pwc-archive/papers-with-abstracts huggingface dataset:
Graph Defense Diffusion Model
Authors: Xin He, Wenqi Fan, Yili Wang, Chengyi Liu, Rui Miao, Xin Juan, Xin Wang
Date: 2025-01-20 00:00:00
Tasks: Denoising, model

Graph Neural Networks (GNNs) demonstrate significant potential in various applications but remain highly vulnerable to adversarial attacks, which can greatly degrade their performance. Existing graph purification methods attempt to address this issue by filtering attacked graphs; however, they struggle to effectively defend against multiple types of adversarial attacks simultaneously due to their limited flexibility, and they lack comprehensive modeling of graph data due to their heavy reliance on heuristic prior knowledge. To overcome these challenges, we propose a more versatile approach for defending against adversarial attacks on graphs. In this work, we introduce the Graph Defense Diffusion Model (GDDM), a flexible purification method that leverages the denoising and modeling capabilities of diffusion models. The iterative nature of diffusion models aligns well with the stepwise process of adversarial attacks, making them particularly suitable for defense. By iteratively adding and removing noise, GDDM effectively purifies attacked graphs, restoring their original structure and features. Our GDDM consists of two key components: (1) Graph Structure-Driven Refiner, which preserves the basic fidelity of the graph during the denoising process, and ensures that the generated graph remains consistent with the original scope; and (2) Node Feature-Constrained Regularizer, which removes residual impurities from the denoised graph, further enhances the purification effect. Additionally, we design tailored denoising strategies to handle different types of adversarial attacks, improving the model's adaptability to various attack scenarios. Extensive experiments conducted on three real-world datasets demonstrate that GDDM outperforms state-of-the-art methods in defending against a wide range of adversarial attacks, showcasing its robustness and effectiveness.
Secure Resource Allocation via Constrained Deep Reinforcement Learning
Authors: Jianfei Sun, Qiang Gao, Cong Wu, Yuxian Li, Jiacheng Wang, Dusit Niyato
Date: 2025-01-20 00:00:00
Tasks: Deep Reinforcement Learning, Distributed Computing, Edge-computing, reinforcement-learning, Reinforcement Learning

The proliferation of Internet of Things (IoT) devices and the advent of 6G technologies have introduced computationally intensive tasks that often surpass the processing capabilities of user devices. Efficient and secure resource allocation in serverless multi-cloud edge computing environments is essential for supporting these demands and advancing distributed computing. However, existing solutions frequently struggle with the complexity of multi-cloud infrastructures, robust security integration, and effective application of traditional deep reinforcement learning (DRL) techniques under system constraints. To address these challenges, we present SARMTO, a novel framework that integrates an action-constrained DRL model. SARMTO dynamically balances resource allocation, task offloading, security, and performance by utilizing a Markov decision process formulation, an adaptive security mechanism, and sophisticated optimization techniques. Extensive simulations across varying scenarios, including different task loads, data sizes, and MEC capacities, show that SARMTO consistently outperforms five baseline approaches, achieving up to a 40% reduction in system costs and a 41.5% improvement in energy efficiency over state-of-the-art methods. These enhancements highlight SARMTO's potential to revolutionize resource management in intricate distributed computing environments, opening the door to more efficient and secure IoT and edge computing applications.
DLinear-based Prediction of Remaining Useful Life of Lithium-Ion Batteries: Feature Engineering through Explainable Artificial Intelligence
Authors: Minsu Kim, Jaehyun Oh, Sang-Young Lee, Junghwan Kim
Date: 2025-01-20 00:00:00
Tasks: Explainable artificial intelligence, Feature Engineering, feature selection, Prediction

Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is essential for ensuring safety, reducing maintenance costs, and optimizing usage. However, predicting RUL is challenging due to the nonlinear characteristics of the degradation caused by complex chemical reactions. Machine learning allows precise predictions by learning the latent functions of degradation relationships based on cycling behavior. This study introduces an accurate RUL prediction approach based on feature engineering and DLinear, applied to the dataset from NASA's Prognostics Center of Excellence. Among the 20 features generated from current, voltage, temperature, and time provided in this dataset, key features contributing to degradation are selected using Pearson correlation coefficient and Shapley values. Shapley value-based feature selection effectively reflects cell-to-cell variability, showing similar importance rankings across all cells. The DLinear-based RUL prediction using key features efficiently captures the time-series trend, demonstrating significantly better performance compared to Long Short-Term Memory and Transformer models.
A Hands-free Spatial Selection and Interaction Technique using Gaze and Blink Input with Blink Prediction for Extended Reality
Authors: Tim Rolff, Jenny Gabel, Lauren Zerbin, Niklas Hypki, Susanne Schmidt, Markus Lappe, Frank Steinicke
Date: 2025-01-20 00:00:00

