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The joint effects of physical activity and sleep duration on risk of osteoporosis in Taiwanese adult population: The Taiwan Biobank Study

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Osteoporosis International 2024年第3期35卷 523-531页

作者： Chen, Kun-Hui Su, Chen-Ming Liu, Wei-Ju Tzeng, Huey-En Lee, Chia-Lin Tsai, Chun-Hao Department of Orthopedic Surgery Taichung Veterans General Hospital Taichung 407219 Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung 402204 Taiwan Department of Computer Science & Information Engineering College of Computing and Informatics Providence University Taichung City 43301 Taiwan Department of Sports Medicine China Medical University Taichung 406040 Taiwan Department of Medical Research Taichung Veterans General Hospital Taichung 407219 Taiwan Intelligent Data Mining Laboratory Department of Medical Research Taichung Veterans General Hospital Taichung 407219 Taiwan School of Medicine Taipei Medical University Taipei 11031 Taiwan Division of Transfusion Medicine /Hematology - Oncology Taichung Veterans General Hospital Taichung 40705 Taiwan Department of Public Health College of Public Health China Medical University Taichung 406040 Taiwan Department of Medicine School of Medicine National Yang Ming Chiao Tung University Taipei 112304 Taiwan Department of Orthopedics China Medical University Hospital Taichung 406040 Taiwan College of Biomedical Engineering China Medical University Taichung 406040 Taiwan

Summary: Most studies investigating the association between physical activity and osteoporosis prevention only focused on specific types of physical activity. This study’s evidence regarding the combined effects or interaction of sleep duration and physical activity. The findings emphasize the role of sleep duration and physical activity in association with osteoporosis. Purpose: The associations between physical activity, sleep duration, and prevalent osteoporosis in Taiwanese adults were studied in this cross-sectional study. Methods: The Taiwan Biobank enrolled a community-based cohort of ~ 120,000 volunteers (as of April 30, 2020) between 30 and 76 years of age with no history of cancer. Amongst, bone mineral density (BMD) measures by dual-energy X-ray absorptiometry (DXA) were available in 22,402 participants. After excluding individuals who had no complete data of BMI (n = 23), MET score (n = 207), T-score (n = 8,826), and sleep duration (n = 16), 13,330 subjects were included as the primary cohort. Univariate and multivariable regression analyses were performed to determine the associations between the presence of osteoporosis, physical activity level, sleep duration, and other variables. Results: The results showed that after adjustment, subjects with physical activity < 20 METs/week and ≥ 20 METs/week (aOR = 1.017 and 0.767, respectively) were associated with risk of osteoporosis than those with zero MET. The odds of osteoporosis were not significantly lower in subjects who slept for ≥ 8 h/day (aOR = 0.934,p=0.266). In addition, compared to short sleepers with no physical activity, adults with increased physical activity ≥ 20 METs/week and sleep ≥ 8 h/day had a significantly lowest likelihood of osteoporosis (aOR = 0.702). Those with medium physical activity (< 20 METs/week) plus average sleep duration (6.5–8 h/day) did not have significant higher odds of osteoporosis (aOR = 1.129,p=0.151). Conclusion: The findings emphasize the joint role of sleep duration

关键词： Bone mineral density (BMD) Exercise habit Osteoporosis Sleep duration Taiwan biobank

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Refracting Reconfigurable Intelligent Surface Assisted URLLC for Millimeter Wave High-Speed Train Communication Coverage Enhancement

arXiv

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arXiv 2024年

作者： Liu, Changzhu He, Ruisi Niu, Yong Mao, Shiwen Ai, Bo Chen, Ruifeng The School of Electronics and Information Engineering The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China The School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The Institute of Computing Technology China Academy of Railway Sciences Corporation Limited Beijing100081 China

High-speed train (HST) has garnered significant attention from both academia and industry due to the rapid development of railways worldwide. Millimeter wave (mmWave) communication, known for its large bandwidth is an effective way to address performance bottlenecks in cellular network based HST wireless communication systems. However, mmWave signals suffer from significant path loss when traversing carriage, posing substantial challenges to cellular networks. To address this issue, reconfigurable intelligent surfaces (RIS) have gained considerable interest for its ability to enhance cell coverage by reflecting signals toward receiver. Ensuring communication reliability, a core performance indicators of ultra-reliable and low-latency communications (URLLC) in fifth-generation systems, is crucial for providing steady and reliable data transmissions along railways, particularly for delivering safety and control messages and monitoring HST signaling information. In this paper, we investigate a refracting RIS-assisted multi-user multiple-input single-output URLLC system in mmWave HST communications. We propose a sum rate maximization problem, subject to base station beamforming constraint, as well as refracting RIS discrete phase shifts and reliability constraints. To solve this optimization problem, we design a joint optimization algorithm based on alternating optimization method. This involves decoupling the original optimization problem into active beamforming design and packet error probability optimization subproblem, and discrete phase shift design subproblems. These subproblems are addressed exploiting Lagrangian dual method and the local search method, respectively. Simulation results demonstrate the fast convergence of the proposed algorithm and highlight the benefits of refracting RIS adoption for sum rate improvement in mmWave HST networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Mobile telecommunication systems

