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A wide range of verification methods have been proposed to verify the safety
properties of deep neural networks ensuring that the networks function
correctly in critical applications. However, many well-known verification tools
still struggle with complicated network architectures and large network sizes.
In this work, we propose a network reduction technique as a pre-processing
method prior to verification. The proposed method reduces neural networks via
eliminating stable ReLU neurons, and transforming them into a sequential neural
network consisting of ReLU and Affine layers which can be handled by the most
verification tools. We instantiate the reduction technique on the
state-of-the-art complete and incomplete verification tools, including
alpha-beta-crown, VeriNet and PRIMA. Our experiments on a large set of
benchmarks indicate that the proposed technique can significantly reduce neural
networks and speed up existing verification tools. Furthermore, the experiment
results also show that network reduction can improve the availability of
existing verification tools on many networks by reducing them into sequential
neural networks. | [
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] |
We study Off-Policy Evaluation (OPE) in contextual bandit settings with large
action spaces. The benchmark estimators suffer from severe bias and variance
tradeoffs. Parametric approaches suffer from bias due to difficulty specifying
the correct model, whereas ones with importance weight suffer from variance. To
overcome these limitations, Marginalized Inverse Propensity Scoring (MIPS) was
proposed to mitigate the estimator's variance via embeddings of an action. To
make the estimator more accurate, we propose the doubly robust estimator of
MIPS called the Marginalized Doubly Robust (MDR) estimator. Theoretical
analysis shows that the proposed estimator is unbiased under weaker assumptions
than MIPS while maintaining variance reduction against IPS, which was the main
advantage of MIPS. The empirical experiment verifies the supremacy of MDR
against existing estimators. | [
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] |
In recent years, the traditional feature engineering process for training
machine learning models is being automated by the feature extraction layers
integrated in deep learning architectures. In wireless networks, many studies
were conducted in automatic learning of feature representations for
domain-related challenges. However, most of the existing works assume some
supervision along the learning process by using labels to optimize the model.
In this paper, we investigate an approach to learning feature representations
for wireless transmission clustering in a completely unsupervised manner, i.e.
requiring no labels in the process. We propose a model based on convolutional
neural networks that automatically learns a reduced dimensionality
representation of the input data with 99.3% less components compared to a
baseline principal component analysis (PCA). We show that the automatic
representation learning is able to extract fine-grained clusters containing the
shapes of the wireless transmission bursts, while the baseline enables only
general separability of the data based on the background noise. | [
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] |
Large language models (LLMs) are incredibly powerful at comprehending and
generating data in the form of text, but are brittle and error-prone. There has
been an advent of toolkits and recipes centered around so-called prompt
engineering-the process of asking an LLM to do something via a series of
prompts. However, for LLM-powered data processing workflows, in particular,
optimizing for quality, while keeping cost bounded, is a tedious, manual
process. We put forth a vision for declarative prompt engineering. We view LLMs
like crowd workers and leverage ideas from the declarative crowdsourcing
literature-including leveraging multiple prompting strategies, ensuring
internal consistency, and exploring hybrid-LLM-non-LLM approaches-to make
prompt engineering a more principled process. Preliminary case studies on
sorting, entity resolution, and imputation demonstrate the promise of our
approach | [
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] |
Motion planning can be cast as a trajectory optimisation problem where a cost
is minimised as a function of the trajectory being generated. In complex
environments with several obstacles and complicated geometry, this optimisation
problem is usually difficult to solve and prone to local minima. However,
recent advancements in computing hardware allow for parallel trajectory
optimisation where multiple solutions are obtained simultaneously, each
initialised from a different starting point. Unfortunately, without a strategy
preventing two solutions to collapse on each other, naive parallel optimisation
can suffer from mode collapse diminishing the efficiency of the approach and
the likelihood of finding a global solution. In this paper we leverage on
recent advances in the theory of rough paths to devise an algorithm for
parallel trajectory optimisation that promotes diversity over the range of
solutions, therefore avoiding mode collapses and achieving better global
properties. Our approach builds on path signatures and Hilbert space
representations of trajectories, and connects parallel variational inference
for trajectory estimation with diversity promoting kernels. We empirically
demonstrate that this strategy achieves lower average costs than competing
alternatives on a range of problems, from 2D navigation to robotic manipulators
operating in cluttered environments. | [
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As data shift or new data become available, updating clinical machine
learning models may be necessary to maintain or improve performance over time.
However, updating a model can introduce compatibility issues when the behavior
of the updated model does not align with user expectations, resulting in poor
user-model team performance. Existing compatibility measures depend on model
decision thresholds, limiting their applicability in settings where models are
used to generate rankings based on estimated risk. To address this limitation,
we propose a novel rank-based compatibility measure, $C^R$, and a new loss
function that aims to optimize discriminative performance while encouraging
good compatibility. Applied to a case study in mortality risk stratification
leveraging data from MIMIC, our approach yields more compatible models while
maintaining discriminative performance compared to existing model selection
techniques, with an increase in $C^R$ of $0.019$ ($95\%$ confidence interval:
$0.005$, $0.035$). This work provides new tools to analyze and update risk
stratification models used in clinical care. | [
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] |
Large Language Models (LLMs) have emerged as a transformative force,
revolutionizing numerous fields well beyond the conventional domain of Natural
Language Processing (NLP) and garnering unprecedented attention. As LLM
technology continues to progress, the telecom industry is facing the prospect
of its potential impact on its landscape. To elucidate these implications, we
delve into the inner workings of LLMs, providing insights into their current
capabilities and limitations. We also examine the use cases that can be readily
implemented in the telecom industry, streamlining numerous tasks that currently
hinder operational efficiency and demand significant manpower and engineering
expertise. Furthermore, we uncover essential research directions that deal with
the distinctive challenges of utilizing the LLMs within the telecom domain.
Addressing these challenges represents a significant stride towards fully
harnessing the potential of LLMs and unlocking their capabilities to the
fullest extent within the telecom domain. | [
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] |
Traffic management systems play a vital role in ensuring safe and efficient
transportation on roads. However, the use of advanced technologies in traffic
management systems has introduced new safety challenges. Therefore, it is
important to ensure the safety of these systems to prevent accidents and
minimize their impact on road users. In this survey, we provide a comprehensive
review of the literature on safety in traffic management systems. Specifically,
we discuss the different safety issues that arise in traffic management
systems, the current state of research on safety in these systems, and the
techniques and methods proposed to ensure the safety of these systems. We also
identify the limitations of the existing research and suggest future research
directions. | [
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] |
The unmanned aerial vehicle (UAV) network is popular these years due to its
various applications. In the UAV network, routing is significantly affected by
the distributed network topology, leading to the issue that UAVs are vulnerable
to deliberate damage. Hence, this paper focuses on the routing plan and
recovery for UAV networks with attacks. In detail, a deliberate attack model
based on the importance of nodes is designed to represent enemy attacks. Then,
a node importance ranking mechanism is presented, considering the degree of
nodes and link importance. However, it is intractable to handle the routing
problem by traditional methods for UAV networks, since link connections change
with the UAV availability. Hence, an intelligent algorithm based on
reinforcement learning is proposed to recover the routing path when UAVs are
attacked. Simulations are conducted and numerical results verify the proposed
mechanism performs better than other referred methods. | [
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] |
Blackjack or "21" is a popular card-based game of chance and skill. The
objective of the game is to win by obtaining a hand total higher than the
dealer's without exceeding 21. The ideal blackjack strategy will maximize
financial return in the long run while avoiding gambler's ruin. The stochastic
environment and inherent reward structure of blackjack presents an appealing
problem to better understand reinforcement learning agents in the presence of
environment variations. Here we consider a q-learning solution for optimal play
and investigate the rate of learning convergence of the algorithm as a function
of deck size. A blackjack simulator allowing for universal blackjack rules is
also implemented to demonstrate the extent to which a card counter perfectly
using the basic strategy and hi-lo system can bring the house to bankruptcy and
how environment variations impact this outcome. The novelty of our work is to
place this conceptual understanding of the impact of deck size in the context
of learning agent convergence. | [
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] |
Time domain astronomy is advancing towards the analysis of multiple massive
datasets in real time, prompting the development of multi-stream machine
learning models. In this work, we study Domain Adaptation (DA) for real/bogus
classification of astronomical alerts using four different datasets: HiTS, DES,
ATLAS, and ZTF. We study the domain shift between these datasets, and improve a
naive deep learning classification model by using a fine tuning approach and
semi-supervised deep DA via Minimax Entropy (MME). We compare the balanced
accuracy of these models for different source-target scenarios. We find that
both the fine tuning and MME models improve significantly the base model with
as few as one labeled item per class coming from the target dataset, but that
the MME does not compromise its performance on the source dataset. | [
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] |
Novel data sources bring new opportunities to improve the quality of
recommender systems. Impressions are a novel data source containing past
recommendations (shown items) and traditional interactions. Researchers may use
impressions to refine user preferences and overcome the current limitations in
recommender systems research. The relevance and interest of impressions have
increased over the years; hence, the need for a review of relevant work on this
type of recommenders. We present a systematic literature review on recommender
systems using impressions, focusing on three fundamental angles in research:
recommenders, datasets, and evaluation methodologies. We provide three
categorizations of papers describing recommenders using impressions, present
each reviewed paper in detail, describe datasets with impressions, and analyze
the existing evaluation methodologies. Lastly, we present open questions and
future directions of interest, highlighting aspects missing in the literature
that can be addressed in future works. | [
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] |
The task of inductive knowledge graph completion requires models to learn
inference patterns from a training graph, which can then be used to make
predictions on a disjoint test graph. Rule-based methods seem like a natural
fit for this task, but in practice they significantly underperform
state-of-the-art methods based on Graph Neural Networks (GNNs), such as NBFNet.
