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On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors | Recently, there has been a growing focus and interest in applying machine
learning (ML) to the field of cybersecurity, particularly in malware detection
and prevention. Several research works on malware analysis have been proposed,
offering promising results for both academic and practical applications. In
these works, the use of Generative Adversarial Networks (GANs) or Reinforcement
Learning (RL) can aid malware creators in crafting metamorphic malware that
evades antivirus software. In this study, we propose a mutation system to
counteract ensemble learning-based detectors by combining GANs and an RL model,
overcoming the limitations of the MalGAN model. Our proposed FeaGAN model is
built based on MalGAN by incorporating an RL model called the Deep Q-network
anti-malware Engines Attacking Framework (DQEAF). The RL model addresses three
key challenges in performing adversarial attacks on Windows Portable Executable
malware, including format preservation, executability preservation, and
maliciousness preservation. In the FeaGAN model, ensemble learning is utilized
to enhance the malware detector's evasion ability, with the generated
adversarial patterns. The experimental results demonstrate that 100\% of the
selected mutant samples preserve the format of executable files, while certain
successes in both executability preservation and maliciousness preservation are
achieved, reaching a stable success rate. | [
"Trong-Nghia To",
"Danh Le Kim",
"Do Thi Thu Hien",
"Nghi Hoang Khoa",
"Hien Do Hoang",
"Phan The Duy",
"Van-Hau Pham"
] | 2023-09-25 02:57:27 | http://arxiv.org/abs/2309.13841v1 | http://arxiv.org/pdf/2309.13841v1 | 2309.13841v1 |
Penalized Principal Component Analysis using Nesterov Smoothing | Principal components computed via PCA (principal component analysis) are
traditionally used to reduce dimensionality in genomic data or to correct for
population stratification. In this paper, we explore the penalized eigenvalue
problem (PEP) which reformulates the computation of the first eigenvector as an
optimization problem and adds an L1 penalty constraint. The contribution of our
article is threefold. First, we extend PEP by applying Nesterov smoothing to
the original LASSO-type L1 penalty. This allows one to compute analytical
gradients which enable faster and more efficient minimization of the objective
function associated with the optimization problem. Second, we demonstrate how
higher order eigenvectors can be calculated with PEP using established results
from singular value decomposition (SVD). Third, using data from the 1000 Genome
Project dataset, we empirically demonstrate that our proposed smoothed PEP
allows one to increase numerical stability and obtain meaningful eigenvectors.
We further investigate the utility of the penalized eigenvector approach over
traditional PCA. | [
"Rebecca M. Hurwitz",
"Georg Hahn"
] | 2023-09-25 02:50:22 | http://arxiv.org/abs/2309.13838v1 | http://arxiv.org/pdf/2309.13838v1 | 2309.13838v1 |
Backorder Prediction in Inventory Management: Classification Techniques and Cost Considerations | This article introduces an advanced analytical approach for predicting
backorders in inventory management. Backorder refers to an order that cannot be
immediately fulfilled due to stock depletion. Multiple classification
techniques, including Balanced Bagging Classifiers, Fuzzy Logic, Variational
Autoencoder - Generative Adversarial Networks, and Multi-layer Perceptron
classifiers, are assessed in this work using performance evaluation metrics
such as ROC-AUC and PR-AUC. Moreover, this work incorporates a profit function
and misclassification costs, considering the financial implications and costs
associated with inventory management and backorder handling. The results
demonstrate the effectiveness of the predictive model in enhancing inventory
system service levels, which leads to customer satisfaction and overall
organizational performance. Considering interpretability is a significant
aspect of using AI in commercial applications, permutation importance is
applied to the selected model to determine the importance of features. This
research contributes to the advancement of predictive analytics and offers
valuable insights for future investigations in backorder forecasting and
inventory control optimization for decision-making. | [
"Sarit Maitra",
"Sukanya Kundu"
] | 2023-09-25 02:50:20 | http://arxiv.org/abs/2309.13837v2 | http://arxiv.org/pdf/2309.13837v2 | 2309.13837v2 |
Sampling - Variational Auto Encoder - Ensemble: In the Quest of Explainable Artificial Intelligence | Explainable Artificial Intelligence (XAI) models have recently attracted a
great deal of interest from a variety of application sectors. Despite
significant developments in this area, there are still no standardized methods
or approaches for understanding AI model outputs. A systematic and cohesive
framework is also increasingly necessary to incorporate new techniques like
discriminative and generative models to close the gap. This paper contributes
to the discourse on XAI by presenting an empirical evaluation based on a novel
framework: Sampling - Variational Auto Encoder (VAE) - Ensemble Anomaly
Detection (SVEAD). It is a hybrid architecture where VAE combined with ensemble
stacking and SHapley Additive exPlanations are used for imbalanced
classification. The finding reveals that combining ensemble stacking, VAE, and
SHAP can. not only lead to better model performance but also provide an easily
explainable framework. This work has used SHAP combined with Permutation
Importance and Individual Conditional Expectations to create a powerful
interpretability of the model. The finding has an important implication in the
real world, where the need for XAI is paramount to boost confidence in AI
applications. | [
"Sarit Maitra",
"Vivek Mishra",
"Pratima Verma",
"Manav Chopra",
"Priyanka Nath"
] | 2023-09-25 02:46:19 | http://arxiv.org/abs/2309.14385v1 | http://arxiv.org/pdf/2309.14385v1 | 2309.14385v1 |
NSOTree: Neural Survival Oblique Tree | Survival analysis is a statistical method employed to scrutinize the duration
until a specific event of interest transpires, known as time-to-event
information characterized by censorship. Recently, deep learning-based methods
have dominated this field due to their representational capacity and
state-of-the-art performance. However, the black-box nature of the deep neural
network hinders its interpretability, which is desired in real-world survival
applications but has been largely neglected by previous works. In contrast,
conventional tree-based methods are advantageous with respect to
interpretability, while consistently grappling with an inability to approximate
the global optima due to greedy expansion. In this paper, we leverage the
strengths of both neural networks and tree-based methods, capitalizing on their
ability to approximate intricate functions while maintaining interpretability.
To this end, we propose a Neural Survival Oblique Tree (NSOTree) for survival
analysis. Specifically, the NSOTree was derived from the ReLU network and can
be easily incorporated into existing survival models in a plug-and-play
fashion. Evaluations on both simulated and real survival datasets demonstrated
the effectiveness of the proposed method in terms of performance and
interpretability. | [
"Xiaotong Sun",
"Peijie Qiu"
] | 2023-09-25 02:14:15 | http://arxiv.org/abs/2309.13825v1 | http://arxiv.org/pdf/2309.13825v1 | 2309.13825v1 |
Forecasting large collections of time series: feature-based methods | In economics and many other forecasting domains, the real world problems are
too complex for a single model that assumes a specific data generation process.
The forecasting performance of different methods changes depending on the
nature of the time series. When forecasting large collections of time series,
two lines of approaches have been developed using time series features, namely
feature-based model selection and feature-based model combination. This chapter
discusses the state-of-the-art feature-based methods, with reference to
open-source software implementations. | [
"Li Li",
"Feng Li",
"Yanfei Kang"
] | 2023-09-25 01:23:02 | http://arxiv.org/abs/2309.13807v1 | http://arxiv.org/pdf/2309.13807v1 | 2309.13807v1 |
Evaluating Cognitive Maps and Planning in Large Language Models with CogEval | Recently an influx of studies claim emergent cognitive abilities in large
language models (LLMs). Yet, most rely on anecdotes, overlook contamination of
training sets, or lack systematic Evaluation involving multiple tasks, control
conditions, multiple iterations, and statistical robustness tests. Here we make
two major contributions. First, we propose CogEval, a cognitive
science-inspired protocol for the systematic evaluation of cognitive capacities
in Large Language Models. The CogEval protocol can be followed for the
evaluation of various abilities. Second, here we follow CogEval to
systematically evaluate cognitive maps and planning ability across eight LLMs
(OpenAI GPT-4, GPT-3.5-turbo-175B, davinci-003-175B, Google Bard,
Cohere-xlarge-52.4B, Anthropic Claude-1-52B, LLaMA-13B, and Alpaca-7B). We base
our task prompts on human experiments, which offer both established construct
validity for evaluating planning, and are absent from LLM training sets. We
find that, while LLMs show apparent competence in a few planning tasks with
simpler structures, systematic evaluation reveals striking failure modes in
planning tasks, including hallucinations of invalid trajectories and getting
trapped in loops. These findings do not support the idea of emergent
out-of-the-box planning ability in LLMs. This could be because LLMs do not
understand the latent relational structures underlying planning problems, known
as cognitive maps, and fail at unrolling goal-directed trajectories based on
the underlying structure. Implications for application and future directions
are discussed. | [
"Ida Momennejad",
"Hosein Hasanbeig",
"Felipe Vieira",
"Hiteshi Sharma",
"Robert Osazuwa Ness",
"Nebojsa Jojic",
"Hamid Palangi",
"Jonathan Larson"
] | 2023-09-25 01:20:13 | http://arxiv.org/abs/2309.15129v1 | http://arxiv.org/pdf/2309.15129v1 | 2309.15129v1 |
Benchmarking Local Robustness of High-Accuracy Binary Neural Networks for Enhanced Traffic Sign Recognition | Traffic signs play a critical role in road safety and traffic management for
autonomous driving systems. Accurate traffic sign classification is essential
but challenging due to real-world complexities like adversarial examples and
occlusions. To address these issues, binary neural networks offer promise in
constructing classifiers suitable for resource-constrained devices.
In our previous work, we proposed high-accuracy BNN models for traffic sign
recognition, focusing on compact size for limited computation and energy
resources. To evaluate their local robustness, this paper introduces a set of
benchmark problems featuring layers that challenge state-of-the-art
verification tools. These layers include binarized convolutions, max pooling,
batch normalization, fully connected. The difficulty of the verification
problem is given by the high number of network parameters (905k - 1.7 M), of
the input dimension (2.7k-12k), and of the number of regions (43) as well by
the fact that the neural networks are not sparse.
The proposed BNN models and local robustness properties can be checked at
https://github.com/ChristopherBrix/vnncomp2023_benchmarks/tree/main/benchmarks/traffic_signs_recognition.
The results of the 4th International Verification of Neural Networks
Competition (VNN-COMP'23) revealed the fact that 4, out of 7, solvers can
handle many of our benchmarks randomly selected (minimum is 6, maximum is 36,
out of 45). Surprisingly, tools output also wrong results or missing
counterexample (ranging from 1 to 4). Currently, our focus lies in exploring
the possibility of achieving a greater count of solved instances by extending
the allotted time (previously set at 8 minutes). Furthermore, we are intrigued
by the reasons behind the erroneous outcomes provided by the tools for certain
benchmarks. | [
"Andreea Postovan",
"Mădălina Eraşcu"
] | 2023-09-25 01:17:14 | http://arxiv.org/abs/2310.03033v1 | http://arxiv.org/pdf/2310.03033v1 | 2310.03033v1 |
Projected Randomized Smoothing for Certified Adversarial Robustness | Randomized smoothing is the current state-of-the-art method for producing
provably robust classifiers. While randomized smoothing typically yields robust
$\ell_2$-ball certificates, recent research has generalized provable robustness
to different norm balls as well as anisotropic regions. This work considers a
classifier architecture that first projects onto a low-dimensional
approximation of the data manifold and then applies a standard classifier. By
performing randomized smoothing in the low-dimensional projected space, we
characterize the certified region of our smoothed composite classifier back in
the high-dimensional input space and prove a tractable lower bound on its
volume. We show experimentally on CIFAR-10 and SVHN that classifiers without
the initial projection are vulnerable to perturbations that are normal to the
data manifold and yet are captured by the certified regions of our method. We
compare the volume of our certified regions against various baselines and show
that our method improves on the state-of-the-art by many orders of magnitude. | [
"Samuel Pfrommer",
"Brendon G. Anderson",
"Somayeh Sojoudi"
] | 2023-09-25 01:12:55 | http://arxiv.org/abs/2309.13794v1 | http://arxiv.org/pdf/2309.13794v1 | 2309.13794v1 |
ReMasker: Imputing Tabular Data with Masked Autoencoding | We present ReMasker, a new method of imputing missing values in tabular data
by extending the masked autoencoding framework. Compared with prior work,
ReMasker is both simple -- besides the missing values (i.e., naturally masked),
we randomly ``re-mask'' another set of values, optimize the autoencoder by
reconstructing this re-masked set, and apply the trained model to predict the
missing values; and effective -- with extensive evaluation on benchmark
datasets, we show that ReMasker performs on par with or outperforms
state-of-the-art methods in terms of both imputation fidelity and utility under
various missingness settings, while its performance advantage often increases
with the ratio of missing data. We further explore theoretical justification
for its effectiveness, showing that ReMasker tends to learn
missingness-invariant representations of tabular data. Our findings indicate
that masked modeling represents a promising direction for further research on
tabular data imputation. The code is publicly available. | [
"Tianyu Du",
"Luca Melis",
"Ting Wang"
] | 2023-09-25 01:03:45 | http://arxiv.org/abs/2309.13793v1 | http://arxiv.org/pdf/2309.13793v1 | 2309.13793v1 |
Can LLM-Generated Misinformation Be Detected? | The advent of Large Language Models (LLMs) has made a transformative impact.
However, the potential that LLMs such as ChatGPT can be exploited to generate
misinformation has posed a serious concern to online safety and public trust. A
fundamental research question is: will LLM-generated misinformation cause more
harm than human-written misinformation? We propose to tackle this question from
the perspective of detection difficulty. We first build a taxonomy of
LLM-generated misinformation. Then we categorize and validate the potential
real-world methods for generating misinformation with LLMs. Then, through
extensive empirical investigation, we discover that LLM-generated
misinformation can be harder to detect for humans and detectors compared to
human-written misinformation with the same semantics, which suggests it can
have more deceptive styles and potentially cause more harm. We also discuss the
implications of our discovery on combating misinformation in the age of LLMs
and the countermeasures. | [
"Canyu Chen",
"Kai Shu"
] | 2023-09-25 00:45:07 | http://arxiv.org/abs/2309.13788v1 | http://arxiv.org/pdf/2309.13788v1 | 2309.13788v1 |
Distribution-Free Statistical Dispersion Control for Societal Applications | Explicit finite-sample statistical guarantees on model performance are an
important ingredient in responsible machine learning. Previous work has focused
mainly on bounding either the expected loss of a predictor or the probability
that an individual prediction will incur a loss value in a specified range.
However, for many high-stakes applications, it is crucial to understand and
control the dispersion of a loss distribution, or the extent to which different
members of a population experience unequal effects of algorithmic decisions. We
initiate the study of distribution-free control of statistical dispersion
measures with societal implications and propose a simple yet flexible framework
that allows us to handle a much richer class of statistical functionals beyond
previous work. Our methods are verified through experiments in toxic comment
detection, medical imaging, and film recommendation. | [
"Zhun Deng",
"Thomas P. Zollo",
"Jake C. Snell",
"Toniann Pitassi",
"Richard Zemel"
] | 2023-09-25 00:31:55 | http://arxiv.org/abs/2309.13786v1 | http://arxiv.org/pdf/2309.13786v1 | 2309.13786v1 |
On the Computational Benefit of Multimodal Learning | Human perception inherently operates in a multimodal manner. Similarly, as
machines interpret the empirical world, their learning processes ought to be
multimodal. The recent, remarkable successes in empirical multimodal learning
underscore the significance of understanding this paradigm. Yet, a solid
theoretical foundation for multimodal learning has eluded the field for some
time. While a recent study by Lu (2023) has shown the superior sample
complexity of multimodal learning compared to its unimodal counterpart, another
basic question remains: does multimodal learning also offer computational
advantages over unimodal learning? This work initiates a study on the
computational benefit of multimodal learning. We demonstrate that, under
certain conditions, multimodal learning can outpace unimodal learning
exponentially in terms of computation. Specifically, we present a learning task
that is NP-hard for unimodal learning but is solvable in polynomial time by a
multimodal algorithm. Our construction is based on a novel modification to the
intersection of two half-spaces problem. | [
"Zhou Lu"
] | 2023-09-25 00:20:50 | http://arxiv.org/abs/2309.13782v1 | http://arxiv.org/pdf/2309.13782v1 | 2309.13782v1 |
Explainable Machine Learning for ICU Readmission Prediction | The intensive care unit (ICU) comprises a complex hospital environment, where
decisions made by clinicians have a high level of risk for the patients' lives.
A comprehensive care pathway must then be followed to reduce p complications.
