not require a dedicated workbench or robot hand, thereby
enhancing the versatility of the proposed method. Finally, experiments were
performed to estimate the knotting performance of the real robot while
executing overhand knots and bowknots on rope and its success rate. The
experimental results established the effectiveness and high performance of the
proposed method. | [
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] |
Cyber attacks pose crucial threats to computer system security, and put
digital treasuries at excessive risks. This leads to an urgent call for an
effective intrusion detection system that can identify the intrusion attacks
with high accuracy. It is challenging to classify the intrusion events due to
the wide variety of attacks. Furthermore, in a normal network environment, a
majority of the connections are initiated by benign behaviors. The class
imbalance issue in intrusion detection forces the classifier to be biased
toward the majority/benign class, thus leave many attack incidents undetected.
Spurred by the success of deep neural networks in computer vision and natural
language processing, in this paper, we design a new system named DeepIDEA that
takes full advantage of deep learning to enable intrusion detection and
classification. To achieve high detection accuracy on imbalanced data, we
design a novel attack-sharing loss function that can effectively move the
decision boundary towards the attack classes and eliminates the bias towards
the majority/benign class. By using this loss function, DeepIDEA respects the
fact that the intrusion mis-classification should receive higher penalty than
the attack mis-classification. Extensive experimental results on three
benchmark datasets demonstrate the high detection accuracy of DeepIDEA. In
particular, compared with eight state-of-the-art approaches, DeepIDEA always
provides the best class-balanced accuracy. | [
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] |
Background: With the rising popularity of Artificial Intelligence (AI), there
is a growing need to build large and complex AI-based systems in a
cost-effective and manageable way. Like with traditional software, Technical
Debt (TD) will emerge naturally over time in these systems, therefore leading
to challenges and risks if not managed appropriately. The influence of data
science and the stochastic nature of AI-based systems may also lead to new
types of TD or antipatterns, which are not yet fully understood by researchers
and practitioners. Objective: The goal of our study is to provide a clear
overview and characterization of the types of TD (both established and new
ones) that appear in AI-based systems, as well as the antipatterns and related
solutions that have been proposed. Method: Following the process of a
systematic mapping study, 21 primary studies are identified and analyzed.
Results: Our results show that (i) established TD types, variations of them,
and four new TD types (data, model, configuration, and ethics debt) are present
in AI-based systems, (ii) 72 antipatterns are discussed in the literature, the
majority related to data and model deficiencies, and (iii) 46 solutions have
been proposed, either to address specific TD types, antipatterns, or TD in
general. Conclusions: Our results can support AI professionals with reasoning
about and communicating aspects of TD present in their systems. Additionally,
they can serve as a foundation for future research to further our understanding
of TD in AI-based systems. | [
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Visualization recommendation systems simplify exploratory data analysis (EDA)
and make understanding data more accessible to users of all skill levels by
automatically generating visualizations for users to explore. However, most
existing visualization recommendation systems focus on ranking all
visualizations into a single list or set of groups based on particular
attributes or encodings. This global ranking makes it difficult and
time-consuming for users to find the most interesting or relevant insights. To
address these limitations, we introduce a novel class of visualization
recommendation systems that automatically rank and recommend both groups of
related insights as well as the most important insights within each group. Our
proposed approach combines results from many different learning-based methods
to discover insights automatically. A key advantage is that this approach
generalizes to a wide variety of attribute types such as categorical,
numerical, and temporal, as well as complex non-trivial combinations of these
different attribute types. To evaluate the effectiveness of our approach, we
implemented a new insight-centric visualization recommendation system,
SpotLight, which generates and ranks annotated visualizations to explain each
insight. We conducted a user study with 12 participants and two datasets which
showed that users are able to quickly understand and find relevant insights in
unfamiliar data. | [
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] |
Optimal performance is critical for decision-making tasks from medicine to
autonomous driving, however common performance measures may be too general or
too specific. For binary classifiers, diagnostic tests or prognosis at a
timepoint, measures such as the area under the receiver operating
characteristic curve, or the area under the precision recall curve, are too
general because they include unrealistic decision thresholds. On the other
hand, measures such as accuracy, sensitivity or the F1 score are measures at a
single threshold that reflect an individual single probability or predicted
risk, rather than a range of individuals or risk. We propose a method in
between, deep ROC analysis, that examines groups of probabilities or predicted
risks for more insightful analysis. We translate esoteric measures into
familiar terms: AUC and the normalized concordant partial AUC are balanced
average accuracy (a new finding); the normalized partial AUC is average
sensitivity; and the normalized horizontal partial AUC is average specificity.
Along with post-test measures, we provide a method that can improve model
selection in some cases and provide interpretation and assurance for patients
in each risk group. We demonstrate deep ROC analysis in two case studies and
provide a toolkit in Python. | [
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] |
There has been a recent resurgence of interest in explainable artificial
intelligence (XAI) that aims to reduce the opaqueness of AI-based
decision-making systems, allowing humans to scrutinize and trust them. Prior
work in this context has focused on the attribution of responsibility for an
algorithm's decisions to its inputs wherein responsibility is typically
approached as a purely associational concept. In this paper, we propose a
principled causality-based approach for explaining black-box decision-making
systems that addresses limitations of existing methods in XAI. At the core of
our framework lies probabilistic contrastive counterfactuals, a concept that
can be traced back to philosophical, cognitive, and social foundations of
theories on how humans generate and select explanations. We show how such
counterfactuals can quantify the direct and indirect influences of a variable
on decisions made by an algorithm, and provide actionable recourse for
individuals negatively affected by the algorithm's decision. Unlike prior work,
our system, LEWIS: (1)can compute provably effective explanations and recourse
at local, global and contextual levels (2)is designed to work with users with
varying levels of background knowledge of the underlying causal model and
(3)makes no assumptions about the internals of an algorithmic system except for
the availability of its input-output data. We empirically evaluate LEWIS on
three real-world datasets and show that it generates human-understandable
explanations that improve upon state-of-the-art approaches in XAI, including
the popular LIME and SHAP. Experiments on synthetic data further demonstrate
the correctness of LEWIS's explanations and the scalability of its recourse
algorithm. | [
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] |
Reinforcement learning (RL) agents with pre-specified reward functions cannot
provide guaranteed safety across variety of circumstances that an uncertain
system might encounter. To guarantee performance while assuring satisfaction of
safety constraints across variety of circumstances, an assured autonomous
control framework is presented in this paper by empowering RL algorithms with
metacognitive learning capabilities. More specifically, adapting the reward
function parameters of the RL agent is performed in a metacognitive
decision-making layer to assure the feasibility of RL agent. That is, to assure
that the learned policy by the RL agent satisfies safety constraints specified
by signal temporal logic while achieving as much performance as possible. The
metacognitive layer monitors any possible future safety violation under the
actions of the RL agent and employs a higher-layer Bayesian RL algorithm to
proactively adapt the reward function for the lower-layer RL agent. To minimize
the higher-layer Bayesian RL intervention, a fitness function is leveraged by
the metacognitive layer as a metric to evaluate success of the lower-layer RL
agent in satisfaction of safety and liveness specifications, and the
higher-layer Bayesian RL intervenes only if there is a risk of lower-layer RL
failure. Finally, a simulation example is provided to validate the
effectiveness of the proposed approach. | [
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] |
Increasing availability of machine learning (ML) frameworks and tools, as
well as their promise to improve solutions to data-driven decision problems,
has resulted in popularity of using ML techniques in software systems. However,
end-to-end development of ML-enabled systems, as well as their seamless
deployment and operations, remain a challenge. One reason is that development
and deployment of ML-enabled systems involves three distinct workflows,
perspectives, and roles, which include data science, software engineering, and
operations. These three distinct perspectives, when misaligned due to incorrect
assumptions, cause ML mismatches which can result in failed systems. We
conducted an interview and survey study where we collected and validated common
types of mismatches that occur in end-to-end development of ML-enabled systems.
Our analysis shows that how each role prioritizes the importance of relevant
mismatches varies, potentially contributing to these mismatched assumptions. In
addition, the mismatch categories we identified can be specified as machine
readable descriptors contributing to improved ML-enabled system development. In
this paper, we report our findings and their implications for improving
end-to-end ML-enabled system development. | [
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] |
Deep learning has grown rapidly thanks to its state-of-the-art performance
across a wide range of real-world applications. While neural networks have been
trained using IEEE-754 binary32 arithmetic, the rapid growth of computational
demands in deep learning has boosted interest in faster, low precision
training. Mixed-precision training that combines IEEE-754 binary16 with
IEEE-754 binary32 has been tried, and other $16$-bit formats, for example
Google's bfloat16, have become popular. In floating-point arithmetic there is a
tradeoff between precision and representation range as the number of exponent
bits changes; denormal numbers extend the representation range. This raises
questions of how much exponent range is needed, of whether there is a format
between binary16 (5 exponent bits) and bfloat16 (8 exponent bits) that works
better than either of them, and whether or not denormals are necessary.
In the current paper we study the need for denormal numbers for
mixed-precision training, and we propose a 1/6/9 format, i.e., 6-bit exponent
and 9-bit explicit mantissa, that offers a better range-precision tradeoff. We
show that 1/6/9 mixed-precision training is able to speed up training on
hardware that incurs a performance slowdown on denormal operations or
eliminates the need for denormal numbers altogether. And, for a number of fully
connected and convolutional neural networks in computer vision and natural
language processing, 1/6/9 achieves numerical parity to standard
mixed-precision. | [
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This paper presents a new Network Intrusion Detection System (NIDS) based on
Graph Neural Networks (GNNs). GNNs are a relatively new sub-field of deep
neural networks, which can leverage the inherent structure of graph-based data.
