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arXiv 2025年

作者： Zhu, Yan Zhu, Jingyang Wang, Ting Shi, Yuanming Jiang, Chunxiao Letaief, Khaled Ben MoE Engineering Research-Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University Shanghai200062 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Advancements in artificial intelligence (AI) and low-earth orbit (LEO) satellites have promoted the application of large remote sensing foundation models for various downstream tasks. However, direct downloading of these models for fine-tuning on the ground is impeded by privacy concerns and limited bandwidth. Satellite federated learning (FL) offers a solution by enabling model fine-tuning directly on-board satellites and aggregating model updates without data downloading. Nevertheless, for large foundation models, the computational capacity of satellites is insufficient to support effective on-board fine-tuning in traditional satellite FL frameworks. To address these challenges, we propose a satellite-ground collaborative federated fine-tuning framework. The key of the framework lies in how to reasonably decompose and allocate model components to alleviate insufficient on-board computation capabilities. During fine-tuning, satellites exchange intermediate results with ground stations or other satellites for forward propagation and back propagation, which brings communication challenges due to the special communication topology of space transmission networks, such as intermittent satellite-ground communication, short duration of satellite-ground communication windows, and unstable inter-orbit inter-satellite links (ISLs). To reduce transmission delays, we further introduce tailored communication strategies that integrate both communication and computing resources. Specifically, we propose a parallel intra-orbit communication strategy, a topology-aware satellite-ground communication strategy, and a latency-minimalization inter-orbit communication strategy to reduce space communication costs. Simulation results demonstrate significant reductions in training time with improvements of approximately 33%. Copyright © 2025, The Authors. All rights reserved.

关键词： Satellite communication systems

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Broadband high efficiency polarization grating with continuously varying periodicity based on liquid crystal polymer

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Journal of Molecular Liquids 2025年 426卷

作者： Zhao, Yadi Wang, Jianguo Chen, Yi Wang, Fajia Wang, Jundong Zhu, Xiangyu Hong, Ruijin Tao, Chunxian Zhang, Dawei Lian, Yafei Zhao, Yuan-an Research and Development Center of Thin Film Optics Shanghai Institute of Optics and Fine Mechanics No.390 Qinghe Road Jiading District Shanghai201800 China Key Laboratory of Materials for High Power Laser Chinese Academy of Sciences Shanghai201800 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Liquid crystal polarization gratings (LCPGs) exhibit unique optical characteristics, making them widely applicable in various optical fields such as optical field manipulation, beam deflection, and laser radar. Consequently, improving their achromatic performance holds significant importance. In this work, we demonstrate a broadband, high diffraction efficiency, and varying periodicity liquid crystal polarization grating fabricated through the photoalignment technique. A spherical wave is employed for pattern exposure within the interference optical path, with the surface anchoring periodicity varying from 2.56um to 12.04um. Subsequently, a double-layer twisted structure is introduced to construct a high diffraction efficiency PG, which attains an average diffraction efficiency exceeding 98 % within the 520–700 nm spectral range. The structural and diffraction properties of the grating are thoroughly investigated. The manufacturing process for this grating structure is both straightforward and efficient. This work has significant implications for the application and advancement of LCPGs in achromatic performance and contributes substantially to the fields of optical field manipulation and beam deflection. © 2025

关键词： Diffraction efficiency

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Practical Strategies to Improve Test Efficiency

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Tsinghua Science and technology 2007年第S1期12卷 250-254页

作者：丁志刚汪泓澄凌良合 Shanghai Development Center of Software Technology Shanghai Key Laboratory of Computer Software Evaluating and Testing

This paper introduces strategies to detect software bugs in earlier life cycle stage in order to improve test efficiency. Static analysis tool is one of the effective methods to reveal software bugs during software development. Three popular static analysis tools are introduced, two of which, PolySpace and Splint, are compared with each other by analyzing a set of test cases generatedd by the authors. PolySpace can reveal 60% bugs with 100% R/W ratio (ratio of real bugs and total warnings), while Splint reveal 73.3% bugs with 44% R/W ratio. And they are good at finding different categories of bugs. Two strategies are concluded to improve test efficiency, under the guideline that static analysis tools should be used in finding different categories of bugs according to their features. The first one aims at finding bugs as many as possible, while the second concentrates to reduce the average time on bug revelation. Experimental data shows the first strategy can find 100% bugs with 60% R/W ratio, the second one find 80% bugs with 66.7% R/W ratio. Experiment results prove that these two strategies can improve the test efficiency in both fault coverage and testing time.

