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TVEG: Model Selection of the Time-Varying Exponential Family Distributions Graphical Models

IEEE Transactions on Network Science and engineering 2025年

作者： Liu, Juan Mei, Guofeng Xia, Yuanqing Wu, Xiaoqun Lu, Jinhu Beijing Forestry University School of Technology Beijing100107 China Italy Zhongyuan University of Technology Henan 451191 China Wuhan University School of Mathematics and Statistics Wuhan430072 China Beihang University State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence School of Automation Science and Electrical Engineering Beijing100191 China

The undirected graphical model, a popular class of statistical model, offers a way to describe and explain the relationships among a set of variables. However, it remains a challenge to choose a certain graphical model to explain the relationships of variables adequately, especially when the relationships of variables are rewiring over time. This paper proposes the Time-Varying Exponential Family Distributions Graphical (TVEG) models, with time-varying structures and exponential family node-wise conditional distributions. TVEG models extend the scope of available graph models and can be applied to time-varying and exponential family distribution observation data in reality. We propose the Temporally Smoothed L1regularized exponential family graphical estimator (TSLEG), an estimator to infer the structure of TVEG from observations. We derive sufficient conditions for the TSLEG to recover the block partition and sparse pattern with high probability. We derive a message-passing optimization method to solve the TSLEG for time-varying Ising, Gaussian, exponential, and Poisson graphs based on the ADMM. The synthetic network simulations corroborate the theoretical analysis. Analysing of real data of stocks and the US Senate by the time-varying exponential model and Poisson model indicates the effectiveness and practicality of TVEG models. © 2025 IEEE. All rights reserved.

关键词： ADMM exponential family distributions Graphical model pseudo maximum likelihood estimation

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An Exact Potential Game-Based End-Edge-Cloud Collaborative Task Offloading Approach

An Exact Potential Game-Based End-Edge-Cloud Collaborative T...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Yue Shen Bowen Liu Fei Dai Wanchun Dou State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China

With the development of Industrial Internet of Things(IIoT), massive industrial devices produce a large number of delay-sensitive and computing-intensive tasks. Offloading tasks under the end-edge-cloud collaboration mode can improve the efficiency of completing tasks. However, on the one hand, tasks need to be finished before corresponding deadlines and pay for consuming resources, so delay and cost constitute the main factors affecting task offloading decisions. On the other hand, limited real-time resources in the system may not meet the needs of all devices, so we should ensure that as many devices can complete tasks on time as possible while optimizing task delay and cost of each device. To tackle the challenges, we propose an end-edge-cloud collaborative task offloading approach based on exact potential game. Aiming at optimizing delay-cost of each device and task completion rate of the whole system, the end-edge-cloud collaborative task offloading problem is modeled as an exact potential game and solved. Extensive experiments show that the approach can improve the task completion rate of the system while optimizing the delay-cost of each device.

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Graph Neural Network Enabled Fluid Antenna Systems: A Two-Stage Approach

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IEEE Transactions on Vehicular Technology 2025年

作者： He, Changpeng Lu, Yang Chen, Wei Ai, Bo Wong, Kai-Kit Niyato, Dusit State Key Laboratory of Advanced Rail Autonomous Operation China Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China University College London Department of Electronic and Electrical Engineering LondonWC1E 6BT United Kingdom Yonsei University Yonsei Frontier Laboratory School of Integrated Technology Seoul03722 Korea Republic of Nanyang Technological University College of Computing and Data Science 639798 Singapore

An emerging fluid antenna system (FAS) brings a new dimension, i.e., the antenna positions, to deal with the deep fading, but simultaneously introduces challenges related to the transmit design. This paper proposes an "unsupervised learning to optimize" paradigm to optimize the FAS. Particularly, we formulate the sum-rate and energy efficiency (EE) maximization problems for a multiple-user multiple-input single-output (MU-MISO) FAS and solved by a two-stage graph neural network (GNN) where the first stage and the second stage are for the inference of antenna positions and beamforming vectors, respectively. The outputs of the two stages are jointly input into an unsupervised loss function to train the two-stage GNN. The numerical results demonstrate that the advantages of the FAS for performance improvement and the two-stage GNN for real-time and scalable optimization. Besides, the two stages can function separately. © 1967-2012 IEEE.

