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Generative AI for Space-Air-Ground Integrated Networks

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arXiv 2023年

作者： Zhang, Ruichen Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Zhang, Ping Kim, Dong In The College of Computing and Data Science Nanyang Technological University Singapore The Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong 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 University of Sydney SydneyNSW2006 Australia The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Recently, generative AI technologies have emerged as a significant advancement in artificial intelligence field, renowned for their language and image generation capabilities. Meantime, space-air-ground integrated network (SAGIN) is an integral part of future B5G/6G for achieving ubiquitous connectivity. Inspired by this, this article explores an integration of generative AI in SAGIN, focusing on potential applications and case study. We first provide a comprehensive review of SAGIN and generative AI models, highlighting their capabilities and opportunities of their integration. Benefiting from generative AI’s ability to generate useful data and facilitate advanced decision-making processes, it can be applied to various scenarios of SAGIN. Accordingly, we present a brief survey on their integration, including channel modeling and channel state information (CSI) estimation, joint air-space-ground resource allocation, intelligent network deployment, semantic communications, image extraction and processing, security and privacy enhancement. Next, we propose a framework that utilizes a generative diffusion model (GDM) to construct channel information map to enhance quality of service for SAGIN. Simulation results demonstrate the effectiveness of the proposed framework. Finally, we discuss potential research directions for generative AI-enabled SAGIN. © 2023, CC BY.

关键词： Generative adversarial networks

Twenty-One Days of Bed Rest Alter Motor Unit Properties and Neuromuscular Junction Transmission in Young Adults

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Journal of applied physiology (Bethesda, Md. : 1985) 2025年第6期138卷 2025 May 12页

作者： Fabio Sarto Miloš Kalc Evgeniia S Motanova Martino V Franchi Daniel Stashuk Nina Murks Giacomo Valli Samuele Negro Tomaž Prašnikar Mladen Gasparini Giovanni Martino Giuseppe De Vito Aleš Holobar Boštjan Šimunič Rado Pišot Marco V Narici Department of Biomedical Sciences University of Padova Padova Italy. Centre of Studies and Activities for Space "Giuseppe Colombo" University of Padova Padova Italy. Science and Research Center Koper Institute for Kinesiology Research Koper Slovenia. CIR-MYO Myology Center University of Padova Padova Italy. Department of Systems Design Engineering University of Waterloo Ontario Canada. Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia. Department of Clinical and Experimental Sciences University of Brescia Brescia Italy. Izola General Hospital Izola Slovenia.

Previous studies showed that properties of higher-threshold motor units (MUs) and neuromuscular junction (NMJ) function are preserved during short-term disuse. This study aimed to test how a longer disuse period affects MU properties, NMJ transmission, and NMJ morphology remodeling. Nine young healthy males (age: 18-29 years) underwent 21 days of horizontal bed rest. Pre- (BR0) and post-bed rest (BR21), quadriceps maximal voluntary contraction (MVC), and size were assessed. We combined intramuscular electromyography (iEMG) and high-density surface electromyography (HDsEMG) recordings on the vastus lateralis to assess MU properties at 25% and 50% of MVC. Muscle biopsies and blood samples were also collected. Quadriceps MVC and size decreased at BR21. We found alterations in MU properties at both contraction intensities, including reduced discharge rate, MU potential area changes, and increased complexity. NMJ transmission was found to be reduced at BR21 at 25% MVC. This functional NMJ impairment was biochemically corroborated by an increase in serum C-terminal agrin fragment concentration, a biomarker of NMJ instability. In addition, a direct assessment of NMJ morphology revealed the presence of some denervated NMJs exclusively at BR21. In conclusion, 21-day bed rest altered MU properties across different contraction intensities and impaired NMJ transmission with initial signs of remodeling/denervation. Disuse duration appears to be a critical factor, as previous shorter studies failed to detect some of these changes. We believe these findings are clinically relevant for disuse after trauma, surgery, or illness, and may support the development of effective countermeasures.

