Search is not available for this dataset
text
string | activations
sequence |
|---|---|
Support Vector Machine (SVM) is a robust machine learning algorithm with
broad applications in classification, regression, and outlier detection. SVM
requires tuning the regularization parameter (RP) which controls the model
capacity and the generalization performance. Conventionally, the optimum RP is
found by comparison of a range of values through the Cross-Validation (CV)
procedure. In addition, for non-linearly separable data, the SVM uses kernels
where a set of kernels, each with a set of parameters, denoted as a grid of
kernels, are considered. The optimal choice of RP and the grid of kernels is
through the grid-search of CV. By stochastically analyzing the behavior of the
regularization parameter, this work shows that the SVM performance can be
modeled as a function of separability and scatteredness (S&S) of the data.
Separability is a measure of the distance between classes, and scatteredness is
the ratio of the spread of data points. In particular, for the hinge loss cost
function, an S&S ratio-based table provides the optimum RP. The S&S ratio is a
powerful value that can automatically detect linear or non-linear separability
before using the SVM algorithm. The provided S&S ratio-based table can also
provide the optimum kernel and its parameters before using the SVM algorithm.
Consequently, the computational complexity of the CV grid-search is reduced to
only one time use of the SVM. The simulation results on the real dataset
confirm the superiority and efficiency of the proposed approach in the sense of
computational complexity over the grid-search CV method. | [
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Biological signals, such as electroencephalograms (EEG), play a crucial role
in numerous clinical applications, exhibiting diverse data formats and quality
profiles. Current deep learning models for biosignals are typically specialized
for specific datasets and clinical settings, limiting their broader
applicability. Motivated by the success of large language models in text
processing, we explore the development of foundational models that are trained
from multiple data sources and can be fine-tuned on different downstream
biosignal tasks.
To overcome the unique challenges associated with biosignals of various
formats, such as mismatched channels, variable sample lengths, and prevalent
missing values, we propose a Biosignal Transformer (\method). The proposed
\method model can enable cross-data learning with mismatched channels, variable
lengths, and missing values by tokenizing diverse biosignals into unified
"biosignal sentences". Specifically, we tokenize each channel into fixed-length
segments containing local signal features, flattening them to form consistent
"sentences". Channel embeddings and {\em relative} position embeddings are
added to preserve spatio-temporal features.
The \method model is versatile and applicable to various biosignal learning
settings across different datasets, including joint pre-training for larger
models. Comprehensive evaluations on EEG, electrocardiogram (ECG), and human
activity sensory signals demonstrate that \method outperforms robust baselines
in common settings and facilitates learning across multiple datasets with
different formats. Use CHB-MIT seizure detection task as an example, our
vanilla \method model shows 3\% improvement over baselines in balanced
accuracy, and the pre-trained \method models (optimized from other data
sources) can further bring up to 4\% improvements. | [
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] |
This document serves as a position paper that outlines the authors' vision
for a potential pathway towards generalist robots. The purpose of this document
is to share the excitement of the authors with the community and highlight a
promising research direction in robotics and AI. The authors believe the
proposed paradigm is a feasible path towards accomplishing the long-standing
goal of robotics research: deploying robots, or embodied AI agents more
broadly, in various non-factory real-world settings to perform diverse tasks.
This document presents a specific idea for mining knowledge in the latest
large-scale foundation models for robotics research. Instead of directly using
or adapting these models to produce low-level policies and actions, it
advocates for a fully automated generative pipeline (termed as generative
simulation), which uses these models to generate diversified tasks, scenes and
training supervisions at scale, thereby scaling up low-level skill learning and
ultimately leading to a foundation model for robotics that empowers generalist
robots. The authors are actively pursuing this direction, but in the meantime,
they recognize that the ambitious goal of building generalist robots with
large-scale policy training demands significant resources such as computing
power and hardware, and research groups in academia alone may face severe
resource constraints in implementing the entire vision. Therefore, the authors
believe sharing their thoughts at this early stage could foster discussions,
attract interest towards the proposed pathway and related topics from industry
groups, and potentially spur significant technical advancements in the field. | [
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Abstaining classifiers have the option to abstain from making predictions on
inputs that they are unsure about. These classifiers are becoming increasingly
popular in high-stakes decision-making problems, as they can withhold uncertain
predictions to improve their reliability and safety. When evaluating black-box
abstaining classifier(s), however, we lack a principled approach that accounts
for what the classifier would have predicted on its abstentions. These missing
predictions matter when they can eventually be utilized, either directly or as
a backup option in a failure mode. In this paper, we introduce a novel approach
and perspective to the problem of evaluating and comparing abstaining
classifiers by treating abstentions as missing data. Our evaluation approach is
centered around defining the counterfactual score of an abstaining classifier,
defined as the expected performance of the classifier had it not been allowed
to abstain. We specify the conditions under which the counterfactual score is
identifiable: if the abstentions are stochastic, and if the evaluation data is
independent of the training data (ensuring that the predictions are missing at
random), then the score is identifiable. Note that, if abstentions are
deterministic, then the score is unidentifiable because the classifier can
perform arbitrarily poorly on its abstentions. Leveraging tools from
observational causal inference, we then develop nonparametric and doubly robust
methods to efficiently estimate this quantity under identification. Our
approach is examined in both simulated and real data experiments. | [
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Systems for serving inference requests on graph neural networks (GNN) must
combine low latency with high throughout, but they face irregular computation
due to skew in the number of sampled graph nodes and aggregated GNN features.
This makes it challenging to exploit GPUs effectively: using GPUs to sample
only a few graph nodes yields lower performance than CPU-based sampling; and
aggregating many features exhibits high data movement costs between GPUs and
CPUs. Therefore, current GNN serving systems use CPUs for graph sampling and
feature aggregation, limiting throughput.
We describe Quiver, a distributed GPU-based GNN serving system with
low-latency and high-throughput. Quiver's key idea is to exploit workload
metrics for predicting the irregular computation of GNN requests, and governing
the use of GPUs for graph sampling and feature aggregation: (1) for graph
sampling, Quiver calculates the probabilistic sampled graph size, a metric that
predicts the degree of parallelism in graph sampling. Quiver uses this metric
to assign sampling tasks to GPUs only when the performance gains surpass
CPU-based sampling; and (2) for feature aggregation, Quiver relies on the
feature access probability to decide which features to partition and replicate
across a distributed GPU NUMA topology. We show that Quiver achieves up to 35
times lower latency with an 8 times higher throughput compared to
state-of-the-art GNN approaches (DGL and PyG). | [
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] |
Artificial Intelligence (AI) is playing a vital role in all aspects of
technology including cyber security. Application of Conversational AI like the
chatbots are also becoming very popular in the medical field to provide timely
and immediate medical assistance to patients in need. As medical chatbots deal
with a lot of sensitive information, the security of these chatbots is crucial.
To secure the confidentiality, integrity, and availability of cloud-hosted
assets like these, medical chatbots can be monitored using AISecOps (Artificial
Intelligence for Secure IT Operations). AISecOPs is an emerging field that
integrates three different but interrelated domains like the IT operation, AI,
and security as one domain, where the expertise from all these three domains
are used cohesively to secure the cyber assets. It considers cloud operations
and security in a holistic framework to collect the metrics required to assess
the security threats and train the AI models to take immediate actions. This
work is focused on applying the STRIDE threat modeling framework to model the
possible threats involved in each component of the chatbot to enable the
automatic threat detection using the AISecOps techniques. This threat modeling
framework is tailored to the medical chatbots that involves sensitive data
sharing but could also be applied for chatbots used in other sectors like the
financial services, public sector, and government sectors that are concerned
with security and compliance. | [
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Accurate short-term predictions of phase-resolved water wave conditions are
crucial for decision-making in ocean engineering. However, the initialization
of remote-sensing-based wave prediction models first requires a reconstruction
of wave surfaces from sparse measurements like radar. Existing reconstruction
methods either rely on computationally intensive optimization procedures or
simplistic modelling assumptions that compromise the real-time capability or
accuracy of the subsequent prediction process. We therefore address these
issues by proposing a novel approach for phase-resolved wave surface
reconstruction using neural networks based on the U-Net and Fourier neural
operator (FNO) architectures. Our approach utilizes synthetic yet highly
realistic training data on uniform one-dimensional grids, that is generated by
the high-order spectral method for wave simulation and a geometric radar
modelling approach. The investigation reveals that both models deliver accurate
wave reconstruction results and show good generalization for different sea
states when trained with spatio-temporal radar data containing multiple
historic radar snapshots in each input. Notably, the FNO demonstrates superior
performance in handling the data structure imposed by wave physics due to its
global approach to learn the mapping between input and output in Fourier space. | [
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] |
Click-Through Rate (CTR) prediction is one of the main tasks of the
recommendation system, which is conducted by a user for different items to give
the recommendation results. Cross-domain CTR prediction models have been
proposed to overcome problems of data sparsity, long tail distribution of
user-item interactions, and cold start of items or users. In order to make
knowledge transfer from source domain to target domain more smoothly, an
innovative deep learning cross-domain CTR prediction model, Domain Adversarial
Deep Interest Network (DADIN) is proposed to convert the cross-domain
recommendation task into a domain adaptation problem. The joint distribution
alignment of two domains is innovatively realized by introducing domain
agnostic layers and specially designed loss, and optimized together with CTR
prediction loss in a way of adversarial training. It is found that the Area
Under Curve (AUC) of DADIN is 0.08% higher than the most competitive baseline
on Huawei dataset and is 0.71% higher than its competitors on Amazon dataset,
achieving the state-of-the-art results on the basis of the evaluation of this
model performance on two real datasets. The ablation study shows that by
introducing adversarial method, this model has respectively led to the AUC
improvements of 2.34% on Huawei dataset and 16.67% on Amazon dataset. | [
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The success of many healthcare programs depends on participants' adherence.
We consider the problem of scheduling interventions in low resource settings
(e.g., placing timely support calls from health workers) to increase adherence
and/or engagement. Past works have successfully developed several classes of
Restless Multi-armed Bandit (RMAB) based solutions for this problem.
Nevertheless, all past RMAB approaches assume that the participants' behaviour
follows the Markov property. We demonstrate significant deviations from the
Markov assumption on real-world data on a maternal health awareness program
from our partner NGO, ARMMAN. Moreover, we extend RMABs to continuous state
spaces, a previously understudied area. To tackle the generalised non-Markovian
RMAB setting we (i) model each participant's trajectory as a time-series, (ii)
leverage the power of time-series forecasting models to learn complex patterns
and dynamics to predict future states, and (iii) propose the Time-series Arm
Ranking Index (TARI) policy, a novel algorithm that selects the RMAB arms that
will benefit the most from an intervention, given our future state predictions.
We evaluate our approach on both synthetic data, and a secondary analysis on
real data from ARMMAN, and demonstrate significant increase in engagement
compared to the SOTA, deployed Whittle index solution. This translates to 16.3
hours of additional content listened, 90.8% more engagement drops prevented,
and reaching more than twice as many high dropout-risk beneficiaries. | [
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Probabilistic logical rule learning has shown great strength in logical rule
mining and knowledge graph completion. It learns logical rules to predict
missing edges by reasoning on existing edges in the knowledge graph. However,
previous efforts have largely been limited to only modeling chain-like Horn
clauses such as $R_1(x,z)\land R_2(z,y)\Rightarrow H(x,y)$. This formulation
overlooks additional contextual information from neighboring sub-graphs of
entity variables $x$, $y$ and $z$. Intuitively, there is a large gap here, as
local sub-graphs have been found to provide important information for knowledge
graph completion. Inspired by these observations, we propose Logical Entity
RePresentation (LERP) to encode contextual information of entities in the
knowledge graph. A LERP is designed as a vector of probabilistic logical
functions on the entity's neighboring sub-graph. It is an interpretable
representation while allowing for differentiable optimization. We can then
incorporate LERP into probabilistic logical rule learning to learn more
expressive rules. Empirical results demonstrate that with LERP, our model
outperforms other rule learning methods in knowledge graph completion and is
comparable or even superior to state-of-the-art black-box methods. Moreover, we
find that our model can discover a more expressive family of logical rules.
LERP can also be further combined with embedding learning methods like TransE
to make it more interpretable. | [
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] |
Deep Reinforcement Learning (DRL) has received a lot of attention from the
research community in recent years. As the technology moves away from game
playing to practical contexts, such as autonomous vehicles and robotics, it is
crucial to evaluate the quality of DRL agents. In this paper, we propose a
search-based approach to test such agents. Our approach, implemented in a tool
called Indago, trains a classifier on failure and non-failure environment
(i.e., pass) configurations resulting from the DRL training process. The
classifier is used at testing time as a surrogate model for the DRL agent
execution in the environment, predicting the extent to which a given
environment configuration induces a failure of the DRL agent under test. The
failure prediction acts as a fitness function, guiding the generation towards
failure environment configurations, while saving computation time by deferring
the execution of the DRL agent in the environment to those configurations that
are more likely to expose failures. Experimental results show that our
search-based approach finds 50% more failures of the DRL agent than
state-of-the-art techniques. Moreover, such failures are, on average, 78% more
diverse; similarly, the behaviors of the DRL agent induced by failure
configurations are 74% more diverse. | [
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] |
Reinforcement learning (RL), imitation learning (IL), and task and motion
planning (TAMP) have demonstrated impressive performance across various robotic
manipulation tasks. However, these approaches have been limited to learning
simple behaviors in current real-world manipulation benchmarks, such as pushing
or pick-and-place. To enable more complex, long-horizon behaviors of an
autonomous robot, we propose to focus on real-world furniture assembly, a
complex, long-horizon robot manipulation task that requires addressing many
current robotic manipulation challenges to solve. We present FurnitureBench, a
reproducible real-world furniture assembly benchmark aimed at providing a low
barrier for entry and being easily reproducible, so that researchers across the
world can reliably test their algorithms and compare them against prior work.
