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Enhanced Acceleration for Generalized Nonconvex Low-Rank Matrix Learning

Chinese Journal of Electronics 2025年第1期34卷 98-113页

作者： Hengmin Zhang Jian Yang Wenli Du Bob Zhang Zhiyuan Zha Bihan Wen School of Electrical and Electronic Engineering Nanyang Technological University School of Computer Science and Engineering Nanjing University of Science and Technology School of Information Science and Engineering East China University of Science and Technology Department of Electrical and Computer Engineering University of Macau

Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion, low-rank representation, and robust matrix regression. We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the ?0-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers, backed by rigorous theoretical analysis for complexity and *** algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.

关键词： Learning systems Image recognition Minimization Computational efficiency Complexity theory Matrix decomposition Optimization Image reconstruction Singular value decomposition Convergence

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Intelligent integrated sensing and communication:a survey

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Science China(Information Sciences) 2025年第3期68卷 5-46页

作者： Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG School of Information and Communication Engineering Dalian University of Technology College of Information Engineering Zhejiang University of Technology School of Information and Electronics Beijing Institute of Technology Department of Electrical and Computer Engineering The University of Texas at Dallas Department of Electrical and Computer Engineering Aristotle University of Thessaloniki

Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

关键词： artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelligence integrated sensing and communication machine learning transfer learning

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A two-stage method with twin autoencoders for the degradation trajectories prediction of lithium-ion batteries

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Journal of Energy Chemistry 2025年第4期103卷 759-772页

作者： Lei Cai Jing Yan Haiyan Jin Jinhao Meng Jichang Peng Bin Wang Wei Liang Remus Teodorescu School of Computer Science and Engineering Xi'an University of Technology School of Electrical Engineering Xi'an Jiaotong University Smart Grid Research Institute Nanjing Institute of Technology Department of Energy Aalborg University

To predict the lithium-ion(Li-ion) battery degradation trajectory in the early phase,arranging the maintenance of battery energy storage systems is of great ***,under different operation conditions,Li-ion batteries present distinct degradation patterns,and it is challenging to capture negligible capacity fade in early *** the data-driven method showing promising performance,insufficient data is still a big issue since the ageing experiments on the batteries are too slow and *** this study,we proposed twin autoencoders integrated into a two-stage method to predict the early cycles' degradation *** two-stage method can properly predict the degradation from course to *** twin autoencoders serve as a feature extractor and a synthetic data generator,***,a learning procedure based on the long-short term memory(LSTM) network is designed to hybridize the learning process between the real and synthetic *** performance of the proposed method is verified on three datasets,and the experimental results show that the proposed method can achieve accurate predictions compared to its competitors.

关键词： Autoencoder Battery degradation trajectory Early prediction Synthetic data generation

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Robust federated contrastive recommender system against targeted model poisoning attack

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Science China(Information Sciences) 2025年第4期68卷 50-65页

作者： Wei YUAN Chaoqun YANG Liang QU Guanhua YE Quoc Viet Hung NGUYEN Hongzhi YIN School of Electrical Engineering and Computer Science The University of Queensland School of Information and Communication Technology Griffith University Deep Neural Computing Company Limited

Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least two ***, the performance of FedRecs is compromised due to highly sparse on-device data for each client. Second, the system's robustness is undermined by the vulnerability to model poisoning attacks launched by malicious users. In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec. Unlike previous contrastive learning approaches in FedRecs that necessitate clients to share their private parameters, our CL4FedRec aligns with the basic FedRec learning protocol, ensuring compatibility with most existing FedRec implementations. We then evaluate the robustness of FedRecs equipped with CL4FedRec by subjecting it to several state-of-the-art model poisoning attacks. Surprisingly, our observations reveal that contrastive learning tends to exacerbate the vulnerability of FedRecs to these attacks. This is attributed to the enhanced embedding uniformity, making the polluted target item embedding easily proximate to popular items. Based on this insight, we propose an enhanced and robust version of CL4FedRec(rCL4FedRec) by introducing a regularizer to maintain the distance among item embeddings with different popularity levels. Extensive experiments conducted on four commonly used recommendation datasets demonstrate that rCL4FedRec significantly enhances both the model's performance and the robustness of FedRecs.