We hypothesise that the underperformance of rule-based methods is due to two
factors: (i) implausible entities are not ranked at all and (ii) only the most
informative path is taken into account when determining the confidence in a
given link prediction answer. To analyse the impact of these factors, we study
a number of variants of a rule-based approach, which are specifically aimed at
addressing the aforementioned issues. We find that the resulting models can
achieve a performance which is close to that of NBFNet. Crucially, the
considered variants only use a small fraction of the evidence that NBFNet
relies on, which means that they largely keep the interpretability advantage of
rule-based methods. Moreover, we show that a further variant, which does look
at the full KG, consistently outperforms NBFNet. | [
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] |
Building a graph neural network (GNN)-based recommender system without
violating user privacy proves challenging. Existing methods can be divided into
federated GNNs and decentralized GNNs. But both methods have undesirable
effects, i.e., low communication efficiency and privacy leakage. This paper
proposes DGREC, a novel decentralized GNN for privacy-preserving
recommendations, where users can choose to publicize their interactions. It
includes three stages, i.e., graph construction, local gradient calculation,
and global gradient passing. The first stage builds a local inner-item
hypergraph for each user and a global inter-user graph. The second stage models
user preference and calculates gradients on each local device. The third stage
designs a local differential privacy mechanism named secure gradient-sharing,
which proves strong privacy-preserving of users' private data. We conduct
extensive experiments on three public datasets to validate the consistent
superiority of our framework. | [
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] |
Quantum machine learning (QML) is a cross-disciplinary subject made up of two
of the most exciting research areas: quantum computing and classical machine
learning (ML), with ML and artificial intelligence (AI) being projected as the
first fields that will be impacted by the rise of quantum machines. Quantum
computers are being used today in drug discovery, material & molecular
modelling and finance. In this work, we discuss some upcoming active new
research areas in application of quantum machine learning (QML) in finance. We
discuss certain QML models that has become areas of active interest in the
financial world for various applications. We use real world financial dataset
and compare models such as qGAN (quantum generative adversarial networks) and
QCBM (quantum circuit Born machine) among others, using simulated environments.
For the qGAN, we define quantum circuits for discriminators and generators and
show promises of future quantum advantage via QML in finance. | [
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] |
Item representation holds significant importance in recommendation systems,
which encompasses domains such as news, retail, and videos. Retrieval and
ranking models utilise item representation to capture the user-item
relationship based on user behaviours. While existing representation learning
methods primarily focus on optimising item-based mechanisms, such as attention
and sequential modelling. However, these methods lack a modelling mechanism to
directly reflect user interests within the learned item representations.
Consequently, these methods may be less effective in capturing user interests
indirectly. To address this challenge, we propose a novel Interest-aware
Capsule network (IaCN) recommendation model, a model-agnostic framework that
directly learns interest-oriented item representations. IaCN serves as an
auxiliary task, enabling the joint learning of both item-based and
interest-based representations. This framework adopts existing recommendation
models without requiring substantial redesign. We evaluate the proposed
approach on benchmark datasets, exploring various scenarios involving different
deep neural networks, behaviour sequence lengths, and joint learning ratios of
interest-oriented item representations. Experimental results demonstrate
significant performance enhancements across diverse recommendation models,
validating the effectiveness of our approach. | [
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] |
Collaborative filtering-based recommender systems (RecSys) rely on learning
representations for users and items to predict preferences accurately.
Representation learning on the hypersphere is a promising approach due to its
desirable properties, such as alignment and uniformity. However, the sparsity
issue arises when it encounters RecSys. To address this issue, we propose a
novel approach, graph-based alignment and uniformity (GraphAU), that explicitly
considers high-order connectivities in the user-item bipartite graph. GraphAU
aligns the user/item embedding to the dense vector representations of
high-order neighbors using a neighborhood aggregator, eliminating the need to
compute the burdensome alignment to high-order neighborhoods individually. To
address the discrepancy in alignment losses, GraphAU includes a layer-wise
alignment pooling module to integrate alignment losses layer-wise. Experiments
on four datasets show that GraphAU significantly alleviates the sparsity issue
and achieves state-of-the-art performance. We open-source GraphAU at
https://github.com/YangLiangwei/GraphAU. | [
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] |
We present Spatial LibriSpeech, a spatial audio dataset with over 650 hours
of 19-channel audio, first-order ambisonics, and optional distractor noise.
Spatial LibriSpeech is designed for machine learning model training, and it
includes labels for source position, speaking direction, room acoustics and
geometry. Spatial LibriSpeech is generated by augmenting LibriSpeech samples
with 200k+ simulated acoustic conditions across 8k+ synthetic rooms. To
demonstrate the utility of our dataset, we train models on four spatial audio
tasks, resulting in a median absolute error of 6.60{\deg} on 3D source
localization, 0.43m on distance, 90.66ms on T30, and 2.74dB on DRR estimation.
We show that the same models generalize well to widely-used evaluation
datasets, e.g., obtaining a median absolute error of 12.43{\deg} on 3D source
localization on TUT Sound Events 2018, and 157.32ms on T30 estimation on ACE
Challenge. | [
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] |
Precision medicine fundamentally aims to establish causality between
dysregulated biochemical mechanisms and cancer subtypes. Omics-based cancer
subtyping has emerged as a revolutionary approach, as different level of omics
records the biochemical products of multistep processes in cancers. This paper
focuses on fully exploiting the potential of multi-omics data to improve cancer
subtyping outcomes, and hence developed MoCLIM, a representation learning
framework. MoCLIM independently extracts the informative features from distinct
omics modalities. Using a unified representation informed by contrastive
learning of different omics modalities, we can well-cluster the subtypes, given
cancer, into a lower latent space. This contrast can be interpreted as a
projection of inter-omics inference observed in biological networks.
Experimental results on six cancer datasets demonstrate that our approach
significantly improves data fit and subtyping performance in fewer
high-dimensional cancer instances. Moreover, our framework incorporates various
medical evaluations as the final component, providing high interpretability in
medical analysis. | [
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] |
This paper introduces our system designed for Track 2, which focuses on
locating manipulated regions, in the second Audio Deepfake Detection Challenge
(ADD 2023). Our approach involves the utilization of multiple detection systems
to identify splicing regions and determine their authenticity. Specifically, we
train and integrate two frame-level systems: one for boundary detection and the
other for deepfake detection. Additionally, we employ a third VAE model trained
exclusively on genuine data to determine the authenticity of a given audio
clip. Through the fusion of these three systems, our top-performing solution
for the ADD challenge achieves an impressive 82.23% sentence accuracy and an F1
score of 60.66%. This results in a final ADD score of 0.6713, securing the
first rank in Track 2 of ADD 2023. | [
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] |
Measures of rank correlation are commonly used in statistics to capture the
degree of concordance between two orderings of the same set of items. Standard
measures like Kendall's tau and Spearman's rho coefficient put equal emphasis
on each position of a ranking. Yet, motivated by applications in which some of
the positions (typically those on the top) are more important than others, a
few weighted variants of these measures have been proposed. Most of these
generalizations fail to meet desirable formal properties, however. Besides,
they are often quite inflexible in the sense of committing to a fixed weighing
scheme. In this paper, we propose a weighted rank correlation measure on the
basis of fuzzy order relations. Our measure, called scaled gamma, is related to
Goodman and Kruskal's gamma rank correlation. It is parametrized by a fuzzy
equivalence relation on the rank positions, which in turn is specified
conveniently by a so-called scaling function. This approach combines soundness
with flexibility: it has a sound formal foundation and allows for weighing rank
positions in a flexible way. | [
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] |
Despite the promising results of machine learning models in malware
detection, they face the problem of concept drift due to malware constant
evolution. This leads to a decline in performance over time, as the data
distribution of the new files differs from the training one, requiring regular
model update. In this work, we propose a model-agnostic protocol to improve a
baseline neural network to handle with the drift problem. We show the
importance of feature reduction and training with the most recent validation
set possible, and propose a loss function named Drift-Resilient Binary
Cross-Entropy, an improvement to the classical Binary Cross-Entropy more
effective against drift. We train our model on the EMBER dataset (2018) and
evaluate it on a dataset of recent malicious files, collected between 2020 and
2023. Our improved model shows promising results, detecting 15.2% more malware
than a baseline model. | [
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] |
Logistic hubs play a pivotal role in the last-mile delivery distance; even a
slight increment in distance negatively impacts the business of the e-commerce
industry while also increasing its carbon footprint. The growth of this
industry, particularly after Covid-19, has further intensified the need for
optimized allocation of resources in an urban environment. In this study, we
use a hybrid approach to optimize the placement of logistic hubs. The approach
sequentially employs different techniques. Initially, delivery points are
clustered using K-Means in relation to their spatial locations. The clustering
method utilizes road network distances as opposed to Euclidean distances.
Non-road network-based approaches have been avoided since they lead to
erroneous and misleading results. Finally, hubs are located using the P-Median
method. The P-Median method also incorporates the number of deliveries and
population as weights. Real-world delivery data from Muller and Phipps (M&P) is
used to demonstrate the effectiveness of the approach. Serving deliveries from
the optimal hub locations results in the saving of 815 (10%) meters per
delivery. | [
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] |
Wav2vec2 has achieved success in applying Transformer architecture and
self-supervised learning to speech recognition. Recently, these have come to be
used not only for speech recognition but also for the entire speech processing.
This paper introduces an effective end-to-end speaker identification model
applied Transformer-based contextual model. We explored the relationship
between the hyper-parameters and the performance in order to discern the
structure of an effective model. Furthermore, we propose a pooling method,
Temporal Gate Pooling, with powerful learning ability for speaker
identification. We applied Conformer as encoder and BEST-RQ for pre-training
and conducted an evaluation utilizing the speaker identification of VoxCeleb1.
The proposed method has achieved an accuracy of 87.1% with 28.5M parameters,
demonstrating comparable precision to wav2vec2 with 317.7M parameters. Code is
available at https://github.com/HarunoriKawano/speaker-identification-with-tgp. | [
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] |
Measures of algorithmic fairness are usually discussed in the context of
binary decisions. We extend the approach to continuous scores. So far,
ROC-based measures have mainly been suggested for this purpose. Other existing
methods depend heavily on the distribution of scores, are unsuitable for
ranking tasks, or their effect sizes are not interpretable. Here, we propose a
distributionally invariant version of fairness measures for continuous scores
with a reasonable interpretation based on the Wasserstein distance. Our
measures are easily computable and well suited for quantifying and interpreting
the strength of group disparities as well as for comparing biases across
different models, datasets, or time points. We derive a link between the
different families of existing fairness measures for scores and show that the
proposed distributionally invariant fairness measures outperform ROC-based
fairness measures because they are more explicit and can quantify significant
biases that ROC-based fairness measures miss. Finally, we demonstrate their
effectiveness through experiments on the most commonly used fairness benchmark
datasets. | [
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] |
In the field of optoelectronics, indium tin oxide (ITO) electrodes play a
crucial role in various applications, such as displays, sensors, and solar
cells. Effective fault detection and diagnosis of the ITO electrodes are
essential to ensure the performance and reliability of the devices. However,
traditional visual inspection is challenging with transparent ITO electrodes,
and existing fault detection methods have limitations in determining the root
causes of the defects, often requiring destructive evaluations. In this study,
an in situ fault diagnosis method is proposed using scattering parameter
(S-parameter) signal processing, offering early detection, high diagnostic
accuracy, noise robustness, and root cause analysis. A comprehensive
S-parameter pattern database is obtained according to defect states. Deep
learning (DL) approaches, including multilayer perceptron (MLP), convolutional
neural network (CNN), and transformer, are then used to simultaneously analyze
the cause and severity of defects. Notably, it is demonstrated that the
diagnostic performance under additive noise levels can be significantly
enhanced by combining different channels of the S-parameters as input to the
learning algorithms, as confirmed through the t-distributed stochastic neighbor
embedding (t-SNE) dimension reduction visualization. | [
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] |
Edge Machine Learning (Edge ML), which shifts computational intelligence from
cloud-based systems to edge devices, is attracting significant interest due to
its evident benefits including reduced latency, enhanced data privacy, and
decreased connectivity reliance. While these advantages are compelling, they
introduce unique challenges absent in traditional cloud-based approaches. In
this paper, we delve into the intricacies of Edge-based learning, examining the
interdependencies among: (i) constrained data storage on Edge devices, (ii)
limited computational power for training, and (iii) the number of learning
classes. Through experiments conducted using our MAGNETO system, that focused
on learning human activities via data collected from mobile sensors, we
highlight these challenges and offer valuable perspectives on Edge ML. | [
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] |
Multi-modal embeddings encode images, sounds, texts, videos, etc. into a
single embedding space, aligning representations across modalities (e.g.,
associate an image of a dog with a barking sound). We show that multi-modal
embeddings can be vulnerable to an attack we call "adversarial illusions."