关键词： Disuse Electromyography Electrophysiology Physical inactivity Unloading

GENERATIVE CAUSAL REPRESENTATION LEARNING FOR OUT-OF-DISTRIBUTION MOTION FORECASTING

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arXiv 2023年

作者： Bagi, Shayan Shirahmad Gale Gharaee, Zahra Schulte, Oliver Crowley, Mark Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada School of Computing Science Simon Fraser University BurnabyBCV5A 1S6 Canada

Conventional supervised learning methods typically assume i.i.d samples and are found to be sensitive to out-of-distribution (OOD) data. We propose Generative Causal Representation Learning (GCRL) which leverages causality to facilitate knowledge transfer under distribution shifts. While we evaluate the effectiveness of our proposed method in human trajectory prediction models, GCRL can be applied to other domains as well. First, we propose a novel causal model that explains the generative factors in motion forecasting datasets using features that are common across all environments and with features that are specific to each environment. Selection variables are used to determine which parts of the model can be directly transferred to a new environment without fine-tuning. Second, we propose an end-to-end variational learning paradigm to learn the causal mechanisms that generate observations from features. GCRL is supported by strong theoretical results that imply identifiability of the causal model under certain assumptions. Experimental results on synthetic and real-world motion forecasting datasets show the robustness and effectiveness of our proposed method for knowledge transfer under zero-shot and low-shot settings by substantially outperforming the prior motion forecasting models on out-of-distribution prediction. Our code is available at https://***/sshirahmad/GCRL. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

Bridging Gaps in HCI: Advancing Education, Research, and Careers in Asia

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Bridging Gaps in HCI: Advancing Education, Research, and Car...

2025 CHI Conference on Human Factors in Computing systems, CHI EA 2025

作者： Gamage, Dilrukshi Cheng, Shiwei Mudliar, Preeti Lu, Zhicong Zhao, Shengdong Ahmed, Nova Ma, Xiaojuan Lee, Uichin Wang, Ding Department of Computation and Intelligent Systems University of Colombo School of Computing Colombo Sri Lanka Zhejiang University of Technology Hangzhou China International Institute of Information Technology Bangalore Bangalore India Department of Computer Science City University of Hong Kong Hong Kong School of Creative Media City University of Hong Kong Hong Kong Design Inclusion and Access Lab. Department of Electrical and Computer Engineering North South University Dhaka Dhaka Bangladesh Hong Kong University of Science and Technology Hong Kong Hong Kong School of Computing KAIST Daejeon South Korea Google Atlanta GA United States

ISBN: (纸本)9798400713958

Asia’s Human-computer Interaction (HCI) landscape is rapidly evolving, yet it faces distinct challenges in curriculum development, research establishment, and career navigation. This panel discussion, hosted by Asia SIGCHI Committee (ASC) at CHI2025, will bring together distinguished experts to address these challenges and explore strategies for fostering a robust and inclusive HCI community in Asia. Panelists will discuss the integration of culturally relevant content in HCI education, approaches to enhancing research visibility on international platforms, and initiatives to bridge gaps between academic training and industry expectations. Through an engaging discussion and audience interaction, the panel aims to identify actionable solutions, promote collaboration, and inspire future initiatives that strengthen HCI’s growth across the region. Participants will gain valuable insights into overcoming systemic barriers while building sustainable and impactful HCI programs tailored to Asia’s unique sociocultural dynamics. This panel seeks to advance the global relevance and contributions of HCI in Asia. © 2025 Copyright held by the owner/author(s).

关键词： Asia Curricula HCI HCI Education HCI Teaching

Performance of Quantum Preprocessing under Phase Noise

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arXiv 2022年

作者： Amiri, Zuhra Bash, Boulat A. Nötzel, Janis Emmy-Noether Group Theoretical Quantum Systems Design Lehrstuhl für Theoretische Informationstechnik Technische Universität München Germany Electrical & Computer Engineering Optical Sciences University of Arizona TucsonAZ United States

Optical fiber transmission systems form the backbone of today’s communication networks and will be of high importance for future networks as well. Among the prominent noise effects in optical fiber is phase noise, which is induced by the Kerr effect. This effect limits the data transmission capacity of these networks and incurs high processing load on the receiver. At the same time, quantum information processing techniques offer more efficient solutions but are believed to be inefficient in terms of size, power consumption and resistance to noise. Here we investigate the concept of an all-optical joint detection receiver. We show how it contributes to enabling higher baud-rates for optical transmission systems when used as a pre-processor, even under high levels of noise induced by the Kerr effect. Copyright © 2022, The Authors. All rights reserved.

关键词： Phase noise

Zero-Forcing Based Downlink Virtual MIMO-NOMA Communications in IoT Networks

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arXiv 2022年

作者： Shi, Zheng Wang, Hong Fu, Yaru Yang, Guanghua Ma, Shaodan Hou, Fen Tsiftsis, Theodoros A. Institute of Physical Internet The School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China The School of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Department of Information Systems Technology and Design Singapore University of Technology and Design Singapore The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau 999078 China

