pwc-archive/papers-with-abstracts huggingface dataset:
Cross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point Clouds
Authors: Mu Cai, Chenxu Luo, Yong Jae Lee, Xiaodong Yang
Date: 2024-09-10 00:00:00
Tasks: 3D Object Detection, 3D Semantic Segmentation, Autonomous Driving, Contrastive Learning, object-detection, Object Detection, Self-Supervised Learning, Semantic Segmentation

3D perception in LiDAR point clouds is crucial for a self-driving vehicle to properly act in 3D environment. However, manually labeling point clouds is hard and costly. There has been a growing interest in self-supervised pre-training of 3D perception models. Following the success of contrastive learning in images, current methods mostly conduct contrastive pre-training on point clouds only. Yet an autonomous driving vehicle is typically supplied with multiple sensors including cameras and LiDAR. In this context, we systematically study single modality, cross-modality, and multi-modality for contrastive learning of point clouds, and show that cross-modality wins over other alternatives. In addition, considering the huge difference between the training sources in 2D images and 3D point clouds, it remains unclear how to design more effective contrastive units for LiDAR. We therefore propose the instance-aware and similarity-balanced contrastive units that are tailored for self-driving point clouds. Extensive experiments reveal that our approach achieves remarkable performance gains over various point cloud models across the downstream perception tasks of LiDAR based 3D object detection and 3D semantic segmentation on the four popular benchmarks including Waymo Open Dataset, nuScenes, SemanticKITTI and ONCE.
Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts
Authors: Assefa Seyoum Wahd, Banafshe Felfeliyan, Yuyue Zhou, Shrimanti Ghosh, Adam McArthur, Jiechen Zhang, Jacob L. Jaremko, Abhilash Hareendranathan
Date: 2024-09-10 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, parameter-efficient fine-tuning, Prompt Learning, Robot Manipulation Generalization, Segmentation, Semantic Segmentation

Foundation models like the segment anything model require high-quality manual prompts for medical image segmentation, which is time-consuming and requires expertise. SAM and its variants often fail to segment structures in ultrasound (US) images due to domain shift. We propose Sam2Rad, a prompt learning approach to adapt SAM and its variants for US bone segmentation without human prompts. It introduces a prompt predictor network (PPN) with a cross-attention module to predict prompt embeddings from image encoder features. PPN outputs bounding box and mask prompts, and 256-dimensional embeddings for regions of interest. The framework allows optional manual prompting and can be trained end-to-end using parameter-efficient fine-tuning (PEFT). Sam2Rad was tested on 3 musculoskeletal US datasets: wrist (3822 images), rotator cuff (1605 images), and hip (4849 images). It improved performance across all datasets without manual prompts, increasing Dice scores by 2-7% for hip/wrist and up to 33% for shoulder data. Sam2Rad can be trained with as few as 10 labeled images and is compatible with any SAM architecture for automatic segmentation.
Object Modeling from Underwater Forward-Scan Sonar Imagery with Sea-Surface Multipath
Authors: YuHan Liu, Shahriar Negaharipour
Date: 2024-09-10 00:00:00
Tasks: Object

We propose an optimization technique for 3-D underwater object modeling from 2-D forward-scan sonar images at known poses. A key contribution, for objects imaged in the proximity of the sea surface, is to resolve the multipath artifacts due to the air-water interface. Here, the object image formed by the direct target backscatter is almost always corrupted by the ghost and sometimes by the mirror components (generated by the multipath propagation). Assuming a planar air-water interface, we model, localize, and discard the corrupted object region within each view, thus avoiding the distortion of recovered 3-D shape. Additionally, complementary visual cues from the boundary of the mirror component, distinct at suitable sonar poses, are employed to enhance the 3-D modeling accuracy. The optimization is implemented as iterative shape adjustment by displacing the vertices of triangular patches in the 3-D surface mesh model, in order to minimize the discrepancy between the data and synthesized views of the 3-D object model. To this end, we first determine 2-D motion fields that align the object regions in the data and synthesized views, then calculate the 3-D motion of triangular patch centers, and finally the model vertices. The 3-D model is initialized with the solution of an earlier space carving method applied to the same data. The same parameters are applied in various experiments with 2 real data sets, mixed real-synthetic data set, and computer-generated data guided by general findings from a real experiment, to explore the impact of non-flat air-water interface. The results confirm the generation of a refined 3-D model in about half-dozen iterations.
Human Motion Synthesis_ A Diffusion Approach for Motion Stitching and In-Betweening
Authors: Michael Adewole, Oluwaseyi Giwa, Favour Nerrise, Martins Osifeko, Ajibola Oyedeji
Date: 2024-09-10 00:00:00
Tasks: Diversity, Motion Generation