Given an image or a sound, an adversary can perturb it so as to make its
embedding close to an arbitrary, adversary-chosen input in another modality.
This enables the adversary to align any image and any sound with any text.
Adversarial illusions exploit proximity in the embedding space and are thus
agnostic to downstream tasks. Using ImageBind embeddings, we demonstrate how
adversarially aligned inputs, generated without knowledge of specific
downstream tasks, mislead image generation, text generation, and zero-shot
classification. | [
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] |
Deep neural networks (DNN) are singular statistical models which exhibit
complex degeneracies. In this work, we illustrate how a quantity known as the
\emph{learning coefficient} introduced in singular learning theory quantifies
precisely the degree of degeneracy in deep neural networks. Importantly, we
will demonstrate that degeneracy in DNN cannot be accounted for by simply
counting the number of "flat" directions. We propose a computationally scalable
approximation of a localized version of the learning coefficient using
stochastic gradient Langevin dynamics. To validate our approach, we demonstrate
its accuracy in low-dimensional models with known theoretical values.
Importantly, the local learning coefficient can correctly recover the ordering
of degeneracy between various parameter regions of interest. An experiment on
MNIST shows the local learning coefficient can reveal the inductive bias of
stochastic opitmizers for more or less degenerate critical points. | [
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] |
Customer retention or churn prevention is a challenging task of a telecom
operator. One of the effective approaches is to offer some attractive incentive
or additional services or money to the subscribers for keeping them engaged and
make sure they stay in the operator's network for longer time. Often, operators
allocate certain amount of monetary budget to carry out the offer campaign. The
difficult part of this campaign is the selection of a set of customers from a
large subscriber-base and deciding the amount that should be offered to an
individual so that operator's objective is achieved. There may be multiple
objectives (e.g., maximizing revenue, minimizing number of churns) for
selection of subscriber and selection of an offer to the selected subscriber.
Apart from monetary benefit, offers may include additional data, SMS, hots-spot
tethering, and many more. This problem is known as offer optimization. In this
paper, we propose a novel combinatorial algorithm for solving offer
optimization under heterogeneous offers by maximizing expected revenue under
the scenario of subscriber churn, which is, in general, seen in telecom domain.
The proposed algorithm is efficient and accurate even for a very large
subscriber-base. | [
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] |
With the rising popularity of autonomous navigation research, Formula Student
(FS) events are introducing a Driverless Vehicle (DV) category to their event
list. This paper presents the initial investigation into utilising Deep
Reinforcement Learning (RL) for end-to-end control of an autonomous FS race car
for these competitions. We train two state-of-the-art RL algorithms in
simulation on tracks analogous to the full-scale design on a Turtlebot2
platform. The results demonstrate that our approach can successfully learn to
race in simulation and then transfer to a real-world racetrack on the physical
platform. Finally, we provide insights into the limitations of the presented
approach and guidance into the future directions for applying RL toward
full-scale autonomous FS racing. | [
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Large language models (LLMs) have significantly improved the ability to
perform tasks in the field of code generation. However, there is still a gap
between LLMs being capable coders and being top-tier software engineers. Based
on the observation that top-level software engineers often ask clarifying
questions to reduce ambiguity in both requirements and coding solutions, we
argue that the same should be applied to LLMs for code generation tasks. By
asking probing questions in various topics before generating the final code,
the challenges of programming with LLMs, such as unclear intent specification,
lack of computational thinking, and undesired code quality, may be alleviated.
This, in turn, increases confidence in the generated code. In this work, we
explore how to leverage better communication skills to achieve greater
confidence in generated code. We propose a communication-centered process that
uses an LLM-generated communicator to identify issues with high ambiguity or
low confidence in problem descriptions and generated code. We then ask
clarifying questions to obtain responses from users for refining the code. | [
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] |
This study delves into the intricacies of emotional contagion and its impact
on performance within dyadic interactions. Specifically, it focuses on the
context of stereotype-based stress (SBS) during collaborative problem-solving
tasks among female pairs. Through an exploration of emotional contagion, this
study seeks to unveil its underlying mechanisms and effects. Leveraging
EEG-based hyperscanning technology, we introduced an innovative approach known
as the functional Graph Contrastive Learning (fGCL), which extracts
subject-invariant representations of neural activity patterns from feedback
trials. These representations are further subjected to analysis using the
Dynamic Graph Classification (DGC) model, aimed at dissecting the process of
emotional contagion along three independent temporal stages. The results
underscore the substantial role of emotional contagion in shaping the
trajectories of participants' performance during collaborative tasks in the
presence of SBS conditions. Overall, our research contributes invaluable
insights into the neural underpinnings of emotional contagion, thereby
enriching our comprehension of the complexities underlying social interactions
and emotional dynamics. | [
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] |
The scale and complexity of workloads in modern cloud services have brought
into sharper focus a critical challenge in automated index tuning -- the need
to recommend high-quality indexes while maintaining index tuning scalability.
This challenge is further compounded by the requirement for automated index
implementations to introduce minimal query performance regressions in
production deployments, representing a significant barrier to achieving
scalability and full automation. This paper directs attention to these
challenges within automated index tuning and explores ways in which machine
learning (ML) techniques provide new opportunities in their mitigation. In
particular, we reflect on recent efforts in developing ML techniques for
workload selection, candidate index filtering, speeding up index configuration
search, reducing the amount of query optimizer calls, and lowering the chances
of performance regressions. We highlight the key takeaways from these efforts
and underline the gaps that need to be closed for their effective functioning
within the traditional index tuning framework. Additionally, we present a
preliminary cross-platform design aimed at democratizing index tuning across
multiple SQL-like systems -- an imperative in today's continuously expanding
data system landscape. We believe our findings will help provide context and
impetus to the research and development efforts in automated index tuning. | [
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Recommendation strategies are typically evaluated by using previously logged
data, employing off-policy evaluation methods to estimate their expected
performance. However, for strategies that present users with slates of multiple
items, the resulting combinatorial action space renders many of these methods
impractical. Prior work has developed estimators that leverage the structure in
slates to estimate the expected off-policy performance, but the estimation of
the entire performance distribution remains elusive. Estimating the complete
distribution allows for a more comprehensive evaluation of recommendation
strategies, particularly along the axes of risk and fairness that employ
metrics computable from the distribution. In this paper, we propose an
estimator for the complete off-policy performance distribution for slates and
establish conditions under which the estimator is unbiased and consistent. This
builds upon prior work on off-policy evaluation for slates and off-policy
distribution estimation in reinforcement learning. We validate the efficacy of
our method empirically on synthetic data as well as on a slate recommendation
simulator constructed from real-world data (MovieLens-20M). Our results show a
significant reduction in estimation variance and improved sample efficiency
over prior work across a range of slate structures. | [
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] |
Artificial intelligence (AI) promises immense benefits across sectors, yet
also poses risks from dual-use potentials, biases, and unintended behaviors.
This paper reviews emerging issues with opaque and uncontrollable AI systems
and proposes an integrative framework called violet teaming to develop reliable
and responsible AI. Violet teaming combines adversarial vulnerability probing
(red teaming) with solutions for safety and security (blue teaming) while
prioritizing ethics and social benefit. It emerged from AI safety research to
manage risks proactively by design. The paper traces the evolution of red,
blue, and purple teaming toward violet teaming, and then discusses applying
violet techniques to address biosecurity risks of AI in biotechnology.
Additional sections review key perspectives across law, ethics, cybersecurity,
macrostrategy, and industry best practices essential for operationalizing
responsible AI through holistic technical and social considerations. Violet
teaming provides both philosophy and method for steering AI trajectories toward
societal good. With conscience and wisdom, the extraordinary capabilities of AI
can enrich humanity. But without adequate precaution, the risks could prove
catastrophic. Violet teaming aims to empower moral technology for the common
welfare. | [
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] |
A particularly challenging problem in AI safety is providing guarantees on
the behavior of high-dimensional autonomous systems. Verification approaches
centered around reachability analysis fail to scale, and purely statistical
approaches are constrained by the distributional assumptions about the sampling
process. Instead, we pose a distributionally robust version of the statistical
verification problem for black-box systems, where our performance guarantees
hold over a large family of distributions. This paper proposes a novel approach
based on a combination of active learning, uncertainty quantification, and
neural network verification. A central piece of our approach is an ensemble
technique called Imprecise Neural Networks, which provides the uncertainty to
guide active learning. The active learning uses an exhaustive neural-network
verification tool Sherlock to collect samples. An evaluation on multiple
physical simulators in the openAI gym Mujoco environments with
reinforcement-learned controllers demonstrates that our approach can provide
useful and scalable guarantees for high-dimensional systems. | [
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] |
Ergonomic efficiency is essential to the mass and prolonged adoption of VR/AR
experiences. While VR/AR head-mounted displays unlock users' natural wide-range
head movements during viewing, their neck muscle comfort is inevitably
compromised by the added hardware weight. Unfortunately, little quantitative
knowledge for understanding and addressing such an issue is available so far.
