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Mobile person re-identification with a lightweight trident CNN

science China(Information sciences) 2020年第11期63卷 293-297页

作者： Mingfu XIONG Dan CHEN Xiaoqiang LU Engineering Research Center of Hubei Province for Clothing Information School of Mathematics and Computer ScienceWuhan Textile University National Engineering Research Center for Multimedia Software School of Computer ScienceWuhan University Center for OPTical IMagery Analysis and Learning (OPTIMAL) State Key Laboratory of Transient Optics and Photonics Xi'an Institute of Optics and Precision MechanicsChinese Academy of Sciences

Dear editor,Person re-identification (re-ID) aims to recognize or associate a person and time at non-overlapping physical locations after the person had been previously observed visually elsewhere. This technology is ... 详细信息

关键词： Smart Mobile Device Person Re-identification Trident CNN Smart City Video Surveillance

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Simulation and Analysis of Back Siltation in a Navigation Channel Using MIKE 21

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Journal of Ocean University of China 2022年第4期21卷 893-902页

作者： ZHANG Kuncheng LI Qingjie ZHANG Jing SHI Hongyuan YU Jing GUO Xinchang DU Yonggang School of Marxism/Institute of Marine Development Ocean University of ChinaQingdao 266100China Guangxi Key Laboratory of Marine Environmental Science Guangxi Academy of SciencesGuangxi 530007China Yantai Marine Environment Monitoring Center Station State Oceanic AdministrationYantai 264006China Qingdao Ocean Shipping Mariners College Teaching and Research Department of Mathematics and Computer ScienceQingdao 266100China Civil Engineering College Ludong UniversityYantai 264025China College of Oceanic and Atmospheric Sciences/Institute of Marine Development Ocean University of ChinaQingdao266100China

The channel back-siltation problem has been restricting the development of channels,and its monitoring is limited by funds and natural ***,predicting the channel back-siltation situation in a timely and accurate manner is ***,a numerical simulation of the back-siltation problem in the sea area near the channel is of great significance to the maintenance of a *** this study,the back siltation of a deep-water channel in the Lanshan Port area of the Port of Rizhao after dredging is *** paper relies on the MIKE 21 software to establish the wave,tidal current,and sediment numerical models and uses measured data from two observation stations in the study area for *** this basis,taking one month as an example,the entire project channel was divided into five sections,and three observation points were set on each *** results show that the area with offshore siltation is located in the northerly direction of the artificial anti-wave *** occurred on the northern seabed in the sea a little farther from the *** occurred on the seabed surface far away from the shoreline,and with the increase in the distance from the shoreline,the amount of siltation in the south,center,and north became gradually closed,and the results can be used to guide actual engineering *** study will play a positive role in promoting the dredging project of Rizhao Lanshan Port.

关键词： channel back siltation numerical simulation back silting analysis and prediction

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Denoising Reuse: Exploiting Inter-frame Motion Consistency for Efficient Video Generation

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Chenyu Yan, Shuo Chen, Yixuan Wang, Xianwei Wang, Yujiang Dong, Mingzhi Yang, Xiaochen Li, Dongsheng Zhu, Rui Clifton, David A. Dick, Robert P. Lv, Qin Yang, Fan Lu, Tun Gu, Ning Shang, Li Fudan University School of Computer Science Shanghai China Fudan University Shanghai Key Laboratory of Data Science Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China Oxford Suzhou Centre for Advanced Research Suzhou China University of Bath Bath United Kingdom University of Glasgow School of Mathematics and Statistics United Kingdom Microsoft Research Asia Shanghai China University of London Bayes Business School City United Kingdom University of Oxford Department of Engineering Science Oxford United Kingdom University of Michigan Department of Electrical Engineering and Computer Science College of Engineering Ann ArborMI United States University of Colorado Boulder BoulderCO United States

Denoising-based diffusion models have attained impressive image synthesis;however, their applications on videos can lead to unaffordable computational costs due to the per-frame denoising operations. In pursuit of efficient video generation, we present a Diffusion Reuse MOtion (Dr. Mo) network to accelerate the video-based denoising process. Our crucial observation is that the latent representations in early denoising steps between adjacent video frames exhibit high consistencies with motion clues. Inspired by the discovery, we propose to accelerate the video denoising process by incorporating lightweight, learnable motion features. Specifically, Dr. Mo will only compute all denoising steps for base frames. For a non-based frame, Dr. Mo will propagate the pre-computed based latents of a particular step with interframe motions to obtain a fast estimation of its coarse-grained latent representation, from which the denoising will continue to obtain more sensitive and fine-grained representations. On top of this, Dr. Mo employs a meta-network named Denoising Step Selector (DSS) to dynamically determine the step to perform motion-based propagations for each frame, ensuring the correct transformation of multi-granularity visual features. Extensive evaluations on video generation and editing tasks indicate that Dr. Mo delivers widely applicable acceleration for diffusion-based video generations while effectively retaining the visual quality and style. Video generation and visualization results can be found at https://***. © 1991-2012 IEEE.