Training and evaluation data for NIDSs are typically represented as flow
records, which can naturally be represented in a graph format. In this paper,
we propose E-GraphSAGE, a GNN approach that allows capturing both the edge
features of a graph as well as the topological information for network
intrusion detection in IoT networks. To the best of our knowledge, our proposal
is the first successful, practical, and extensively evaluated approach of
applying GNNs on the problem of network intrusion detection for IoT using
flow-based data. Our extensive experimental evaluation on four recent NIDS
benchmark datasets shows that our approach outperforms the state-of-the-art in
terms of key classification metrics, which demonstrates the potential of GNNs
in network intrusion detection, and provides motivation for further research. | [
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] |
Learning from Demonstration (LfD) has been established as the dominant
paradigm for efficiently transferring skills from human teachers to robots. In
this context, the Federated Learning (FL) conceptualization has very recently
been introduced for developing large-scale human-robot collaborative
environments, targeting to robustly address, among others, the critical
challenges of multi-agent learning and long-term autonomy. In the current work,
the latter scheme is further extended and enhanced, by designing and
integrating a novel user profile formulation for providing a fine-grained
representation of the exhibited human behavior, adopting a Deep Learning
(DL)-based formalism. In particular, a hierarchically organized set of key
information sources is considered, including: a) User attributes (e.g.
demographic, anthropomorphic, educational, etc.), b) User state (e.g. fatigue
detection, stress detection, emotion recognition, etc.) and c)
Psychophysiological measurements (e.g. gaze, electrodermal activity, heart
rate, etc.) related data. Then, a combination of Long Short-Term Memory (LSTM)
and stacked autoencoders, with appropriately defined neural network
architectures, is employed for the modelling step. The overall designed scheme
enables both short- and long-term analysis/interpretation of the human behavior
(as observed during the feedback capturing sessions), so as to adaptively
adjust the importance of the collected feedback samples when aggregating
information originating from the same and different human teachers,
respectively. | [
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] |
Events are happening in real-world and real-time, which can be planned and
organized for occasions, such as social gatherings, festival celebrations,
influential meetings or sports activities. Social media platforms generate a
lot of real-time text information regarding public events with different
topics. However, mining social events is challenging because events typically
exhibit heterogeneous texture and metadata are often ambiguous. In this paper,
we first design a novel event-based meta-schema to characterize the semantic
relatedness of social events and then build an event-based heterogeneous
information network (HIN) integrating information from external knowledge base.
Second, we propose a novel Pairwise Popularity Graph Convolutional Network,
named as PP-GCN, based on weighted meta-path instance similarity and textual
semantic representation as inputs, to perform fine-grained social event
categorization and learn the optimal weights of meta-paths in different tasks.
Third, we propose a streaming social event detection and evolution discovery
framework for HINs based on meta-path similarity search, historical information
about meta-paths, and heterogeneous DBSCAN clustering method. Comprehensive
experiments on real-world streaming social text data are conducted to compare
various social event detection and evolution discovery algorithms. Experimental
results demonstrate that our proposed framework outperforms other alternative
social event detection and evolution discovery techniques. | [
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Conventional works that learn grasping affordance from demonstrations need to
explicitly predict grasping configurations, such as gripper approaching angles
or grasping preshapes. Classic motion planners could then sample trajectories
by using such predicted configurations. In this work, our goal is instead to
fill the gap between affordance discovery and affordance-based policy learning
by integrating the two objectives in an end-to-end imitation learning framework
based on deep neural networks. From a psychological perspective, there is a
close association between attention and affordance. Therefore, with an
end-to-end neural network, we propose to learn affordance cues as visual
attention that serves as a useful indicating signal of how a demonstrator
accomplishes tasks, instead of explicitly modeling affordances. To achieve
this, we propose a contrastive learning framework that consists of a Siamese
encoder and a trajectory decoder. We further introduce a coupled triplet loss
to encourage the discovered affordance cues to be more affordance-relevant. Our
experimental results demonstrate that our model with the coupled triplet loss
achieves the highest grasping success rate in a simulated robot environment.
Our project website can be accessed at
https://sites.google.com/asu.edu/affordance-aware-imitation/project. | [
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] |
Lack of adequate training samples and noisy high-dimensional features are key
challenges faced by Motor Imagery (MI) decoding algorithms for
electroencephalogram (EEG) based Brain-Computer Interface (BCI). To address
these challenges, inspired from neuro-physiological signatures of MI, this
paper proposes a novel Filter-Bank Convolutional Network (FBCNet) for MI
classification. FBCNet employs a multi-view data representation followed by
spatial filtering to extract spectro-spatially discriminative features. This
multistage approach enables efficient training of the network even when limited
training data is available. More significantly, in FBCNet, we propose a novel
Variance layer that effectively aggregates the EEG time-domain information.
With this design, we compare FBCNet with state-of-the-art (SOTA) BCI algorithm
on four MI datasets: The BCI competition IV dataset 2a (BCIC-IV-2a), the
OpenBMI dataset, and two large datasets from chronic stroke patients. The
results show that, by achieving 76.20% 4-class classification accuracy, FBCNet
sets a new SOTA for BCIC-IV-2a dataset. On the other three datasets, FBCNet
yields up to 8% higher binary classification accuracies. Additionally, using
explainable AI techniques we present one of the first reports about the
differences in discriminative EEG features between healthy subjects and stroke
patients. Also, the FBCNet source code is available at
https://github.com/ravikiran-mane/FBCNet. | [
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] |
Gradient-based adversarial attacks on deep neural networks pose a serious
threat, since they can be deployed by adding imperceptible perturbations to the
test data of any network, and the risk they introduce cannot be assessed
through the network's original training performance. Denoising and
dimensionality reduction are two distinct methods that have been independently
investigated to combat such attacks. While denoising offers the ability to
tailor the defense to the specific nature of the attack, dimensionality
reduction offers the advantage of potentially removing previously unseen
perturbations, along with reducing the training time of the network being
defended. We propose strategies to combine the advantages of these two defense
mechanisms. First, we propose the cascaded defense, which involves denoising
followed by dimensionality reduction. To reduce the training time of the
defense for a small trade-off in performance, we propose the hidden layer
defense, which involves feeding the output of the encoder of a denoising
autoencoder into the network. Further, we discuss how adaptive attacks against
these defenses could become significantly weak when an alternative defense is
used, or when no defense is used. In this light, we propose a new metric to
evaluate a defense which measures the sensitivity of the adaptive attack to
modifications in the defense. Finally, we present a guideline for building an
ordered repertoire of defenses, a.k.a. a defense infrastructure, that adjusts
to limited computational resources in presence of uncertainty about the attack
strategy. | [
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Given two large lists of records, the task in entity resolution (ER) is to
find the pairs from the Cartesian product of the lists that correspond to the
same real world entity. Typically, passive learning methods on such tasks
require large amounts of labeled data to yield useful models. Active Learning
is a promising approach for ER in low resource settings. However, the search
space, to find informative samples for the user to label, grows quadratically
for instance-pair tasks making active learning hard to scale. Previous works,
in this setting, rely on hand-crafted predicates, pre-trained language model
embeddings, or rule learning to prune away unlikely pairs from the Cartesian
product. This blocking step can miss out on important regions in the product
space leading to low recall. We propose DIAL, a scalable active learning
approach that jointly learns embeddings to maximize recall for blocking and
accuracy for matching blocked pairs. DIAL uses an Index-By-Committee framework,
where each committee member learns representations based on powerful
pre-trained transformer language models. We highlight surprising differences
between the matcher and the blocker in the creation of the training data and
the objective used to train their parameters. Experiments on five benchmark
datasets and a multilingual record matching dataset show the effectiveness of
our approach in terms of precision, recall and running time. Code is available
at https://github.com/ArjitJ/DIAL | [
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] |
Optimizing the quality of result (QoR) and the quality of service (QoS) of
AI-empowered autonomous systems simultaneously is very challenging. First,
there are multiple input sources, e.g., multi-modal data from different
sensors, requiring diverse data preprocessing, sensor fusion, and feature
aggregation. Second, there are multiple tasks that require various AI models to
run simultaneously, e.g., perception, localization, and control. Third, the
computing and control system is heterogeneous, composed of hardware components
with varied features, such as embedded CPUs, GPUs, FPGAs, and dedicated
accelerators. Therefore, autonomous systems essentially require multi-modal
multi-task (MMMT) learning which must be aware of hardware performance and
implementation strategies. While MMMT learning has been attracting intensive
research interests, its applications in autonomous systems are still
underexplored. In this paper, we first discuss the opportunities of applying
MMMT techniques in autonomous systems and then discuss the unique challenges
that must be solved. In addition, we discuss the necessity and opportunities of
MMMT model and hardware co-design, which is critical for autonomous systems
especially with power/resource-limited or heterogeneous platforms. We formulate
the MMMT model and heterogeneous hardware implementation co-design as a
differentiable optimization problem, with the objective of improving the
solution quality and reducing the overall power consumption and critical path
latency. We advocate for further explorations of MMMT in autonomous systems and
software/hardware co-design solutions. | [
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In September 2019, the Council of Europe's Committee of Ministers adopted the
terms of reference for the Ad Hoc Committee on Artificial Intelligence (CAHAI).
The CAHAI is charged with examining the feasibility and potential elements of a
legal framework for the design, development, and deployment of AI systems that
accord with Council of Europe standards across the interrelated areas of human
rights, democracy, and the rule of law. As a first and necessary step in
carrying out this responsibility, the CAHAI's Feasibility Study, adopted by its
plenary in December 2020, has explored options for an international legal
response that fills existing gaps in legislation and tailors the use of binding
and non-binding legal instruments to the specific risks and opportunities
presented by AI systems. The Study examines how the fundamental rights and
freedoms that are already codified in international human rights law can be
used as the basis for such a legal framework. The purpose of this primer is to
introduce the main concepts and principles presented in the CAHAI's Feasibility
Study for a general, non-technical audience. It also aims to provide some
background information on the areas of AI innovation, human rights law,
technology policy, and compliance mechanisms covered therein. In keeping with
the Council of Europe's commitment to broad multi-stakeholder consultations,
outreach, and engagement, this primer has been designed to help facilitate the
meaningful and informed participation of an inclusive group of stakeholders as
the CAHAI seeks feedback and guidance regarding the essential issues raised by
the Feasibility Study. | [
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] |
This paper addresses the problem of multi-step time series forecasting for
non-stationary signals that can present sudden changes. Current
state-of-the-art deep learning forecasting methods, often trained with variants
of the MSE, lack the ability to provide sharp predictions in deterministic and
probabilistic contexts. To handle these challenges, we propose to incorporate
shape and temporal criteria in the training objective of deep models. We define
shape and temporal similarities and dissimilarities, based on a smooth
relaxation of Dynamic Time Warping (DTW) and Temporal Distortion Index (TDI),
that enable to build differentiable loss functions and positive semi-definite
(PSD) kernels. With these tools, we introduce DILATE (DIstortion Loss including
shApe and TimE), a new objective for deterministic forecasting, that explicitly
incorporates two terms supporting precise shape and temporal change detection.
For probabilistic forecasting, we introduce STRIPE++ (Shape and Time diverRsIty
in Probabilistic forEcasting), a framework for providing a set of sharp and
diverse forecasts, where the structured shape and time diversity is enforced
with a determinantal point process (DPP) diversity loss. Extensive experiments
and ablations studies on synthetic and real-world datasets confirm the benefits
of leveraging shape and time features in time series forecasting. | [
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] |
Machine Learning (ML) techniques are becoming an invaluable support for
network intrusion detection, especially in revealing anomalous flows, which
often hide cyber-threats. Typically, ML algorithms are exploited to
classify/recognize data traffic on the basis of statistical features such as
inter-arrival times, packets length distribution, mean number of flows, etc.