Leveraging electromyography devices, we measure, model, and predict VR users'
neck muscle contraction levels (MCL) while they move their heads to interact
with the virtual environment. Specifically, by learning from collected
physiological data, we develop a bio-physically inspired computational model to
predict neck MCL under diverse head kinematic states. Beyond quantifying the
cumulative MCL of completed head movements, our model can also predict
potential MCL requirements with target head poses only. A series of objective
evaluations and user studies demonstrate its prediction accuracy and
generality, as well as its ability in reducing users' neck discomfort by
optimizing the layout of visual targets. We hope this research will motivate
new ergonomic-centered designs for VR/AR and interactive graphics applications.
Source code is released at:
https://github.com/NYU-ICL/xr-ergonomics-neck-comfort. | [
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] |
The goal of this work is to reconstruct high quality speech from lip motions
alone, a task also known as lip-to-speech. A key challenge of lip-to-speech
systems is the one-to-many mapping caused by (1) the existence of homophenes
and (2) multiple speech variations, resulting in a mispronounced and
over-smoothed speech. In this paper, we propose a novel lip-to-speech system
that significantly improves the generation quality by alleviating the
one-to-many mapping problem from multiple perspectives. Specifically, we
incorporate (1) self-supervised speech representations to disambiguate
homophenes, and (2) acoustic variance information to model diverse speech
styles. Additionally, to better solve the aforementioned problem, we employ a
flow based post-net which captures and refines the details of the generated
speech. We perform extensive experiments and demonstrate that our method
achieves the generation quality close to that of real human utterance,
outperforming existing methods in terms of speech naturalness and
intelligibility by a large margin. Synthesised samples are available at the
anonymous demo page: https://mm.kaist.ac.kr/projects/LTBS. | [
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Modern High Performance Computing (HPC) systems are complex machines, with
major impacts on economy and society. Along with their computational
capability, their energy consumption is also steadily raising, representing a
critical issue given the ongoing environmental and energetic crisis. Therefore,
developing strategies to optimize HPC system management has paramount
importance, both to guarantee top-tier performance and to improve energy
efficiency. One strategy is to act at the workload level and highlight the jobs
that are most likely to fail, prior to their execution on the system. Jobs
failing during their execution unnecessarily occupy resources which could delay
other jobs, adversely affecting the system performance and energy consumption.
In this paper, we study job failure prediction at submit-time using classical
machine learning algorithms. Our novelty lies in (i) the combination of these
algorithms with Natural Language Processing (NLP) tools to represent jobs and
(ii) the design of the approach to work in an online fashion in a real system.
The study is based on a dataset extracted from a production machine hosted at
the HPC centre CINECA in Italy. Experimental results show that our approach is
promising. | [
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] |
The proliferation of misinformation has emerged as a new form of warfare in
the information age. This type of warfare involves cyberwarriors, who
deliberately propagate messages aimed at defaming opponents or fostering unity
among allies. In this study, we investigate the level of activity exhibited by
cyberwarriors within a large online forum, and remarkably, we discover that
only a minute fraction of cyberwarriors are active users. Surprisingly, despite
their expected role of actively disseminating misinformation, cyberwarriors
remain predominantly silent during peacetime and only spring into action when
necessary. Moreover, we analyze the challenges associated with identifying
cyberwarriors and provide evidence that detecting inactive cyberwarriors is
considerably more challenging than identifying their active counterparts.
Finally, we discuss potential methodologies to more effectively identify
cyberwarriors during their inactive phases, offering insights into better
capturing their presence and actions. The experimental code is released for
reproducibility:
\url{https://github.com/Ryaninthegame/Detect-Inactive-Spammers-on-PTT}. | [
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] |
Probabilistic logic programs are logic programs where some facts hold with a
specified probability. Here, we investigate these programs with a causal
framework that allows counterfactual queries. Learning the program structure
from observational data is usually done through heuristic search relying on
statistical tests. However, these statistical tests lack information about the
causal mechanism generating the data, which makes it unfeasible to use the
resulting programs for counterfactual reasoning. To address this, we propose a
language fragment that allows reconstructing a program from its induced
distribution. This further enables us to learn programs supporting
counterfactual queries. | [
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] |
Reinforcement Learning (RL) has emerged as a transformative approach in the
domains of automation and robotics, offering powerful solutions to complex
problems that conventional methods struggle to address. In scenarios where the
problem definitions are elusive and challenging to quantify, learning-based
solutions such as RL become particularly valuable. One instance of such
complexity can be found in the realm of car racing, a dynamic and unpredictable
environment that demands sophisticated decision-making algorithms. This study
focuses on developing and training an RL agent to navigate a racing environment
solely using feedforward raw lidar and velocity data in a simulated context.
The agent's performance, trained in the simulation environment, is then
experimentally evaluated in a real-world racing scenario. This exploration
underlines the feasibility and potential benefits of RL algorithm enhancing
autonomous racing performance, especially in the environments where prior map
information is not available. | [
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] |
Scientific computing has experienced a surge empowered by advancements in
technologies such as neural networks. However, certain important tasks are less
amenable to these technologies, benefiting from innovations to traditional
inference schemes. One such task is protein re-design. Recently a new re-design
algorithm, AOBB-K*, was introduced and was competitive with state-of-the-art
BBK* on small protein re-design problems. However, AOBB-K* did not scale well.
In this work we focus on scaling up AOBB-K* and introduce three new versions:
AOBB-K*-b (boosted), AOBB-K*-DH (with dynamic heuristics), and AOBB-K*-UFO
(with underflow optimization) that significantly enhance scalability. | [
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] |
Explaining opaque Machine Learning (ML) models is an increasingly relevant
problem. Current explanation in AI (XAI) methods suffer several shortcomings,
among others an insufficient incorporation of background knowledge, and a lack
of abstraction and interactivity with the user. We propose REASONX, an
explanation method based on Constraint Logic Programming (CLP). REASONX can
provide declarative, interactive explanations for decision trees, which can be
the ML models under analysis or global/local surrogate models of any black-box
model. Users can express background or common sense knowledge using linear
constraints and MILP optimization over features of factual and contrastive
instances, and interact with the answer constraints at different levels of
abstraction through constraint projection. We present here the architecture of
REASONX, which consists of a Python layer, closer to the user, and a CLP layer.
REASONX's core execution engine is a Prolog meta-program with declarative
semantics in terms of logic theories. | [
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] |
In light of the challenges and costs of real-world testing, autonomous
vehicle developers often rely on testing in simulation for the creation of
reliable systems. A key element of effective simulation is the incorporation of
realistic traffic models that align with human knowledge, an aspect that has
proven challenging due to the need to balance realism and diversity. This works
aims to address this by developing a framework that employs reinforcement
learning with human preference (RLHF) to enhance the realism of existing
traffic models. This study also identifies two main challenges: capturing the
nuances of human preferences on realism and the unification of diverse traffic
simulation models. To tackle these issues, we propose using human feedback for
alignment and employ RLHF due to its sample efficiency. We also introduce the
first dataset for realism alignment in traffic modeling to support such
research. Our framework, named TrafficRLHF, demonstrates its proficiency in
generating realistic traffic scenarios that are well-aligned with human
preferences, as corroborated by comprehensive evaluations on the nuScenes
dataset. | [
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] |
This paper introduces an approach for learning to solve continuous constraint
satisfaction problems (CCSP) in robotic reasoning and planning. Previous
methods primarily rely on hand-engineering or learning generators for specific
constraint types and then rejecting the value assignments when other
constraints are violated. By contrast, our model, the compositional diffusion
continuous constraint solver (Diffusion-CCSP) derives global solutions to CCSPs
by representing them as factor graphs and combining the energies of diffusion
models trained to sample for individual constraint types. Diffusion-CCSP
exhibits strong generalization to novel combinations of known constraints, and
it can be integrated into a task and motion planner to devise long-horizon
plans that include actions with both discrete and continuous parameters.
Project site: https://diffusion-ccsp.github.io/ | [
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] |
Many real-world manipulation tasks consist of a series of subtasks that are
significantly different from one another. Such long-horizon, complex tasks
highlight the potential of dexterous hands, which possess adaptability and
versatility, capable of seamlessly transitioning between different modes of
functionality without the need for re-grasping or external tools. However, the
challenges arise due to the high-dimensional action space of dexterous hand and
complex compositional dynamics of the long-horizon tasks. We present Sequential
Dexterity, a general system based on reinforcement learning (RL) that chains
multiple dexterous policies for achieving long-horizon task goals. The core of
the system is a transition feasibility function that progressively finetunes
the sub-policies for enhancing chaining success rate, while also enables
autonomous policy-switching for recovery from failures and bypassing redundant
stages. Despite being trained only in simulation with a few task objects, our
system demonstrates generalization capability to novel object shapes and is
able to zero-shot transfer to a real-world robot equipped with a dexterous
hand. Code and videos are available at https://sequential-dexterity.github.io | [
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] |
Financial fraud detection is essential for preventing significant financial
losses and maintaining the reputation of financial institutions. However,
conventional methods of detecting financial fraud have limited effectiveness,
necessitating the need for new approaches to improve detection rates. In this
paper, we propose a novel approach for detecting financial fraud using Quantum
Graph Neural Networks (QGNNs). QGNNs are a type of neural network that can
process graph-structured data and leverage the power of Quantum Computing (QC)
to perform computations more efficiently than classical neural networks. Our
approach uses Variational Quantum Circuits (VQC) to enhance the performance of
the QGNN. In order to evaluate the efficiency of our proposed method, we
compared the performance of QGNNs to Classical Graph Neural Networks using a
real-world financial fraud detection dataset. The results of our experiments
showed that QGNNs achieved an AUC of $0.85$, which outperformed classical GNNs.