关键词： HTTP

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Source-Free Elastic Model Adaptation for Vision-and-Language Navigation

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IEEE Transactions on Multimedia 2025年

作者： Tan, Mingkui Chen, Peihao Zhi, Hongyan Mai, Jiajie Rosman, Benjamin Ji, Dongyu Zeng, Runhao South China University of Technology School of Software Engineering Guangzhou China City University of Hong Kong Hong Kong University of the Witwatersrand School of Computer Science and Applied Mathematics Johannesburg South Africa Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Shenzhen China

Vision-and-Language Navigation (VLN) requires an agent to follow given instructions to navigate. Despite the significant progress, the model trained on seen environments has a performance drop on unseen environments due to distribution shift. To improve the generalization, existing method attempts to apply test-time adaptation to VLN. However, it needs to access the training data and all testing data for updating the model before inference. The setting is not suitable for the real application because it is hard for the agent to access training data and all testing data when the agent is applied in a new environment. In this paper, we consider a more practical setting with sourcefree and online-inference test-time adaption. In other words, the model can only access one testing sample for test-time adaptation. In this setting, the model may suffer from catastrophic forgetting of the learned knowledge and unstable parameter update issues. To solve these challenges, we propose an elastic adaptation model (EAM) that consists of an auxiliary decision model and a sample replay mechanism. We use the online testing samples to adapt the auxiliary decision model to new environments, which cooperates with the frozen original model to make better action decisions. The sample replay mechanism stores the historical testing samples to make the adaptation process more stable. Our method is model-agnostic and is effortless to be applied to most existing methods. Experimental results show that our method achieves stable performance improvement based on three existing methods on three VLN benchmark datasets. © 1999-2012 IEEE.

关键词： Metadata

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A mathematical reaction–diffusion model describing the connection between smokers and tuberculosis patients

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Partial Differential Equations in Applied mathematics 2025年 14卷

作者： Khaloufi, Issam Karim, Marouane Rhila, Soukaina Ben Rachik, Mostafa Laboratory of Analysis Modelling and Simulation Department of Mathematics and Computer Science Faculty of Sciences Ben M'Sik Hassan II University Casablanca Casablanca Sidi Othman BP 7955 Morocco Multidisciplinary Research and Innovation Laboratory (LPRI) Moroccan School of Engineering Sciences (EMSI) Casablanca Morocco

In this paper, we introduce a new mathematical model that incorporates both temporal and spatial factors to characterize the relationship between the smoking population and tuberculosis (TB) cases. We validate the model's relevance by demonstrating the existence and positivity of the solutions. Furthermore, we propose three control measures aimed at reducing TB and smoking transmission, focusing on limiting contact between smokers and non-smokers, improving awareness of the health risks associated with smoking, and ensuring timely and effective medical treatment for TB patients. Mathematical analysis is employed to demonstrate the existence and characteristics of optimal controls for these strategies. The theoretical results are verified using numerical simulations, which also confirm the effectiveness of the suggested control strategies. Our results underline the importance of considering spatiotemporal dynamics in disease modeling and control, and they offer crucial insights for public health initiatives aimed at reducing the prevalence of smoking and TB. © 2025 The Authors

关键词： Mathematical model Optimal control Smoking Spatiotemporal dynamics Tuberculosis

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High-speed and low-power molecular dynamics processing unit(MDPU)with ab initio accuracy

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npj Computational Materials 2024年第1期10卷 559-568页