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Optimizing Generative AI Networking: A Dual Perspective with Multi-Agent Systems and Mixture of Experts

arXiv

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arXiv 2024年

作者： Zhang, Ruichen Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Zhang, Ping Kim, Dong In The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

In the continued development of next-generation networking and artificial intelligence content generation (AIGC) services, the integration of multi-agent systems (MAS) and the mixture of experts (MoE) frameworks is becoming increasingly important. Motivated by this, this article studies the contrasting and converging of MAS and MoE in AIGC-enabled networking. First, we discuss the architectural designs, operational procedures, and inherent advantages of using MAS and MoE in generative AI to explore its functionality and applications fully. Next, we review the applications of MAS and MoE frameworks in content generation and resource allocation, emphasizing their impact on networking operations. Subsequently, we propose a novel multi-agent-enabled MoE-proximal policy optimization (MoE-PPO) framework for 3D object generation and data transfer scenarios. The framework uses MAS for dynamic task coordination of each network service provider agent and MoE for expert-driven execution of respective tasks, thereby improving overall system efficiency and adaptability. The simulation results demonstrate the effectiveness of our proposed framework and significantly improve the performance indicators under different network conditions. Finally, we outline potential future research directions. © 2024, CC BY.

关键词： Multi agent systems

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Fact-checking based fake news detection: a review

arXiv

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arXiv 2024年

作者： Yang, Yuzhou Zhou, Yangming Ying, Qichao Qian, Zhenxing Zeng, Dan Liu, Liang School of Computer Science Fudan University Shanghai200433 China Key Laboratory of Culture & Tourism Intelligent Computing of Ministry of Culture & Tourism Fudan University Shanghai200433 China School of Communication & Information Engineering Shanghai University Shanghai200444 China Technical Application Department of Audiovisual New Media Center China Media Group Beijing100026 China

There are many groundbreaking methods in the field of fake news detection based on deep learning that can automatically detect fake news through feature extraction and detection. A common methodology framework consists of extracting features from news content by pre-trained models and developing algorithms for detection. Major approaches within the scope identify fake news by learning common feature patterns in them, such as writing style, word usage, etc.. The performance of such models highly relies on large well-annotated data sets, but obtaining and annotating fake news data is laborious. Moreover, newly forged fake news often avoids utilizing the writing style of previous fake news, resulting in poor generalization ability in terms of timeliness. In recent research, fake news detection based on fact verification provides new ideas to address the above problems. Approaches within the scope verify the authenticity of the news event, matching between description and factual information, and so on, to provide more reliable and explicable detection, greatly addressing the bias of previous methods that rely on semantic and writing style features. In this paper, we sort out the research findings of fact-based fake news detection from the perspectives of tasks and problems, algorithms, datasets, and so on. First, this paper illustrates the task definition and core problems of fact-based fake news detection. Next, existing approaches are summarized and organized in terms of algorithms. Subsequently, classic and newly published datasets are analyzed, and extended by summarizing experimental results evaluation. Finally, this paper elaborates on the pros and cons of existing approaches from an overall perspective, pointing out some substantial challenges and expectations of this field. It is expected to provide references for future works in the field of fake news detection. © 2024, CC BY.

关键词： Semantics

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PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators

arXiv

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arXiv 2023年

作者： Cong, Runmin Yang, Wenyu Zhang, Wei Li, Chongyi Guo, Chun-Le Huang, Qingming Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China College of Computer Science Nankai University Tianjin300350 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China Department of Computer Science City University of Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

Due to the light absorption and scattering induced by the water medium, underwater images usually suffer from some degradation problems, such as low contrast, color distortion, and blurring details, which aggravate the difficulty of downstream underwater understanding tasks. Therefore, how to obtain clear and visually pleasant images has become a common concern of people, and the task of underwater image enhancement (UIE) has also emerged as the times require. Among existing UIE methods, Generative Adversarial Networks (GANs) based methods perform well in visual aesthetics, while the physical model-based methods have better scene adaptability. Inheriting the advantages of the above two types of models, we propose a physical model-guided GAN model for UIE in this paper, referred to as PUGAN. The entire network is under the GAN architecture. On the one hand, we design a Parameters Estimation subnetwork (Par-subnet) to learn the parameters for physical model inversion, and use the generated color enhancement image as auxiliary information for the Two-Stream Interaction Enhancement subnetwork (TSIE-subnet). Meanwhile, we design a Degradation Quantization (DQ) module in TSIE-subnet to quantize scene degradation, thereby achieving reinforcing enhancement of key regions. On the other hand, we design the Dual-Discriminators for the style-content adversarial constraint, promoting the authenticity and visual aesthetics of the results. Extensive experiments on three benchmark datasets demonstrate that our PUGAN outperforms state-of-the-art methods in both qualitative and quantitative metrics. The code and results can be found from the link of https://***/proj_***. © 2023, CC BY-NC-SA.

