Existing models often rely on self-attention mechanisms to capture spatio-temporal dependencies in traffic sequences, leading to high computational complexity. Additionally, most models use static ...

Abstract: There is a growing interest in using spatio-temporal graph networks to predict sea surface temperature (SST). Nevertheless, numerous existing methodologies struggle to accurately capture ...

By combining both time and spatial information, MTF can capture local details and global signal trends, providing more accurate information for ECG classification. MTF, by modeling the temporal ...

Here, we present stGuide, an attention-based supervised graph learning model designed for cross ... while also maintaining temporal coherence within the spatial transcriptomic landscape, thereby ...