作者： Pinghui Mo Yujia Zhang Zhuoying Zhao Hanhan Sun Junhua Li Dawei Guan Xi Ding Xin Zhang Bo Chen Mengchao Shi Duo Zhang Denghui Lu Yinan Wang Jianxing Huang Fei Liu Xinyu Li Mohan Chen Jun Cheng Bin Liang Weinan E Jiayu Dai Linfeng Zhang Han Wang Jie Liu College of Integrated Circuits Hunan UniversityChangsha410082P.R.China College of Electrical and Information Engineering Hunan UniversityChangsha410082P.R.China College of Computer National University of Defense TechnologyChangsha410073P.R.China Department of Physics National University of Defense TechnologyChangsha410073P.R.China DP Technology 100080 BeijingP.R.China AI for Science Institute 100080 BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking University100871 BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking University100871 BeijingP.R.China School of Mathematical Science Peking University100871 BeijingP.R.China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEMCollege of Chemistry and Chemical EngineeringXiamen UniversityXiamen361005P.R.China School of Integrated Circuits Peking University100871 BeijingP.R.China Center for Machine Learning Research Peking University100871 BeijingP.R.China Institute of Applied Physics and Computational Mathematics 100088 BeijingP.R.China Greater Bay Area Institute for Innovation Hunan UniversityGuangzhou511300P.R.China Department of Electrical and Computer Engineering University of WashingtonSeattleWA98195USA

Molecular dynamics(MD)is an indispensable atomistic-scale computational tool widely-used in various *** the past decades,nearly all ab initio MD and machine-learning MD have been based on the general-purpose central/graphics processing units(CPU/GPU),which are well-known to suffer from their intrinsic“memory wall”and“power wall”***,nowadays MD calculations with ab initio accuracy are extremely time-consuming and power-consuming,imposing serious restrictions on the MD simulation size and *** solve this problem,here we propose a special-purpose MD processing unit(MDPU),which could reduce MD time and power consumption by about 103 times(109 times)compared to state-of-the-art machine-learningMD(ab initio MD)based on CPU/GPU,while keeping ab initio *** significantly-enhanced performance,the proposed MDPU may pave a way for the accurate atomistic-scale analysis of large-size and/or longduration problems which were impossible/impractical to compute before.

关键词： consuming power MDP

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STAR-RIS and UAV Combination in MEC Networks: Simultaneous Task Offloading and Communications

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IEEE Transactions on Communications 2025年

作者： Xiao, Han Hu, Xiaoyan Wang, Wenjie Su, Zhou Wong, Kai-Kit Yang, Kun Xi'an Jiaotong University School of Information and Communications Engineering Xi'an710049 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China University College London Department of Electronic and Electrical Engineering Torrington Place LondonWC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of Nanjing University State Key Laboratory of Novel Software Technology Nanjing210008 China School of Intelligent Software and Engineering Suzhou215163 China University of Essex School of Computer Science and Electronic Engineering Essex United Kingdom

This paper explores a simultaneous tasks offloading and communications (STOC) scheme in mobile edge computing (MEC) networks, supported by the combination of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and the unmanned aerial vehicle (UAV). Different from the traditional MEC schemes, the proposed scheme concurrently considers the computation and communication capabilities of the MEC networks, which is actually more practical in reality. Specifically, an optimization problem is devised to maximize the weighted sum of the minimum computed task data and communication data, while ensuring the quality of service (QoS) constraints for STOC through joint design of time scheduling, resource allocation, active and passive beamforming, alongside with the UAV trajectory planning. This non-convex problem with strong couplings among variables is challenging to solve directly. Then, a novel alternating optimization method is proposed, leveraging the successive convex approximation (SCA) and semi-definite relaxation (SDR) techniques. We provide sufficient numerical results to validate the effectiveness of the proposed STOC scheme, which demonstrate that the proposed scheme supported by STAR-RIS and UAV outperforms five benchmark schemes in terms of performance gain. It is important to note that the proposed scheme offers a feasible and realistic way for the implementations of STOC in practical MEC networks. © 1972-2012 IEEE.

关键词： Convex optimization

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Deep Learning Approaches for Recognizing Daily Human Activities Using Smart Home Sensors

Deep Learning Approaches for Recognizing Daily Human Activit...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Worrakit Sanpote Ponnipa Jantawong Narit Hnoohom Anuchit Jitpattanakul Sakorn Mekruksavanich Department of Computer Science School of Information and Communication Technology University of Phayao Phayao Thailand Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Computer Engineering Image Information and Intelligence Laboratory Faculty of Engineering Mahidol University Nakorn Pathom Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