Human motion generation is an important area of research in many fields. In this work, we tackle the problem of motion stitching and in-betweening. Current methods either require manual efforts, or are incapable of handling longer sequences. To address these challenges, we propose a diffusion model with a transformer-based denoiser to generate realistic human motion. Our method demonstrated strong performance in generating in-betweening sequences, transforming a variable number of input poses into smooth and realistic motion sequences consisting of 75 frames at 15 fps, resulting in a total duration of 5 seconds. We present the performance evaluation of our method using quantitative metrics such as Frechet Inception Distance (FID), Diversity, and Multimodality, along with visual assessments of the generated outputs.
gsplat: An Open-Source Library for Gaussian Splatting
Authors: Vickie Ye, RuiLong Li, Justin Kerr, Matias Turkulainen, Brent Yi, Zhuoyang Pan, Otto Seiskari, Jianbo Ye, Jeffrey Hu, Matthew Tancik, Angjoo Kanazawa
Date: 2024-09-10 00:00:00

gsplat is an open-source library designed for training and developing Gaussian Splatting methods. It features a front-end with Python bindings compatible with the PyTorch library and a back-end with highly optimized CUDA kernels. gsplat offers numerous features that enhance the optimization of Gaussian Splatting models, which include optimization improvements for speed, memory, and convergence times. Experimental results demonstrate that gsplat achieves up to 10% less training time and 4x less memory than the original implementation. Utilized in several research projects, gsplat is actively maintained on GitHub. Source code is available at https://github.com/nerfstudio-project/gsplat under Apache License 2.0. We welcome contributions from the open-source community.
Asymptotics of Stochastic Gradient Descent with Dropout Regularization in Linear Models
Authors: Jiaqi Li, Johannes Schmidt-Hieber, Wei Biao Wu
Date: 2024-09-11 00:00:00

This paper proposes an asymptotic theory for online inference of the stochastic gradient descent (SGD) iterates with dropout regularization in linear regression. Specifically, we establish the geometric-moment contraction (GMC) for constant step-size SGD dropout iterates to show the existence of a unique stationary distribution of the dropout recursive function. By the GMC property, we provide quenched central limit theorems (CLT) for the difference between dropout and $\ell^2$-regularized iterates, regardless of initialization. The CLT for the difference between the Ruppert-Polyak averaged SGD (ASGD) with dropout and $\ell^2$-regularized iterates is also presented. Based on these asymptotic normality results, we further introduce an online estimator for the long-run covariance matrix of ASGD dropout to facilitate inference in a recursive manner with efficiency in computational time and memory. The numerical experiments demonstrate that for sufficiently large samples, the proposed confidence intervals for ASGD with dropout nearly achieve the nominal coverage probability.
Towards Fairer Health Recommendations: finding informative unbiased samples via Word Sense Disambiguation
Authors: Gavin Butts, Pegah Emdad, Jethro Lee, Shannon Song, Chiman Salavati, Willmar Sosa Diaz, Shiri Dori-Hacohen, Fabricio Murai
Date: 2024-09-11 00:00:00
Tasks: Bias Detection, Fairness, Recommendation Systems, Word Sense Disambiguation

