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NUDIF: A Non-Uniform Deployment Framework for Distributed Inference in Heterogeneous Edge Clusters

Future Internet 2025年第4期17卷 168-168页

作者： Li, Peng Qing, Chen Liu, Hao National Key Laboratory of Complex Aviation System Simulation Chengdu610036 China Southwest China Institute of Electronic Technology Chengdu610036 China Beijing100876 China

Distributed inference in resource-constrained heterogeneous edge clusters is fundamentally limited by disparities in device capabilities and load imbalance issues. Existing methods predominantly focus on optimizing single-pipeline allocation schemes for partitioned sub-models. However, such approaches often lead to load imbalance and suboptimal resource utilization under concurrent batch processing scenarios. To address these challenges, we propose a non-uniform deployment inference framework (NUDIF), which achieves high-throughput distributed inference service by adapting to heterogeneous resources and balancing inter-stage processing capabilities. Formulated as a mixed-integer nonlinear programming (MINLP) problem, NUDIF is responsible for planning the number of instances for each sub-model and determining the specific devices for deploying these instances, while considering computational capacity, memory constraints, and communication latency. This optimization minimizes inter-stage processing discrepancies and maximizes resource utilization. Experimental evaluations demonstrate that NUDIF enhances system throughput by an average of 9.95% compared to traditional single-pipeline optimization methods under various scales of cluster device configurations. © 2025 by the authors.

关键词： Deep neural networks

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Robust Practical Stabilization for complex Dynamical Networks With DoS Attacks and Actuator Saturation

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3108-3118页

作者： Shi, Xueya Peng, Zhinan Yu, Junzhi Li, Hongfei Southwest University College of Electronic and Information Engineering Chongqing400715 China Sichuan Artificial Intelligence Research Institute Yibin644000 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China

This article addresses the problem of designing an attack-resilient adaptive event-triggered (AET) controller for complex dynamical networks (CDNs) under DoS attacks and actuator saturation, with a focus on robust practical stability (RPS). First, considering the impact of DoS attacks on closed-loop systems, an AET controller against DoS attacks is designed. Unlike other event-triggered controllers, the complete timeline is divided, and the AET controller is built with two switching modes based on the intervals of dormant and active periods of DoS attacks in which the system is located. Second, to reconcile AET controller with actuator saturation, a switched system modeling approach is established that explicitly incorporates saturation constraints into the coupled network dynamics. Third, a switched Lyapunov-Krasovskii functional (LKF) is proposed, with which sufficient conditions are provided to ensured the RPS, and a joint design strategy is developed for the desired triggered matrix and feedback gain using linear matrix inequalities (LMIs). Moreover, the results are generalized to the case of actuator faults, indicating that the system is able to achieve RPS with actuator faults. Finally, the proposed method is verified through an example. © 2013 IEEE.

关键词： Lyapunov functions

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Generating Targeted Universal Adversarial Perturbation against Automatic Speech Recognition via Phoneme Tailoring

Generating Targeted Universal Adversarial Perturbation again...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Yujun Chen, Yanqu Wang, Jiakai Hu, Jin Tao, Renshuai Liu, Xianglong State Key Laboratory of Complex & Critical Software Environment Beihang University China School of Computer Science and Engineering Beihang University China College of Computer Science Beijing University of Technology China Zhongguancun Laboratory China School of Computer and Information Technology Beijing Jiaotong University China Institute of Data Space Hefei Comprehensive National Science Center China

ISBN: (纸本)9798350368741

There is a growing concern about adversarial attacks against automatic speech recognition (ASR) systems. Although research into targeted universal adversarial examples (AEs) has progressed, current methods are constrained by inefficient exploitation of audio features, demonstrating insufficient attack ability and robustness in the physical world. To solve this problem, we propose a phoneme-tailored attack (PTA) to improve the quality of the generated AEs. Specifically, to improve attack ability, we propose a Diverse Audio Composition Enrichment method, which enhances the utilization of audio features through phoneme-level slicing and recombination. To adapt AEs to complex environments, we propose a Natural Noise Pattern Guidance method to align AEs with natural noise patterns to improve their robustness. Experiments show that our method achieves an average accuracy of more than 72.34% and 98% with and without a norm constraint, and also demonstrates excellent performance in terms of generalization across datasets and resilience to MP3 compression. © 2025 IEEE.

