Fuelled by increasing computer power and algorithmic advances, machine learning
techniques have become powerful tools for finding patterns in data. Quantum systems …

Our knowledge of the fundamental particles of nature and their interactions is summarized
by the standard model of particle physics. Advancing our understanding in this field has …

A bstract Recent progress in applying machine learning for jet physics has been built upon
an analogy between calorimeters and images. In this work, we present a novel class of …

Machine learning methods incorporating deep neural networks have been the subject of
recent proposals for new hadronic resonance taggers. These methods require training on a …

In quantum information science, a major challenge is to look for an efficient means for
classifying quantum states. An attractive proposal is to utilize Bell's inequality as an …

In proton-proton and heavy-ion collisions, the study of charm hadrons plays a pivotal role in
understanding the QCD medium and provides an undisputed testing ground for the theory of …

Data intensive studies in the domain of accelerator-based High Energy Physics, HEP, have
become increasingly more achievable due to the emergence of machine learning with high …

We study the emergence of collective spatio-temporal objects in biological systems by
representing individually the elementary interactions between their microscopic …

In this paper we propose to use convolutional neural networks (CNNs) to improve the
precision measurement of the Higgs boson-gluon effective coupling at lepton colliders. The …

Stacking fault energy (SFE) is an important parameter to be considered in the design of
austenitic stainless steels (SS) due to its influence on magnetic susceptibility, atomic order …