There have been growing concerns around high-stake applications that rely on models trained with biased data, which consequently produce biased predictions, often harming the most vulnerable. In particular, biased medical data could cause health-related applications and recommender systems to create outputs that jeopardize patient care and widen disparities in health outcomes. A recent framework titled Fairness via AI posits that, instead of attempting to correct model biases, researchers must focus on their root causes by using AI to debias data. Inspired by this framework, we tackle bias detection in medical curricula using NLP models, including LLMs, and evaluate them on a gold standard dataset containing 4,105 excerpts annotated by medical experts for bias from a large corpus. We build on previous work by coauthors which augments the set of negative samples with non-annotated text containing social identifier terms. However, some of these terms, especially those related to race and ethnicity, can carry different meanings (e.g., "white matter of spinal cord"). To address this issue, we propose the use of Word Sense Disambiguation models to refine dataset quality by removing irrelevant sentences. We then evaluate fine-tuned variations of BERT models as well as GPT models with zero- and few-shot prompting. We found LLMs, considered SOTA on many NLP tasks, unsuitable for bias detection, while fine-tuned BERT models generally perform well across all evaluated metrics.
Manifold Learning via Foliations and Knowledge Transfer
Authors: E. Tron, E. Fioresi
Date: 2024-09-11 00:00:00
Tasks: Transfer Learning

Understanding how real data is distributed in high dimensional spaces is the key to many tasks in machine learning. We want to provide a natural geometric structure on the space of data employing a deep ReLU neural network trained as a classifier. Through the data information matrix (DIM), a variation of the Fisher information matrix, the model will discern a singular foliation structure on the space of data. We show that the singular points of such foliation are contained in a measure zero set, and that a local regular foliation exists almost everywhere. Experiments show that the data is correlated with leaves of such foliation. Moreover we show the potential of our approach for knowledge transfer by analyzing the spectrum of the DIM to measure distances between datasets.
Convergence of continuous-time stochastic gradient descent with applications to linear deep neural networks
Authors: Gabor Lugosi, Eulalia Nualart
Date: 2024-09-11 00:00:00

We study a continuous-time approximation of the stochastic gradient descent process for minimizing the expected loss in learning problems. The main results establish general sufficient conditions for the convergence, extending the results of Chatterjee (2022) established for (nonstochastic) gradient descent. We show how the main result can be applied to the case of overparametrized linear neural network training.
Revisiting Static Feature-Based Android Malware Detection
Authors: Md Tanvirul Alam, Dipkamal Bhusal, Nidhi Rastogi
Date: 2024-09-11 00:00:00
Tasks: Android Malware Detection, Computer Security, Continual Learning, Malware Analysis, Malware Classification, Malware Detection

The increasing reliance on machine learning (ML) in computer security, particularly for malware classification, has driven significant advancements. However, the replicability and reproducibility of these results are often overlooked, leading to challenges in verifying research findings. This paper highlights critical pitfalls that undermine the validity of ML research in Android malware detection, focusing on dataset and methodological issues. We comprehensively analyze Android malware detection using two datasets and assess offline and continual learning settings with six widely used ML models. Our study reveals that when properly tuned, simpler baseline methods can often outperform more complex models. To address reproducibility challenges, we propose solutions for improving datasets and methodological practices, enabling fairer model comparisons. Additionally, we open-source our code to facilitate malware analysis, making it extensible for new models and datasets. Our paper aims to support future research in Android malware detection and other security domains, enhancing the reliability and reproducibility of published results.
A Scalable Algorithm for Active Learning
Authors: Youguang Chen, Zheyu Wen, George Biros
Date: 2024-09-11 00:00:00
Tasks: Active Learning

FIRAL is a recently proposed deterministic active learning algorithm for multiclass classification using logistic regression. It was shown to outperform the state-of-the-art in terms of accuracy and robustness and comes with theoretical performance guarantees. However, its scalability suffers when dealing with datasets featuring a large number of points $n$, dimensions $d$, and classes $c$, due to its $\mathcal{O}(c^2d^2+nc^2d)$ storage and $\mathcal{O}(c^3(nd^2 + bd^3 + bn))$ computational complexity where $b$ is the number of points to select in active learning. To address these challenges, we propose an approximate algorithm with storage requirements reduced to $\mathcal{O}(n(d+c) + cd^2)$ and a computational complexity of $\mathcal{O}(bncd^2)$. Additionally, we present a parallel implementation on GPUs. We demonstrate the accuracy and scalability of our approach using MNIST, CIFAR-10, Caltech101, and ImageNet. The accuracy tests reveal no deterioration in accuracy compared to FIRAL. We report strong and weak scaling tests on up to 12 GPUs, for three million point synthetic dataset.
D-CAPTCHA++: A Study of Resilience of Deepfake CAPTCHA under Transferable Imperceptible Adversarial Attack
Authors: Hong-Hanh Nguyen-Le, Van-Tuan Tran, Dinh-Thuc Nguyen, Nhien-An Le-Khac
Date: 2024-09-11 00:00:00
Tasks: Adversarial Attack, Audio Synthesis, Face Swapping, text-to-speech, Text to Speech, Voice Conversion

