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arXiv 2025年

作者： Wang, Shiyu Avagyan, Mariam Shen, Yihan Lamy, Arnaud Wang, Tingran Márka, Szabolcs Márka, Zsuzsa Wright, John Department of Electrical Engineering Columbia University New York United States Data Science Institute Columbia University New York United States Department of Computer Science Columbia University New York United States Department of Physics Columbia University New York United States Columbia Astrophysics Laboratory Columbia University New York United States Department of Applied Physics and Applied Mathematics Columbia University New York United States

Learned denoisers play a fundamental role in various signal generation (e.g., diffusion models) and reconstruction (e.g., compressed sensing) architectures, whose success derives from their ability to leverage low-dimensional structure in data. Existing denoising methods, however, either rely on local approximations that require a linear scan of the entire dataset or treat denoising as generic function approximation problems, often sacrificing efficiency and interpretability. We consider the problem of efficiently denoising a new noisy data point sampled from an unknown ddimensional manifold M ∈ D, using only noisy samples. This work proposes a framework for test-time efficient manifold denoising, by framing the concept of "learning-to-denoise" as "learning-to-optimize". We have two technical innovations: (i) online learning methods which learn to optimize over the manifold of clean signals using only noisy data, effectively "growing" an optimizer one sample at a time. (ii) mixed-order methods which guarantee that the learned optimizers achieve global optimality, ensuring both efficiency and near-optimal denoising performance. We corroborate these claims with theoretical analyses of both the complexity and denoising performance of mixed-order traversal. Our experiments on scientific manifolds demonstrate significantly improved complexity-performance tradeoffs compared to nearest neighbor search, which underpins existing provable denoising approaches based on exhaustive search. Copyright © 2025, The Authors. All rights reserved.

关键词： Nearest neighbor search

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Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural Networks

arXiv

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arXiv 2025年

作者： Khot, Ayush Wang, Xiwei Roy, Avik Kindratenko, Volodymyr Neubauer, Mark S. Department of Physics University of Illinois Urbana-Champaign UrbanaIL61801 United States The Grainger College of Engineering Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States Center for Artificial Intelligence Innovation National Center for Supercomputing Applications University of Illinois Urbana-Champaign UrbanaIL61801 United States The Grainger College of Engineering Siebel School of Computing and Data Science University of Illinois Urbana-Champaign UrbanaIL61801 United States

Current methods commonly used for uncertainty quantification (UQ) in deep learning (DL) models utilize Bayesian methods which are computationally expensive and time-consuming. In this paper, we provide a detailed study of UQ based on evidential deep learning (EDL) for deep neural network models designed to identify jets in high energy proton-proton collisions at the Large Hadron Collider and explore its utility in anomaly detection. EDL is a DL approach that treats learning as an evidence acquisition process designed to provide confidence (or epistemic uncertainty) about test data. Using publicly available datasets for jet classification benchmarking, we explore hyperparameter optimizations for EDL applied to the challenge of UQ for jet identification. We also investigate how the uncertainty is distributed for each jet class, how this method can be implemented for the detection of anomalies, how the uncertainty compares with Bayesian ensemble methods, and how the uncertainty maps onto latent spaces for the models. Our studies uncover some pitfalls of EDL applied to anomaly detection and a more effective way to quantify uncertainty from EDL as compared with the foundational EDL setup. These studies illustrate a methodological approach to interpreting EDL in jet classification models, providing new insights on how EDL quantifies uncertainty and detects out-of-distribution data which may lead to improved EDL methods for DL models applied to classification tasks. © 2025, CC BY.