Dealing with the vast diversity and number of features that typically
characterize data traffic is a hard problem. This results in the following
issues: i) the presence of so many features leads to lengthy training processes
(particularly when features are highly correlated), while prediction accuracy
does not proportionally improve; ii) some of the features may introduce bias
during the classification process, particularly those that have scarce relation
with the data traffic to be classified. To this end, by reducing the feature
space and retaining only the most significant features, Feature Selection (FS)
becomes a crucial pre-processing step in network management and, specifically,
for the purposes of network intrusion detection. In this review paper, we
complement other surveys in multiple ways: i) evaluating more recent datasets
(updated w.r.t. obsolete KDD 99) by means of a designed-from-scratch
Python-based procedure; ii) providing a synopsis of most credited FS approaches
in the field of intrusion detection, including Multi-Objective Evolutionary
techniques; iii) assessing various experimental analyses such as feature
correlation, time complexity, and performance. Our comparisons offer useful
guidelines to network/security managers who are considering the incorporation
of ML concepts into network intrusion detection, where trade-offs between
performance and resource consumption are crucial. | [
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] |
We describe a robotic learning system for autonomous exploration and
navigation in diverse, open-world environments. At the core of our method is a
learned latent variable model of distances and actions, along with a
non-parametric topological memory of images. We use an information bottleneck
to regularize the learned policy, giving us (i) a compact visual representation
of goals, (ii) improved generalization capabilities, and (iii) a mechanism for
sampling feasible goals for exploration. Trained on a large offline dataset of
prior experience, the model acquires a representation of visual goals that is
robust to task-irrelevant distractors. We demonstrate our method on a mobile
ground robot in open-world exploration scenarios. Given an image of a goal that
is up to 80 meters away, our method leverages its representation to explore and
discover the goal in under 20 minutes, even amidst previously-unseen obstacles
and weather conditions. Please check out the project website for videos of our
experiments and information about the real-world dataset used at
https://sites.google.com/view/recon-robot. | [
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In Volt/Var control (VVC) of active distribution networks(ADNs), both slow
timescale discrete devices (STDDs) and fast timescale continuous devices
(FTCDs) are involved. The STDDs such as on-load tap changers (OLTC) and FTCDs
such as distributed generators should be coordinated in time sequence. Such VCC
is formulated as a two-timescale optimization problem to jointly optimize FTCDs
and STDDs in ADNs. Traditional optimization methods are heavily based on
accurate models of the system, but sometimes impractical because of their
unaffordable effort on modelling. In this paper, a novel bi-level off-policy
reinforcement learning (RL) algorithm is proposed to solve this problem in a
model-free manner. A Bi-level Markov decision process (BMDP) is defined to
describe the two-timescale VVC problem and separate agents are set up for the
slow and fast timescale sub-problems. For the fast timescale sub-problem, we
adopt an off-policy RL method soft actor-critic with high sample efficiency.
For the slow one, we develop an off-policy multi-discrete soft actor-critic
(MDSAC) algorithm to address the curse of dimensionality with various STDDs. To
mitigate the non-stationary issue existing the two agents' learning processes,
we propose a multi-timescale off-policy correction (MTOPC) method by adopting
importance sampling technique. Comprehensive numerical studies not only
demonstrate that the proposed method can achieve stable and satisfactory
optimization of both STDDs and FTCDs without any model information, but also
support that the proposed method outperforms existing two-timescale VVC
methods. | [
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] |
In recent years, Graph Neural Networks (GNNs) appear to be state-of-the-art
algorithms for analyzing non-euclidean graph data. By applying deep-learning to
extract high-level representations from graph structures, GNNs achieve
extraordinary accuracy and great generalization ability in various tasks.
However, with the ever-increasing graph sizes, more and more complicated GNN
layers, and higher feature dimensions, the computational complexity of GNNs
grows exponentially. How to inference GNNs in real time has become a
challenging problem, especially for some resource-limited edge-computing
platforms.
To tackle this challenge, we propose BlockGNN, a software-hardware co-design
approach to realize efficient GNN acceleration. At the algorithm level, we
propose to leverage block-circulant weight matrices to greatly reduce the
complexity of various GNN models. At the hardware design level, we propose a
pipelined CirCore architecture, which supports efficient block-circulant
matrices computation. Basing on CirCore, we present a novel BlockGNN
accelerator to compute various GNNs with low latency. Moreover, to determine
the optimal configurations for diverse deployed tasks, we also introduce a
performance and resource model that helps choose the optimal hardware
parameters automatically. Comprehensive experiments on the ZC706 FPGA platform
demonstrate that on various GNN tasks, BlockGNN achieves up to $8.3\times$
speedup compared to the baseline HyGCN architecture and $111.9\times$ energy
reduction compared to the Intel Xeon CPU platform. | [
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] |
Uncertainty quantification provides quantitative measures on the reliability
of candidate solutions of ill-posed inverse problems. Due to their sequential
nature, Monte Carlo sampling methods require large numbers of sampling steps
for accurate Bayesian inference and are often computationally infeasible for
large-scale inverse problems, such as seismic imaging. Our main contribution is
a data-driven variational inference approach where we train a normalizing flow
(NF), a type of invertible neural net, capable of cheaply sampling the
posterior distribution given previously unseen seismic data from neighboring
surveys. To arrive at this result, we train the NF on pairs of low- and
high-fidelity migrated images. In our numerical example, we obtain
high-fidelity images from the Parihaka dataset and low-fidelity images are
derived from these images through the process of demigration, followed by
adding noise and migration. During inference, given shot records from a new
neighboring seismic survey, we first compute the reverse-time migration image.
Next, by feeding this low-fidelity migrated image to the NF we gain access to
samples from the posterior distribution virtually for free. We use these
samples to compute a high-fidelity image including a first assessment of the
image's reliability. To our knowledge, this is the first attempt to train a
conditional network on what we know from neighboring images to improve the
current image and assess its reliability. | [
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] |
Extra virgin olive oil (EVOO) is the highest quality of olive oil and is
characterized by highly beneficial nutritional properties. The large increase
in both consumption and fraud, for example through adulteration, creates new
challenges and an increasing demand for developing new quality assessment
methodologies that are easier and cheaper to perform. As of today, the
determination of olive oil quality is performed by producers through chemical
analysis and organoleptic evaluation. The chemical analysis requires the
advanced equipment and chemical knowledge of certified laboratories, and has
therefore a limited accessibility. In this work a minimalist, portable and
low-cost sensor is presented, which can perform olive oil quality assessment
using fluorescence spectroscopy. The potential of the proposed technology is
explored by analyzing several olive oils of different quality levels, EVOO,
virgin olive oil (VOO), and lampante olive oil (LOO). The spectral data were
analyzed using a large number of machine learning methods, including artificial
neural networks. The analysis performed in this work demonstrates the
possibility of performing classification of olive oil in the three mentioned
classes with an accuracy of 100$\%$. These results confirm that this minimalist
low-cost sensor has the potential of substituting expensive and complex
chemical analysis. | [
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] |
Automatic modulation classification can be a core component for intelligent
spectrally efficient wireless communication networks, and deep learning
techniques have recently been shown to deliver superior performance to
conventional model-based strategies, particularly when distinguishing between a
large number of modulation types. However, such deep learning techniques have
also been recently shown to be vulnerable to gradient-based adversarial attacks
that rely on subtle input perturbations, which would be particularly feasible
in a wireless setting via jamming. One such potent attack is the one known as
the Carlini-Wagner attack, which we consider in this work. We further consider
a data-driven subsampling setting, where several recently introduced
deep-learning-based algorithms are employed to select a subset of samples that
lead to reducing the final classifier's training time with minimal loss in
accuracy. In this setting, the attacker has to make an assumption about the
employed subsampling strategy, in order to calculate the loss gradient. Based
on state of the art techniques available to both the attacker and defender, we
evaluate best strategies under various assumptions on the knowledge of the
other party's strategy. Interestingly, in presence of knowledgeable attackers,
we identify computational cost reduction opportunities for the defender with no
or minimal loss in performance. | [
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] |
Data-intensive science is increasingly reliant on real-time processing
capabilities and machine learning workflows, in order to filter and analyze the
extreme volumes of data being collected. This is especially true at the energy
and intensity frontiers of particle physics where bandwidths of raw data can
exceed 100 Tb/s of heterogeneous, high-dimensional data sourced from hundreds
of millions of individual sensors. In this paper, we introduce a new
data-driven approach for designing and optimizing high-throughput data
filtering and trigger systems such as those in use at physics facilities like
the Large Hadron Collider (LHC). Concretely, our goal is to design a
data-driven filtering system with a minimal run-time cost for determining which
data event to keep, while preserving (and potentially improving upon) the
distribution of the output as generated by the hand-designed trigger system. We
introduce key insights from interpretable predictive modeling and
cost-sensitive learning in order to account for non-local inefficiencies in the
current paradigm and construct a cost-effective data filtering and trigger
model that does not compromise physics coverage. | [
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] |
With the increasing scale, complexity, and heterogeneity of the next
generation networked systems, seamless control, management, and security of
such systems becomes increasingly challenging. Many diverse applications have
driven interest in networked systems, including large-scale distributed
learning, multi-agent optimization, 5G service provisioning, and network
slicing, etc. In this paper, we propose trust as a measure to evaluate the
status of network agents and improve the decision-making process. We interpret
trust as a relation among entities that participate in various protocols. Trust
relations are based on evidence created by the interactions of entities within
a protocol and may be a composite of multiple metrics such as availability,
reliability, resilience, etc. depending on application context. We first
elaborate on the importance of trust as a metric and then present a
mathematical framework for trust computation and aggregation within a network.
Then we show in practice, how trust can be integrated into network
decision-making processes by presenting two examples. In the first example, we
show how utilizing the trust evidence can improve the performance and the
security of Federated Learning. Second, we show how a 5G network resource
provisioning framework can be improved when augmented with a trust-aware
decision-making scheme. We verify the validity of our trust-based approach
through simulations. Finally, we explain the challenges associated with
aggregating the trust evidence and briefly explain our ideas to tackle them. | [
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] |
In the last three years, the largest dense deep learning models have grown
over 1000x to reach hundreds of billions of parameters, while the GPU memory
has only grown by 5x (16 GB to 80 GB). Therefore, the growth in model scale has
been supported primarily though system innovations that allow large models to
fit in the aggregate GPU memory of multiple GPUs. However, we are getting close
to the GPU memory wall. It requires 800 NVIDIA V100 GPUs just to fit a trillion
parameter model for training, and such clusters are simply out of reach for
most data scientists. In addition, training models at that scale requires
complex combinations of parallelism techniques that puts a big burden on the
data scientists to refactor their model.
