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A survey on cross-user federated recommendation

Science China(Information Sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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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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Integrated Framework of Multisource Data Fusion for Outage Location in Looped Distribution systems

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IEEE Transactions on Smart Grid 2025年第3期16卷 2635-2646页

作者： Liu, Liming Yuan, Yuxuan Wang, Zhaoyu Yao, Yiyun Ding, Fei Iowa State University Department of Electrical and Computer Engineering AmesIA50011 United States National Renewable Energy Laboratory Power System Engineering Center GoldenCO80214 United States

Accurate outage location is essential for expediting post-outage power restoration, minimizing outage duration, and enhancing the resilience of distribution networks. With the advent of advanced metering infrastructure, data-driven outage location methods have significantly advanced beyond traditional approaches that rely on manual inspections. However, existing methods still face critical challenges, like reliance on single-source data, limited ability to handle partially observable systems or difficulties with loop networks. To the best of our knowledge, no single approach has comprehensively addressed all of these challenges at once. To this end, this paper proposes a comprehensive multisource data fusion framework for outage locations via probabilistic graph networks. The framework consists of three key phases. First, a novel method for reconstituting distribution networks with loops is developed, transforming looped networks into multiple radial subnetworks that retain all outage causalities of the original network. Second, Bayesian network (BN) models are established for each subnetwork, integrating multiple data sources and network structures. Finally, a joint Gibbs sampling mechanism, featuring forward and backward information flow, is designed to merge data from separate BN models and maximize the utilization of limited evidence, ensuring accurate outage location identification. The framework was validated on two modified public test systems, and comparative studies confirmed its effectiveness. © 2010-2012 IEEE.

关键词： Soft sensors Data models Topology Probabilistic logic Data integration Accuracy Renewable energy sources Observability Government Support vector machines

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A Novel Sleep Modulation and Quality Classification Strategy Based EEG Signals and Multi-Scale Attention Deep Learning

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IEEE Internet of Things Journal 2025年第11期12卷 17377-17386页

作者： Lou, Yajun Zhou, Lu Song, Fanlei Xu, Fanyao Wang, Xuejing Li, Weitao Qian, Zhiyu Nanjing University of Aeronautics and Astronautics Key Laboratory of Multimodal Brain-Computer Precision Drive Ministry of Industry and Information Technology the Key Laboratory of Digital Medical Equipment and Technology of Jiangsu Province Department of Biomedical Engineering College of Automation Engineering Nanjing210016 China

Sleep disorders have become the second most prevalent mental health condition globally, posing a significant challenge to public health. Consequently, there is an imperative need for the development of effective, non-pharmacological interventions to enhance sleep quality. In this study, a novel sleep modulation technique utilizing transcutaneous electrical nerve stimulation (TENS) is introduced. A specialized stimulation mode is designed by integrating sine waves of varying frequencies, including spindle waveform components. Additionally, to facilitate an accurate evaluation of sleep quality and enable individualized interventions, a multi-scale attention-based deep learning model (MSA-Net) is proposed for sleep quality classification. It consists of a two-branch convolution block (TBC-block), an adaptive feature recalibration (AFR) module, three dense blocks, three attention transition blocks. By extracting the deep features of the electroencephalography (EEG) raw data, the sleep quality classification is realized. Experimental results indicate that the proposed stimulation effectively enhances and stabilizes slow-wave activity, significantly extending the duration of deep sleep and enhancing overall sleep stability. In addition, the MSA-Net demonstrates robust performance, achieving exceptional performance with an overall accuracy of 0.881, F1 score of 0.903, and Kappa score of 0.845, outperforming baseline models across all evaluation metrics. These results demonstrate the potential of combining TENS-based sleep modulation with deep learning techniques, offering an effective framework for enhancing sleep quality and advancing personalized sleep modulation strategies. © 2025 IEEE.

