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arXiv 2023年

作者： Sun, Shiyuan Mei, Weidong Yang, Fang An, Nan Song, Jian Zhang, Rui The Department of Electronic Engineering Tsinghua University Beijing100084 China The Key Laboratory of Digital TV System of Shenzhen City Research Institute of Tsinghua University in Shenzhen Shenzhen518057 China The Shenzhen International Graduate School Tsinghua University Shenzhen518055 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen518172 China

Although the multi-antenna or so-called multiple-input multiple-output (MIMO) transmission has been the enabling technology for the past generations of radio-frequency (RF)-based wireless communication systems, its application to the visible light communication (VLC) still faces a critical challenge as the MIMO spatial multiplexing gain can be hardly attained in VLC channels due to their strong spatial correlation. In this paper, we tackle this problem by deploying the optical intelligent reflecting surface (OIRS) in the environment to boost the capacity of MIMO VLC. Firstly, based on the extremely near-field channel condition in VLC, we propose a new channel model for OIRS-assisted MIMO VLC and reveal its peculiar "no crosstalk" property, where the OIRS reflecting elements can be respectively configured to align with one pair of transmitter and receiver antennas without causing crosstalk to each other. Next, we characterize the OIRS-assisted MIMO VLC capacities under different practical power constraints and then proceed to maximize them by jointly optimizing the OIRS element alignment and transmitter emission power. In particular, for optimizing the OIRS element alignment, we propose two algorithms, namely, location-aided interior-point algorithm and log-det-based alternating optimization algorithm, to balance the performance versus complexity trade-off;while the optimal transmitter emission power is derived in closed form. Numerical results are provided, which validate the capacity improvement of OIRS-assisted MIMO VLC against the VLC without OIRS and demonstrate the superior performance of the proposed algorithms compared to baseline schemes. Copyright © 2023, The Authors. All rights reserved.

关键词： Visible light communication

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids with Coupled Electricity-Hydrogen-Ammonia under Uncertainties

arXiv

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arXiv 2024年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2024, CC BY.

关键词： Ammonia

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FedCache: A Knowledge Cache-driven Federated Learning Architecture for Personalized Edge Intelligence

arXiv

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arXiv 2023年

作者： Wu, Zhiyuan Sun, Sheng Wang, Yuwei Liu, Min Xu, Ke Wang, Wen Jiang, Xuefeng Gao, Bo Lu, Jinda The Institute of Computing Technology Chinese Academy of Sciences Beijing China The University of Chinese Academy of Sciences Beijing China The Zhongguancun Laboratory Beijing China The Department of Computer Science and Technology Tsinghua University Beijing China The School of Computer and Information Technology The Engineering Research Center of Network Management Technology for High-Speed Railway Ministry of Education Beijing Jiaotong University Beijing China The School of Information Science and Technology University of Science and Technology of China Hefei China

Edge Intelligence (EI) allows Artificial Intelligence (AI) applications to run at the edge, where data analysis and decision-making can be performed in real-time and close to data sources. To protect data privacy and unify data silos distributed among end devices in EI, Federated Learning (FL) is proposed for collaborative training of shared AI models across multiple devices without compromising data privacy. However, the prevailing FL approaches cannot guarantee model generalization and adaptation on heterogeneous clients. Recently, Personalized Federated Learning (PFL) has drawn growing awareness in EI, as it enables a productive balance between local-specific training requirements inherent in devices and global-generalized optimization objectives for satisfactory performance. However, most existing PFL methods are based on the Parameters Interaction-based Architecture (PIA) represented by FedAvg, which suffers from unaffordable communication burdens due to large-scale parameters transmission between devices and the edge server. In contrast, Logits Interaction-based Architecture (LIA) allows to update model parameters with logits transfer and gains the advantages of communication lightweight and heterogeneous on-device model allowance compared to PIA. Nevertheless, previous LIA methods attempt to achieve satisfactory performance either relying on unrealistic public datasets or increasing communication overhead for additional information transmission other than logits. To tackle this dilemma, we propose a knowledge cache-driven PFL architecture, named FedCache, which reserves a knowledge cache on the server for fetching personalized knowledge from the samples with similar hashes to each given on-device sample. During the training phase, ensemble distillation is applied to on-device models for constructive optimization with personalized knowledge transferred from the server-side knowledge cache. Empirical experiments on four datasets demonstrate that FedCache achiev

关键词： Distillation

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Age of Information Minimization in Intelligent Reflecting Surface-Aided Covert Communications

Age of Information Minimization in Intelligent Reflecting Su...