Uncertain, competing and unplanned aspects within this environment increase the
difficulty in uniformly implementing the care pathway. Readmission contributes
to this pathway's difficulty, occurring when patients are admitted again to the
ICU in a short timeframe, resulting in high mortality rates and high resource
utilisation. Several works have tried to predict readmission through patients'
medical information. Although they have some level of success while predicting
readmission, those works do not properly assess, characterise and understand
readmission prediction. This work proposes a standardised and explainable
machine learning pipeline to model patient readmission on a multicentric
database (i.e., the eICU cohort with 166,355 patients, 200,859 admissions and
6,021 readmissions) while validating it on monocentric (i.e., the MIMIC IV
cohort with 382,278 patients, 523,740 admissions and 5,984 readmissions) and
multicentric settings. Our machine learning pipeline achieved predictive
performance in terms of the area of the receiver operating characteristic curve
(AUC) up to 0.7 with a Random Forest classification model, yielding an overall
good calibration and consistency on validation sets. From explanations provided
by the constructed models, we could also derive a set of insightful
conclusions, primarily on variables related to vital signs and blood tests
(e.g., albumin, blood urea nitrogen and hemoglobin levels), demographics (e.g.,
age, and admission height and weight), and ICU-associated variables (e.g., unit
type). These insights provide an invaluable source of information during
clinicians' decision-making while discharging ICU patients. | [
"Alex G. C. de Sá",
"Daniel Gould",
"Anna Fedyukova",
"Mitchell Nicholas",
"Lucy Dockrell",
"Calvin Fletcher",
"David Pilcher",
"Daniel Capurro",
"David B. Ascher",
"Khaled El-Khawas",
"Douglas E. V. Pires"
] | 2023-09-25 00:16:43 | http://arxiv.org/abs/2309.13781v3 | http://arxiv.org/pdf/2309.13781v3 | 2309.13781v3 |
Multi-Task Learning For Reduced Popularity Bias In Multi-Territory Video Recommendations | Various data imbalances that naturally arise in a multi-territory
personalized recommender system can lead to a significant item bias for
globally prevalent items. A locally popular item can be overshadowed by a
globally prevalent item. Moreover, users' viewership patterns/statistics can
drastically change from one geographic location to another which may suggest to
learn specific user embeddings. In this paper, we propose a multi-task learning
(MTL) technique, along with an adaptive upsampling method to reduce popularity
bias in multi-territory recommendations. Our proposed framework is designed to
enrich training examples with active users representation through upsampling,
and capable of learning geographic-based user embeddings by leveraging MTL.
Through experiments, we demonstrate the effectiveness of our framework in
multiple territories compared to a baseline not incorporating our proposed
techniques.~Noticeably, we show improved relative gain of up to $65.27\%$ in
PR-AUC metric. A case study is presented to demonstrate the advantages of our
methods in attenuating the popularity bias of global items. | [
"Phanideep Gampa",
"Farnoosh Javadi",
"Belhassen Bayar",
"Ainur Yessenalina"
] | 2023-09-25 00:11:33 | http://arxiv.org/abs/2310.03148v1 | http://arxiv.org/pdf/2310.03148v1 | 2310.03148v1 |
Design Principles of Robust Multi-Armed Bandit Framework in Video Recommendations | Current multi-armed bandit approaches in recommender systems (RS) have
focused more on devising effective exploration techniques, while not adequately
addressing common exploitation challenges related to distributional changes and
item cannibalization. Little work exists to guide the design of robust bandit
frameworks that can address these frequent challenges in RS. In this paper, we
propose a new design principles to (i) make bandit models robust to
time-variant metadata signals, (ii) less prone to item cannibalization, and
(iii) prevent their weights fluctuating due to data sparsity. Through a series
of experiments, we systematically examine the influence of several important
bandit design choices. We demonstrate the advantage of our proposed design
principles at making bandit models robust to dynamic behavioral changes through
in-depth analyses. Noticeably, we show improved relative gain compared to a
baseline bandit model not incorporating our design choices of up to $11.88\%$
and $44.85\%$, respectively in ROC-AUC and PR-AUC. Case studies about fairness
in recommending specific popular and unpopular titles are presented, to
demonstrate the robustness of our proposed design at addressing popularity
biases. | [
"Belhassen Bayar",
"Phanideep Gampa",
"Ainur Yessenalina",
"Zhen Wen"
] | 2023-09-24 23:44:48 | http://arxiv.org/abs/2310.01419v1 | http://arxiv.org/pdf/2310.01419v1 | 2310.01419v1 |
Diffeomorphic Multi-Resolution Deep Learning Registration for Applications in Breast MRI | In breast surgical planning, accurate registration of MR images across
patient positions has the potential to improve the localisation of tumours
during breast cancer treatment. While learning-based registration methods have
recently become the state-of-the-art approach for most medical image
registration tasks, these methods have yet to make inroads into breast image
registration due to certain difficulties-the lack of rich texture information
in breast MR images and the need for the deformations to be diffeomophic. In
this work, we propose learning strategies for breast MR image registration that
are amenable to diffeomorphic constraints, together with early experimental
results from in-silico and in-vivo experiments. One key contribution of this
work is a registration network which produces superior registration outcomes
for breast images in addition to providing diffeomorphic guarantees. | [
"Matthew G. French",
"Gonzalo D. Maso Talou",
"Thiranja P. Babarenda Gamage",
"Martyn P. Nash",
"Poul M. Nielsen",
"Anthony J. Doyle",
"Juan Eugenio Iglesias",
"Yaël Balbastre",
"Sean I. Young"
] | 2023-09-24 23:16:38 | http://arxiv.org/abs/2309.13777v2 | http://arxiv.org/pdf/2309.13777v2 | 2309.13777v2 |
The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance | Quantifying variable importance is essential for answering high-stakes
questions in fields like genetics, public policy, and medicine. Current methods
generally calculate variable importance for a given model trained on a given
dataset. However, for a given dataset, there may be many models that explain
the target outcome equally well; without accounting for all possible
explanations, different researchers may arrive at many conflicting yet equally
valid conclusions given the same data. Additionally, even when accounting for
all possible explanations for a given dataset, these insights may not
generalize because not all good explanations are stable across reasonable data
perturbations. We propose a new variable importance framework that quantifies
the importance of a variable across the set of all good models and is stable
across the data distribution. Our framework is extremely flexible and can be
integrated with most existing model classes and global variable importance
metrics. We demonstrate through experiments that our framework recovers
variable importance rankings for complex simulation setups where other methods
fail. Further, we show that our framework accurately estimates the true
importance of a variable for the underlying data distribution. We provide
theoretical guarantees on the consistency and finite sample error rates for our
estimator. Finally, we demonstrate its utility with a real-world case study
exploring which genes are important for predicting HIV load in persons with
HIV, highlighting an important gene that has not previously been studied in
connection with HIV. Code is available here. | [
"Jon Donnelly",
"Srikar Katta",
"Cynthia Rudin",
"Edward P. Browne"
] | 2023-09-24 23:09:48 | http://arxiv.org/abs/2309.13775v2 | http://arxiv.org/pdf/2309.13775v2 | 2309.13775v2 |
GHN-QAT: Training Graph Hypernetworks to Predict Quantization-Robust Parameters of Unseen Limited Precision Neural Networks | Graph Hypernetworks (GHN) can predict the parameters of varying unseen CNN
architectures with surprisingly good accuracy at a fraction of the cost of
iterative optimization. Following these successes, preliminary research has
explored the use of GHNs to predict quantization-robust parameters for 8-bit
and 4-bit quantized CNNs. However, this early work leveraged full-precision
float32 training and only quantized for testing. We explore the impact of
quantization-aware training and/or other quantization-based training strategies
on quantized robustness and performance of GHN predicted parameters for
low-precision CNNs. We show that quantization-aware training can significantly
improve quantized accuracy for GHN predicted parameters of 4-bit quantized CNNs
and even lead to greater-than-random accuracy for 2-bit quantized CNNs. These
promising results open the door for future explorations such as investigating
the use of GHN predicted parameters as initialization for further quantized
training of individual CNNs, further exploration of "extreme bitwidth"
quantization, and mixed precision quantization schemes. | [
"Stone Yun",
"Alexander Wong"
] | 2023-09-24 23:01:00 | http://arxiv.org/abs/2309.13773v1 | http://arxiv.org/pdf/2309.13773v1 | 2309.13773v1 |
Devil in the Number: Towards Robust Multi-modality Data Filter | In order to appropriately filter multi-modality data sets on a web-scale, it
becomes crucial to employ suitable filtering methods to boost performance and
reduce training costs. For instance, LAION papers employs the CLIP score filter
to select data with CLIP scores surpassing a certain threshold. On the other
hand, T-MARS achieves high-quality data filtering by detecting and masking text
within images and then filtering by CLIP score. Through analyzing the dataset,
we observe a significant proportion of redundant information, such as numbers,
present in the textual content. Our experiments on a subset of the data unveil
the profound impact of these redundant elements on the CLIP scores. A logical
approach would involve reevaluating the CLIP scores after eliminating these
influences. Experimentally, our text-based CLIP filter outperforms the
top-ranked method on the ``small scale" of DataComp (a data filtering
benchmark) on ImageNet distribution shifts, achieving a 3.6% performance
improvement. The results also demonstrate that our proposed text-masked filter
outperforms the original CLIP score filter when selecting the top 40% of the
data. The impact of numbers on CLIP and their handling provide valuable
insights for improving the effectiveness of CLIP training, including language
rewrite techniques. | [
"Yichen Xu",
"Zihan Xu",
"Wenhao Chai",
"Zhonghan Zhao",
"Enxin Song",
"Gaoang Wang"
] | 2023-09-24 22:52:35 | http://arxiv.org/abs/2309.13770v1 | http://arxiv.org/pdf/2309.13770v1 | 2309.13770v1 |
Improving Robustness of Deep Convolutional Neural Networks via Multiresolution Learning | The current learning process of deep learning, regardless of any deep neural
network (DNN) architecture and/or learning algorithm used, is essentially a
single resolution training. We explore multiresolution learning and show that
multiresolution learning can significantly improve robustness of DNN models for
both 1D signal and 2D signal (image) prediction problems. We demonstrate this
improvement in terms of both noise and adversarial robustness as well as with
small training dataset size. Our results also suggest that it may not be
necessary to trade standard accuracy for robustness with multiresolution
learning, which is, interestingly, contrary to the observation obtained from
the traditional single resolution learning setting. | [
"Hongyan Zhou",
"Yao Liang"
] | 2023-09-24 21:04:56 | http://arxiv.org/abs/2309.13752v2 | http://arxiv.org/pdf/2309.13752v2 | 2309.13752v2 |
Generative Residual Diffusion Modeling for Km-scale Atmospheric Downscaling | The state of the art for physical hazard prediction from weather and climate
requires expensive km-scale numerical simulations driven by coarser resolution
global inputs. Here, a km-scale downscaling diffusion model is presented as a
cost effective alternative. The model is trained from a regional
high-resolution weather model over Taiwan, and conditioned on ERA5 reanalysis
data. To address the downscaling uncertainties, large resolution ratios (25km
to 2km), different physics involved at different scales and predict channels
that are not in the input data, we employ a two-step approach
(\textit{ResDiff}) where a (UNet) regression predicts the mean in the first
step and a diffusion model predicts the residual in the second step.
\textit{ResDiff} exhibits encouraging skill in bulk RMSE and CRPS scores. The
predicted spectra and distributions from ResDiff faithfully recover important
power law relationships regulating damaging wind and rain extremes. Case
studies of coherent weather phenomena reveal appropriate multivariate
relationships reminiscent of learnt physics. This includes the sharp wind and
temperature variations that co-locate with intense rainfall in a cold front,
and the extreme winds and rainfall bands that surround the eyewall of typhoons.
Some evidence of simultaneous bias correction is found. A first attempt at
downscaling directly from an operational global forecast model successfully
retains many of these benefits. The implication is that a new era of fully
end-to-end, global-to-regional machine learning weather prediction is likely
near at hand. | [
"Morteza Mardani",
"Noah Brenowitz",
"Yair Cohen",
"Jaideep Pathak",
"Chieh-Yu Chen",
"Cheng-Chin Liu",
"Arash Vahdat",
"Karthik Kashinath",
"Jan Kautz",
"Mike Pritchard"
] | 2023-09-24 19:57:22 | http://arxiv.org/abs/2309.15214v2 | http://arxiv.org/pdf/2309.15214v2 | 2309.15214v2 |
Geometry of Linear Neural Networks: Equivariance and Invariance under Permutation Groups | The set of functions parameterized by a linear fully-connected neural network
is a determinantal variety. We investigate the subvariety of functions that are
equivariant or invariant under the action of a permutation group. Examples of
such group actions are translations or $90^\circ$ rotations on images. For such
equivariant or invariant subvarieties, we provide an explicit description of
their dimension, their degree as well as their Euclidean distance degree, and
their singularities. We fully characterize invariance for arbitrary permutation
groups, and equivariance for cyclic groups. We draw conclusions for the
parameterization and the design of equivariant and invariant linear networks,
such as a weight sharing property, and we prove that all invariant linear
functions can be learned by linear autoencoders. | [
"Kathlén Kohn",
"Anna-Laura Sattelberger",
"Vahid Shahverdi"
] | 2023-09-24 19:40:15 | http://arxiv.org/abs/2309.13736v1 | http://arxiv.org/pdf/2309.13736v1 | 2309.13736v1 |
Towards Tuning-Free Minimum-Volume Nonnegative Matrix Factorization | Nonnegative Matrix Factorization (NMF) is a versatile and powerful tool for
discovering latent structures in data matrices, with many variations proposed
in the literature. Recently, Leplat et al.\@ (2019) introduced a minimum-volume
NMF for the identifiable recovery of rank-deficient matrices in the presence of
noise. The performance of their formulation, however, requires the selection of
a tuning parameter whose optimal value depends on the unknown noise level. In
this work, we propose an alternative formulation of minimum-volume NMF inspired
by the square-root lasso and its tuning-free properties. Our formulation also
requires the selection of a tuning parameter, but its optimal value does not
depend on the noise level. To fit our NMF model, we propose a
majorization-minimization (MM) algorithm that comes with global convergence
guarantees. We show empirically that the optimal choice of our tuning parameter
is insensitive to the noise level in the data. | [
"Duc Toan Nguyen",
"Eric C. Chi"
] | 2023-09-24 19:34:52 | http://arxiv.org/abs/2309.13733v1 | http://arxiv.org/pdf/2309.13733v1 | 2309.13733v1 |
Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey | Cough acoustics contain multitudes of vital information about
pathomorphological alterations in the respiratory system. Reliable and accurate
detection of cough events by investigating the underlying cough latent features
and disease diagnosis can play an indispensable role in revitalizing the
healthcare practices. The recent application of Artificial Intelligence (AI)
and advances of ubiquitous computing for respiratory disease prediction has
created an auspicious trend and myriad of future possibilities in the medical
domain. In particular, there is an expeditiously emerging trend of Machine
learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting
cough signatures. The enormous body of literature on cough-based AI algorithms
demonstrate that these models can play a significant role for detecting the
onset of a specific respiratory disease. However, it is pertinent to collect
the information from all relevant studies in an exhaustive manner for the
medical experts and AI scientists to analyze the decisive role of AI/ML. This
survey offers a comprehensive overview of the cough data-driven ML/DL detection
and preliminary diagnosis frameworks, along with a detailed list of significant
features. We investigate the mechanism that causes cough and the latent cough
features of the respiratory modalities. We also analyze the customized cough
monitoring application, and their AI-powered recognition algorithms. Challenges
and prospective future research directions to develop practical, robust, and
ubiquitous solutions are also discussed in detail. | [
"Aneeqa Ijaz",
"Muhammad Nabeel",
"Usama Masood",
"Tahir Mahmood",
"Mydah Sajid Hashmi",
"Iryna Posokhova",
"Ali Rizwan",
"Ali Imran"
] | 2023-09-24 19:03:46 | http://arxiv.org/abs/2309.14383v1 | http://arxiv.org/pdf/2309.14383v1 | 2309.14383v1 |
Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense | Recently, several deep learning (DL) methods for approximating
high-dimensional partial differential equations (PDEs) have been proposed. The
interest that these methods have generated in the literature is in large part
due to simulations which appear to demonstrate that such DL methods have the
capacity to overcome the curse of dimensionality (COD) for PDEs in the sense
that the number of computational operations they require to achieve a certain
approximation accuracy $\varepsilon\in(0,\infty)$ grows at most polynomially in
the PDE dimension $d\in\mathbb N$ and the reciprocal of $\varepsilon$. While
there is thus far no mathematical result that proves that one of such methods
is indeed capable of overcoming the COD, there are now a number of rigorous
results in the literature that show that deep neural networks (DNNs) have the
expressive power to approximate PDE solutions without the COD in the sense that
the number of parameters used to describe the approximating DNN grows at most
polynomially in both the PDE dimension $d\in\mathbb N$ and the reciprocal of
the approximation accuracy $\varepsilon>0$. Roughly speaking, in the literature
it is has been proved for every $T>0$ that solutions $u_d\colon
[0,T]\times\mathbb R^d\to \mathbb R$, $d\in\mathbb N$, of semilinear heat PDEs
with Lipschitz continuous nonlinearities can be approximated by DNNs with ReLU
activation at the terminal time in the $L^2$-sense without the COD provided
that the initial value functions $\mathbb R^d\ni x\mapsto u_d(0,x)\in\mathbb
R$, $d\in\mathbb N$, can be approximated by ReLU DNNs without the COD. It is
the key contribution of this work to generalize this result by establishing
this statement in the $L^p$-sense with $p\in(0,\infty)$ and by allowing the
activation function to be more general covering the ReLU, the leaky ReLU, and
the softplus activation functions as special cases. | [
"Julia Ackermann",
"Arnulf Jentzen",
"Thomas Kruse",
"Benno Kuckuck",
"Joshua Lee Padgett"
] | 2023-09-24 18:58:18 | http://arxiv.org/abs/2309.13722v1 | http://arxiv.org/pdf/2309.13722v1 | 2309.13722v1 |
ORLA*: Mobile Manipulator-Based Object Rearrangement with Lazy A* | Effectively performing object rearrangement is an essential skill for mobile
manipulators, e.g., setting up a dinner table or organizing a desk. A key
challenge in such problems is deciding an appropriate manipulation order for
objects to effectively untangle dependencies between objects while considering
the necessary motions for realizing the manipulations (e.g., pick and place).