The advancements in generative AI have enabled the improvement of audio synthesis models, including text-to-speech and voice conversion. This raises concerns about its potential misuse in social manipulation and political interference, as synthetic speech has become indistinguishable from natural human speech. Several speech-generation programs are utilized for malicious purposes, especially impersonating individuals through phone calls. Therefore, detecting fake audio is crucial to maintain social security and safeguard the integrity of information. Recent research has proposed a D-CAPTCHA system based on the challenge-response protocol to differentiate fake phone calls from real ones. In this work, we study the resilience of this system and introduce a more robust version, D-CAPTCHA++, to defend against fake calls. Specifically, we first expose the vulnerability of the D-CAPTCHA system under transferable imperceptible adversarial attack. Secondly, we mitigate such vulnerability by improving the robustness of the system by using adversarial training in D-CAPTCHA deepfake detectors and task classifiers.
A Contrastive Symmetric Forward-Forward Algorithm (SFFA) for Continual Learning Tasks
Authors: Erik B. Terres-Escudero, Javier Del Ser, Pablo Garcia Bringas
Date: 2024-09-11 00:00:00
Tasks: Continual Learning, image-classification, Image Classification

The so-called Forward-Forward Algorithm (FFA) has recently gained momentum as an alternative to the conventional back-propagation algorithm for neural network learning, yielding competitive performance across various modeling tasks. By replacing the backward pass of gradient back-propagation with two contrastive forward passes, the FFA avoids several shortcomings undergone by its predecessor (e.g., vanishing/exploding gradient) by enabling layer-wise training heuristics. In classification tasks, this contrastive method has been proven to effectively create a latent sparse representation of the input data, ultimately favoring discriminability. However, FFA exhibits an inherent asymmetric gradient behavior due to an imbalanced loss function between positive and negative data, adversely impacting on the model's generalization capabilities and leading to an accuracy degradation. To address this issue, this work proposes the Symmetric Forward-Forward Algorithm (SFFA), a novel modification of the original FFA which partitions each layer into positive and negative neurons. This allows the local fitness function to be defined as the ratio between the activation of positive neurons and the overall layer activity, resulting in a symmetric loss landscape during the training phase. To evaluate the enhanced convergence of our method, we conduct several experiments using multiple image classification benchmarks, comparing the accuracy of models trained with SFFA to those trained with its FFA counterpart. As a byproduct of this reformulation, we explore the advantages of using a layer-wise training algorithm for Continual Learning (CL) tasks. The specialization of neurons and the sparsity of their activations induced by layer-wise training algorithms enable efficient CL strategies that incorporate new knowledge (classes) into the neural network, while preventing catastrophic forgetting of previously...
Training-Free Guidance for Discrete Diffusion Models for Molecular Generation
Authors: Thomas J. Kerby, Kevin R. Moon
Date: 2024-09-11 00:00:00
Tasks: Graph Generation, Molecular Graph Generation

Training-free guidance methods for continuous data have seen an explosion of interest due to the fact that they enable foundation diffusion models to be paired with interchangable guidance models. Currently, equivalent guidance methods for discrete diffusion models are unknown. We present a framework for applying training-free guidance to discrete data and demonstrate its utility on molecular graph generation tasks using the discrete diffusion model architecture of DiGress. We pair this model with guidance functions that return the proportion of heavy atoms that are a specific atom type and the molecular weight of the heavy atoms and demonstrate our method's ability to guide the data generation.
The Role of Explainable AI in Revolutionizing Human Health Monitoring: A Review
Authors: Abdullah Alharthi, Ahmed Alqurashi, Turki Alharbi, Mohammed Alammar, Nasser Aldosari, Houssem Bouchekara, Yusuf Shaaban, Mohammad Shoaib Shahriar, Abdulrahman Al Ayidh
Date: 2024-09-11 00:00:00
Tasks: Decision Making, Diagnostic, Medical Diagnosis

