Spatially and temporally overlapping target and distractor are both rhythmically sampled at ~1 Hz, and the phase relationship between target sampling and distractor sampling predicts behavior.

Abstract: Graph neural networks (GNNs) could directly deal with the data of graph structure. Current GNNs are confined to the spatial domain and learn real low-dimensional embeddings in graph ...

Decrease the displayed range on the temperature graph, to only focus on the operating area. Being able to see from 0 degrees and up isn't super useful, in my opinion, and I would personally rather ...

Abstract: Complex spatial dependencies in transportation networks make traffic prediction extremely challenging. Much existing work is devoted to learning dynamic graph structures among sensors, and ...