To our knowledge, computing time-optimal multi-object rearrangement solutions
for mobile manipulators remains a largely untapped research direction. In this
research, we propose ORLA*, which leverages delayed (lazy) evaluation in
searching for a high-quality object pick and place sequence that considers both
end-effector and mobile robot base travel. ORLA* also supports multi-layered
rearrangement tasks considering pile stability using machine learning.
Employing an optimal solver for finding temporary locations for displacing
objects, ORLA* can achieve global optimality. Through extensive simulation and
ablation study, we confirm the effectiveness of ORLA* delivering quality
solutions for challenging rearrangement instances. Supplementary materials are
available at: https://gaokai15.github.io/ORLA-Star/ | [
"Kai Gao",
"Yan Ding",
"Shiqi Zhang",
"Jingjin Yu"
] | 2023-09-24 17:40:19 | http://arxiv.org/abs/2309.13707v1 | http://arxiv.org/pdf/2309.13707v1 | 2309.13707v1 |
A Neural-Guided Dynamic Symbolic Network for Exploring Mathematical Expressions from Data | Symbolic regression (SR) is a powerful technique for discovering the
underlying mathematical expressions from observed data. Inspired by the success
of deep learning, recent efforts have focused on two categories for SR methods.
One is using a neural network or genetic programming to search the expression
tree directly. Although this has shown promising results, the large search
space poses difficulties in learning constant factors and processing
high-dimensional problems. Another approach is leveraging a transformer-based
model training on synthetic data and offers advantages in inference speed.
However, this method is limited to fixed small numbers of dimensions and may
encounter inference problems when given data is out-of-distribution compared to
the synthetic data. In this work, we propose DySymNet, a novel neural-guided
Dynamic Symbolic Network for SR. Instead of searching for expressions within a
large search space, we explore DySymNet with various structures and optimize
them to identify expressions that better-fitting the data. With a topology
structure like neural networks, DySymNet not only tackles the challenge of
high-dimensional problems but also proves effective in optimizing constants.
Based on extensive numerical experiments using low-dimensional public standard
benchmarks and the well-known SRBench with more variables, our method achieves
state-of-the-art performance in terms of fitting accuracy and robustness to
noise. | [
"Wenqiang Li",
"Weijun Li",
"Lina Yu",
"Min Wu",
"Jingyi Liu",
"Yanjie Li"
] | 2023-09-24 17:37:45 | http://arxiv.org/abs/2309.13705v1 | http://arxiv.org/pdf/2309.13705v1 | 2309.13705v1 |
Federated Deep Multi-View Clustering with Global Self-Supervision | Federated multi-view clustering has the potential to learn a global
clustering model from data distributed across multiple devices. In this
setting, label information is unknown and data privacy must be preserved,
leading to two major challenges. First, views on different clients often have
feature heterogeneity, and mining their complementary cluster information is
not trivial. Second, the storage and usage of data from multiple clients in a
distributed environment can lead to incompleteness of multi-view data. To
address these challenges, we propose a novel federated deep multi-view
clustering method that can mine complementary cluster structures from multiple
clients, while dealing with data incompleteness and privacy concerns.
Specifically, in the server environment, we propose sample alignment and data
extension techniques to explore the complementary cluster structures of
multiple views. The server then distributes global prototypes and global
pseudo-labels to each client as global self-supervised information. In the
client environment, multiple clients use the global self-supervised information
and deep autoencoders to learn view-specific cluster assignments and embedded
features, which are then uploaded to the server for refining the global
self-supervised information. Finally, the results of our extensive experiments
demonstrate that our proposed method exhibits superior performance in
addressing the challenges of incomplete multi-view data in distributed
environments. | [
"Xinyue Chen",
"Jie Xu",
"Yazhou Ren",
"Xiaorong Pu",
"Ce Zhu",
"Xiaofeng Zhu",
"Zhifeng Hao",
"Lifang He"
] | 2023-09-24 17:07:01 | http://arxiv.org/abs/2309.13697v1 | http://arxiv.org/pdf/2309.13697v1 | 2309.13697v1 |
Performance Evaluation of Equal-Weight Portfolio and Optimum Risk Portfolio on Indian Stocks | Designing an optimum portfolio for allocating suitable weights to its
constituent assets so that the return and risk associated with the portfolio
are optimized is a computationally hard problem. The seminal work of Markowitz
that attempted to solve the problem by estimating the future returns of the
stocks is found to perform sub-optimally on real-world stock market data. This
is because the estimation task becomes extremely challenging due to the
stochastic and volatile nature of stock prices. This work illustrates three
approaches to portfolio design minimizing the risk, optimizing the risk, and
assigning equal weights to the stocks of a portfolio. Thirteen critical sectors
listed on the National Stock Exchange (NSE) of India are first chosen. Three
portfolios are designed following the above approaches choosing the top ten
stocks from each sector based on their free-float market capitalization. The
portfolios are designed using the historical prices of the stocks from Jan 1,
2017, to Dec 31, 2022. The portfolios are evaluated on the stock price data
from Jan 1, 2022, to Dec 31, 2022. The performances of the portfolios are
compared, and the portfolio yielding the higher return for each sector is
identified. | [
"Abhiraj Sen",
"Jaydip Sen"
] | 2023-09-24 17:06:58 | http://arxiv.org/abs/2309.13696v1 | http://arxiv.org/pdf/2309.13696v1 | 2309.13696v1 |
Regularization and Optimal Multiclass Learning | The quintessential learning algorithm of empirical risk minimization (ERM) is
known to fail in various settings for which uniform convergence does not
characterize learning. It is therefore unsurprising that the practice of
machine learning is rife with considerably richer algorithmic techniques for
successfully controlling model capacity. Nevertheless, no such technique or
principle has broken away from the pack to characterize optimal learning in
these more general settings.
The purpose of this work is to characterize the role of regularization in
perhaps the simplest setting for which ERM fails: multiclass learning with
arbitrary label sets. Using one-inclusion graphs (OIGs), we exhibit optimal
learning algorithms that dovetail with tried-and-true algorithmic principles:
Occam's Razor as embodied by structural risk minimization (SRM), the principle
of maximum entropy, and Bayesian reasoning. Most notably, we introduce an
optimal learner which relaxes structural risk minimization on two dimensions:
it allows the regularization function to be "local" to datapoints, and uses an
unsupervised learning stage to learn this regularizer at the outset. We justify
these relaxations by showing that they are necessary: removing either dimension
fails to yield a near-optimal learner. We also extract from OIGs a
combinatorial sequence we term the Hall complexity, which is the first to
characterize a problem's transductive error rate exactly.
Lastly, we introduce a generalization of OIGs and the transductive learning
setting to the agnostic case, where we show that optimal orientations of
Hamming graphs -- judged using nodes' outdegrees minus a system of
node-dependent credits -- characterize optimal learners exactly. We demonstrate
that an agnostic version of the Hall complexity again characterizes error rates
exactly, and exhibit an optimal learner using maximum entropy programs. | [
"Julian Asilis",
"Siddartha Devic",
"Shaddin Dughmi",
"Vatsal Sharan",
"Shang-Hua Teng"
] | 2023-09-24 16:49:55 | http://arxiv.org/abs/2309.13692v1 | http://arxiv.org/pdf/2309.13692v1 | 2309.13692v1 |
Smart OMVI: Obfuscated Malware Variant Identification using a novel dataset | Cybersecurity has become a significant issue in the digital era as a result
of the growth in everyday computer use. Cybercriminals now engage in more than
virus distribution and computer hacking. Cyberwarfare has developed as a result
because it has become a threat to a nation's survival. Malware analysis serves
as the first line of defence against an attack and is a significant component
of cybercrime. Every day, malware attacks target a large number of computer
users, businesses, and governmental agencies, causing billions of dollars in
losses. Malware may evade multiple AV software with a very minor, cunning tweak
made by its designers, despite the fact that security experts have a variety of
tools at their disposal to identify it. To address this challenge, a new
dataset called the Obfuscated Malware Dataset (OMD) has been developed. This
dataset comprises 40 distinct malware families having 21924 samples, and it
incorporates obfuscation techniques that mimic the strategies employed by
malware creators to make their malware variations different from the original
samples. The purpose of this dataset is to provide a more realistic and
representative environment for evaluating the effectiveness of malware analysis
techniques. Different conventional machine learning algorithms including but
not limited to Support Vector Machine (SVM), Random Forrest (RF), Extreme
Gradient Boosting (XGBOOST) etc are applied and contrasted. The results
demonstrated that XGBoost outperformed the other algorithms, achieving an
accuracy of f 82%, precision of 88%, recall of 80%, and an F1-Score of 83%. | [
"Suleman Qamar"
] | 2023-09-24 16:28:35 | http://arxiv.org/abs/2310.10670v1 | http://arxiv.org/pdf/2310.10670v1 | 2310.10670v1 |
Causal-DFQ: Causality Guided Data-free Network Quantization | Model quantization, which aims to compress deep neural networks and
accelerate inference speed, has greatly facilitated the development of
cumbersome models on mobile and edge devices. There is a common assumption in
quantization methods from prior works that training data is available. In
practice, however, this assumption cannot always be fulfilled due to reasons of
privacy and security, rendering these methods inapplicable in real-life
situations. Thus, data-free network quantization has recently received
significant attention in neural network compression. Causal reasoning provides
an intuitive way to model causal relationships to eliminate data-driven
correlations, making causality an essential component of analyzing data-free
problems. However, causal formulations of data-free quantization are inadequate
in the literature. To bridge this gap, we construct a causal graph to model the
data generation and discrepancy reduction between the pre-trained and quantized
models. Inspired by the causal understanding, we propose the Causality-guided
Data-free Network Quantization method, Causal-DFQ, to eliminate the reliance on
data via approaching an equilibrium of causality-driven intervened
distributions. Specifically, we design a content-style-decoupled generator,
synthesizing images conditioned on the relevant and irrelevant factors; then we
propose a discrepancy reduction loss to align the intervened distributions of
the pre-trained and quantized models. It is worth noting that our work is the
first attempt towards introducing causality to data-free quantization problem.
Extensive experiments demonstrate the efficacy of Causal-DFQ. The code is
available at https://github.com/42Shawn/Causal-DFQ. | [
"Yuzhang Shang",
"Bingxin Xu",
"Gaowen Liu",
"Ramana Kompella",
"Yan Yan"
] | 2023-09-24 16:11:58 | http://arxiv.org/abs/2309.13682v1 | http://arxiv.org/pdf/2309.13682v1 | 2309.13682v1 |
Accelerating Large Batch Training via Gradient Signal to Noise Ratio (GSNR) | As models for nature language processing (NLP), computer vision (CV) and
recommendation systems (RS) require surging computation, a large number of
GPUs/TPUs are paralleled as a large batch (LB) to improve training throughput.
However, training such LB tasks often meets large generalization gap and
downgrades final precision, which limits enlarging the batch size. In this
work, we develop the variance reduced gradient descent technique (VRGD) based
on the gradient signal to noise ratio (GSNR) and apply it onto popular
optimizers such as SGD/Adam/LARS/LAMB. We carry out a theoretical analysis of
convergence rate to explain its fast training dynamics, and a generalization
analysis to demonstrate its smaller generalization gap on LB training.
Comprehensive experiments demonstrate that VRGD can accelerate training ($1\sim
2 \times$), narrow generalization gap and improve final accuracy. We push the
batch size limit of BERT pretraining up to 128k/64k and DLRM to 512k without
noticeable accuracy loss. We improve ImageNet Top-1 accuracy at 96k by $0.52pp$
than LARS. The generalization gap of BERT and ImageNet training is
significantly reduce by over $65\%$. | [
"Guo-qing Jiang",
"Jinlong Liu",
"Zixiang Ding",
"Lin Guo",
"Wei Lin"
] | 2023-09-24 16:08:21 | http://arxiv.org/abs/2309.13681v1 | http://arxiv.org/pdf/2309.13681v1 | 2309.13681v1 |
Joint inversion of Time-Lapse Surface Gravity and Seismic Data for Monitoring of 3D CO$_2$ Plumes via Deep Learning | We introduce a fully 3D, deep learning-based approach for the joint inversion
of time-lapse surface gravity and seismic data for reconstructing subsurface
density and velocity models. The target application of this proposed inversion
approach is the prediction of subsurface CO2 plumes as a complementary tool for
monitoring CO2 sequestration deployments. Our joint inversion technique
outperforms deep learning-based gravity-only and seismic-only inversion models,
achieving improved density and velocity reconstruction, accurate segmentation,
and higher R-squared coefficients. These results indicate that deep
learning-based joint inversion is an effective tool for CO$_2$ storage
monitoring. Future work will focus on validating our approach with larger
datasets, simulations with other geological storage sites, and ultimately field
data. | [
"Adrian Celaya",
"Mauricio Araya-Polo"
] | 2023-09-24 15:41:40 | http://arxiv.org/abs/2310.04430v1 | http://arxiv.org/pdf/2310.04430v1 | 2310.04430v1 |
VoiceLDM: Text-to-Speech with Environmental Context | This paper presents VoiceLDM, a model designed to produce audio that
accurately follows two distinct natural language text prompts: the description
prompt and the content prompt. The former provides information about the
overall environmental context of the audio, while the latter conveys the
linguistic content. To achieve this, we adopt a text-to-audio (TTA) model based
on latent diffusion models and extend its functionality to incorporate an
additional content prompt as a conditional input. By utilizing pretrained
contrastive language-audio pretraining (CLAP) and Whisper, VoiceLDM is trained
on large amounts of real-world audio without manual annotations or
transcriptions. Additionally, we employ dual classifier-free guidance to
further enhance the controllability of VoiceLDM. Experimental results
demonstrate that VoiceLDM is capable of generating plausible audio that aligns
well with both input conditions, even surpassing the speech intelligibility of
the ground truth audio on the AudioCaps test set. Furthermore, we explore the
text-to-speech (TTS) and zero-shot text-to-audio capabilities of VoiceLDM and
show that it achieves competitive results. Demos and code are available at
https://voiceldm.github.io. | [
"Yeonghyeon Lee",
"Inmo Yeon",
"Juhan Nam",
"Joon Son Chung"
] | 2023-09-24 15:20:59 | http://arxiv.org/abs/2309.13664v1 | http://arxiv.org/pdf/2309.13664v1 | 2309.13664v1 |
Topology-Agnostic Detection of Temporal Money Laundering Flows in Billion-Scale Transactions | Money launderers exploit the weaknesses in detection systems by purposefully
placing their ill-gotten money into multiple accounts, at different banks. That
money is then layered and moved around among mule accounts to obscure the
origin and the flow of transactions. Consequently, the money is integrated into
the financial system without raising suspicion. Path finding algorithms that
aim at tracking suspicious flows of money usually struggle with scale and
complexity. Existing community detection techniques also fail to properly
capture the time-dependent relationships. This is particularly evident when
performing analytics over massive transaction graphs. We propose a framework
(called FaSTMAN), adapted for domain-specific constraints, to efficiently
construct a temporal graph of sequential transactions. The framework includes a
weighting method, using 2nd order graph representation, to quantify the
significance of the edges. This method enables us to distribute complex queries
on smaller and densely connected networks of flows. Finally, based on those
queries, we can effectively identify networks of suspicious flows. We
extensively evaluate the scalability and the effectiveness of our framework
against two state-of-the-art solutions for detecting suspicious flows of
transactions. For a dataset of over 1 Billion transactions from multiple large
European banks, the results show a clear superiority of our framework both in
efficiency and usefulness. | [
"Haseeb Tariq",
"Marwan Hassani"
] | 2023-09-24 15:11:58 | http://arxiv.org/abs/2309.13662v1 | http://arxiv.org/pdf/2309.13662v1 | 2309.13662v1 |
Fantastic Generalization Measures are Nowhere to be Found | Numerous generalization bounds have been proposed in the literature as
potential explanations for the ability of neural networks to generalize in the
overparameterized setting. However, none of these bounds are tight. For
instance, in their paper ``Fantastic Generalization Measures and Where to Find
Them'', Jiang et al. (2020) examine more than a dozen generalization bounds,
and show empirically that none of them imply guarantees that can explain the
remarkable performance of neural networks. This raises the question of whether
tight generalization bounds are at all possible. We consider two types of
generalization bounds common in the literature: (1) bounds that depend on the
training set and the output of the learning algorithm. There are multiple
bounds of this type in the literature (e.g., norm-based and margin-based
bounds), but we prove mathematically that no such bound can be uniformly tight
in the overparameterized setting; (2) bounds that depend on the training set
and on the learning algorithm (e.g., stability bounds). For these bounds, we
show a trade-off between the algorithm's performance and the bound's tightness.