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IEEE Globecom Workshops

作者： Chao Wang Zan Li Yue Zhao Derrick Wing Kwan Ng Naofal Al-Dhahir State Key Laboratory of Integrated Service Network Xidian University Xi’an China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia Department of Electrical and Computer Engineering the University of Texas at Dallas Richardson TX USA

ISBN: (纸本)9781665459761

In this work, we investigate the covert information freshness in intelligent reflecting surface (IRS)-aided multi antenna communications, where a public full-duplex user (Alice) and a private full-duplex user (Bob) exchange information in the presence of a watchful warden (Willie). Specifically, the covert transmission from Bob to Alice is shielded from Willie under the mask of Alice’s undisguised signal transmission. To minimize the covert information age, we exploit the channel statistics to jointly design the active beamforming at Alice and Bob, the passive beamforming at the IRS, and the blocklength of the confidential data packet under the constraints of the maximum tolerable age of information (AoI) at Bob and communication covertness. Although the considered joint optimization problem is nonconvex, we employ the successive convex approximation to design an iterative algorithm to obtain an efficient suboptimal solution. Numerical results demonstrate the superiority of our proposed joint optimization algorithm and further show that the enhanced covert channel quality can reduce the required blocklength to improve information freshness.

关键词： Array signal processing Error probability Simulation Full-duplex system Information age Approximation algorithms Reflector antennas

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Meta-Computing Enhanced Federated Learning in IIoT: Satisfaction-Aware Incentive Scheme via DRL-Based Stackelberg Game

arXiv

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arXiv 2025年

作者： Li, Xiaohuan Qin, Shaowen Tang, Xin Kang, Jiawen Ye, Jin Zhao, Zhonghua Zheng, Yusi Niyato, Dusit Guangxi University Key Laboratory of Intelligent Networking and Scenario System School of Information and Communication Guilin University of Electronic Technology Guilin541004 China National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology Guangxi Nanning530001 China School of Automation Guangdong University of Technology Guangzhou510006 China Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530000 China School of Computer and Electronic Information Guangxi University Nanning530000 China Department of Interdisciplinary Studies Lingnan University Hong Kong College of Computing and Data Science Nanyang Technological University Singapore

The Industrial Internet of Things (IIoT) leverages Federated Learning (FL) for distributed model training while preserving data privacy, and meta-computing enhances FL by optimizing and integrating distributed computing resources, improving efficiency and scalability. Efficient IIoT operations require a trade-off between model quality and training latency. Consequently, a primary challenge of FL in IIoT is to optimize overall system performance by balancing model quality and training latency. This paper designs a satisfaction function that accounts for data size, Age of Information (AoI), and training latency for meta-computing. Additionally, the satisfaction function is incorporated into the utility functions to incentivize nodes in IIoT participation in model training. We model the utility functions of servers and nodes as a two-stage Stackelberg game and employ a deep reinforcement learning approach to learn the Stackelberg equilibrium. This approach ensures balanced rewards and enhances the applicability of the incentive scheme for IIoT. Simulation results demonstrate that, under the same budget constraints, the proposed incentive scheme improves utility by at least 23.7% compared to existing FL schemes without compromising model accuracy. Copyright © 2025, The Authors. All rights reserved.

