Fine-grained Spatio-temporal Event Prediction with Self-adaptive Anchor Graph
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China（电子科技大学深圳高等研究院）；Kashi Institute of Electronics and Information Industry（喀什电子信息产业研究院）
Event prediction tasks often handle spatio-temporal data distributed in a large spatial area. Different regions in the area exhibit different characteristics while having latent correlations. This spatial heterogeneity and correlations greatly affect the spatio-temporal distributions of event occurrences, which has not been addressed by state-of-the-art models. Learning spatial dependencies of events in a continuous space is challenging due to its fine granularity and a lack of prior knowledge. In this work, we propose a novel Graph Spatio-Temporal Point Process (GSTPP) model for fine-grained event prediction. It adopts an encoder-decoder architecture that jointly models the state dynamics of spatially localized regions using neural Ordinary Differential Equations (ODEs). The state evolution is built on the foundation of a novel Self-Adaptive Anchor Graph (SAAG) that captures spatial dependencies. By adaptively localizing the anchor nodes in the space and jointly constructing the correlation edges between them, the SAAG enhances the model's ability of learning complex spatial event patterns. The proposed GSTPP model greatly improves the accuracy of fine-grained event prediction. Extensive experimental results show that our method greatly improves the prediction accuracy over existing spatio-temporal event prediction approaches.
AnchorGK: Anchor-based Incremental and Stratified Graph Learning Framework for Inductive Spatio-Temporal Kriging
School of Computer Science（计算机科学学院）；University of Auckland（奥克兰大学）；Shenzhen Key Laboratory of Internet Information Collaboration（互联网信息协作深圳实验室）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学深圳学院）
Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification
MILA, Montreal Institute for Learning Algorithms（MILA蒙特利尔学习算法研究所）；Paris-Saclay University（巴黎萨克雷大学）；CentraleSupélec Paris (ECP)（巴黎中央理工学院）；INRIA Paris（法国国家信息与自动化研究所巴黎分部）；Operations Research Center, MIT（麻省理工学院运筹学中心）；Université de Montréal（蒙特利尔大学）；University of Toronto（多伦多大学）；McGill University（麦吉尔大学）
Decoupling Spatio-Temporal Adapter for Fine-Grained Badminton Action Localization
School of Economics and Management, Beihang University（北京航空航天大学经济管理学院）；Key Laboratory of Data Intelligence and Management, Beihang University, Ministry of Industry and Information Technology（信息产业部北京航空航天大学数据智能与管理重点实验室）
STeP-Diff: Spatio-Temporal Physics-Informed Diffusion Models for Mobile Fine-Grained Pollution Forecasting
Shenzhen International Graduate School, Tsinghua University, China（清华大学深圳国际研究生院）；Department of Electronic Engineering, Beijing National Research Center for Information Science and Tech- nology (BNRist), Tsinghua University, China（电子工程系，北京信息科学与技术国家研究中心（BNRist），清华大学）；Shenzhen Smartcity Communication, Shenzhen, China（深圳智慧城市通信）；College of Soil and Water Conservation, Southwest Forestry University, China（水土保持学院，西南林业大学）；College of Civil Engineering, Southwest Forestry University, China（土木工程学院，西南林业大学）
When Context Compensates for Sparse Event History: AlphaEarth for Spatio-Temporal Point-Process Forecasting
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Université Mohammed VI Polytechnique（穆罕默德六世理工大学）；University of Oxford（牛津大学）；Imperial College London（帝国理工学院）；Rhineland-Palatinate Technical University of Kaiserslautern-Landau (RPTU)（莱茵兰-普法尔茨凯泽斯劳滕-兰道工业大学）
Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition
University of Chinese Academy of Sciences（中国科学院大学）；National Key Laboratory of Space Integrated Information System（空间信息集成系统国家重点实验室）；Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）；Department of Computer Science and Technology（计算机科学与技术系）