For ease of use, we provide 200+ hours of pre-collected data (5000+
demonstrations), 3D printable furniture models, a robotic environment setup
guide, and systematic task initialization. Furthermore, we provide
FurnitureSim, a fast and realistic simulator of FurnitureBench. We benchmark
the performance of offline RL and IL algorithms on our assembly tasks and
demonstrate the need to improve such algorithms to be able to solve our tasks
in the real world, providing ample opportunities for future research. | [
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In-memory computing for Machine Learning (ML) applications remedies the von
Neumann bottlenecks by organizing computation to exploit parallelism and
locality. Non-volatile memory devices such as Resistive RAM (ReRAM) offer
integrated switching and storage capabilities showing promising performance for
ML applications. However, ReRAM devices have design challenges, such as
non-linear digital-analog conversion and circuit overheads. This paper proposes
an In-Memory Boolean-to-Current Inference Architecture (IMBUE) that uses
ReRAM-transistor cells to eliminate the need for such conversions. IMBUE
processes Boolean feature inputs expressed as digital voltages and generates
parallel current paths based on resistive memory states. The proportional
column current is then translated back to the Boolean domain for further
digital processing. The IMBUE architecture is inspired by the Tsetlin Machine
(TM), an emerging ML algorithm based on intrinsically Boolean logic. The IMBUE
architecture demonstrates significant performance improvements over binarized
convolutional neural networks and digital TM in-memory implementations,
achieving up to a 12.99x and 5.28x increase, respectively. | [
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] |
Depression is the most prevalent and serious mental illness, which induces
grave financial and societal ramifications. Depression detection is key for
early intervention to mitigate those consequences. Such a high-stake decision
inherently necessitates interpretability. Although a few depression detection
studies attempt to explain the decision based on the importance score or
attention weights, these explanations misalign with the clinical depression
diagnosis criterion that is based on depressive symptoms. To fill this gap, we
follow the computational design science paradigm to develop a novel Multi-Scale
Temporal Prototype Network (MSTPNet). MSTPNet innovatively detects and
interprets depressive symptoms as well as how long they last. Extensive
empirical analyses using a large-scale dataset show that MSTPNet outperforms
state-of-the-art depression detection methods with an F1-score of 0.851. This
result also reveals new symptoms that are unnoted in the survey approach, such
as sharing admiration for a different life. We further conduct a user study to
demonstrate its superiority over the benchmarks in interpretability. This study
contributes to IS literature with a novel interpretable deep learning model for
depression detection in social media. In practice, our proposed method can be
implemented in social media platforms to provide personalized online resources
for detected depressed patients. | [
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] |
Generating desirable molecular structures in 3D is a fundamental problem for
drug discovery. Despite the considerable progress we have achieved, existing
methods usually generate molecules in atom resolution and ignore intrinsic
local structures such as rings, which leads to poor quality in generated
structures, especially when generating large molecules. Fragment-based molecule
generation is a promising strategy, however, it is nontrivial to be adapted for
3D non-autoregressive generations because of the combinational optimization
problems. In this paper, we utilize a coarse-to-fine strategy to tackle this
problem, in which a Hierarchical Diffusion-based model (i.e.~HierDiff) is
proposed to preserve the validity of local segments without relying on
autoregressive modeling. Specifically, HierDiff first generates coarse-grained
molecule geometries via an equivariant diffusion process, where each
coarse-grained node reflects a fragment in a molecule. Then the coarse-grained
nodes are decoded into fine-grained fragments by a message-passing process and
a newly designed iterative refined sampling module. Lastly, the fine-grained
fragments are then assembled to derive a complete atomic molecular structure.
Extensive experiments demonstrate that HierDiff consistently improves the
quality of molecule generation over existing methods | [
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] |
Physics is a field of science that has traditionally used the scientific
method to answer questions about why natural phenomena occur and to make
testable models that explain the phenomena. Discovering equations, laws and
principles that are invariant, robust and causal explanations of the world has
been fundamental in physical sciences throughout the centuries. Discoveries
emerge from observing the world and, when possible, performing interventional
studies in the system under study. With the advent of big data and the use of
data-driven methods, causal and equation discovery fields have grown and made
progress in computer science, physics, statistics, philosophy, and many applied
fields. All these domains are intertwined and can be used to discover causal
relations, physical laws, and equations from observational data. This paper
reviews the concepts, methods, and relevant works on causal and equation
discovery in the broad field of Physics and outlines the most important
challenges and promising future lines of research. We also provide a taxonomy
for observational causal and equation discovery, point out connections, and
showcase a complete set of case studies in Earth and climate sciences, fluid
dynamics and mechanics, and the neurosciences. This review demonstrates that
discovering fundamental laws and causal relations by observing natural
phenomena is being revolutionised with the efficient exploitation of
observational data, modern machine learning algorithms and the interaction with
domain knowledge. Exciting times are ahead with many challenges and
opportunities to improve our understanding of complex systems. | [
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Concerns regarding unfairness and discrimination in the context of artificial
intelligence (AI) systems have recently received increased attention from both
legal and computer science scholars. Yet, the degree of overlap between notions
of algorithmic bias and fairness on the one hand, and legal notions of
discrimination and equality on the other, is often unclear, leading to
misunderstandings between computer science and law. What types of bias and
unfairness does the law address when it prohibits discrimination? What role can
fairness metrics play in establishing legal compliance? In this paper, we aim
to illustrate to what extent European Union (EU) non-discrimination law
coincides with notions of algorithmic fairness proposed in computer science
literature and where they differ. The contributions of this paper are as
follows. First, we analyse seminal examples of algorithmic unfairness through
the lens of EU non-discrimination law, drawing parallels with EU case law.
Second, we set out the normative underpinnings of fairness metrics and
technical interventions and compare these to the legal reasoning of the Court
of Justice of the EU. Specifically, we show how normative assumptions often
remain implicit in both disciplinary approaches and explain the ensuing
limitations of current AI practice and non-discrimination law. We conclude with
implications for AI practitioners and regulators. | [
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] |
Integrating the brain structural and functional connectivity features is of
great significance in both exploring brain science and analyzing cognitive
impairment clinically. However, it remains a challenge to effectively fuse
structural and functional features in exploring the brain network. In this
paper, a novel brain structure-function fusing-representation learning (BSFL)
model is proposed to effectively learn fused representation from diffusion
tensor imaging (DTI) and resting-state functional magnetic resonance imaging
(fMRI) for mild cognitive impairment (MCI) analysis. Specifically, the
decomposition-fusion framework is developed to first decompose the feature
space into the union of the uniform and the unique spaces for each modality,
and then adaptively fuse the decomposed features to learn MCI-related
representation. Moreover, a knowledge-aware transformer module is designed to
automatically capture local and global connectivity features throughout the
brain. Also, a uniform-unique contrastive loss is further devised to make the
decomposition more effective and enhance the complementarity of structural and
functional features. The extensive experiments demonstrate that the proposed
model achieves better performance than other competitive methods in predicting
and analyzing MCI. More importantly, the proposed model could be a potential
tool for reconstructing unified brain networks and predicting abnormal
connections during the degenerative processes in MCI. | [
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] |
In this paper we present a novel solution that combines the capabilities of
Large Language Models (LLMs) with Formal Verification strategies to verify and
automatically repair software vulnerabilities. Initially, we employ Bounded
Model Checking (BMC) to locate the software vulnerability and derive a
counterexample. The counterexample provides evidence that the system behaves
incorrectly or contains a vulnerability. The counterexample that has been
detected, along with the source code, are provided to the LLM engine. Our
approach involves establishing a specialized prompt language for conducting
code debugging and generation to understand the vulnerability's root cause and
repair the code. Finally, we use BMC to verify the corrected version of the
code generated by the LLM. As a proof of concept, we create ESBMC-AI based on
the Efficient SMT-based Context-Bounded Model Checker (ESBMC) and a pre-trained
Transformer model, specifically gpt-3.5-turbo, to detect and fix errors in C
programs. Our experimentation involved generating a dataset comprising 1000 C
code samples, each consisting of 20 to 50 lines of code. Notably, our proposed
method achieved an impressive success rate of up to 80% in repairing vulnerable
code encompassing buffer overflow and pointer dereference failures. We assert
that this automated approach can effectively incorporate into the software
development lifecycle's continuous integration and deployment (CI/CD) process. | [
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] |
Evaluating canine electrocardiograms (ECG) require skilled veterinarians, but
current availability of veterinary cardiologists for ECG interpretation and
diagnostic support is limited. Developing tools for automated assessment of ECG
sequences can improve veterinary care by providing clinicians real-time results
and decision support tools. We implement a deep convolutional neural network
(CNN) approach for classifying canine electrocardiogram sequences as either
normal or abnormal. ECG records are converted into 8 second Lead II sequences
and classified as either normal (no evidence of cardiac abnormalities) or
abnormal (presence of one or more cardiac abnormalities). For training ECG
sequences are randomly augmented using RandomAugmentECG, a new augmentation
library implemented specifically for this project. Each chunk is then is
converted using a continuous wavelet transform into a 2D scalogram. The 2D
scalogram are then classified as either normal or abnormal by a binary CNN
classifier. Experimental results are validated against three boarded veterinary
cardiologists achieving an AUC-ROC score of 0.9506 on test dataset matching
human level performance. Additionally, we describe model deployment to
Microsoft Azure using an MLOps approach. To our knowledge, this work is one of
the first attempts to implement a deep learning model to automatically classify
ECG sequences for canines.Implementing automated ECG classification will
enhance veterinary care through improved diagnostic performance and increased
clinic efficiency. | [
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] |
Recent progress in music generation has been remarkably advanced by the
state-of-the-art MusicLM, which comprises a hierarchy of three LMs,
respectively, for semantic, coarse acoustic, and fine acoustic modelings. Yet,
sampling with the MusicLM requires processing through these LMs one by one to
obtain the fine-grained acoustic tokens, making it computationally expensive
and prohibitive for a real-time generation. Efficient music generation with a
quality on par with MusicLM remains a significant challenge. In this paper, we
present MeLoDy (M for music; L for LM; D for diffusion), an LM-guided diffusion
model that generates music audios of state-of-the-art quality meanwhile
reducing 95.7% or 99.6% forward passes in MusicLM, respectively, for sampling
10s or 30s music. MeLoDy inherits the highest-level LM from MusicLM for
semantic modeling, and applies a novel dual-path diffusion (DPD) model and an
audio VAE-GAN to efficiently decode the conditioning semantic tokens into
waveform. DPD is proposed to simultaneously model the coarse and fine acoustics
by incorporating the semantic information into segments of latents effectively
via cross-attention at each denoising step. Our experimental results suggest
the superiority of MeLoDy, not only in its practical advantages on sampling
speed and infinitely continuable generation, but also in its state-of-the-art
musicality, audio quality, and text correlation.
Our samples are available at https://Efficient-MeLoDy.github.io/. | [
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Independent Component Analysis (ICA) aims to recover independent latent
variables from observed mixtures thereof. Causal Representation Learning (CRL)
aims instead to infer causally related (thus often statistically dependent)
latent variables, together with the unknown graph encoding their causal
relationships. We introduce an intermediate problem termed Causal Component
Analysis (CauCA). CauCA can be viewed as a generalization of ICA, modelling the
causal dependence among the latent components, and as a special case of CRL. In
contrast to CRL, it presupposes knowledge of the causal graph, focusing solely
on learning the unmixing function and the causal mechanisms. Any impossibility
results regarding the recovery of the ground truth in CauCA also apply for CRL,
while possibility results may serve as a stepping stone for extensions to CRL.
We characterize CauCA identifiability from multiple datasets generated through
different types of interventions on the latent causal variables. As a
corollary, this interventional perspective also leads to new identifiability
results for nonlinear ICA -- a special case of CauCA with an empty graph --
requiring strictly fewer datasets than previous results. We introduce a
likelihood-based approach using normalizing flows to estimate both the unmixing
function and the causal mechanisms, and demonstrate its effectiveness through
extensive synthetic experiments in the CauCA and ICA setting. | [
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] |
Recent advancements in conversational large language models (LLMs), such as
ChatGPT, have demonstrated remarkable promise in various domains, including
drug discovery. However, existing works mainly focus on investigating the
capabilities of conversational LLMs on chemical reaction and retrosynthesis.
While drug editing, a critical task in the drug discovery pipeline, remains
largely unexplored. To bridge this gap, we propose ChatDrug, a framework to
facilitate the systematic investigation of drug editing using LLMs. ChatDrug
jointly leverages a prompt module, a retrieval and domain feedback (ReDF)
module, and a conversation module to streamline effective drug editing. We
empirically show that ChatDrug reaches the best performance on 33 out of 39
drug editing tasks, encompassing small molecules, peptides, and proteins. We
further demonstrate, through 10 case studies, that ChatDrug can successfully
identify the key substructures (e.g., the molecule functional groups, peptide
motifs, and protein structures) for manipulation, generating diverse and valid
suggestions for drug editing. Promisingly, we also show that ChatDrug can offer
insightful explanations from a domain-specific perspective, enhancing
interpretability and enabling informed decision-making. This research sheds
light on the potential of ChatGPT and conversational LLMs for drug editing. It
paves the way for a more efficient and collaborative drug discovery pipeline,
contributing to the advancement of pharmaceutical research and development. | [
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] |
Many high-performing machine learning models are not interpretable. As they
are increasingly used in decision scenarios that can critically affect
individuals, it is necessary to develop tools to better understand their
outputs. Popular explanation methods include contrastive explanations. However,
they suffer several shortcomings, among others an insufficient incorporation of
background knowledge, and a lack of interactivity. While (dialogue-like)
interactivity is important to better communicate an explanation, background
knowledge has the potential to significantly improve their quality, e.g., by
adapting the explanation to the needs of the end-user. To close this gap, we
present REASONX, an explanation tool based on Constraint Logic Programming
(CLP). REASONX provides interactive contrastive explanations that can be
augmented by background knowledge, and allows to operate under a setting of
under-specified information, leading to increased flexibility in the provided
explanations. REASONX computes factual and constrative decision rules, as well
as closest constrative examples. It provides explanations for decision trees,
which can be the ML models under analysis, or global/local surrogate models of
any ML model. While the core part of REASONX is built on CLP, we also provide a
program layer that allows to compute the explanations via Python, making the
tool accessible to a wider audience. We illustrate the capability of REASONX on
a synthetic data set, and on a a well-developed example in the credit domain.
In both cases, we can show how REASONX can be flexibly used and tailored to the
needs of the user. | [
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] |
Large Language Models (LLMs) pre-trained on code have recently emerged as the
dominant approach to program synthesis. However, the code that these models
produce can violate basic language-level invariants, leading to lower
performance in downstream tasks. We address this issue through an approach,
called RLCF, that further trains a pre-trained LLM using feedback from a code
compiler. RLCF views the LLM as an RL agent that generates code step by step
and receives: (i) compiler-derived feedback on whether the code it generates
passes a set of correctness checks; and (ii) feedback from a different LLM on
whether the generated code is similar to a set of reference programs in the
training corpus. Together, these feedback mechanisms help the generated code
remain within the target distribution while passing all static correctness
checks. RLCF is model- and language-agnostic. We empirically evaluate it on the
MBJP and MathQA tasks for Java. Our experiments show that RLCF significantly
raises the odds that an LLM-generated program compiles, is executable, and
produces the right output on tests, often allowing LLMs to match the
performance of 2x-8x larger LLMs. | [
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] |
Recently, diffusion models have achieved remarkable success in generating
tasks, including image and audio generation. However, like other generative
models, diffusion models are prone to privacy issues. In this paper, we propose
an efficient query-based membership inference attack (MIA), namely Proximal
Initialization Attack (PIA), which utilizes groundtruth trajectory obtained by
$\epsilon$ initialized in $t=0$ and predicted point to infer memberships.
