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arXiv 2025年

作者： Hossain, Abrar Badawy, Abdel-Hameed A. Islam, Mohammad A. Patki, Tapasya Ahmed, Kishwar Department of Electrical Engineering and Computer Science The University of Toledo United States Department of Electrical and Computer Engineering New Mexico State University United States Department of Computer Science and Engineering The University of Texas Arlington United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory

The growing necessity for enhanced processing capabilities in edge devices with limited resources has led us to develop effective methods for improving high-performance computing (HPC) applications. In this paper, we introduce LASP (Lightweight Autotuning of scientific Application Parameters), a novel strategy designed to address the parameter search space challenge in edge devices. Our strategy employs a multi-armed bandit (MAB) technique focused on online exploration and exploitation. Notably, LASP takes a dynamic approach, adapting seamlessly to changing environments. We tested LASP with four HPC applications: Lulesh, Kripke, Clomp, and Hypre. Its lightweight nature makes it particularly well-suited for resource-constrained edge devices. By employing the MAB framework to efficiently navigate the search space, we achieved significant performance improvements while adhering to the stringent computational limits of edge devices. Our experimental results demonstrate the effectiveness of LASP in optimizing parameter search on edge devices. © 2025, CC BY-NC-ND.

关键词： Edge computing

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Semi-Asynchronous Federated Split Learning for computing-Limited Devices in Wireless Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Ao, Huiqing Tian, Hui Ni, Wanli Nie, Gaofeng Niyato, Dusit Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Tsinghua University Department of Electronic Engineering Beijing100084 China Nanyang Technological University College of Computing and Data Science Singapore

The rapid evolution of edge computing and artificial intelligence (AI) paves the way for pervasive intelligence in the next-generation network. As a hybrid training paradigm, federated split learning (FSL) leverages data and model parallelism to enhance training efficiency. However, existing FSL encounters unacceptable waiting latency due to device heterogeneity and synchronous model aggregation. To address this issue, we propose a semi-asynchronous FSL (SAFSL) framework that enables personalized model splitting and aperiodic model aggregation. We derive the convergence upper bound by considering factors such as the number of devices, training iterations, and data heterogeneity. To minimize the long-term average training latency while maintaining high energy efficiency in resource-constrained wireless networks, we formulate a stochastic mixed-integer nonlinear programming problem. By decomposing it into multiple sub-problems in each round, we propose a Lyapunov-based alternating optimization algorithm to solve it in an online manner. Numerical results demonstrate that our SAFSL achieves faster convergence with reduced communication overhead while maintaining high prediction performance under non-independent and identically distributed data, outperforming state-of-the-art benchmarks. Moreover, our algorithm achieves a low training latency, highlighting its superior performance and effectiveness. © 2002-2012 IEEE.

关键词： Federated learning

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Complexity of Tensor Product Functions in Representing Antisymmetry

arXiv

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arXiv 2025年

作者： Wang, Yuyang Hu, Yukuan Liu, Xin State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China CERMICS Ecole des Ponts IP Paris Marne-la-Vallée77455 France

Tensor product function (TPF) approximations have been widely adopted in solving high-dimensional problems, such as partial differential equations and eigenvalue problems, achieving desirable accuracy with computational overhead that scales linearly with problem dimensions. However, recent studies have underscored the extraordinarily high computational cost of TPFs on quantum many-body problems, even for systems with as few as three particles. A key distinction in these problems is the antisymmetry requirement on the unknown functions. In the present work, we rigorously establish that the minimum number of involved terms for a class of TPFs to be exactly antisymmetric increases exponentially fast with the problem dimension. This class encompasses both traditionally discretized TPFs and the recent ones parameterized by neural networks. Our proof exploits the link between the antisymmetric TPFs in this class and the corresponding antisymmetric tensors and focuses on the Canonical Polyadic rank of the latter. As a result, our findings uncover a fundamental incompatibility between antisymmetry and low-rank TPFs in high-dimensional contexts and offer new insights for further developments. Copyright © 2025, The Authors. All rights reserved.

