Urban big data fusion creates huge values for urban computing in solving urban problems.
In recent years, various models and algorithms based on deep learning have been …

Diffusion models, a family of generative models based on deep learning, have become
increasingly prominent in cutting-edge machine learning research. With distinguished …

Abstract Spatio-Temporal Graph (STG) forecasting is a fundamental task in many real-world
applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular …

While deep learning has shown tremendous success in a wide range of domains, it remains
a grand challenge to incorporate physical principles in a systematic manner to the design …

During the past few years, severe air-pollution problem has garnered worldwide attention
due to its effect on health and wellbeing of individuals. As a result, the analysis and …

Air pollution is a crucial issue affecting human health and livelihoods, as well as one of the
barriers to economic growth. Forecasting air quality has become an increasingly important …

Air quality forecasting has been regarded as the key problem of air pollution early warning
and control management. In this article, we propose a novel deep learning model for air …

Urban spatiotemporal flow prediction is of great importance to traffic management, land use,
public safety. This prediction task is affected by several complex and dynamic factors, such …

Traffic accidents usually lead to severe human casualties and huge economic losses in real-
world scenarios. Timely accurate prediction of traffic accidents has great potential to protect …

Spatial-temporal prediction is a fundamental problem for constructing smart city, which is
useful for tasks such as traffic control, taxi dispatching, and environment policy making. Due …