Experimental results indicate that the proposed method can achieve competitive
performance with only two queries on both discrete-time and continuous-time
diffusion models. Moreover, previous works on the privacy of diffusion models
have focused on vision tasks without considering audio tasks. Therefore, we
also explore the robustness of diffusion models to MIA in the text-to-speech
(TTS) task, which is an audio generation task. To the best of our knowledge,
this work is the first to study the robustness of diffusion models to MIA in
the TTS task. Experimental results indicate that models with mel-spectrogram
(image-like) output are vulnerable to MIA, while models with audio output are
relatively robust to MIA. {Code is available at
\url{https://github.com/kong13661/PIA}}. | [
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] |
At Airbnb, an online marketplace for stays and experiences, guests often
spend weeks exploring and comparing multiple items before making a final
reservation request. Each reservation request may then potentially be rejected
or cancelled by the host prior to check-in. The long and exploratory nature of
the search journey, as well as the need to balance both guest and host
preferences, present unique challenges for Airbnb search ranking. In this
paper, we present Journey Ranker, a new multi-task deep learning model
architecture that addresses these challenges. Journey Ranker leverages
intermediate guest actions as milestones, both positive and negative, to better
progress the guest towards a successful booking. It also uses contextual
information such as guest state and search query to balance guest and host
preferences. Its modular and extensible design, consisting of four modules with
clear separation of concerns, allows for easy application to use cases beyond
the Airbnb search ranking context. We conducted offline and online testing of
the Journey Ranker and successfully deployed it in production to four different
Airbnb products with significant business metrics improvements. | [
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] |
Generative AI tools such as ChatGPT have recently gained significant
attention in higher education. This study aims to understand how universities
establish policies regarding the use of AI tools and explore the factors that
influence their decisions. Our study examines ChatGPT policies implemented at
universities around the world, including their existence, content, and issuance
dates. Specifically, we analyzed the top 500 universities according to the 2022
QS World University Rankings. Our findings indicate that there is significant
variation in university policies. Less than one-third of the universities
included in the study had implemented ChatGPT policies. Of the universities
with ChatGPT policies, approximately 67 percent embraced ChatGPT in teaching
and learning, more than twice the number of universities that banned it. The
majority of the universities that ban the use of ChatGPT in assessments allow
individual instructors to deviate from this restrictive policy. Our empirical
analysis identifies several factors that are significantly and positively
correlated with a university's likelihood of having a ChatGPT policy, including
the university's academic reputation score, being in an English-speaking
country, and the general public attitudes toward ChatGPT. In addition, we found
that a university's likelihood of having a ban policy is positively associated
with faculty student ratio, citations, and the English-speaking country dummy,
while negatively associated with the number of peer universities within the
same country that have banned ChatGPT. We discuss the challenges faced by
universities based our empirical findings. | [
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] |
In this paper, we consider non-convex multi-block bilevel optimization (MBBO)
problems, which involve $m\gg 1$ lower level problems and have important
applications in machine learning. Designing a stochastic gradient and
controlling its variance is more intricate due to the hierarchical sampling of
blocks and data and the unique challenge of estimating hyper-gradient. We aim
to achieve three nice properties for our algorithm: (a) matching the
state-of-the-art complexity of standard BO problems with a single block; (b)
achieving parallel speedup by sampling $I$ blocks and sampling $B$ samples for
each sampled block per-iteration; (c) avoiding the computation of the inverse
of a high-dimensional Hessian matrix estimator. However, it is non-trivial to
achieve all of these by observing that existing works only achieve one or two
of these properties. To address the involved challenges for achieving (a, b,
c), we propose two stochastic algorithms by using advanced blockwise
variance-reduction techniques for tracking the Hessian matrices (for
low-dimensional problems) or the Hessian-vector products (for high-dimensional
problems), and prove an iteration complexity of
$O(\frac{m\epsilon^{-3}\mathbb{I}(I<m)}{I\sqrt{I}} +
\frac{m\epsilon^{-3}}{I\sqrt{B}})$ for finding an $\epsilon$-stationary point
under appropriate conditions. We also conduct experiments to verify the
effectiveness of the proposed algorithms comparing with existing MBBO
algorithms. | [
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] |
Aiding humans with scientific designs is one of the most exciting of
artificial intelligence (AI) and machine learning (ML), due to their potential
for the discovery of new drugs, design of new materials and chemical compounds,
etc. However, scientific design typically requires complex domain knowledge
that is not familiar to AI researchers. Further, scientific studies involve
professional skills to perform experiments and evaluations. These obstacles
prevent AI researchers from developing specialized methods for scientific
designs. To take a step towards easy-to-understand and reproducible research of
scientific design, we propose a benchmark for the inverse design of
nanophotonic devices, which can be verified computationally and accurately.
Specifically, we implemented three different nanophotonic design problems,
namely a radiative cooler, a selective emitter for thermophotovoltaics, and
structural color filters, all of which are different in design parameter
spaces, complexity, and design targets. The benchmark environments are
implemented with an open-source simulator. We further implemented 10 different
inverse design algorithms and compared them in a reproducible and fair
framework. The results revealed the strengths and weaknesses of existing
methods, which shed light on several future directions for developing more
efficient inverse design algorithms. Our benchmark can also serve as the
starting point for more challenging scientific design problems. The code of
IDToolkit is available at https://github.com/ThyrixYang/IDToolkit. | [
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The optimal multicast tree problem in the Software-Defined Networking (SDN)
multicast routing is an NP-hard combinatorial optimization problem. Although
existing SDN intelligent solution methods, which are based on deep
reinforcement learning, can dynamically adapt to complex network link state
changes, these methods are plagued by problems such as redundant branches,
large action space, and slow agent convergence. In this paper, an SDN
intelligent multicast routing algorithm based on deep hierarchical
reinforcement learning is proposed to circumvent the aforementioned problems.
First, the multicast tree construction problem is decomposed into two
sub-problems: the fork node selection problem and the construction of the
optimal path from the fork node to the destination node. Second, based on the
information characteristics of SDN global network perception, the multicast
tree state matrix, link bandwidth matrix, link delay matrix, link packet loss
rate matrix, and sub-goal matrix are designed as the state space of intrinsic
and meta controllers. Then, in order to mitigate the excessive action space,
our approach constructs different action spaces at the upper and lower levels.
The meta-controller generates an action space using network nodes to select the
fork node, and the intrinsic controller uses the adjacent edges of the current
node as its action space, thus implementing four different action selection
strategies in the construction of the multicast tree. To facilitate the
intelligent agent in constructing the optimal multicast tree with greater
speed, we developed alternative reward strategies that distinguish between
single-step node actions and multi-step actions towards multiple destination
nodes. | [
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Model predictive control (MPC) is a popular approach for trajectory
optimization in practical robotics applications. MPC policies can optimize
trajectory parameters under kinodynamic and safety constraints and provide
guarantees on safety, optimality, generalizability, interpretability, and
explainability. However, some behaviors are complex and it is difficult to
hand-craft an MPC objective function. A special class of MPC policies called
Learnable-MPC addresses this difficulty using imitation learning from expert
demonstrations. However, they require the demonstrator and the imitator agents
to be identical which is hard to satisfy in many real world applications of
robotics. In this paper, we address the practical problem of training
Learnable-MPC policies when the demonstrator and the imitator do not share the
same dynamics and their state spaces may have a partial overlap. We propose a
novel approach that uses a generative adversarial network (GAN) to minimize the
Jensen-Shannon divergence between the state-trajectory distributions of the
demonstrator and the imitator. We evaluate our approach on a variety of
simulated robotics tasks of DeepMind Control suite and demonstrate the efficacy
of our approach at learning the demonstrator's behavior without having to copy
their actions. | [
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] |
One prevalent property we find empirically in real-world graph anomaly
detection (GAD) datasets is a one-class homophily, i.e., normal nodes tend to
have strong connection/affinity with each other, while the homophily in
abnormal nodes is significantly weaker than normal nodes. However, this
anomaly-discriminative property is ignored by existing GAD methods that are
typically built using a conventional anomaly detection objective, such as data
reconstruction. In this work, we explore this property to introduce a novel
unsupervised anomaly scoring measure for GAD -- local node affinity -- that
assigns a larger anomaly score to nodes that are less affiliated with their
neighbors, with the affinity defined as similarity on node
attributes/representations. We further propose Truncated Affinity Maximization
(TAM) that learns tailored node representations for our anomaly measure by
maximizing the local affinity of nodes to their neighbors. Optimizing on the
original graph structure can be biased by non-homophily edges (i.e., edges
connecting normal and abnormal nodes). Thus, TAM is instead optimized on
truncated graphs where non-homophily edges are removed iteratively to mitigate
this bias. The learned representations result in significantly stronger local
affinity for normal nodes than abnormal nodes. Extensive empirical results on
six real-world GAD datasets show that TAM substantially outperforms seven
competing models, achieving over 10% increase in AUROC/AUPRC compared to the
best contenders on challenging datasets. Our code will be made available at
https: //github.com/mala-lab/TAM-master/. | [
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] |
Twitter as one of the most popular social networks, offers a means for
communication and online discourse, which unfortunately has been the target of
bots and fake accounts, leading to the manipulation and spreading of false
information. Towards this end, we gather a challenging, multilingual dataset of
social discourse on Twitter, originating from 9M users regarding the recent
Russo-Ukrainian war, in order to detect the bot accounts and the conversation
involving them. We collect the ground truth for our dataset through the Twitter
API suspended accounts collection, containing approximately 343K of bot
accounts and 8M of normal users. Additionally, we use a dataset provided by
Botometer-V3 with 1,777 Varol, 483 German accounts, and 1,321 US accounts.
Besides the publicly available datasets, we also manage to collect 2
independent datasets around popular discussion topics of the 2022 energy crisis
and the 2022 conspiracy discussions. Both of the datasets were labeled
according to the Twitter suspension mechanism. We build a novel ML model for
bot detection using the state-of-the-art XGBoost model. We combine the model
with a high volume of labeled tweets according to the Twitter suspension
mechanism ground truth. This requires a limited set of profile features
allowing labeling of the dataset in different time periods from the collection,
as it is independent of the Twitter API. In comparison with Botometer our
methodology achieves an average 11% higher ROC-AUC score over two real-case
scenario datasets. | [
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] |
Quantum machine learning is often highlighted as one of the most promising
uses for a quantum computer to solve practical problems. However, a major
obstacle to the widespread use of quantum machine learning models in practice
is that these models, even once trained, still require access to a quantum
computer in order to be evaluated on new data. To solve this issue, we suggest
that following the training phase of a quantum model, a quantum computer could
be used to generate what we call a classical shadow of this model, i.e., a
classically computable approximation of the learned function. While recent
works already explore this idea and suggest approaches to construct such shadow
models, they also raise the possibility that a completely classical model could
be trained instead, thus circumventing the need for a quantum computer in the
first place. In this work, we take a novel approach to define shadow models
based on the frameworks of quantum linear models and classical shadow
tomography. This approach allows us to show that there exist shadow models
which can solve certain learning tasks that are intractable for fully classical
models, based on widely-believed cryptography assumptions. We also discuss the
(un)likeliness that all quantum models could be shadowfiable, based on common
assumptions in complexity theory. | [
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] |
We present Language-Image Value learning (LIV), a unified objective for
vision-language representation and reward learning from action-free videos with
text annotations. Exploiting a novel connection between dual reinforcement
learning and mutual information contrastive learning, the LIV objective trains
a multi-modal representation that implicitly encodes a universal value function
for tasks specified as language or image goals. We use LIV to pre-train the
first control-centric vision-language representation from large human video
datasets such as EpicKitchen. Given only a language or image goal, the
pre-trained LIV model can assign dense rewards to each frame in videos of
unseen robots or humans attempting that task in unseen environments. Further,
when some target domain-specific data is available, the same objective can be
used to fine-tune and improve LIV and even other pre-trained representations
for robotic control and reward specification in that domain. In our experiments
on several simulated and real-world robot environments, LIV models consistently
outperform the best prior input state representations for imitation learning,
as well as reward specification methods for policy synthesis. Our results
validate the advantages of joint vision-language representation and reward
learning within the unified, compact LIV framework. | [
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] |
Session-based recommendation, which aims to predict the next item of users'
interest as per an existing sequence interaction of items, has attracted
growing applications of Contrastive Learning (CL) with improved user and item
representations. However, these contrastive objectives: (1) serve a similar
role as the cross-entropy loss while ignoring the item representation space
optimisation; and (2) commonly require complicated modelling, including complex
positive/negative sample constructions and extra data augmentation. In this
work, we introduce Self-Contrastive Learning (SCL), which simplifies the
application of CL and enhances the performance of state-of-the-art CL-based
recommendation techniques. Specifically, SCL is formulated as an objective
function that directly promotes a uniform distribution among item
representations and efficiently replaces all the existing contrastive objective
components of state-of-the-art models. Unlike previous works, SCL eliminates
the need for any positive/negative sample construction or data augmentation,
leading to enhanced interpretability of the item representation space and
facilitating its extensibility to existing recommender systems. Through
experiments on three benchmark datasets, we demonstrate that SCL consistently
improves the performance of state-of-the-art models with statistical
significance. Notably, our experiments show that SCL improves the performance
of two best-performing models by 8.2% and 9.5% in P@10 (Precision) and 9.9% and
11.2% in MRR@10 (Mean Reciprocal Rank) on average across different benchmarks.
