Multivariate time series prediction is a critical problem that encountered in many fields, and recurrent neural network (RNN)-based approaches have been widely used to address this problem. However, traditional RNN-based for predicting multivariate are still facing challenges, as often related each other historical observations real-world applications. To limitation, paper proposes spatiotempor...

Coherent structures form spontaneously in nonlinear spatiotemporal systems and are found at all spatial scales in natural phenomena from laboratory hydrodynamic flows and chemical reactions to ocean, atmosphere, and planetary climate dynamics. Phenomenologically, they appear as key components that organize the macroscopic behaviors in such systems. Despite a century of effort, they have eluded ...

To determine whether spatiotemporal information could help predict multidrug resistance at the time of tuberculosis diagnosis, we investigated tuberculosis patients who underwent drug susceptibility testing in Lima, Peru, during 2005-2007. We found that crude representation of spatial location at the level of the health center improved prediction of multidrug resistance.

The accurate characterization of the spatiotemporal pattern of electrical activation and repolarization during ischemia is relevant for diagnosis and sudden cardiac death prediction. Although several animal studies have comprehensively described changes in acute ischemic models in the dog, cat, and pig, data from whole-heart mapping of acute ischemia in the in vivo human heart are scant and are...