Learning the embeddings for urban regions from human mobility data can reveal the functionality of regions, and then enables the correlated but distinct tasks such as crime prediction. Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks. In this paper, we propose multi-graph fusion networks (MGFN) to enable the cross domain prediction tasks. First, we integrate the graphs with spatio-temporal similarity as mobility patterns through a mobility graph fusion module. Then, in the mobility pattern joint learning module, we design the multi-level cross-attention mechanism to learn the comprehensive embeddings from multiple mobility patterns based on intra-pattern and inter-pattern messages. Finally, we conduct extensive experiments on real-world urban datasets. Experimental results demonstrate that the proposed MGFN outperforms the state-of-the-art methods by up to 12.35% improvement.
Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；JD Technology（京东科技）；JD Intelligent Cities Research（京东智能城市研究院）；Tsinghua University（清华大学）；Southwest Jiaotong University（西南交通大学）
UrbanGraphEmbeddings: Learning and Evaluating Spatially Grounded Multimodal Embeddings for Urban Science
National University of Singapore Department of Architecture Singapore；The Chinese University of Hong Kong Dept of Systems Eng. \& Eng. Mgmt. China；Singapore Management University School of Computing \& Info. Systems Singapore；National University of Singapore；Department of Architecture；The Chinese University of Hong Kong；Singapore Management University
Synergistic Fusion of Topological Structure and Temporal Semantics of Mobility for Urban Region Embedding
Urban AI Institute, Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院城市人工智能研究院）；Graduate School of Data Science, KAIST（韩国科学技术院数据科学研究生院）；Department of Industrial Engineering and Systems, KAIST（韩国科学技术院工业工程与系统系）；Department of Civil and Environmental Engineering, KAIST（韩国科学技术院土木与环境工程系）
MedFuse: Multiplicative Embedding Fusion For Irregular Clinical Time Series
Graduate Institute of Communication Engineering, National Taiwan University (NTU)（国立台湾大学通信工程研究所）；Department of Electrical Engineering, NTU（国立台湾大学电子工程系）；Center for Advanced Computing and Imaging in Biomedicine, NTU（国立台湾大学生物医学先进计算与影像中心）；Smart Medicine and Health Informatics Program, NTU（国立台湾大学智慧医疗与健康资讯学程）
UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Alibaba Cloud Intelligence Group（阿里云智能事业群）
Enhancing Regional Airbnb Trend Forecasting Using LLM-Based Embeddings of Accessibility and Human Mobility
Graduate School of Data Science, KAIST（韩国科学技术院数据科学研究生院）；School of Computing, KAIST（韩国科学技术院计算机科学学院）；Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington（印第安纳大学布卢明顿分校信息学、计算与工程学院）
Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena
MIT（麻省理工学院）；National University of Singapore（新加坡国立大学）；Carnegie Mellon University（卡内基梅隆大学）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工学院科研联盟）；University of Southern California（南加州大学）
Learning Universal Human Mobility Patterns with a Foundation Model for Cross-domain Data Fusion
Department of Civil and Environmental Engineering, University of California, Los Angeles（加州大学洛杉矶分校土木与环境工程系）；Novateur Research Solutions（Novateur研究解决方案）；Department of Civil and Environmental Engineering, University of California, Berkeley（加州大学伯克利分校土木与环境工程系）
Temporal-Spatial Tubelet Embedding for Cloud-Robust MSI Reconstruction using MSI-SAR Fusion: A Multi-Head Self-Attention Video Vision Transformer Approach
Jiangsu University of Science and Technology（江苏科技大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Chongqing University of Post and Telecommunications（重庆邮电大学）
MTA-RL: Robust Urban Driving via Multi-modal Transformer-based 3D Affordances and Reinforcement Learning
Dongguan Key Laboratory of Intelligent Equipment and Smart Industry, School of Advanced Engineering, Great Bay University（东莞智能装备与智能制造重点实验室，先进工程学院，大湾大学）；Chair of Applied Statistics, Technische Universität Dresden（应用统计学教授职位，德累斯顿技术大学）；Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI)（可扩展数据解析与人工智能中心（ScaDS.AI））；College of Automation, Guangdong University of Technology（自动化学院，广东技术大学）
本文提出MTA-RL框架，通过多模态Transformer-based 3D affordances和强化学习实现可靠的城市自动驾驶，提升样本效率和稳定性，优于现有基线方法。
Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena
National University of Singapore（新加坡国立大学）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工学院研究与技术联盟）；MIT（麻省理工学院）；Carnegie Mellon University（卡内基梅隆大学）；University of Southern California（南加州大学）
