18 papers
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…
Measuring Healthcare Accessibility and Resilience for Smart and Connected Rural Communities: A Florida Panhandle Case Study
Dahai Yu, Dahai YU, Zhe He +3
Rural communities in the United States face persistent healthcare disparities, with fewer providers and facilities, longer trips to care, lower health literacy, and weaker transpor…
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.…
TrAC: Trace-Conditioned Answer Consistency for Efficient Uncertainty Quantification in LLMs
Dahai Yu, Lin Jiang, Rongchao Xu +1
Large language models (LLMs) can generate fluent reasoning traces that nevertheless lead to incorrect answers, making response-level uncertainty estimation important for abstention…
ChainUQ: Reasoning Consistency-Aware Uncertainty Quantification for Large Language Models
Dahai Yu, Rongchao Xu, Lin Jiang +2
While large language models (LLMs) exhibit impressive reasoning capabilities, response-level confidence may remain unreliable when intermediate claims conflict with the final concl…
SymboUQ: Symbolic Uncertainty Quantification for Spatial Reasoning in LLMs
Dahai Yu, Lin Jiang, Rongchao Xu +1
Although large language models (LLMs) can produce fluent spatial reasoning traces, their intermediate relations may fail to support the final conclusion, making token-level confide…
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…
MobiDiff: Semantic-Aware Multi-Channel Discrete Diffusion for Human Mobility Data Generation
Rongchao Xu, Lin Jiang, Dahai Yu +5
Human mobility data are essential for transportation optimization, urban planning, and resource allocation, yet real-world mobility data are costly to collect and difficult to shar…
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…
EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction
Dahai Yu, Rongchao Xu, Lin Jiang +1
Energy consumption prediction is essential for efficient grid management, demand-side optimization, and sustainable energy planning. Although advanced machine learning methods have…
SynHAT: A Two-stage Coarse-to-Fine Diffusion Framework for Synthesizing Human Activity Traces
Rongchao Xu, Lin Jiang, Dahai Yu +2
Human activity traces (HATs) are critical for many applications, including human mobility modeling and point-of-interest (POI) recommendation. However, growing privacy concerns hav…
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…
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…