Additionally, our analysis elucidates the improvement in terms of alignment and
uniformity of representations, as well as the effectiveness of SCL with a low
computational cost. | [
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Multi-robot path finding in dynamic environments is a highly challenging
classic problem. In the movement process, robots need to avoid collisions with
other moving robots while minimizing their travel distance. Previous methods
for this problem either continuously replan paths using heuristic search
methods to avoid conflicts or choose appropriate collision avoidance strategies
based on learning approaches. The former may result in long travel distances
due to frequent replanning, while the latter may have low learning efficiency
due to low sample exploration and utilization, and causing high training costs
for the model. To address these issues, we propose a path planning method,
MAPPOHR, which combines heuristic search, empirical rules, and multi-agent
reinforcement learning. The method consists of two layers: a real-time planner
based on the multi-agent reinforcement learning algorithm, MAPPO, which embeds
empirical rules in the action output layer and reward functions, and a
heuristic search planner used to create a global guiding path. During movement,
the heuristic search planner replans new paths based on the instructions of the
real-time planner. We tested our method in 10 different conflict scenarios. The
experiments show that the planning performance of MAPPOHR is better than that
of existing learning and heuristic methods. Due to the utilization of empirical
knowledge and heuristic search, the learning efficiency of MAPPOHR is higher
than that of existing learning methods. | [
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] |
Software vulnerabilities bear enterprises significant costs. Despite
extensive efforts in research and development of software vulnerability
detection methods, uncaught vulnerabilities continue to put software owners and
users at risk. Many current vulnerability detection methods require that code
snippets can compile and build before attempting detection. This,
unfortunately, introduces a long latency between the time a vulnerability is
injected to the time it is removed, which can substantially increases the cost
of fixing a vulnerability. We recognize that the current advances in machine
learning can be used to detect vulnerable code patterns on syntactically
incomplete code snippets as the developer is writing the code at EditTime. In
this paper we present a practical system that leverages deep learning on a
large-scale data set of vulnerable code patterns to learn complex
manifestations of more than 250 vulnerability types and detect vulnerable code
patterns at EditTime. We discuss zero-shot, few-shot, and fine-tuning
approaches on state of the art pre-trained Large Language Models (LLMs). We
show that in comparison with state of the art vulnerability detection models
our approach improves the state of the art by 10%. We also evaluate our
approach to detect vulnerability in auto-generated code by code LLMs.
Evaluation on a benchmark of high-risk code scenarios shows a reduction of up
to 90% vulnerability reduction. | [
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Deep neural networks (DNNs) are emerging as a potential solution to solve
NP-hard wireless resource allocation problems. However, in the presence of
intricate constraints, e.g., users' quality-of-service (QoS) constraints,
guaranteeing constraint satisfaction becomes a fundamental challenge. In this
paper, we propose a novel unsupervised learning framework to solve the
classical power control problem in a multi-user interference channel, where the
objective is to maximize the network sumrate under users' minimum data rate or
QoS requirements and power budget constraints. Utilizing a differentiable
projection function, two novel deep learning (DL) solutions are pursued. The
first is called Deep Implicit Projection Network (DIPNet), and the second is
called Deep Explicit Projection Network (DEPNet). DIPNet utilizes a
differentiable convex optimization layer to implicitly define a projection
function. On the other hand, DEPNet uses an explicitly-defined projection
function, which has an iterative nature and relies on a differentiable
correction process. DIPNet requires convex constraints; whereas, the DEPNet
does not require convexity and has a reduced computational complexity. To
enhance the sum-rate performance of the proposed models even further,
Frank-Wolfe algorithm (FW) has been applied to the output of the proposed
models. Extensive simulations depict that the proposed DNN solutions not only
improve the achievable data rate but also achieve zero constraint violation
probability, compared to the existing DNNs. The proposed solutions outperform
the classic optimization methods in terms of computation time complexity. | [
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] |
User modeling, which learns to represent users into a low-dimensional
representation space based on their past behaviors, got a surge of interest
from the industry for providing personalized services to users. Previous
efforts in user modeling mainly focus on learning a task-specific user
representation that is designed for a single task. However, since learning
task-specific user representations for every task is infeasible, recent studies
introduce the concept of universal user representation, which is a more
generalized representation of a user that is relevant to a variety of tasks.
Despite their effectiveness, existing approaches for learning universal user
representations are impractical in real-world applications due to the data
requirement, catastrophic forgetting and the limited learning capability for
continually added tasks. In this paper, we propose a novel continual user
representation learning method, called TERACON, whose learning capability is
not limited as the number of learned tasks increases while capturing the
relationship between the tasks. The main idea is to introduce an embedding for
each task, i.e., task embedding, which is utilized to generate task-specific
soft masks that not only allow the entire model parameters to be updated until
the end of training sequence, but also facilitate the relationship between the
tasks to be captured. Moreover, we introduce a novel knowledge retention module
with pseudo-labeling strategy that successfully alleviates the long-standing
problem of continual learning, i.e., catastrophic forgetting. Extensive
experiments on public and proprietary real-world datasets demonstrate the
superiority and practicality of TERACON. Our code is available at
https://github.com/Sein-Kim/TERACON. | [
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The task of the session-based recommendation is to predict the next
interaction of the user based on the anonymized user's behavior pattern. And
personalized version of this system is a promising research field due to its
availability to deal with user information. However, there's a problem that the
user's preferences and historical sessions were not considered in the typical
session-based recommendation since it concentrates only on user-item
interaction. In addition, the existing personalized session-based
recommendation model has a limited capability in that it only considers the
preference of the current user without considering those of similar users. It
means there can be the loss of information included within the hierarchical
data structure of the user-session-item. To tackle with this problem, we
propose USP-SBR(abbr. of User Similarity Powered - Session Based Recommender).
To model global historical sessions of users, we propose UserGraph that has two
types of nodes - ItemNode and UserNode. We then connect the nodes with three
types of edges. The first type of edges connects ItemNode as chronological
order, and the second connects ItemNode to UserNode, and the last connects
UserNode to ItemNode. With these user embeddings, we propose additional
contrastive loss, that makes users with similar intention be close to each
other in the vector space. we apply graph neural network on these UserGraph and
update nodes. Experimental results on two real-world datasets demonstrate that
our method outperforms some state-of-the-art approaches. | [
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] |
The adoption of self-driving cars will certainly revolutionize our lives,
even though they may take more time to become fully autonomous than initially
predicted. The first vehicles are already present in certain cities of the
world, as part of experimental robot-taxi services. However, most existing
studies focus on the navigation part of such vehicles. We currently miss
methods, datasets, and studies to assess the in-cabin human component of the
adoption of such technology in real-world conditions. This paper proposes an
experimental framework to study the activities of occupants of self-driving
cars using a multidisciplinary approach (computer vision associated with human
and social sciences), particularly non-driving related activities. The
framework is composed of an experimentation scenario, and a data acquisition
module. We seek firstly to capture real-world data about the usage of the
vehicle in the nearest possible, real-world conditions, and secondly to create
a dataset containing in-cabin human activities to foster the development and
evaluation of computer vision algorithms. The acquisition module records
multiple views of the front seats of the vehicle (Intel RGB-D and GoPro
cameras); in addition to survey data about the internal states and attitudes of
participants towards this type of vehicle before, during, and after the
experimentation. We evaluated the proposed framework with the realization of
real-world experimentation with 30 participants (1 hour each) to study the
acceptance of SDCs of SAE level 4. | [
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] |
Many recent works in simulation-based inference (SBI) rely on deep generative
models to approximate complex, high-dimensional posterior distributions.
However, evaluating whether or not these approximations can be trusted remains
a challenge. Most approaches evaluate the posterior estimator only in
expectation over the observation space. This limits their interpretability and
is not sufficient to identify for which observations the approximation can be
trusted or should be improved. Building upon the well-known classifier
two-sample test (C2ST), we introduce L-C2ST, a new method that allows for a
local evaluation of the posterior estimator at any given observation. It offers
theoretically grounded and easy to interpret -- e.g. graphical -- diagnostics,
and unlike C2ST, does not require access to samples from the true posterior. In
the case of normalizing flow-based posterior estimators, L-C2ST can be
specialized to offer better statistical power, while being computationally more
efficient. On standard SBI benchmarks, L-C2ST provides comparable results to
C2ST and outperforms alternative local approaches such as coverage tests based
on highest predictive density (HPD). We further highlight the importance of
local evaluation and the benefit of interpretability of L-C2ST on a challenging
application from computational neuroscience. | [
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Load safety assessment and compliance is an essential step in the corporate
process of every logistics service provider. In 2020, a total of 11,371 police
checks of trucks were carried out, during which 9.6% (1091) violations against
the load safety regulations were detected. For a logistic service provider,
every load safety violation results in height fines and damage to reputation.
An assessment of load safety supported by artificial intelligence (AI) will
reduce the risk of accidents by unsecured loads and fines during safety
assessments. This work shows how photos of the load, taken by the truck driver
or the loadmaster after the loading process, can be used to assess load safety.
By a trained two-stage artificial neural network (ANN), these photos are
classified into three different classes I) cargo loaded safely, II) cargo
loaded unsafely, and III) unusable image. By applying several architectures of
convolutional neural networks (CNN), it can be shown that it is possible to
distinguish between unusable and usable images for cargo safety assessment.
This distinction is quite crucial since the truck driver and the loadmaster
sometimes provide photos without the essential image features like the case
structure of the truck and the whole cargo. A human operator or another ANN
will then assess the load safety within the second stage. | [
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] |
We propose and implement an interpretable machine learning classification
model for Explainable AI (XAI) based on expressive Boolean formulas. Potential
applications include credit scoring and diagnosis of medical conditions. The
Boolean formula defines a rule with tunable complexity (or interpretability),
according to which input data are classified. Such a formula can include any
operator that can be applied to one or more Boolean variables, thus providing
higher expressivity compared to more rigid rule-based and tree-based
approaches. The classifier is trained using native local optimization
techniques, efficiently searching the space of feasible formulas. Shallow rules
can be determined by fast Integer Linear Programming (ILP) or Quadratic
Unconstrained Binary Optimization (QUBO) solvers, potentially powered by
special purpose hardware or quantum devices. We combine the expressivity and
efficiency of the native local optimizer with the fast operation of these
devices by executing non-local moves that optimize over subtrees of the full
Boolean formula. We provide extensive numerical benchmarking results featuring
several baselines on well-known public datasets. Based on the results, we find
that the native local rule classifier is generally competitive with the other
classifiers. The addition of non-local moves achieves similar results with
fewer iterations, and therefore using specialized or quantum hardware could
lead to a speedup by fast proposal of non-local moves. | [
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One of the goals of causal inference is to generalize from past experiments
and observational data to novel conditions. While it is in principle possible
to eventually learn a mapping from a novel experimental condition to an outcome
of interest, provided a sufficient variety of experiments is available in the
training data, coping with a large combinatorial space of possible
interventions is hard. Under a typical sparse experimental design, this mapping
is ill-posed without relying on heavy regularization or prior distributions.
Such assumptions may or may not be reliable, and can be hard to defend or test.
In this paper, we take a close look at how to warrant a leap from past
experiments to novel conditions based on minimal assumptions about the
factorization of the distribution of the manipulated system, communicated in
the well-understood language of factor graph models. A postulated
$\textit{interventional factor model}$ (IFM) may not always be informative, but
it conveniently abstracts away a need for explicitly modeling unmeasured
confounding and feedback mechanisms, leading to directly testable claims. Given
an IFM and datasets from a collection of experimental regimes, we derive
conditions for identifiability of the expected outcomes of new regimes never
observed in these training data. We implement our framework using several
efficient algorithms, and apply them on a range of semi-synthetic experiments. | [
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Collaborative Mobile Crowd Sensing (CMCS) enhances data quality and coverage
by promoting teamwork in task sensing, with worker recruitment representing a
complex multi-objective optimization problem. Existing strategies mainly focus
on the characteristics of workers themselves, neglecting the asymmetric trust
relationships between them, which affects the rationality of task utility
evaluation. To address this, this paper first employs the Mini-Batch K-Means
clustering algorithm and deploys edge servers to enable efficient distributed
worker recruitment. Historical data and task requirements are utilized to
obtain workers' ability types and distances. A trust-directed graph in the
worker's social network is input into the Graph Convolutional Network (GCN)
framework for training, capturing asymmetric trustworthiness between worker
pairs. Privacy leakage is prevented in CMCS scenarios through high trust values
between workers. Ultimately, an undirected recruitment graph is constructed
using workers' abilities, trust values, and distance weights, transforming the
worker recruitment problem into a Maximum Weight Average Subgraph Problem
(MWASP). A Tabu Search Recruitment (TSR) algorithm is proposed to rationally
recruit a balanced multi-objective optimal task utility worker set for each
task. Extensive simulation experiments on four real-world datasets demonstrate
the effectiveness of the proposed strategy, outperforming other strategies. | [
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As society transitions towards an AI-based decision-making infrastructure, an
ever-increasing number of decisions once under control of humans are now
delegated to automated systems. Even though such developments make various
parts of society more efficient, a large body of evidence suggests that a great
deal of care needs to be taken to make such automated decision-making systems
fair and equitable, namely, taking into account sensitive attributes such as
gender, race, and religion. In this paper, we study a specific decision-making
task called outcome control in which an automated system aims to optimize an
outcome variable $Y$ while being fair and equitable. The interest in such a
setting ranges from interventions related to criminal justice and welfare, all
the way to clinical decision-making and public health. In this paper, we first
analyze through causal lenses the notion of benefit, which captures how much a
specific individual would benefit from a positive decision, counterfactually
speaking, when contrasted with an alternative, negative one. We introduce the
notion of benefit fairness, which can be seen as the minimal fairness
requirement in decision-making, and develop an algorithm for satisfying it. We
then note that the benefit itself may be influenced by the protected attribute,
and propose causal tools which can be used to analyze this. Finally, if some of
the variations of the protected attribute in the benefit are considered as
discriminatory, the notion of benefit fairness may need to be strengthened,
which leads us to articulating a notion of causal benefit fairness. Using this
notion, we develop a new optimization procedure capable of maximizing $Y$ while
ascertaining causal fairness in the decision process. | [
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Concentration of drivers on traffic is a vital safety issue; thus, monitoring
a driver being on road becomes an essential requirement. The key purpose of
supervision is to detect abnormal behaviours of the driver and promptly send
warnings to him her for avoiding incidents related to traffic accidents. In
this paper, to meet the requirement, based on radar sensors applications, the
authors first use a small sized millimetre wave radar installed at the steering
wheel of the vehicle to collect signals from different head movements of the
driver. The received signals consist of the reflection patterns that change in
response to the head movements of the driver. Then, in order to distinguish
these different movements, a classifier based on the measured signal of the
radar sensor is designed. However, since the collected data set is not large,
in this paper, the authors propose One shot learning to classify four cases of
driver's head movements. The experimental results indicate that the proposed
method can classify the four types of cases according to the various head
movements of the driver with a high accuracy reaching up to 100. In addition,
the classification performance of the proposed method is significantly better
than that of the convolutional neural network model. | [
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] |
Deep learning (DL) models have become increasingly popular in identifying
software vulnerabilities. Prior studies found that vulnerabilities across
different vulnerable programs may exhibit similar vulnerable scopes, implicitly
forming discernible vulnerability patterns that can be learned by DL models
through supervised training. However, vulnerable scopes still manifest in
various spatial locations and formats within a program, posing challenges for
models to accurately identify vulnerable statements. Despite this challenge,
state-of-the-art vulnerability detection approaches fail to exploit the
vulnerability patterns that arise in vulnerable programs. To take full
advantage of vulnerability patterns and unleash the ability of DL models, we
propose a novel vulnerability-matching approach in this paper, drawing
inspiration from program analysis tools that locate vulnerabilities based on
pre-defined patterns. Specifically, a vulnerability codebook is learned, which
consists of quantized vectors representing various vulnerability patterns.
