检索结果-内蒙古大学图书馆

Linear Convergent Distributed Nash Equilibrium Seeking With Compression

IEEE Transactions on Automatic control 2025年第5期70卷 3316-3323页

作者： Chen, Xiaomeng Wu, Yuchi Yi, Xinlei Huang, Minyi Shi, Ling Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China College of Electronics and Information Engineering Tongji University Department of Control Science and Engineering Shanghai201804 China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Carleton University School of Mathematics and Statistics OttawaONK1S 5B6 Canada

Information compression techniques are majorly employed to reduce communication cost over peer-to-peer links. In this article, we investigate distributed Nash equilibrium (NE) seeking problems in a class of noncooperative games over directed graphs with information compression. To improve communication efficiency, a compressed distributed NE seeking (C-DNES) algorithm is proposed to obtain an NE for games, where the differences between decision vectors and their estimates are compressed. The proposed algorithm is compatible with a general class of compressors, including both unbiased and biased compressors. Moreover, our approach only requires the adjacency matrix of the directed graph to be row-stochastic, in contrast to past works that relied on balancedness or specific global network parameters. It is shown that C-DNES not only inherits the advantages of conventional distributed NE algorithms, achieving linear convergence rate for games with restricted strongly monotone mappings, but also saves communication costs in terms of transmitted bits. Finally, numerical simulations illustrate the advantages of C-DNES in saving communication cost by an order of magnitude under different compressors. © 1963-2012 IEEE.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Enhancing the transferability of adversarial attacks via Scale Enriching

引用

Neural Networks 2025年 189卷 107549页

作者： Zhao, Yuhang Zheng, Jun Gao, Xianfeng Liu, Lu Zhang, Yaoyuan Zhang, Quanxin School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

Deep learning models are vulnerable to adversarial attacks. Transfer-based adversarial examples are crafted against surrogate models and transferred to victim models. However, under the black-box settings, most adversaries have poor transferability on models with different input sizes. In this work, we propose the Scale Enriching Method (SEM) to enhance the transferability of adversarial examples using an input scale-enriching framework. By scaling the surrogate model's input in a specific range, our method enriches the attention areas that the surrogate model perceives and enlarges the tolerance of the distinction among different models, significantly improving the transferability. Notably, SEM avoids introducing extraneous noise during perturbation generation, thereby preserving the inherent textural features corresponding to different scales within the original images. Experiments on ImageNet show that our method successfully mitigates the gap of transferability between models with different input sizes. Furthermore, we demonstrate that our method can integrate with existing methods and bypass a variety of defense methods with over 90% success rate. © 2025 Elsevier Ltd

关键词： Adversarial attack Adversarial example Transfer attack

来源：评论

学校读者我要写书评

暂无评论

High Efficiency Wiener Filter-based Point Cloud Quality Enhancement for MPEG G-PCC

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wei, Yuxuan Wang, Zehan Guo, Tian Liu, Hao Shen, Liquan Yuan, Hui Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Yantai University School of Computer and Control Engineering Yantai264005 China Shanghai University Shanghai Institute for Advanced Communication and Data Science Shanghai200072 China

Point clouds, which directly record the geometry and attributes of scenes or objects by a large number of points, are widely used in various applications such as virtual reality and immersive communication. However, due to the huge data volume and unstructured geometry, efficient compression of point clouds is very crucial. The Moving Picture Expert Group is establishing a geometry-based point cloud compression (G-PCC) standard for both static and dynamic point clouds in recent years. Although lossy compression of G-PCC can achieve a very high compression ratio, the reconstruction quality is relatively low, especially at low bitrates. To mitigate this problem, we propose a high efficiency Wiener filter that can be integrated into the encoder and decoder pipeline of G-PCC to improve the reconstruction quality as well as the rate-distortion performance for dynamic point clouds. Specifically, we first propose a basic Wiener filter, and then improve it by introducing coefficients inheritance and variance-based point classification for the Luma component. Besides, to reduce the complexity of the nearest neighbor search during the application of the Wiener filter, we also propose a Morton code-based fast nearest neighbor search algorithm for efficient calculation of filter coefficients. Experimental results demonstrate that the proposed method can achieve average Bjøntegaard delta rates of -6.1%, -7.3%, and -8.0% for Luma, Chroma Cb, and Chroma Cr components, respectively, under the condition of lossless-geometry-lossy-attributes configuration compared to the latest G-PCC encoding platform (i.e., geometry-based solid content test model version 7.0 release candidate 2) by consuming affordable computational complexity. © 2025 IEEE. All rights reserved.

关键词： Wiener filtering

来源：评论

学校读者我要写书评

暂无评论

Decomposition of Differences of Independent and Identically Distributed Random Variables

引用

Moscow University Computational Mathematics and Cybernetics 2025年第1期49卷 67-72页

作者： Ushakov, V. G. Ushakov, N. G. Department of Computational Mathematics and Cybernetics Moscow State University Moscow Russia Institute of Informatics Problems Federal Research Center ‘‘Computer Science and Control’’ Russian Academy of Sciences Moscow Russia Norwegian University of Science and Technology Trondheim Norway

The authors consider conditions where the distribution of the components of the difference between two independent identically distributed random variables can be uniquely reconstructed with an accuracy of up to a shift and reflection. This uniqueness is essential for solving a number of characterization problems in mathematical statistics. An algorithm for estimating the components is presented for when data are given in a symmetrized form.

