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Two-Component GMM Source Coding and Optimization

IEEE Transactions on Intelligent Transportation Systems 2025年

作者： Song, Dan Ren, Jinkai Wang, Lin Wu, Huihui Chen, Jun Chen, Guanrong Jimei University Navigation College Xiamen 361021 China China Academy of Railway Sciences Corporation Ltd Chengdu 610213 China Xiamen University Department of Information and Communication Engineering Xiamen 361005 China Eastern Institute of Technology Ningbo Institute of Digital Twin Ningbo 315200 China McMaster University Department of Electrical and Computer Engineering Hamilton L8S 4K1 ON Canada City University of Hong Kong Department of Electrical Engineering Hong Kong

A protograph low-density parity-check (P-LDPC) code-based lossy coding system is proposed for compressing the Gaussian mixture model source, as a particular case of shipping transportation systems. The compression performance is benchmarked against the rate-distortion bounds for this source. Some effective methods are developed to improve both the encoder and the decoder to reduce the compression distortion. Experimental results demonstrate that the proposed methods achieve good performance while maintaining low complexity. © 2000-2011 IEEE.

关键词： Gaussian mixture model Lossy source coding optimal coding algorithm P-LDPC codes rate distortion function

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Dynamic Precoding for Near-Field Secure Communications: Implementation and Performance Analysis

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IEEE Internet of Things Journal 2025年

作者： Teng, Zihao An, Jiancheng Masouros, Christos Li, Hongbin Gan, Lu Ng, Derrick Wing Kwan University of Electronic Science and Technology of China (UESTC) School of Information and Communication Engineering Chengdu 611731 China Nanyang Technological University (NTU) School of Electrical and Electronics Engineering Singapore 639798 Singapore University College London Department of Electronic and Electrical Engineering London WC1E 7JE United Kingdom Stevens Institute of Technology Department of Electrical and Computer Engineering Hoboken 07030 NJ United States Yibin Institute of UESTC Sichuan Yibin 643000 China University of New South Wales (UNSW) School of Electrical Engineering and Telecommunications Sydney 2052 NSW Australia

The increase in antenna apertures and transmission frequencies in next-generation wireless networks is catalyzing advancements in near-field communications (NFC). In this paper, we investigate secure transmission in near-field multi-user multiple-input single-output (MU-MISO) scenarios. Specifically, with the advent of extremely large-scale antenna arrays (ELAA) applied in the NFC regime, the spatial degrees of freedom in the channel matrix are significantly enhanced. This creates an expanded null space that can be exploited for designing secure communication schemes. Motivated by this observation, we propose a near-field dynamic hybrid beamforming architecture incorporating artificial noise, which effectively disrupts eavesdroppers at any undesired positions, even in the absence of their channel state information (CSI). Furthermore, we comprehensively analyze the dynamic precoder's performance in terms of the average signal-to-interference-plus-noise ratio, achievable rate, secrecy capacity, secrecy outage probability, and the size of the secrecy zone. In contrast to far-field secure transmission techniques that only enhance security in the angular dimension, the proposed algorithm exploits the unique properties of spherical wave characteristics in NFC to achieve secure transmission in both the angular and distance dimensions. Remarkably, the proposed algorithm is applicable to arbitrary modulation types and array configurations. Numerical results demonstrate that the proposed method achieves approximately 20% higher rate capacity compared to zero-forcing and the weighted minimum mean squared error precoders. © 2014 IEEE.

关键词： multi-user multiple-input single-output (MU-MISO) Near-field communications (NFC) physical layer security (PLS)

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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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A Dataset for Investigations of Amine-Impregnated Solid Adsorbent for Direct Air Capture

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Scientific Data 2025年第1期12卷 1-24页

作者： Wang, Eryu Luo, Liping Wang, Jiachuan Dai, Jiaxin Li, Shuangyin Chen, Lei Li, Jia Innovation Policy and Entrepreneurship Thrust Society Hub The Hong Kong University of Science and Technology (Guangzhou) No.1 Duxue Road Nansha Guangzhou 511453 China Data Science and Analytics Thrust Information Hub The Hong Kong University of Science and Technology (Guangzhou) No.1 Duxue Road Nansha Guangzhou 511453 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Clearwater Bay Kowloon 999077 Hong Kong School of Computer Science South China Normal University Guangzhou China School of Interdisciplinary Studies Lingnan University Tuen Mun Hong Kong

Amine-impregnated solid adsorbents are widely explored for point source capture and direct air capture (DAC) to address climate change. Existing literature serves as a valuable source for the investigation of amine-functionalized solid adsorbents. This study selected 52 articles from bibliographic platforms using GPT-assisted data source screening. A total of 1,336 data points were manually collected. Each data point is characterized by 28 features including the CO₂ capture performance of various adsorbents from diluted to concentrated sources, resulting in 29,857 records. The methodology addresses inconsistencies in units and terminologies in the published articles and demonstrates database reliability, regularity and integrity through statistical analysis. The diverse types of amines and mesoporous solids in the database offer innovation potential for future research. In addition, two machine learning models were trained to promote dataset reuse by scientists from lab-based research and cheminformatics. This study provides opportunities to explore the use of machine learning on small databases and encourages data sharing and uniform reporting among DAC communities. © The Author(s) 2025.