Gaze-based interaction techniques have created significant interest in the field of spatial interaction. Many of these methods require additional input modalities, such as hand gestures (e.g., gaze coupled with pinch). Those can be uncomfortable and difficult to perform in public or limited spaces, and pose challenges for users who are unable to execute pinch gestures. To address these aspects, we propose a novel, hands-free Gaze+Blink interaction technique that leverages the user's gaze and intentional eye blinks. This technique enables users to perform selections by executing intentional blinks. It facilitates continuous interactions, such as scrolling or drag-and-drop, through eye blinks coupled with head movements. So far, this concept has not been explored for hands-free spatial interaction techniques. We evaluated the performance and user experience (UX) of our Gaze+Blink method with two user studies and compared it with Gaze+Pinch in a realistic user interface setup featuring common menu interaction tasks. Study 1 demonstrated that while Gaze+Blink achieved comparable selection speeds, it was prone to accidental selections resulting from unintentional blinks. In Study 2 we explored an enhanced technique employing a deep learning algorithms for filtering out unintentional blinks.
DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals
Authors: Atik Faysal, Taha Boushine, Mohammad Rostami, Reihaneh Gh. Roshan, Huaxia Wang, Nikhil Muralidhar, Avimanyu Sahoo, Yu-Dong Yao
Date: 2025-01-20 00:00:00
Tasks: Denoising

We propose Denoising Masked Autoencoder (Deno-MAE), a novel multimodal autoencoder framework for denoising modulation signals during pretraining. DenoMAE extends the concept of masked autoencoders by incorporating multiple input modalities, including noise as an explicit modality, to enhance cross-modal learning and improve denoising performance. The network is pre-trained using unlabeled noisy modulation signals and constellation diagrams, effectively learning to reconstruct their equivalent noiseless signals and diagrams. Deno-MAE achieves state-of-the-art accuracy in automatic modulation classification tasks with significantly fewer training samples, demonstrating a 10% reduction in unlabeled pretraining data and a 3% reduction in labeled fine-tuning data compared to existing approaches. Moreover, our model exhibits robust performance across varying signal-to-noise ratios (SNRs) and supports extrapolation on unseen lower SNRs. The results indicate that DenoMAE is an efficient, flexible, and data-efficient solution for denoising and classifying modulation signals in challenging noise-intensive environments.
Dual Contouring of Hermite Data
Authors: Tao Ju, Frank Losasso, Scott Schaefer, Joe Warren
Date: 2002-07-01 00:00:00
Proceeding: ACM Transactions on Graphics (TOG), Volume 21, Issue 3 2002 7

This paper describes a new method for contouring a signed grid whose edges are tagged by Hermite data (i.e; exact intersection points and normals). This method avoids the need to explicitly identify and process “features” as required in previous Hermite contouring methods. Using a new, numerically stable representation for quadratic error functions, we develop an octree-based method for simplifying contours produced by this method. We next extend our contouring method to these simplified octrees. This new method imposes no constraints on the octree (such as being a restricted octree) and requires no “crack patching”. We conclude with a simple test for preserving the topology of the contour during simplification.
GraPix: Exploring Graph Modularity Optimization for Unsupervised Pixel Clustering
Authors: Sonal Kumar, Arijit Sur, Rashmi Dutta Baruah
Date: 2024-12-04 00:00:00
Proceeding: 27th International Conference on Pattern Recognition (ICPR) 2024 12
Tasks: Attribute, Clustering, Object Localization, Segmentation, Semantic Segmentation, Unsupervised Semantic Segmentation

