We study characteristics of receptive fields of units in deep convolutional networks. The
receptive field size is a crucial issue in many visual tasks, as the output must respond to …

Dilated convolutions support expanding receptive field without parameter exploration or
resolution loss, which turn out to be suitable for pixel-level prediction problems. In this paper …

Abstract Motivation The Rank Product (RP) is a statistical technique widely used to detect
differentially expressed features in molecular profiling experiments such as transcriptomics …

Visible light communication (VLC) systems using the indoor lighting system to also provide
downlink communications require high-average optical powers to satisfy the illumination …

Convolutional neural networks (CNNs) have demonstrated excellent capability in various
visual recognition tasks but impose an excessive computational burden. The latter problem …

The study of motifs can help researchers uncover links between the structure and function of
networks in biology, sociology, economics, and many other areas. Empirical studies of …

Deep learning now offers state-of-the-art accuracy for many prediction tasks. A form of deep
learning called deep convolutional neural networks (CNNs) are especially popular on …

We explore the ZN parafermionic clock-model generalizations of the p-wave Majorana wire
model. In particular, we examine whether zero-mode operators analogous to Majorana zero …

Stirling's Approximation for Central Extended Binomial Coefficients Page 1 NOTES
Edited by Sergei Tabachnikov Stirling’s Approximation for Central Extended Binomial …

Going wireless is one of the key industrial trends, which assists the emergence of new
manufacturing and maintenance processes by reducing the complexity and cost of physical …