In this paper we present ZeRO-Infinity, a novel heterogeneous system
technology that leverages GPU, CPU, and NVMe memory to allow for unprecedented
model scale on limited resources without requiring model code refactoring. At
the same time it achieves excellent training throughput and scalability,
unencumbered by the limited CPU or NVMe bandwidth. ZeRO-Infinity can fit models
with tens and even hundreds of trillions of parameters for training on current
generation GPU clusters. It can be used to fine-tune trillion parameter models
on a single NVIDIA DGX-2 node, making large models more accessible. In terms of
training throughput and scalability, it sustains over 25 petaflops on 512
NVIDIA V100 GPUs(40% of peak), while also demonstrating super linear
scalability. An open source implementation of ZeRO-Infinity is available
through DeepSpeed, a deep learning optimization library that makes distributed
training easy, efficient, and effective. | [
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] |
Recommending medications for patients using electronic health records (EHRs)
is a crucial data mining task for an intelligent healthcare system. It can
assist doctors in making clinical decisions more efficiently. However, the
inherent complexity of the EHR data renders it as a challenging task: (1)
Multilevel structures: the EHR data typically contains multilevel structures
which are closely related with the decision-making pathways, e.g., laboratory
results lead to disease diagnoses, and then contribute to the prescribed
medications; (2) Multiple sequences interactions: multiple sequences in EHR
data are usually closely correlated with each other; (3) Abundant noise: lots
of task-unrelated features or noise information within EHR data generally
result in suboptimal performance. To tackle the above challenges, we propose a
multilevel selective and interactive network (MeSIN) for medication
recommendation. Specifically, MeSIN is designed with three components. First,
an attentional selective module (ASM) is applied to assign flexible attention
scores to different medical codes embeddings by their relevance to the
recommended medications in every admission. Second, we incorporate a novel
interactive long-short term memory network (InLSTM) to reinforce the
interactions of multilevel medical sequences in EHR data with the help of the
calibrated memory-augmented cell and an enhanced input gate. Finally, we employ
a global selective fusion module (GSFM) to infuse the multi-sourced information
embeddings into final patient representations for medications recommendation.
To validate our method, extensive experiments have been conducted on a
real-world clinical dataset. The results demonstrate a consistent superiority
of our framework over several baselines and testify the effectiveness of our
proposed approach. | [
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] |
We investigated the data-driven relationship between features in the tumor
microenvironment (TME) and the overall and 5-year survival in triple-negative
breast cancer (TNBC) and non-TNBC (NTNBC) patients by using Explainable
Artificial Intelligence (XAI) models. We used clinical information from
patients with invasive breast carcinoma from The Cancer Genome Atlas and from
two studies from the cbioPortal, the PanCanAtlas project and the GDAC Firehose
study. In this study, we used a normalized RNA sequencing data-driven cohort
from 1,015 breast cancer patients, alive or deceased, from the UCSC Xena data
set and performed integrated deconvolution with the EPIC method to estimate the
percentage of seven different immune and stromal cells from RNA sequencing
data. Novel insights derived from our XAI model showed that CD4+ T cells and B
cells are more critical than other TME features for enhanced prognosis for both
TNBC and NTNBC patients. Our XAI model revealed the critical inflection points
(i.e., threshold fractions) of CD4+ T cells and B cells above or below which
5-year survival rates improve. Subsequently, we ascertained the conditional
probabilities of $\geq$ 5-year survival in both TNBC and NTNBC patients under
specific conditions inferred from the inflection points. In particular, the XAI
models revealed that a B-cell fraction exceeding 0.018 in the TME could ensure
100% 5-year survival for NTNBC patients. The findings from this research could
lead to more accurate clinical predictions and enhanced immunotherapies and to
the design of innovative strategies to reprogram the TME of breast cancer
patients. | [
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] |
Unlike areas such as computer vision and speech recognition where
convolutional and recurrent neural networks-based approaches have proven
effective to the nature of the respective areas of application, deep learning
(DL) still lacks a general approach suitable for the unique nature and
challenges of RF systems such as radar, signals intelligence, electronic
warfare, and communications. Existing approaches face problems in robustness,
consistency, efficiency, repeatability and scalability. One of the main
challenges in RF sensing such as radar target identification is the difficulty
and cost of obtaining data. Hundreds to thousands of samples per class are
typically used when training for classifying signals into 2 to 12 classes with
reported accuracy ranging from 87% to 99%, where accuracy generally decreases
with more classes added. In this paper, we present a new DL approach based on
multistage training and demonstrate it on RF sensing signal classification. We
consistently achieve over 99% accuracy for up to 17 diverse classes using only
11 samples per class for training, yielding up to 35% improvement in accuracy
over standard DL approaches. | [
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The COVID-19 pandemic considerably affects public health systems around the
world. The lack of knowledge about the virus, the extension of this phenomenon,
and the speed of the evolution of the infection are all factors that highlight
the necessity of employing new approaches to study these events. Artificial
intelligence techniques may be useful in analyzing data related to areas
affected by the virus. The aim of this work is to investigate any possible
relationships between air quality and confirmed cases of COVID-19 in Italian
districts. Specifically, we report an analysis of the correlation between daily
COVID-19 cases and environmental factors, such as temperature, relative
humidity, and atmospheric pollutants. Our analysis confirms a significant
association of some environmental parameters with the spread of the virus. This
suggests that machine learning models trained on the environmental parameters
to predict the number of future infected cases may be accurate. Predictive
models may be useful for helping institutions in making decisions for
protecting the population and contrasting the pandemic. | [
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] |
Conversational search systems, such as Google Assistant and Microsoft
Cortana, enable users to interact with search systems in multiple rounds
through natural language dialogues. Evaluating such systems is very challenging
given that any natural language responses could be generated, and users
commonly interact for multiple semantically coherent rounds to accomplish a
search task. Although prior studies proposed many evaluation metrics, the
extent of how those measures effectively capture user preference remains to be
investigated. In this paper, we systematically meta-evaluate a variety of
conversational search metrics. We specifically study three perspectives on
those metrics: (1) reliability: the ability to detect "actual" performance
differences as opposed to those observed by chance; (2) fidelity: the ability
to agree with ultimate user preference; and (3) intuitiveness: the ability to
capture any property deemed important: adequacy, informativeness, and fluency
in the context of conversational search. By conducting experiments on two test
collections, we find that the performance of different metrics varies
significantly across different scenarios whereas consistent with prior studies,
existing metrics only achieve a weak correlation with ultimate user preference
and satisfaction. METEOR is, comparatively speaking, the best existing
single-turn metric considering all three perspectives. We also demonstrate that
adapted session-based evaluation metrics can be used to measure multi-turn
conversational search, achieving moderate concordance with user satisfaction.
To our knowledge, our work establishes the most comprehensive meta-evaluation
for conversational search to date. | [
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] |
Navigating through intersections is one of the main challenging tasks for an
autonomous vehicle. However, for the majority of intersections regulated by
traffic lights, the problem could be solved by a simple rule-based method in
which the autonomous vehicle behavior is closely related to the traffic light
states. In this work, we focus on the implementation of a system able to
navigate through intersections where only traffic signs are provided. We
propose a multi-agent system using a continuous, model-free Deep Reinforcement
Learning algorithm used to train a neural network for predicting both the
acceleration and the steering angle at each time step. We demonstrate that
agents learn both the basic rules needed to handle intersections by
understanding the priorities of other learners inside the environment, and to
drive safely along their paths. Moreover, a comparison between our system and a
rule-based method proves that our model achieves better results especially with
dense traffic conditions. Finally, we test our system on real world scenarios
using real recorded traffic data, proving that our module is able to generalize
both to unseen environments and to different traffic conditions. | [
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] |
Societal biases resonate in the retrieved contents of information retrieval
(IR) systems, resulting in reinforcing existing stereotypes. Approaching this
issue requires established measures of fairness in respect to the
representation of various social groups in retrieval results, as well as
methods to mitigate such biases, particularly in the light of the advances in
deep ranking models. In this work, we first provide a novel framework to
measure the fairness in the retrieved text contents of ranking models.
Introducing a ranker-agnostic measurement, the framework also enables the
disentanglement of the effect on fairness of collection from that of rankers.
To mitigate these biases, we propose AdvBert, a ranking model achieved by
adapting adversarial bias mitigation for IR, which jointly learns to predict
relevance and remove protected attributes. We conduct experiments on two
passage retrieval collections (MSMARCO Passage Re-ranking and TREC Deep
Learning 2019 Passage Re-ranking), which we extend by fairness annotations of a
selected subset of queries regarding gender attributes. Our results on the
MSMARCO benchmark show that, (1) all ranking models are less fair in comparison
with ranker-agnostic baselines, and (2) the fairness of Bert rankers
significantly improves when using the proposed AdvBert models. Lastly, we
investigate the trade-off between fairness and utility, showing that we can
maintain the significant improvements in fairness without any significant loss
in utility. | [
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] |
Collision avoidance algorithms are of central interest to many drone
applications. In particular, decentralized approaches may be the key to
enabling robust drone swarm solutions in cases where centralized communication
becomes computationally prohibitive. In this work, we draw biological
inspiration from flocks of starlings (Sturnus vulgaris) and apply the insight
to end-to-end learned decentralized collision avoidance. More specifically, we
propose a new, scalable observation model following a biomimetic
nearest-neighbor information constraint that leads to fast learning and good
collision avoidance behavior. By proposing a general reinforcement learning
approach, we obtain an end-to-end learning-based approach to integrating
collision avoidance with arbitrary tasks such as package collection and
formation change. To validate the generality of this approach, we successfully
apply our methodology through motion models of medium complexity, modeling
momentum and nonetheless allowing direct application to real world quadrotors
in conjunction with a standard PID controller. In contrast to prior works, we
find that in our sufficiently rich motion model, nearest-neighbor information
is indeed enough to learn effective collision avoidance behavior. Our learned
policies are tested in simulation and subsequently transferred to real-world
drones to validate their real-world applicability. | [
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] |
Wireless channels can be inherently privacy-preserving by distorting the
received signals due to channel noise, and superpositioning multiple signals
over-the-air. By harnessing these natural distortions and superpositions by
wireless channels, we propose a novel privacy-preserving machine learning (ML)
framework at the network edge, coined over-the-air mixup ML (AirMixML). In
AirMixML, multiple workers transmit analog-modulated signals of their private
data samples to an edge server who trains an ML model using the received
noisy-and superpositioned samples. AirMixML coincides with model training using
mixup data augmentation achieving comparable accuracy to that with raw data
samples. From a privacy perspective, AirMixML is a differentially private (DP)
mechanism limiting the disclosure of each worker's private sample information
at the server, while the worker's transmit power determines the privacy
disclosure level. To this end, we develop a fractional channel-inversion power
control (PC) method, {\alpha}-Dirichlet mixup PC (DirMix({\alpha})-PC), wherein
for a given global power scaling factor after channel inversion, each worker's
local power contribution to the superpositioned signal is controlled by the
Dirichlet dispersion ratio {\alpha}. Mathematically, we derive a closed-form
expression clarifying the relationship between the local and global PC factors
to guarantee a target DP level. By simulations, we provide DirMix({\alpha})-PC
design guidelines to improve accuracy, privacy, and energy-efficiency. Finally,
AirMixML with DirMix({\alpha})-PC is shown to achieve reasonable accuracy
compared to a privacy-violating baseline with neither superposition nor PC. | [
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] |
Recent studies have shown that providing personalized explanations alongside
recommendations increases trust and perceived quality. Furthermore, it gives
users an opportunity to refine the recommendations by critiquing parts of the
explanations. On one hand, current recommender systems model the
recommendation, explanation, and critiquing objectives jointly, but this
creates an inherent trade-off between their respective performance. On the
other hand, although recent latent linear critiquing approaches are built upon
an existing recommender system, they suffer from computational inefficiency at
inference due to the objective optimized at each conversation's turn. We
address these deficiencies with M&Ms-VAE, a novel variational autoencoder for
recommendation and explanation that is based on multimodal modeling
assumptions. We train the model under a weak supervision scheme to simulate
both fully and partially observed variables. Then, we leverage the
generalization ability of a trained M&Ms-VAE model to embed the user preference
and the critique separately. Our work's most important innovation is our
critiquing module, which is built upon and trained in a self-supervised manner
with a simple ranking objective. Experiments on four real-world datasets
demonstrate that among state-of-the-art models, our system is the first to
dominate or match the performance in terms of recommendation, explanation, and
multi-step critiquing. Moreover, M&Ms-VAE processes the critiques up to 25.6x
faster than the best baselines. Finally, we show that our model infers coherent
joint and cross generation, even under weak supervision, thanks to our
multimodal-based modeling and training scheme. | [
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] |
Influenced by the field of Computer Vision, Generative Adversarial Networks
(GANs) are often adopted for the audio domain using fixed-size two-dimensional
spectrogram representations as the "image data". However, in the (musical)
audio domain, it is often desired to generate output of variable duration. This
paper presents VQCPC-GAN, an adversarial framework for synthesizing
variable-length audio by exploiting Vector-Quantized Contrastive Predictive
Coding (VQCPC). A sequence of VQCPC tokens extracted from real audio data
serves as conditional input to a GAN architecture, providing step-wise
time-dependent features of the generated content. The input noise z
(characteristic in adversarial architectures) remains fixed over time, ensuring
temporal consistency of global features. We evaluate the proposed model by
comparing a diverse set of metrics against various strong baselines. Results
show that, even though the baselines score best, VQCPC-GAN achieves comparable
performance even when generating variable-length audio. Numerous sound examples
are provided in the accompanying website, and we release the code for
reproducibility. | [
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] |
Recent advances in the e-commerce fashion industry have led to an exploration
of novel ways to enhance buyer experience via improved personalization.