关键词： static analysis PolySpace splint software bug

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Imperceptible and Targeted Physical Attacks on Deep Learning-Based Speech Semantic Communications

Imperceptible and Targeted Physical Attacks on Deep Learning...

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IEEE Conference on Wireless Communications and Networking

作者： Yuhao Hua Yang Xu Chen Lyu Jia Liu Yulong Shen Weidong Yang Norio Shiratori Hangzhou Institute of Technology Xidian University Hangzhou China School of Computer Science and Technology Xidian University Xi'an China MoE Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics Shanghai China Center for Strategic Cyber Resilience Research and Development National Institute of Informatics Tokyo Japan Research and Development Initiative Chuo University Tokyo Japan

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The deep learning-based semantic communication system (DeepSC) is designed to improve the efficiency and accuracy of information transmission, by leveraging joint source-channel coding techniques to extract relevant semantic features. However, existing research on attack methods targeting DeepSC has primarily focused on text and image domains, leaving the speech domain largely unexplored. To this end, this paper proposes Iterative Semantic Gradient Update (ISGU), a novel approach for crafting physical layer adversarial attacks on DeepSC for speech transmission (DeepSC-ST). Specifically, we introduce a joint loss that combines the semantic similarity loss with Connectionist Temporal Classification loss to expedite the generation process of targeted attacks against the DeepSC-ST. In addition, we design an algorithm to generate adversarial examples, enhancing their imperceptibility by meticulously controlling the perturbation power added to the input speech. Extensive experiments indicate that ISGU is capable of rapidly generating highly covert adversarial examples, with a notably high attack success rate.

关键词： Training Perturbation methods Oral communication Information processing Speech enhancement Semantic communication Physical layer Feature extraction Security Iterative methods

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Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution

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IEEE Transactions on Multimedia 2025年

作者： Fu, Congrui Yuan, Hui Jiang, Shiqi Zhang, Guanghui Shen, Liquan Hamzaoui, Raouf Center of Experimental Teaching China Shandong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Machine Intelligence and System Control China Shandong University School of software Shandong Jinan China Shandong University School of Computer Science and Technology China Shanghai University School of Communication and Information Engineering Shanghai China De Montfort University School of Engineering and Sustainable Development LeicesterLE1 9BH United Kingdom

By converting low-frame-rate, low-resolution videos into high-frame-rate, high-resolution ones, space-time video super-resolution techniques can enhance visual experiences and facilitate more efficient information dissemination. We propose a convolutional neural network (CNN) for space-time video super-resolution, namely GIRNet. Our method combines long-term global information and short-term local information from the video to better extract complete and accurate spatial-temporal information. To generate highly accurate features and thus improve performance, the proposed network integrates a feature-level temporal interpolation module with deformable convolutions and a global spatial-temporal information-based residual convolutional long short-term memory (convLSTM) module. In the feature-level temporal interpolation module, we leverage deformable convolution, which adapts to deformations and scale variations of objects across different scene locations. This provides a more efficient solution than conventional convolution for extracting features from moving objects. Our network effectively uses forward and backward feature information to determine inter-frame offsets, leading to the direct generation of interpolated frame features. In the global spatial-temporal information-based residual convLSTM module, the first convLSTM is used to derive global spatial-temporal information from the input features, and the second convLSTM uses the previously computed global spatial-temporal information feature as its initial cell state. This second convLSTM adopts residual connections to preserve spatial information, thereby enhancing the output features. Experiments on the Vimeo90K dataset show that the proposed method outperforms open source state-of-the-art techniques in peak signal-to-noise-ratio (by 1.45 dB, 1.14 dB, and 0.2 dB over STARnet, TMNet, and 3DAttGAN, respectively), structural similarity index(by 0.027, 0.023, and 0.006 over STARnet, TMNet, and 3DAttGAN, respectiv

