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Resource Allocation for Twin Maintenance and Task Processing in Vehicular Edge Computing Network

IEEE Internet of Things Journal 2025年

作者： Xie, Yu Wu, Qiong Fan, Pingyi Cheng, Nan Chen, Wen Wang, Jiangzhou Letaief, Khaled B. Jiangnan University School of Internet of Things Engineering Wuxi 214122 China Nanchang University School of Information Engineering Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications Nanchang 330031 China Tsinghua University Department of Electronic Engineering State Key Laboratory of Space Network and Communications Beijing National Research Center for Information Science and Technology Beijing 100084 China Xidian University State Key Lab. of ISN School of Telecommunications Engineering Xi’an 710071 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai 200240 China Southeast University School of Information Science and Engineering Nanjing 211111 China Hong Kong University of Science and Technology Department of Electrical and Computer Engineering Hong Kong

In the digital twin mobile edge network, the maintenance of the vehicle twin model and vehicular task processing in the server require the support of computing resources. In addition, they are performed simultaneously. Therefore, how to allocate resources for twin maintenance and task processing under limited server resources is crucial. However, current research tends to ignore the aspect of resource competition for twin maintenance. In this study, we analyze the delays of these two affected by resource allocation under a generic digital twin mobile edge network (DTMEN) to construct the optimization problem. For this problem, we transformed the problem using a Markov decision process. Meanwhile, we propose a multi-agent reinforcement learning (MADRL) based twin maintenance and task processing resource collaborative scheduling (TMTPRCS) algorithm to solve the problem. Experiments show that our proposed approach is effective in terms of resource allocation compared to other alternative algorithms. © 2014 IEEE.

关键词： Resource allocation Twin maintenance Vehicular edge computing

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What improves the generalization of graph transformers? a theoretical dive into the self-attention and positional encoding 24

What improves the generalization of graph transformers? a th...

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Proceedings of the 41st International Conference on Machine Learning

作者： Hongkang Li Meng Wang Tengfei Ma Sijia Liu Zaixi Zhang Pin-Yu Chen Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute Troy NY Department of Biomedical Informatics Stony Brook University Stony Brook NY Department of Computer Science and Engineering Michigan State University East Lansing MI and MIT-IBM Watson AI Lab IBM Research MA Department of Computer Science and Engineering University of Science and Technology of China Hefei Anhui China IBM Thomas J. Watson Research Center Yorktown Heights NY

Graph Transformers, which incorporate self-attention and positional encoding, have recently emerged as a powerful architecture for various graph learning tasks. Despite their impressive performance, the complex non-convex interactions across layers and the recursive graph structure have made it challenging to establish a theoretical foundation for learning and generalization. This study introduces the first theoretical investigation of a shallow Graph Transformer for semi-supervised node classification, comprising a self-attention layer with relative positional encoding and a two-layer perceptron. Focusing on a graph data model with discriminative nodes that determine node labels and non-discriminative nodes that are class-irrelevant, we characterize the sample complexity required to achieve a desirable generalization error by training with stochastic gradient descent (SGD). This paper provides the quantitative characterization of the sample complexity and number of iterations for convergence dependent on the fraction of discriminative nodes, the dominant patterns, and the initial model errors. Furthermore, we demonstrate that self-attention and positional encoding enhance generalization by making the attention map sparse and promoting the core neighborhood during training, which explains the superior feature representation of Graph Transformers. Our theoretical results are supported by empirical experiments on synthetic and real-world benchmarks.

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BED: A Block-Level Deduplication-Based Container Deployment Framework 15th

BED: A Block-Level Deduplication-Based Container Deployment ...

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15th International Conference on Green, Pervasive, and Cloud Computing, GPC 2020

作者： Zhang, Shiqiang Wu, Song Fan, Hao Zou, Deqing Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Scinece and Technology Wuhan430074 China

ISBN: (纸本)9783030642426

Container technology has gained great popularity in cloud environment since containers provide near-native performance and are lighter and less expensive than traditional virtual machines. However, starting up a non-local container, whose image is unavailable locally, is time-consuming. The reason is that pulling an image from a remote registry requires a long time. In this paper, we experimentally find that a lot of redundant data exists among image layers when we pull images from the registry. This redundant data causes additional pull time and makes the startup of a non-local container slower. To minimize the amount of pulled data while providing intact images, we propose a container deployment framework, BED, based on block-level deduplication. To be specific, BED stores an image layer as numerous data blocks and a fingerprint list which is generated based on these data blocks. When BED needs to pull an image, it pulls the fingerprint lists of the image, deduplicates these fingerprint lists, and pulls the data blocks non-existing locally from the registry. Based on the local and pulled blocks, BED reconstructs the image layers for a container. Experiments show that compared with original Docker, BED reduces the time of pulling images by 35% on average and saves about 48% data transmission in the network. © 2020, Springer Nature Switzerland AG.

关键词： Containers

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Globally Exponential Regulation of Hypersonic Vehicle with Strong Nonlinearies

Globally Exponential Regulation of Hypersonic Vehicle with S...

