Given a (periodic) function $f$ I would like to implement its convolution operator $C_f$, i.e. an operator that I can later apply on a function to give $C_f g = \int ...

Abstract: In recent years, convolutional neural networks have become increasingly important in deep learning. Convolution operation is the core computing unit in convolutional neural networks, which ...

Abstract: The aim object of this paper is to study some classes of the differential subordination concept for the subclasses of univalent functions by using the convolution operator. Some interesting ...

We study hypercyclicity properties of a family of non-convolution operators defined on the spaces of entire functions on ℂN. These operators are a composition of a differentiation operator and an ...

The semantic segmentation task in computer vision involves partitioning an image into a set of multiple non-overlapping and semantically interpretable regions. This entails assigning pixel-wise class ...

When using the advanced measurement approach to determine required regulatory capital for operational risk, expert opinion is applied via scenario analysis to help quantify exposure to high-severity ...

This paper is an introduction to C*-algebra methods for studying the spectral behavior of large truncated Wiener-Hopf operators. We compute the limit of the norms of the inverses of truncated ...

There is no author summary for this book yet. Authors can add summaries to their books on ScienceOpen to make them more accessible to a non-specialist audience.

I has been using derivatives of your repo (https://github.com/yongqyu/MolGAN-pytorch, https://github.com/danielmanu93/GraphGANFed/). And this slide triggered me to ...