关键词： Automatic speech recognition natural noise pattern phoneme-level targeted universal adversarial examples

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A Compact Manifold Mixup Feature-Based Open-Set Recognition Approach for Unknown Signals

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China Communications 2025年第4期22卷 322-338页

作者： Yang Ying Zhu Lidong Li Chengjie Sun Hong National Key Laboratory of Wireless Communications(University of Electronic Science and Technology of China) Chengdu 611731China College of Computer Science and Cyber Security(Chengdu University of Technology) Chengdu 610059China The Key Laboratory for Computer Systems of State Ethnic Affairs Commission School of Computer Science and Technology(Southwest Minzu University)Chengdu 610064China China Communications Magazine Co.Ltd. Beijing 100029China

There are all kinds of unknown and known signals in the actual electromagnetic environment,which hinders the development of practical cognitive radio ***,most existing signal recognition models are difficult to discover unknown signals while recognizing known *** this paper,a compact manifold mixup feature-based open-set recognition approach(OR-CMMF)is proposed to address the above ***,the proposed approach utilizes the center loss to constrain decision boundaries so that it obtains the compact latent signal feature representations and extends the low-confidence feature ***,the latent signal feature representations are used to construct synthetic representations as substitutes for unknown categories of ***,these constructed representations can occupy the extended low-confidence ***,the proposed approach applies the distillation loss to adjust the decision boundaries between the known categories signals and the constructed unknown categories substitutes so that it accurately discovers unknown *** OR-CMMF approach outperformed other state-of-the-art open-set recognition methods in comprehensive recognition performance and running time,as demonstrated by simulation experiments on two public datasets RML2016.10a and ORACLE.

关键词： manifold mixup open-set recognition synthetic representation unknown signal recognition

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Microstructure and rheological properties evolution of bi-disperse magnetorheological fluids: From three-dimensional particle flow simulation to experiment

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Journal of Molecular Liquids 2025年 425卷

作者： Wang, Shun Han, Wenjiao Yu, Hailong Jiang, Min He, Bin Institute of Launch Dynamics Nanjing University of Science and Technology Nanjing210094 China National Key Laboratory of Complex Multibody Systems Dynamics Nanjing University of Science and Technology Nanjing210094 China

Many studies have confirmed that the addition of nanoscale magnetizable particles to conventional magnetorheological fluids can effectively improve the magnetorheological effect. However, the influence of particle size ratio on shear strength has no consistent conclusion, and the mechanism is not investigated sufficiently, especially the explanation of macroscopic properties from microstructures. In this work, the microstructure and macroscopic rheological properties of bi-disperse magnetorheological fluids under a wide range of particle size ratio are studied with particle-level simulation and verified by experiments. The fluid field is simulated on the regular Eulerian grids by the lattice Boltzmann method. The Lagrangian particle motion is governed by the discrete element method and the fluid–structure interactions are coupled by the immersed moving boundary scheme. simulation results show that the static yield stress goes through three stages: increasing, decreasing and increasing as the particle size ratio decreases from 1 to 0.2. Experimental data obtained using a rotational rheometer also shows the same trend. The maximum yield stress occurs at a particle size ratio of 0.2, and the minimum yield stress occurs at a particle size ratio between 0.4 and 0.5. This trend is explained for the first time by quantitatively describing the competitive relationship between chain number and chain strength. Furthermore, the effect of particle size ratio on response time is investigated, and we find that the response time increases significantly only at a small particle size ratio. © 2025 Elsevier B.V.

关键词： Yield stress

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Cryo-EM structure of a minimal reaction center–light-harvesting complex from the phototrophic bacterium Chloroflexus aurantiacus

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Journal of Integrative Plant Biology 2025年第4期 967-978页

作者： Guoqiang Huang Shishang Dong Lin Ma Lin Li Jinxin Ju Mei-Jiao Wang Jian-Ping Zhang Sen-Fang Sui Xiaochun Qin State Key Laboratory of Membrane Biology Beijing Advanced Innovation Center for Structural Biology School of Life SciencesTsinghua University Multiscale Research Institute of Complex Systems Fudan University School of Biological Science and Technology University of Jinan School of Chemistry and Life Resources Renmin University of China School of Life Sciences Southern University of Science and Technology

Photosynthetic organisms have developed various light-harvesting antenna systems to capture light and transfer energy to reaction centers（RCs）.Simultaneous utilization of the integral membrane light-harvesting antenna（LH complex） and the extrinsic antenna（chlorosomes） makes the phototrophic bacterium Chloroflexus（Cfx.） aurantiacus an ideal model for studying filamentous anoxygenic phototrophs（FAPs）. Here, we determined the structure of a minimal RC–LH photocomplex from Cfx. aurantiacus J-10-fl（CaRC–LH） at 3.05-? resolution. The CaRC–LH binds only to seven LH subunits, which form a crescent-shaped antenna surrounding the movable menaquinone-10（QB）binding site of Ca RC. In this complex with minimal LH units, an extra antenna is required to ensure sufficient light-gathering, providing a clear explanation for the presence of chlorosomes in Cfx. aurantiacus. More importantly, the semicircle of the antenna represents a novel RC–LH assembly pattern. Our structure provides a basis for understanding the existence of chlorosomes in *** and the possible assembly pattern of RC–LH.

