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The production of light hypernuclei in relativistic heavy-ion collisions provides a unique opportunity to probe hyperon--nucleon interactions and possible three-body forces, which are central to the resolution of the hyperon puzzle in…
This work presents the first experimental observation of the attractive strong interaction between a proton and a multi-strange baryon (hyperon) $\Xi^-$. The result is extracted from two-particle correlations of combined…
The rapid advancement of machine learning and artificial intelligence (AI)-driven techniques is revolutionizing materials discovery, property prediction, and material design by minimizing human intervention and accelerating scientific…
Artificial Intelligence (AI) technology is based on theory and development of computer systems able to perform tasks that normally require human intelligence. In this context, deep learning is a family of computational methods that allow an…
Artificial Intelligence (AI) is revolutionizing emergency medicine by enhancing diagnostic processes and improving patient outcomes. This article provides a review of the current applications of AI in emergency imaging studies, focusing on…
In overview of the recent activity of the newly funded EXCLusives with AI and Machine learning (EXCLAIM) collaboration is presented. The main goal of the collaboration is to develop a framework to implement AI and machine learning…
In recent years, artificial neural networks and their applications for large data sets have became a crucial part of scientific research. In this work, we implement the Multilayer Perceptron (MLP), which is a class of feedforward artificial…
There has been a longstanding demand for artificial intelligence with human-level cognitive sophistication to address loopholes in Bell-type experiments. In this study, we propose a novel experimental framework that integrates advanced deep…
I discuss strangeness production in nucleus-nucleus reactions at ultrarelativistic energies (up to 200 AGeV). In these reactions matter may be created with densities and temperatures in the transition region between quark-gluon plasma (QGP)…
Data on mean numbers of $\Lambda$, $\bar\Lambda$, K's and on the total number $<s\bar s>$ of pairs of strange valence quarks in final state hadrons in hadronic and nuclear collisions at CERN- SPS energies are studied as a function of the…
We study the strangeness production in antiproton-nucleus collisions at the beam momenta from 200 MeV/c to 15 GeV/c within the Giessen Botzmann-Uehling-Uhlenbeck (GiBUU) transport model. The GiBUU model contains a very detailed description…
A key problem in the modern study of AI is predicting and understanding emergent capabilities in models during training. Inspired by methods for studying reactions in quantum chemistry, we present the ``2-datapoint reduced density matrix".…
Particle identification is one of the core tasks in the data analysis pipeline at the Large Hadron Collider (LHC). Statistically, this entails the identification of rare signal events buried in immense backgrounds that mimic the properties…
Much of the present-day Artificial Intelligence (AI) utilizes artificial neural networks, which are sophisticated computational models designed to recognize patterns and solve complex problems by learning from data. However, a major…
Deep neural networks are a powerful technique that have found ample applications in several branches of Physics. In this work, we apply machine learning algorithms to a specific problem of Cosmic Ray Physics: the estimation of the muon…
Convolutional Neural Nets, which is a powerful method of Deep Learning, is applied to classify equation of state of heavy-ion collision event generated within the UrQMD model. Event-by-event transverse momentum and azimuthal angle…
Bound-systems of $\Xi^-$--$^{14}_{}{\rm N}$ are studied via $\Xi^-$ capture at rest followed by emission of a twin single-$\Lambda$ hypernucleus in the emulsion detectors. Two events forming extremely deep $\Xi^-$ bound states were obtained…
This work concerns the control of unknown nonlinear systems corrupted by disturbances. For such systems, we propose an anti-disturbance dual control approach with active learning of the disturbances. Our approach holds the dual property of…
In studying solidification process by simulations on the atomic scale, the modeling of crystal nucleation or amorphisation requires the construction of interatomic interactions that are able to reproduce the properties of both the solid and…
We uncover a phenomenon largely overlooked by the scientific community utilizing AI: neural networks exhibit high susceptibility to minute perturbations, resulting in significant deviations in their outputs. Through an analysis of five…