A vision transformer learns high-quality patch embeddings during the self-supervised training, which plays a crucial role in many unsupervised downstream tasks like object localization, object detection, and sparse semantic segmentation. Such downstream tasks explore the various properties of the patch affinity graph to achieve state-of-the-art performance in an unsupervised setting. However, the true potential of the patch affinity graph is yet to be harnessed for the dense semantic segmentation task. The existing works show that modularity is an essential property of a graph, which reflects the strength of the existing graph partitions. We argue that joint optimization of feature clustering in patch embedding space and graph modularity in node attribute space leads to a smooth training convergence and achieves better results. In this paper, we propose a novel end-to-end unsupervised learning method called GraPix, which utilizes the hidden property of patch embeddings extracted from a self-supervised vision transformer for the dense semantic segmentation task. The GraPix constructs an affinity graph based on patch similarities in their embedding space. Next, it learns highly discriminative centroid embeddings for dense semantic segmentation with our novel joint feature clustering and graph modularity optimization objective. The experiment results show that GraPix outperforms the state-of-the-art method on the SUIM dataset and achieves the second-best performance on the Cityscapes dataset. Also, we perform a detailed ablation to justify the choice of model components and hyper-parameters. The code is available at https://github.com/SonalKumar95/GraPix.
SE(3)-Based Trajectory Optimization and Target Tracking in UAV-Enabled ISAC Systems
Authors: Dongxiao Xu, Xinyang Li, Vlad C. Andrei, Moritz Wiese, Ullrich J. Moenich, Holger Boche
Date: 2025-01-20 00:00:00
Tasks: ISAC, parameter estimation

This paper presents a novel approach to enhance sensing capabilities in UAV-enabled MIMO-OFDM ISAC systems by leveraging UAV mobility as a mono-static radar. By integrating uniform planar arrays (UPAs) and modeling the UAV dynamics in $SE(3)$, we address key challenges such as 3D space sensing and trajectory design. We propose a target tracking scheme using extended Kalman filtering (EKF) in $SE(3)$, along with trajectory optimization based on the conditional Posterior Cramer-Rao bound (CPCRB). Numerical results demonstrate the effectiveness of the proposed trajectory design in enhancing performance of target tracking and physical parameter estimation in UAV-enabled MIMO-OFDM ISAC systems.
The Streaming Batch Model for Efficient and Fault-Tolerant Heterogeneous Execution
Authors: Frank Sifei Luan, Ziming Mao, Ron Yifeng Wang, Charlotte Lin, Amog Kamsetty, Hao Chen, Cheng Su, Balaji Veeramani, Scott Lee, SangBin Cho, Clark Zinzow, Eric Liang, Ion Stoica, Stephanie Wang
Date: 2025-01-16 00:00:00
Tasks: CPU, GPU

While ML model training and inference are both GPU-intensive, CPU-based data processing is often the bottleneck. Distributed data processing systems based on the batch or stream processing models assume homogeneous resource requirements. They excel at CPU-based computation but either under-utilize heterogeneous resources or impose high overheads on failure and reconfiguration. We introduce the streaming batch model, a hybrid of the two models that enables efficient and fault-tolerant heterogeneous execution. The key idea is to execute one partition at a time to allow lineage-based recovery with dynamic resource allocation. This enables memory-efficient pipelining across heterogeneous resources, similar to stream processing, but also offers the elasticity and fault tolerance properties of batch processing. We present Ray Data, an implementation of the streaming batch model that improves throughput on heterogeneous batch inference pipelines by 3--8$\times$ compared to traditional batch and stream processing systems. When training Stable Diffusion, Ray Data matches the throughput of single-node ML data loaders while additionally leveraging distributed heterogeneous clusters to further improve training throughput by 31%.
Tackling Small Sample Survival Analysis via Transfer Learning: A Study of Colorectal Cancer Prognosis
Authors: Yonghao Zhao, Changtao Li, Chi Shu, Qingbin Wu, Hong Li, Chuan Xu, Tianrui Li, Ziqiang Wang, Zhipeng Luo, Yazhou He
Date: 2025-01-21 00:00:00
Tasks: Prognosis, Survival Analysis, Transfer Learning

