This important study presents a new method for longitudinally tracking cells in two-photon imaging data that addresses the specific challenges of imaging neurons in the developing cortex. It provides ...

The openshift-client-python library aims to provide a readable, concise, comprehensive, and fluent API for rich interactions with an OpenShift cluster. Unlike other clients, this library exclusively ...

Graph convolutional neural netwoks (GCNNs) have been emerged to handle graph-structured data in recent years. Most existing GCNNs are either spatial approaches working on neighborhood of each node, or ...

Graph embedding, aiming to learn low-dimensional representations (aka. embeddings) of nodes in graphs, has received significant attention. In recent years, there has been a surge of efforts, among ...

This repository hosts the scripts and some of the pre-trained models presented in out paper "ViGAT: Bottom-up event recognition and explanation in video using factorized graph attention network", IEEE ...