关键词： Long short-term memory

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SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning

arXiv

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arXiv 2025年

作者： Li, Nan Wang, Xiaolu Du, Xiao Cai, Puyu Wang, Ting Engineering Research Center of Software/Hardware Co-Design Technology and Application Ministry of Education the Shangh ai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China Software Engineering Institute East China Normal University Shanghai200050 China Computer Science Department New York University New YorkNY10012 United States

Federated Learning (FL) enables clients to collaboratively train machine learning models without sharing local data, preserving privacy in diverse environments. While traditional FL approaches preserve privacy, they often struggle with high computational and communication overhead. To address these issues, model pruning is introduced as a strategy to streamline computations. However, existing pruning methods, when applied solely based on local data, often produce sub-models that inadequately reflect clients’ specific tasks due to data insufficiency. To overcome these challenges, this paper introduces SAFL (Structure-Aware Federated Learning), a novel framework that enhances personalized federated learning through client-specific clustering and Similar Client Structure Information (SCSI)-guided model pruning. SAFL employs a two-stage process: initially, it groups clients based on data similarities and uses aggregated pruning criteria to guide the pruning process, facilitating the identification of optimal sub-models. Subsequently, clients train these pruned models and engage in server-based aggregation, ensuring tailored and efficient models for each client. This method significantly reduces computational overhead while improving inference accuracy. Extensive experiments demonstrate that SAFL markedly diminishes model size and improves performance, making it highly effective in federated environments characterized by heterogeneous data. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Neural Rendering and Flow-assisted Unsupervised Multi-view Stereo for Real-time Monocular Tracking and Scene Perception

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IEEE Transactions on Automation Science and Engineering 2025年

作者： Tong, Kevin W. Cai, Yandong Jie, Yu-Wen Duan, Ya Hou, Yuhong Wu, Edmond Q. Nanjing University of Posts and Telecommunications College of Automation College of Artificial Intelligence Nanjing210023 China Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Dalian116024 China Shanxi University School of Automation and Software Engineering Shanxi 237016 China Flight Test Technology and Engineering Center of the Chinese Flight Test Establishment Xian710089 China China Civil Aviation Shanghai Aircraft Airworthiness Certification Center Shanghai200335 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China

The existing camera tracking and perception methods mainly rely on sparse SLAM, which limits the dense perception ability of the scene and affects the reliability of auxiliary decision-making. Different from this, this work proposes a real-time tracking and unsupervised dense sensing framework. Firstly, the dense depth value of the scene is predicted by unsupervised multi-view stereo to remove the dependence on labeled data. Then, the quality of synthetic pseudo-reference image is quantified according to the predicted depth map and used as a weighted guidance to train the unsupervised model, thus reducing the ambiguity of feature matching in areas such as specular reflection. Moreover, the sparse optical flow of the keyframes is solved by real-time and robust ORB feature matching operator, which assists the high-precision training of unsupervised depth inference model. To increase the prediction accuracy of occluded area, a novel rendering consistency loss via neural radiance fields is designed to constrain the geometric characteristics of object surface. Finally, dense direct image alignment is performed from a global model to improve the tracking robustness, which is incrementally constructed from dense depth prediction. Extensive experiments on synthetic datasets and real datasets validate the effectiveness and practicability of the proposed work, which is an effective supplement to the existing SLAM work. © 2025 IEEE.

关键词： Stereo image processing

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Collaborative prediction for bus arrival time based on CPS

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Journal of Central South University 2014年第3期21卷 1242-1248页

作者：蔡雪松 Software College East China Normal University Vehicle Information Department Shanghai Development Center of Computer Software Technology

To improve the accuracy of real-time public transport information release system, a collaborative prediction model was proposed based on cyber-physical systems architecture. In the model, the total bus travel time was divided into three parts: running time, dwell time and intersection delay time, and the data were divided into three categories of historical data, static data and real-time data. The bus arrival time was obtained by fusion computing the real-time data in perception layer together with historical data and static data in collaborative layer. The validity of the collaborative model was verified by the data of a typical urban bus line in shanghai, and 1538 sets of data were collected and analyzed from three different perspectives. By comparing the experimental results with the actual results, it is shown that the experimental results are with higher prediction accuracy, and the collaborative prediction model adopted is able to meet the demand for bus arrival prediction.

