Quantum walks sound abstract, but they sit at the center of a very concrete race: who will harness quantum mechanics to solve problems that overwhelm today’s most powerful supercomputers. Instead of ...

Abstract: For many years, topological data analysis (TDA) and deep learning (DL) have been considered separate data analysis and representation learning approaches, which have nothing in common. The ...

Knowledge graphs are a powerful tool for bringing together information from biological databases and linking what is already ...

Understanding how cells decide their fate is a central challenge in biology, complicated by the fact that single-cell RNA ...

This repository contains a refactoring of the code used in the paper "Learning Latent Graph Structures and Their Uncertainty" (ICML 2025). The code is designed to be modular and easy to use, allowing ...

Abstract: Graph neural networks (GNNs), as a cutting-edge technology in deep learning, perform particularly well in various tasks that process graph structure data. However, their foundation on ...

Icinga Web Module for Performance Data Graphs. This module enables graphs on the Host and Service Detail View for the respective performance data. The data is fetched by a "backend module", at least ...