This paper proposes a new graph convolutional operator called central difference convolution (CDGC) for skeleton based action recognition. It is not only able to aggregate node information like vanilla operation but also gradient information. Without introducing any additional parameters, CDGC can replace in existing Graph Convolutional Networks (GCNs). In addition, an accelerated version of th...

Abstract In this work, we describe a new flux splitting method for an ideal MHD in the 3D curvilinear coordinate system. The is designed to create dissipation term by zero of split and central difference scheme MHD. Numerical tests are performed demonstrate accuracy capability capture complex waves