关键词： Adversarial machine learning

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data-Driven Model Order Reduction for T-Product-Based Dynamical Systems

arXiv

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arXiv 2025年

作者： Mei, Shenghan He, Ziqin Mei, Yidan Mao, Xin Dong, Anqi Wang, Ren Chen, Can Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States School of Data Science and Society University of North Carolina at Chapel Hill Chapel HillNC27599 United States Division of Decision and Control Systems the Department of Mathematics KTH Royal Institute of Technology StockholmSE-100 44 Sweden Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States School of Data Science and Society the Department of Mathematics the Department of Biostatistics University of North Carolina at Chapel Hill Chapel HillNC27599 United States

— Model order reduction plays a crucial role in simplifying complex systems while preserving their essential dynamic characteristics, making it an invaluable tool in a wide range of applications, including robotic systems, signal processing, and fluid dynamics. However, traditional model order reduction techniques like balanced truncation are not designed to handle tensor data directly and instead require unfolding the data, which may lead to the loss of important higher-order structural information. In this article, we introduce a novel framework for data-driven model order reduction of T-product-based dynamical systems (TPDSs), which are often used to capture the evolution of third-order tensor data such as images and videos through the T-product. Specifically, we develop advanced T-product-based techniques, including T-balanced truncation, T-balanced proper orthogonal decomposition, and the T-eigensystem realization algorithm for input-output TPDSs by leveraging the unique properties of T-singular value decomposition. We demonstrate that these techniques offer significant memory and computational savings while achieving reduction errors that are comparable to those of conventional methods. The effectiveness of the proposed framework is further validated through synthetic and real-world *** Codes 93C05, 15A69, 93B30, 65F99 © 2025, CC BY.

关键词： Eigenvalues and eigenfunctions

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AFDM-Enabled Integrated Sensing and Communication: Theoretical Framework and Pilot Design

arXiv

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arXiv 2025年

作者： Zhang, Fan Wang, Zhaocheng Mao, Tianqi Jiao, Tianyu Zhuo, Yinxiao Wen, Miaowen Xiang, Wei Chen, Sheng Karagiannidis, George K. Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Laboratory of CNS/ATM Beijing Institute of Technology Beijing100081 China Zhuhai519088 China School of Electronic and Information Engineering South China University of Technology Guangzhou China School of Computing Engineering and Mathematical Sciences La Trobe University MelbourneVIC3086 Australia School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Greece

The integrated sensing and communication (ISAC) has been envisioned as one representative usage scenario of sixth-generation (6G) network. However, the unprecedented characteristics of 6G, especially the doubly dispersive channel, make classical ISAC waveforms rather challenging to guarantee a desirable performance level. The recently proposed affine frequency division multiplexing (AFDM) can attain full diversity even under doubly dispersive effects, thus becoming a competitive candidate for next-generation ISAC waveforms. Relevant investigations are still at an early stage, which involve only straightforward design lacking explicit theoretical analysis. This paper provides an in-depth investigation on AFDM waveform design for ISAC applications. Specifically, the closed-form Crámer-Rao bounds of target detection for AFDM are derived, followed by a demonstration on its merits over existing counterparts. Furthermore, we formulate the ambiguity function of the pilot-assisted AFDM waveform for the first time, revealing conditions for stable sensing performance. To further enhance both the communication and sensing performance of the AFDM waveform, we propose a novel pilot design by exploiting the characteristics of AFDM signals. The proposed design is analytically validated to be capable of optimizing the ambiguity function property and channel estimation accuracy simultaneously as well as overcoming the sensing and channel estimation range limitation originated from the pilot spacing. Numerical results have verified the superiority of the proposed pilot design in terms of dual-functional performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Cramer-Rao bounds

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Randomized CNN based deep learning technique for the cyber-attacks detection in SCADA industrial control systems

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Measurement 2025年 254卷

作者： Sinil Mubarak Mohamed Hadi Habaebi Md Rafiqul Islam Nubila Jaleel Mohammed Tahir Siddique Department of Electrical and Computer Engineering International Islamic University Malaysia Department of Computer Science and Engineering Bearys Institute of Technology Mangalore India Department of Computing University of Turku Finland