Predicting a proper size for an item to recommend is an important
personalization challenge, and is being studied in this work. Earlier works in
this field either focused on modeling explicit buyer fitment feedback or
modeling of only a single aspect of the problem (e.g., specific category,
brand, etc.). More recent works proposed richer models, either content-based or
sequence-based, better accounting for content-based aspects of the problem or
better modeling the buyer's online journey. However, both these approaches fail
in certain scenarios: either when encountering unseen items (sequence-based
models) or when encountering new users (content-based models).
To address the aforementioned gaps, we propose PreSizE - a novel deep
learning framework which utilizes Transformers for accurate size prediction.
PreSizE models the effect of both content-based attributes, such as brand and
category, and the buyer's purchase history on her size preferences. Using an
extensive set of experiments on a large-scale e-commerce dataset, we
demonstrate that PreSizE is capable of achieving superior prediction
performance compared to previous state-of-the-art baselines. By encoding item
attributes, PreSizE better handles cold-start cases with unseen items, and
cases where buyers have little past purchase data. As a proof of concept, we
demonstrate that size predictions made by PreSizE can be effectively integrated
into an existing production recommender system yielding very effective features
and significantly improving recommendations. | [
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] |
Decision-making for self-adaptation approaches need to address different
challenges, including the quantification of the uncertainty of events that
cannot be foreseen in advance and their effects, and dealing with conflicting
objectives that inherently involve multi-objective decision making (e.g.,
avoiding costs vs. providing reliable service). To enable researchers to
evaluate and compare decision-making techniques for self-adaptation, we present
the RDMSim exemplar. RDMSim enables researchers to evaluate and compare
techniques for decision-making under environmental uncertainty that support
self-adaptation. The focus of the exemplar is on the domain problem related to
Remote Data Mirroring, which gives opportunity to face the challenges described
above. RDMSim provides probe and effector components for easy integration with
external adaptation managers, which are associated with decision-making
techniques and based on the MAPE-K loop. Specifically, the paper presents (i)
RDMSim, a simulator for real-world experimentation, (ii) a set of realistic
simulation scenarios that can be used for experimentation and comparison
purposes, (iii) data for the sake of comparison. | [
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] |
AI-based systems are software systems with functionalities enabled by at
least one AI component (e.g., for image- and speech-recognition, and autonomous
driving). AI-based systems are becoming pervasive in society due to advances in
AI. However, there is limited synthesized knowledge on Software Engineering
(SE) approaches for building, operating, and maintaining AI-based systems. To
collect and analyze state-of-the-art knowledge about SE for AI-based systems,
we conducted a systematic mapping study. We considered 248 studies published
between January 2010 and March 2020. SE for AI-based systems is an emerging
research area, where more than 2/3 of the studies have been published since
2018. The most studied properties of AI-based systems are dependability and
safety. We identified multiple SE approaches for AI-based systems, which we
classified according to the SWEBOK areas. Studies related to software testing
and software quality are very prevalent, while areas like software maintenance
seem neglected. Data-related issues are the most recurrent challenges. Our
results are valuable for: researchers, to quickly understand the state of the
art and learn which topics need more research; practitioners, to learn about
the approaches and challenges that SE entails for AI-based systems; and,
educators, to bridge the gap among SE and AI in their curricula. | [
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] |
Although neural networks are capable of reaching astonishing performances on
a wide variety of contexts, properly training networks on complicated tasks
requires expertise and can be expensive from a computational perspective. In
industrial applications, data coming from an open-world setting might widely
differ from the benchmark datasets on which a network was trained. Being able
to monitor the presence of such variations without retraining the network is of
crucial importance. In this article, we develop a method to monitor trained
neural networks based on the topological properties of their activation graphs.
To each new observation, we assign a Topological Uncertainty, a score that aims
to assess the reliability of the predictions by investigating the whole network
instead of its final layer only, as typically done by practitioners. Our
approach entirely works at a post-training level and does not require any
assumption on the network architecture, optimization scheme, nor the use of
data augmentation or auxiliary datasets; and can be faithfully applied on a
large range of network architectures and data types. We showcase experimentally
the potential of Topological Uncertainty in the context of trained network
selection, Out-Of-Distribution detection, and shift-detection, both on
synthetic and real datasets of images and graphs. | [
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In educational applications, Knowledge Tracing (KT), the problem of
accurately predicting students' responses to future questions by summarizing
their knowledge states, has been widely studied for decades as it is considered
a fundamental task towards adaptive online learning. Among all the proposed KT
methods, Deep Knowledge Tracing (DKT) and its variants are by far the most
effective ones due to the high flexibility of the neural network. However, DKT
often ignores the inherent differences between students (e.g. memory skills,
reasoning skills, ...), averaging the performances of all students, leading to
the lack of personalization, and therefore was considered insufficient for
adaptive learning. To alleviate this problem, in this paper, we proposed
Leveled Attentive KNowledge TrAcing (LANA), which firstly uses a novel
student-related features extractor (SRFE) to distill students' unique inherent
properties from their respective interactive sequences. Secondly, the pivot
module was utilized to dynamically reconstruct the decoder of the neural
network on attention of the extracted features, successfully distinguishing the
performance between students over time. Moreover, inspired by Item Response
Theory (IRT), the interpretable Rasch model was used to cluster students by
their ability levels, and thereby utilizing leveled learning to assign
different encoders to different groups of students. With pivot module
reconstructed the decoder for individual students and leveled learning
specialized encoders for groups, personalized DKT was achieved. Extensive
experiments conducted on two real-world large-scale datasets demonstrated that
our proposed LANA improves the AUC score by at least 1.00% (i.e. EdNet 1.46%
and RAIEd2020 1.00%), substantially surpassing the other State-Of-The-Art KT
methods. | [
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] |
Recent years have witnessed the fast development of the emerging topic of
Graph Learning based Recommender Systems (GLRS). GLRS employ advanced graph
learning approaches to model users' preferences and intentions as well as
items' characteristics for recommendations. Differently from other RS
approaches, including content-based filtering and collaborative filtering, GLRS
are built on graphs where the important objects, e.g., users, items, and
attributes, are either explicitly or implicitly connected. With the rapid
development of graph learning techniques, exploring and exploiting homogeneous
or heterogeneous relations in graphs are a promising direction for building
more effective RS. In this paper, we provide a systematic review of GLRS, by
discussing how they extract important knowledge from graph-based
representations to improve the accuracy, reliability and explainability of the
recommendations. First, we characterize and formalize GLRS, and then summarize
and categorize the key challenges and main progress in this novel research
area. Finally, we share some new research directions in this vibrant area. | [
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] |
Reservoir computing (RC) offers efficient temporal data processing with a low
training cost by separating recurrent neural networks into a fixed network with
recurrent connections and a trainable linear network. The quality of the fixed
network, called reservoir, is the most important factor that determines the
performance of the RC system. In this paper, we investigate the influence of
the hierarchical reservoir structure on the properties of the reservoir and the
performance of the RC system. Analogous to deep neural networks, stacking
sub-reservoirs in series is an efficient way to enhance the nonlinearity of
data transformation to high-dimensional space and expand the diversity of
temporal information captured by the reservoir. These deep reservoir systems
offer better performance when compared to simply increasing the size of the
reservoir or the number of sub-reservoirs. Low frequency components are mainly
captured by the sub-reservoirs in later stage of the deep reservoir structure,
similar to observations that more abstract information can be extracted by
layers in the late stage of deep neural networks. When the total size of the
reservoir is fixed, tradeoff between the number of sub-reservoirs and the size
of each sub-reservoir needs to be carefully considered, due to the degraded
ability of individual sub-reservoirs at small sizes. Improved performance of
the deep reservoir structure alleviates the difficulty of implementing the RC
system on hardware systems. | [
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] |
There is a growing interest in using electric vehicles (EVs) and drones for
many applications. However, battery-oriented issues, including range anxiety
and battery degradation, impede adoption. Battery swap stations are one
alternative to reduce these concerns that allow the swap of depleted for full
batteries in minutes. We consider the problem of deriving actions at a battery
swap station when explicitly considering the uncertain arrival of swap demand,
battery degradation, and replacement. We model the operations at a battery swap
station using a finite horizon Markov Decision Process model for the stochastic
scheduling, allocation, and inventory replenishment problem (SAIRP), which
determines when and how many batteries are charged, discharged, and replaced
over time. We present theoretical proofs for the monotonicity of the value
function and monotone structure of an optimal policy for special SAIRP cases.