关键词： Tensors

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S-MGHSTN: Towards An Effective Streaming Traffic Accident Risk Prediction Framework

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IEEE Transactions on Knowledge and Data engineering 2025年

作者： Chen, Minxiao Yuan, Haitao Jiang, Nan Zheng, Zhihan Bao, Zhifeng Zhou, Ao Jiang, Jiaxin Wang, Shangguang Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore RMIT University School of Computing Technologies MelbourneVIC3000 Australia National University of Singapore School of Computing 117417 Singapore

Traffic accidents pose a significant risk to human health and property safety. To address this issue, predicting their risks has garnered growing interest. We argue that a desired prediction solution should demonstrate resilience to the complexity of traffic accidents. In particular, it should adequately consider the streaming nature of data and key related aspects, such as regional background, accurately capture both proximity and similarity while bridging the disparities, and effectively address the sparsity. However, these factors are often overlooked or difficult to incorporate. In this paper, we propose a novel streaming multi-granularity hierarchical spatio-temporal network. Initially, we innovate by incorporating remote sensing data, facilitating the creation of hierarchical multi-granularity structure and the comprehension of regional background. We construct multiple high-level risk prediction tasks to enhance model's ability to cope with sparsity. Subsequently, to capture and bridge spatial proximity and semantic similarity, region features and multi-view graph undergo encoding processes to distill effective representations, followed by a graph-enhanced representation alignment module that reconciles their disparities. At last, an alternating experience replay with a dual-memory buffer is employed to accommodate streaming data scenarios. Extensive experiments on two real datasets verify the superiority of our model against the state-of-the-art methods. © 1989-2012 IEEE.

关键词： Remote sensing Spatio-temporal data Streaming data Traffic accident risk prediction

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Experimental realization of valley vortex states in water wave crystals

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Science Bulletin 2025年第7期70卷 1034-1037页

作者： Zijian Qin Qiaolu Chen Lian Shen Yihao Yang Hongsheng Chen Huaping Wang Key Laboratory of Ocean Observation-Imaging Testbed of Zhejiang Province Ocean CollegeZhejiang UniversityHangzhou 310058China State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation CenterZhejiang UniversityHangzhou 310027China Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical InstrumentationCollege of Information Science and Electronic EngineeringZhejiang UniversityHangzhou 310027China Key Lab of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang Jinhua Institute of Zhejiang UniversityZhejiang UniversityJinhua 321099China Shaoxing Institute of Zhejiang University Zhejiang UniversityShaoxing 312000China International Joint Innovation Center The Electromagnetics Academy at Zhejiang UniversityZhejiang UniversityHaining 314400China

The valley degree of freedom(DoF)plays a crucial role in valleytronics,paralleling the spin DoF in spintronics,and serves as a novel conduit for information and *** momentum space,valley DoF is characterized by discrete energy extremal states,which are present in conventional semiconductors and twodimensional(2D)crystal *** wave behaviors in periodic structures allow for the application of valley dispersion relations to wave systems,including optics[1],acoustics[2,3],and water waves[4].When the wavelength is comparable to the period of an artificial crystal structure,classical waves experience strong Bragg scattering,resulting in a band gap and new propagation *** to their macroscopic features and controllable geometric symmetry,these classical wave systems have become an ideal platform for studying topological *** progress has been made in classical wave systems,conclusive evidence of topological valley states in water waves—a typical classical wave system—has not yet been obtained.

关键词： spin dof valley dispersion relations valley degree freedom dof plays discrete energy extremal stateswhich valley vortex states wave behaviors wave systemsincluding conventional semiconductors

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Learning Adaptive Jamming and Beamforming for Hybrid IRS-assisted Secure NOMA Transmissions

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IEEE Transactions on Communications 2025年

作者： Zhou, Defeng Li, Lanhua Gong, Shimin Gu, Bo Chen, Gaojie Niyato, Dusit School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University China State Key Laboratory of Optoelectronic Materials and Technologies Guangdong Guangzhou510275 China Nanyang Technological University College of Computing and Data Science Singapore Singapore

In this paper, we investigate hybrid passive and active intelligent reflecting surface (IRS)-assisted secure non-orthogonal multiple access (NOMA) networks. Multiple users concurrently transmit sensitive data to an access point (AP) in the presence of an eavesdropper (Eve). The hybrid IRS is employed to enhance the NOMA users’ sum rates while simultaneously performing jamming beamforming against the Eve by optimizing the communication channels of NOMA users and injecting controllable noise into the Eve’s channel. We formulate a sum secrecy rate maximization problem by jointly optimizing the users’ scheduling policy, the hybrid IRS’s working mode and beamforming, and the AP’s receiving beamforming. To address combinatorial user scheduling and high-dimensional beamforming design, we develop a dual-cycling deep reinforcement learning (DRL) framework. We first determine the NOMA users’ scheduling strategy and the hybrid IRS’s working mode using a proximal policy optimization (PPO)-based learning algorithm. Then, we optimize the AP’s receiving beamforming and hybrid IRS’s beamforming strategies using an alternating optimization (AO) algorithm. The joint beamforming optimization can significantly enhance the DRL’s learning efficiency by limiting its action space. Moreover, we propose a lightweight two-phase algorithm with approximation techniques to reduce computational complexity by eliminating double-nested loops in AO, while maintaining secrecy performance close to optimum. Numerical results demonstrate that the proposed dual-cycling DRL scheme achieves 54.85% gains in the secrecy rate compared to traditional DRL schemes. © 1972-2012 IEEE.