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A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical Usage

TechRxiv

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TechRxiv 2025年

作者： Hadi, Muhammad Usman Al-Tashi, Qasem Qureshi, Rizwan Shah, Abbas Muneer, Amgad Irfan, Muhammad Zafar, Anas Shaikh, Muhammad Bilal Akhtar, Naveed Al-Garadi, Mohammed Ali Hassan, Syed Zohaib Shoman, Maged Wu, Jia Mirjalili, Seyedali Shah, Mubarak School of Engineering Ulster University BelfastBT15 1AP United Kingdom Department of Imaging Physics The University of Texas MD Anderson Cancer Center HoustonTX77030 United States Department of Electronics Engineering Mehran University of Engineering and Technology Jamshoro76062 Pakistan of Engineering Sciences and Technology Swabi23460 Pakistan Department of Computer Science National University of Computer and Emerging Sciences Karachi Pakistan Edith Cowan University 270 Joondalup Drive Joondalup PerthWA6027 Australia Computing and Information Systems The University of Melbourne 700 Swanston Street CarltonVIC3010 Australia Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Civil Environmental and Construction Engineering The University of Central Florida OrlandoFL United States Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia University Research and Innovation Center Obuda University Budapest1034 Hungary Center for Research in Computer Vision The University of Central Florida OrlandoFL United States

Within the vast expanse of computerized language processing, a revolutionary entity known as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to comprehend intricate linguistic patterns and conjure coherent and contextually fitting responses. LLMs are a type of artificial intelligence (AI) that have emerged as powerful tools for a wide range of tasks, including natural language processing (NLP), machine translation, vision applications, and question-answering. This survey provides a comprehensive overview of LLMs, including their history, architecture, datasets, training methods, applications, challenges, and future prospects. We begin by discussing the fundamental concepts of generative AI and the architecture of generative pre-trained transformers (GPT). We then provide an overview of the history of LLMs, their evolution over time, and the different training methods. We also present benchmark dataset for training and fine-tuning and evaluating LLMs. We then discuss the wide range of tasks where they are used and also discuss applications of LLMs in different domains, including medicine, education, finance, engineering, agriculture, media, entertainment, politics, and law. We also discuss how LLMs are shaping the future of AI and their increasing role in scientific discovery, and how they can be used to solve real-world problems. Next, we explore the challenges associated with deploying LLMs in real-world scenarios, including ethical considerations, model biases, interpretability, privacy concerns, and computational resource requirements. This survey also highlights techniques for enhancing the robustness and controllability of LLMs and addressing bias, fairness, and quality issues in Generative AI. Finally, we conclude by highlighting the future of LLM research and the challenges that need to be addressed in order to make this technology more reliable and useful. This survey is intended to provide researchers, practitioners, and ent

关键词： Natural language processing systems

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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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Efficient Model Training in Edge Networks with Hierarchical Split Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Zhang, Songge Wu, Wen Song, Lingyang Shen, Xuemin Peking University Shenzhen Graduate School School of Electronic and Computer Engineering Shenzhen 518055 China Pengcheng Laboratory Frontier Research Center Shenzhen 518055 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing 100871 China Xiangtan University Hunan Institute of Advanced Sensing and Information Technology Xiangtan China University of Waterloo Department of Electrical and Computer Engineering Waterloo N2L 3G1 Canada

In this paper, we propose an efficient model training scheme, named Group-based Hierarchical Split Learning (GHSL), which can accelerate the artificial intelligence (AI) training process in edge networks in a “first-sequential-then-parallel” manner. Specifically, the proposed scheme hierarchically splits an AI model into a user-side and server-side model, while dividing a number of users into multiple groups. Users in each group train user-side models with the interaction of the shared server-side model sequentially;different groups perform the above training process parallelly;the AI models of each group are aggregated into a global model. We also carry out the convergence analysis for the proposed scheme over non-independent and identically distributed data, which reveals that the convergence rate depends on user grouping. Furthermore, we propose a data-driven two-stage user grouping algorithm to minimize the overall training delay, taking user resource heterogeneity and the black-box training process into account. The proposed algorithm first utilizes the Gaussian process regression approach to determine the number of groups, and then employs the coalition game theory to determine the optimal user grouping decision. Comprehensive simulation results demonstrate that the proposed scheme can reduce training delay, user-side computational workload, and communication overhead by up to 19%, 53%, and 54%, respectively, comparing to state-of-the-art benchmarks. © 2002-2012 IEEE.