关键词：

来源：评论

学校读者我要写书评

暂无评论

MKMGCN-DDI: Predicting Drug-Drug Interactions via Magnetic Graph Convolutional Network With Multiple Kernels

IEEE Transactions on Computational Biology and Bioinformatic...

引用

IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 469-480页

作者： Ming Chen Yunhan Pan Xiujuan Lei Chunyan Ji Yinglong Dai Yi Pan Department of Artificial Intelligence Hunan Normal University Changsha Hunan China School of Computer Science Shaanxi Normal University Xi’an China Computer Science Department BNU-HKBU United International College Zhuhai China Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Polypharmacy is a common means of clinical treatments, but detecting drug-drug interactions (DDIs) behind unexpected effects can be costly and faces clinical limitations. Recently, graph neural networks (GNNs) have demonstrated encouraging performance in predicting DDIs. However, most studies overlook the comprehensive aspects of DDIs, such as the coexistence of types of pharmacological changes and the asymmetric roles of drugs. In this article, we define new prediction tasks, taking into account both enhancive or depressive changes and the roles of drugs, and then establish spectral GNNs to predict comprehensive information of DDIs. First, we formally define several tasks, including joint prediction tasks designed to leverage both types and directions. These tasks deduce to sub-tasks in previous studies. Then, we propose a unified framework, the MKMGCN-DDI, via introducing two Magnetic Laplacian matrices to encode comprehension information within DDIs, defining multiple graph filters, and designing multiple-kernel based Magnetic graph convolutional networks (MKMGCN). Experiments across three datasets show that it not only has good adaptability to multiple tasks but also significantly improves results on simple tasks. Case studies on breast neoplasms and lung neoplasms verify its feasibility, as over half of top-10 items are supported.

关键词： Drugs Predictive models Magnetic separation Laplace equations Bioinformatics Sodium Market research Graph convolutional networks computer science Computational modeling

来源：评论

学校读者我要写书评

暂无评论

A cost function approximation method for dynamic vehicle routing with docking and LIFO constraints

Multimodal Transportation

引用

Multimodal Transportation 2025年第1期4卷

作者： Horváth, Markó Kis, Tamás Györgyi, Péter HUN-REN Institute for Computer Science and Control H-1111 Budapest Kende u. 13-17 Hungary

In this paper, we study a dynamic pickup and delivery problem with docking constraints. There is a homogeneous fleet of vehicles to serve pickup-and-delivery requests at given locations. The vehicles can be loaded up to their capacity, while unloading has to follow the last-in-first-out (LIFO) rule. The locations have a limited number of docking ports for loading and unloading, which may force the vehicles to wait. The problem is dynamic since the transportation requests arrive real-time, over the day. Accordingly, the routes of the vehicles are to be determined dynamically. The goal is to satisfy all the requests such that a combination of tardiness penalties and traveling costs is minimized. We propose a cost function approximation based solution method. In each decision epoch, we solve the respective optimization problem with a perturbed objective function to ensure the solutions remain adaptable to accommodate new requests. We penalize waiting times and idle vehicles. We propose a variable neighborhood search based method for solving the optimization problems, and we apply two existing local search operators, and we also introduce a new one. We evaluate our method using a widely adopted benchmark dataset, and the results demonstrate that our approach significantly surpasses the current state-of-the-art methods. © 2025 The Author(s)

关键词： Cost function approximation Docking constraints Dynamic pickup and delivery problem Variable neighborhood search

来源：评论

学校读者我要写书评

暂无评论

Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review

arXiv

引用

arXiv 2025年

作者： Gu, Yan Liu, Zhaoze Dai, Shuhong Liu, Cong Wang, Ying Wang, Shen Theodoropoulos, Georgios Cheng, Long The School of Control and Computer Engineering North China Electric Power University Beijing China The School of Computer Science and Technology Shandong University of Technology China The Institute of Computing Technology Chinese Academy of Sciences Beijing China The School of Computer Science University College Dublin Ireland Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Cloud computing has revolutionized the provisioning of computing resources, offering scalable, flexible, and on-demand services to meet the diverse requirements of modern applications. At the heart of efficient cloud operations are job scheduling and resource management, which are critical for optimizing system performance and ensuring timely and cost-effective service delivery. However, the dynamic and heterogeneous nature of cloud environments presents significant challenges for these tasks, as workloads and resource availability can fluctuate unpredictably. Traditional approaches, including heuristic and meta-heuristic algorithms, often struggle to adapt to these real-time changes due to their reliance on static models or predefined rules. Deep Reinforcement Learning (DRL) has emerged as a promising solution to these challenges by enabling systems to learn and adapt policies based on continuous observations of the environment, facilitating intelligent and responsive decision-making. This survey provides a comprehensive review of DRL-based algorithms for job scheduling and resource management in cloud computing, analyzing their methodologies, performance metrics, and practical applications. We also highlight emerging trends and future research directions, offering valuable insights into leveraging DRL to advance both job scheduling and resource management in cloud computing. © 2025, CC BY.