During inference, the codebook is iterated to match all learned patterns and
predict the presence of potential vulnerabilities within a given program. Our
approach was extensively evaluated on a real-world dataset comprising more than
188,000 C/C++ functions. The evaluation results show that our approach achieves
an F1-score of 94% (6% higher than the previous best) and 82% (19% higher than
the previous best) for function and statement-level vulnerability
identification, respectively. These substantial enhancements highlight the
effectiveness of our approach to identifying vulnerabilities. The training code
and pre-trained models are available at https://github.com/optimatch/optimatch. | [
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] |
Power quality (PQ) events are recorded by PQ meters whenever anomalous events
are detected on the power grid. Using neural networks with machine learning can
aid in accurately classifying the recorded waveforms and help power system
engineers diagnose and rectify the root causes of problems. However, many of
the waveforms captured during a disturbance in the power system need to be
labeled for supervised learning, leaving a large number of data recordings for
engineers to process manually or go unseen. This paper presents an autoencoder
and K-means clustering-based unsupervised technique that can be used to cluster
PQ events into categories like sag, interruption, transients, normal, and
harmonic distortion to enable filtering of anomalous waveforms from recurring
or normal waveforms. The method is demonstrated using three-phase,
field-obtained voltage waveforms recorded in a distribution grid. First, a
convolutional autoencoder compresses the input signals into a set of lower
feature dimensions which, after further processing, is passed to the K-means
algorithm to identify data clusters. Using a small, labeled dataset, numerical
labels are then assigned to events based on a cosine similarity analysis.
Finally, the study analyzes the clusters using the t-distributed stochastic
neighbor embedding (t-SNE) visualization tool, demonstrating that the technique
can help investigate a large number of captured events in a quick manner. | [
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Reinforcement learning has gained significant traction in the field of
robotic navigation. However, a persistent challenge is its sample inefficiency,
primarily due to the inherent complexities of encouraging exploration. During
training, the mobile agent must explore as much as possible to efficiently
learn optimal behaviors. We introduce Ada-NAV, a novel adaptive trajectory
length scheme designed to enhance the training sample efficiency of
reinforcement learning algorithms in robotic navigation tasks. Unlike
traditional approaches that treat trajectory length as a fixed hyperparameter,
Ada-NAV dynamically adjusts it based on the entropy of the underlying
navigation policy. We empirically validate the efficacy of AdaNAV using two
popular policy gradient methods: REINFORCE and Proximal Policy Optimization
(PPO). We demonstrate through both simulated and real-world robotic experiments
that Ada-NAV outperforms conventional methods that employ constant or randomly
sampled trajectory lengths. Specifically, for a fixed sample budget, Ada-NAV
achieves an 18% increase in navigation success rate, a 20-38% reduction in
navigation path length, and a 9.32% decrease in elevation costs. Furthermore,
we showcase the versatility of Ada-NAV by integrating it with the Clearpath
Husky robot, illustrating its applicability in complex, outdoor environments. | [
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Accurate measurement of optical absorption coefficients from photoacoustic
imaging (PAI) data would enable direct mapping of molecular concentrations,
providing vital clinical insight. The ill-posed nature of the problem of
absorption coefficient recovery has prohibited PAI from achieving this goal in
living systems due to the domain gap between simulation and experiment. To
bridge this gap, we introduce a collection of experimentally well-characterised
imaging phantoms and their digital twins. This first-of-a-kind phantom data set
enables supervised training of a U-Net on experimental data for pixel-wise
estimation of absorption coefficients. We show that training on simulated data
results in artefacts and biases in the estimates, reinforcing the existence of
a domain gap between simulation and experiment. Training on experimentally
acquired data, however, yielded more accurate and robust estimates of optical
absorption coefficients. We compare the results to fluence correction with a
Monte Carlo model from reference optical properties of the materials, which
yields a quantification error of approximately 20%. Application of the trained
U-Nets to a blood flow phantom demonstrated spectral biases when training on
simulated data, while application to a mouse model highlighted the ability of
both learning-based approaches to recover the depth-dependent loss of signal
intensity. We demonstrate that training on experimental phantoms can restore
the correlation of signal amplitudes measured in depth. While the absolute
quantification error remains high and further improvements are needed, our
results highlight the promise of deep learning to advance quantitative PAI. | [
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] |
We present Strokes2Surface, an offline geometry reconstruction pipeline that
recovers well-connected curve networks from imprecise 4D sketches to bridge
concept design and digital modeling stages in architectural design. The input
to our pipeline consists of 3D strokes' polyline vertices and their timestamps
as the 4th dimension, along with additional metadata recorded throughout
sketching. Inspired by architectural sketching practices, our pipeline combines
a classifier and two clustering models to achieve its goal. First, with a set
of extracted hand-engineered features from the sketch, the classifier
recognizes the type of individual strokes between those depicting boundaries
(Shape strokes) and those depicting enclosed areas (Scribble strokes). Next,
the two clustering models parse strokes of each type into distinct groups, each
representing an individual edge or face of the intended architectural object.
Curve networks are then formed through topology recovery of consolidated Shape
clusters and surfaced using Scribble clusters guiding the cycle discovery. Our
evaluation is threefold: We confirm the usability of the Strokes2Surface
pipeline in architectural design use cases via a user study, we validate our
choice of features via statistical analysis and ablation studies on our
collected dataset, and we compare our outputs against a range of
reconstructions computed using alternative methods. | [
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Quadruped animals seamlessly transition between gaits as they change
locomotion speeds. While the most widely accepted explanation for gait
transitions is energy efficiency, there is no clear consensus on the
determining factor, nor on the potential effects from terrain properties. In
this article, we propose that viability, i.e. the avoidance of falls,
represents an important criterion for gait transitions. We investigate the
emergence of gait transitions through the interaction between supraspinal drive
(brain), the central pattern generator in the spinal cord, the body, and
exteroceptive sensing by leveraging deep reinforcement learning and robotics
tools. Consistent with quadruped animal data, we show that the walk-trot gait
transition for quadruped robots on flat terrain improves both viability and
energy efficiency. Furthermore, we investigate the effects of discrete terrain
(i.e. crossing successive gaps) on imposing gait transitions, and find the
emergence of trot-pronk transitions to avoid non-viable states. Compared with
other potential criteria such as peak forces and energy efficiency, viability
is the only improved factor after gait transitions on both flat and discrete
gap terrains, suggesting that viability could be a primary and universal
objective of gait transitions, while other criteria are secondary objectives
and/or a consequence of viability. Moreover, we deploy our learned controller
in sim-to-real hardware experiments and demonstrate state-of-the-art quadruped
agility in challenging scenarios, where the Unitree A1 quadruped autonomously
transitions gaits between trot and pronk to cross consecutive gaps of up to 30
cm (83.3 % of the body-length) at over 1.3 m/s. | [
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The Gaussian process latent variable model (GPLVM) is a popular probabilistic
method used for nonlinear dimension reduction, matrix factorization, and
state-space modeling. Inference for GPLVMs is computationally tractable only
when the data likelihood is Gaussian. Moreover, inference for GPLVMs has
typically been restricted to obtaining maximum a posteriori point estimates,
which can lead to overfitting, or variational approximations, which
mischaracterize the posterior uncertainty. Here, we present a method to perform
Markov chain Monte Carlo (MCMC) inference for generalized Bayesian nonlinear
latent variable modeling. The crucial insight necessary to generalize GPLVMs to
arbitrary observation models is that we approximate the kernel function in the
Gaussian process mappings with random Fourier features; this allows us to
compute the gradient of the posterior in closed form with respect to the latent
variables. We show that we can generalize GPLVMs to non-Gaussian observations,
such as Poisson, negative binomial, and multinomial distributions, using our
random feature latent variable model (RFLVM). Our generalized RFLVMs perform on
par with state-of-the-art latent variable models on a wide range of
applications, including motion capture, images, and text data for the purpose
of estimating the latent structure and imputing the missing data of these
complex data sets. | [
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] |
This paper presents a Predictive Maneuver Planning with Deep Reinforcement
Learning (PMP-DRL) model for maneuver planning. Traditional rule-based maneuver
planning approaches often have to improve their abilities to handle the
variabilities of real-world driving scenarios. By learning from its experience,
a Reinforcement Learning (RL)-based driving agent can adapt to changing driving
conditions and improve its performance over time. Our proposed approach
combines a predictive model and an RL agent to plan for comfortable and safe
maneuvers. The predictive model is trained using historical driving data to
predict the future positions of other surrounding vehicles. The surrounding
vehicles' past and predicted future positions are embedded in context-aware
grid maps. At the same time, the RL agent learns to make maneuvers based on
this spatio-temporal context information. Performance evaluation of PMP-DRL has
been carried out using simulated environments generated from publicly available
NGSIM US101 and I80 datasets. The training sequence shows the continuous
improvement in the driving experiences. It shows that proposed PMP-DRL can
learn the trade-off between safety and comfortability. The decisions generated
by the recent imitation learning-based model are compared with the proposed
PMP-DRL for unseen scenarios. The results clearly show that PMP-DRL can handle
complex real-world scenarios and make better comfortable and safe maneuver
decisions than rule-based and imitative models. | [
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] |
Supervised machine learning approaches have been increasingly used in
accelerating electronic structure prediction as surrogates of first-principle
computational methods, such as density functional theory (DFT). While numerous
quantum chemistry datasets focus on chemical properties and atomic forces, the
ability to achieve accurate and efficient prediction of the Hamiltonian matrix
is highly desired, as it is the most important and fundamental physical
quantity that determines the quantum states of physical systems and chemical
properties. In this work, we generate a new Quantum Hamiltonian dataset, named
as QH9, to provide precise Hamiltonian matrices for 2,399 molecular dynamics
trajectories and 130,831 stable molecular geometries, based on the QM9 dataset.
By designing benchmark tasks with various molecules, we show that current
machine learning models have the capacity to predict Hamiltonian matrices for
arbitrary molecules. Both the QH9 dataset and the baseline models are provided
to the community through an open-source benchmark, which can be highly valuable
for developing machine learning methods and accelerating molecular and
materials design for scientific and technological applications. Our benchmark
is publicly available at
https://github.com/divelab/AIRS/tree/main/OpenDFT/QHBench. | [
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] |
We consider the problem of subset selection where one is given multiple
rankings of items and the goal is to select the highest ``quality'' subset.
Score functions from the multiwinner voting literature have been used to
aggregate rankings into quality scores for subsets. We study this setting of
subset selection problems when, in addition, rankings may contain systemic or
unconscious biases toward a group of items. For a general model of input
rankings and biases, we show that requiring the selected subset to satisfy
group fairness constraints can improve the quality of the selection with
respect to unbiased rankings. Importantly, we show that for fairness
constraints to be effective, different multiwinner score functions may require
a drastically different number of rankings: While for some functions, fairness
constraints need an exponential number of rankings to recover a
close-to-optimal solution, for others, this dependency is only polynomial. This
result relies on a novel notion of ``smoothness'' of submodular functions in
this setting that quantifies how well a function can ``correctly'' assess the
quality of items in the presence of bias. The results in this paper can be used
to guide the choice of multiwinner score functions for the subset selection
setting considered here; we additionally provide a tool to empirically enable
this. | [
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] |
Machine learning techniques paired with the availability of massive datasets
dramatically enhance our ability to explore the chemical compound space by
providing fast and accurate predictions of molecular properties. However,
learning on large datasets is strongly limited by the availability of
computational resources and can be infeasible in some scenarios. Moreover, the
instances in the datasets may not yet be labelled and generating the labels can
be costly, as in the case of quantum chemistry computations. Thus, there is a
need to select small training subsets from large pools of unlabelled data
points and to develop reliable ML methods that can effectively learn from small
training sets. This work focuses on predicting the molecules atomization energy
in the QM9 dataset. We investigate the advantages of employing domain
knowledge-based data sampling methods for an efficient training set selection
combined with informed ML techniques. In particular, we show how maximizing
molecular diversity in the training set selection process increases the
robustness of linear and nonlinear regression techniques such as kernel methods
and graph neural networks. We also check the reliability of the predictions
made by the graph neural network with a model-agnostic explainer based on the
rate distortion explanation framework. | [
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] |
Foley sound generation aims to synthesise the background sound for multimedia
content. Previous models usually employ a large development set with labels as
input (e.g., single numbers or one-hot vector). In this work, we propose a
diffusion model based system for Foley sound generation with text conditions.
To alleviate the data scarcity issue, our model is initially pre-trained with
large-scale datasets and fine-tuned to this task via transfer learning using
the contrastive language-audio pertaining (CLAP) technique. We have observed
that the feature embedding extracted by the text encoder can significantly
affect the performance of the generation model. Hence, we introduce a trainable
layer after the encoder to improve the text embedding produced by the encoder.
