Global routing plays a critical role in modern chip design. The routing paths generated by global routers often form a rectilinear Steiner tree (RST).

Notwithstanding astonishing advances in computer vision technologies, detecting ships and floating matters in these images is challenging due to factors such as object distance.

Afundamental challenge indeep metric learning isthegeneralization capability of the feature embedding network model since the embedding network learned ontraining classes needtobeevaluated onnewtestclasses.

Human communication has a multi-modal and multi-affect nature.

The main observation behind our approach is that separation does not require optimization butcan besolvedinclosed-form prior totraining and plugged into a network.

More specifically,for the output features ofthe penultimate layer, for each class the within-class features converge to their means, and the means of different classes exhibit a certain tight frame structure, which is also aligned withthelastlayer'sclassifier.