Due to the curses of dimensionality, we develop a new monotone approximate
dynamic programming (ADP) method, which intelligently initializes a value
function approximation using regression. In computational tests, we demonstrate
the superior performance of the new regression-based monotone ADP method as
compared to exact methods and other monotone ADP methods. Further, with the
tests, we deduce policy insights for drone swap stations. | [
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] |
Academia and industry have developed several platforms to support the popular
privacy-preserving distributed learning method -- Federated Learning (FL).
However, these platforms are complex to use and require a deep understanding of
FL, which imposes high barriers to entry for beginners, limits the productivity
of researchers, and compromises deployment efficiency. In this paper, we
propose the first low-code FL platform, EasyFL, to enable users with various
levels of expertise to experiment and prototype FL applications with little
coding. We achieve this goal while ensuring great flexibility and extensibility
for customization by unifying simple API design, modular design, and granular
training flow abstraction. With only a few lines of code, EasyFL empowers them
with many out-of-the-box functionalities to accelerate experimentation and
deployment. These practical functionalities are heterogeneity simulation,
comprehensive tracking, distributed training optimization, and seamless
deployment. They are proposed based on challenges identified in the proposed FL
life cycle. Compared with other platforms, EasyFL not only requires just three
lines of code (at least 10x lesser) to build a vanilla FL application but also
incurs lower training overhead. Besides, our evaluations demonstrate that
EasyFL expedites distributed training by 1.5x. It also improves the efficiency
of deployment. We believe that EasyFL will increase the productivity of
researchers and democratize FL to wider audiences. | [
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] |
Graph Neural Networks have emerged as a useful tool to learn on the data by
applying additional constraints based on the graph structure. These graphs are
often created with assumed intrinsic relations between the entities. In recent
years, there have been tremendous improvements in the architecture design,
pushing the performance up in various prediction tasks. In general, these
neural architectures combine layer depth and node feature aggregation steps.
This makes it challenging to analyze the importance of features at various hops
and the expressiveness of the neural network layers. As different graph
datasets show varying levels of homophily and heterophily in features and class
label distribution, it becomes essential to understand which features are
important for the prediction tasks without any prior information. In this work,
we decouple the node feature aggregation step and depth of graph neural network
and introduce several key design strategies for graph neural networks. More
specifically, we propose to use softmax as a regularizer and "Soft-Selector" of
features aggregated from neighbors at different hop distances; and
"Hop-Normalization" over GNN layers. Combining these techniques, we present a
simple and shallow model, Feature Selection Graph Neural Network (FSGNN), and
show empirically that the proposed model outperforms other state of the art GNN
models and achieves up to 64% improvements in accuracy on node classification
tasks. Moreover, analyzing the learned soft-selection parameters of the model
provides a simple way to study the importance of features in the prediction
tasks. Finally, we demonstrate with experiments that the model is scalable for
large graphs with millions of nodes and billions of edges. | [
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Near-term quantum computers provide a promising platform for finding ground
states of quantum systems, which is an essential task in physics, chemistry,
and materials science. Near-term approaches, however, are constrained by the
effects of noise as well as the limited resources of near-term quantum
hardware. We introduce "neural error mitigation," which uses neural networks to
improve estimates of ground states and ground-state observables obtained using
near-term quantum simulations. To demonstrate our method's broad applicability,
we employ neural error mitigation to find the ground states of the H$_2$ and
LiH molecular Hamiltonians, as well as the lattice Schwinger model, prepared
via the variational quantum eigensolver (VQE). Our results show that neural
error mitigation improves numerical and experimental VQE computations to yield
low energy errors, high fidelities, and accurate estimations of more-complex
observables like order parameters and entanglement entropy, without requiring
additional quantum resources. Furthermore, neural error mitigation is agnostic
with respect to the quantum state preparation algorithm used, the quantum
hardware it is implemented on, and the particular noise channel affecting the
experiment, contributing to its versatility as a tool for quantum simulation. | [
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] |
Machine-generated artworks are now part of the contemporary art scene: they
are attracting significant investments and they are presented in exhibitions
together with those created by human artists. These artworks are mainly based
on generative deep learning techniques, which have seen a formidable
development and remarkable refinement in the very recent years. Given the
inherent characteristics of these techniques, a series of novel legal problems
arise. In this article, we consider a set of key questions in the area of
generative deep learning for the arts, including the following: is it possible
to use copyrighted works as training set for generative models? How do we
legally store their copies in order to perform the training process? Who (if
someone) will own the copyright on the generated data? We try to answer these
questions considering the law in force in both the United States of America and
the European Union, and potential future alternatives. We then extend our
analysis to code generation, which is an emerging area of generative deep
learning. Finally, we also formulate a set of practical guidelines for artists
and developers working on deep learning generated art, as well as some policy
suggestions for policymakers. | [
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] |
Recommender systems are gaining increasing and critical impacts on human and
society since a growing number of users use them for information seeking and
decision making. Therefore, it is crucial to address the potential unfairness
problems in recommendations. Just like users have personalized preferences on
items, users' demands for fairness are also personalized in many scenarios.
Therefore, it is important to provide personalized fair recommendations for
users to satisfy their personalized fairness demands. Besides, previous works
on fair recommendation mainly focus on association-based fairness. However, it
is important to advance from associative fairness notions to causal fairness
notions for assessing fairness more properly in recommender systems. Based on
the above considerations, this paper focuses on achieving personalized
counterfactual fairness for users in recommender systems. To this end, we
introduce a framework for achieving counterfactually fair recommendations
through adversary learning by generating feature-independent user embeddings
for recommendation. The framework allows recommender systems to achieve
personalized fairness for users while also covering non-personalized
situations. Experiments on two real-world datasets with shallow and deep
recommendation algorithms show that our method can generate fairer
recommendations for users with a desirable recommendation performance. | [
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Reinforcement learning (RL) can be used to create a decision-making agent for
autonomous driving. However, previous approaches provide only black-box
solutions, which do not offer information on how confident the agent is about
its decisions. An estimate of both the aleatoric and epistemic uncertainty of
the agent's decisions is fundamental for real-world applications of autonomous
driving. Therefore, this paper introduces the Ensemble Quantile Networks (EQN)
method, which combines distributional RL with an ensemble approach, to obtain a
complete uncertainty estimate. The distribution over returns is estimated by
learning its quantile function implicitly, which gives the aleatoric
uncertainty, whereas an ensemble of agents is trained on bootstrapped data to
provide a Bayesian estimation of the epistemic uncertainty. A criterion for
classifying which decisions that have an unacceptable uncertainty is also
introduced. The results show that the EQN method can balance risk and time
efficiency in different occluded intersection scenarios, by considering the
estimated aleatoric uncertainty. Furthermore, it is shown that the trained
agent can use the epistemic uncertainty information to identify situations that
the agent has not been trained for and thereby avoid making unfounded,
potentially dangerous, decisions outside of the training distribution. | [
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] |
The threats faced by cyber-physical systems (CPSs) in critical infrastructure
have motivated research into a multitude of attack detection mechanisms,
including anomaly detectors based on neural network models. The effectiveness
of anomaly detectors can be assessed by subjecting them to test suites of
attacks, but less consideration has been given to adversarial attackers that
craft noise specifically designed to deceive them. While successfully applied
in domains such as images and audio, adversarial attacks are much harder to
implement in CPSs due to the presence of other built-in defence mechanisms such
as rule checkers(or invariant checkers). In this work, we present an
adversarial attack that simultaneously evades the anomaly detectors and rule
checkers of a CPS. Inspired by existing gradient-based approaches, our
adversarial attack crafts noise over the sensor and actuator values, then uses
a genetic algorithm to optimise the latter, ensuring that the neural network
and the rule checking system are both deceived.We implemented our approach for
two real-world critical infrastructure testbeds, successfully reducing the
classification accuracy of their detectors by over 50% on average, while
simultaneously avoiding detection by rule checkers. Finally, we explore whether
these attacks can be mitigated by training the detectors on adversarial
samples. | [
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] |
Before researchers rush to reason across all available data or try complex
methods, perhaps it is prudent to first check for simpler alternatives.
Specifically, if the historical data has the most information in some small
region, perhaps a model learned from that region would suffice for the rest of
the project.
To support this claim, we offer a case study with 240 projects, where we find
that the information in those projects "clump" towards the earliest parts of
the project. A quality prediction model learned from just the first 150 commits
works as well, or better than state-of-the-art alternatives. Using just this
"early bird" data, we can build models very quickly and very early in the
project life cycle. Moreover, using this early bird method, we have shown that
a simple model (with just a few features) generalizes to hundreds of projects.
Based on this experience, we doubt that prior work on generalizing quality
models may have needlessly complicated an inherently simple process. Further,
prior work that focused on later-life cycle data needs to be revisited since
their conclusions were drawn from relatively uninformative regions.
Replication note: all our data and scripts are available here:
https://github.com/snaraya7/early-bird | [
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Deep learning is gaining increasing popularity for spatiotemporal
forecasting. However, prior works have mostly focused on point estimates
without quantifying the uncertainty of the predictions. In high stakes domains,
being able to generate probabilistic forecasts with confidence intervals is
critical to risk assessment and decision making. Hence, a systematic study of
uncertainty quantification (UQ) methods for spatiotemporal forecasting is
missing in the community. In this paper, we describe two types of
spatiotemporal forecasting problems: regular grid-based and graph-based. Then
we analyze UQ methods from both the Bayesian and the frequentist point of view,
casting in a unified framework via statistical decision theory. Through
extensive experiments on real-world road network traffic, epidemics, and air
quality forecasting tasks, we reveal the statistical and computational
trade-offs for different UQ methods: Bayesian methods are typically more robust
in mean prediction, while confidence levels obtained from frequentist methods
provide more extensive coverage over data variations. Computationally, quantile
regression type methods are cheaper for a single confidence interval but
require re-training for different intervals. Sampling based methods generate
samples that can form multiple confidence intervals, albeit at a higher
computational cost. | [
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] |
We consider the problem of modeling trajectories of drivers in a road network
from the perspective of inverse reinforcement learning. Cars are detected by
sensors placed on sparsely distributed points on the street network of a city.