Namely, if the algorithm achieves good accuracy on certain distributions in the
overparameterized setting, then no generalization bound can be tight for it. We
conclude that generalization bounds in the overparameterized setting cannot be
tight without suitable assumptions on the population distribution. | [
"Michael Gastpar",
"Ido Nachum",
"Jonathan Shafer",
"Thomas Weinberger"
] | 2023-09-24 14:53:51 | http://arxiv.org/abs/2309.13658v1 | http://arxiv.org/pdf/2309.13658v1 | 2309.13658v1 |
A Probabilistic Model for Data Redundancy in the Feature Domain | In this paper, we use a probabilistic model to estimate the number of
uncorrelated features in a large dataset. Our model allows for both pairwise
feature correlation (collinearity) and interdependency of multiple features
(multicollinearity) and we use the probabilistic method to obtain upper and
lower bounds of the same order, for the size of a feature set that exhibits low
collinearity and low multicollinearity. We also prove an auxiliary result
regarding mutually good constrained sets that is of independent interest. | [
"Ghurumuruhan Ganesan"
] | 2023-09-24 14:51:53 | http://arxiv.org/abs/2309.13657v1 | http://arxiv.org/pdf/2309.13657v1 | 2309.13657v1 |
REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning over Mobile Devices | Participant selection (PS) helps to accelerate federated learning (FL)
convergence, which is essential for the practical deployment of FL over mobile
devices. While most existing PS approaches focus on improving training accuracy
and efficiency rather than residual energy of mobile devices, which
fundamentally determines whether the selected devices can participate.
Meanwhile, the impacts of mobile devices' heterogeneous wireless transmission
rates on PS and FL training efficiency are largely ignored. Moreover, PS causes
the staleness issue. Prior research exploits isolated functions to force
long-neglected devices to participate, which is decoupled from original PS
designs. In this paper, we propose a residual energy and wireless aware PS
design for efficient FL training over mobile devices (REWAFL). REW AFL
introduces a novel PS utility function that jointly considers global FL
training utilities and local energy utility, which integrates energy
consumption and residual battery energy of candidate mobile devices. Under the
proposed PS utility function framework, REW AFL further presents a residual
energy and wireless aware local computing policy. Besides, REWAFL buries the
staleness solution into its utility function and local computing policy. The
experimental results show that REW AFL is effective in improving training
accuracy and efficiency, while avoiding "flat battery" of mobile devices. | [
"Y. Li",
"X. Qin",
"J. Geng",
"R. Chen",
"Y. Hou",
"Y. Gong",
"M. Pan",
"P. Zhang"
] | 2023-09-24 14:04:30 | http://arxiv.org/abs/2309.13643v1 | http://arxiv.org/pdf/2309.13643v1 | 2309.13643v1 |
Development of an intelligent system for the detection of corona virus using artificial neural network | This paper presents the development of an intelligent system for the
detection of coronavirus using artificial neural network. This was done after
series of literature review which indicated that high fever accounts for 87.9%
of the COVID-19 symptoms. 683 temperature data of COVID-19 patients at >= 38C^o
were collected from Colliery hospital Enugu, Nigeria and used to train an
artificial neural network detective model for the detection of COVID-19. The
reference model generated was used converted into Verilog codes using Hardware
Description Language (HDL) and then burn into a Field Programming Gate Array
(FPGA) controller using FPGA tool in Matlab. The performance of the model when
evaluated using confusion matrix, regression and means square error (MSE)
showed that the regression value is 0.967; the accuracy is 97% and then MSE is
0.00100Mu. These results all implied that the new detection system for is
reliable and very effective for the detection of COVID-19. | [
"Nwafor Emmanuel O",
"Ngozi Maryrose Umeh",
"Ikechukwu Ekene Onyenwe"
] | 2023-09-24 13:30:50 | http://arxiv.org/abs/2309.13636v1 | http://arxiv.org/pdf/2309.13636v1 | 2309.13636v1 |
PanopticNDT: Efficient and Robust Panoptic Mapping | As the application scenarios of mobile robots are getting more complex and
challenging, scene understanding becomes increasingly crucial. A mobile robot
that is supposed to operate autonomously in indoor environments must have
precise knowledge about what objects are present, where they are, what their
spatial extent is, and how they can be reached; i.e., information about free
space is also crucial. Panoptic mapping is a powerful instrument providing such
information. However, building 3D panoptic maps with high spatial resolution is
challenging on mobile robots, given their limited computing capabilities. In
this paper, we propose PanopticNDT - an efficient and robust panoptic mapping
approach based on occupancy normal distribution transform (NDT) mapping. We
evaluate our approach on the publicly available datasets Hypersim and
ScanNetV2. The results reveal that our approach can represent panoptic
information at a higher level of detail than other state-of-the-art approaches
while enabling real-time panoptic mapping on mobile robots. Finally, we prove
the real-world applicability of PanopticNDT with qualitative results in a
domestic application. | [
"Daniel Seichter",
"Benedict Stephan",
"Söhnke Benedikt Fischedick",
"Steffen Müller",
"Leonard Rabes",
"Horst-Michael Gross"
] | 2023-09-24 13:21:33 | http://arxiv.org/abs/2309.13635v1 | http://arxiv.org/pdf/2309.13635v1 | 2309.13635v1 |
A Multi-channel EEG Data Analysis for Poor Neuro-prognostication in Comatose Patients with Self and Cross-channel Attention Mechanism | This work investigates the predictive potential of bipolar
electroencephalogram (EEG) recordings towards efficient prediction of poor
neurological outcomes. A retrospective design using a hybrid deep learning
approach is utilized to optimize an objective function aiming for high
specificity, i.e., true positive rate (TPR) with reduced false positives (<
0.05). A multi-channel EEG array of 18 bipolar channel pairs from a randomly
selected 5-minute segment in an hour is kept. In order to determine the outcome
prediction, a combination of a feature encoder with 1-D convolutional layers,
learnable position encoding, a context network with attention mechanisms, and
finally, a regressor and classifier blocks are used. The feature encoder
extricates local temporal and spatial features, while the following position
encoding and attention mechanisms attempt to capture global temporal
dependencies. Results: The proposed framework by our team, OUS IVS, when
validated on the challenge hidden validation data, exhibited a score of 0.57. | [
"Hemin Ali Qadir",
"Naimahmed Nesaragi",
"Per Steiner Halvorsen",
"Ilangko Balasingham"
] | 2023-09-24 13:13:29 | http://arxiv.org/abs/2310.03756v1 | http://arxiv.org/pdf/2310.03756v1 | 2310.03756v1 |
Crack-Net: Prediction of Crack Propagation in Composites | Computational solid mechanics has become an indispensable approach in
engineering, and numerical investigation of fracture in composites is essential
as composites are widely used in structural applications. Crack evolution in
composites is the bridge to elucidate the relationship between the
microstructure and fracture performance, but crack-based finite element methods
are computationally expensive and time-consuming, limiting their application in
computation-intensive scenarios. Here we propose a deep learning framework
called Crack-Net, which incorporates the relationship between crack evolution
and stress response to predict the fracture process in composites. Trained on a
high-precision fracture development dataset generated using the phase field
method, Crack-Net demonstrates a remarkable capability to accurately forecast
the long-term evolution of crack growth patterns and the stress-strain curve
for a given composite design. The Crack-Net captures the essential principle of
crack growth, which enables it to handle more complex microstructures such as
binary co-continuous structures. Moreover, transfer learning is adopted to
further improve the generalization ability of Crack-Net for composite materials
with reinforcements of different strengths. The proposed Crack-Net holds great
promise for practical applications in engineering and materials science, in
which accurate and efficient fracture prediction is crucial for optimizing
material performance and microstructural design. | [
"Hao Xu",
"Wei Fan",
"Ambrose C. Taylor",
"Dongxiao Zhang",
"Lecheng Ruan",
"Rundong Shi"
] | 2023-09-24 12:57:35 | http://arxiv.org/abs/2309.13626v1 | http://arxiv.org/pdf/2309.13626v1 | 2309.13626v1 |
Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions | Feature transformation aims to generate new pattern-discriminative feature
space from original features to improve downstream machine learning (ML) task
performances. However, the discrete search space for the optimal feature
explosively grows on the basis of combinations of features and operations from
low-order forms to high-order forms. Existing methods, such as exhaustive
search, expansion reduction, evolutionary algorithms, reinforcement learning,
and iterative greedy, suffer from large search space. Overly emphasizing
efficiency in algorithm design usually sacrifices stability or robustness. To
fundamentally fill this gap, we reformulate discrete feature transformation as
a continuous space optimization task and develop an
embedding-optimization-reconstruction framework. This framework includes four
steps: 1) reinforcement-enhanced data preparation, aiming to prepare
high-quality transformation-accuracy training data; 2) feature transformation
operation sequence embedding, intending to encapsulate the knowledge of
prepared training data within a continuous space; 3) gradient-steered optimal
embedding search, dedicating to uncover potentially superior embeddings within
the learned space; 4) transformation operation sequence reconstruction,
striving to reproduce the feature transformation solution to pinpoint the
optimal feature space. | [
"Dongjie Wang",
"Meng Xiao",
"Min Wu",
"Pengfei Wang",
"Yuanchun Zhou",
"Yanjie Fu"
] | 2023-09-24 12:18:37 | http://arxiv.org/abs/2309.13618v1 | http://arxiv.org/pdf/2309.13618v1 | 2309.13618v1 |
A Text Classification-Based Approach for Evaluating and Enhancing the Machine Interpretability of Building Codes | Interpreting regulatory documents or building codes into computer-processable
formats is essential for the intelligent design and construction of buildings
and infrastructures. Although automated rule interpretation (ARI) methods have
been investigated for years, most of them highly depend on the early and manual
filtering of interpretable clauses from a building code. While few of them
considered machine interpretability, which represents the potential to be
transformed into a computer-processable format, from both clause- and
document-level. Therefore, this research aims to propose a novel approach to
automatically evaluate and enhance the machine interpretability of single
clause and building codes. First, a few categories are introduced to classify
each clause in a building code considering the requirements for rule
interpretation, and a dataset is developed for model training. Then, an
efficient text classification model is developed based on a pretrained
domain-specific language model and transfer learning techniques. Finally, a
quantitative evaluation method is proposed to assess the overall
interpretability of building codes. Experiments show that the proposed text
classification algorithm outperforms the existing CNN- or RNN-based methods,
improving the F1-score from 72.16% to 93.60%. It is also illustrated that the
proposed classification method can enhance downstream ARI methods with an
improvement of 4%. Furthermore, analyzing the results of more than 150 building
codes in China showed that their average interpretability is 34.40%, which
implies that it is still hard to fully transform the entire regulatory document
into computer-processable formats. It is also argued that the interpretability
of building codes should be further improved both from the human side and the
machine side. | [
"Zhe Zheng",
"Yu-Cheng Zhou",
"Ke-Yin Chen",
"Xin-Zheng Lu",
"Zhong-Tian She",
"Jia-Rui Lin"
] | 2023-09-24 11:36:21 | http://arxiv.org/abs/2309.14374v1 | http://arxiv.org/pdf/2309.14374v1 | 2309.14374v1 |
DPA-WNO: A gray box model for a class of stochastic mechanics problem | The well-known governing physics in science and engineering is often based on
certain assumptions and approximations. Therefore, analyses and designs carried
out based on these equations are also approximate. The emergence of data-driven
models has, to a certain degree, addressed this challenge; however, the purely
data-driven models often (a) lack interpretability, (b) are data-hungry, and
(c) do not generalize beyond the training window. Operator learning has
recently been proposed as a potential alternative to address the aforementioned
challenges; however, the challenges are still persistent. We here argue that
one of the possible solutions resides in data-physics fusion, where the
data-driven model is used to correct/identify the missing physics. To that end,
we propose a novel Differentiable Physics Augmented Wavelet Neural Operator
(DPA-WNO). The proposed DPA-WNO blends a differentiable physics solver with the
Wavelet Neural Operator (WNO), where the role of WNO is to model the missing
physics. This empowers the proposed framework to exploit the capability of WNO
to learn from data while retaining the interpretability and generalizability
associated with physics-based solvers. We illustrate the applicability of the
proposed approach in solving time-dependent uncertainty quantification problems
due to randomness in the initial condition. Four benchmark uncertainty
quantification and reliability analysis examples from various fields of science
and engineering are solved using the proposed approach. The results presented
illustrate interesting features of the proposed approach. | [
"Tushar",
"Souvik Chakraborty"
] | 2023-09-24 11:15:06 | http://arxiv.org/abs/2309.15128v2 | http://arxiv.org/pdf/2309.15128v2 | 2309.15128v2 |
Multi-Dimensional Hyena for Spatial Inductive Bias | In recent years, Vision Transformers have attracted increasing interest from
computer vision researchers. However, the advantage of these transformers over
CNNs is only fully manifested when trained over a large dataset, mainly due to
the reduced inductive bias towards spatial locality within the transformer's
self-attention mechanism. In this work, we present a data-efficient vision
transformer that does not rely on self-attention. Instead, it employs a novel
generalization to multiple axes of the very recent Hyena layer. We propose
several alternative approaches for obtaining this generalization and delve into
their unique distinctions and considerations from both empirical and
theoretical perspectives.
Our empirical findings indicate that the proposed Hyena N-D layer boosts the
performance of various Vision Transformer architectures, such as ViT, Swin, and
DeiT across multiple datasets. Furthermore, in the small dataset regime, our
Hyena-based ViT is favorable to ViT variants from the recent literature that
are specifically designed for solving the same challenge, i.e., working with
small datasets or incorporating image-specific inductive bias into the
self-attention mechanism. Finally, we show that a hybrid approach that is based
on Hyena N-D for the first layers in ViT, followed by layers that incorporate
conventional attention, consistently boosts the performance of various vision
transformer architectures. | [
"Itamar Zimerman",
"Lior Wolf"
] | 2023-09-24 10:22:35 | http://arxiv.org/abs/2309.13600v1 | http://arxiv.org/pdf/2309.13600v1 | 2309.13600v1 |
From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited | Graph-based semi-supervised learning (GSSL) has long been a hot research
topic. Traditional methods are generally shallow learners, based on the cluster
assumption. Recently, graph convolutional networks (GCNs) have become the
predominant techniques for their promising performance. In this paper, we
theoretically discuss the relationship between these two types of methods in a
unified optimization framework. One of the most intriguing findings is that,
unlike traditional ones, typical GCNs may not jointly consider the graph
structure and label information at each layer. Motivated by this, we further
propose three simple but powerful graph convolution methods. The first is a
supervised method OGC which guides the graph convolution process with labels.