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Exploring Communication-efficient Federated Learning via Stateless In-network Aggregation

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IEEE Transactions on Mobile Computing 2025年

作者： Xia, Junxu Cheng, Geyao Wu, Wenfei Luo, Lailong Guo, Deke National University of Defense Technology National Key Laboratory of Information Systems Engineering Changsha Hunan410073 China National University of Defense Technology State Key Laboratory of Complex & Critical Software Environment College of Information and Communication Changsha Hunan410073 China Peking University School of Computer Science Beijing100091 China

As an ambitious training paradigm, federated learning has garnered increasing attention in recent years, which enables collaborative training of a global model without accessing users' private data. However, due to the simultaneous and constant model updates gathering from massive distributed clients, the central server generally becomes a performance bottleneck. Additionally, the stateful aggregation (retaining all the updates from each client) conducted by the central server further poses potential threats to privacy, since it may recover the raw data based on such model updates inversely. The state-of-the-art methodologies, however, fail to address these two problems concurrently and efficiently. To this end, we propose GAIN, a secure aggregation acceleration service for federated learning. At its core, GAIN leverages programmable switches deployed at the edge network to aggregate model updates in a stateless manner before transmitting them to the central server. Consequently, GAIN can accelerate the transmission and aggregation of model updates while eliminating the chance of recovering private data. We evaluate the performance of GAIN through FPGA-based experiments and large-scale simulations. The results show that GAIN can effectively reduce bandwidth overhead and achieve up to 4.11× training throughput acceleration while prioritizing privacy protection. © 2025 IEEE.

关键词： Differential privacy

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Federated Services: A Smart Service Ecology with Federated Security for Aligned Data Supply and Scenario-Oriented Demands

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IEEE/CAA Journal of Automatica Sinica 2025年第5期12卷 925-936页

作者： Xiaofeng Jia Juanjuan Li Shouwen Wang Hongwei Qi Fei-Yue Wang Rui Qin Min Zhang Xiaolong Liang Beijing Big Data Centre Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China Datatang (Beijing) Technology Company Ltd. Beijing China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

This paper introduces federated services as a smart service ecology with federated security to align distributed data supply with diversified service demands spanning digital and societal contexts. It presents the comprehensive researches on the theoretical foundation and technical system of federated services, aiming at advancing our understanding and implementation of this novel service paradigm. First, a thorough examination of the characteristics of federated security within federated services is conducted. Then, a five-layer technical framework is formulated under a decentralized intelligent architecture, ensuring secure, agile, and adaptable service provision. On this basis, the operational mechanisms underlying data federation and service confederation is analyzed, with emphasis on the smart supply-demand matching model. Furthermore, a scenario-oriented taxonomy of federated services accompanied by illustrative examples is proposed. Our work offers actionable insights and roadmap for realizing and advancing federated services, contributing to the refinement and wider adoption of this transformative service paradigm in the digital era.

关键词： Adaptation models Biological system modeling Large language models Taxonomy Smart contracts Distributed databases Ecology Data models Security Research and development

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In Memory of Wolter J. Fabrycky: A Pioneer of systems Engineering and US-Sino Academic Exchange

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IEEE/CAA Journal of Automatica Sinica 2025年第5期12卷 839-840页

作者： Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China

The passing of Professor Wolter “Wolt” Fabrycky, an outstanding member and great leader, is a big loss to our international systems engineering professional community. “Wolt was a legend in the systems engineering community with his teaching, advising, and dissemination of knowledge through the books he authored.”, as stated by Professor Eileen Aken, a former student of Wolt and the head of the Virginia Tech's Grado Department of Industrial and systems Engineeirng where Wolt had served and led for 30 years and retired as John L. Lawrence Professor emeritus.

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HRSTORY: Historical News Review Based Online Story Discovery 25

HRSTORY: Historical News Review Based Online Story Discovery

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Renjie Zhou Haoran Ye Jian Wan Yong Liao Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education Hangzhou Dianzi University Hangzhou Zhejiang China Zhejiang University of Science and Technology Hangzhou Zhejiang China University of Science and Technology of China Hefei Anhui China

ISBN: (纸本)9798400712456

Story discovery on news streams can help people quickly find story from vast amounts of news, improving the efficiency of information acquisition. Recent online story discovery methods encode text topics and then cluster articles into stories based on similarity. However, the results obtained by these methods are one-time, and clustered news cannot adaptively update in a continuous news stream. Additionally, the inadequate quality of article encoding and the presence of noise data deteriorate the performance of story discovery. To this end, we propose HRSTORY for online story discovery on news streams, which employs a historical news review method to enable news to continuously adapt to the latest environment in the stream data and make corrections and updates. Furthermore, HRSTORY captures better article embeddings through modeling multi-layer relational dependencies within the text. By using sentence-level noise masking, HRSTORY improves the relevance of news article representation to core topics and reduces the interference of noise data. Experiments on real news datasets show that HRSTORY outperforms the state-of-the-art algorithms in unsupervised online story discovery performance.

关键词： historical news

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