As rational agents, drivers are trying to maximize some reward function unknown
to an external observer. We apply the concept of random utility from
econometrics to model the unknown reward function as a function of observed and
unobserved features. In contrast to current inverse reinforcement learning
approaches, we do not assume that agents act according to a stochastic policy;
rather, we assume that agents act according to a deterministic optimal policy
and show that randomness in data arises because the exact rewards are not fully
observed by an external observer. We introduce the concept of extended state to
cope with unobserved features and develop a Markov decision process formulation
of drivers decisions. We present theoretical results which guarantee the
existence of solutions and show that maximum entropy inverse reinforcement
learning is a particular case of our approach. Finally, we illustrate Bayesian
inference on model parameters through a case study with real trajectory data
from a large city in Brazil. | [
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] |
A community reveals the features and connections of its members that are
different from those in other communities in a network. Detecting communities
is of great significance in network analysis. Despite the classical spectral
clustering and statistical inference methods, we notice a significant
development of deep learning techniques for community detection in recent years
with their advantages in handling high dimensional network data. Hence, a
comprehensive overview of community detection's latest progress through deep
learning is timely to academics and practitioners. This survey devises and
proposes a new taxonomy covering different state-of-the-art methods, including
deep learning-based models upon deep neural networks, deep nonnegative matrix
factorization and deep sparse filtering. The main category, i.e., deep neural
networks, is further divided into convolutional networks, graph attention
networks, generative adversarial networks and autoencoders. The survey also
summarizes the popular benchmark data sets, evaluation metrics, and open-source
implementations to address experimentation settings. We then discuss the
practical applications of community detection in various domains and point to
implementation scenarios. Finally, we outline future directions by suggesting
challenging topics in this fast-growing deep learning field. | [
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] |
The lack of explainability is one of the most prominent disadvantages of deep
learning applications in omics. This "black box" problem can undermine the
credibility and limit the practical implementation of biomedical deep learning
models. Here we present XOmiVAE, a variational autoencoder (VAE) based
interpretable deep learning model for cancer classification using
high-dimensional omics data. XOmiVAE is capable of revealing the contribution
of each gene and latent dimension for each classification prediction, and the
correlation between each gene and each latent dimension. It is also
demonstrated that XOmiVAE can explain not only the supervised classification
but the unsupervised clustering results from the deep learning network. To the
best of our knowledge, XOmiVAE is one of the first activation level-based
interpretable deep learning models explaining novel clusters generated by VAE.
The explainable results generated by XOmiVAE were validated by both the
performance of downstream tasks and the biomedical knowledge. In our
experiments, XOmiVAE explanations of deep learning based cancer classification
and clustering aligned with current domain knowledge including biological
annotation and academic literature, which shows great potential for novel
biomedical knowledge discovery from deep learning models. | [
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] |
We present a novel approach based on artificial neural networks, so-called
geodesyNets, and present compelling evidence of their ability to serve as
accurate geodetic models of highly irregular bodies using minimal prior
information on the body. The approach does not rely on the body shape
information but, if available, can harness it. GeodesyNets learn a
three-dimensional, differentiable, function representing the body density,
which we call neural density field. The body shape, as well as other geodetic
properties, can easily be recovered. We investigate six different shapes
including the bodies 101955 Bennu, 67P Churyumov-Gerasimenko, 433 Eros and
25143 Itokawa for which shape models developed during close proximity surveys
are available. Both heterogeneous and homogeneous mass distributions are
considered. The gravitational acceleration computed from the trained
geodesyNets models, as well as the inferred body shape, show great accuracy in
all cases with a relative error on the predicted acceleration smaller than 1\%
even close to the asteroid surface. When the body shape information is
available, geodesyNets can seamlessly exploit it and be trained to represent a
high-fidelity neural density field able to give insights into the internal
structure of the body. This work introduces a new unexplored approach to
geodesy, adding a powerful tool to consolidated ones based on spherical
harmonics, mascon models and polyhedral gravity. | [
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] |
Large-scale ride-sharing systems combine real-time dispatching and routing
optimization over a rolling time horizon with a model predictive control (MPC)
component that relocates idle vehicles to anticipate the demand. The MPC
optimization operates over a longer time horizon to compensate for the inherent
myopic nature of the real-time dispatching. These longer time horizons are
beneficial for the quality of relocation decisions but increase computational
complexity. Consequently, the ride-sharing operators are often forced to use a
relatively short time horizon. To address this computational challenge, this
paper proposes a hybrid approach that combines machine learning and
optimization. The machine-learning component learns the optimal solution to the
MPC on the aggregated level to overcome the sparsity and high-dimensionality of
the solution. The optimization component transforms the machine-learning
prediction back to the original granularity through a tractable transportation
model. As a consequence, the original NP-hard MPC problem is reduced to a
polynomial time prediction and optimization, which allows the ride-sharing
operators to consider a longer time horizon. Experimental results show that the
hybrid approach achieves significantly better service quality than the MPC
optimization in terms of average rider waiting time, due to its ability to
model a longer horizon. | [
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] |
With the advent of Artificial Intelligence (AI)-empowered communications,
industry, academia, and standardization organizations are progressing on the
definition of mechanisms and procedures to address the increasing complexity of
future 5G and beyond communications. In this context, the International
Telecommunication Union (ITU) organized the first AI for 5G Challenge to bring
industry and academia together to introduce and solve representative problems
related to the application of Machine Learning (ML) to networks. In this paper,
we present the results gathered from Problem Statement~13 (PS-013), organized
by Universitat Pompeu Fabra (UPF), which primary goal was predicting the
performance of next-generation Wireless Local Area Networks (WLANs) applying
Channel Bonding (CB) techniques. In particular, we overview the ML models
proposed by participants (including Artificial Neural Networks, Graph Neural
Networks, Random Forest regression, and gradient boosting) and analyze their
performance on an open dataset generated using the IEEE 802.11ax-oriented
Komondor network simulator. The accuracy achieved by the proposed methods
demonstrates the suitability of ML for predicting the performance of WLANs.
Moreover, we discuss the importance of abstracting WLAN interactions to achieve
better results, and we argue that there is certainly room for improvement in
throughput prediction through ML. | [
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] |
The identification of protein-ligand interaction plays a key role in
biochemical research and drug discovery. Although deep learning has recently
shown great promise in discovering new drugs, there remains a gap between deep
learning-based and experimental approaches. Here we propose a novel framework,
named AIMEE, integrating AI Model and Enzymology Experiments, to identify
inhibitors against 3CL protease of SARS-CoV-2, which has taken a significant
toll on people across the globe. From a bioactive chemical library, we have
conducted two rounds of experiments and identified six novel inhibitors with a
hit rate of 29.41%, and four of them showed an IC50 value less than 3 {\mu}M.
Moreover, we explored the interpretability of the central model in AIMEE,
mapping the deep learning extracted features to domain knowledge of chemical
properties. Based on this knowledge, a commercially available compound was
selected and proven to be an activity-based probe of 3CLpro. This work
highlights the great potential of combining deep learning models and
biochemical experiments for intelligent iteration and expanding the boundaries
of drug discovery. | [
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Together with the improvements in state-of-the-art accuracies of various
tasks, deep learning models are getting significantly larger. However, it is
extremely difficult to implement these large models because limited GPU memory
makes it impossible to fit large models into a single GPU or even a GPU server.
Besides, it is highly necessary to reduce the training time for large models.
Previous methods like Megatron-LM implemented a 1-Dimensional distributed
method to use GPUs to speed up the training. However, these methods have a high
communication overhead and a low scaling efficiency on large-scale clusters. To
solve these problems, we propose Tesseract, a highly scalable tensor
parallelism with a novel design. It increases efficiency by reducing
communication overhead and lowers the memory required for each GPU. By
introducing the novel dimension into tensor parallelism, Tesseract greatly
increases the memory capacity of tensor parallelism. Concretely, this new
dimension furthermore increases the degree of tensor parallelism. Compared to
previous 1-D and 2-D methods, Tesseract manages to reduce the communication
cost on each layer, resulting in speedups of 1.38x and 1.53x respectively with
strong scaling. In weak scaling experiments, Tesseract achieves a maximum of
4.0/1.7 times inference speedup and 3.4/1.7 times throughput improvement
compared to 1-D/2-D methods, respectively. By introducing Tesseract, we offer a
more efficient and scalable way to implement large deep learning models with
limited GPU resources. | [
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] |
Dynamic Network Embedding (DNE) has recently attracted considerable attention
due to the advantage of network embedding in various fields and the dynamic
nature of many real-world networks. An input dynamic network to DNE is often
assumed to have smooth changes over snapshots, which however would not hold for
all real-world scenarios. It is natural to ask if existing DNE methods can
perform well for an input dynamic network without smooth changes. To quantify
it, an index called Degree of Changes (DoCs) is suggested so that the smaller
DoCs indicates the smoother changes. Our comparative study shows several DNE
methods are not robust enough to different DoCs even if the corresponding input
dynamic networks come from the same dataset, which would make these methods
unreliable and hard to use for unknown real-world applications. To propose an
effective and more robust DNE method, we follow the notion of ensembles where
each base learner adopts an incremental Skip-Gram embedding model. To further
boost the performance, a simple yet effective strategy is designed to enhance
the diversity among base learners at each timestep by capturing different
levels of local-global topology. Extensive experiments demonstrate the superior
effectiveness and robustness of the proposed method compared to
state-of-the-art DNE methods, as well as the benefits of special designs in the
proposed method and its scalability. | [
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] |
Although deep-learning has been successfully applied in a variety of science
and engineering problems owing to its strong high-dimensional nonlinear mapping
capability, it is of limited use in scientific knowledge discovery. In this
work, we propose a deep-learning based framework to discover the macroscopic
governing equation of viscous gravity current based on high-resolution
microscopic simulation data without the need for prior knowledge of underlying
terms. For two typical scenarios with different viscosity ratios, the
deep-learning based equations exactly capture the same dominated terms as the
theoretically derived equations for describing long-term asymptotic behaviors,
which validates the proposed framework. Unknown macroscopic equations are then
obtained for describing short-term behaviors, and additional deep-learned
compensation terms are eventually discovered. Comparison of posterior tests
shows that the deep-learning based PDEs actually perform better than the
theoretically derived PDEs in predicting evolving viscous gravity currents for
both long-term and short-term regimes. Moreover, the proposed framework is
proven to be very robust against non-biased data noise for training, which is
up to 20%. Consequently, the presented deep-learning framework shows
considerable potential for discovering unrevealed intrinsic laws in scientific
semantic space from raw experimental or simulation results in data space. | [
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Internet-of-Things (IoT) and cyber-physical systems (CPSs) may consist of
thousands of devices connected in a complex network topology. The diversity and
complexity of these components present an enormous attack surface, allowing an
adversary to exploit security vulnerabilities of different devices to execute a
potent attack. Though significant efforts have been made to improve the
security of individual devices in these systems, little attention has been paid
to security at the aggregate level. In this article, we describe a
comprehensive risk management system, called GRAVITAS, for IoT/CPS that can
identify undiscovered attack vectors and optimize the placement of defenses
within the system for optimal performance and cost. While existing risk
management systems consider only known attacks, our model employs a machine
learning approach to extrapolate undiscovered exploits, enabling us to identify
attacks overlooked by manual penetration testing (pen-testing). The model is
flexible enough to analyze practically any IoT/CPS and provide the system
administrator with a concrete list of suggested defenses that can reduce system
vulnerability at optimal cost. GRAVITAS can be employed by governments,
companies, and system administrators to design secure IoT/CPS at scale,
providing a quantitative measure of security and efficiency in a world where
IoT/CPS devices will soon be ubiquitous. | [
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] |
How to learn a good predictor on data with missing values? Most efforts focus
on first imputing as well as possible and second learning on the completed data
to predict the outcome. Yet, this widespread practice has no theoretical
grounding. Here we show that for almost all imputation functions, an
impute-then-regress procedure with a powerful learner is Bayes optimal. This
result holds for all missing-values mechanisms, in contrast with the classic
statistical results that require missing-at-random settings to use imputation
in probabilistic modeling. Moreover, it implies that perfect conditional
imputation is not needed for good prediction asymptotically. In fact, we show
that on perfectly imputed data the best regression function will generally be
discontinuous, which makes it hard to learn. Crafting instead the imputation so
as to leave the regression function unchanged simply shifts the problem to
learning discontinuous imputations. Rather, we suggest that it is easier to
learn imputation and regression jointly. We propose such a procedure, adapting
NeuMiss, a neural network capturing the conditional links across observed and
unobserved variables whatever the missing-value pattern. Experiments confirm
that joint imputation and regression through NeuMiss is better than various two
step procedures in our experiments with finite number of samples. | [
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] |
The main objective of eXplainable Artificial Intelligence (XAI) is to provide
effective explanations for black-box classifiers. The existing literature lists
many desirable properties for explanations to be useful, but there is no
consensus on how to quantitatively evaluate explanations in practice. Moreover,
explanations are typically used only to inspect black-box models, and the
proactive use of explanations as a decision support is generally overlooked.