关键词： Sleep research

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Exploring Multiple Functionalities of Cyber-Physical Power system Within digital Substation-Based laboratory Environment

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IEEE Access 2025年 13卷 82821-82833页

作者： Pham, Le Nam Hai Riquelme-Dominguez, Jose Miguel Gonzalez-Longatt, Francisco University of South-Eastern Norway Digital Energy System Laboratory Department of Electrical Engineering IT and Cybernetic Porsgrunn3918 Norway Universidad de Seville Department of Electrical Engineering Seville41004 Spain Loughborough University Wolfson School of Mechanical Electrical and Manufacturing Engineering LoughboroughLE11 3TU United Kingdom

The landscape of control centre technologies is dynamically adapting to the swift transformations in the physical world, specifically targeting wide-area monitoring, control, and protection in energy systems. In response to these changes, Cyber-Physical Power system (CPPS) has emerged as a promising solution to develop control centre technologies and is gaining significant attention from scholars and researchers with similar interests. However, the widespread adoption of CPPS technology faces several challenges, such as the need for methodologies to develop further CPPS or the detailed deployment of multiple functions within the CPPS framework. To address these challenges, a specialised CPPS testbed within a laboratory environment is presented in this study. In addition, this paper delves into various functions integrated into CPPS, involving critical aspects such as real-time monitoring, digital communication protocol, state estimation, outlier detection, optimal power flow and self-healing capabilities. By implementing a co-simulation platform based on two real-time simulators, Typhoon HIL, the physical and cyber layers can exchange data through Ethernet LAN within the laboratory environment through communication protocols, IEEE C37.118.1 and IEC 61850. This research lays the foundation for a deeper understanding of the potential applications of the CPPS testbed for future control centre technology as well as serves as a catalyst for further innovations, driving a paradigm toward smart, flexible energy infrastructure. © 2013 IEEE.

关键词： digital communication systems

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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Fast alternating minimization algorithm for coded aperture snapshot spectral imaging based on sparsity and deep image priors

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Applied Optics 2025年第14期64卷 4121-4131页

作者： Zhao, Qile Zhao, Xianhong Ma, Xu Chen, Xudong Arce, Gonzalo R. Key Laboratory of Photoelectronic Imaging Technology System of Ministry of Education of China School of Optics and Photonics Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

Coded aperture snapshot spectral imaging is a computational imaging technique used to reconstruct three-dimensional hyperspectral images from one or several two-dimensional projection measurements. However, fewer projection measurements or more spectral channels lead to a severely ill-posed problem, in which case regularization methods have to be applied. In order to significantly improve the accuracy of reconstruction, this paper proposes a fast alternating minimization algorithm based on the sparsity and deep image priors of natural images. By integrating deep image prior into the principle of compressive sensing reconstruction, the proposed algorithm can achieve state-of-the-art results without any training dataset. Extensive experiments show that the Fama-SDIP method significantly outperforms prevailing methods on simulation and real HSI datasets. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved. © 2025 Optica Publishing Group (formerly OSA). All rights reserved.

关键词： Computational Imaging

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A trajectory-level robot skill learning method based on fuzzy systems

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Science China(Information Sciences) 2025年第5期68卷 38-52页

作者： Chao ZENG Zhen DENG Boyang TI Yongsheng GAO Department of Computer Science University of Liverpool School of Mechanical Engineering and Automation Fuzhou University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics and System Harbin Institute of Technology

Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.

关键词： robot skill learning movement primitives TSK fuzzy systems gradient descent robotics

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Low Chirp Dual-Racetrack Modulator for PAM-4 Modulation with Low Driving Voltage

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Journal of Lightwave Technology 2025年第10期43卷 4840-4847页

作者： Yu, Fuhao Zeng, Zhaobang Tang, Kaifei Yang, Shichong Ji, Xiang Xin, Yu Gu, Tingyi Jiang, Wei Nanjing University Key Laboratory of Intelligent Optical Sensing and Manipulation Engineering Research Center for Precision Photonics Integration & System Applications Ministry of Education Nanjing210023 China Nanjing University National Laboratory of Solid-State Microstructures College of Engineering and Applied Sciences Institute of Optical Communication Engineering Nanjing210023 China Nanjing University of Information Science & Technology School of Physics and Optoelectronic Engineering Nanjing210044 China University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States

Micro-ring modulators with a small footprint and energy consumption have been explored for densely integrated high throughput optical interconnects. However, their strong dispersive response results in frequency chirp and pulse broadening, which limits their signal transmission distance. Here we explore the low chirp operation of a dual-racetrack modulator symmetrically coupled to a bus waveguide. With opposite index changes in the two resonators, the coherent couplings of two resonators' optical fields with the bus waveguide and their interference can cancel out the output phase variation due to a single resonator. This allows the push-pull operation to achieve a lower chirp compared to the single-drive operation. The advantage of lower chirp operation is experimentally verified through open eye diagram with 100 Gbps PAM-4 transmitted over 2 km single-mode fiber (SMF), with a peak-to-peak driving voltage of 1.6 V. © 1983-2012 IEEE.

关键词： Single mode fibers

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