To support massive connectivity and boost spectral efficiency for internet of things (IoT), a downlink scheme combining virtual multiple-input multiple-output (MIMO) and nonorthogonal multiple access (NOMA) is proposed. All the single-antenna IoT devices in each cluster cooperate with each other to establish a virtual MIMO entity, and multiple independent data streams are requested by each cluster. NOMA is employed to superimpose all the requested data streams, and each cluster leverages zero-forcing detection to de-multiplex the input data streams. Only statistical channel state information (CSI) is available at base station to avoid the waste of the energy and bandwidth on frequent CSI estimations. The outage probability and goodput of the virtual MIMO-NOMA system are thoroughly investigated by considering Kronecker model, which embraces both the transmit and receive correlations. Furthermore, the asymptotic results facilitate not only the exploration of physical insights but also the goodput maximization. In particular, the asymptotic outage expressions provide quantitative impacts of various system parameters and enable the investigation of diversity-multiplexing tradeoff (DMT). Moreover, power allocation coefficients and/or transmission rates can be properly chosen to achieve the maximal goodput. By favor of Karush-Kuhn-Tucker conditions, the goodput maximization problems can be solved in closed-form, with which the joint power and rate selection is realized by using alternately iterating optimization. Besides, the optimization algorithms tend to allocate more power to clusters under unfavorable channel conditions and support clusters with higher transmission rate under benign channel conditions. © 2022, CC BY.

关键词： Internet of things

Numerical simulation of heat pipes in different applications

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International Journal of Thermofluids 2022年 16卷

作者： Maghrabie, Hussein M. Olabi, A.G. Alami, Abdul Hai Radi, Muaz Al Zwayyed, Firas salamah, Tareq Wilberforce, Tabbi Abdelkareem, Mohammad Ali Department of Mechanical Engineering Faculty of Engineering South Valley University Qena83521 Egypt Sustainable Energy & Power Systems Research Centre RISE University of Sharjah P.O. Box 27272 Sharjah United Arab Emirates Mechanical Engineering and Design Aston University School of Engineering and Applied Science Aston Triangle BirminghamB4 7ET United Kingdom Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Faculty of Engineering Minia University Elminia Egypt

Nowadays heat pipes are considered to be popular passive heat transfer technologies due to their high thermal performance. The heat pipe is a superior heat transfer apparatus in which latent heat of vaporization is employed to transfer heat for an extended distance under a limited operating temperature difference. Numerical simulation of heat transfer devices is a principal step before implementing in real-life applications as many parameters can be tested in cost-and time-effective behaviors. The present study provides a review of the numerical simulations of various heat pipes in different applications such as cooling of electronic components, heating, ventilation, and air conditioning (HVAC), nuclear reactors, solar energy systems, electric vehicles, waste heat recovery systems, cryogenic, etc. Firstly, this work introduces a background about the main components of heat pipes such as an evacuated tube, wick, and working fluid. The fluid flow and thermal performance characteristics of heat pips are discussed, considering the optimum parameters. Finally, the critical challenges and recommendations for future work encountering the broad application of heat pipes are thoroughly studied. © 2022

关键词： Heat pipes

Integrating Edge Intelligence and Blockchain: What, Why, and How

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TechRxiv

TechRxiv 2022年

作者： Wang, Xiaofei Ren, Xiaoxu Qiu, Chao Xiong, Zehui Yao, Haipeng Leung, Victor C.M. College of Intelligence and Computing Tianjin University Tianjin300072 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design 487372 Singapore Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100088 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

Driven by an unprecedented boom in artificial intelligence (AI) and Internet of Things (IoT), edge intelligence (EI) pushes the frontier of AI from cloud to network edge, serving as a remarkable solution that unlocks the full potential of AI services. It is yet facing critical challenges in its decentralized management and security, limiting its capabilities to support services with numerous requirements. In this context, blockchain (BC) has been seen as a promising solution to tackle the above issues, and further support EI. Based on the number of citations or the relevance of emerging methods, this paper presents the results of a literature survey on the integration of EI and BC. Accordingly, we summarize the recent research efforts reported in the existing works on EI and BC. We then paint a comprehensive picture of the limitations of EI and why BC could benefit from EI. From there, we explore how BC benefits EI in terms of computing power management, data administration, and model optimization. In order to narrow the gap between immature BC and EI-amicable BC, we also probe into how to tailor BC to EI from four perspectives, including flexible consensus protocol, effective incentive, intellectuality smart contract, and scalability. Finally, some research challenges and future directions are presented. Different from existing surveys, our work focuses on the integration of EI and BC, develops some general models to help the reader build relevant optimization models in the integrated system, as well as provides detailed tutorials on implementation. We anticipate that this survey will motivate further discussions on the synergy of EI and BC, and offer some guidance in EI, BC, future networks, and other areas. © 2022, CC BY-NC-SA.

关键词： Blockchain