关键词： Job shop scheduling

来源：评论

学校读者我要写书评

暂无评论

Toward Long Video Understanding via Fine-Detailed Video Story Generation

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年第5期35卷 4592-4607页

作者： You, Zeng Wen, Zhiquan Chen, Yaofo Li, Xin Zeng, Runhao Wang, Yaowei Tan, Mingkui South China University of Technology School of Future Technology Guangzhou510006 China Peng Cheng Laboratory Shenzhen518066 China South China University of Technology School of Software Engineering Guangzhou510006 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Harbin Institute of Technology School of Computer Science and Technology Shenzhen518055 China

Long video understanding has become a critical task in computer vision, driving advancements across numerous applications from surveillance to content retrieval. Existing video understanding methods suffer from two challenges when dealing with long video understanding: intricate long-context relationship modeling and interference from redundancy. To tackle these challenges, we introduce Fine-Detailed Video Story generation (FDVS), which interprets long videos into detailed textual representations. Specifically, to achieve fine-grained modeling of long-temporal content, we propose a Bottom-up Video Interpretation Mechanism that progressively interprets video content from clips to video. To avoid interference from redundant information in videos, we introduce a Semantic Redundancy Reduction mechanism that removes redundancy at both the visual and textual levels. Our method transforms long videos into hierarchical textual representations that contain multi-granularity information of the video. With these representations, FDVS is applicable to various tasks without any fine-tuning. We evaluate the proposed method across eight datasets spanning three tasks. The performance demonstrates the effectiveness and versatility of our method. © 1991-2012 IEEE.

关键词： Video analysis

来源：评论

学校读者我要写书评

暂无评论

Enriching Physical-Virtual Interaction in AR Gaming by Tracking Identical Real Objects

arXiv

引用

arXiv 2025年

作者： Yu, Liuchuan Huang, Ching-I Wang, Hsueh-Cheng Yu, Lap-Fai Computer Science Department George Mason University FairfaxVA United States Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Taiwan Faculty of Electrical and Computer Engineering Department and Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Taiwan Faculty of Computer Science Department George Mason University FairfaxVA United States

Augmented reality (AR) games, particularly those designed for headsets, have become increasingly prevalent with advancements in both hardware and software. However, the majority of AR games still rely on pre-scanned or static scenes, and interaction mechanisms are often limited to controllers or hand-tracking. Additionally, the presence of identical objects in AR games poses challenges for conventional object tracking techniques, which often struggle to differentiate between identical objects or necessitate the installation of fixed cameras for global object movement tracking. In response to these limitations, we present a novel approach to address the tracking of identical objects in an AR scene to enrich physical-virtual interaction. Our method leverages partial scene observations captured by an AR headset, utilizing the perspective and spatial data provided by this technology. Object identities within the scene are determined through the solution of a label assignment problem using integer programming. To enhance computational efficiency, we incorporate a Voronoi diagram-based pruning method into our approach. Our implementation of this approach in a farm-to-table AR game demonstrates its satisfactory performance and robustness. Furthermore, we showcase the versatility and practicality of our method through applications in AR storytelling and a simulated gaming robot. Our video demo is available at: https://***/ rPGkLYuKvCQ. Copyright © 2025, The Authors. All rights reserved.

关键词： Computational efficiency

来源：评论

学校读者我要写书评

暂无评论

MFE-Net: A remote sensing image dehazing model for the visual system of a flight simulator 6

MFE-Net: A remote sensing image dehazing model for the visua...

引用

6th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Ge, Wenyi Wang, Bo Liu, Qi Qin, Xiaolin Tan, Shihan Wang, Shengjie Yuan, Xia College of Computer Science Chengdu University of Information Technology Chengdu610255 China Chengdu Institute of Computer Applications Chinese Academy of Sciences Chengdu610213 China Jiuzhou Air Trafic Control Sichuan Jiuzhou Investment Holding GroupCo. Ltd Mianyang621000 China The Key Laboratory of Flight Techniques and Flight Safety Civil Aviation Flight University of China Guanghan618307 China

ISBN: (纸本)9781510688018

Obtaining high-quality remote sensing images is crucial in generating a three-dimensional terrain for flight simulators. However, due to the presence of haze and other impact factors, collected remote sensing images usually suffer from blurred details, which limits its applicability real-world applications. To address this issue a deep learning-based dehazing model is proposed to advance hazy remote sensing images to build flight simulator visual systems. The encoder-decoder architecture is proposed to perform the image dehazing, in which an improved feature extraction module is designed to extract both global and local semantic features across multi-resolutions. In addition, the pixel-pixel and region-region mechanism are innovatively designed to achieve deep mapping relationships and further enhance image dehazing. Experimental results demonstrate that the proposed model outperforms other comparative baselines, achieving high-confidence dehazing in multi-scene remote sensing images, and with good generality and applicability in multi-resolution flight simulators and multi-terrain remote sensing. © 2025 SPIE.

关键词： Flight simulators

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：