Among the many approaches to XAI, a widely adopted paradigm is Local Linear
Explanations - with LIME and SHAP emerging as state-of-the-art methods. We show
that these methods are plagued by many defects including unstable explanations,
divergence of actual implementations from the promised theoretical properties,
and explanations for the wrong label. This highlights the need to have standard
and unbiased evaluation procedures for Local Linear Explanations in the XAI
field. In this paper we address the problem of identifying a clear and
unambiguous set of metrics for the evaluation of Local Linear Explanations.
This set includes both existing and novel metrics defined specifically for this
class of explanations. All metrics have been included in an open Python
framework, named LEAF. The purpose of LEAF is to provide a reference for end
users to evaluate explanations in a standardised and unbiased way, and to guide
researchers towards developing improved explainable techniques. | [
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] |
We posit a new mechanism for cooperation in multi-agent reinforcement
learning (MARL) based upon any nonlinear function of the team's long-term
state-action occupancy measure, i.e., a \emph{general utility}. This subsumes
the cumulative return but also allows one to incorporate risk-sensitivity,
exploration, and priors. % We derive the {\bf D}ecentralized {\bf S}hadow
Reward {\bf A}ctor-{\bf C}ritic (DSAC) in which agents alternate between policy
evaluation (critic), weighted averaging with neighbors (information mixing),
and local gradient updates for their policy parameters (actor). DSAC augments
the classic critic step by requiring agents to (i) estimate their local
occupancy measure in order to (ii) estimate the derivative of the local utility
with respect to their occupancy measure, i.e., the "shadow reward". DSAC
converges to $\epsilon$-stationarity in $\mathcal{O}(1/\epsilon^{2.5})$
(Theorem \ref{theorem:final}) or faster $\mathcal{O}(1/\epsilon^{2})$
(Corollary \ref{corollary:communication}) steps with high probability,
depending on the amount of communications. We further establish the
non-existence of spurious stationary points for this problem, that is, DSAC
finds the globally optimal policy (Corollary \ref{corollary:global}).
Experiments demonstrate the merits of goals beyond the cumulative return in
cooperative MARL. | [
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] |
Finding different solutions to the same problem is a key aspect of
intelligence associated with creativity and adaptation to novel situations. In
reinforcement learning, a set of diverse policies can be useful for
exploration, transfer, hierarchy, and robustness. We propose Diverse Successive
Policies, a method for discovering policies that are diverse in the space of
Successor Features, while assuring that they are near optimal. We formalize the
problem as a Constrained Markov Decision Process (CMDP) where the goal is to
find policies that maximize diversity, characterized by an intrinsic diversity
reward, while remaining near-optimal with respect to the extrinsic reward of
the MDP. We also analyze how recently proposed robustness and discrimination
rewards perform and find that they are sensitive to the initialization of the
procedure and may converge to sub-optimal solutions. To alleviate this, we
propose new explicit diversity rewards that aim to minimize the correlation
between the Successor Features of the policies in the set. We compare the
different diversity mechanisms in the DeepMind Control Suite and find that the
type of explicit diversity we are proposing is important to discover distinct
behavior, like for example different locomotion patterns. | [
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] |
Disentangling two or more cables requires many steps to remove crossings
between and within cables. We formalize the problem of disentangling multiple
cables and present an algorithm, Iterative Reduction Of Non-planar Multiple
cAble kNots (IRON-MAN), that outputs robot actions to remove crossings from
multi-cable knotted structures. We instantiate this algorithm with a learned
perception system, inspired by prior work in single-cable untying that given an
image input, can disentangle two-cable twists, three-cable braids, and knots of
two or three cables, such as overhand, square, carrick bend, sheet bend, crown,
and fisherman's knots. IRON-MAN keeps track of task-relevant keypoints
corresponding to target cable endpoints and crossings and iteratively
disentangles the cables by identifying and undoing crossings that are critical
to knot structure. Using a da Vinci surgical robot, we experimentally evaluate
the effectiveness of IRON-MAN on untangling multi-cable knots of types that
appear in the training data, as well as generalizing to novel classes of
multi-cable knots. Results suggest that IRON-MAN is effective in disentangling
knots involving up to three cables with 80.5% success and generalizing to knot
types that are not present during training, with cables of both distinct or
identical colors. | [
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] |
Multi-regional interaction among neuronal populations underlies the brain's
processing of rich sensory information in our daily lives. Recent neuroscience
and neuroimaging studies have increasingly used naturalistic stimuli and
experimental design to identify such realistic sensory computation in the
brain. However, existing methods for cross-areal interaction analysis with
dimensionality reduction, such as reduced-rank regression and canonical
correlation analysis, have limited applicability and interpretability in
naturalistic settings because they usually do not appropriately 'demix' neural
interactions into those associated with different types of task parameters or
stimulus features (e.g., visual or audio). In this paper, we develop a new
method for cross-areal interaction analysis that uses the rich task or stimulus
parameters to reveal how and what types of information are shared by different
neural populations. The proposed neural demixed shared component analysis
combines existing dimensionality reduction methods with a practical neural
network implementation of functional analysis of variance with latent
variables, thereby efficiently demixing nonlinear effects of continuous and
multimodal stimuli. We also propose a simplifying alternative under the
assumptions of linear effects and unimodal stimuli. To demonstrate our methods,
we analyzed two human neuroimaging datasets of participants watching
naturalistic videos of movies and dance movements. The results demonstrate that
our methods provide new insights into multi-regional interaction in the brain
during naturalistic sensory inputs, which cannot be captured by conventional
techniques. | [
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] |
In E-commerce, vouchers are important marketing tools to enhance users'
engagement and boost sales and revenue. The likelihood that a user redeems a
voucher is a key factor in voucher distribution decision. User-item
Click-Through-Rate (CTR) models are often applied to predict the user-voucher
redemption rate. However, the voucher scenario involves more complicated
relations among users, items and vouchers. The users' historical behavior in a
voucher collection activity reflects users' voucher usage patterns, which is
nevertheless overlooked by the CTR-based solutions. In this paper, we propose a
Deep Multi-behavior Graph Networks (DMBGN) to shed light on this field for the
voucher redemption rate prediction. The complex structural user-voucher-item
relationships are captured by a User-Behavior Voucher Graph (UVG). User
behavior happening both before and after voucher collection is taken into
consideration, and a high-level representation is extracted by Higher-order
Graph Neural Networks. On top of a sequence of UVGs, an attention network is
built which can help to learn users' long-term voucher redemption preference.
Extensive experiments on three large-scale production datasets demonstrate the
proposed DMBGN model is effective, with 10% to 16% relative AUC improvement
over Deep Neural Networks (DNN), and 2% to 4% AUC improvement over Deep
Interest Network (DIN). Source code and a sample dataset are made publicly
available to facilitate future research. | [
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] |
In e-commerce advertising, it is crucial to jointly consider various
performance metrics, e.g., user experience, advertiser utility, and platform
revenue. Traditional auction mechanisms, such as GSP and VCG auctions, can be
suboptimal due to their fixed allocation rules to optimize a single performance
metric (e.g., revenue or social welfare). Recently, data-driven auctions,
learned directly from auction outcomes to optimize multiple performance
metrics, have attracted increasing research interests. However, the procedure
of auction mechanisms involves various discrete calculation operations, making
it challenging to be compatible with continuous optimization pipelines in
machine learning. In this paper, we design \underline{D}eep \underline{N}eural
\underline{A}uctions (DNAs) to enable end-to-end auction learning by proposing
a differentiable model to relax the discrete sorting operation, a key component
in auctions. We optimize the performance metrics by developing deep models to
efficiently extract contexts from auctions, providing rich features for auction
design. We further integrate the game theoretical conditions within the model
design, to guarantee the stability of the auctions. DNAs have been successfully
deployed in the e-commerce advertising system at Taobao. Experimental
evaluation results on both large-scale data set as well as online A/B test
demonstrated that DNAs significantly outperformed other mechanisms widely
adopted in industry. | [
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] |
Given a stream of graph edges from a dynamic graph, how can we assign anomaly
scores to edges and subgraphs in an online manner, for the purpose of detecting
unusual behavior, using constant time and memory? For example, in intrusion
detection, existing work seeks to detect either anomalous edges or anomalous
subgraphs, but not both. In this paper, we first extend the count-min sketch
data structure to a higher-order sketch. This higher-order sketch has the
useful property of preserving the dense subgraph structure (dense subgraphs in
the input turn into dense submatrices in the data structure). We then propose 4
online algorithms that utilize this enhanced data structure, which (a) detect
both edge and graph anomalies; (b) process each edge and graph in constant
memory and constant update time per newly arriving edge, and; (c) outperform
state-of-the-art baselines on 4 real-world datasets. Our method is the first
streaming approach that incorporates dense subgraph search to detect graph
anomalies in constant memory and time. | [
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