关键词： prediction model cyber-physical system architecture bus arrival time collaborative prediction

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Multi-Modal Multi-Kernel Graph Learning for Autism Prediction and Biomarker Discovery

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 842-854页

作者： Jin Liu Junbin Mao Hanhe Lin Hulin Kuang Shirui Pan Xusheng Wu Shan Xie Fei Liu Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China School of Science and Engineering University of Dundee Dundee U.K. School of Information and Communication Technology Griffith University Gold Coast QLD Australia Shenzhen Health Development Research and Data Management Center Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology.

关键词： Diseases Autism Random access memory Data mining Kernel Electronic mail Training Neuroimaging Graph convolutional networks Bioinformatics

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Paleolithic divergence and multiple Neolithic expansions of ancestral nomadic emperor-related paternal lineages

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Journal of Genetics and Genomics 2025年第4期52卷 502-512页

作者： Mengge Wang Qiuxia Sun Yuhang Feng Lan-Hai Wei Kaijun Liu Lintao Luo Yuguo Huang Kun Zhou Haibing Yuan Hongliang Lv Yu Lu Jing Cheng Shaoqing Wen Chuan-Chao Wang Renkuan Tang Fengxiao Bu Chao Liu Huijun Yuan Zhiyong Wang Guanglin He Institute of Rare Diseases Frontiers Science Center for Disease-related Molecular NetworkWest China HospitalSichuan UniversityChengduSichuan 610000China Center for Archaeological Science Sichuan UniversityChengduSichuan 610000China Anti-Drug Technology Center of Guangdong Province GuangzhouGuangdong 510230China Department of Forensic Medicine College of Basic MedicineChongqing Medical UniversityChongqing 400331China Human Genetics and Forensic Genomics Research Institute College of Basic MedicineChongqing Medical UniversityChongqing 400331China Institute of Humanities and Human Sciences Inner Mongolia Normal UniversityHohhotInner Mongolia 010022China Institute of 23Mofang Tianfu Software ParkChengduSichuan 610042China MOE Laboratory for National Development and Intelligent Governance Fudan UniversityShanghai 200433China State Key Laboratory of Cellular Stress Biology School of Life SciencesXiamen UniversityXiamenFujian 361102China

The reconstruction of demographic history using ancient and modern genomic resources reveals extensive interactions and admixture between ancient nomadic pastoralists and the social organizations of the Chinese Central ***,the extent to which Y-chromosome genetic legacies from nomadic emperor-related ancestral lineages influence the Chinese paternal gene pool remains ***,we genotype 2717 ethnolinguistically diverse samples belonging to C2a lineages,perform whole-genome sequencing on 997 representative samples,and integrate these data with ancient genomic *** reconstruct the evolutionary histories of Northern Zhou-,Qing emperor-,and pastoralist-related lineages to assess their genetic impact on modern Chinese *** reassembled fine-scale Ychromosome phylogeny identifies deep divergence and five Neolithic expansion events contributing differently to the formation of northern Chinese *** modeling indicates that the nomadic empires of the Northern Zhou and Qing dynasties genetically originated from the Mongolian *** topology and shared haplotype patterns show that three upstream ancestors of Northern Zhou(C2a1a1b1a2a1b-FGC28857),Donghu tribe(C2a1a1b1-F1756),and Qing(C2a1a3a2-F10283)emperor-related lineages expanded during the middle Neolithic,contributing significantly to genetic flow between ancient northeastern Asians and modern East ***,this study reveals limited direct contributions of Emperor Wu of Northern Zhou’s lineages to modern East Asians.

关键词： Paternal lineage Founding lineage Genetic origin Emperor lineage Y-chromosome

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