Our research highlights the potential of QGNNs and suggests that QGNNs are a
promising new approach for improving financial fraud detection. | [
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] |
Traditionally, social choice theory has only been applicable to choices among
a few predetermined alternatives but not to more complex decisions such as
collectively selecting a textual statement. We introduce generative social
choice, a framework that combines the mathematical rigor of social choice
theory with large language models' capability to generate text and extrapolate
preferences. This framework divides the design of AI-augmented democratic
processes into two components: first, proving that the process satisfies
rigorous representation guarantees when given access to oracle queries; second,
empirically validating that these queries can be approximately implemented
using a large language model. We illustrate this framework by applying it to
the problem of generating a slate of statements that is representative of
opinions expressed as free-form text, for instance in an online deliberative
process. | [
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] |
The widespread adoption of the Android operating system has made malicious
Android applications an appealing target for attackers. Machine learning-based
(ML-based) Android malware detection (AMD) methods are crucial in addressing
this problem; however, their vulnerability to adversarial examples raises
concerns. Current attacks against ML-based AMD methods demonstrate remarkable
performance but rely on strong assumptions that may not be realistic in
real-world scenarios, e.g., the knowledge requirements about feature space,
model parameters, and training dataset. To address this limitation, we
introduce AdvDroidZero, an efficient query-based attack framework against
ML-based AMD methods that operates under the zero knowledge setting. Our
extensive evaluation shows that AdvDroidZero is effective against various
mainstream ML-based AMD methods, in particular, state-of-the-art such methods
and real-world antivirus solutions. | [
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Training specific deep learning models for particular tasks is common across
various domains within seismology. However, this approach encounters two
limitations: inadequate labeled data for certain tasks and limited
generalization across regions. To address these challenges, we develop
SeisCLIP, a seismology foundation model trained through contrastive learning
from multi-modal data. It consists of a transformer encoder for extracting
crucial features from time-frequency seismic spectrum and an MLP encoder for
integrating the phase and source information of the same event. These encoders
are jointly pre-trained on a vast dataset and the spectrum encoder is
subsequently fine-tuned on smaller datasets for various downstream tasks.
Notably, SeisCLIP's performance surpasses that of baseline methods in event
classification, localization, and focal mechanism analysis tasks, employing
distinct datasets from different regions. In conclusion, SeisCLIP holds
significant potential as a foundational model in the field of seismology,
paving the way for innovative directions in foundation-model-based seismology
research. | [
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] |
Robotic manipulation tasks, such as object rearrangement, play a crucial role
in enabling robots to interact with complex and arbitrary environments.
Existing work focuses primarily on single-level rearrangement planning and,
even if multiple levels exist, dependency relations among substructures are
geometrically simpler, like tower stacking. We propose Structural Concept
Learning (SCL), a deep learning approach that leverages graph attention
networks to perform multi-level object rearrangement planning for scenes with
structural dependency hierarchies. It is trained on a self-generated simulation
data set with intuitive structures, works for unseen scenes with an arbitrary
number of objects and higher complexity of structures, infers independent
substructures to allow for task parallelization over multiple manipulators, and
generalizes to the real world. We compare our method with a range of classical
and model-based baselines to show that our method leverages its scene
understanding to achieve better performance, flexibility, and efficiency. The
dataset, supplementary details, videos, and code implementation are available
at: https://manavkulshrestha.github.io/scl | [
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] |
Reliability is extremely important for large-scale cloud systems like
Microsoft 365. Cloud failures such as disk failure, node failure, etc. threaten
service reliability, resulting in online service interruptions and economic
loss. Existing works focus on predicting cloud failures and proactively taking
action before failures happen. However, they suffer from poor data quality like
data missing in model training and prediction, which limits the performance. In
this paper, we focus on enhancing data quality through data imputation by the
proposed Diffusion+, a sample-efficient diffusion model, to impute the missing
data efficiently based on the observed data. Our experiments and application
practice show that our model contributes to improving the performance of the
downstream failure prediction task. | [
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Recent studies have verified that equivariant methods can significantly
improve the data efficiency, generalizability, and robustness in robot
learning. Meanwhile, denoising diffusion-based generative modeling has recently
gained significant attention as a promising approach for robotic manipulation
learning from demonstrations with stochastic behaviors. In this paper, we
present Diffusion-EDFs, a novel approach that incorporates spatial
roto-translation equivariance, i.e., SE(3)-equivariance to diffusion generative
modeling. By integrating SE(3)-equivariance into our model architectures, we
demonstrate that our proposed method exhibits remarkable data efficiency,
requiring only 5 to 10 task demonstrations for effective end-to-end training.
Furthermore, our approach showcases superior generalizability compared to
previous diffusion-based manipulation methods. | [
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] |
We present a new, efficient procedure to establish Markov equivalence between
directed graphs that may or may not contain cycles under the
\textit{d}-separation criterion. It is based on the Cyclic Equivalence Theorem
(CET) in the seminal works on cyclic models by Thomas Richardson in the mid
'90s, but now rephrased from an ancestral perspective. The resulting
characterization leads to a procedure for establishing Markov equivalence
between graphs that no longer requires tests for d-separation, leading to a
significantly reduced algorithmic complexity. The conceptually simplified
characterization may help to reinvigorate theoretical research towards sound
and complete cyclic discovery in the presence of latent confounders. This
version includes a correction to rule (iv) in Theorem 1, and the subsequent
adjustment in part 2 of Algorithm 2. | [
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] |
The widespread application of multimodal machine learning models like GPT-4
has revolutionized various research fields including computer vision and
natural language processing. However, its implementation in materials
informatics remains underexplored, despite the presence of materials data
across diverse modalities, such as composition and structure. The effectiveness
of machine learning models trained on large calculated datasets depends on the
accuracy of calculations, while experimental datasets often have limited data
availability and incomplete information. This paper introduces a novel approach
to multimodal machine learning in materials science via composition-structure
bimodal learning. The proposed COmposition-Structure Bimodal Network (COSNet)
is designed to enhance learning and predictions of experimentally measured
materials properties that have incomplete structure information. Bimodal
learning significantly reduces prediction errors across distinct materials
properties including Li conductivity in solid electrolyte, band gap, refractive
index, dielectric constant, energy, and magnetic moment, surpassing
composition-only learning methods. Furthermore, we identified that data
augmentation based on modal availability plays a pivotal role in the success of
bimodal learning. | [
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] |
The echo index counts the number of simultaneously stable asymptotic
responses of a nonautonomous (i.e. input-driven) dynamical system. It
generalizes the well-known echo state property for recurrent neural networks -
this corresponds to the echo index being equal to one. In this paper, we
investigate how the echo index depends on parameters that govern typical
responses to a finite-state ergodic external input that forces the dynamics. We
consider the echo index for a nonautonomous system that switches between a
finite set of maps, where we assume that each map possesses a finite set of
hyperbolic equilibrium attractors. We find the minimum and maximum repetitions
of each map are crucial for the resulting echo index. Casting our theoretical
findings in the RNN computing framework, we obtain that for small amplitude
forcing the echo index corresponds to the number of attractors for the
input-free system, while for large amplitude forcing, the echo index reduces to
one. The intermediate regime is the most interesting; in this region the echo
index depends not just on the amplitude of forcing but also on more subtle
properties of the input. | [
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] |
Commit messages (CMs) are an essential part of version control. By providing
important context in regard to what has changed and why, they strongly support
software maintenance and evolution. But writing good CMs is difficult and often
neglected by developers. So far, there is no tool suitable for practice that
automatically assesses how well a CM is written, including its meaning and
context. Since this task is challenging, we ask the research question: how well
can the CM quality, including semantics and context, be measured with machine
learning methods? By considering all rules from the most popular CM quality
guideline, creating datasets for those rules, and training and evaluating
state-of-the-art machine learning models to check those rules, we can answer
the research question with: sufficiently well for practice, with the lowest
F$_1$ score of 82.9\%, for the most challenging task. We develop a full-fledged
open-source framework that checks all these CM quality rules. It is useful for
research, e.g., automatic CM generation, but most importantly for software
practitioners to raise the quality of CMs and thus the maintainability and
evolution speed of their software. | [
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] |
Recent advances in machine learning, particularly deep learning, have enabled
autonomous systems to perceive and comprehend objects and their environments in
a perceptual subsymbolic manner. These systems can now perform object
detection, sensor data fusion, and language understanding tasks. However, there
is a growing need to enhance these systems to understand objects and their
environments more conceptually and symbolically. It is essential to consider
both the explicit teaching provided by humans (e.g., describing a situation or
explaining how to act) and the implicit teaching obtained by observing human
behavior (e.g., through the system's sensors) to achieve this level of powerful
artificial intelligence. Thus, the system must be designed with multimodal
input and output capabilities to support implicit and explicit interaction
models. In this position paper, we argue for considering both types of inputs,
as well as human-in-the-loop and incremental learning techniques, for advancing
the field of artificial intelligence and enabling autonomous systems to learn
like humans. We propose several hypotheses and design guidelines and highlight
a use case from related work to achieve this goal. | [
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] |
In this paper, we introduce two local graph features for missing link
prediction tasks on ogbl-citation2. We define the features as Circle Features,
which are borrowed from the concept of circle of friends. We propose the
detailed computing formulas for the above features. Firstly, we define the
first circle feature as modified swing for common graph, which comes from
bipartite graph. Secondly, we define the second circle feature as bridge, which
indicates the importance of two nodes for different circle of friends. In
addition, we firstly propose the above features as bias to enhance graph
transformer neural network, such that graph self-attention mechanism can be
improved. We implement a Circled Feature aware Graph transformer (CFG) model
based on SIEG network, which utilizes a double tower structure to capture both
global and local structure features. Experimental results show that CFG
achieves the state-of-the-art performance on dataset ogbl-citation2. | [
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] |
Electrocardiogram (ECG) is one of the most important diagnostic tools in
clinical applications. With the advent of advanced algorithms, various deep
learning models have been adopted for ECG tasks. However, the potential of
Transformers for ECG data is not yet realized, despite their widespread success
in computer vision and natural language processing. In this work, we present a
useful masked Transformer method for ECG classification referred to as MTECG,
which expands the application of masked autoencoders to ECG time series. We
construct a dataset comprising 220,251 ECG recordings with a broad range of
diagnoses annoated by medical experts to explore the properties of MTECG. Under
the proposed training strategies, a lightweight model with 5.7M parameters
performs stably well on a broad range of masking ratios (5%-75%). The ablation
studies highlight the importance of fluctuated reconstruction targets, training
schedule length, layer-wise LR decay and DropPath rate. The experiments on both
private and public ECG datasets demonstrate that MTECG-T significantly
outperforms the recent state-of-the-art algorithms in ECG classification. | [
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In the domain of cardiovascular healthcare, the Electrocardiogram (ECG)
serves as a critical, non-invasive diagnostic tool. Although recent strides in
self-supervised learning (SSL) have been promising for ECG representation
learning, these techniques often require annotated samples and struggle with
classes not present in the fine-tuning stages. To address these limitations, we
introduce ECG-Text Pre-training (ETP), an innovative framework designed to
learn cross-modal representations that link ECG signals with textual reports.