The others are two unsupervised methods: GGC and its multi-scale version GGCM,
both aiming to preserve the graph structure information during the convolution
process. Finally, we conduct extensive experiments to show the effectiveness of
our methods. | [
"Zheng Wang",
"Hongming Ding",
"Li Pan",
"Jianhua Li",
"Zhiguo Gong",
"Philip S. Yu"
] | 2023-09-24 10:10:21 | http://arxiv.org/abs/2309.13599v1 | http://arxiv.org/pdf/2309.13599v1 | 2309.13599v1 |
On the Posterior Distribution in Denoising: Application to Uncertainty Quantification | Denoisers play a central role in many applications, from noise suppression in
low-grade imaging sensors, to empowering score-based generative models. The
latter category of methods makes use of Tweedie's formula, which links the
posterior mean in Gaussian denoising (i.e., the minimum MSE denoiser) with the
score of the data distribution. Here, we derive a fundamental relation between
the higher-order central moments of the posterior distribution, and the
higher-order derivatives of the posterior mean. We harness this result for
uncertainty quantification of pre-trained denoisers. Particularly, we show how
to efficiently compute the principal components of the posterior distribution
for any desired region of an image, as well as to approximate the full marginal
distribution along those (or any other) one-dimensional directions. Our method
is fast and memory efficient, as it does not explicitly compute or store the
high-order moment tensors and it requires no training or fine tuning of the
denoiser. Code and examples are available on the project's webpage in
https://hilamanor.github.io/GaussianDenoisingPosterior/ | [
"Hila Manor",
"Tomer Michaeli"
] | 2023-09-24 10:07:40 | http://arxiv.org/abs/2309.13598v1 | http://arxiv.org/pdf/2309.13598v1 | 2309.13598v1 |
Self-Tuning Hamiltonian Monte Carlo for Accelerated Sampling | The performance of Hamiltonian Monte Carlo crucially depends on its
parameters, in particular the integration timestep and the number of
integration steps. We present an adaptive general-purpose framework to
automatically tune these parameters based on a loss function which promotes the
fast exploration of phase-space. For this, we make use of a
fully-differentiable set-up and use backpropagation for optimization. An
attention-like loss is defined which allows for the gradient driven learning of
the distribution of integration steps. We also highlight the importance of
jittering for a smooth loss-surface. Our approach is demonstrated for the
one-dimensional harmonic oscillator and alanine dipeptide, a small protein
common as a test-case for simulation methods. We find a good correspondence
between our loss and the autocorrelation times, resulting in well-tuned
parameters for Hamiltonian Monte Carlo. | [
"Henrik Christiansen",
"Federico Errica",
"Francesco Alesiani"
] | 2023-09-24 09:35:25 | http://arxiv.org/abs/2309.13593v1 | http://arxiv.org/pdf/2309.13593v1 | 2309.13593v1 |
Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity | The theory underlying robust distributed learning algorithms, designed to
resist adversarial machines, matches empirical observations when data is
homogeneous. Under data heterogeneity however, which is the norm in practical
scenarios, established lower bounds on the learning error are essentially
vacuous and greatly mismatch empirical observations. This is because the
heterogeneity model considered is too restrictive and does not cover basic
learning tasks such as least-squares regression. We consider in this paper a
more realistic heterogeneity model, namely (G,B)-gradient dissimilarity, and
show that it covers a larger class of learning problems than existing theory.
Notably, we show that the breakdown point under heterogeneity is lower than the
classical fraction 1/2. We also prove a new lower bound on the learning error
of any distributed learning algorithm. We derive a matching upper bound for a
robust variant of distributed gradient descent, and empirically show that our
analysis reduces the gap between theory and practice. | [
"Youssef Allouah",
"Rachid Guerraoui",
"Nirupam Gupta",
"Rafaël Pinot",
"Geovani Rizk"
] | 2023-09-24 09:29:28 | http://arxiv.org/abs/2309.13591v1 | http://arxiv.org/pdf/2309.13591v1 | 2309.13591v1 |
Benchmarking Encoder-Decoder Architectures for Biplanar X-ray to 3D Shape Reconstruction | Various deep learning models have been proposed for 3D bone shape
reconstruction from two orthogonal (biplanar) X-ray images. However, it is
unclear how these models compare against each other since they are evaluated on
different anatomy, cohort and (often privately held) datasets. Moreover, the
impact of the commonly optimized image-based segmentation metrics such as dice
score on the estimation of clinical parameters relevant in 2D-3D bone shape
reconstruction is not well known. To move closer toward clinical translation,
we propose a benchmarking framework that evaluates tasks relevant to real-world
clinical scenarios, including reconstruction of fractured bones, bones with
implants, robustness to population shift, and error in estimating clinical
parameters. Our open-source platform provides reference implementations of 8
models (many of whose implementations were not publicly available), APIs to
easily collect and preprocess 6 public datasets, and the implementation of
automatic clinical parameter and landmark extraction methods. We present an
extensive evaluation of 8 2D-3D models on equal footing using 6 public datasets
comprising images for four different anatomies. Our results show that
attention-based methods that capture global spatial relationships tend to
perform better across all anatomies and datasets; performance on clinically
relevant subgroups may be overestimated without disaggregated reporting; ribs
are substantially more difficult to reconstruct compared to femur, hip and
spine; and the dice score improvement does not always bring a corresponding
improvement in the automatic estimation of clinically relevant parameters. | [
"Mahesh Shakya",
"Bishesh Khanal"
] | 2023-09-24 09:05:35 | http://arxiv.org/abs/2309.13587v2 | http://arxiv.org/pdf/2309.13587v2 | 2309.13587v2 |
Solving Low-Dose CT Reconstruction via GAN with Local Coherence | The Computed Tomography (CT) for diagnosis of lesions in human internal
organs is one of the most fundamental topics in medical imaging. Low-dose CT,
which offers reduced radiation exposure, is preferred over standard-dose CT,
and therefore its reconstruction approaches have been extensively studied.
However, current low-dose CT reconstruction techniques mainly rely on
model-based methods or deep-learning-based techniques, which often ignore the
coherence and smoothness for sequential CT slices. To address this issue, we
propose a novel approach using generative adversarial networks (GANs) with
enhanced local coherence. The proposed method can capture the local coherence
of adjacent images by optical flow, which yields significant improvements in
the precision and stability of the constructed images. We evaluate our proposed
method on real datasets and the experimental results suggest that it can
outperform existing state-of-the-art reconstruction approaches significantly. | [
"Wenjie Liu"
] | 2023-09-24 08:55:42 | http://arxiv.org/abs/2309.13584v1 | http://arxiv.org/pdf/2309.13584v1 | 2309.13584v1 |
Probabilistic Weight Fixing: Large-scale training of neural network weight uncertainties for quantization | Weight-sharing quantization has emerged as a technique to reduce energy
expenditure during inference in large neural networks by constraining their
weights to a limited set of values. However, existing methods for
weight-sharing quantization often make assumptions about the treatment of
weights based on value alone that neglect the unique role weight position
plays. This paper proposes a probabilistic framework based on Bayesian neural
networks (BNNs) and a variational relaxation to identify which weights can be
moved to which cluster centre and to what degree based on their individual
position-specific learned uncertainty distributions. We introduce a new
initialisation setting and a regularisation term which allow for the training
of BNNs under complex dataset-model combinations. By leveraging the flexibility
of weight values captured through a probability distribution, we enhance noise
resilience and downstream compressibility. Our iterative clustering procedure
demonstrates superior compressibility and higher accuracy compared to
state-of-the-art methods on both ResNet models and the more complex
transformer-based architectures. In particular, our method outperforms the
state-of-the-art quantization method top-1 accuracy by 1.6% on ImageNet using
DeiT-Tiny, with its 5 million+ weights now represented by only 296 unique
values. | [
"Christopher Subia-Waud",
"Srinandan Dasmahapatra"
] | 2023-09-24 08:04:28 | http://arxiv.org/abs/2309.13575v3 | http://arxiv.org/pdf/2309.13575v3 | 2309.13575v3 |
Physics Informed Neural Network Code for 2D Transient Problems (PINN-2DT) Compatible with Google Colab | We present an open-source Physics Informed Neural Network environment for
simulations of transient phenomena on two-dimensional rectangular domains, with
the following features: (1) it is compatible with Google Colab which allows
automatic execution on cloud environment; (2) it supports two dimensional
time-dependent PDEs; (3) it provides simple interface for definition of the
residual loss, boundary condition and initial loss, together with their
weights; (4) it support Neumann and Dirichlet boundary conditions; (5) it
allows for customizing the number of layers and neurons per layer, as well as
for arbitrary activation function; (6) the learning rate and number of epochs
are available as parameters; (7) it automatically differentiates PINN with
respect to spatial and temporal variables; (8) it provides routines for
plotting the convergence (with running average), initial conditions learnt, 2D
and 3D snapshots from the simulation and movies (9) it includes a library of
problems: (a) non-stationary heat transfer; (b) wave equation modeling a
tsunami; (c) atmospheric simulations including thermal inversion; (d) tumor
growth simulations. | [
"Paweł Maczuga",
"Maciej Skoczeń",
"Przemysław Rożnawski",
"Filip Tłuszcz",
"Marcin Szubert",
"Marcin Łoś",
"Witold Dzwinel",
"Keshav Pingali",
"Maciej Paszyński"
] | 2023-09-24 07:08:36 | http://arxiv.org/abs/2310.03755v1 | http://arxiv.org/pdf/2310.03755v1 | 2310.03755v1 |
Generalized Dice Focal Loss trained 3D Residual UNet for Automated Lesion Segmentation in Whole-Body FDG PET/CT Images | Automated segmentation of cancerous lesions in PET/CT images is a vital
initial task for quantitative analysis. However, it is often challenging to
train deep learning-based segmentation methods to high degree of accuracy due
to the diversity of lesions in terms of their shapes, sizes, and radiotracer
uptake levels. These lesions can be found in various parts of the body, often
close to healthy organs that also show significant uptake. Consequently,
developing a comprehensive PET/CT lesion segmentation model is a demanding
endeavor for routine quantitative image analysis. In this work, we train a 3D
Residual UNet using Generalized Dice Focal Loss function on the AutoPET
challenge 2023 training dataset. We develop our models in a 5-fold
cross-validation setting and ensemble the five models via average and
weighted-average ensembling. On the preliminary test phase, the average
ensemble achieved a Dice similarity coefficient (DSC), false-positive volume
(FPV) and false negative volume (FNV) of 0.5417, 0.8261 ml, and 0.2538 ml,
respectively, while the weighted-average ensemble achieved 0.5417, 0.8186 ml,
and 0.2538 ml, respectively. Our algorithm can be accessed via this link:
https://github.com/ahxmeds/autosegnet. | [
"Shadab Ahamed",
"Arman Rahmim"
] | 2023-09-24 05:29:45 | http://arxiv.org/abs/2309.13553v1 | http://arxiv.org/pdf/2309.13553v1 | 2309.13553v1 |
DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning | Federated Learning (FL) is a privacy-constrained decentralized machine
learning paradigm in which clients enable collaborative training without
compromising private data. However, how to learn a robust global model in the
data-heterogeneous and model-heterogeneous FL scenarios is challenging. To
address it, we resort to data-free knowledge distillation to propose a new FL
method (namely DFRD). DFRD equips a conditional generator on the server to
approximate the training space of the local models uploaded by clients, and
systematically investigates its training in terms of fidelity, transferability}
and diversity. To overcome the catastrophic forgetting of the global model
caused by the distribution shifts of the generator across communication rounds,
we maintain an exponential moving average copy of the generator on the server.
Additionally, we propose dynamic weighting and label sampling to accurately
extract knowledge from local models. Finally, our extensive experiments on
various image classification tasks illustrate that DFRD achieves significant
performance gains compared to SOTA baselines. | [
"Kangyang Luo",
"Shuai Wang",
"Yexuan Fu",
"Xiang Li",
"Yunshi Lan",
"Ming Gao"
] | 2023-09-24 04:29:22 | http://arxiv.org/abs/2309.13546v2 | http://arxiv.org/pdf/2309.13546v2 | 2309.13546v2 |
Related Rhythms: Recommendation System To Discover Music You May Like | Machine Learning models are being utilized extensively to drive recommender
systems, which is a widely explored topic today. This is especially true of the
music industry, where we are witnessing a surge in growth. Besides a large
chunk of active users, these systems are fueled by massive amounts of data.
These large-scale systems yield applications that aim to provide a better user
experience and to keep customers actively engaged. In this paper, a distributed
Machine Learning (ML) pipeline is delineated, which is capable of taking a
subset of songs as input and producing a new subset of songs identified as
being similar to the inputted subset. The publicly accessible Million Songs
Dataset (MSD) enables researchers to develop and explore reasonably efficient
systems for audio track analysis and recommendations, without having to access
a commercialized music platform. The objective of the proposed application is
to leverage an ML system trained to optimally recommend songs that a user might
like. | [
"Rahul Singh",
"Pranav Kanuparthi"
] | 2023-09-24 04:18:40 | http://arxiv.org/abs/2309.13544v1 | http://arxiv.org/pdf/2309.13544v1 | 2309.13544v1 |
Substituting Data Annotation with Balanced Updates and Collective Loss in Multi-label Text Classification | Multi-label text classification (MLTC) is the task of assigning multiple
labels to a given text, and has a wide range of application domains. Most
existing approaches require an enormous amount of annotated data to learn a
classifier and/or a set of well-defined constraints on the label space
structure, such as hierarchical relations which may be complicated to provide
as the number of labels increases. In this paper, we study the MLTC problem in
annotation-free and scarce-annotation settings in which the magnitude of
available supervision signals is linear to the number of labels. Our method
follows three steps, (1) mapping input text into a set of preliminary label
likelihoods by natural language inference using a pre-trained language model,
(2) calculating a signed label dependency graph by label descriptions, and (3)
updating the preliminary label likelihoods with message passing along the label
dependency graph, driven with a collective loss function that injects the
information of expected label frequency and average multi-label cardinality of
predictions. The experiments show that the proposed framework achieves
effective performance under low supervision settings with almost imperceptible
computational and memory overheads added to the usage of pre-trained language
model outperforming its initial performance by 70\% in terms of example-based
F1 score. | [
"Muberra Ozmen",
"Joseph Cotnareanu",
"Mark Coates"
] | 2023-09-24 04:12:52 | http://arxiv.org/abs/2309.13543v1 | http://arxiv.org/pdf/2309.13543v1 | 2309.13543v1 |
Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution | Embedding plays a critical role in modern recommender systems because they
are virtual representations of real-world entities and the foundation for
subsequent decision models. In this paper, we propose a novel embedding update
mechanism, Structure-aware Embedding Evolution (SEvo for short), to encourage
related nodes to evolve similarly at each step. Unlike GNN (Graph Neural
Network) that typically serves as an intermediate part, SEvo is able to
directly inject the graph structure information into embedding with negligible
computational overhead in training. The convergence properties of SEvo as well
as its possible variants are theoretically analyzed to justify the validity of
the designs. Moreover, SEvo can be seamlessly integrated into existing
optimizers for state-of-the-art performance. In particular, SEvo-enhanced AdamW
with moment estimate correction demonstrates consistent improvements across a
spectrum of models and datasets, suggesting a novel technical route to
effectively utilize graph structure information beyond explicit GNN modules. | [
"Cong Xu",
"Jun Wang",
"Jianyong Wang",
"Wei Zhang"
] | 2023-09-24 04:09:16 | http://arxiv.org/abs/2310.03032v1 | http://arxiv.org/pdf/2310.03032v1 | 2310.03032v1 |
Human Transcription Quality Improvement | High quality transcription data is crucial for training automatic speech
recognition (ASR) systems. However, the existing industry-level data collection
pipelines are expensive to researchers, while the quality of crowdsourced
transcription is low. In this paper, we propose a reliable method to collect
speech transcriptions. We introduce two mechanisms to improve transcription
quality: confidence estimation based reprocessing at labeling stage, and
automatic word error correction at post-labeling stage. We collect and release
LibriCrowd - a large-scale crowdsourced dataset of audio transcriptions on 100
hours of English speech. Experiment shows the Transcription WER is reduced by
over 50%. We further investigate the impact of transcription error on ASR model
performance and found a strong correlation. The transcription quality
improvement provides over 10% relative WER reduction for ASR models. We release
the dataset and code to benefit the research community. | [
"Jian Gao",
"Hanbo Sun",
"Cheng Cao",
"Zheng Du"
] | 2023-09-24 03:39:43 | http://arxiv.org/abs/2309.14372v1 | http://arxiv.org/pdf/2309.14372v1 | 2309.14372v1 |
Tackling the Unlimited Staleness in Federated Learning with Intertwined Data and Device Heterogeneities | The efficiency of Federated Learning (FL) is often affected by both data and
device heterogeneities. Data heterogeneity is defined as the heterogeneity of
data distributions on different clients. Device heterogeneity is defined as the
clients' variant latencies in uploading their local model updates due to
heterogeneous conditions of local hardware resources, and causes the problem of
staleness when being addressed by asynchronous FL. Traditional schemes of
tackling the impact of staleness consider data and device heterogeneities as
two separate and independent aspects in FL, but this assumption is unrealistic
in many practical FL scenarios where data and device heterogeneities are
intertwined. In these cases, traditional schemes of weighted aggregation in FL
have been proved to be ineffective, and a better approach is to convert a stale
model update into a non-stale one. In this paper, we present a new FL framework
that leverages the gradient inversion technique for such conversion, hence
efficiently tackling unlimited staleness in clients' model updates. Our basic
idea is to use gradient inversion to get estimations of clients' local training
data from their uploaded stale model updates, and use these estimations to
compute non-stale client model updates. In this way, we address the problem of
possible data quality drop when using gradient inversion, while still
preserving the clients' local data privacy. We compared our approach with the
existing FL strategies on mainstream datasets and models, and experiment
results demonstrate that when tackling unlimited staleness, our approach can
significantly improve the trained model accuracy by up to 20% and speed up the
FL training progress by up to 35%. | [
"Haoming Wang",
"Wei Gao"
] | 2023-09-24 03:19:40 | http://arxiv.org/abs/2309.13536v1 | http://arxiv.org/pdf/2309.13536v1 | 2309.13536v1 |
Iterative Reachability Estimation for Safe Reinforcement Learning | Ensuring safety is important for the practical deployment of reinforcement
learning (RL). Various challenges must be addressed, such as handling
stochasticity in the environments, providing rigorous guarantees of persistent
state-wise safety satisfaction, and avoiding overly conservative behaviors that
sacrifice performance. We propose a new framework, Reachability Estimation for
Safe Policy Optimization (RESPO), for safety-constrained RL in general
stochastic settings. In the feasible set where there exist violation-free
policies, we optimize for rewards while maintaining persistent safety. Outside
this feasible set, our optimization produces the safest behavior by
guaranteeing entrance into the feasible set whenever possible with the least
cumulative discounted violations. We introduce a class of algorithms using our
novel reachability estimation function to optimize in our proposed framework
and in similar frameworks such as those concurrently handling multiple hard and
soft constraints. We theoretically establish that our algorithms almost surely
converge to locally optimal policies of our safe optimization framework. We
evaluate the proposed methods on a diverse suite of safe RL environments from
Safety Gym, PyBullet, and MuJoCo, and show the benefits in improving both
reward performance and safety compared with state-of-the-art baselines. | [
"Milan Ganai",
"Zheng Gong",
"Chenning Yu",
"Sylvia Herbert",
"Sicun Gao"
] | 2023-09-24 02:36:42 | http://arxiv.org/abs/2309.13528v1 | http://arxiv.org/pdf/2309.13528v1 | 2309.13528v1 |
Data-Driven Modeling of an Unsaturated Bentonite Buffer Model Test Under High Temperatures Using an Enhanced Axisymmetric Reproducing Kernel Particle Method | In deep geological repositories for high level nuclear waste with close
canister spacings, bentonite buffers can experience temperatures higher than
100 {\deg}C. In this range of extreme temperatures, phenomenological
constitutive laws face limitations in capturing the thermo-hydro-mechanical
(THM) behavior of the bentonite, since the pre-defined functional constitutive
laws often lack generality and flexibility to capture a wide range of complex
coupling phenomena as well as the effects of stress state and path dependency.