关键词： Beamforming

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A Hybrid Federated Learning Framework for Task-Oriented Semantic Communication

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IEEE Internet of Things Journal 2025年

作者： Sun, Haofeng Ni, Wanli Tian, Hui Zheng, Jingheng Nie, Gaofeng Niyato, Dusit Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Tsinghua University Department of Electronic Engineering Beijing100084 China Nanyang Technological University College of Computing and Data Science 117583 Singapore

In existing deep learning-based semantic communication systems, centralized training of semantic models brings a risk of privacy leakage, whereas distributed training imposes a huge computational burden on user equipments (UEs). To address these challenges, we propose a hybrid federated learning (Hybrid-FL) framework to alleviate the computational burden on UEs while protecting the user privacy. Specifically, each UE uploads local gradients and semantic symbols to the base station for the collaborative training of global and local semantic models. Furthermore, we propose a joint communication and computation scheme for supporting the model aggregation and semantics transmission. To gain deep insights, we expose the joint impact of communication and computation on the convergence behavior of Hybrid-FL by deriving an upper bound. Then, we formulate a mixed-integer non-linear programming problem to improve the convergence performance of Hybrid-FL, which is then effectively solved by using our proposed algorithm that developed based on alternating and matching theory. Experimental results demonstrate that Hybrid-FL outperforms conventional FL by achieving a 20% accuracy gain and a 80% latency reduction. © 2014 IEEE.

关键词： Federated learning

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From quasi-Newton methods to non-quasi-Newton methods

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PROGRESS IN NATURAL SCIENCE-MATERIALS INTERNATIONAL 1997年第1期7卷 14-23页

作者： Yuan, YX State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Chinese Academy of Sciences Beijing 100080 China

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A novel deep high-level concept-mining jointing hashing model for unsupervised cross-modal retrieval

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High-Confidence computing 2025年第2期5卷

作者： Dong, Chun-Ru Zhang, Jun-Yan Zhang, Feng Hua, Qiang Xu, Dachuan Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding071002 China Beijing Institute for Scientific and Engineering Computing Beijing University of Technology Beijing100124 China

Unsupervised cross-modal hashing has achieved great success in various information retrieval applications owing to its efficient storage usage and fast retrieval speed. Recent studies have primarily focused on training the hash-encoded networks by calculating a sample-based similarity matrix to improve the retrieval performance. However, there are two issues remain to solve: (1) The current sample-based similarity matrix only considers the similarity between image-text pairs, ignoring the different information densities of each modality, which may introduce additional noise and fail to mine key information for retrieval;(2) Most existing unsupervised cross-modal hashing methods only consider alignment between different modalities, while ignoring consistency between each modality, resulting in semantic conflicts. To tackle these challenges, a novel Deep High-level Concept-mining Jointing Hashing (DHCJH) model for unsupervised cross-modal retrieval is proposed in this study. DHCJH is able to capture the essential high-level semantic information from image modalities and integrate into the text modalities to improve the accuracy of guidance information. Additionally, a new hashing loss with a regularization term is introduced to avoid the cross-modal semantic collision and false positive pairs problems. To validate the proposed method, extensive comparison experiments on benchmark datasets are conducted. Experimental findings reveal that DHCJH achieves superior performance in both accuracy and efficiency. The code of DHCJH is available at Github. © 2024 The Author(s)

关键词： Online searching

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A potential reduction algorithm for an extended SDP problem

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Science China Mathematics 2000年第1期43卷 35-46页

作者：聂家旺袁亚湘 State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Chinese Academy of Sciences Beijing 100080 China

An extended semi-definite programming, the SDP with an additional quadratic term in the objective function, is studied. Our generalization is similar to the generalization from linear programming to quadratic programming. Optimal conditions for this new class of problems are discussed and a potential reduction algorithm for solving QSDP problems is presented. The convergence properties of this algorithm are also given.

关键词： quadratic semi-definite programming potential reduction method convergence.

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