Survival prognosis is crucial for medical informatics. Practitioners often confront small-sized clinical data, especially cancer patient cases, which can be insufficient to induce useful patterns for survival predictions. This study deals with small sample survival analysis by leveraging transfer learning, a useful machine learning technique that can enhance the target analysis with related knowledge pre-learned from other data. We propose and develop various transfer learning methods designed for common survival models. For parametric models such as DeepSurv, Cox-CC (Cox-based neural networks), and DeepHit (end-to-end deep learning model), we apply standard transfer learning techniques like pretraining and fine-tuning. For non-parametric models such as Random Survival Forest, we propose a new transfer survival forest (TSF) model that transfers tree structures from source tasks and fine-tunes them with target data. We evaluated the transfer learning methods on colorectal cancer (CRC) prognosis. The source data are 27,379 SEER CRC stage I patients, and the target data are 728 CRC stage I patients from the West China Hospital. When enhanced by transfer learning, Cox-CC's $C^{td}$ value was boosted from 0.7868 to 0.8111, DeepHit's from 0.8085 to 0.8135, DeepSurv's from 0.7722 to 0.8043, and RSF's from 0.7940 to 0.8297 (the highest performance). All models trained with data as small as 50 demonstrated even more significant improvement. Conclusions: Therefore, the current survival models used for cancer prognosis can be enhanced and improved by properly designed transfer learning techniques. The source code used in this study is available at https://github.com/YonghaoZhao722/TSF.
Online Clustering with Bandit Information
Authors: G Dhinesh Chandran, Srinivas Reddy Kota, Srikrishna Bhashyam
Date: 2025-01-20 00:00:00
Tasks: Clustering, Online Clustering

We study the problem of online clustering within the multi-armed bandit framework under the fixed confidence setting. In this multi-armed bandit problem, we have $M$ arms, each providing i.i.d. samples that follow a multivariate Gaussian distribution with an {\em unknown} mean and a known unit covariance. The arms are grouped into $K$ clusters based on the distance between their means using the Single Linkage (SLINK) clustering algorithm on the means of the arms. Since the true means are unknown, the objective is to obtain the above clustering of the arms with the minimum number of samples drawn from the arms, subject to an upper bound on the error probability. We introduce a novel algorithm, Average Tracking Bandit Online Clustering (ATBOC), and prove that this algorithm is order optimal, meaning that the upper bound on its expected sample complexity for given error probability $\delta$ is within a factor of 2 of an instance-dependent lower bound as $\delta \rightarrow 0$. Furthermore, we propose a computationally more efficient algorithm, Lower and Upper Confidence Bound-based Bandit Online Clustering (LUCBBOC), inspired by the LUCB algorithm for best arm identification. Simulation results demonstrate that the performance of LUCBBOC is comparable to that of ATBOC. We numerically assess the effectiveness of the proposed algorithms through numerical experiments on both synthetic datasets and the real-world MovieLens dataset. To the best of our knowledge, this is the first work on bandit online clustering that allows arms with different means in a cluster and $K$ greater than 2.
UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital Collaboration
Authors: Chung-ju Huang, Yuanpeng He, Xiao Han, Wenpin Jiao, Zhi Jin, Leye Wang
Date: 2025-01-20 00:00:00
Tasks: Federated Learning, Privacy Preserving, Representation Learning, Transfer Learning, Vertical Federated Learning

Cross-hospital collaboration has the potential to address disparities in medical resources across different regions. However, strict privacy regulations prohibit the direct sharing of sensitive patient information between hospitals. Vertical federated learning (VFL) offers a novel privacy-preserving machine learning paradigm that maximizes data utility across multiple hospitals. Traditional VFL methods, however, primarily benefit patients with overlapping data, leaving vulnerable non-overlapping patients without guaranteed improvements in medical prediction services. While some knowledge transfer techniques can enhance the prediction performance for non-overlapping patients, they fall short in addressing scenarios where overlapping and non-overlapping patients belong to different domains, resulting in challenges such as feature heterogeneity and label heterogeneity. To address these issues, we propose a novel unified vertical federated knowledge transfer framework (Unitrans). Our framework consists of three key steps. First, we extract the federated representation of overlapping patients by employing an effective vertical federated representation learning method to model multi-party joint features online. Next, each hospital learns a local knowledge transfer module offline, enabling the transfer of knowledge from the federated representation of overlapping patients to the enriched representation of local non-overlapping patients in a domain-adaptive manner. Finally, hospitals utilize these enriched local representations to enhance performance across various downstream medical prediction tasks. Experiments on real-world medical datasets validate the framework's dual effectiveness in both intra-domain and cross-domain knowledge transfer. The code of \method is available at \url{https://github.com/Chung-ju/Unitrans}.
Adaptive parameters identification for nonlinear dynamics using deep permutation invariant networks
Authors: Mouad Elaarabi, Domenico Borzacchiello, Yves Le Guennec, Philippe Le Bot, Sebastien Comas-Cardona
Date: 2025-01-20 00:00:00
Tasks: Time Series, Time Series Forecasting

