6 papers
PhysFire-WM: A Physics-Informed World Model for Emulating Fire Spread Dynamics
Nan Zhou, Huandong Wang, Jiahao Li +4
Fine-grained fire prediction plays a crucial role in emergency response. Infrared images and fire masks provide complementary thermal and boundary information, yet current methods…
STeP-Diff: Spatio-Temporal Physics-Informed Diffusion Models for Mobile Fine-Grained Pollution Forecasting
Nan Zhou, Weijie Hong, Huandong Wang +6
Fine-grained air pollution forecasting is crucial for urban management and the development of healthy buildings. Deploying portable sensors on mobile platforms such as cars and bus…
FireSentry: A Multi-Modal Spatio-temporal Benchmark Dataset for Fine-Grained Wildfire Spread Forecasting
Nan Zhou, Huandong Wang, Jiahao Li +5
Fine-grained wildfire spread prediction is crucial for enhancing emergency response efficacy and decision-making precision. However, existing research predominantly focuses on coar…
Progressive Supernet Training for Efficient Visual Autoregressive Modeling
Xiaoyue Chen, Yuling Shi, Kaiyuan Li +5
Visual Auto-Regressive (VAR) models significantly reduce inference steps through the "next-scale" prediction paradigm. However, progressive multi-scale generation incurs substantia…
PhyxMamba: Chaotic System Reconstruction from Short Context Observations with Generative State-Space Models
Chang Liu, Bohao Zhao, Jingtao Ding +2
Understanding chaotic dynamics is a fundamental problem across scientific disciplines, including climate science, neuroscience, and fluid dynamics, yet direct experimentation and i…
Context-Aware Sentiment Forecasting via LLM-based Multi-Perspective Role-Playing Agents
Fanhang Man, Huandong Wang, Jianjie Fang +4
User sentiment on social media reveals the underlying social trends, crises, and needs. Researchers have analyzed users' past messages to trace the evolution of sentiments and reco…