The increasing digitization and connectivity of Industrial Control Systems (ICS) have exposed them to highly sophisticated cyber threats to a great extent. Traditional security mechanisms like rule-based and signature-based intrusion detection systems fail to detect new and emerging attacks. In such limitations, the current research is on the Randomized Convolutional Neural Network (R-CNN) model for cyber-attack detection in ICS networks. The proposed model leverages convolutional layers to improve feature extraction and reduce the likelihood of overfitting. Additionally, advanced data preprocessing, augmentation, and hyperparameter optimization methods to maximize classification performance. With the increasing frequency of cybersecurity attacks in the Industrial Internet of Things (IIoT), addressing such challenges are hindered by outdated public datasets and scarcity of testbeds, to design appropriate solutions to detect and prevent cyberattacks. To overcome this limitation, we have developed an in-house cyber testbed based on standard industrial operations, simulated with hacking scenarios. The model is trained and tuned with ICS cyber-attack benchmark datasets on the various attacks. Randomized layers provide controlled variability at training time, enhancing the model to recognize known and unknown attacks. Experimental results indicate that the proposed R-CNN method performs better than the baseline machine learning and conventional CNN methods with a detection accuracy of 98.7%. The model also achieves 98.2% precision, 97.9% recall, and an F1-score of 98.0%, making the threat detection robust and the false positive rate imperceptible. The computational overhead of the architecture also enables real-time deployment within an industrial setup. The improved Randomized CNN-based IDS in deep learning-based cyber security applications facilitates real-time cyber threat detection feasibility within ICS. It enhances the model for zero-day attack detection and integr

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Post-exascale supercomputing:research opportunities abound

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Frontiers of Information Technology & Electronic engineering 2018年第10期19卷 1203-1208页

作者： Zuo-ning CHEN Jack DONGARRA Zhi-wei XU Chinese Academy of Engineering Electrical Engineering and Computer Science Department University of Tennessee Institute of Computing Technology Chinese Academy of Sciences

Exascale supercomputing refers to the scientific research efforts and activities to build and use supercomputers that can perform scientific computing at the speed of exaflops,or 10^(18) floating-point(64-bit)operatio... 详细信息

关键词：计算研究机科学研究浮点操作

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

arXiv

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Deng, Pengchao Liu, Chongpei Sebe, Nicu Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Information Engineering and Computer Science University of Trento Trento38123 Italy School of Computing and Communications Lancaster University LA1 4YW United Kingdom Department of Computer Science The University of Western Australia WA6009 Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://***/CNJianLiu/Diff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Augmented reality

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Obesity Prediction from Structural MRI Using Conformal Deep Learning with Uncertainty Quantification

Obesity Prediction from Structural MRI Using Conformal Deep ...

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Medical Imaging 2025: computer-Aided Diagnosis

作者： Farzana, W. Temtam, A. Humud-Arboleda, B. Ma, L. Bean, M. Moeller, F. Gerard Iftekharuddin, K.M. Vision Lab Department of Electrical and Computer Engineering Old Dominion University NorfolkVA23529 United States Institute of Drug and Alcohol Studies Virginia Commonwealth University RichmondVA23284-2520 United States Department of Psychiatry Virginia Commonwealth University RichmondVA23284-2520 United States Data Science Institute Old Dominion University Virginia BeachVA23462 United States

ISBN: (纸本)9781510685925

Obesity arises from a neurobehavioral disorder in which the brain’s regulation of hunger and food intake is impaired, leading to an imbalance between energy consumption and expenditure. One key phenotype associated with obesity is body mass index (BMI). BMI is influenced by multiple causal pathways driven by behavioral, metabolic, and genetic factors. Traditional obesity prediction studies often rely on magnetic resonance imaging (MRI) voxel-based morphometry to correlate BMI with obesity-related clinical measurements and brain structure, predominantly gray matter volume (GMV). However, the altered brain regions are variable and widespread between studies, with some literature presenting contradictory results between BMI and GMV within certain brain regions. Therefore, it is crucial to quantify uncertainty in the analysis to provide user-defined confidence, interpretability in the prediction. To address these limitations, we propose a computational model to predict obesity directly from individual T1-weighted structural MRI (sMRI) data. The proposed conformal deep learning (CDL) model achieves a 5-fold cross validated average precision of 77.65% and an F1-score of 75.42%, effectively classifying a patient as obese or healthy with uncertainty score from sMRI. Furthermore, our model provides probabilistic uncertainty quantification paired with gradient-based localization maps that discover key brain regions, such as frontal lobe, caudate, and thalamus from the individual sMRI of patient. Comparisons with standard probabilistic subcortical atlas and existing literature demonstrate that our CDL model offers complementary evidence linking obesity with subcortical regions. The findings highlight specific brain regions linked to obese individuals, with potential towards developing brain region-focused behavioral therapy designs to control impulsivity for individuals who are overweight or obese. © 2025 SPIE.