In this work, a deep neural network (DNN)-based soil-water retention curve
(SWRC) of bentonite is introduced and integrated into a Reproducing Kernel
Particle Method (RKPM) for conducting THM simulations of the bentonite buffer.
The DNN-SWRC model incorporates temperature as an additional input variable,
allowing it to learn the relationship between suction and degree of saturation
under the general non-isothermal condition, which is difficult to represent
using a phenomenological SWRC. For effective modeling of the tank-scale test,
new axisymmetric Reproducing Kernel basis functions enriched with singular
Dirichlet enforcement representing heater placement and an effective convective
heat transfer coefficient representing thin-layer composite tank construction
are developed. The proposed method is demonstrated through the modeling of a
tank-scale experiment involving a cylindrical layer of MX-80 bentonite exposed
to central heating. | [
"Jonghyuk Baek",
"Yanran Wang",
"Xiaolong He",
"Yu Lu",
"John S. McCartney",
"J. S. Chen"
] | 2023-09-24 01:22:23 | http://arxiv.org/abs/2309.13519v1 | http://arxiv.org/pdf/2309.13519v1 | 2309.13519v1 |
Guided Cooperation in Hierarchical Reinforcement Learning via Model-based Rollout | Goal-conditioned hierarchical reinforcement learning (HRL) presents a
promising approach for enabling effective exploration in complex long-horizon
reinforcement learning (RL) tasks via temporal abstraction. Yet, most
goal-conditioned HRL algorithms focused on the subgoal discovery, regardless of
inter-level coupling. In essence, for hierarchical systems, the increased
inter-level communication and coordination can induce more stable and robust
policy improvement. Here, we present a goal-conditioned HRL framework with
Guided Cooperation via Model-based Rollout (GCMR), which estimates forward
dynamics to promote inter-level cooperation. The GCMR alleviates the
state-transition error within off-policy correction through a model-based
rollout, further improving the sample efficiency. Meanwhile, to avoid being
disrupted by these corrected but possibly unseen or faraway goals, lower-level
Q-function gradients are constrained using a gradient penalty with a
model-inferred upper bound, leading to a more stable behavioral policy.
Besides, we propose a one-step rollout-based planning to further facilitate
inter-level cooperation, where the higher-level Q-function is used to guide the
lower-level policy by estimating the value of future states so that global task
information is transmitted downwards to avoid local pitfalls. Experimental
results demonstrate that incorporating the proposed GCMR framework with ACLG, a
disentangled variant of HIGL, yields more stable and robust policy improvement
than baselines and substantially outperforms previous state-of-the-art (SOTA)
HRL algorithms in both hard-exploration problems and robotic control. | [
"Haoran Wang",
"Yaoru Sun",
"Fang Wang",
"Yeming Chen"
] | 2023-09-24 00:13:16 | http://arxiv.org/abs/2309.13508v1 | http://arxiv.org/pdf/2309.13508v1 | 2309.13508v1 |
Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy | As an emerging education strategy, learnersourcing offers the potential for
personalized learning content creation, but also grapples with the challenge of
predicting student performance due to inherent noise in student-generated data.
While graph-based methods excel in capturing dense learner-question
interactions, they falter in cold start scenarios, characterized by limited
interactions, as seen when questions lack substantial learner responses. In
response, we introduce an innovative strategy that synergizes the potential of
integrating Signed Graph Neural Networks (SGNNs) and Large Language Model (LLM)
embeddings. Our methodology employs a signed bipartite graph to comprehensively
model student answers, complemented by a contrastive learning framework that
enhances noise resilience. Furthermore, LLM's contribution lies in generating
foundational question embeddings, proving especially advantageous in addressing
cold start scenarios characterized by limited graph data interactions.
Validation across five real-world datasets sourced from the PeerWise platform
underscores our approach's effectiveness. Our method outperforms baselines,
showcasing enhanced predictive accuracy and robustness. | [
"Lin Ni",
"Sijie Wang",
"Zeyu Zhang",
"Xiaoxuan Li",
"Xianda Zheng",
"Paul Denny",
"Jiamou Liu"
] | 2023-09-23 23:37:55 | http://arxiv.org/abs/2309.13500v1 | http://arxiv.org/pdf/2309.13500v1 | 2309.13500v1 |
Interpretable and Flexible Target-Conditioned Neural Planners For Autonomous Vehicles | Learning-based approaches to autonomous vehicle planners have the potential
to scale to many complicated real-world driving scenarios by leveraging huge
amounts of driver demonstrations. However, prior work only learns to estimate a
single planning trajectory, while there may be multiple acceptable plans in
real-world scenarios. To solve the problem, we propose an interpretable neural
planner to regress a heatmap, which effectively represents multiple potential
goals in the bird's-eye view of an autonomous vehicle. The planner employs an
adaptive Gaussian kernel and relaxed hourglass loss to better capture the
uncertainty of planning problems. We also use a negative Gaussian kernel to add
supervision to the heatmap regression, enabling the model to learn collision
avoidance effectively. Our systematic evaluation on the Lyft Open Dataset
across a diverse range of real-world driving scenarios shows that our model
achieves a safer and more flexible driving performance than prior works. | [
"Haolan Liu",
"Jishen Zhao",
"Liangjun Zhang"
] | 2023-09-23 22:13:03 | http://arxiv.org/abs/2309.13485v1 | http://arxiv.org/pdf/2309.13485v1 | 2309.13485v1 |
Enhancing Prediction and Analysis of UK Road Traffic Accident Severity Using AI: Integration of Machine Learning, Econometric Techniques, and Time Series Forecasting in Public Health Research | This research investigates road traffic accident severity in the UK, using a
combination of machine learning, econometric, and statistical methods on
historical data. We employed various techniques, including correlation
analysis, regression models, GMM for error term issues, and time-series
forecasting with VAR and ARIMA models. Our approach outperforms naive
forecasting with an MASE of 0.800 and ME of -73.80. We also built a random
forest classifier with 73% precision, 78% recall, and a 73% F1-score.
Optimizing with H2O AutoML led to an XGBoost model with an RMSE of 0.176 and
MAE of 0.087. Factor Analysis identified key variables, and we used SHAP for
Explainable AI, highlighting influential factors like Driver_Home_Area_Type and
Road_Type. Our study enhances understanding of accident severity and offers
insights for evidence-based road safety policies. | [
"Md Abu Sufian",
"Jayasree Varadarajan"
] | 2023-09-23 21:46:43 | http://arxiv.org/abs/2309.13483v1 | http://arxiv.org/pdf/2309.13483v1 | 2309.13483v1 |
A Unified Scheme of ResNet and Softmax | Large language models (LLMs) have brought significant changes to human
society. Softmax regression and residual neural networks (ResNet) are two
important techniques in deep learning: they not only serve as significant
theoretical components supporting the functionality of LLMs but also are
related to many other machine learning and theoretical computer science fields,
including but not limited to image classification, object detection, semantic
segmentation, and tensors.
Previous research works studied these two concepts separately. In this paper,
we provide a theoretical analysis of the regression problem: $\| \langle
\exp(Ax) + A x , {\bf 1}_n \rangle^{-1} ( \exp(Ax) + Ax ) - b \|_2^2$, where
$A$ is a matrix in $\mathbb{R}^{n \times d}$, $b$ is a vector in
$\mathbb{R}^n$, and ${\bf 1}_n$ is the $n$-dimensional vector whose entries are
all $1$. This regression problem is a unified scheme that combines softmax
regression and ResNet, which has never been done before. We derive the
gradient, Hessian, and Lipschitz properties of the loss function. The Hessian
is shown to be positive semidefinite, and its structure is characterized as the
sum of a low-rank matrix and a diagonal matrix. This enables an efficient
approximate Newton method.
As a result, this unified scheme helps to connect two previously thought
unrelated fields and provides novel insight into loss landscape and
optimization for emerging over-parameterized neural networks, which is
meaningful for future research in deep learning models. | [
"Zhao Song",
"Weixin Wang",
"Junze Yin"
] | 2023-09-23 21:41:01 | http://arxiv.org/abs/2309.13482v1 | http://arxiv.org/pdf/2309.13482v1 | 2309.13482v1 |
Real-time Bandwidth Estimation from Offline Expert Demonstrations | In this work, we tackle the problem of bandwidth estimation (BWE) for
real-time communication systems; however, in contrast to previous works, we
leverage the vast efforts of prior heuristic-based BWE methods and synergize
these approaches with deep learning-based techniques. Our work addresses
challenges in generalizing to unseen network dynamics and extracting rich
representations from prior experience, two key challenges in integrating
data-driven bandwidth estimators into real-time systems. To that end, we
propose Merlin, the first purely offline, data-driven solution to BWE that
harnesses prior heuristic-based methods to extract an expert BWE policy.
Through a series of experiments, we demonstrate that Merlin surpasses
state-of-the-art heuristic-based and deep learning-based bandwidth estimators
in terms of objective quality of experience metrics while generalizing beyond
the offline world to in-the-wild network deployments where Merlin achieves a
42.85% and 12.8% reduction in packet loss and delay, respectively, when
compared against WebRTC in inter-continental videoconferencing calls. We hope
that Merlin's offline-oriented design fosters new strategies for real-time
network control. | [
"Aashish Gottipati",
"Sami Khairy",
"Gabriel Mittag",
"Vishak Gopal",
"Ross Cutler"
] | 2023-09-23 21:39:51 | http://arxiv.org/abs/2309.13481v1 | http://arxiv.org/pdf/2309.13481v1 | 2309.13481v1 |
CA-PCA: Manifold Dimension Estimation, Adapted for Curvature | The success of algorithms in the analysis of high-dimensional data is often
attributed to the manifold hypothesis, which supposes that this data lie on or
near a manifold of much lower dimension. It is often useful to determine or
estimate the dimension of this manifold before performing dimension reduction,
for instance. Existing methods for dimension estimation are calibrated using a
flat unit ball. In this paper, we develop CA-PCA, a version of local PCA based
instead on a calibration of a quadratic embedding, acknowledging the curvature
of the underlying manifold. Numerous careful experiments show that this
adaptation improves the estimator in a wide range of settings. | [
"Anna C. Gilbert",
"Kevin O'Neill"
] | 2023-09-23 21:06:17 | http://arxiv.org/abs/2309.13478v1 | http://arxiv.org/pdf/2309.13478v1 | 2309.13478v1 |
Detecting and Mitigating System-Level Anomalies of Vision-Based Controllers | Autonomous systems, such as self-driving cars and drones, have made
significant strides in recent years by leveraging visual inputs and machine
learning for decision-making and control. Despite their impressive performance,
these vision-based controllers can make erroneous predictions when faced with
novel or out-of-distribution inputs. Such errors can cascade to catastrophic
system failures and compromise system safety. In this work, we introduce a
run-time anomaly monitor to detect and mitigate such closed-loop, system-level
failures. Specifically, we leverage a reachability-based framework to
stress-test the vision-based controller offline and mine its system-level
failures. This data is then used to train a classifier that is leveraged online
to flag inputs that might cause system breakdowns. The anomaly detector
highlights issues that transcend individual modules and pertain to the safety
of the overall system. We also design a fallback controller that robustly
handles these detected anomalies to preserve system safety. We validate the
proposed approach on an autonomous aircraft taxiing system that uses a
vision-based controller for taxiing. Our results show the efficacy of the
proposed approach in identifying and handling system-level anomalies,
outperforming methods such as prediction error-based detection, and ensembling,
thereby enhancing the overall safety and robustness of autonomous systems. | [
"Aryaman Gupta",
"Kaustav Chakraborty",
"Somil Bansal"
] | 2023-09-23 20:33:38 | http://arxiv.org/abs/2309.13475v1 | http://arxiv.org/pdf/2309.13475v1 | 2309.13475v1 |
SUDS: Sanitizing Universal and Dependent Steganography | Steganography, or hiding messages in plain sight, is a form of information
hiding that is most commonly used for covert communication. As modern
steganographic mediums include images, text, audio, and video, this
communication method is being increasingly used by bad actors to propagate
malware, exfiltrate data, and discreetly communicate. Current protection
mechanisms rely upon steganalysis, or the detection of steganography, but these
approaches are dependent upon prior knowledge, such as steganographic
signatures from publicly available tools and statistical knowledge about known
hiding methods. These dependencies render steganalysis useless against new or
unique hiding methods, which are becoming increasingly common with the
application of deep learning models. To mitigate the shortcomings of
steganalysis, this work focuses on a deep learning sanitization technique
called SUDS that is not reliant upon knowledge of steganographic hiding
techniques and is able to sanitize universal and dependent steganography. SUDS
is tested using least significant bit method (LSB), dependent deep hiding
(DDH), and universal deep hiding (UDH). We demonstrate the capabilities and
limitations of SUDS by answering five research questions, including baseline
comparisons and an ablation study. Additionally, we apply SUDS to a real-world
scenario, where it is able to increase the resistance of a poisoned classifier
against attacks by 1375%. | [
"Preston K. Robinette",
"Hanchen D. Wang",
"Nishan Shehadeh",
"Daniel Moyer",
"Taylor T. Johnson"
] | 2023-09-23 19:39:44 | http://arxiv.org/abs/2309.13467v1 | http://arxiv.org/pdf/2309.13467v1 | 2309.13467v1 |
Personalised and Adjustable Interval Type-2 Fuzzy-Based PPG Quality Assessment for the Edge | Most of today's wearable technology provides seamless cardiac activity
monitoring. Specifically, the vast majority employ Photoplethysmography (PPG)
sensors to acquire blood volume pulse information, which is further analysed to
extract useful and physiologically related features. Nevertheless, PPG-based
signal reliability presents different challenges that strongly affect such data
processing. This is mainly related to the fact of PPG morphological wave
distortion due to motion artefacts, which can lead to erroneous interpretation
of the extracted cardiac-related features. On this basis, in this paper, we
propose a novel personalised and adjustable Interval Type-2 Fuzzy Logic System
(IT2FLS) for assessing the quality of PPG signals. The proposed system employs
a personalised approach to adapt the IT2FLS parameters to the unique
characteristics of each individual's PPG signals.Additionally, the system
provides adjustable levels of personalisation, allowing healthcare providers to
adjust the system to meet specific requirements for different applications. The
proposed system obtained up to 93.72\% for average accuracy during validation.
