Spatio-temporal modeling of wireless access latency is of great importance for connected-vehicular systems. The quality of the molded results rely heavily on the number and quality of samples which can vary significantly due to the sensor deployment density as well as traffic volume and density. This paper proposes LaMI (Latency Model Inpainting), a novel framework to generate a comprehensive spatio-temporal of wireless access latency of a connected vehicles across a wide geographical area. LaMI adopts the idea from image inpainting and synthesizing and can reconstruct the missing latency samples by a two-step procedure. In particular, it first discovers the spatial correlation between samples collected in various regions using a patching-based approach and then feeds the original and highly correlated samples into a Variational Autoencoder (VAE), a deep generative model, to create latency samples with similar probability distribution with the original samples. Finally, LaMI establishes the empirical PDF of latency performance and maps the PDFs into the confidence levels of different vehicular service requirements. Extensive performance evaluation has been conducted using the real traces collected in a commercial LTE network in a university campus. Simulation results show that our proposed model can significantly improve the accuracy of latency modeling especially compared to existing popular solutions such as interpolation and nearest neighbor-based methods.
Confidence-Regulated Generative Diffusion Models for Reliable AI Agent Migration in Vehicular Metaverses
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；School of Cyberspace Science and Technology, Beijing Jiaotong University（北京交通大学网络空间科学与技术学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）；Department of Informatics, University of Oslo（奥斯陆大学信息学院）；Simula Research Laboratory（Simula研究实验室）
Cross-reality location privacy protection in 6G-enabled vehicular metaverses: an LLM-enhanced hybrid generative diffusion model-based approach
School of Automation, Guangdong University of Technology（自动化学院，广东技术大学）；School of Computer Science and Engineering, Nanyang Technological University（计算机科学与工程学院，南洋理工大学）；Department of Electrical and Computer Engineering, University of Manitoba（电气与计算机工程系，曼尼托巴大学）；Department of Electrical and Computer Engineering, Sungkyunkwan University（电气与计算机工程系，成均馆大学）
Generative AI Enables EEG Super-Resolution via Spatio-Temporal Adaptive Diffusion Learning
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；The University of Hong Kong（香港大学）；Guangdong University of Technology（广东工业大学）
Generative diffusion models for spatiotemporal influenza forecasting
Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill（流行病学系，全球公共卫生学院，北卡罗来纳大学查佩尔希尔分校）；Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA（流行病学系，约翰·霍普金斯伯恩斯坦公共卫生学院，巴尔的摩，MD 21205, USA）；Carolina Population Center, University of North Carolina at Chapel Hill（卡罗来纳人口中心，北卡罗来纳大学查佩尔希尔分校）
Spatiotemporal Satellite Image Downscaling with Transfer Encoders and Autoregressive Generative Models
University of Toronto（多伦多大学）；Harvard T.H. Chan School of Public Health（哈佛大学T.H. Chan公共卫生学院）；Harvard Medical School（哈佛医学院）；VA Healthcare System Boston, U.S. Department of Veterans Affairs（美国退伍军人事务部波士顿医疗系统）
A Temporally Disentangled Contrastive Diffusion Model for Spatiotemporal Imputation
School of Computer Science, University of Technology Sydney（悉尼科技大学计算机科学学院）；School of Computer Science and Technology, Zhejiang Normal University（浙江师范大学计算机科学与技术学院）；Department of Computer Science and Engineering, University of California, San Diego（加利福尼亚大学圣地亚哥分校计算机科学与工程学院）
Sequential and Generative Models for Vehicular Distributed MIMO Channel Prediction
College of computing GVSU（GVSU计算学院）；Univ Rennes, CNRS, IETR, UMR 6164（雷恩大学，法国国家科学研究中心，IETR，UMR 6164）；Univ Lille, CNRS, IEMN, UMR 8520（里尔大学，法国国家科学研究中心，IEMN，UMR 8520）
