2 citations · 2 across the 17 of their papers we have counts for
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Cross-View Time-Frequency Diffusion for Extreme-Aware Time-Series Generation
Lin Jiang, Rongchao Xu, Guang Wang
Generating realistic time-series requires faithfully reproducing rare but high-impact extreme events. Existing approaches often rely on specialized representations or extreme-focus…
EviRec: Continual Evidence Learning for Dual Cold-Start POI Recommendation
Rongchao Xu, Lin Jiang, Guang Wang
Point-of-Interest (POI) recommendation is a core task in location-based services, yet most existing methods assume a fixed user population and POI catalog. Through a large-scale da…
ZeroHAT: Behavior-Conditioned Zero-Shot Human Activity Trace Generation
Rongchao Xu, Dahai Yu, Lin Jiang +1
Human activity traces record individuals' timestamped visits to points of interest and are essential for applications such as mobility prediction and urban simulation. However, acc…
SynEHR: Joint Modeling Inter-visit Temporal Evolution and Intra-visit Clinical Structure for Longitudinal EHR Synthesis
Ximiao Li, Lin Jiang, Rongchao Xu +3
Longitudinal electronic health records (EHRs) document patients' sequences of clinical visits over time, preserving the temporal evolution of disease progression and care delivery.…
SynEnergy: Anomaly Semantic-Guided Diffusion for Synthetic Energy Data Generation
Lin Jiang, Dahai Yu, Ravikumar Gelli +1
Fine-grained energy consumption data are essential for applications such as demand forecasting, demand response planning, and grid reliability assessment. However, access to such d…
MBDiff: Multi-view Behavior-aware Diffusion Model for Probabilistic Utility Data Imputation
Rongchao Xu, Lin Jiang, Dahai Yu +2
Utility data (e.g., electricity, water, and gas consumption), collected by ubiquitous sensors and embedded devices, often contains substantial missing values due to various factors…
Diffusion Model-Based Data Assimilation for Real-World Energy Consumption Forecasting
Ruoyu Hu, Dahai Yu, Feng Bao +2
Accurate estimation and forecasting of energy consumption are important for power-system operation, planning, and demand-side management. In practice, however, complete and timely…
E4GEN: Event-level Explainable Extreme-Enhanced Time-series Generation
Lin Jiang, Dahai Yu, Ximiao Li +1
Generating realistic time series is essential for scientific research and real-world applications. However, existing methods often emphasize overall distributional fidelity while f…
HealthMamba: An Uncertainty-aware Spatiotemporal Graph State Space Model for Effective and Reliable Healthcare Facility Visit Prediction
Dahai Yu, Lin Jiang, Rongchao Xu +1
Healthcare facility visit prediction is essential for optimizing healthcare resource allocation and informing public health policy. Despite advanced machine learning methods being…
TrustEnergy: A Unified Framework for Accurate and Reliable User-level Energy Usage Prediction
Dahai Yu, Rongchao Xu, Dingyi Zhuang +3
Energy usage prediction is important for various real-world applications, including grid management, infrastructure planning, and disaster response. Although a plethora of deep lea…
GeoGen: A Two-stage Coarse-to-Fine Framework for Fine-grained Synthetic Location-based Social Network Trajectory Generation
Rongchao Xu, Kunlin Cai, Lin Jiang +3
Location-Based Social Network (LBSN) check-in trajectory data are important for many practical applications, like POI recommendation, advertising, and pandemic intervention. Howeve…
UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
Dahai Yu, Dingyi Zhuang, Lin Jiang +5
Spatiotemporal prediction plays a critical role in numerous real-world applications such as urban planning, transportation optimization, disaster response, and pandemic control. In…
HCRide: Harmonizing Passenger Fairness and Driver Preference for Human-Centered Ride-Hailing
Lin Jiang, Yu Yang, Guang Wang
Order dispatch systems play a vital role in ride-hailing services, which directly influence operator revenue, driver profit, and passenger experience. Most existing work focuses on…
Uncertainty-aware Predict-Then-Optimize Framework for Equitable Post-Disaster Power Restoration
Lin Jiang, Dahai Yu, Rongchao Xu +2
The increasing frequency of extreme weather events, such as hurricanes, highlights the urgent need for efficient and equitable power system restoration. Many electricity providers…
FusionTransNet for Smart Urban Mobility: Spatiotemporal Traffic Forecasting Through Multimodal Network Integration
Binwu Wang, Yan Leng, Guang Wang +1
This study develops FusionTransNet, a framework designed for Origin-Destination (OD) flow predictions within smart and multimodal urban transportation systems. Urban transportation…