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

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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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 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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An automatic approach to develop the fourth-order and L2-stable lattice Boltzmann model for diagonal-anisotropic diffusion equations

arXiv

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arXiv 2025年

作者： Chen, Ying Chai, Zhenhua Shi, Baochang School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan430074 China Institute of Interdisciplinary Research for Mathematics and Applied Science Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan430074 China The State Key Laboratory of Intelligent Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China

This paper discusses how to develop a high-order multiple-relaxation-time lattice Boltzmann (MRT-LB) model for the general d(≥ 1)-dimensional diagonal-anisotropic diffusion equation. Such an MRT-LB model considers the transformation matrix constructed in a natural way and the DdQ(2d2 + 1) [(2d2 + 1) discrete velocities in d-dimensional space] lattice structure. A key step in developing the high-order MRT-LB model is to determine the additional adjustable relaxation parameters and weight coefficients, which are used to eliminate the truncation errors at some certain orders of the MRT-LB model, while ensuring the stability of the MRT-LB model. In this work, we first present a unified MRT-LB model for the d-dimensional diagonal-anisotropic diffusion equation. Then, through the direct Taylor expansion, we analyze the macroscopic modified equations of the MRT-LB model up to fourth-order at the diffusive scaling, and further derive the conditions that ensure the MRT-LB model to be fourth-order consistent with the diagonal-anisotropic diffusion equation. In particular, when the diagonal-anisotropic diffusion equation is reduced to the isotropic type, we propose another MRT-LB model with the DdQ(2d+ 1) lattice structure [fewer discrete velocities than the DdQ(2d2 + 1) lattice structure], and the fourth-order conditions are similarly derived. Additionally, we also construct the fourth-order initialization scheme for the present LB method. After that, the condition which guarantees that the MRT-LB model can satisfy the stability structure is explicitly given, and we would like to point out that from a numerical perspective, once the stability structure is satisfied, the MRT-LB model must be L2 stable. In combination with the fourth-order consistent and L2 stability conditions, the relaxation parameters and weight coefficients of the MRT-LB model can be automatically given by a simple computer code. Finally, we perform numerical simulations of several benchmark problems, and f

关键词： Anisotropy

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Convexity of the implicit Lagrangian

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 1997年第2期92卷 331-341页

作者： Peng, JM Assistant Professor State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific Engineering Computing Academia Sinica Beijing China

The implicit Lagrangian has attracted much attention recently because of its utility in reformulating complementarity and variational inequality problems as unconstrained minimization problems, II was first proposed by Mangasarian and Solodov as a merit function for the nonlinear complementarity problem (Ref. 1). Three open problems were also raised in the same paper, This paper addresses, among other issues, one of these problems by giving the properties of the implicit Lagrangian and establishing its convexity under appropriate assumptions.

关键词： complementarity problems implicit Lagrangian functions

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A Clustering-Based Color Reordering Method for Reversible Data Hiding in Palette Images

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IET Image Processing 2025年第1期19卷

作者： Deng, Jianxuan Chen, Yi Wang, Hongxia Guo, Chun Cui, Yunhe Shen, Guowei State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Guizhou University Guiyang China School of Cyber Science and Engineering Sichuan University Chengdu China

A recent research work pointed out that the reversible data hiding algorithms proposed for gray-scale images can be implemented on the reconstructed palette images to improve embedding capacity and visual quality by reordering the color table. However, the reordering effect has a significant impact on performance improvement. Therefore, we propose a clustering-based color reordering method for reversible data hiding in palette images to improve the reordering effect and further enhance the performance. In this method, we first design a centroid initialization method to select the initial centroids and then exploit the K-means algorithm to generate (Formula presented.) clusters for the colors in the original color table. In the following, our proposed method, respectively, reorders the colors of these clusters by a greedy strategy and concatenates them into the reordered color table. Based on the relationship between the original and the reordered color tables, a novel index matrix can be reconstructed. Finally, state-of-the-art reversible data hiding algorithms can be implemented on the reconstructed index matrix for performance improvement. Since our proposed method improves the reordering effect, enhances the correlation of the reconstructed index matrix, and reduces the length of the encoded location map, the maximal embedding capacities and the visual quality under the fixed embedding capacities are improved. We conducted experiments on two image datasets and six standard images to verify that the performance improvement of our proposed reordering method is better than that of the state-of-the-art methods. © 2025 The Author(s). IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Embeddings