For the first time, this framework leverages the zero-shot classification task
in the ECG domain. ETP employs an ECG encoder along with a pre-trained language
model to align ECG signals with their corresponding textual reports. The
proposed framework excels in both linear evaluation and zero-shot
classification tasks, as demonstrated on the PTB-XL and CPSC2018 datasets,
showcasing its ability for robust and generalizable cross-modal ECG feature
learning. | [
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] |
Maximizing influences in complex networks is a practically important but
computationally challenging task for social network analysis, due to its NP-
hard nature. Most current approximation or heuristic methods either require
tremendous human design efforts or achieve unsatisfying balances between
effectiveness and efficiency. Recent machine learning attempts only focus on
speed but lack performance enhancement. In this paper, different from previous
attempts, we propose an effective deep reinforcement learning model that
achieves superior performances over traditional best influence maximization
algorithms. Specifically, we design an end-to-end learning framework that
combines graph neural network as the encoder and reinforcement learning as the
decoder, named DREIM. Trough extensive training on small synthetic graphs,
DREIM outperforms the state-of-the-art baseline methods on very large synthetic
and real-world networks on solution quality, and we also empirically show its
linear scalability with regard to the network size, which demonstrates its
superiority in solving this problem. | [
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] |
Deep neural networks have shown many fruitful applications in this decade. A
network can get the generalized function through training with a finite
dataset. The degree of generalization is a realization of the proximity scale
in the data space. Specifically, the scale is not clear if the dataset is
complicated. Here we consider a network for the distribution estimation of the
dataset. We show the estimation is unstable and the instability depends on the
data density and training duration. We derive the kernel-balanced equation,
which gives a short phenomenological description of the solution. The equation
tells us the reason for the instability and the mechanism of the scale. The
network outputs a local average of the dataset as a prediction and the scale of
averaging is determined along the equation. The scale gradually decreases along
training and finally results in instability in our case. | [
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Recently sequential recommendations and next-item prediction task has become
increasingly popular in the field of recommender systems. Currently, two
state-of-the-art baselines are Transformer-based models SASRec and BERT4Rec.
Over the past few years, there have been quite a few publications comparing
these two algorithms and proposing new state-of-the-art models. In most of the
publications, BERT4Rec achieves better performance than SASRec. But BERT4Rec
uses cross-entropy over softmax for all items, while SASRec uses negative
sampling and calculates binary cross-entropy loss for one positive and one
negative item. In our work, we show that if both models are trained with the
same loss, which is used by BERT4Rec, then SASRec will significantly outperform
BERT4Rec both in terms of quality and training speed. In addition, we show that
SASRec could be effectively trained with negative sampling and still outperform
BERT4Rec, but the number of negative examples should be much larger than one. | [
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] |
People use web search engines to find information before forming opinions,
which can lead to practical decisions with different levels of impact. The
cognitive effort of search can leave opinionated users vulnerable to cognitive
biases, e.g., the confirmation bias. In this paper, we investigate whether
stance labels and their explanations can help users consume more diverse search
results. We automatically classify and label search results on three topics
(i.e., intellectual property rights, school uniforms, and atheism) as against,
neutral, and in favor, and generate explanations for these labels. In a user
study (N =203), we then investigate whether search result stance bias (balanced
vs biased) and the level of explanation (plain text, label only, label and
explanation) influence the diversity of search results clicked. We find that
stance labels and explanations lead to a more diverse search result
consumption. However, we do not find evidence for systematic opinion change
among users in this context. We believe these results can help designers of
search engines to make more informed design decisions. | [
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] |
A resampling scheme provides a way to switch low-weight particles for
sequential Monte Carlo with higher-weight particles representing the objective
distribution. The less the variance of the weight distribution is, the more
concentrated the effective particles are, and the quicker and more accurate it
is to approximate the hidden Markov model, especially for the nonlinear case.
We propose a repetitive deterministic domain with median ergodicity for
resampling and have achieved the lowest variances compared to the other
resampling methods. As the size of the deterministic domain $M\ll N$ (the size
of population), given a feasible size of particles, our algorithm is faster
than the state of the art, which is verified by theoretical deduction and
experiments of a hidden Markov model in both the linear and non-linear cases. | [
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] |
This paper presents an innovative approach to enhance control over audio
generation by emphasizing the alignment between audio and text representations
during model training. In the context of language model-based audio generation,
the model leverages input from both textual and audio token representations to
predict subsequent audio tokens. However, the current configuration lacks
explicit regularization to ensure the alignment between the chosen text
representation and the language model's predictions. Our proposal involves the
incorporation of audio and text representation regularization, particularly
during the classifier-free guidance (CFG) phase, where the text condition is
excluded from cross attention during language model training. The aim of this
proposed representation regularization is to minimize discrepancies in audio
and text similarity compared to other samples within the same training batch.
Experimental results on both music and audio generation tasks demonstrate that
our proposed methods lead to improvements in objective metrics for both audio
and music generation, as well as an enhancement in the human perception for
audio generation. | [
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] |
Fact-checking in financial domain is under explored, and there is a shortage
of quality dataset in this domain. In this paper, we propose Fin-Fact, a
benchmark dataset for multimodal fact-checking within the financial domain.
Notably, it includes professional fact-checker annotations and justifications,
providing expertise and credibility. With its multimodal nature encompassing
both textual and visual content, Fin-Fact provides complementary information
sources to enhance factuality analysis. Its primary objective is combating
misinformation in finance, fostering transparency, and building trust in
financial reporting and news dissemination. By offering insightful
explanations, Fin-Fact empowers users, including domain experts and end-users,
to understand the reasoning behind fact-checking decisions, validating claim
credibility, and fostering trust in the fact-checking process. The Fin-Fact
dataset, along with our experimental codes is available at
https://github.com/IIT-DM/Fin-Fact/. | [
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] |
Reinforcement learning (RL) is a class of artificial intelligence algorithms
being used to design adaptive optimal controllers through online learning. This
paper presents a model-free, real-time, data-efficient Q-learning-based
algorithm to solve the H$_{\infty}$ control of linear discrete-time systems.
The computational complexity is shown to reduce from
$\mathcal{O}(\underline{q}^3)$ in the literature to
$\mathcal{O}(\underline{q}^2)$ in the proposed algorithm, where $\underline{q}$
is quadratic in the sum of the size of state variables, control inputs, and
disturbance. An adaptive optimal controller is designed and the parameters of
the action and critic networks are learned online without the knowledge of the
system dynamics, making the proposed algorithm completely model-free. Also, a
sufficient probing noise is only needed in the first iteration and does not
affect the proposed algorithm. With no need for an initial stabilizing policy,
the algorithm converges to the closed-form solution obtained by solving the
Riccati equation. A simulation study is performed by applying the proposed
algorithm to real-time control of an autonomous mobility-on-demand (AMoD)
system for a real-world case study to evaluate the effectiveness of the
proposed algorithm. | [
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In explainable artificial intelligence (XAI) research, the predominant focus
has been on interpreting models for experts and practitioners. Model agnostic
and local explanation approaches are deemed interpretable and sufficient in
many applications. However, in domains like healthcare, where end users are
patients without AI or domain expertise, there is an urgent need for model
explanations that are more comprehensible and instil trust in the model's
operations. We hypothesise that generating model explanations that are
narrative, patient-specific and global(holistic of the model) would enable
better understandability and enable decision-making. We test this using a
decision tree model to generate both local and global explanations for patients
identified as having a high risk of coronary heart disease. These explanations
are presented to non-expert users. We find a strong individual preference for a
specific type of explanation. The majority of participants prefer global
explanations, while a smaller group prefers local explanations. A task based
evaluation of mental models of these participants provide valuable feedback to
enhance narrative global explanations. This, in turn, guides the design of
health informatics systems that are both trustworthy and actionable. | [
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] |
Learning from human demonstrations (behavior cloning) is a cornerstone of
robot learning. However, most behavior cloning algorithms require a large
number of demonstrations to learn a task, especially for general tasks that
have a large variety of initial conditions. Humans, however, can learn to
complete tasks, even complex ones, after only seeing one or two demonstrations.
Our work seeks to emulate this ability, using behavior cloning to learn a task
given only a single human demonstration. We achieve this goal by using linear
transforms to augment the single demonstration, generating a set of
trajectories for a wide range of initial conditions. With these demonstrations,
we are able to train a behavior cloning agent to successfully complete three
block manipulation tasks. Additionally, we developed a novel addition to the
temporal ensembling method used by action chunking agents during inference. By
incorporating the standard deviation of the action predictions into the
ensembling method, our approach is more robust to unforeseen changes in the
environment, resulting in significant performance improvements. | [
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] |
This paper addresses the prediction stability, prediction accuracy and
control capability of the current probabilistic model-based reinforcement
learning (MBRL) built on neural networks. A novel approach dropout-based
probabilistic ensembles with trajectory sampling (DPETS) is proposed where the
system uncertainty is stably predicted by combining the Monte-Carlo dropout and
trajectory sampling in one framework. Its loss function is designed to correct
the fitting error of neural networks for more accurate prediction of
probabilistic models. The state propagation in its policy is extended to filter
the aleatoric uncertainty for superior control capability. Evaluated by several
Mujoco benchmark control tasks under additional disturbances and one practical
robot arm manipulation task, DPETS outperforms related MBRL approaches in both
average return and convergence velocity while achieving superior performance
than well-known model-free baselines with significant sample efficiency. The
open source code of DPETS is available at https://github.com/mrjun123/DPETS. | [
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0.0396604910492897,
0.00387338874861598
] |
We present C$\cdot$ASE, an efficient and effective framework that learns
conditional Adversarial Skill Embeddings for physics-based characters. Our
physically simulated character can learn a diverse repertoire of skills while
providing controllability in the form of direct manipulation of the skills to
be performed. C$\cdot$ASE divides the heterogeneous skill motions into distinct
subsets containing homogeneous samples for training a low-level conditional
model to learn conditional behavior distribution. The skill-conditioned
imitation learning naturally offers explicit control over the character's
skills after training. The training course incorporates the focal skill
sampling, skeletal residual forces, and element-wise feature masking to balance
diverse skills of varying complexities, mitigate dynamics mismatch to master
agile motions and capture more general behavior characteristics, respectively.
Once trained, the conditional model can produce highly diverse and realistic
skills, outperforming state-of-the-art models, and can be repurposed in various
downstream tasks. In particular, the explicit skill control handle allows a
high-level policy or user to direct the character with desired skill
specifications, which we demonstrate is advantageous for interactive character
animation. | [
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] |
Automatic Speech Recognition systems have been shown to be vulnerable to
adversarial attacks that manipulate the command executed on the device. Recent
research has focused on exploring methods to create such attacks, however, some
issues relating to Over-The-Air (OTA) attacks have not been properly addressed.