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International Symposium on Autonomous systems (ISAS)

作者： Shaohua Yang Xia Li Zhen Liu Yanjun Zhang Institute of Automation College of Engineering Qufu Normal University Qufu China School of Cyber Science and Engineering Qufu Normal University Qufu China Integrated Information System Research Center Institute of Automation Chinese Academy of Sciences Beijing China School of Automation Beijing Institute of Technology Beijing China

This paper considers a regulation problem for a longitudinal dynamical model of hypersonic vehicles with strong nonlinearies. The problem is reduced to a stabilization problem by shifting the desired equilibrium position to the origin. By means of a control input domination idea and suitable coordinate transformations, the longitudinal dynamics of hypersonic vehicles with engine throttle setting dynamics are divided into a flight-path angle controlled subsystem and a velocity controlled subsystem. A new nonlinear state-feedback control scheme is proposed using a parameter separation technique for dealing with strong nonlinearies and a backstepping approach for designing virtual and actual controllers. The proposed controller ensures the closed-loop signals global boundedness and achieves exponential regulation throughout the vehicle’s operational envelope. Simulation results show the desired control system performance for the hypersonic vehicle models with strong nonlinearies.

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Neural Topic Modeling with Deep Mutual information Estimation

arXiv

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arXiv 2022年

作者： Xu, Kang Lu, Xiaoqiu Li, Yuan-Fang Wu, Tongtong Qi, Guilin Ye, Ning Wang, Dong Zhou, Zheng School of Computer Science Nanjing University of Posts and Telecommunications China School of Computer Science and Engineering Southeast University China Faculty of Information Technology Monash University Australia Department of Earth System Science Tsinghua University Beijing100084 China

The emerging neural topic models make topic modeling more easily adaptable and extendable in unsupervised text mining. However, the existing neural topic models is difficult to retain representative information of the documents within the learnt topic representation. In this paper, we propose a neural topic model which incorporates deep mutual information estimation, i.e., Neural Topic Modeling with Deep Mutual information Estimation(NTM-DMIE). NTM-DMIE is a neural network method for topic learning which maximizes the mutual information between the input documents and their latent topic representation. To learn robust topic representation, we incorporate the discriminator to discriminate negative examples and positive examples via adversarial learning. Moreover, we use both global and local mutual information to preserve the rich information of the input documents in the topic representation. We evaluate NTM-DMIE on several metrics, including accuracy of text clustering, with topic representation, topic uniqueness and topic coherence. Compared to the existing methods, the experimental results show that NTM-DMIE can outperform in all the metrics on the four datasets. © 2022, CC BY.

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Towards ISAC-Empowered Vehicular Networks: Framework, Advances, and Opportunities

arXiv

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arXiv 2023年

作者： Du, Zhen Liu, Fan Li, Yunxin Yuan, Weijie Cui, Yuanhao Zhang, Zenghui Masouros, Christos Ai, Bo School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China

Connected and autonomous vehicle (CAV) networks face several challenges, such as low throughput, high latency, and poor localization accuracy. These challenges severely impede the implementation of CAV networks for immersive metaverse applications and driving safety in future 6G wireless networks. To alleviate these issues, integrated sensing and communications (ISAC) is envisioned as a game-changing technology for future CAV networks. This article presents a comprehensive overview on the application of ISAC techniques in vehicle-to-infrastructure (V2I) networks. We cover the general system framework, representative advances, and a detailed case study on using the 5G New Radio (NR) waveform for sensing-assisted communications in V2I networks. Finally, we highlight open problems and opportunities in the field. Copyright © 2023, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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Diff5T: Benchmarking Human Brain Diffusion MRI with an Extensive 5.0 Tesla K-Space and Spatial Dataset

arXiv

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arXiv 2024年

作者： Wang, Shanshan Yu, Shoujun Cheng, Jian Jia, Sen Tie, Changjun Zhu, Jiayu Peng, Haohao Dong, Yijing He, Jianzhong Zhang, Fan Xing, Yaowen Jia, Xiuqin Yang, Qi Tian, Qiyuan Guo, Hua Li, Guobin Zheng, Hairong Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Beihang University Beijing China United-Imaging Healthcare Shanghai China School of Biomedical Engineering Tsinghua University Beijing China Department of Radiology Beijing Chaoyang Hospital Capital Medical University Key Lab of Medical Engineering for Cardiovascular Disease Ministry of Education Beijing China Institute of Information Processing and Automation College of Information Engineering Zhejiang University of Technology China University of Electronic Science and Technology of China Chengdu China

Diffusion magnetic resonance imaging (dMRI) provides critical insights into the microstructural and connectional organization of the human brain. However, the availability of high-field, open-access datasets that include raw k-space data for advanced research remains limited. To address this gap, we introduce Diff5T, a first comprehensive 5.0 Tesla diffusion MRI dataset focusing on the human brain. This dataset includes raw k-space data and reconstructed diffusion images, acquired using a variety of imaging protocols. Diff5T is designed to support the development and benchmarking of innovative methods in artifact correction, image reconstruction, image preprocessing, diffusion modelling and tractography. The dataset features a wide range of diffusion parameters, including multiple b-values and gradient directions, allowing extensive research applications in studying human brain microstructure and connectivity. With its emphasis on open accessibility and detailed benchmarks, Diff5T serves as a valuable resource for advancing human brain mapping research using diffusion MRI, fostering reproducibility, and enabling collaboration across the neuroscience and medical imaging communities. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Extending E Prover with Fully Use Binary Clauses Algorithm Based on Standard Contradiction Separation Rule 16

Extending E Prover with Fully Use Binary Clauses Algorithm B...

