Convolution-based recurrent neural networks and convolutional have been used extensively in spatiotemporal prediction. However, these methods tend to concentrate on fixed-scale state transitions disregard the complexity of motion. Through statistical analysis, we found that distribution sequence variety motion exhibit some regularity. In light statistics observations, propose Multi-scale Spatio...

Accurate and timely precipitation forecasts can help people organizations make informed decisions, plan for potential weather-related disruptions, protect lives property. Instead of using physics-based numerical forecasts, which be computationally prohibitive, there has been a growing interest in deep learning techniques prediction recent years due to the success these approaches various other ...

Language in social media is a dynamic system, constantly evolving and adapting, with words and concepts rapidly emerging, disappearing, and changing their meaning. These changes can be estimated using word representations in context, over time and across locations. A number of methods have been proposed to track these spatiotemporal changes but no general method exists to evaluate the quality o...

Interest has been growing with regard to the use of remote sensing data characterized by a fine spatial resolution and frequent coverage for the monitoring of land surface dynamics. However, current satellite sensors are fundamentally limited by a trade-off between their spatial and temporal resolutions. Spatiotemporal fusion thus provides a feasible solution to overcome this limitation, and ma...

We designed a model-based analysis to predict the occurrence of population patterns in distributed spiking activity. Using a maximum entropy principle with a Markovian assumption, we obtain a model that accounts for both spatial and temporal pairwise correlations among neurons. This model is tested on data generated with a Glauber spin-glass system and is shown to correctly predict the occurren...