关键词： federated recommender system contrastive learning model poisoning attack and defense

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Privacy-preserving explainable AI: a survey

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Science China(Information Sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN Phi Le NGUYEN Hongzhi YIN Quoc Viet Hung NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer Science University of Bremen Faculty of Information Technology HUTECH University Department of Computer Science Hanoi University of Science and Technology School of Electrical Engineering and Computer Science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

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Experimental study on microstructural and corrosion behaviors of friction stir processed steel with CoCrFeCuTi HEA reinforcement and effect of annealing

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Science China(Technological Sciences) 2025年第1期68卷 229-246页

作者： S.Aravind KRISHNA N.RADHIKA Alokesh PRAMANIK N.JEYAPRAKASH Department of Mechanical Engineering Amrita School of EngineeringAmrita Vishwa VidyapeethamCoimbatore 641112India School of Civil and Mechanical Engineering Curtin UniversityBentley WA 6102Australia School of Mechanical and Electrical Engineering China University of Mining and TechnologyXuzhou 221116China

The ability to perform against extreme corrosion environments in stainless steel depreciates under challenging conditions in industrial *** can be resolved by employing surface modification techniques to modify the surface with the required *** present study investigates the outcomes of friction stir processing of SS304 with CoCrFeCuTi high entropy alloy(HEA)as reinforcement and the property enhancements *** synthesized CoCrFeCuTi HEA exhibited irregular fragments with 17μm mean particle size and body centred cubic(BCC)*** microstructure of the processed surface revealed ultra-refined grains with a uniform distribution of HEA *** microstructures of the samples annealed at 400℃,550℃,700℃and 850℃for 2 h also showed significant grain growth and grain *** processed sample with HEA reinforcement annealed at 700℃exhibited enhanced microhardness and tensile strength by 63%and 0.88 times than that of the processed sample with HEA *** subsequent electrochemical corrosion test displayed better corrosion resistance for the HEA-processed and annealed *** corrosion tests at 900℃showcased excellent resistance for the HEA processed and annealed samples by 14.92%,12.75%,and 14.83%against the samples pre-annealed under three different salt mixtures,and the corroded surface was analyzed.

关键词： high entropy alloys friction stir processing annealing grain refinement corrosion resistance

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Real-time holographic camera for obtaining real 3D scene hologram

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Light(Science & Applications) 2025年第3期14卷 789-799页

作者： Zhao-Song Li Chao Liu Xiao-Wei Li Yi Zheng Qian Huang Yi-Wei Zheng Ye-Hao Hou Chen-Liang Chang Da-Wei Zhang Song-Lin Zhuang Di Wang Qiong-Hua Wang School of Instrumentation and Optoelectronic Engineering Beihang UniversityBeijing100191China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China

As a frontier technology,holography has important research values in fields such as bio-micrographic imaging,light feld modulation and data ***,the real-time acquisition of 3D scenes and high-fidelity reconstruction technology has not yet made a breakthrough,which has seriously hindered the development of ***,a novel holographic camera is proposed to solve the above inherent problems *** proposed holographic camera consists of the acquisition end and the calculation *** the acquisition end of the holographic camera,specially configured liquid materials and liquid lens structure based on voice-coil motor-driving are used to produce the liquid camera,so that the liquid camera can quickly capture the focus stack of the real 3D scene within 15 *** the calculation end,a new structured focus stack network(FS-Net)is designed for hologram *** training the FS-Net with the focus stack renderer and learnable Zernike phase,it enables hologram calculation within 13 *** the first device to achieve real-time incoherent acquisition and high-fidelity holographic reconstruction of a real 3D scene,our proposed holographic camera breaks technical bottlenecks of difficulty in acquiring the real 3D scene,low quality of the holographic reconstructed image,and incorrect defocus *** experimental results demonstrate the effectiveness of our holographic camera in the acquisition of focal plane information and hologram calculation of the real 3D *** proposed holographic camera opens up a new way for the application of holography in fields such as 3D display,light field modulation,and 3D measurement.