In addition, we further refine the generated waveform by generating multiple
candidate audio clips simultaneously and selecting the best one, which is
determined in terms of the similarity score between the embedding of the
candidate clips and the embedding of the target text label. Using the proposed
method, our system ranks ${1}^{st}$ among the systems submitted to DCASE
Challenge 2023 Task 7. The results of the ablation studies illustrate that the
proposed techniques significantly improve sound generation performance. The
codes for implementing the proposed system are available online. | [
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In the investment industry, it is often essential to carry out fine-grained
company similarity quantification for a range of purposes, including market
mapping, competitor analysis, and mergers and acquisitions. We propose and
publish a knowledge graph, named CompanyKG, to represent and learn diverse
company features and relations. Specifically, 1.17 million companies are
represented as nodes enriched with company description embeddings; and 15
different inter-company relations result in 51.06 million weighted edges. To
enable a comprehensive assessment of methods for company similarity
quantification, we have devised and compiled three evaluation tasks with
annotated test sets: similarity prediction, competitor retrieval and similarity
ranking. We present extensive benchmarking results for 11 reproducible
predictive methods categorized into three groups: node-only, edge-only, and
node+edge. To the best of our knowledge, CompanyKG is the first large-scale
heterogeneous graph dataset originating from a real-world investment platform,
tailored for quantifying inter-company similarity. | [
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] |
Inference and prediction of routes have become of interest over the past
decade owing to a dramatic increase in package delivery and ride-sharing
services. Given the underlying combinatorial structure and the incorporation of
probabilities, route prediction involves techniques from both formal methods
and machine learning. One promising approach for predicting routes uses
decision diagrams that are augmented with probability values. However, the
effectiveness of this approach depends on the size of the compiled decision
diagrams. The scalability of the approach is limited owing to its empirical
runtime and space complexity. In this work, our contributions are two-fold:
first, we introduce a relaxed encoding that uses a linear number of variables
with respect to the number of vertices in a road network graph to significantly
reduce the size of resultant decision diagrams. Secondly, instead of a stepwise
sampling procedure, we propose a single pass sampling-based route prediction.
In our evaluations arising from a real-world road network, we demonstrate that
the resulting system achieves around twice the quality of suggested routes
while being an order of magnitude faster compared to state-of-the-art. | [
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] |
The recommendation algorithm based on knowledge graphs is at a relatively
mature stage. However, there are still some problems in the recommendation of
specific areas. For example, in the tourism field, selecting suitable tourist
attraction attributes process is complicated as the recommendation basis for
tourist attractions. In this paper, we propose the improved Attention Knowledge
Graph Convolution Network model, named ($Att-KGCN$), which automatically
discovers the neighboring entities of the target scenic spot semantically. The
attention layer aggregates relatively similar locations and represents them
with an adjacent vector. Then, according to the tourist's preferred choices,
the model predicts the probability of similar spots as a recommendation system.
A knowledge graph dataset of tourist attractions used based on tourism data on
Socotra Island-Yemen. Through experiments, it is verified that the Attention
Knowledge Graph Convolution Network has a good effect on the recommendation of
tourist attractions and can make more recommendations for tourists' choices. | [
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] |
Fully Homomorphic Encryption (FHE) is a cryptographic method that guarantees
the privacy and security of user data during computation. FHE algorithms can
perform unlimited arithmetic computations directly on encrypted data without
decrypting it. Thus, even when processed by untrusted systems, confidential
data is never exposed. In this work, we develop new techniques for accelerated
encrypted execution and demonstrate the significant performance advantages of
our approach. Our current focus is the Fully Homomorphic Encryption over the
Torus (CGGI) scheme, which is a current state-of-the-art method for evaluating
arbitrary functions in the encrypted domain. CGGI represents a computation as a
graph of homomorphic logic gates and each individual bit of the plaintext is
transformed into a polynomial in the encrypted domain. Arithmetic on such data
becomes very expensive: operations on bits become operations on entire
polynomials. Therefore, evaluating even relatively simple nonlinear functions,
such as a sigmoid, can take thousands of seconds on a single CPU thread. Using
our novel framework for end-to-end accelerated encrypted execution called
ArctyrEX, developers with no knowledge of complex FHE libraries can simply
describe their computation as a C program that is evaluated over $40\times$
faster on an NVIDIA DGX A100 and $6\times$ faster with a single A100 relative
to a 256-threaded CPU baseline. | [
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The development of variational quantum algorithms is crucial for the
application of NISQ computers. Such algorithms require short quantum circuits,
which are more amenable to implementation on near-term hardware, and many such
methods have been developed. One of particular interest is the so-called the
variational diagonalization method, which constitutes an important algorithmic
subroutine, and it can be used directly for working with data encoded in
quantum states. In particular, it can be applied to discern the features of
quantum states, such as entanglement properties of a system, or in quantum
machine learning algorithms. In this work, we tackle the problem of designing a
very shallow quantum circuit, required in the quantum state diagonalization
task, by utilizing reinforcement learning. To achieve this, we utilize a novel
encoding method that can be used to tackle the problem of circuit depth
optimization using a reinforcement learning approach. We demonstrate that our
approach provides a solid approximation to the diagonalization task while using
a small number of gates. The circuits proposed by the reinforcement learning
methods are shallower than the standard variational quantum state
diagonalization algorithm, and thus can be used in situations where the depth
of quantum circuits is limited by the hardware capabilities. | [
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] |
We introduce PyRCA, an open-source Python machine learning library of Root
Cause Analysis (RCA) for Artificial Intelligence for IT Operations (AIOps). It
provides a holistic framework to uncover the complicated metric causal
dependencies and automatically locate root causes of incidents. It offers a
unified interface for multiple commonly used RCA models, encompassing both
graph construction and scoring tasks. This library aims to provide IT
operations staff, data scientists, and researchers a one-step solution to rapid
model development, model evaluation and deployment to online applications. In
particular, our library includes various causal discovery methods to support
causal graph construction, and multiple types of root cause scoring methods
inspired by Bayesian analysis, graph analysis and causal analysis, etc. Our GUI
dashboard offers practitioners an intuitive point-and-click interface,
empowering them to easily inject expert knowledge through human interaction.
With the ability to visualize causal graphs and the root cause of incidents,
practitioners can quickly gain insights and improve their workflow efficiency.
This technical report introduces PyRCA's architecture and major
functionalities, while also presenting benchmark performance numbers in
comparison to various baseline models. Additionally, we demonstrate PyRCA's
capabilities through several example use cases. | [
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] |
Rapid advancements in artificial intelligence (AI) have sparked growing
concerns among experts, policymakers, and world leaders regarding the potential
for increasingly advanced AI systems to pose catastrophic risks. Although
numerous risks have been detailed separately, there is a pressing need for a
systematic discussion and illustration of the potential dangers to better
inform efforts to mitigate them. This paper provides an overview of the main
sources of catastrophic AI risks, which we organize into four categories:
malicious use, in which individuals or groups intentionally use AIs to cause
harm; AI race, in which competitive environments compel actors to deploy unsafe
AIs or cede control to AIs; organizational risks, highlighting how human
factors and complex systems can increase the chances of catastrophic accidents;
and rogue AIs, describing the inherent difficulty in controlling agents far
more intelligent than humans. For each category of risk, we describe specific
hazards, present illustrative stories, envision ideal scenarios, and propose
practical suggestions for mitigating these dangers. Our goal is to foster a
comprehensive understanding of these risks and inspire collective and proactive
efforts to ensure that AIs are developed and deployed in a safe manner.
Ultimately, we hope this will allow us to realize the benefits of this powerful
technology while minimizing the potential for catastrophic outcomes. | [
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Detecting duplicate patient participation in clinical trials is a major
challenge because repeated patients can undermine the credibility and accuracy
of the trial's findings and result in significant health and financial risks.
Developing accurate automated speaker verification (ASV) models is crucial to
verify the identity of enrolled individuals and remove duplicates, but the size
and quality of data influence ASV performance. However, there has been limited
investigation into the factors that can affect ASV capabilities in clinical
environments. In this paper, we bridge the gap by conducting analysis of how
participant demographic characteristics, audio quality criteria, and severity
level of Alzheimer's disease (AD) impact the performance of ASV utilizing a
dataset of speech recordings from 659 participants with varying levels of AD,
obtained through multiple speech tasks. Our results indicate that ASV
performance: 1) is slightly better on male speakers than on female speakers; 2)
degrades for individuals who are above 70 years old; 3) is comparatively better
for non-native English speakers than for native English speakers; 4) is
negatively affected by clinician interference, noisy background, and unclear
participant speech; 5) tends to decrease with an increase in the severity level
of AD. Our study finds that voice biometrics raise fairness concerns as certain
subgroups exhibit different ASV performances owing to their inherent voice
characteristics. Moreover, the performance of ASV is influenced by the quality
of speech recordings, which underscores the importance of improving the data
collection settings in clinical trials. | [
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] |
Machine learning algorithms are powerful tools for data driven tasks such as
image classification and feature detection, however their vulnerability to
adversarial examples - input samples manipulated to fool the algorithm -
remains a serious challenge. The integration of machine learning with quantum
computing has the potential to yield tools offering not only better accuracy
and computational efficiency, but also superior robustness against adversarial
attacks. Indeed, recent work has employed quantum mechanical phenomena to
defend against adversarial attacks, spurring the rapid development of the field
of quantum adversarial machine learning (QAML) and potentially yielding a new
source of quantum advantage. Despite promising early results, there remain
challenges towards building robust real-world QAML tools. In this review we
discuss recent progress in QAML and identify key challenges. We also suggest
future research directions which could determine the route to practicality for
QAML approaches as quantum computing hardware scales up and noise levels are
reduced. | [
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] |
Over the past few years, due to the rapid development of machine learning
(ML) models for weather forecasting, state-of-the-art ML models have shown
superior performance compared to the European Centre for Medium-Range Weather
Forecasts (ECMWF)'s high-resolution forecast (HRES) in 10-day forecasts at a
spatial resolution of 0.25 degree. However, the challenge remains to perform
comparably to the ECMWF ensemble mean (EM) in 15-day forecasts. Previous
studies have demonstrated the importance of mitigating the accumulation of
forecast errors for effective long-term forecasts. Despite numerous efforts to
reduce accumulation errors, including autoregressive multi-time step loss,
using a single model is found to be insufficient to achieve optimal performance
in both short and long lead times. Therefore, we present FuXi, a cascaded ML
weather forecasting system that provides 15-day global forecasts with a
temporal resolution of 6 hours and a spatial resolution of 0.25 degree. FuXi is
developed using 39 years of the ECMWF ERA5 reanalysis dataset. The performance
evaluation, based on latitude-weighted root mean square error (RMSE) and
anomaly correlation coefficient (ACC), demonstrates that FuXi has comparable
forecast performance to ECMWF EM in 15-day forecasts, making FuXi the first
ML-based weather forecasting system to accomplish this achievement. | [
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] |
Epilepsy is one of the most serious neurological diseases, affecting 1-2% of
the world's population. The diagnosis of epilepsy depends heavily on the
recognition of epileptic waves, i.e., disordered electrical brainwave activity
in the patient's brain. Existing works have begun to employ machine learning
models to detect epileptic waves via cortical electroencephalogram (EEG).
However, the recently developed stereoelectrocorticography (SEEG) method
provides information in stereo that is more precise than conventional EEG, and
has been broadly applied in clinical practice. Therefore, we propose the first
data-driven study to detect epileptic waves in a real-world SEEG dataset. While
offering new opportunities, SEEG also poses several challenges. In clinical
practice, epileptic wave activities are considered to propagate between
different regions in the brain. These propagation paths, also known as the
epileptogenic network, are deemed to be a key factor in the context of epilepsy
surgery. However, the question of how to extract an exact epileptogenic network
for each patient remains an open problem in the field of neuroscience. To
address these challenges, we propose a novel model (BrainNet) that jointly
learns the dynamic diffusion graphs and models the brain wave diffusion
patterns. In addition, our model effectively aids in resisting label imbalance
and severe noise by employing several self-supervised learning tasks and a
hierarchical framework. By experimenting with the extensive real SEEG dataset
obtained from multiple patients, we find that BrainNet outperforms several
latest state-of-the-art baselines derived from time-series analysis. | [
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Brain signals are important quantitative data for understanding physiological
activities and diseases of human brain. Most existing studies pay attention to
supervised learning methods, which, however, require high-cost clinical labels.
In addition, the huge difference in the clinical patterns of brain signals
measured by invasive (e.g., SEEG) and non-invasive (e.g., EEG) methods leads to
the lack of a unified method. To handle the above issues, we propose to study
the self-supervised learning (SSL) framework for brain signals that can be
applied to pre-train either SEEG or EEG data. Intuitively, brain signals,
generated by the firing of neurons, are transmitted among different connecting
structures in human brain. Inspired by this, we propose MBrain to learn
implicit spatial and temporal correlations between different channels (i.e.,
contacts of the electrode, corresponding to different brain areas) as the
cornerstone for uniformly modeling different types of brain signals.
Specifically, we represent the spatial correlation by a graph structure, which
is built with proposed multi-channel CPC. We theoretically prove that
optimizing the goal of multi-channel CPC can lead to a better predictive
representation and apply the instantaneou-time-shift prediction task based on
it. Then we capture the temporal correlation by designing the
delayed-time-shift prediction task. Finally, replace-discriminative-learning
task is proposed to preserve the characteristics of each channel. Extensive
experiments of seizure detection on both EEG and SEEG large-scale real-world
datasets demonstrate that our model outperforms several state-of-the-art time
series SSL and unsupervised models, and has the ability to be deployed to
clinical practice. | [
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] |
Multimedia content is of predominance in the modern Web era. In real
scenarios, multiple modalities reveal different aspects of item attributes and
usually possess different importance to user purchase decisions. However, it is
difficult for models to figure out users' true preference towards different
modalities since there exists strong statistical correlation between
modalities. Even worse, the strong statistical correlation might mislead models
to learn the spurious preference towards inconsequential modalities. As a
result, when data (modal features) distribution shifts, the learned spurious
preference might not guarantee to be as effective on the inference set as on
the training set. We propose a novel MOdality DEcorrelating STable learning
framework, MODEST for brevity, to learn users' stable preference. Inspired by
sample re-weighting techniques, the proposed method aims to estimate a weight
for each item, such that the features from different modalities in the weighted
distribution are decorrelated. We adopt Hilbert Schmidt Independence Criterion
(HSIC) as independence testing measure which is a kernel-based method capable
of evaluating the correlation degree between two multi-dimensional and
non-linear variables. Our method could be served as a play-and-plug module for
existing multimedia recommendation backbones. Extensive experiments on four
public datasets and four state-of-the-art multimedia recommendation backbones
unequivocally show that our proposed method can improve the performances by a
large margin. | [
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Sequential recommendation requires understanding the dynamic patterns of
users' behaviors, contexts, and preferences from their historical interactions.
Most existing works focus on modeling user-item interactions only from the item
level, ignoring that they are driven by latent shopping intentions (e.g.,
ballpoint pens, miniatures, etc). The detection of the underlying shopping
intentions of users based on their historical interactions is a crucial aspect
for e-commerce platforms, such as Amazon, to enhance the convenience and
efficiency of their customers' shopping experiences. Despite its significance,
the area of main shopping intention detection remains under-investigated in the
academic literature. To fill this gap, we propose a graph-regularized
stochastic Transformer method, G-STO. By considering intentions as sets of
products and user preferences as compositions of intentions, we model both of
them as stochastic Gaussian embeddings in the latent representation space.
