1 citations · 1 across the 10 of their papers we have counts for
10 papers
Interpretable Kolmogorov-Arnold Network with Feature-Isolated Temporal Attention Mechanism for Electricity Load Forecasting
Jinhao Li, Hao Wang
Accurate electricity load forecasting is a crucial prerequisite for stable power system operations. While prevalent deep learning models present competitive performance, they often…
ImProNCDE: Impulse-Corrected Neural Controlled Differential Equations with Prototype Learning for Longitudinal Prognosis Prediction
Hao Wang, Yupeng Xu, Jinghao Lin +5
Longitudinal ophthalmic imaging analysis is an essential step for prognosis prediction in ophthalmic diseases. However, AI-assisted prognosis models are challenged by follow-up seq…
LGNO: A Local-Global Neural Operator for Hyperbolic Conservation Laws
Hao Wang, Chi-Wang Shu, Qi Tang
Solutions of hyperbolic conservation laws exhibit both smooth structures across large scales and sharp localized features such as shocks and contact discontinuities, making them di…
Spatio-Temporal Wildfire Spread Prediction in Canada using a Video Swin-Hybrid-U-Net and Satellite Imagery
Maulik Srivastava, Esha Saha, Hao Wang
Background: Wildfires in Canada present increasing threats to ecosystems, communities, and infrastructure, demanding accurate forecasting tools to aid mitigation efforts. Existing…
Deep ZakaiJ: Structured Filtering for Jump-Diffusion Time Series Forecasting
Yan Leng, Thibaut Mastrolia, Hao Wang
Time series driven by unobserved latent states frequently exhibit abrupt jump discontinuities whose timing and magnitude cannot be predicted from observed history alone. Classical…
Turning mechanistic models into forecasters by using machine learning
Amit K. Chakraborty, Hao Wang, Pouria Ramazi
The equations of complex dynamical systems may not be identified by expert knowledge, especially if the underlying mechanisms are unknown. Data-driven discovery methods address thi…
PiXTime: A Model for Federated Time Series Forecasting with Heterogeneous Data across Nodes
Yiming Zhou, Jiahao Wang, Mingyue Cheng +3
While collaborative forecasting on distributed time series is highly desirable, directly pooling localized datasets is often impractical due to data sharing constraints. Federated…
Deep Learning for Disease Outbreak Prediction: A Robust Early Warning Signal for Transcritical Bifurcations
Reza Miry, Amit K. Chakraborty, Russell Greiner +4
Early Warning Signals (EWSs) are vital for implementing preventive measures before a disease turns into a pandemic. While new diseases exhibit unique behaviors, they often share fu…
Early detection of disease outbreaks and non-outbreaks using incidence data
Shan Gao, Amit K. Chakraborty, Russell Greiner +2
Forecasting the occurrence and absence of novel disease outbreaks is essential for disease management. Here, we develop a general model, with no real-world training data, that accu…
An early warning indicator trained on stochastic disease-spreading models with different noises
Amit K. Chakraborty, Shan Gao, Reza Miry +4
The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies. Nevertheless, the intric…