The presented system has the potential to enable ultra-low complexity and
real-time PPG quality assessment, improving the accuracy and reliability of
PPG-based health monitoring systems at the edge. | [
"Jose A. Miranda",
"Celia López-Ongil",
"Javier Andreu-Perez"
] | 2023-09-23 19:35:00 | http://arxiv.org/abs/2309.13464v1 | http://arxiv.org/pdf/2309.13464v1 | 2309.13464v1 |
Tight bounds on Pauli channel learning without entanglement | Entanglement is a useful resource for learning, but a precise
characterization of its advantage can be challenging. In this work, we consider
learning algorithms without entanglement to be those that only utilize
separable states, measurements, and operations between the main system of
interest and an ancillary system. These algorithms are equivalent to those that
apply quantum circuits on the main system interleaved with mid-circuit
measurements and classical feedforward. We prove a tight lower bound for
learning Pauli channels without entanglement that closes a cubic gap between
the best-known upper and lower bound. In particular, we show that
$\Theta(2^n\varepsilon^{-2})$ rounds of measurements are required to estimate
each eigenvalue of an $n$-qubit Pauli channel to $\varepsilon$ error with high
probability when learning without entanglement. In contrast, a learning
algorithm with entanglement only needs $\Theta(\varepsilon^{-2})$ rounds of
measurements. The tight lower bound strengthens the foundation for an
experimental demonstration of entanglement-enhanced advantages for
characterizing Pauli noise. | [
"Senrui Chen",
"Changhun Oh",
"Sisi Zhou",
"Hsin-Yuan Huang",
"Liang Jiang"
] | 2023-09-23 19:12:29 | http://arxiv.org/abs/2309.13461v1 | http://arxiv.org/pdf/2309.13461v1 | 2309.13461v1 |
A Model-Agnostic Graph Neural Network for Integrating Local and Global Information | Graph Neural Networks (GNNs) have achieved promising performance in a variety
of graph-focused tasks. Despite their success, existing GNNs suffer from two
significant limitations: a lack of interpretability in results due to their
black-box nature, and an inability to learn representations of varying orders.
To tackle these issues, we propose a novel Model-agnostic Graph Neural Network
(MaGNet) framework, which is able to sequentially integrate information of
various orders, extract knowledge from high-order neighbors, and provide
meaningful and interpretable results by identifying influential compact graph
structures. In particular, MaGNet consists of two components: an estimation
model for the latent representation of complex relationships under graph
topology, and an interpretation model that identifies influential nodes, edges,
and important node features. Theoretically, we establish the generalization
error bound for MaGNet via empirical Rademacher complexity, and showcase its
power to represent layer-wise neighborhood mixing. We conduct comprehensive
numerical studies using simulated data to demonstrate the superior performance
of MaGNet in comparison to several state-of-the-art alternatives. Furthermore,
we apply MaGNet to a real-world case study aimed at extracting task-critical
information from brain activity data, thereby highlighting its effectiveness in
advancing scientific research. | [
"Wenzhuo Zhou",
"Annie Qu",
"Keiland W. Cooper",
"Norbert Fortin",
"Babak Shahbaba"
] | 2023-09-23 19:07:03 | http://arxiv.org/abs/2309.13459v2 | http://arxiv.org/pdf/2309.13459v2 | 2309.13459v2 |
Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data | Analysis of compressible turbulent flows is essential for applications
related to propulsion, energy generation, and the environment. Here, we present
BLASTNet 2.0, a 2.2 TB network-of-datasets containing 744 full-domain samples
from 34 high-fidelity direct numerical simulations, which addresses the current
limited availability of 3D high-fidelity reacting and non-reacting compressible
turbulent flow simulation data. With this data, we benchmark a total of 49
variations of five deep learning approaches for 3D super-resolution - which can
be applied for improving scientific imaging, simulations, turbulence models, as
well as in computer vision applications. We perform neural scaling analysis on
these models to examine the performance of different machine learning (ML)
approaches, including two scientific ML techniques. We demonstrate that (i)
predictive performance can scale with model size and cost, (ii) architecture
matters significantly, especially for smaller models, and (iii) the benefits of
physics-based losses can persist with increasing model size. The outcomes of
this benchmark study are anticipated to offer insights that can aid the design
of 3D super-resolution models, especially for turbulence models, while this
data is expected to foster ML methods for a broad range of flow physics
applications. This data is publicly available with download links and browsing
tools consolidated at https://blastnet.github.io. | [
"Wai Tong Chung",
"Bassem Akoush",
"Pushan Sharma",
"Alex Tamkin",
"Ki Sung Jung",
"Jacqueline H. Chen",
"Jack Guo",
"Davy Brouzet",
"Mohsen Talei",
"Bruno Savard",
"Alexei Y. Poludnenko",
"Matthias Ihme"
] | 2023-09-23 18:57:02 | http://arxiv.org/abs/2309.13457v2 | http://arxiv.org/pdf/2309.13457v2 | 2309.13457v2 |
EMGTFNet: Fuzzy Vision Transformer to decode Upperlimb sEMG signals for Hand Gestures Recognition | Myoelectric control is an area of electromyography of increasing interest
nowadays, particularly in applications such as Hand Gesture Recognition (HGR)
for bionic prostheses. Today's focus is on pattern recognition using Machine
Learning and, more recently, Deep Learning methods. Despite achieving good
results on sparse sEMG signals, the latter models typically require large
datasets and training times. Furthermore, due to the nature of stochastic sEMG
signals, traditional models fail to generalize samples for atypical or noisy
values. In this paper, we propose the design of a Vision Transformer (ViT)
based architecture with a Fuzzy Neural Block (FNB) called EMGTFNet to perform
Hand Gesture Recognition from surface electromyography (sEMG) signals. The
proposed EMGTFNet architecture can accurately classify a variety of hand
gestures without any need for data augmentation techniques, transfer learning
or a significant increase in the number of parameters in the network. The
accuracy of the proposed model is tested using the publicly available NinaPro
database consisting of 49 different hand gestures. Experiments yield an average
test accuracy of 83.57\% \& 3.5\% using a 200 ms window size and only 56,793
trainable parameters. Our results outperform the ViT without FNB, thus
demonstrating that including FNB improves its performance. Our proposal
framework EMGTFNet reported the significant potential for its practical
application for prosthetic control. | [
"Joseph Cherre Córdova",
"Christian Flores",
"Javier Andreu-Perez"
] | 2023-09-23 18:55:26 | http://arxiv.org/abs/2310.03754v1 | http://arxiv.org/pdf/2310.03754v1 | 2310.03754v1 |
Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data | Time-Series Forecasting based on Cumulative Data (TSFCD) is a crucial problem
in decision-making across various industrial scenarios. However, existing
time-series forecasting methods often overlook two important characteristics of
cumulative data, namely monotonicity and irregularity, which limit their
practical applicability. To address this limitation, we propose a principled
approach called Monotonic neural Ordinary Differential Equation (MODE) within
the framework of neural ordinary differential equations. By leveraging MODE, we
are able to effectively capture and represent the monotonicity and irregularity
in practical cumulative data. Through extensive experiments conducted in a
bonus allocation scenario, we demonstrate that MODE outperforms
state-of-the-art methods, showcasing its ability to handle both monotonicity
and irregularity in cumulative data and delivering superior forecasting
performance. | [
"Zhichao Chen",
"Leilei Ding",
"Zhixuan Chu",
"Yucheng Qi",
"Jianmin Huang",
"Hao Wang"
] | 2023-09-23 18:40:10 | http://arxiv.org/abs/2309.13452v1 | http://arxiv.org/pdf/2309.13452v1 | 2309.13452v1 |
NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series Imaging | Network data analytics are now at the core of almost every networking
solution. Nonetheless, limited access to networking data has been an enduring
challenge due to many reasons including complexity of modern networks,
commercial sensitivity, privacy and regulatory constraints. In this work, we
explore how to leverage recent advancements in Diffusion Models (DM) to
generate synthetic network traffic data. We develop an end-to-end framework -
NetDiffus that first converts one-dimensional time-series network traffic into
two-dimensional images, and then synthesizes representative images for the
original data. We demonstrate that NetDiffus outperforms the state-of-the-art
traffic generation methods based on Generative Adversarial Networks (GANs) by
providing 66.4% increase in fidelity of the generated data and 18.1% increase
in downstream machine learning tasks. We evaluate NetDiffus on seven diverse
traffic traces and show that utilizing synthetic data significantly improves
traffic fingerprinting, anomaly detection and traffic classification. | [
"Nirhoshan Sivaroopan",
"Dumindu Bandara",
"Chamara Madarasingha",
"Guilluame Jourjon",
"Anura Jayasumana",
"Kanchana Thilakarathna"
] | 2023-09-23 18:13:12 | http://arxiv.org/abs/2310.04429v1 | http://arxiv.org/pdf/2310.04429v1 | 2310.04429v1 |
Early Classification for Dynamic Inference of Neural Networks | Deep neural networks (DNNs) have been successfully applied in various fields.
In DNNs, a large number of multiply-accumulate (MAC) operations is required to
be performed, posing critical challenges in applying them in
resource-constrained platforms, e.g., edge devices. Dynamic neural networks
have been introduced to allow a structural adaption, e.g., early-exit,
according to different inputs to reduce the computational cost of DNNs.
Existing early-exit techniques deploy classifiers at intermediate layers of
DNNs to push them to make a classification decision as early as possible.
However, the learned features at early layers might not be sufficient to
exclude all the irrelevant classes and decide the correct class, leading to
suboptimal results. To address this challenge, in this paper, we propose a
class-based early-exit for dynamic inference. Instead of pushing DNNs to make a
dynamic decision at intermediate layers, we take advantages of the learned
features in these layers to exclude as many irrelevant classes as possible, so
that later layers only have to determine the target class among the remaining
classes. Until at a layer only one class remains, this class is the
corresponding classification result. To realize this class-based exclusion, we
assign each class with a classifier at intermediate layers and train the
networks together with these classifiers. Afterwards, an exclusion strategy is
developed to exclude irrelevant classes at early layers. Experimental results
demonstrate the computational cost of DNNs in inference can be reduced
significantly. | [
"Jingcun Wang",
"Bing Li",
"Grace Li Zhang"
] | 2023-09-23 18:12:27 | http://arxiv.org/abs/2309.13443v1 | http://arxiv.org/pdf/2309.13443v1 | 2309.13443v1 |
How Do Drivers Behave at Roundabouts in a Mixed Traffic? A Case Study Using Machine Learning | Driving behavior is considered a unique driving habit of each driver and has
a significant impact on road safety. Classifying driving behavior and
introducing policies based on the results can reduce the severity of crashes on
the road. Roundabouts are particularly interesting because of the
interconnected interaction between different road users at the area of
roundabouts, which different driving behavior is hypothesized. This study
investigates driving behavior at roundabouts in a mixed traffic environment
using a data-driven unsupervised machine learning to classify driving behavior
at three roundabouts in Germany. We used a dataset of vehicle kinematics to a
group of different vehicles and vulnerable road users (VRUs) at roundabouts and
classified them into three categories (i.e., conservative, normal, and
aggressive). Results showed that most of the drivers proceeding through a
roundabout can be mostly classified into two driving styles: conservative and
normal because traffic speeds in roundabouts are relatively lower than in other
signalized and unsignalized intersections. Results also showed that about 77%
of drivers who interacted with pedestrians or cyclists were classified as
conservative drivers compared to about 42% of conservative drivers that did not
interact or about 51% from all drivers. It seems that drivers tend to behave
abnormally as they interact with VRUs at roundabouts, which increases the risk
of crashes when an intersection is multimodal. Results of this study could be
helpful in improving the safety of roads by allowing policymakers to determine
the effective and suitable safety countermeasures. Results will also be
beneficial for the Advanced Driver Assistance System (ADAS) as the technology
is being deployed in a mixed traffic environment. | [
"Farah Abu Hamad",
"Rama Hasiba",
"Deema Shahwan",
"Huthaifa I. Ashqar"
] | 2023-09-23 18:02:57 | http://arxiv.org/abs/2309.13442v1 | http://arxiv.org/pdf/2309.13442v1 | 2309.13442v1 |
Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning | The success of contrastive learning is well known to be dependent on data
augmentation. Although the degree of data augmentations has been well
controlled by utilizing pre-defined techniques in some domains like vision,
time-series data augmentation is less explored and remains a challenging
problem due to the complexity of the data generation mechanism, such as the
intricate mechanism involved in the cardiovascular system. Moreover, there is
no widely recognized and general time-series augmentation method that can be
applied across different tasks. In this paper, we propose a novel data
augmentation method for quasi-periodic time-series tasks that aims to connect
intra-class samples together, and thereby find order in the latent space. Our
method builds upon the well-known mixup technique by incorporating a novel
approach that accounts for the periodic nature of non-stationary time-series.
Also, by controlling the degree of chaos created by data augmentation, our
method leads to improved feature representations and performance on downstream
tasks. We evaluate our proposed method on three time-series tasks, including
heart rate estimation, human activity recognition, and cardiovascular disease
detection. Extensive experiments against state-of-the-art methods show that the
proposed approach outperforms prior works on optimal data generation and known
data augmentation techniques in the three tasks, reflecting the effectiveness
of the presented method. Source code:
https://github.com/eth-siplab/Finding_Order_in_Chaos | [
"Berken Utku Demirel",
"Christian Holz"
] | 2023-09-23 17:42:13 | http://arxiv.org/abs/2309.13439v1 | http://arxiv.org/pdf/2309.13439v1 | 2309.13439v1 |
Modeling Student Performance in Game-Based Learning Environments | This study investigates game-based learning in the context of the educational
game "Jo Wilder and the Capitol Case," focusing on predicting student
performance using various machine learning models, including K-Nearest
Neighbors (KNN), Multi-Layer Perceptron (MLP), and Random Forest. The research
aims to identify the features most predictive of student performance and
correct question answering. By leveraging gameplay data, we establish complete
benchmarks for these models and explore the importance of applying proper data
aggregation methods. By compressing all numeric data to min/max/mean/sum and
categorical data to first, last, count, and nunique, we reduced the size of the
original training data from 4.6 GB to 48 MB of preprocessed training data,
maintaining high F1 scores and accuracy.
Our findings suggest that proper preprocessing techniques can be vital in
enhancing the performance of non-deep-learning-based models. The MLP model
outperformed the current state-of-the-art French Touch model, achieving an F-1
score of 0.83 and an accuracy of 0.74, suggesting its suitability for this
dataset. Future research should explore using larger datasets, other
preprocessing techniques, more advanced deep learning techniques, and
real-world applications to provide personalized learning recommendations to
students based on their predicted performance. This paper contributes to the
understanding of game-based learning and provides insights into optimizing
educational game experiences for improved student outcomes and skill
development. | [
"Hyunbae Jeon",
"Harry He",
"Anthony Wang",
"Susanna Spooner"
] | 2023-09-23 16:53:07 | http://arxiv.org/abs/2309.13429v1 | http://arxiv.org/pdf/2309.13429v1 | 2309.13429v1 |
ECGNet: A generative adversarial network (GAN) approach to the synthesis of 12-lead ECG signals from single lead inputs | Electrocardiography (ECG) signal generation has been heavily explored using
generative adversarial networks (GAN) because the implementation of 12-lead
ECGs is not always feasible. The GAN models have achieved remarkable results in
reproducing ECG signals but are only designed for multiple lead inputs and the
features the GAN model preserves have not been identified-limiting the
generated signals use in cardiovascular disease (CVD)-predictive models. This
paper presents ECGNet which is a procedure that generates a complete set of
12-lead ECG signals from any single lead input using a GAN framework with a
bidirectional long short-term memory (LSTM) generator and a convolutional
neural network (CNN) discriminator. Cross and auto-correlation analysis
performed on the generated signals identifies features conserved during the
signal generation-i.e., features that can characterize the unique-nature of
each signal and thus likely indicators of CVD. Finally, by using ECG signals
annotated with the CVD-indicative features detailed by the correlation analysis
as inputs for a CVD-onset-predictive CNN model, we overcome challenges
preventing the prediction of multiple-CVD targets. Our models are experimented
on 15s 12-lead ECG dataset recorded using MyoVista's wavECG. Functional outcome
data for each patient is recorded and used in the CVD-predictive model. Our
best GAN model achieves state-of-the-art accuracy with Frechet Distance (FD)
scores of 4.73, 4.89, 5.18, 4.77, 4.71, and 5.55 on the V1-V6 pre-cordial leads
respectively and shows strength in preserving the P-Q segments and R-peaks in
the generated signals. To the best of our knowledge, ECGNet is the first to
predict all of the remaining eleven leads from the input of any single lead. | [
"Max Bagga",
"Hyunbae Jeon",
"Alex Issokson"
] | 2023-09-23 16:43:31 | http://arxiv.org/abs/2310.03753v1 | http://arxiv.org/pdf/2310.03753v1 | 2310.03753v1 |
MiliPoint: A Point Cloud Dataset for mmWave Radar | Millimetre-wave (mmWave) radar has emerged as an attractive and
cost-effective alternative for human activity sensing compared to traditional
camera-based systems. mmWave radars are also non-intrusive, providing better
protection for user privacy. However, as a Radio Frequency (RF) based
technology, mmWave radars rely on capturing reflected signals from objects,
making them more prone to noise compared to cameras. This raises an intriguing
question for the deep learning community: Can we develop more effective point
set-based deep learning methods for such attractive sensors?