In our work, we examine the needed properties of robust attacks compatible with
the OTA model, and we design a method of generating attacks with arbitrary such
desired properties, namely the invariance to synchronization, and the
robustness to filtering: this allows a Denial-of-Service (DoS) attack against
ASR systems. We achieve these characteristics by constructing attacks in a
modified frequency domain through an inverse Fourier transform. We evaluate our
method on standard keyword classification tasks and analyze it in OTA, and we
analyze the properties of the cross-domain attacks to explain the efficiency of
the approach. | [
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] |
Electroencephalogram (EEG) data compression is necessary for wireless
recording applications to reduce the amount of data that needs to be
transmitted. In this paper, an asymmetrical sparse autoencoder with a discrete
cosine transform (DCT) layer is proposed to compress EEG signals. The encoder
module of the autoencoder has a combination of a fully connected linear layer
and the DCT layer to reduce redundant data using hard-thresholding
nonlinearity. Furthermore, the DCT layer includes trainable hard-thresholding
parameters and scaling layers to give emphasis or de-emphasis on individual DCT
coefficients. Finally, the one-by-one convolutional layer generates the latent
space. The sparsity penalty-based cost function is employed to keep the feature
map as sparse as possible in the latent space. The latent space data is
transmitted to the receiver. The decoder module of the autoencoder is designed
using the inverse DCT and two fully connected linear layers to improve the
accuracy of data reconstruction. In comparison to other state-of-the-art
methods, the proposed method significantly improves the average quality score
in various data compression experiments. | [
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] |
Federated Learning (FL) has revolutionized how we train deep neural networks
by enabling decentralized collaboration while safeguarding sensitive data and
improving model performance. However, FL faces two crucial challenges: the
diverse nature of data held by individual clients and the vulnerability of the
FL system to security breaches. This paper introduces an innovative solution
named Estimated Mean Aggregation (EMA) that not only addresses these challenges
but also provides a fundamental reference point as a $\mathsf{baseline}$ for
advanced aggregation techniques in FL systems. EMA's significance lies in its
dual role: enhancing model security by effectively handling malicious outliers
through trimmed means and uncovering data heterogeneity to ensure that trained
models are adaptable across various client datasets. Through a wealth of
experiments, EMA consistently demonstrates high accuracy and area under the
curve (AUC) compared to alternative methods, establishing itself as a robust
baseline for evaluating the effectiveness and security of FL aggregation
methods. EMA's contributions thus offer a crucial step forward in advancing the
efficiency, security, and versatility of decentralized deep learning in the
context of FL. | [
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] |
Conformers have recently been proposed as a promising modelling approach for
automatic speech recognition (ASR), outperforming recurrent neural
network-based approaches and transformers. Nevertheless, in general, the
performance of these end-to-end models, especially attention-based models, is
particularly degraded in the case of long utterances. To address this
limitation, we propose adding a fully-differentiable memory-augmented neural
network between the encoder and decoder of a conformer. This external memory
can enrich the generalization for longer utterances since it allows the system
to store and retrieve more information recurrently. Notably, we explore the
neural Turing machine (NTM) that results in our proposed Conformer-NTM model
architecture for ASR. Experimental results using Librispeech train-clean-100
and train-960 sets show that the proposed system outperforms the baseline
conformer without memory for long utterances. | [
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] |
In utilizing large language models (LLMs) for mathematical reasoning,
addressing the errors in the reasoning and calculation present in the generated
text by LLMs is a crucial challenge. In this paper, we propose a novel
framework that integrates the Chain-of-Thought (CoT) method with an external
tool (Python REPL). We discovered that by prompting LLMs to generate structured
text in XML-like markup language, we could seamlessly integrate CoT and the
external tool and control the undesired behaviors of LLMs. With our approach,
LLMs can utilize Python computation to rectify errors within CoT. We applied
our method to ChatGPT (GPT-3.5) to solve challenging mathematical problems and
demonstrated that combining CoT and Python REPL through the markup language
enhances the reasoning capability of LLMs. Our approach enables LLMs to write
the markup language and perform advanced mathematical reasoning using only
zero-shot prompting. | [
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] |
Automating warehouse operations can reduce logistics overhead costs,
ultimately driving down the final price for consumers, increasing the speed of
delivery, and enhancing the resiliency to market fluctuations.
This extended abstract showcases a large-scale package manipulation from
unstructured piles in Amazon Robotics' Robot Induction (Robin) fleet, which is
used for picking and singulating up to 6 million packages per day and so far
has manipulated over 2 billion packages. It describes the various heuristic
methods developed over time and their successor, which utilizes a pick success
predictor trained on real production data.
To the best of the authors' knowledge, this work is the first large-scale
deployment of learned pick quality estimation methods in a real production
system. | [
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] |
Most of today's wearable technology provides seamless cardiac activity
monitoring. Specifically, the vast majority employ Photoplethysmography (PPG)
sensors to acquire blood volume pulse information, which is further analysed to
extract useful and physiologically related features. Nevertheless, PPG-based
signal reliability presents different challenges that strongly affect such data
processing. This is mainly related to the fact of PPG morphological wave
distortion due to motion artefacts, which can lead to erroneous interpretation
of the extracted cardiac-related features. On this basis, in this paper, we
propose a novel personalised and adjustable Interval Type-2 Fuzzy Logic System
(IT2FLS) for assessing the quality of PPG signals. The proposed system employs
a personalised approach to adapt the IT2FLS parameters to the unique
characteristics of each individual's PPG signals.Additionally, the system
provides adjustable levels of personalisation, allowing healthcare providers to
adjust the system to meet specific requirements for different applications. The
proposed system obtained up to 93.72\% for average accuracy during validation.
The presented system has the potential to enable ultra-low complexity and
real-time PPG quality assessment, improving the accuracy and reliability of
PPG-based health monitoring systems at the edge. | [
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] |
This research investigates road traffic accident severity in the UK, using a
combination of machine learning, econometric, and statistical methods on
historical data. We employed various techniques, including correlation
analysis, regression models, GMM for error term issues, and time-series
forecasting with VAR and ARIMA models. Our approach outperforms naive
forecasting with an MASE of 0.800 and ME of -73.80. We also built a random
forest classifier with 73% precision, 78% recall, and a 73% F1-score.
Optimizing with H2O AutoML led to an XGBoost model with an RMSE of 0.176 and
MAE of 0.087. Factor Analysis identified key variables, and we used SHAP for
Explainable AI, highlighting influential factors like Driver_Home_Area_Type and
Road_Type. Our study enhances understanding of accident severity and offers
insights for evidence-based road safety policies. | [
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] |
As the application scenarios of mobile robots are getting more complex and
challenging, scene understanding becomes increasingly crucial. A mobile robot
that is supposed to operate autonomously in indoor environments must have
precise knowledge about what objects are present, where they are, what their
spatial extent is, and how they can be reached; i.e., information about free
space is also crucial. Panoptic mapping is a powerful instrument providing such
information. However, building 3D panoptic maps with high spatial resolution is
challenging on mobile robots, given their limited computing capabilities. In
this paper, we propose PanopticNDT - an efficient and robust panoptic mapping
approach based on occupancy normal distribution transform (NDT) mapping. We
evaluate our approach on the publicly available datasets Hypersim and
ScanNetV2. The results reveal that our approach can represent panoptic
information at a higher level of detail than other state-of-the-art approaches
while enabling real-time panoptic mapping on mobile robots. Finally, we prove
the real-world applicability of PanopticNDT with qualitative results in a
domestic application. | [
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] |
Effectively performing object rearrangement is an essential skill for mobile
manipulators, e.g., setting up a dinner table or organizing a desk. A key
challenge in such problems is deciding an appropriate manipulation order for
objects to effectively untangle dependencies between objects while considering
the necessary motions for realizing the manipulations (e.g., pick and place).
To our knowledge, computing time-optimal multi-object rearrangement solutions
for mobile manipulators remains a largely untapped research direction. In this
research, we propose ORLA*, which leverages delayed (lazy) evaluation in
searching for a high-quality object pick and place sequence that considers both
end-effector and mobile robot base travel. ORLA* also supports multi-layered
rearrangement tasks considering pile stability using machine learning.
Employing an optimal solver for finding temporary locations for displacing
objects, ORLA* can achieve global optimality. Through extensive simulation and
ablation study, we confirm the effectiveness of ORLA* delivering quality
solutions for challenging rearrangement instances. Supplementary materials are
available at: https://gaokai15.github.io/ORLA-Star/ | [
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] |
In order to solve the problem of non-ideal training sets (i.e., the
less-complete or over-complete sets) and implement one-iteration learning, a
novel efficient quantum perceptron algorithm based on unitary weights is
proposed, where the singular value decomposition of the total weight matrix
from the training set is calculated to make the weight matrix to be unitary.
The example validation of quantum gates {H, S, T, CNOT, Toffoli, Fredkin} shows
that our algorithm can accurately implement arbitrary quantum gates within one
iteration. The performance comparison between our algorithm and other quantum
perceptron algorithms demonstrates the advantages of our algorithm in terms of
applicability, accuracy, and availability. For further validating the
applicability of our algorithm, a quantum composite gate which consists of
several basic quantum gates is also illustrated. | [
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] |
Evolutionary robotics offers a powerful framework for designing and evolving
robot morphologies, particularly in the context of modular robots. However, the
role of query mechanisms during the genotype-to-phenotype mapping process has
been largely overlooked. This research addresses this gap by conducting a
comparative analysis of query mechanisms in the brain-body co-evolution of
modular robots. Using two different query mechanisms, Breadth-First Search
(BFS) and Random Query, within the context of evolving robot morphologies using
CPPNs and robot controllers using tensors, and testing them in two evolutionary
frameworks, Lamarckian and Darwinian systems, this study investigates their
influence on evolutionary outcomes and performance. The findings demonstrate
the impact of the two query mechanisms on the evolution and performance of
modular robot bodies, including morphological intelligence, diversity, and
morphological traits. This study suggests that BFS is both more effective and
efficient in producing highly performing robots. It also reveals that
initially, robot diversity was higher with BFS compared to Random Query, but in
the Lamarckian system, it declines faster, converging to superior designs,
while in the Darwinian system, BFS led to higher end-process diversity. | [
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] |
Shape servoing, a robotic task dedicated to controlling objects to desired
goal shapes, is a promising approach to deformable object manipulation. An
issue arises, however, with the reliance on the specification of a goal shape.