Instead of training the stochastic representations from scratch, we develop a
global intention relational graph as prior knowledge for regularization,
allowing relevant shopping intentions to be distributionally close. Finally, we
feed the newly regularized stochastic embeddings into Transformer-based models
to encode sequential information from the intention transitions. We evaluate
our main shopping intention identification model on three different real-world
datasets, where G-STO achieves significantly superior performances to the
baselines by 18.08% in Hit@1, 7.01% in Hit@10, and 6.11% in NDCG@10 on average. | [
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] |
Avatars, as promising digital assistants in Vehicular Metaverses, can enable
drivers and passengers to immerse in 3D virtual spaces, serving as a practical
emerging example of Artificial Intelligence of Things (AIoT) in intelligent
vehicular environments. The immersive experience is achieved through seamless
human-avatar interaction, e.g., augmented reality navigation, which requires
intensive resources that are inefficient and impractical to process on
intelligent vehicles locally. Fortunately, offloading avatar tasks to RoadSide
Units (RSUs) or cloud servers for remote execution can effectively reduce
resource consumption. However, the high mobility of vehicles, the dynamic
workload of RSUs, and the heterogeneity of RSUs pose novel challenges to making
avatar migration decisions. To address these challenges, in this paper, we
propose a dynamic migration framework for avatar tasks based on real-time
trajectory prediction and Multi-Agent Deep Reinforcement Learning (MADRL).
Specifically, we propose a model to predict the future trajectories of
intelligent vehicles based on their historical data, indicating the future
workloads of RSUs.Based on the expected workloads of RSUs, we formulate the
avatar task migration problem as a long-term mixed integer programming problem.
To tackle this problem efficiently, the problem is transformed into a Partially
Observable Markov Decision Process (POMDP) and solved by multiple DRL agents
with hybrid continuous and discrete actions in decentralized. Numerical results
demonstrate that our proposed algorithm can effectively reduce the latency of
executing avatar tasks by around 25% without prediction and 30% with prediction
and enhance user immersive experiences in the AIoT-enabled Vehicular Metaverse
(AeVeM). | [
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Cybersecurity solutions have shown promising performance when detecting
ransomware samples that use fixed algorithms and encryption rates. However, due
to the current explosion of Artificial Intelligence (AI), sooner than later,
ransomware (and malware in general) will incorporate AI techniques to
intelligently and dynamically adapt its encryption behavior to be undetected.
It might result in ineffective and obsolete cybersecurity solutions, but the
literature lacks AI-powered ransomware to verify it. Thus, this work proposes
RansomAI, a Reinforcement Learning-based framework that can be integrated into
existing ransomware samples to adapt their encryption behavior and stay
stealthy while encrypting files. RansomAI presents an agent that learns the
best encryption algorithm, rate, and duration that minimizes its detection
(using a reward mechanism and a fingerprinting intelligent detection system)
while maximizing its damage function. The proposed framework was validated in a
ransomware, Ransomware-PoC, that infected a Raspberry Pi 4, acting as a
crowdsensor. A pool of experiments with Deep Q-Learning and Isolation Forest
(deployed on the agent and detection system, respectively) has demonstrated
that RansomAI evades the detection of Ransomware-PoC affecting the Raspberry Pi
4 in a few minutes with >90% accuracy. | [
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Dataflow scheduling decisions are of vital importance to neural network (NN)
accelerators. Recent scalable NN accelerators support a rich set of advanced
dataflow techniques. The problems of comprehensively representing and quickly
finding optimized dataflow schemes thus become significantly more complicated
and challenging. In this work, we first propose comprehensive and pragmatic
dataflow representations for temporal and spatial scheduling on scalable
multi-node NN architectures. An informal hierarchical taxonomy highlights the
tight coupling across different levels of the dataflow space as the major
difficulty for fast design exploration. A set of formal tensor-centric
directives accurately express various inter-layer and intra-layer schemes, and
allow for quickly determining their validity and efficiency. We then build a
generic, optimized, and fast dataflow solver, KAPLA, which makes use of the
pragmatic directives to explore the design space with effective validity check
and efficiency estimation. KAPLA decouples the upper inter-layer level for fast
pruning, and solves the lower intra-layer schemes with a novel bottom-up cost
descending method. KAPLA achieves within only 2.2% and 7.7% energy overheads on
the result dataflow for training and inference, respectively, compared to the
exhaustively searched optimal schemes. It also outperforms random and
machine-learning-based approaches, with more optimized results and orders of
magnitude faster search speedup. | [
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The recurrent neural network has been greatly developed for effectively
solving time-varying problems corresponding to complex environments. However,
limited by the way of centralized processing, the model performance is greatly
affected by factors like the silos problems of the models and data in reality.
Therefore, the emergence of distributed artificial intelligence such as
federated learning (FL) makes it possible for the dynamic aggregation among
models. However, the integration process of FL is still server-dependent, which
may cause a great risk to the overall model. Also, it only allows collaboration
between homogeneous models, and does not have a good solution for the
interaction between heterogeneous models. Therefore, we propose a Distributed
Computation Model (DCM) based on the consortium blockchain network to improve
the credibility of the overall model and effective coordination among
heterogeneous models. In addition, a Distributed Hierarchical Integration (DHI)
algorithm is also designed for the global solution process. Within a group,
permissioned nodes collect the local models' results from different
permissionless nodes and then sends the aggregated results back to all the
permissionless nodes to regularize the processing of the local models. After
the iteration is completed, the secondary integration of the local results will
be performed between permission nodes to obtain the global results. In the
experiments, we verify the efficiency of DCM, where the results show that the
proposed model outperforms many state-of-the-art models based on a federated
learning framework. | [
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With the widespread digitization of finance and the increasing popularity of
cryptocurrencies, the sophistication of fraud schemes devised by cybercriminals
is growing. Money laundering -- the movement of illicit funds to conceal their
origins -- can cross bank and national boundaries, producing complex
transaction patterns. The UN estimates 2-5\% of global GDP or \$0.8 - \$2.0
trillion dollars are laundered globally each year. Unfortunately, real data to
train machine learning models to detect laundering is generally not available,
and previous synthetic data generators have had significant shortcomings. A
realistic, standardized, publicly-available benchmark is needed for comparing
models and for the advancement of the area. To this end, this paper contributes
a synthetic financial transaction dataset generator and a set of synthetically
generated AML (Anti-Money Laundering) datasets. We have calibrated this
agent-based generator to match real transactions as closely as possible and
made the datasets public. We describe the generator in detail and demonstrate
how the datasets generated can help compare different Graph Neural Networks in
terms of their AML abilities. In a key way, using synthetic data in these
comparisons can be even better than using real data: the ground truth labels
are complete, whilst many laundering transactions in real data are never
detected. | [
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Disk scrubbing is a process aimed at resolving read errors on disks by
reading data from the disk. However, scrubbing the entire storage array at once
can adversely impact system performance, particularly during periods of high
input/output operations. Additionally, the continuous reading of data from
disks when scrubbing can result in wear and tear, especially on larger capacity
disks, due to the significant time and energy consumption involved. To address
these issues, we propose a selective disk scrubbing method that enhances the
overall reliability and power efficiency in data centers. Our method employs a
Machine Learning model based on Mondrian Conformal prediction to identify
specific disks for scrubbing, by proactively predicting the health status of
each disk in the storage pool, forecasting n-days in advance, and using an
open-source dataset. For disks predicted as non-healthy, we mark them for
replacement without further action. For healthy drives, we create a set and
quantify their relative health across the entire storage pool based on the
predictor's confidence. This enables us to prioritize selective scrubbing for
drives with established scrubbing frequency based on the scrub cycle. The
method we propose provides an efficient and dependable solution for managing
enterprise disk drives. By scrubbing just 22.7% of the total storage disks, we
can achieve optimized energy consumption and reduce the carbon footprint of the
data center. | [
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] |
3D perceptual representations are well suited for robot manipulation as they
easily encode occlusions and simplify spatial reasoning. Many manipulation
tasks require high spatial precision in end-effector pose prediction, which
typically demands high-resolution 3D feature grids that are computationally
expensive to process. As a result, most manipulation policies operate directly
in 2D, foregoing 3D inductive biases. In this paper, we introduce Act3D, a
manipulation policy transformer that represents the robot's workspace using a
3D feature field with adaptive resolutions dependent on the task at hand. The
model lifts 2D pre-trained features to 3D using sensed depth, and attends to
them to compute features for sampled 3D points. It samples 3D point grids in a
coarse to fine manner, featurizes them using relative-position attention, and
selects where to focus the next round of point sampling. In this way, it
efficiently computes 3D action maps of high spatial resolution. Act3D sets a
new state-of-the-art in RL-Bench, an established manipulation benchmark, where
it achieves 10% absolute improvement over the previous SOTA 2D multi-view
policy on 74 RLBench tasks and 22% absolute improvement with 3x less compute
over the previous SOTA 3D policy. We quantify the importance of relative
spatial attention, large-scale vision-language pre-trained 2D backbones, and
weight tying across coarse-to-fine attentions in ablative experiments. Code and
videos are available on our project website: https://act3d.github.io/. | [
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Flaky tests are problematic because they non-deterministically pass or fail
for the same software version under test, causing confusion and wasting
developer time. While machine learning models have been used to predict
flakiness and its root causes, there is less work on providing support to fix
the problem. To address this gap, we propose a framework that automatically
generates labeled datasets for 13 fix categories and train models to predict
the fix category of a flaky test by analyzing the test code only. Though it is
unrealistic at this stage to accurately predict the fix itself, the categories
provide precise guidance about what part of the test code to look at. Our
approach is based on language models, namely CodeBERT and UniXcoder, whose
output is fine-tuned with a Feed Forward Neural Network (FNN) or a Siamese
Network-based Few Shot Learning (FSL). Our experimental results show that
UniXcoder outperforms CodeBERT, in correctly predicting most of the categories
of fixes a developer should apply. Furthermore, FSL does not appear to have any
significant effect. Given the high accuracy obtained for most fix categories,
our proposed framework has the potential to help developers to fix flaky tests
quickly and accurately.To aid future research, we make our automated labeling
tool, dataset, prediction models, and experimental infrastructure publicly
available. | [
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Despite its broad practical applications such as in fraud prevention,
open-set speaker identification (OSI) has received less attention in the
speaker recognition community compared to speaker verification (SV). OSI deals
with determining if a test speech sample belongs to a speaker from a set of
pre-enrolled individuals (in-set) or if it is from an out-of-set speaker. In
addition to the typical challenges associated with speech variability, OSI is
prone to the "false-alarm problem"; as the size of the in-set speaker
population (a.k.a watchlist) grows, the out-of-set scores become larger,
leading to increased false alarm rates. This is in particular challenging for
applications in financial institutions and border security where the watchlist
size is typically of the order of several thousand speakers. Therefore, it is
important to systematically quantify the false-alarm problem, and develop
techniques that alleviate the impact of watchlist size on detection
performance. Prior studies on this problem are sparse, and lack a common
benchmark for systematic evaluations. In this paper, we present the first
public benchmark for OSI, developed using the VoxCeleb dataset. We quantify the
effect of the watchlist size and speech duration on the watchlist-based speaker
detection task using three strong neural network based systems. In contrast to
the findings from prior research, we show that the commonly adopted adaptive
score normalization is not guaranteed to improve the performance for this task.
On the other hand, we show that score calibration and score fusion, two other
commonly used techniques in SV, result in significant improvements in OSI
performance. | [
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Event logs are widely used to record the status of high-tech systems, making
log anomaly detection important for monitoring those systems. Most existing log
anomaly detection methods take a log event count matrix or log event sequences
as input, exploiting quantitative and/or sequential relationships between log
events to detect anomalies. Unfortunately, only considering quantitative or
sequential relationships may result in low detection accuracy. To alleviate
this problem, we propose a graph-based method for unsupervised log anomaly
detection, dubbed Logs2Graphs, which first converts event logs into attributed,
directed, and weighted graphs, and then leverages graph neural networks to
perform graph-level anomaly detection. Specifically, we introduce One-Class
Digraph Inception Convolutional Networks, abbreviated as OCDiGCN, a novel graph
neural network model for detecting graph-level anomalies in a collection of
attributed, directed, and weighted graphs. By coupling the graph representation
and anomaly detection steps, OCDiGCN can learn a representation that is
especially suited for anomaly detection, resulting in a high detection
accuracy. Importantly, for each identified anomaly, we additionally provide a
small subset of nodes that play a crucial role in OCDiGCN's prediction as
explanations, which can offer valuable cues for subsequent root cause
diagnosis. Experiments on five benchmark datasets show that Logs2Graphs
performs at least on par with state-of-the-art log anomaly detection methods on
simple datasets while largely outperforming state-of-the-art log anomaly
detection methods on complicated datasets. | [
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] |
Entity resolution (ER) is the process of identifying records that refer to
the same entities within one or across multiple databases. Numerous techniques
have been developed to tackle ER challenges over the years, with recent
emphasis placed on machine and deep learning methods for the matching phase.
However, the quality of the benchmark datasets typically used in the
experimental evaluations of learning-based matching algorithms has not been
examined in the literature. To cover this gap, we propose four different
approaches to assessing the difficulty and appropriateness of 13 established
datasets: two theoretical approaches, which involve new measures of linearity
and existing measures of complexity, and two practical approaches: the
difference between the best non-linear and linear matchers, as well as the
difference between the best learning-based matcher and the perfect oracle. Our
analysis demonstrates that most of the popular datasets pose rather easy
classification tasks. As a result, they are not suitable for properly
evaluating learning-based matching algorithms. To address this issue, we
propose a new methodology for yielding benchmark datasets. We put it into
practice by creating four new matching tasks, and we verify that these new
benchmarks are more challenging and therefore more suitable for further
advancements in the field. | [
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] |
The Data Science domain has expanded monumentally in both research and
industry communities during the past decade, predominantly owing to the Big
Data revolution. Artificial Intelligence (AI) and Machine Learning (ML) are
bringing more complexities to data engineering applications, which are now
integrated into data processing pipelines to process terabytes of data.