Nowadays, one of the most important objectives in health-related research is the improvement of the living condition and well-being of people. Smart home systems can provide health protection for residents based on the results of daily activity recognition. Recent advances and developments in sensor technology have increased the need for sensor-compatible goods and services in smart homes. Consequently, the ever-increasing volume of data requires the field of deep learning (DL) for auto-matic human motion recognition. Recent research has modeled spatiotemporal sequences gathered by smart home sensors using long short-term memory networks. In this work, ResNeXt-based models that learn to classify human activities in smart homes were proposed to improve recognition performance. Experiments conducted on Center for Advanced Studies in Adaptive Systems (CASAS) data, a publicly available benchmark dataset, shows that the proposed ResNeXt-based techniques are significantly superior to the existing DL methods and provide better results compared to the existing literature. The ResNeXt model achieved the averaged accuracy over the benchmark method to 84.81%, 93.57%, and 90.38% for the CASAS_Cairo, CASAS_Milan and CASAS_Kyoto3 datasets, respectively.

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Non-Convex Potential Games for Finding Global Solutions to Sensor Network Localization

Non-Convex Potential Games for Finding Global Solutions to S...

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European Control Conference (ECC)

作者： Gehui Xu Guanpu Chen Yiguang Hong Baris Fidan Thomas Parisini Karl H. Johansson Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering Tongji University Shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Department of Electrical and Electronic Engineering Imperial College London London UK Department of Engineering and Architecture University of Trieste Trieste Italy

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Sensor network localization (SNL) problems require determining the physical coordinates of all sensors in a network. This process relies on the global coordinates of anchors and the available measurements between non-anchor and anchor nodes. Attributed to the intrinsic non-convexity, obtaining a globally optimal solution to SNL is challenging, as well as implementing corresponding algorithms. In this paper, we formulate a non-convex multi-player potential game for a generic SNL problem to investigate the identification condition of the global Nash equilibrium (NE) therein, where the global NE represents the global solution of SNL. We employ canonical duality theory to transform the non-convex game into a complementary dual problem. Then we develop a conjugation-based algorithm to compute the stationary points of the complementary dual problem. On this basis, we show an identification condition of the global NE: the stationary point of the proposed algorithm satisfies a duality relation. Finally, simulation results are provided to validate the effectiveness of the theoretical results.

关键词： Location awareness Coordinate measuring machines Simulation Current measurement Noise Europe Games

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pFedLVM: A Large Vision Model (LVM)-Driven and Latent Feature-Based Personalized Federated Learning Framework in Autonomous Driving

arXiv

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arXiv 2024年

作者： Kou, Wei-Bin Lin, Qingfeng Tang, Ming Xu, Sheng Ye, Rongguang Leng, Yang Wang, Shuai Li, Guofa Chen, Zhenyu Zhu, Guangxu Wu, Yik-Chung Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Shenzhen Research Institute of Big Data Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Research Institute of Electronic Science and Technology University of Electronic Science and technology Chengdu China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China College of Mechanical and Vehicle Engineering Chongqing University Chongqing China

Deep learning-based Autonomous Driving (AD) semantic segmentation (SSeg) models often exhibit poor generalization due to data heterogeneity in an ever domain-shifting environment. While Federated Learning (FL) could improve the generalization of an AD SSeg model (known as FedAD system), conventional models often struggle with under-fitting as the amount of accumulated training data progressively increases. To address this issue, instead of conventional small models, employing Large Vision Models (LVMs) in FedAD is a viable option for better learning of representations from a vast volume of data. However, implementing LVMs in FedAD introduces three challenges: (I) the extremely high communication overheads associated with transmitting LVMs between participating vehicles and a central server;(II) lack of computing resource to deploy LVMs on each vehicle;(III) the performance drop due to LVM focusing on shared features but overlooking local vehicle characteristics. To overcome these challenges, we propose pFedLVM, a LVM-Driven, Latent Feature-Based Personalized Federated Learning framework. In this approach, the LVM is deployed only on central server, which effectively alleviates the computational burden on individual vehicles. Furthermore, the exchange between central server and vehicles are the learned features rather than the LVM parameters, which significantly reduces communication overhead. In addition, we utilize both shared features from all participating vehicles and individual characteristics from each vehicle to establish a personalized learning mechanism. This enables each vehicle’s model to learn features from others while preserving its personalized characteristics, thereby outperforming globally shared models trained in general FL. Extensive experiments demonstrate that pFedLVM outperforms the existing state-of-the-art approach by 18.47%, 25.60%, 51.03% and 14.19% in terms of mIoU, mF1, mPrecision and mRecall, respectively. © 2024, CC BY-NC-ND.

关键词： Semantic Segmentation

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