This goal has been obtained either by a laborious domain knowledge engineering
process or by manually manipulating the object into the desired shape and
capturing the goal shape at that specific moment, both of which are impractical
in various robotic applications. In this paper, we solve this problem by
developing a novel neural network DefGoalNet, which learns deformable object
goal shapes directly from a small number of human demonstrations. We
demonstrate our method's effectiveness on various robotic tasks, both in
simulation and on a physical robot. Notably, in the surgical retraction task,
even when trained with as few as 10 demonstrations, our method achieves a
median success percentage of nearly 90%. These results mark a substantial
advancement in enabling shape servoing methods to bring deformable object
manipulation closer to practical, real-world applications. | [
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] |
Precise perception of contact interactions is essential for the fine-grained
manipulation skills for robots. In this paper, we present the design of
feedback skills for robots that must learn to stack complex-shaped objects on
top of each other. To design such a system, a robot should be able to reason
about the stability of placement from very gentle contact interactions. Our
results demonstrate that it is possible to infer the stability of object
placement based on tactile readings during contact formation between the object
and its environment. In particular, we estimate the contact patch between a
grasped object and its environment using force and tactile observations to
estimate the stability of the object during a contact formation. The contact
patch could be used to estimate the stability of the object upon the release of
the grasp. The proposed method is demonstrated on various pairs of objects that
are used in a very popular board game. | [
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] |
This paper addresses the advancements in on-road trajectory planning for
Autonomous Passenger Vehicles (APV). Trajectory planning aims to produce a
globally optimal route for APVs, considering various factors such as vehicle
dynamics, constraints, and detected obstacles. Traditional techniques involve a
combination of sampling methods followed by optimization algorithms, where the
former ensures global awareness and the latter refines for local optima.
Notably, the Constrained Iterative Linear Quadratic Regulator (CILQR)
optimization algorithm has recently emerged, adapted for APV systems,
emphasizing improved safety and comfort. However, existing implementations
utilizing the vehicle bicycle kinematic model may not guarantee controllable
trajectories. We augment this model by incorporating higher-order terms,
including the first and second-order derivatives of curvature and longitudinal
jerk. This inclusion facilitates a richer representation in our cost and
constraint design. We also address roadway compliance, emphasizing adherence to
lane boundaries and directions, which past work often overlooked. Lastly, we
adopt a relaxed logarithmic barrier function to address the CILQR's dependency
on feasible initial trajectories. The proposed methodology is then validated
through simulation and real-world experiment driving scenes in real time. | [
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] |
In this paper, a novel Multi-agent Reinforcement Learning (MARL) approach,
Multi-Agent Continuous Dynamic Policy Gradient (MACDPP) was proposed to tackle
the issues of limited capability and sample efficiency in various scenarios
controlled by multiple agents. It alleviates the inconsistency of multiple
agents' policy updates by introducing the relative entropy regularization to
the Centralized Training with Decentralized Execution (CTDE) framework with the
Actor-Critic (AC) structure. Evaluated by multi-agent cooperation and
competition tasks and traditional control tasks including OpenAI benchmarks and
robot arm manipulation, MACDPP demonstrates significant superiority in learning
capability and sample efficiency compared with both related multi-agent and
widely implemented signal-agent baselines and therefore expands the potential
of MARL in effectively learning challenging control scenarios. | [
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Advances in neural speech synthesis have brought us technology that is not
only close to human naturalness, but is also capable of instant voice cloning
with little data, and is highly accessible with pre-trained models available.
Naturally, the potential flood of generated content raises the need for
synthetic speech detection and watermarking. Recently, considerable research
effort in synthetic speech detection has been related to the Automatic Speaker
Verification and Spoofing Countermeasure Challenge (ASVspoof), which focuses on
passive countermeasures. This paper takes a complementary view to generated
speech detection: a synthesis system should make an active effort to watermark
the generated speech in a way that aids detection by another machine, but
remains transparent to a human listener. We propose a collaborative training
scheme for synthetic speech watermarking and show that a HiFi-GAN neural
vocoder collaborating with the ASVspoof 2021 baseline countermeasure models
consistently improves detection performance over conventional classifier
training. Furthermore, we demonstrate how collaborative training can be paired
with augmentation strategies for added robustness against noise and
time-stretching. Finally, listening tests demonstrate that collaborative
training has little adverse effect on perceptual quality of vocoded speech. | [
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] |
Machine learning (ML) has the potential to revolutionize a wide range of
research areas and industries, but many ML projects never progress past the
proof-of-concept stage. To address this issue, we introduce Model Share AI
(AIMS), an easy-to-use MLOps platform designed to streamline collaborative
model development, model provenance tracking, and model deployment, as well as
a host of other functions aiming to maximize the real-world impact of ML
research. AIMS features collaborative project spaces and a standardized model
evaluation process that ranks model submissions based on their performance on
unseen evaluation data, enabling collaborative model development and
crowd-sourcing. Model performance and various model metadata are automatically
captured to facilitate provenance tracking and allow users to learn from and
build on previous submissions. Additionally, AIMS allows users to deploy ML
models built in Scikit-Learn, TensorFlow Keras, PyTorch, and ONNX into live
REST APIs and automatically generated web apps with minimal code. The ability
to deploy models with minimal effort and to make them accessible to
non-technical end-users through web apps has the potential to make ML research
more applicable to real-world challenges. | [
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] |
The cost of hyperparameter tuning in deep learning has been rising with model
sizes, prompting practitioners to find new tuning methods using a proxy of
smaller networks. One such proposal uses $\mu$P parameterized networks, where
the optimal hyperparameters for small width networks transfer to networks with
arbitrarily large width. However, in this scheme, hyperparameters do not
transfer across depths. As a remedy, we study residual networks with a residual
branch scale of $1/\sqrt{\text{depth}}$ in combination with the $\mu$P
parameterization. We provide experiments demonstrating that residual
architectures including convolutional ResNets and Vision Transformers trained
with this parameterization exhibit transfer of optimal hyperparameters across
width and depth on CIFAR-10 and ImageNet. Furthermore, our empirical findings
are supported and motivated by theory. Using recent developments in the
dynamical mean field theory (DMFT) description of neural network learning
dynamics, we show that this parameterization of ResNets admits a well-defined
feature learning joint infinite-width and infinite-depth limit and show
convergence of finite-size network dynamics towards this limit. | [
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] |
In this paper, we aim to explore the use of uplink semantic communications
with the assistance of UAV in order to improve data collection effiicency for
metaverse users in remote areas. To reduce the time for uplink data collection
while balancing the trade-off between reconstruction quality and computational
energy cost, we propose a hybrid action reinforcement learning (RL) framework
to make decisions on semantic model scale, channel allocation, transmission
power, and UAV trajectory. The variables are classified into discrete type and
continuous type, which are optimized by two different RL agents to generate the
combined action. Simulation results indicate that the proposed hybrid action
reinforcement learning framework can effectively improve the efficiency of
uplink semantic data collection under different parameter settings and
outperforms the benchmark scenarios. | [
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] |
We introduce MORPH, a method for co-optimization of hardware design
parameters and control policies in simulation using reinforcement learning.
Like most co-optimization methods, MORPH relies on a model of the hardware
being optimized, usually simulated based on the laws of physics. However, such
a model is often difficult to integrate into an effective optimization routine.
To address this, we introduce a proxy hardware model, which is always
differentiable and enables efficient co-optimization alongside a long-horizon
control policy using RL. MORPH is designed to ensure that the optimized
hardware proxy remains as close as possible to its realistic counterpart, while
still enabling task completion. We demonstrate our approach on simulated 2D
reaching and 3D multi-fingered manipulation tasks. | [
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] |
Recent results suggest that splitting topological navigation into
robot-independent and robot-specific components improves navigation performance
by enabling the robot-independent part to be trained with data collected by
different robot types. However, the navigation methods are still limited by the
scarcity of suitable training data and suffer from poor computational scaling.
In this work, we present PlaceNav, subdividing the robot-independent part into
navigation-specific and generic computer vision components. We utilize visual
place recognition for the subgoal selection of the topological navigation
pipeline. This makes subgoal selection more efficient and enables leveraging
large-scale datasets from non-robotics sources, increasing training data
availability. Bayesian filtering, enabled by place recognition, further
improves navigation performance by increasing the temporal consistency of
subgoals. Our experimental results verify the design and the new model obtains
a 76% higher success rate in indoor and 23% higher in outdoor navigation tasks
with higher computational efficiency. | [
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] |
Autonomous vehicle (AV) evaluation has been the subject of increased interest
in recent years both in industry and in academia. This paper focuses on the
development of a novel framework for generating adversarial driving behavior of
background vehicle interfering against the AV to expose effective and rational
risky events. Specifically, the adversarial behavior is learned by a
reinforcement learning (RL) approach incorporated with the cumulative prospect
theory (CPT) which allows representation of human risk cognition. Then, the
extended version of deep deterministic policy gradient (DDPG) technique is
proposed for training the adversarial policy while ensuring training stability
as the CPT action-value function is leveraged. A comparative case study
regarding the cut-in scenario is conducted on a high fidelity
Hardware-in-the-Loop (HiL) platform and the results demonstrate the adversarial
effectiveness to infer the weakness of the tested AV. | [
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] |
Developing machine intelligence abilities in robots and autonomous systems is
an expensive and time consuming process. Existing solutions are tailored to
specific applications and are harder to generalize. Furthermore, scarcity of
training data adds a layer of complexity in deploying deep machine learning
models. We present a new platform for General Robot Intelligence Development
(GRID) to address both of these issues. The platform enables robots to learn,
compose and adapt skills to their physical capabilities, environmental
constraints and goals. The platform addresses AI problems in robotics via
foundation models that know the physical world. GRID is designed from the
ground up to be extensible to accommodate new types of robots, vehicles,
hardware platforms and software protocols. In addition, the modular design
enables various deep ML components and existing foundation models to be easily
usable in a wider variety of robot-centric problems. We demonstrate the
platform in various aerial robotics scenarios and demonstrate how the platform
dramatically accelerates development of machine intelligent robots. | [
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Neuromorphic photonic accelerators are becoming increasingly popular, since
they can significantly improve computation speed and energy efficiency, leading
to femtojoule per MAC efficiency. However, deploying existing DL models on such
platforms is not trivial, since a great range of photonic neural network
architectures relies on incoherent setups and power addition operational
schemes that cannot natively represent negative quantities. This results in
additional hardware complexity that increases cost and reduces energy
efficiency. To overcome this, we can train non-negative neural networks and
potentially exploit the full range of incoherent neuromorphic photonic
capabilities. However, existing approaches cannot achieve the same level of
accuracy as their regular counterparts, due to training difficulties, as also
recent evidence suggests. To this end, we introduce a methodology to obtain the
non-negative isomorphic equivalents of regular neural networks that meet
requirements of neuromorphic hardware, overcoming the aforementioned
limitations. Furthermore, we also introduce a sign-preserving optimization
approach that enables training of such isomorphic networks in a non-negative
manner. | [
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