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Clustering-based Low Rank Approximation Method

arXiv

引用

arXiv 2025年

作者： Zhu, Yujun Zhu, Jie Arshad, Hizba Wang, Zhongming Ming, Ju School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan China Department of Mathematics and Statistics Florida International University MiamiFL United States

We propose a clustering-based generalized low rank approximation method, which takes advantage of appealing features from both the generalized low rank approximation of matrices (GLRAM) and cluster analysis. It exploits a more general form of clustering generators and similarity metrics so that it is more suitable for matrix-structured data relative to conventional partitioning methods. In our approach, we first pre-classify the initial matrix collection into several small subset clusters and then sequentially compress the matrices within the clusters. This strategy enhances the numerical precision of the low-rank approximation. In essence, we combine the ideas of GLRAM and clustering into a hybrid algorithm for dimensionality reduction. The proposed algorithm can be viewed as the generalization of both techniques. Theoretical analysis and numerical experiments are established to validate the feasibility and effectiveness of the proposed algorithm. © 2025, CC BY-NC-ND.

关键词： Matrix algebra

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SELF-SUPERVISED DIFFUSION MRI DENOISING VIA ITERATIVE AND STABLE REFINEMENT

arXiv

引用

arXiv 2025年

作者： Wu, Chenxu Kong, Qingpeng Jiang, Zihang Kevin Zhou, S. School of Biomedical Engineering Division of Life Sciences and Medicine USTC China MIRACLE Center Suzhou Institute for Advance Research USTC China State Key Laboratory of Precision and Intelligent Chemistry USTC China Key Laboratory of Intelligent Information Processing of CAS Institute of Computing Technology CAS China

Magnetic Resonance Imaging (MRI), including diffusion MRI (dMRI), serves as a "microscope" for anatomical structures and routinely mitigates the influence of low signal-to-noise ratio scans by compromising temporal or spatial resolution. However, these compromises fail to meet clinical demands for both efficiency and precision. Consequently, denoising is a vital preprocessing step, particularly for dMRI, where clean data is unavailable. In this paper, we introduce Di-Fusion, a fully self-supervised denoising method that leverages the latter diffusion steps and an adaptive sampling process. Unlike previous approaches, our single-stage framework achieves efficient and stable training without extra noise model training and offers adaptive and controllable results in the sampling process. Our thorough experiments on real and simulated data demonstrate that Di-Fusion achieves state-of-the-art performance in microstructure modeling, tractography tracking, and other downstream tasks. Code is available at https://***/FouierL/Di-Fusion. Copyright © 2025, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Influence of Froude Number on the Development and Evolution of Secondary Flows in a Sharply Curved Bend: An Experimental and Numerical Study

SSRN

引用

SSRN 2025年

作者： Hu, Chengwei Liu, Yujiao Yu, Minghui River Research Department Yangtze River Scientific Research Institute Wuhan430010 China Key Laboratory of River and Lake Regulation and Flood Control in the Middle and Lower Reaches of the Changjiang River of Ministry of Water Resources Yangtze River Scientific Research Institute Wuhan430010 China State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan University Wuhan430072 China

Meandering rivers sculpt landscapes and foster diverse ecosystems, with secondary flows in bends exerting a pivotal influence on sediment transport and channel morphology. Although the Froude number typically remains below 0.3 in natural meanders, the interplay of secondary flows under these low-Froude conditions is still poorly understood. This study addresses this knowledge gap by systematically examining the influence of Froude numbers (Fr = 0.12-0.21) on secondary flow structures in sharply curved channels through high-resolution flume experiments and numerical simulations. Results reveal that even slight variations in Froude number can markedly alter vortex dynamics and secondary flow complexity, underscoring a delicate balance between inertial and turbulent forces. In particular, the stability of S2-type secondary flows depends on the precise alignment of advective, centrifugal, and turbulence-induced vorticity. Minor shifts in inertial forcing can rapidly destabilize S2, leading to significant changes in velocity distributions. Additionally, a time or spatial lag between the onset of secondary circulation and the point of maximum velocity inversion points to a dynamic, two-way feedback between the secondary flow and the main flow, evolving from robust vortex growth at lower Fr to flow decay at higher Fr. These findings advance our understanding of secondary flow mechanisms in natural rivers and offer practical insights for river engineering and flood management, informing more effective strategies for sediment control and bank stability. © 2025, The Authors. All rights reserved.

关键词： Froude number

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