关键词： Discriminators

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

arXiv

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arXiv 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 School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom Yonsei Frontier Laboratory The School of Integrated Technology Yonsei University Seoul03722 Korea Republic of College of Computing and Data Science Nanyang Technological University Singapore639798 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 a unsupervised loss function to train the two-stage GNN. The numerical results demonstrates 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. © 2025, CC BY.

关键词： Graph neural networks

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DQPS: An Intelligent Multi-Hop Computation Offloading Scheme for Workflow Applications in Vehicular Edge computing Networks

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IEEE Transactions on Consumer Electronics 2024年

作者： Lin, Bing Chen, Qiaoxin Chen, Xing Jia, Wen-Kang Lu, Yu Xiong, Neal N. Fujian Normal University College of Physics and Energy Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials Fuzhou350117 China Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering Fuzhou350117 China Peking University School of Computer Science 100871 China Xiamen University Department of Informatics and Communication Engineering Xiamen361005 China Fuzhou University College of Computer and Data Science Fuzhou350118 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350118 China Fujian Normal University College of Photonic and Electronic Engineering Fuzhou350117 China Fujian Normal University Concord University College Fuzhou350117 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States

Vehicular Edge computing (VEC) is a feasible solution for autonomous driving as it can offload latency-sensitive and computation-intensive tasks from vehicle terminals to roadside units (RSUs) for real-time processing. Due to the high computational resource the workflow applications (i.e., autonomous driving) required, the small vehicle-to-RSUs communication range, and the scarce available resources for each vehicle, it might be difficult to accomplish complex workflow applications through the single-hop offloading paradigm in the Internet of Vehicles (IoV). In this paper, we propose a reinforcement learning (RL) based multi-hop computation offloading scheme for workflow applications to reduce their execution latency in the VEC networks, which considers the data dependency in workflow applications as well as the trust relationship and communication interference among RSUs. Firstly, a preprocessing method is employed to merge the cut-edges in a workflow to reduce the offloading scale of tasks and compress the encoding dimension of offloading solutions. Then, a Deep Q-network algorithm using Phase-optimal State update (DQPS) is proposed to update the offloading policy distribution, in which the deviation of the phase-optimal state from the current state is estimated as the reward of RL to promote the algorithm's convergence to adapt the dynamic VEC networks. Simulation results show that DQPS has the best performance compared to other benchmark schemes. Moreover, the latency of the identical applications can be reduced by 2.5%-25.3% through our offloading scheme with a multi-hop model compared to that with the single-hop paradigm in IoV. © 1975-2011 IEEE.

关键词： Computation offloading

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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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PMU-Spill: Performance Monitor Unit Counters Leak Secrets in Transient Executions

PMU-Spill: Performance Monitor Unit Counters Leak Secrets in...

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IEEE Asian Hardware-Oriented Security and Trust (AsianHOST)

作者： Pengfei Qiu Qiang Gao Dongsheng Wang Yongqiang Lyu Chang Liu Xiaoyong Li Chunlu Wang Gang Qu Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Tsinghua University Beijing China Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Beijing China Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland Maryland USA

ISBN: (纸本)9781665461153

The processor's Performance Monitor Unit (PMU) allows the recording of architectural and microarchitectural events for profiling purposes. In this study, we reveal a security issue caused by the fact that current PMU implementations are capable of recording some events that happened during transient executions. We propose the PMU -Spill attack, a new kind of attack that enables attackers to maliciously leak the secret data in transient executions. We demonstrate on real hardware that PMU -Spill attack can leak the secret data stored in Intel Software Guard Extensions (SGX). In addition, we perform a thorough study to reveal all the vulnerable PMU counters and find that 20 of them can be used to achieve PMU -Spill attack. Our experiments suggest that the throughput of PMU -Spill attack is up to 575.3 bytes per second (Bps) with an average error rate of 1.89% when leaking the SGX-protected secret data.

关键词： Side-channel attacks Throughput Phasor measurement units Hardware Software Recording Security

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Diagnosis of Alzheimer's disease based on logistic regression algorithm

Diagnosis of Alzheimer's disease based on logistic regressio...

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computing, Robotics and System sciences (ICRSS), International Conference on

作者： YuSi Chen Ning Liu PeiKun Chen School of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Quanzhou China School of Public Health Hangzhou Normal University Hangzhou China Department of mathematics and computer science Quanzhou Normal University Fujian Provincial Key Laboratory of Data-Intensive Computing Quanzhou China Xiamen Silicon Field System Engineering Co. Ltd. Xiamen China

Language impairment may be an important early manifestation of dementia. In this study, we propose a multi-feature fusion to distinguish MCI and AD patients. In this paper, a set of acoustic, demographic and linguistic features were selected, and after data processing, logistic regression algorithm was used to diagnose MCI and AD patients. In order to get better results, comparative experiments are also effectively conducted, including different combination methods and different group distinctions with the three most popular algorithms. Studies have shown that this method is more advantageous and the classification results are more accurate.

关键词： Fuses Linguistics Feature extraction Data processing Acoustics Classification algorithms Data mining

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