The complex nature of disease mechanisms and the variability of patient symptoms pose significant challenges in developing effective diagnostic tools. Although machine learning (ML) has made substantial advances in medical diagnosis, the decision-making processes of these models often lack transparency, potentially jeopardizing patient outcomes. This review aims to highlight the role of Explainable AI (XAI) in addressing the interpretability issues of ML models in healthcare, with a focus on chronic conditions such as Parkinson's, stroke, depression, cancer, heart disease, and Alzheimer's disease. A comprehensive literature search was conducted across multiple databases to identify studies that applied XAI techniques in healthcare. The search focused on XAI algorithms used in diagnosing and monitoring chronic diseases. The review identified the application of nine trending XAI algorithms, each evaluated for their advantages and limitations in various healthcare contexts. The findings underscore the importance of transparency in ML models, which is crucial for improving trust and outcomes in clinical practice. While XAI provides significant potential to bridge the gap between complex ML models and clinical practice, challenges such as scalability, validation, and clinician acceptance remain. The review also highlights areas requiring further research, particularly in integrating XAI into healthcare systems. The study concludes that XAI methods offer a promising path forward for enhancing human health monitoring and patient care, though significant challenges must be addressed to fully realize their potential in clinical settings.
ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals
Authors: Chun-Hsiang Chuang, Kong-Yi Chang, Chih-Sheng Huang, Anne-Mei Bessas
Date: 2024-09-11 00:00:00
Tasks: Brain Computer Interface, Component Classification, Denoising, EEG, EEG Denoising

Artifact removal in electroencephalography (EEG) is a longstanding challenge that significantly impacts neuroscientific analysis and brain-computer interface (BCI) performance. Tackling this problem demands advanced algorithms, extensive noisy-clean training data, and thorough evaluation strategies. This study presents the Artifact Removal Transformer (ART), an innovative EEG denoising model employing transformer architecture to adeptly capture the transient millisecond-scale dynamics characteristic of EEG signals. Our approach offers a holistic, end-to-end denoising solution for diverse artifact types in multichannel EEG data. We enhanced the generation of noisy-clean EEG data pairs using an independent component analysis, thus fortifying the training scenarios critical for effective supervised learning. We performed comprehensive validations using a wide range of open datasets from various BCI applications, employing metrics like mean squared error and signal-to-noise ratio, as well as sophisticated techniques such as source localization and EEG component classification. Our evaluations confirm that ART surpasses other deep-learning-based artifact removal methods, setting a new benchmark in EEG signal processing. This advancement not only boosts the accuracy and reliability of artifact removal but also promises to catalyze further innovations in the field, facilitating the study of brain dynamics in naturalistic environments.
Statistically Valid Information Bottleneck via Multiple Hypothesis Testing
Authors: Amirmohammad Farzaneh, Osvaldo Simeone
Date: 2024-09-11 00:00:00
Tasks: valid

The information bottleneck (IB) problem is a widely studied framework in machine learning for extracting compressed features that are informative for downstream tasks. However, current approaches to solving the IB problem rely on a heuristic tuning of hyperparameters, offering no guarantees that the learned features satisfy information-theoretic constraints. In this work, we introduce a statistically valid solution to this problem, referred to as IB via multiple hypothesis testing (IB-MHT), which ensures that the learned features meet the IB constraints with high probability, regardless of the size of the available dataset. The proposed methodology builds on Pareto testing and learn-then-test (LTT), and it wraps around existing IB solvers to provide statistical guarantees on the IB constraints. We demonstrate the performance of IB-MHT on classical and deterministic IB formulations, including experiments on distillation of language models. The results validate the effectiveness of IB-MHT in outperforming conventional methods in terms of statistical robustness and reliability.