In this paper, we apply kernel methods to deep convolutional neural network (DCNN) improve its nonlinear ability. DCNNs have achieved significant improvement in many computer vision tasks. For an image classification task, the accuracy comes saturation when depth and width of are enough appropriate. The will not rise even by increasing width. We find that improving ability can break through acc...

We use a simple model consisting of energy-momentum tensor conservation and Maxwell-Cattaneo equation for its viscous part to study nonlinear phenomena in real relativistic fluid. focus on new types behavior without nonrelativistic equivalents, such as an entropy cascade driven by fluctuations the degrees freedom theory. write down von K\'arm\'an--Howarth equations this kind turbulence consider...

We present a one-stage F ully C onvolutional Li ne P arsing network (F-Clip) that detects line segments from images. The proposed is very simple and flexible with variations gracefully trade off between speed accuracy for different applications. F-Clip in an end-to-end fashion by predicting each line’s center position, length, angle. further customize the design of convolution kernels our fully...