Typically, a significant amount of time is spent on data preprocessing in these
pipelines, and hence improving its e fficiency directly impacts the overall
pipeline performance. The community has recently embraced the concept of
Dataframes as the de-facto data structure for data representation and
manipulation. However, the most widely used serial Dataframes today (R, pandas)
experience performance limitations while working on even moderately large data
sets. We believe that there is plenty of room for improvement by taking a look
at this problem from a high-performance computing point of view. In a prior
publication, we presented a set of parallel processing patterns for distributed
dataframe operators and the reference runtime implementation, Cylon [1]. In
this paper, we are expanding on the initial concept by introducing a cost model
for evaluating the said patterns. Furthermore, we evaluate the performance of
Cylon on the ORNL Summit supercomputer. | [
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The effectiveness of ad creatives is greatly influenced by their visual
appearance. Advertising platforms can generate ad creatives with different
appearances by combining creative elements provided by advertisers. However,
with the increasing number of ad creative elements, it becomes challenging to
select a suitable combination from the countless possibilities. The industry's
mainstream approach is to select individual creative elements independently,
which often overlooks the importance of interaction between creative elements
during the modeling process. In response, this paper proposes a Cross-Element
Combinatorial Selection framework for multiple creative elements, termed CECS.
In the encoder process, a cross-element interaction is adopted to dynamically
adjust the expression of a single creative element based on the current
candidate creatives. In the decoder process, the creative combination problem
is transformed into a cascade selection problem of multiple creative elements.
A pointer mechanism with a cascade design is used to model the associations
among candidates. Comprehensive experiments on real-world datasets show that
CECS achieved the SOTA score on offline metrics. Moreover, the CECS algorithm
has been deployed in our industrial application, resulting in a significant
6.02% CTR and 10.37% GMV lift, which is beneficial to the business. | [
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] |
Machine learning (ML) models are vulnerable to membership inference attacks
(MIAs), which determine whether a given input is used for training the target
model. While there have been many efforts to mitigate MIAs, they often suffer
from limited privacy protection, large accuracy drop, and/or requiring
additional data that may be difficult to acquire. This work proposes a defense
technique, HAMP that can achieve both strong membership privacy and high
accuracy, without requiring extra data. To mitigate MIAs in different forms, we
observe that they can be unified as they all exploit the ML model's
overconfidence in predicting training samples through different proxies. This
motivates our design to enforce less confident prediction by the model, hence
forcing the model to behave similarly on the training and testing samples. HAMP
consists of a novel training framework with high-entropy soft labels and an
entropy-based regularizer to constrain the model's prediction while still
achieving high accuracy. To further reduce privacy risk, HAMP uniformly
modifies all the prediction outputs to become low-confidence outputs while
preserving the accuracy, which effectively obscures the differences between the
prediction on members and non-members. We conduct extensive evaluation on five
benchmark datasets, and show that HAMP provides consistently high accuracy and
strong membership privacy. Our comparison with seven state-of-the-art defenses
shows that HAMP achieves a superior privacy-utility trade off than those
techniques. | [
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] |
Mean decrease in impurity (MDI) is a popular feature importance measure for
random forests (RFs). We show that the MDI for a feature $X_k$ in each tree in
an RF is equivalent to the unnormalized $R^2$ value in a linear regression of
the response on the collection of decision stumps that split on $X_k$. We use
this interpretation to propose a flexible feature importance framework called
MDI+. Specifically, MDI+ generalizes MDI by allowing the analyst to replace the
linear regression model and $R^2$ metric with regularized generalized linear
models (GLMs) and metrics better suited for the given data structure. Moreover,
MDI+ incorporates additional features to mitigate known biases of decision
trees against additive or smooth models. We further provide guidance on how
practitioners can choose an appropriate GLM and metric based upon the
Predictability, Computability, Stability framework for veridical data science.
Extensive data-inspired simulations show that MDI+ significantly outperforms
popular feature importance measures in identifying signal features. We also
apply MDI+ to two real-world case studies on drug response prediction and
breast cancer subtype classification. We show that MDI+ extracts
well-established predictive genes with significantly greater stability compared
to existing feature importance measures. All code and models are released in a
full-fledged python package on Github. | [
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] |
Recent years have witnessed the widespread use of artificial intelligence
(AI) algorithms and machine learning (ML) models. Despite their tremendous
success, a number of vital problems like ML model brittleness, their fairness,
and the lack of interpretability warrant the need for the active developments
in explainable artificial intelligence (XAI) and formal ML model verification.
The two major lines of work in XAI include feature selection methods, e.g.
Anchors, and feature attribution techniques, e.g. LIME and SHAP. Despite their
promise, most of the existing feature selection and attribution approaches are
susceptible to a range of critical issues, including explanation unsoundness
and out-of-distribution sampling. A recent formal approach to XAI (FXAI)
although serving as an alternative to the above and free of these issues
suffers from a few other limitations. For instance and besides the scalability
limitation, the formal approach is unable to tackle the feature attribution
problem. Additionally, a formal explanation despite being formally sound is
typically quite large, which hampers its applicability in practical settings.
Motivated by the above, this paper proposes a way to apply the apparatus of
formal XAI to the case of feature attribution based on formal explanation
enumeration. Formal feature attribution (FFA) is argued to be advantageous over
the existing methods, both formal and non-formal. Given the practical
complexity of the problem, the paper then proposes an efficient technique for
approximating exact FFA. Finally, it offers experimental evidence of the
effectiveness of the proposed approximate FFA in comparison to the existing
feature attribution algorithms not only in terms of feature importance and but
also in terms of their relative order. | [
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Reinforcement learning has been successful across several applications in
which agents have to learn to act in environments with sparse feedback.
However, despite this empirical success there is still a lack of theoretical
understanding of how the parameters of reinforcement learning models and the
features used to represent states interact to control the dynamics of learning.
In this work, we use concepts from statistical physics, to study the typical
case learning curves for temporal difference learning of a value function with
linear function approximators. Our theory is derived under a Gaussian
equivalence hypothesis where averages over the random trajectories are replaced
with temporally correlated Gaussian feature averages and we validate our
assumptions on small scale Markov Decision Processes. We find that the
stochastic semi-gradient noise due to subsampling the space of possible
episodes leads to significant plateaus in the value error, unlike in
traditional gradient descent dynamics. We study how learning dynamics and
plateaus depend on feature structure, learning rate, discount factor, and
reward function. We then analyze how strategies like learning rate annealing
and reward shaping can favorably alter learning dynamics and plateaus. To
conclude, our work introduces new tools to open a new direction towards
developing a theory of learning dynamics in reinforcement learning. | [
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Personalized recommendations have a growing importance in direct marketing,
which motivates research to enhance customer experiences by knowledge graph
(KG) applications. For example, in financial services, companies may benefit
from providing relevant financial articles to their customers to cultivate
relationships, foster client engagement and promote informed financial
decisions. While several approaches center on KG-based recommender systems for
improved content, in this study we focus on interpretable KG-based recommender
systems for decision making.To this end, we present two knowledge graph-based
approaches for personalized article recommendations for a set of customers of a
large multinational financial services company. The first approach employs
Reinforcement Learning and the second approach uses the XGBoost algorithm for
recommending articles to the customers. Both approaches make use of a KG
generated from both structured (tabular data) and unstructured data (a large
body of text data).Using the Reinforcement Learning-based recommender system we
could leverage the graph traversal path leading to the recommendation as a way
to generate interpretations (Path Directed Reasoning (PDR)). In the
XGBoost-based approach, one can also provide explainable results using post-hoc
methods such as SHAP (SHapley Additive exPlanations) and ELI5 (Explain Like I
am Five).Importantly, our approach offers explainable results, promoting better
decision-making. This study underscores the potential of combining advanced
machine learning techniques with KG-driven insights to bolster experience in
customer relationship management. | [
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The dominant theories of rational choice assume logical omniscience. That is,
they assume that when facing a decision problem, an agent can perform all
relevant computations and determine the truth value of all relevant
logical/mathematical claims. This assumption is unrealistic when, for example,
we offer bets on remote digits of pi or when an agent faces a computationally
intractable planning problem. Furthermore, the assumption of logical
omniscience creates contradictions in cases where the environment can contain
descriptions of the agent itself. Importantly, strategic interactions as
studied in game theory are decision problems in which a rational agent is
predicted by its environment (the other players). In this paper, we develop a
theory of rational decision making that does not assume logical omniscience. We
consider agents who repeatedly face decision problems (including ones like
betting on digits of pi or games against other agents). The main contribution
of this paper is to provide a sensible theory of rationality for such agents.
Roughly, we require that a boundedly rational inductive agent tests each
efficiently computable hypothesis infinitely often and follows those hypotheses
that keep their promises of high rewards. We then prove that agents that are
rational in this sense have other desirable properties. For example, they learn
to value random and pseudo-random lotteries at their expected reward. Finally,
we consider strategic interactions between different agents and prove a folk
theorem for what strategies bounded rational inductive agents can converge to. | [
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] |
Self-supervised learning (SSL) has shown promising results in various speech
and natural language processing applications. However, its efficacy in music
information retrieval (MIR) still remains largely unexplored. While previous
SSL models pre-trained on music recordings may have been mostly closed-sourced,
recent speech models such as wav2vec2.0 have shown promise in music modelling.
Nevertheless, research exploring the effectiveness of applying speech SSL
models to music recordings has been limited. We explore the music adaption of
SSL with two distinctive speech-related models, data2vec1.0 and Hubert, and
refer to them as music2vec and musicHuBERT, respectively. We train $12$ SSL
models with 95M parameters under various pre-training configurations and
systematically evaluate the MIR task performances with 13 different MIR tasks.
Our findings suggest that training with music data can generally improve
performance on MIR tasks, even when models are trained using paradigms designed
for speech. However, we identify the limitations of such existing
speech-oriented designs, especially in modelling polyphonic information. Based
on the experimental results, empirical suggestions are also given for designing
future musical SSL strategies and paradigms. | [
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The combination of diverse health data (IoT, EHR, and clinical surveys) and
scalable-adaptable Artificial Intelligence (AI), has enabled the discovery of
physical, behavioral, and psycho-social indicators of pain status. Despite the
hype and promise to fundamentally alter the healthcare system with
technological advancements, much AI adoption in clinical pain evaluation has
been hampered by the heterogeneity of the problem itself and other challenges,
such as personalization and fairness. Studies have revealed that many AI (i.e.,
machine learning or deep learning) models display biases and discriminate
against specific population segments (such as those based on gender or
ethnicity), which breeds skepticism among medical professionals about AI
adaptability. In this paper, we propose a Multi-attribute Fairness Loss (MAFL)
based CNN model that aims to account for any sensitive attributes included in
the data and fairly predict patients' pain status while attempting to minimize
the discrepancies between privileged and unprivileged groups. In order to
determine whether the trade-off between accuracy and fairness can be satisfied,
we compare the proposed model with well-known existing mitigation procedures,
and studies reveal that the implemented model performs favorably in contrast to
state-of-the-art methods. Utilizing NIH All-Of-US data, where a cohort of 868
distinct individuals with wearables and EHR data gathered over 1500 days has
been taken into consideration to analyze our suggested fair pain assessment
system. | [
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Muscle force and joint kinematics estimation from surface electromyography
(sEMG) are essential for real-time biomechanical analysis of the dynamic
interplay among neural muscle stimulation, muscle dynamics, and kinetics.
Recent advances in deep neural networks (DNNs) have shown the potential to
improve biomechanical analysis in a fully automated and reproducible manner.
However, the small sample nature and physical interpretability of biomechanical
analysis limit the applications of DNNs. This paper presents a novel
physics-informed low-shot learning method for sEMG-based estimation of muscle
force and joint kinematics. This method seamlessly integrates Lagrange's
equation of motion and inverse dynamic muscle model into the generative
adversarial network (GAN) framework for structured feature decoding and
extrapolated estimation from the small sample data. Specifically, Lagrange's
equation of motion is introduced into the generative model to restrain the
structured decoding of the high-level features following the laws of physics.
And a physics-informed policy gradient is designed to improve the adversarial
learning efficiency by rewarding the consistent physical representation of the
extrapolated estimations and the physical references. Experimental validations
are conducted on two scenarios (i.e. the walking trials and wrist motion
trials). Results indicate that the estimations of the muscle forces and joint
kinematics are unbiased compared to the physics-based inverse dynamics, which
outperforms the selected benchmark methods, including physics-informed
convolution neural network (PI-CNN), vallina generative adversarial network
(GAN), and multi-layer extreme learning machine (ML-ELM). | [
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] |
Eye-in-hand cameras have shown promise in enabling greater sample efficiency
and generalization in vision-based robotic manipulation. However, for robotic
imitation, it is still expensive to have a human teleoperator collect large
amounts of expert demonstrations with a real robot. Videos of humans performing
tasks, on the other hand, are much cheaper to collect since they eliminate the
need for expertise in robotic teleoperation and can be quickly captured in a
wide range of scenarios. Therefore, human video demonstrations are a promising
data source for learning generalizable robotic manipulation policies at scale.
In this work, we augment narrow robotic imitation datasets with broad unlabeled
human video demonstrations to greatly enhance the generalization of eye-in-hand
visuomotor policies. Although a clear visual domain gap exists between human
and robot data, our framework does not need to employ any explicit domain
adaptation method, as we leverage the partial observability of eye-in-hand
cameras as well as a simple fixed image masking scheme. On a suite of eight
real-world tasks involving both 3-DoF and 6-DoF robot arm control, our method
improves the success rates of eye-in-hand manipulation policies by 58%
(absolute) on average, enabling robots to generalize to both new environment
configurations and new tasks that are unseen in the robot demonstration data.
See video results at https://giving-robots-a-hand.github.io/ . | [
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] |
Existing object-search approaches enable robots to search through free
pathways, however, robots operating in unstructured human-centered environments
frequently also have to manipulate the environment to their needs. In this
work, we introduce a novel interactive multi-object search task in which a
robot has to open doors to navigate rooms and search inside cabinets and
drawers to find target objects. These new challenges require combining
manipulation and navigation skills in unexplored environments. We present
HIMOS, a hierarchical reinforcement learning approach that learns to compose
exploration, navigation, and manipulation skills. To achieve this, we design an
abstract high-level action space around a semantic map memory and leverage the
explored environment as instance navigation points. We perform extensive
experiments in simulation and the real world that demonstrate that, with
accurate perception, the decision making of HIMOS effectively transfers to new
environments in a zero-shot manner. It shows robustness to unseen subpolicies,
failures in their execution, and different robot kinematics. These capabilities
open the door to a wide range of downstream tasks across embodied AI and
real-world use cases. | [
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