To answer this question, our work, termed MiliPoint, delves into this idea by
providing a large-scale, open dataset for the community to explore how mmWave
radars can be utilised for human activity recognition. Moreover, MiliPoint
stands out as it is larger in size than existing datasets, has more diverse
human actions represented, and encompasses all three key tasks in human
activity recognition. We have also established a range of point-based deep
neural networks such as DGCNN, PointNet++ and PointTransformer, on MiliPoint,
which can serve to set the ground baseline for further development. | [
"Han Cui",
"Shu Zhong",
"Jiacheng Wu",
"Zichao Shen",
"Naim Dahnoun",
"Yiren Zhao"
] | 2023-09-23 16:32:36 | http://arxiv.org/abs/2309.13425v1 | http://arxiv.org/pdf/2309.13425v1 | 2309.13425v1 |
Penalties and Rewards for Fair Learning in Paired Kidney Exchange Programs | A kidney exchange program, also called a kidney paired donation program, can
be viewed as a repeated, dynamic trading and allocation mechanism. This
suggests that a dynamic algorithm for transplant exchange selection may have
superior performance in comparison to the repeated use of a static algorithm.
We confirm this hypothesis using a full scale simulation of the Canadian Kidney
Paired Donation Program: learning algorithms, that attempt to learn optimal
patient-donor weights in advance via dynamic simulations, do lead to improved
outcomes. Specifically, our learning algorithms, designed with the objective of
fairness (that is, equity in terms of transplant accessibility across cPRA
groups), also lead to an increased number of transplants and shorter average
waiting times. Indeed, our highest performing learning algorithm improves
egalitarian fairness by 10% whilst also increasing the number of transplants by
6% and decreasing waiting times by 24%. However, our main result is much more
surprising. We find that the most critical factor in determining the
performance of a kidney exchange program is not the judicious assignment of
positive weights (rewards) to patient-donor pairs. Rather, the key factor in
increasing the number of transplants, decreasing waiting times and improving
group fairness is the judicious assignment of a negative weight (penalty) to
the small number of non-directed donors in the kidney exchange program. | [
"Margarida Carvalho",
"Alison Caulfield",
"Yi Lin",
"Adrian Vetta"
] | 2023-09-23 16:25:49 | http://arxiv.org/abs/2309.13421v1 | http://arxiv.org/pdf/2309.13421v1 | 2309.13421v1 |
DenMune: Density peak based clustering using mutual nearest neighbors | Many clustering algorithms fail when clusters are of arbitrary shapes, of
varying densities, or the data classes are unbalanced and close to each other,
even in two dimensions. A novel clustering algorithm, DenMune is presented to
meet this challenge. It is based on identifying dense regions using mutual
nearest neighborhoods of size K, where K is the only parameter required from
the user, besides obeying the mutual nearest neighbor consistency principle.
The algorithm is stable for a wide range of values of K. Moreover, it is able
to automatically detect and remove noise from the clustering process as well as
detecting the target clusters. It produces robust results on various low and
high-dimensional datasets relative to several known state-of-the-art clustering
algorithms. | [
"Mohamed Abbas",
"Adel El-Zoghobi",
"Amin Shoukry"
] | 2023-09-23 16:18:00 | http://arxiv.org/abs/2309.13420v1 | http://arxiv.org/pdf/2309.13420v1 | 2309.13420v1 |
Dream the Impossible: Outlier Imagination with Diffusion Models | Utilizing auxiliary outlier datasets to regularize the machine learning model
has demonstrated promise for out-of-distribution (OOD) detection and safe
prediction. Due to the labor intensity in data collection and cleaning,
automating outlier data generation has been a long-desired alternative. Despite
the appeal, generating photo-realistic outliers in the high dimensional pixel
space has been an open challenge for the field. To tackle the problem, this
paper proposes a new framework DREAM-OOD, which enables imagining
photo-realistic outliers by way of diffusion models, provided with only the
in-distribution (ID) data and classes. Specifically, DREAM-OOD learns a
text-conditioned latent space based on ID data, and then samples outliers in
the low-likelihood region via the latent, which can be decoded into images by
the diffusion model. Different from prior works, DREAM-OOD enables visualizing
and understanding the imagined outliers, directly in the pixel space. We
conduct comprehensive quantitative and qualitative studies to understand the
efficacy of DREAM-OOD, and show that training with the samples generated by
DREAM-OOD can benefit OOD detection performance. Code is publicly available at
https://github.com/deeplearning-wisc/dream-ood. | [
"Xuefeng Du",
"Yiyou Sun",
"Xiaojin Zhu",
"Yixuan Li"
] | 2023-09-23 15:58:27 | http://arxiv.org/abs/2309.13415v1 | http://arxiv.org/pdf/2309.13415v1 | 2309.13415v1 |
State-space Models with Layer-wise Nonlinearity are Universal Approximators with Exponential Decaying Memory | State-space models have gained popularity in sequence modelling due to their
simple and efficient network structures. However, the absence of nonlinear
activation along the temporal direction limits the model's capacity. In this
paper, we prove that stacking state-space models with layer-wise nonlinear
activation is sufficient to approximate any continuous sequence-to-sequence
relationship. Our findings demonstrate that the addition of layer-wise
nonlinear activation enhances the model's capacity to learn complex sequence
patterns. Meanwhile, it can be seen both theoretically and empirically that the
state-space models do not fundamentally resolve the exponential decaying memory
issue. Theoretical results are justified by numerical verifications. | [
"Shida Wang",
"Beichen Xue"
] | 2023-09-23 15:55:12 | http://arxiv.org/abs/2309.13414v2 | http://arxiv.org/pdf/2309.13414v2 | 2309.13414v2 |
Towards Attributions of Input Variables in a Coalition | This paper aims to develop a new attribution method to explain the conflict
between individual variables' attributions and their coalition's attribution
from a fully new perspective. First, we find that the Shapley value can be
reformulated as the allocation of Harsanyi interactions encoded by the AI
model. Second, based the re-alloction of interactions, we extend the Shapley
value to the attribution of coalitions. Third we ective. We derive the
fundamental mechanism behind the conflict. This conflict come from the
interaction containing partial variables in their coalition. | [
"Xinhao Zheng",
"Huiqi Deng",
"Quanshi Zhang"
] | 2023-09-23 15:48:35 | http://arxiv.org/abs/2309.13411v1 | http://arxiv.org/pdf/2309.13411v1 | 2309.13411v1 |
Time-Series Forecasting: Unleashing Long-Term Dependencies with Fractionally Differenced Data | This study introduces a novel forecasting strategy that leverages the power
of fractional differencing (FD) to capture both short- and long-term
dependencies in time series data. Unlike traditional integer differencing
methods, FD preserves memory in series while stabilizing it for modeling
purposes. By applying FD to financial data from the SPY index and incorporating
sentiment analysis from news reports, this empirical analysis explores the
effectiveness of FD in conjunction with binary classification of target
variables. Supervised classification algorithms were employed to validate the
performance of FD series. The results demonstrate the superiority of FD over
integer differencing, as confirmed by Receiver Operating Characteristic/Area
Under the Curve (ROCAUC) and Mathews Correlation Coefficient (MCC) evaluations. | [
"Sarit Maitra",
"Vivek Mishra",
"Srashti Dwivedi",
"Sukanya Kundu",
"Goutam Kumar Kundu"
] | 2023-09-23 15:42:54 | http://arxiv.org/abs/2309.13409v2 | http://arxiv.org/pdf/2309.13409v2 | 2309.13409v2 |
Learning Large-Scale MTP$_2$ Gaussian Graphical Models via Bridge-Block Decomposition | This paper studies the problem of learning the large-scale Gaussian graphical
models that are multivariate totally positive of order two ($\text{MTP}_2$). By
introducing the concept of bridge, which commonly exists in large-scale sparse
graphs, we show that the entire problem can be equivalently optimized through
(1) several smaller-scaled sub-problems induced by a \emph{bridge-block
decomposition} on the thresholded sample covariance graph and (2) a set of
explicit solutions on entries corresponding to bridges. From practical aspect,
this simple and provable discipline can be applied to break down a large
problem into small tractable ones, leading to enormous reduction on the
computational complexity and substantial improvements for all existing
algorithms. The synthetic and real-world experiments demonstrate that our
proposed method presents a significant speed-up compared to the
state-of-the-art benchmarks. | [
"Xiwen Wang",
"Jiaxi Ying",
"Daniel P. Palomar"
] | 2023-09-23 15:30:34 | http://arxiv.org/abs/2309.13405v3 | http://arxiv.org/pdf/2309.13405v3 | 2309.13405v3 |
ML Algorithm Synthesizing Domain Knowledge for Fungal Spores Concentration Prediction | The pulp and paper manufacturing industry requires precise quality control to
ensure pure, contaminant-free end products suitable for various applications.
Fungal spore concentration is a crucial metric that affects paper usability,
and current testing methods are labor-intensive with delayed results, hindering
real-time control strategies. To address this, a machine learning algorithm
utilizing time-series data and domain knowledge was proposed. The optimal model
employed Ridge Regression achieving an MSE of 2.90 on training and validation
data. This approach could lead to significant improvements in efficiency and
sustainability by providing real-time predictions for fungal spore
concentrations. This paper showcases a promising method for real-time fungal
spore concentration prediction, enabling stringent quality control measures in
the pulp-and-paper industry. | [
"Md Asif Bin Syed",
"Azmine Toushik Wasi",
"Imtiaz Ahmed"
] | 2023-09-23 15:27:14 | http://arxiv.org/abs/2309.13402v1 | http://arxiv.org/pdf/2309.13402v1 | 2309.13402v1 |
A Unitary Weights Based One-Iteration Quantum Perceptron Algorithm for Non-Ideal Training Sets | In order to solve the problem of non-ideal training sets (i.e., the
less-complete or over-complete sets) and implement one-iteration learning, a
novel efficient quantum perceptron algorithm based on unitary weights is
proposed, where the singular value decomposition of the total weight matrix
from the training set is calculated to make the weight matrix to be unitary.
The example validation of quantum gates {H, S, T, CNOT, Toffoli, Fredkin} shows
that our algorithm can accurately implement arbitrary quantum gates within one
iteration. The performance comparison between our algorithm and other quantum
perceptron algorithms demonstrates the advantages of our algorithm in terms of
applicability, accuracy, and availability. For further validating the
applicability of our algorithm, a quantum composite gate which consists of
several basic quantum gates is also illustrated. | [
"Wenjie Liu",
"Peipei Gao",
"Yuxiang Wang",
"Wenbin Yu",
"Maojun Zhang"
] | 2023-09-23 15:24:41 | http://arxiv.org/abs/2309.14366v1 | http://arxiv.org/pdf/2309.14366v1 | 2309.14366v1 |
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinement | Cine Magnetic Resonance Imaging (MRI) allows for understanding of the heart's
function and condition in a non-invasive manner. Undersampling of the $k$-space
is employed to reduce the scan duration, thus increasing patient comfort and
reducing the risk of motion artefacts, at the cost of reduced image quality. In
this challenge paper, we investigate the use of a convolutional recurrent
neural network (CRNN) architecture to exploit temporal correlations in
supervised cine cardiac MRI reconstruction. This is combined with a
single-image super-resolution refinement module to improve single coil
reconstruction by 4.4\% in structural similarity and 3.9\% in normalised mean
square error compared to a plain CRNN implementation. We deploy a high-pass
filter to our $\ell_1$ loss to allow greater emphasis on high-frequency details
which are missing in the original data. The proposed model demonstrates
considerable enhancements compared to the baseline case and holds promising
potential for further improving cardiac MRI reconstruction. | [
"Yuyang Xue",
"Yuning Du",
"Gianluca Carloni",
"Eva Pachetti",
"Connor Jordan",
"Sotirios A. Tsaftaris"
] | 2023-09-23 14:07:04 | http://arxiv.org/abs/2309.13385v1 | http://arxiv.org/pdf/2309.13385v1 | 2309.13385v1 |
On the Sweet Spot of Contrastive Views for Knowledge-enhanced Recommendation | In recommender systems, knowledge graph (KG) can offer critical information
that is lacking in the original user-item interaction graph (IG). Recent
process has explored this direction and shows that contrastive learning is a
promising way to integrate both. However, we observe that existing KG-enhanced
recommenders struggle in balancing between the two contrastive views of IG and
KG, making them sometimes even less effective than simply applying contrastive
learning on IG without using KG. In this paper, we propose a new contrastive
learning framework for KG-enhanced recommendation. Specifically, to make full
use of the knowledge, we construct two separate contrastive views for KG and
IG, and maximize their mutual information; to ease the contrastive learning on
the two views, we further fuse KG information into IG in a one-direction
manner.Extensive experimental results on three real-world datasets demonstrate
the effectiveness and efficiency of our method, compared to the
state-of-the-art. Our code is available through the anonymous
link:https://figshare.com/articles/conference_contribution/SimKGCL/22783382 | [
"Haibo Ye",
"Xinjie Li",
"Yuan Yao",
"Hanghang Tong"
] | 2023-09-23 14:05:55 | http://arxiv.org/abs/2309.13384v1 | http://arxiv.org/pdf/2309.13384v1 | 2309.13384v1 |
Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment | Spatio-Temporal Graph (STG) forecasting is a fundamental task in many
real-world applications. Spatio-Temporal Graph Neural Networks have emerged as
the most popular method for STG forecasting, but they often struggle with
temporal out-of-distribution (OoD) issues and dynamic spatial causation. In
this paper, we propose a novel framework called CaST to tackle these two
challenges via causal treatments. Concretely, leveraging a causal lens, we
first build a structural causal model to decipher the data generation process
of STGs. To handle the temporal OoD issue, we employ the back-door adjustment
by a novel disentanglement block to separate invariant parts and temporal
environments from input data. Moreover, we utilize the front-door adjustment
and adopt the Hodge-Laplacian operator for edge-level convolution to model the
ripple effect of causation. Experiments results on three real-world datasets
demonstrate the effectiveness and practicality of CaST, which consistently
outperforms existing methods with good interpretability. | [
"Yutong Xia",
"Yuxuan Liang",
"Haomin Wen",
"Xu Liu",
"Kun Wang",
"Zhengyang Zhou",
"Roger Zimmermann"
] | 2023-09-23 13:51:09 | http://arxiv.org/abs/2309.13378v1 | http://arxiv.org/pdf/2309.13378v1 | 2309.13378v1 |
Learning Invariant Representations with a Nonparametric Nadaraya-Watson Head | Machine learning models will often fail when deployed in an environment with
a data distribution that is different than the training distribution. When
multiple environments are available during training, many methods exist that
learn representations which are invariant across the different distributions,
with the hope that these representations will be transportable to unseen
domains. In this work, we present a nonparametric strategy for learning
invariant representations based on the recently-proposed Nadaraya-Watson (NW)
head. The NW head makes a prediction by comparing the learned representations
of the query to the elements of a support set that consists of labeled data. We
demonstrate that by manipulating the support set, one can encode different
causal assumptions. In particular, restricting the support set to a single
environment encourages the model to learn invariant features that do not depend
on the environment. We present a causally-motivated setup for our modeling and
training strategy and validate on three challenging real-world domain
generalization tasks in computer vision. | [
"Alan Q. Wang",
"Minh Nguyen",
"Mert R. Sabuncu"
] | 2023-09-23 13:46:49 | http://arxiv.org/abs/2309.13377v1 | http://arxiv.org/pdf/2309.13377v1 | 2309.13377v1 |
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