关键词： holographic camera feld modulation hologram calculation liquid lens structured focus stack network D scene reconstruction d scenes real time acquisition

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Multi-Modal Self-Supervised Learning AlgorithmBased Product Recommendation

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IEEE Transactions on Automation Science and engineering 2025年 22卷 15371-15380页

作者： Gao, Li Fan, Haiping Chen, Qingkui Yang, Heyu School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

With the rise of e-commerce, personalized recommendation algorithms have received much attention in recent years. Meanwhile, multimodal recommendation algorithms have become the next competitive track of personalized recommendation. The current mainstream recommendation algorithms (e.g., NeuMF, NGCF) rely too much on empirical data and do not effectively utilize multimodal information to model user preferences, while there is the problem of sparsity of user preference data. Secondly, they are still limited to using the same weights to fuse the item features of different modalities, and have the problem of inaccurate recommendation. For this reason, this paper proposes Multi-Modal Self-Supervised Learning Algorithm Based Product Recommendation (MMSLRec). (1) Data augmentation by self-supervised adversarial perturbation generation learning is performed to supplement the missing interaction information of tailed products to solve the common data sparsity problem in product recommendation. (2) A multimodal representation learning module is introduced to capture deep features in image and text modalities using AlexNet and Bert, respectively, in order to avoid the recommendation accuracy degradation problem caused by insufficient data feature learning. (3) Learning the user's perceived strength in different modalities, using the user co-occurrence matrix to fuse the features and make recommendations. Experiments on Amazon-Electronics, Amazon-Clothing, and Amazon-Baby, with different degrees of data sparsity verify the effectiveness of our MMSLRec in solving the data sparsity problem and the multimodal user preference modeling problem, and compared to the state-of-the-art baseline recommendation model on the three datasets, the Recall@20 improved by 9.26%, 3.11% and 2.92%, respectively. © 2004-2012 IEEE.

关键词： Self-supervised learning

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Orbit-Orbit Interaction in Spatiotemporal Optical Vortex

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engineering 2025年第2期45卷 44-51页

作者： Jian Chen Jie Zhao Xi Shen Dewei Mo Cheng-Wei Qiu Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

While spin-orbit interaction has been extensively studied,few investigations have reported on the interaction between orbital angular momenta(OAMs).In this work,we study a new type of orbit-orbit coupling between the longitudinal OAM and the transverse OAM carried by a three-dimensional(3D)spatiotemporal optical vortex(STOV)in the process of tight *** 3D STOV possesses orthogonal OAMs in the x-y,t-x,and y-t planes,and is preconditioned to overcome the spatiotemporal astigmatism effect.x,y,and t are the axes in the spatiotemporal *** corresponding focused wavepacket is calculated by employing the Debye diffraction theory,showing that a phase singularity ring is generated by the interactions among the transverse and longitudinal vortices in the highly confined *** Fourier-transform decomposition of the Debye integral is employed to analyze the mechanism of the orbit-orbit *** is the first revelation of coupling between the longitudinal OAM and the transverse OAM,paving the way for potential applications in optical trapping,laser machining,nonlinear light-matter interactions,and more.

关键词： Spatiotemporal optical vortex Orbital angular momentum Momentum interaction Highly confined wavepacket Diffraction

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Vector Polynomial Electric Arc Furnace Models

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IEEE Transactions on Power Delivery 2025年第3期40卷 1694-1705页

作者： Babaei, Zahra Samet, Haidar Shiraz University School of Electrical and Computer Engineering Shiraz Iran Eindhoven University of Technology Eindhoven Netherlands

Electric arc furnaces (EAFs) have nonlinear and time-varying voltage-current characteristics which cause voltage flicker and harmonics. An accurate EAF model is required to study its negative power quality impacts and the possible solutions. Although there are many EAF models, most of them are single-phase models. Few others consider the threephase modeling of the EAF feeding system. However, three arcs with independent mathematical equations are used. There is not much distance between the three arcs;one end of the three arc columns is the shared molten bath. So, the voltage and current of each arc depend on the electrical quantities of the other two arcs, which have been neglected in almost all previous EAF models. This paper presents two EAF models where the arc voltage/current is a polynomial function of three-phase arc current/voltage and their derivatives. Many of EAFs' actual records at the Mobarakeh Steel Company in Isfahan, Iran, are used to develop the proposed models. The best model orders are attained using error indices based on actual data. The models' parameters are calculated every half cycle based on the actual records. Then, they are considered time series and modeled with ARMA models to be reproduced using the enclosed MATLAB codes. © 2025 IEEE.

关键词： Electric arc furnaces

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