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16th IEEE International Conference on Intelligent systems and Knowledge engineering, ISKE 2021

作者： Liu, Peiyao Wu, Guanfeng Xu, Yang Cao, Feng Southwest Jiao Tong University School of Mathematics Chengdu610031 China Southwest Jiao Tong University National-Local Joint Engineering Laboratory of System Credibility Automatic Verification Chengdu610031 China School of Information Engineering Jiangxi University of Science and Technology Ganzhou341000 China

ISBN: (纸本)9781665405539

After nearly two decades of development, E prover has made great progress and is a state-of-the-art automated theorem prover for first-order logic in the world. E has routinely come in as one of the top three provers in all CASC competitions since 1999. However, up to now, E still cannot resolve or effectively resolve many problems of the latest benchmarks database TPTP. And the inference rule of E essentially is binary resolution method which lacks the synergetic processing ability for multi-clause. Standard contradiction separation (S-CS) inference rule is a recently proposed theoretical method to overcome the limitation of binary resolution inference rule. Based on this theory, this paper proposes a novel S-CS dynamic deduction algorithm that fully uses binary clauses (in short FUBC). While maintaining the advantages of E, we implement FUBC algorithm on E, forming an extending E prover, denoted as E_FUBC. Then we use the latest benchmarks database TPTP (such as problems of FOF division from CASC-J10 and CASC-27) to evaluate E_FUBC. Experimental results show E-FUBC outperforms E itself and FUBC algorithm effectively improves E prover's ability. © 2021 IEEE.

关键词： Separation

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A Survey on Machine Learning-based Detection and Classification technology of Malware

A Survey on Machine Learning-based Detection and Classificat...

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Computer information science and Artificial Intelligence (CISAI), International Conference on

作者： Yangyang Meng Honglin Zhuang Zhechao Lin Yetao Jia Institute of System Engineering AMS. PLA Beijing China National Key Laboratory of Science and Technology on Information System Security Beijing China

ISBN: (纸本)9781665406932

Under the background of massive malware, traditional methods of malware analysis can no longer efficiently meet the task of malware analysis. The development of machine learning technologies has effectively solved this problem. In recent years, machine learning and deep learning technologies have been gradually applied to the detection of malicious software. Machine learning-based malware classification has become a research hotspot in the field of malware detection. As an overview of machine learning-based detection and classification of malware, we analyze and summarize the system framework of malware detection and classification, introduce the basic categories of malware features and main classifiers of malware detection and classification, and propose a basic framework and technical architecture of feature processing technologies. Then, we summarize some issues and challenges faced by the current malware detection-related work. Finally, we discuss some potential research directions of machine learning-based detection and classification of malware from three aspects.

关键词： Deep learning information science systematics Learning (artificial intelligence) Computer architecture Feature extraction Malware

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Determination of birefringence of myocardial tissue based on PS-OCT with circularly polarized light as reference 16

Determination of birefringence of myocardial tissue based on...

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16th International Conference on Photonics and Imaging in Biology and Medicine, PIBM 2023

作者： Li, Wangbiao Li, Ke Li, Dezi Chen, Haiyu Cao, Wenliang Zhuo, Shufeng Li, Hui Li, Zhifang Key Laboratory of Optoelectronic Science and Technology for Medicine Ministry of Education Fujian Provincial Key Laboratory of Photonics Technology Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application College of Photonic and Electronic Engineering Fujian Normal University Fuzhou Fujian350117 China Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua University Huaihua Hunan418000 China Fujian Medical University Department of Cardiovascular Surgery Fujian Provincial Hospital Fuzhou Fujian350001 China The Internet of Thing and Artificial Intelligence College Fujian Polytechnic of Information Technology Fuzhou Fujian350001 China

ISBN: (纸本)9781510667341

In this study, the modified spectral domain polarization-sensitive optical coherence tomography (SD PS-OCT) is proposed for determining the birefringence of the myocardial tissue. In this modified SD PS-OCT, the circular polarization state of light was generated before entering the beam splitter. Thus, the polarization states in the reference and sample arms are both circular, and the symmetry between them is good without using additional quarter-wave plate (QWP), which reduce the dispersion effect. The results demonstrated that theoretical analysis for determination of birefringence including the phase retardance and the fast axis orientation based on Stokes parameters of backscattered from biological tissue, which is different from the traditional SD PS-OCT. In addition, the phase retardance and the fast axis orientation was used to differentiate the myocaridal tissue in the diastole of the cardiac cycle the from that in the systole of the cardiac cycle. The findings suggest that the SD PS-OCT be a potential tool for the real-Time monitoring the change of the myocardial wall during the cardiac cycle. © 2023 SPIE.

关键词： Birefringence

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