关键词： convergence analysis Hierarchical split learning user grouping

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Nonequilibrium transport and the fluctuation theorem in the thermodynamic behaviors of nonlinear photonic systems

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Physical Review Research 2025年第1期7卷 013084-013084页

作者： Yang Liu Jincheng Lu Zhongfei Xiong Fan O. Wu Demetrios Christodoulides Yuntian Chen Jian-Hua Jiang School of Physical Science and Technology & Collaborative Innovation Center of Suzhou Nano Science and Technology Soochow University 1 Shizi Street Suzhou 215006 China Key Laboratory of Efficient Low-carbon Energy Conversion and Utilization of Jiangsu Provincial Higher Education Institutions School of Physical Science and Technology Suzhou University of Science and Technology Suzhou 215009 China School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan 430074 China CREOL College of Optics and Photonics University of Central Florida Orlando Florida 32816-2700 USA Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles California 90089 USA School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei 230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou 215123 China School of Physical Sciences University of Science and Technology of China Hefei 230026 China

Nonlinear multimode optical systems have attracted substantial attention due to their rich physical properties. Complex interplay between the nonlinear effects and mode couplings makes it difficult to understand the collective dynamics of photons. Authors of recent studies have shown that such collective phenomena can be effectively described by Rayleigh-Jeans thermodynamics theory, which is a powerful tool for the study of nonlinear multimode photonic systems. These systems, in turn, offer a compelling platform for investigating fundamental issues in statistical physics, attributed to their tunability and the ability to access negative temperature regimes. However, to date, a theory for nonequilibrium transport and fluctuations is yet to be established. Here, we employ full counting statistics theory to study the nonequilibrium transport of particles and energy in nonlinear multimode photonic systems in both positive and negative temperature regimes. Furthermore, we discover that, in situations involving two reservoirs of opposite temperatures and chemical potentials, an intriguing phenomenon known as the loop current effect can arise, wherein the current in the positive energy sector runs counter to that in the negative energy sector. In addition, we numerically confirm that the fluctuation theorem remains applicable in optical thermodynamics systems across all regimes, from positive temperatures to negative ones. Our findings closely align with numerical simulations based on first-principles nonlinear wave equations. In this paper, we seek to deepen the understanding of irreversible nonequilibrium processes and statistical fluctuations in nonlinear many-body photonic systems which will enhance our grasp of collective phenomena of photons and foster a fruitful intersection between optics and statistical physics.

关键词： Chemical potential

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A DISTRIBUTION-FREE TECHNIQUE TO ESTIMATE THE ORDER OF MARKOVIAN DEPENDENCE USING ENTROPY

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 1990年第1期9卷 31-54页

作者： KUMAR, AR JOHNSON, DH 1. Computer and Information Technology Institute Department of Electrical and Computer Engineering Rice University P.O. Box 1892 77251 Houston Texas USA

A model-order determination procedure for time series is described which is based on computing the differential entropy of the time series' multivariate amplitude probability density. The procedure, termed the differential entropy method, applies to linear and nonlinear models for the time series. Simulations were performed to determine the performance characteristics of the differential entropy method on first-order autoregressive models with Gaussian and non-Gaussian inputs and on a first-order nonlinear model. It produces accurate model-order estimates for the linear model once the correlation coefficient becomes large enough. The threshold for accurate estimates is dependent on the amplitude distribution of the model's input. This technique estimates the model order of data having nonlinear dependence structure more accurately than thead hoc application of the AIC and MDL methods. Extension of the method to higher-order models is described, but, because of computation complexity issues, the differential entropy method is best used for estimating small model orders.

关键词： performance characteristics ACCEPTABLE INTAKE FOR CHRONIC EXPOSURE amplitude probability density higher-order Developed Countries DEPENDENCE entropy correlation coefficient (r) order linear models Time series Nonlinear Models AIC gene Differential amplitude distribution

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Editorial: Introduction to Web information Systems engineering

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Knowledge and information Systems 2002年第2期4卷 129-131页

作者： Li, Qing Zhang, Yanchun 1Department of Computer Engineering and Information Technology City University of Hong Kong Kowloon Hong Kong 2Department of Mathematics and Computing University of Southern Queensland ToowoombaQLD Australia

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