关键词： Copyrights

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Semantic Learning and Emulation Based Cross-platform Binary Vulnerability Seeker

arXiv

引用

arXiv 2022年

作者： Gao, Jian Jiang, Yu Liu, Zhe Yang, Xin Wang, Cong Jiao, Xun Yang, Zijiang Sun, Jiaguang The School of Software Tsinghua University Beijing National Research Center for Information Science and Technology Key Laboratory for Information System Security Ministry of Education Beijing100084 China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China The Department of Electrical and Computer Engineering Villanova University VillanovaPA19085 United States The Department of Computer Science Western Michigan University KalamazooMI49008 United States

Clone detection is widely exploited for software vulnerability search. The approaches based on source code analysis cannot be applied to binary clone detection because the same source code can produce significantly different binaries due to different operating systems, microprocessor architectures and compilers. In this paper, we present BinSeeker, a cross-platform binary seeker that integrates semantic learning and emulation. With the help of the labeled semantic flow graph, BinSeeker can quickly identify M candidate functions that are most similar to the vulnerability from the target binary. The value of M is relatively large so this semantic learning procedure essentially eliminates those functions that are very unlikely to have the vulnerability. Then, semantic emulation is conducted on these M candidates to obtain their dynamic signature sequences. By comparing signature sequences, BinSeeker produces top-N functions that exhibit most similar behavior to that of the vulnerability. With fast filtering of semantic learning and accurate comparison of semantic emulation, BinSeeker seeks vulnerability precisely with little overhead. The experiments on six widely used programs with fifteen known CVE vulnerabilities demonstrate that BinSeeker outperforms three state-of-the-art tools Genius, Gemini and CACompare. Regarding search accuracy, BinSeeker achieves an MRR value of 0.65 in the target programs, whereas the MRR values by Genius, Gemini and CACompare are 0.17, 0.07 and 0.42, respectively. If we consider ranking a function with the targeted vulnerability in the top-5 as accurate, BinSeeker achieves the accuracy of 93.33 percent, while the accuracy of the other three tools is merely 33.33, 13.33 and 53.33 percent, respectively. Such accuracy is achieved with 0.27s on average to determine whether the target binary function contains a known vulnerability, and the time for the other three tools are 1.57s, 0.15s and 0.98s, respectively. Compared to the time used to manu

关键词： Semantics

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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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Automatic Sleep Spindle Detection and Analysis in Patients with Sleep Disorders 1

引用

2nd International Workshop on Human Brain and Artificial Intelligence, HBAI 2020 held in Conjunction with IJCAI-PRICAI 2020

作者： Chen, Chao Zhu, Xuequan Belkacem, Abdelkader Nasreddine Lu, Lin Hao, Long You, Jia Shin, Duk Tan, Wenjun Huang, Zhaoyang Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin300072 China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin300384 China Department of Computer and Network Engineering College of Information Technology UAE University Al Ain15551 United Arab Emirates Zhonghuan Information College Tianjin University of Technology Tianjin300380 China Department of Electronics and Mechatronics Tokyo Polytechnic University Atsugi243-0297 Japan School of Computer Science and Engineering Northeastern University Shenyang110189 China Department of Neurology Xuanwu Hospital Capital Medical University Beijing100088 China

ISBN: (数字)9789811612886

ISBN: (纸本)9789811612879

Nowadays, Sleep disorder is a common disease, and spindle spindles are important features of the second stage non-rapid eye movement (NREM) sleep. In this paper, we propose an improved automatic detection method of spindles based on wavelet transform. The spindles automatic detector is mainly composed of wavelet transform and clustering. We collected the electroencephalography (EEG) signals of six patients with sleep disorders all night for ten hours, and then preprocessed the data and other operations, and then used our improved method to detect the sleep EEG signals by spindles. By comparing with the previous automatic detection method not improved and another automatic detection method, the results show that the accuracy of sleep spindles detection can be effectively improved. The accuracy of the improved detector is 5.19% higher than before, and 9.7% higher than that of another method based on amplitude threshold. Finally, we made a simple comparison between people with sleep disorders and normal people. We found that there were significant differences in spindle density between people with sleep disorders and people without sleep disorders. The average spindle density in the normal population averaged 2.59 spindles per minute. People with sleep disorders had an average spindle density of 1.32 spindles per minute. In future research, our research direction is to improve the accuracy of spindles automatic detection by improving the spindles detector and study the difference of spindles between patients with sleep disorders and normal people in a large number of samples, so that the difference of spindles can be used as the basis for the diagnosis of sleep disorders. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Wavelet transforms