关键词： Neurodegenerative diseases

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Multi-Agent Reinforcement Learning in Wireless Distributed Networks for 6G

arXiv

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arXiv 2025年

作者： Zhang, Jiayi Liu, Ziheng Zhu, Yiyang Shi, Enyu Xu, Bokai Yuen, Chau Niyato, Dusit Debbah, Mérouane Jin, Shi Ai, Bo Shen, Xuemin School of Electronic and Information Engineering The Frontiers Science Center for Smart High-Speed Railway System Beijing jiaotong University Beijing100044 China School of Electrical and Electronics Engineering Nanyang Technological University 639798 Singapore College of Computing and Data Science Nanyang Technological University 639798 Singapore KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

The introduction of intelligent interconnectivity between the physical and human worlds has attracted great attention for future sixth-generation (6G) networks, emphasizing massive capacity, ultra-low latency, and unparalleled reliability. Wireless distributed networks and multi-agent reinforcement learning (MARL), both of which have evolved from centralized paradigms, are two promising solutions for the great attention. Given their distinct capabilities, such as decentralization and collaborative mechanisms, integrating these two paradigms holds great promise for unleashing the full power of 6G, attracting significant research and development attention. This paper provides a comprehensive study on MARL-assisted wireless distributed networks for 6G. In particular, we introduce the basic mathematical background and evolution of wireless distributed networks and MARL, as well as demonstrate their interrelationships. Subsequently, we analyze different structures of wireless distributed networks from the perspectives of homogeneous and heterogeneous. Furthermore, we introduce the basic concepts of MARL and discuss two typical categories, including model-based and model-free. We then present critical challenges faced by MARL-assisted wireless distributed networks, providing important guidance and insights for actual implementation. We also explore an interplay between MARL-assisted wireless distributed networks and emerging techniques, such as information bottleneck and mirror learning, delivering in-depth analyses and application scenarios. Finally, we outline several compelling research directions for future MARL-assisted wireless distributed networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Hybrid-Generative Diffusion Models for Attack-Oriented Twin Migration in Vehicular Metaverses

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IEEE Transactions on Vehicular Technology 2025年

作者： Kang, Yingkai Wen, Jinbo Kang, Jiawen Zhang, Tao Du, Hongyang Niyato, Dusit Yu, Rong Xie, Shengli Guangdong University of Technology School of Automation Guangzhou510006 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Beijing Jiaotong University School of Cyberspace Science and Technology Beijing100044 China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Nanyang Technological University College of Computing and Data Science 639798 Singapore

The vehicular metaverse is envisioned as a blended immersive domain that promises to bring revolutionary changes to the automotive industry. As a core component of vehicular metaverses, Vehicle Twins (VTs) are digital twins that cover the entire life cycle of vehicles, providing immersive virtual services for Vehicular Metaverse Users (VMUs). Vehicles with limited resources offload the computationally intensive tasks of constructing and updating VTs to edge servers and migrate VTs between these servers, ensuring seamless and immersive experiences for VMUs. However, the high mobility of vehicles, uneven deployment of edge servers, and potential security threats pose challenges to achieving efficient and reliable VT migrations. To address these issues, we propose a secure and reliable VT migration framework in vehicular metaverses. Specifically, we design a two-layer trust evaluation model to comprehensively evaluate the reputation value of edge servers in the network communication and interaction layers. Then, we model the VT migration problem as a partially observable Markov decision process and design a hybrid-Generative Diffusion Model (GDM) algorithm based on deep reinforcement learning to generate optimal migration decisions by taking hybrid actions (i.e., continuous actions and discrete actions). Numerical results demonstrate that the hybrid-GDM algorithm outperforms the baseline algorithms, showing strong adaptability in various settings and highlighting the potential of the hybrid-GDM algorithm for addressing various optimization issues in vehicular metaverses. © 1967-2012 IEEE.

关键词： deep reinforcement learning generative diffusion models reputation twin migration Vehicular metaverse

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