The promising outcomes of dynamical system identification techniques, such as SINDy [Brunton et al. 2016], highlight their advantages in providing qualitative interpretability and extrapolation compared to non-interpretable deep neural networks [Rudin 2019]. These techniques suffer from parameter updating in real-time use cases, especially when the system parameters are likely to change during or between processes. Recently, the OASIS [Bhadriraju et al. 2020] framework introduced a data-driven technique to address the limitations of real-time dynamical system parameters updating, yielding interesting results. Nevertheless, we show in this work that superior performance can be achieved using more advanced model architectures. We present an innovative encoding approach, based mainly on the use of Set Encoding methods of sequence data, which give accurate adaptive model identification for complex dynamic systems, with variable input time series length. Two Set Encoding methods are used, the first is Deep Set [Zaheer et al. 2017], and the second is Set Transformer [Lee et al. 2019]. Comparing Set Transformer to OASIS framework on Lotka Volterra for real-time local dynamical system identification and time series forecasting, we find that the Set Transformer architecture is well adapted to learning relationships within data sets. We then compare the two Set Encoding methods based on the Lorenz system for online global dynamical system identification. Finally, we trained a Deep Set model to perform identification and characterization of abnormalities for 1D heat-transfer problem.
Lee and Seung (2000)'s Algorithms for Non-negative Matrix Factorization: A Supplementary Proof Guide
Authors: Sungjae Cho
Date: 2025-01-20 00:00:00
Tasks: Dimensionality Reduction

Lee and Seung (2000) introduced numerical solutions for non-negative matrix factorization (NMF) using iterative multiplicative update algorithms. These algorithms have been actively utilized as dimensionality reduction tools for high-dimensional non-negative data and learning algorithms for artificial neural networks. Despite a considerable amount of literature on the applications of the NMF algorithms, detailed explanations about their formulation and derivation are lacking. This report provides supplementary details to help understand the formulation and derivation of the proofs as used in the original paper.
A2SB: Audio-to-Audio Schrodinger Bridges
Authors: Zhifeng Kong, Kevin J Shih, Weili Nie, Arash Vahdat, Sang-gil Lee, Joao Felipe Santos, Ante Jukic, Rafael Valle, Bryan Catanzaro
Date: 2025-01-20 00:00:00
Tasks: Bandwidth Extension

Audio in the real world may be perturbed due to numerous factors, causing the audio quality to be degraded. The following work presents an audio restoration model tailored for high-res music at 44.1kHz. Our model, Audio-to-Audio Schrodinger Bridges (A2SB), is capable of both bandwidth extension (predicting high-frequency components) and inpainting (re-generating missing segments). Critically, A2SB is end-to-end without need of a vocoder to predict waveform outputs, able to restore hour-long audio inputs, and trained on permissively licensed music data. A2SB is capable of achieving state-of-the-art bandwidth extension and inpainting quality on several out-of-distribution music test sets. Our demo website is https: //research.nvidia.com/labs/adlr/A2SB/.
Empirical Bayes Estimation for Lasso-Type Regularizers: Analysis of Automatic Relevance Determination
Authors: Tsukasa Yoshida, Kazuho Watanabe
Date: 2025-01-20 00:00:00
Tasks: regression

This paper focuses on linear regression models with non-conjugate sparsity-inducing regularizers such as lasso and group lasso. Although the empirical Bayes approach enables us to estimate the regularization parameter, little is known on the properties of the estimators. In particular, many aspects regarding the specific conditions under which the mechanism of automatic relevance determination (ARD) occurs remain unexplained. In this paper, we derive the empirical Bayes estimators for the group lasso regularized linear regression models with limited parameters. It is shown that the estimators diverge under a specific condition, giving rise to the ARD mechanism. We also prove that empirical Bayes methods can produce the ARD mechanism in general regularized linear regression models and clarify the conditions under which models such as ridge, lasso, and group lasso can do so.