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A sequence-to-sequence approach for document-level relation extraction

arXiv

引用

arXiv 2022年

作者： Giorgi, John Bader, Gary D. Wang, Bo Department of Computer Science University of Toronto Canada Department of Molecular Genetics University of Toronto Canada Department of Laboratory Medicine and Pathobiology University of Toronto Canada Terrence Donnelly Centre for Cellular & Biomolecular Research Canada Vector Institute for Artificial Intelligence The Lunenfeld-Tanenbaum Research Institute Sinai Health System Canada Princess Margaret Cancer Centre University Health Network Canada Peter Munk Cardiac Center University Health Network Canada

Motivated by the fact that many relations cross the sentence boundary, there has been increasing interest in document-level relation extraction (DocRE). DocRE requires integrating information within and across sentences, capturing complex interactions between mentions of entities. Most existing methods are pipeline-based, requiring entities as input. However, jointly learning to extract entities and relations can improve performance and be more efficient due to shared parameters and training steps. In this paper, we develop a sequence-to-sequence approach, seq2rel, that can learn the subtasks of DocRE (entity extraction, coreference resolution and relation extraction) end-to-end, replacing a pipeline of task-specific components. Using a simple strategy we call entity hinting, we compare our approach to existing pipeline-based methods on several popular biomedical datasets, in some cases exceeding their performance. We also report the first end-to-end results on these datasets for future comparison. Finally, we demonstrate that, under our model, an end-to-end approach outperforms a pipeline-based approach. Our code, data and trained models are available at https://***/johngiorgi/seq2rel. An online demo is available at https://***/johngiorgi/seq2rel/main/***. © 2022, CC0.

关键词： Pipelines

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Dynamic nitrogen vacancy magnetometry by single-shot optical streaking microscopy

引用

Photonics research 2022年第9期10卷 2147-2156页

作者： MARK A.KEPPLER ZACHARY A.STEELMAN ZACHARY N.COKER MILOŠNESLÁDEK PHILIP R.HEMMER VLADISLAV V.YAKOVLEV JOEL N.BIXLER Department of Biomedical Engineering Texas A&M UniversityCollege StationTexas 77843USA SAIC JBSA Fort Sam HoustonTexas 78234USA National Research Council Research Associateship Program WashingtonDC 20001USA IMOMEC Division IMECB-3590 DiepenbeekBelgium Institute for Materials Research(IMO) Hasselt UniversityB-3590 DiepenbeekBelgium Czech Technical University in Prague 27201 KladnoCzech Republic Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTexas 77843USA Bioeffects Division Airman System DirectorateAir Force Research LaboratoryJBSA Fort Sam HoustonTexas 78234USA

Nitrogen vacancy diamonds have emerged as sensitive solid-state magnetic field sensors capable of producing diffraction limited and sub-diffraction field ***,for the first time,to our knowledge,we extend those measurements to high-speed imaging,which can be readily applied to analyze currents and magnetic field dynamics in circuits on a microscopic *** overcome detector acquisition rate limitations,we designed an optical streaking nitrogen vacancy microscope to acquire two-dimensional spatiotemporal *** demonstrate magnetic field wave imaging with micro-scale spatial extent and~400μs temporal *** validating this system,we detected magnetic fields down to 10μT for 40 Hz magnetic fields using single-shot imaging and captured the spatial transit of an electromagnetic needle at streak rates as high as 110μm/*** design has the capability to be readily extended to full 3D video acquisition by utilizing compressed sensing techniques and a potential for further improvement of spatial resolution,acquisition speed,and *** device opens opportunities to many potential applications where transient magnetic events can be isolated to a single spatial axis,such as acquiring spatially propagating action potentials for brain imaging and remotely interrogating integrated circuits.

关键词： vacancy utilizing overcome

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