关键词： Graph neural networks

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VMeta: A QoS dataset for Metaverse Services 6th

VMeta: A QoS Dataset for Metaverse Services

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6th International Conference on Blockchain and Trustworthy Systems, BlockSys 2024

作者： Ge, Zhiyuan Jin, Huiying Dong, Hai Ji, Shunhui Li, Jiajia Yang, Shuhan Wang, Qi Zhang, Pengcheng Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing210000 China College of Computer Science and Software Engineering Hohai University Nanjing210000 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210000 China School of Computing Technologies RMIT University Melbourne3000 Australia

ISBN: (纸本)9789819614134

The metaverse is undergoing repaid evolution with metaverse services emerging as a significant focal point of research across virtual reality, augmented reality, and metaverse social networking. However, a key challenge lies in the scarcity of datasets that accurately represent metaverse services, impeding the advancement of metaverse service technologies and applications. To address this challenge, we have categorized metaverse services into virtual scene services and meta-services. Subsequently, we introduced a metaverse service dataset called "VMeta". VMeta encompasses comprehensive information about virtual scene services and meta-services, including essential QoS (Quality of Service) data. This dataset serves as a valuable resource for researchers exploring various aspects of metaverse services, with the ultimate goal of enhancing user satisfaction in the metaverse. We have conducted evaluations and tests on VMeta to optimize metaverse QoS. The results demonstrate its substantial value for metaverse service research. Our datasets are available at https://***/Gelieziman/VMeta. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Virtual environments

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Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning

arXiv

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arXiv 2025年

作者： He, Yuting Wang, Boyu Ge, Rongjun Chen, Yang Yang, Guanyu Li, Shuo Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Nanjing China School of Instrument Science and Engineering Southeast University Nanjing China Department of Computer Science Western University LondonONN6A 3K7 Canada Department of Biomedical Engineering Department of Computer and Data Science Case Western Reserve University ClevelandOH44106 United States

Dense contrastive representation learning (DCRL) has greatly improved the learning efficiency for image dense prediction tasks, showing its great potential to reduce the large costs of medical image collection and dense annotation. However, the properties of medical images make unreliable correspondence discovery, bringing an open problem of large-scale false positive and negative (FP&N) pairs in DCRL. In this paper, we propose GEoMetric vIsual deNse sImilarity (GEMINI) learning which embeds the homeomorphism prior to DCRL and enables a reliable correspondence discovery for effective dense contrast. We proposes a deformable homeomorphism learning (DHL) which models the homeomorphism of medical images and learns to estimate a deformable mapping to predict the pixels’ correspondence under the condition of topological preservation. It effectively reduces the searching space of pairing and drives an implicit and soft learning of negative pairs via gradient. We also proposes a geometric semantic similarity (GSS) which extracts semantic information in features to measure the alignment degree for the correspondence learning. It will promote the learning efficiency and performance of deformation, constructing positive pairs reliably. We implement two practical variants on two typical representation learning tasks in our experiments. Our promising results on seven datasets which outperform the existing methods show our great superiority. We will release our code at a companion website. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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A Method of Realizing External Network Access to Intranet for Embedded Web Server

A Method of Realizing External Network Access to Intranet fo...

