Hierarchical Spatio-Temporal Attention Network with Adaptive Risk-Aware Decision for Forward Collision Warning in Complex Scenarios
organization= School of Automation \& School of Industrial Internet, Chongqing University of Posts；organization= Platform Technology Development Department, AVATR Technology Co. LTD. , city= Chongqing , postcode= 400000 , country= China；organization= School of Vehicle；Mobility, Tsinghua University , city= Beijing , postcode= 100084 , country= China
Forward Collision Warning systems are crucial for vehicle safety and autonomous driving, yet current methods often fail to balance precise multi-agent interaction modeling with real-time decision adaptability, evidenced by the high computational cost for edge deployment and the unreliability stemming from simplified interaction models.To overcome these dual challenges-computational complexity and modeling insufficiency-along with the high false alarm rates of traditional static-threshold warnings, this paper introduces an integrated FCW framework that pairs a Hierarchical Spatio-Temporal Attention Network with a Dynamic Risk Threshold Adjustment algorithm. HSTAN employs a decoupled architecture (Graph Attention Network for spatial, cascaded GRU with self-attention for temporal) to achieve superior performance and efficiency, requiring only 12.3 ms inference time (73% faster than Transformer methods) and reducing the Average Displacement Error (ADE) to 0.73m (42.2% better than Social_LSTM) on the NGSIM dataset. Furthermore, Conformalized Quantile Regression enhances reliability by generating prediction intervals (91.3% coverage at 90% confidence), which the DTRA module then converts into timely warnings via a physics-informed risk potential function and an adaptive threshold mechanism inspired by statistical process control.Tested across multi-scenario datasets, the complete system demonstrates high efficacy, achieving an F1 score of 0.912, a low false alarm rate of 8.2%, and an ample warning lead time of 2.8 seconds, validating the framework's superior performance and practical deployment feasibility in complex environments.
SpatioTemporal Causal Network Diagnostics for Geographic Tipping Point Early Warning
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application（江苏省地理信息资源开发与应用协同创新中心）；National Center for Applied Mathematics, Tianjin University（天津大学国家应用数学中心）
Decision-Oriented Uncertainty Quantification for Risk Control in Earth System Spatiotemporal Foundation Models
Vanderbilt University（范德堡大学）；City University of New York（纽约城市大学）；Yale University（耶鲁大学）；Wyze Inc.（Wyze公司）；Northeastern University（东北大学）；University of Southern California（南加州大学）；Southwest University（西南大学）
Clinically Interpretable Sepsis Early Warning via LLM-Guided Simulation of Temporal Physiological Dynamics
School of Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）；Department of Information Engineering and Computer Science, University of Trento（特伦托大学信息工程与计算机科学系）；Department of Orthopedics, Affiliated Kunshan Hospital of Jiangsu University（江苏大学附属昆山医院骨科部）；Tianjin University Chest Hospital（天津大学胸科医院）；Haihe Hospital, Tianjin University（天津大学海河医院）；Key Laboratory of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University（广西师范大学教育区块链与智能技术重点实验室）
Hierarchical Information-Guided Spatio-Temporal Mamba for Stock Time Series Forecasting
School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；Institute for Artificial Intelligence（人工智能研究院）；National Key Laboratory of General Artificial Intelligence（通用人工智能国家重点实验室）
A Hierarchical Framework with Spatio-Temporal Consistency Learning for Emergence Detection in Complex Adaptive Systems
School of Computer Science and Cyber Engineering, Guangzhou University（广州大学计算机科学与网络工程学院）；Civil Aviation Electronic Information Engineering College, Guangzhou Civil Aviation College（广州民航职业技术学院民航电子信息工程学院）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）
Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in Neuroimaging
Technische Universität Berlin（柏林工业大学）；University of California, San Francisco（加利福尼亚大学旧金山分校）；Central China Normal University（华中师范大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；Korea University（高丽大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Physikalisch-Technische Bundesanstalt（德国联邦物理技术研究院）；Charité – Universitätsmedizin Berlin（柏林夏里特医学院）；Bernstein Center for Computational Neuroscience（伯恩斯坦计算神经科学中心）
Orca: Ocean Significant Wave Height Estimation with Spatio-temporally Aware Large Language Models
School of Data Science and Engineering, East China Normal University（华东师范大学数据科学与工程学院）；The State Key Laboratory of Estuarine and Coastal Research, ECNU（华东师范大学河口海岸学国家重点实验室）
H-FLTN: A Privacy-Preserving Hierarchical Framework for Electric Vehicle Spatio-Temporal Charge Prediction
School of Computer Science, Queensland University of Technology（昆士兰科技大学计算机科学学院）；CSIRO’s Data61（澳大利亚联邦科学与工业研究组织数据61部）；Cyber Security Cooperative Research Centre（网络安全合作研究中心）
HyPV-LEAD: Proactive Early-Warning of Cryptocurrency Anomalies through Data-Driven Structural-Temporal Modeling
Department of Business Administration（商业管理系）；Kumoh National Institute of Technology（Kumoh国立技术学院）；Ewha Womans University（成均馆大学）；Ewha Womans University, Coretrustlink（成均馆大学，Coretrustlink）