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Electronic Information Technology and Smart Agriculture (ICEITSA), International Conference on

作者： Xian Zhang Taiguo Qu Pengju Tang Yiwen Liu School of Computer Science and Engineering Huaihua University Huaihua China Key Laboratory of Intelligent Control Technology for Wuling Mountain Ecological Agriculture in Hunan Province Huaihua China Key Laboratory of Wuling Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua China College of Computer Science and Electronic Engineering Hunan University ChangSha China

ISBN: (纸本)9781665464024

Based on the application requirements of the embedded Web server for external network access to the internal network, this paper designs and builds an embedded Web server scheme for external network access to the internal network based on the embedded Boa server and phddns program. This solution mainly transplants the Boa server and phddns program in embedded devices. Boa provides a Web server application function, and phddns software realizes the mapping and binding mapping between the IP address and the domain name of embedded devices in the intranet. An experiment is designed to test this scheme's connection success rate, response time, stable running time, domain name mapping success rate, CPU utilization and other performance indicators. The test results show that this scheme can better meet the system requirements of external network access to the internal network. Therefore, this scheme can support the information and intelligent monitoring and control of embedded devices and has good practical value and application prospects.

关键词： Performance evaluation Uniform resource locators Smart agriculture Web servers Software Real-time systems Organ transplantation

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Insight-HXMT observations of the extremely bright GRB 221009A

arXiv

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arXiv 2025年

作者： Zhang, Wen-Long Xue, Wang-Chen Li, Cheng-Kui Xiong, Shao-Lin Li, Gang Chen, Yong Cui, Wei-Wei Li, Xiao-Bo Liu, Cong-Zhan Ge, Ming-Yu Tan, Wen-Jun Liu, Jia-Cong Wang, Chen-Wei Zheng, Chao Zhang, Yan-Qiu Wang, Yue Zhang, Zhen Yi, Shu-Xu Xiao, Shuo Cai, Ce Yi, Shuang-Xi Song, Li-Ming Tao, Lian Zhang, Shu Zhang, Shuang-Nan Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China School of Physics and Physical Engineering Qufu Normal University Shandong Qufu273165 China University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100049 China School of Physics and Electronic Science Guizhou Normal University Guiyang550001 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China College of Physics Hebei Key Laboratory of Photophysics Research and Application Hebei Normal University Hebei Shijiazhuang050024 China

The Hard X-ray Modulation Telescope (Insight-HXMT) detected GRB 221009A, the brightest gamma-ray burst observed to date, with all its three telescopes, i.e. High Energy telescope (HE, 20-250 keV), Medium Energy telescope (ME, 5-30 keV), and Low Energy telescope (LE, 1-10 keV). Here we present the detailed observation results of all three telescopes of Insight-HXMT on the prompt emission of GRB 221009A. After dead-time and data saturation correction, we recovered the light curves of HE, ME and LE telescopes and find that they generally track the GECAM-C low gain light curves that are free of data saturation issues. Particularly, the ME light curve matches the GECAM-C light curve in low gain mode above 400 keV, while the LE light curve is more consistent with the GECAM-C above 1.5 MeV. Based on simulation, we find that the signals recorded by the ME and LE are actually caused by the secondary particles produced by the interaction between GRB gamma-ray photons and the material of the satellite. Interestingly, the consistency between ME and LE light curves and GECAM-C demonstrates that ME and LE data could be used to characterize the GRB properties. Espeically, the high time resolution light curve of ME allowed us, for the first time, to calculate the minimum variability timescale (MVT = 0.10 s) of the main burst episode of GRB 221009A. Copyright © 2025, The Authors. All rights reserved.

关键词： Gamma radiography

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Transformer-Based Hierarchical Dynamic Decoders for Salient Object Detection

SSRN

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SSRN 2023年

作者： Zheng, Qingping Zheng, Ling Deng, Jiankang Li, Ying Shang, Changjing Shen, Qiang School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northewestern Polytechnical University Shaanxi Xi’an710129 China Fuzhou Institute of Data Technology Fujian350200 China Department of Computing Imperial College London LondonSW7 2AZ United Kingdom

HighlightsTransformer-Based Hierarchical Dynamic Decoders for Salient Object DetectionQingping Zheng, Ling Zheng, Jiankang Deng, Ying Li, Changjing Shang, Qiang Shen• T-HDDNet employs dynamic upsampling and fusion for data-driven salient object detection.• T-HDDNet uses a spatially sensitive loss to learn salient object structures, yielding a high-resolution map.• Our method exceeds benchmarks at 22.19G MACs and 27M Params, reducing costs compared to the top model. © 2023, The Authors. All rights reserved.

关键词： Object detection

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A Unified Modeling Framework for Automated Penetration Testing

arXiv

引用

arXiv 2025年

作者： Wang, Yunfei Liu, Shixuan Wang, Wenhao Zhou, Changling Zhang, Chao Jin, Jiandong Zhu, Cheng National Key Laboratory of Information Systems Engineering National University of Defense Technology Hunan China Department of Intelligent Data Science College of Computer Science and Technology National University of Defense Technology Hunan China National University of Defense Technology Hunan China Tsinghua University Beijing China Computer Center Peking University Beijing China

The integration of artificial intelligence into automated penetration testing (AutoPT) has highlighted the necessity of simulation modeling for the training of intelligent agents, due to its cost-efficiency and swift feedback capabilities. Despite the proliferation of AutoPT research, there is a recognized gap in the availability of a unified framework for simulation modeling methods. This paper presents a systematic review and synthesis of existing techniques, introducing MDCPM to categorize studies based on literature objectives, network simulation complexity, dependency of technical and tactical operations, and scenario feedback and variation. To bridge the gap in unified method for multi-dimensional and multi-level simulation modeling, dynamic environment modeling, and the scarcity of public datasets, we introduce AutoPT-Sim, a novel modeling framework that based on policy automation and encompasses the combination of all sub dimensions. AutoPT-Sim offers a comprehensive approach to modeling network environments, attackers, and defenders, transcending the constraints of static modeling and accommodating networks of diverse scales. We publicly release a generated standard network environment dataset and the code of Network Generator. By integrating publicly available datasets flexibly, support is offered for various simulation modeling levels focused on policy automation in MDCPM and the network generator help researchers output customized target network data by adjusting parameters or fine-tuning the network generator. Copyright © 2025, The Authors. All rights reserved.

关键词： Intelligent agents

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Non-small-molecule therapeutics for drug addiction:From pharmaco-kinetics modulating to synthetic biology

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Fundamental Research 2024年第6期4卷 1398-1400页

作者： Yuanpeng Li Yibo Wang Lin Lu Jie Shi Xiaohui Wang Laboratory of Chemical Biology Changchun Institute of Applied ChemistryChinese Academy of SciencesChangchun 130022China School of Applied Chemistry and Engineering University of Science and Technology of ChinaHefei 230026China Institute of Mental Health Peking University Sixth Hospital and National Clinical Research Center for Mental DisordersPeking UniversityBeijing 100191China Peking-Tsinghua Center for Life Sciences Peking UniversityBeijing 100191China International Data Group/McGovern Institute for Brain Research at Peking University Peking UniversityBeijing 100191China National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Peking UniversityBeijing 100191China Beijing National Laboratory for Molecular Sciences Beijing 100190China

Drug addiction is a pervasive problem *** not only affects the abuser’s life,but also poses a serious threat to public ***,there is a strong demand for the development of novel and effective therapies for drug *** that small-molecule drugs have only had limited success,there is great interest in developing alternative strategies that extend the reach of small-molecule-based *** antibody-based trapping approach and the enzyme catalytic strategy have been considered as promising ways to reduce the euphoria of drug users by altering drug pharmacokinetics and decreasing drug concentrations in the central nervous ***,these biological macromolecules are generally unstable and their in vivo half-lives are *** the rapid development of gene editing technologies,it is possible to perform ex vivo gene therapy for the long-term and stable delivery of enzymes and other effector proteins,which could free abusers from frequent injections of *** constructing programmed gene switches that regulate spatiotemporal gene expression in response to illicit drugs,this perspective proposed the concept of preventing drug addiction by the drug *** strategy enables the controlled release of therapeutic proteins in vivo and is expected to improve patient safety and *** will open up new opportunities for next-generation medicine and hold great promise for expanding our ability to treat drug addiction.

关键词： Drug abuse Enzymatic degradation Gene therapy Synthetic biology Pharmacokinetics

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