114 citations · 825 across the 41 of their papers we have counts for
42 papers
Citations Are Late: Reading epistemic instability from what papers believe, years before the citation graph catches up
Andrey Ustyuzhanin, Ekaterina Trofimova, Denis Zuenko
Paradigm shifts in science are visible in what researchers assert and contest before they are visible in the citation graph. We ask whether a cheap, content-level signal of epistem…
Machine Learning the Strong Disorder Renormalization Group Method for Disordered Quantum Spin Chains
A. Ustyuzhanin, J. Vahedi, S. Kettemann
We train machine learning algorithms to infer the entanglement structure of disordered long-range interacting quantum spin chains by learning from the strong disorder renormalisati…
Symbolic regression for defect interactions in 2D materials
Mikhail Lazarev, Andrey Ustyuzhanin
Machine learning models have become firmly established across all scientific fields. Extracting features from data and making inferences based on them with neural network models of…
AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
Stephen G. Dale, Nikita Kazeev, Alastair J. A. Price +65
Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, p…
The SND@LHC neutron shielding
LHC Collaboration
The design and construction of a neutron shielding for the SND@LHC detector, which utilizes a combination of plexiglass and borated polyethylene, is presented. FLUKA simulations we…
Energy Underprediction from Symmetry in Machine-Learning Interatomic Potentials
Wei Nong, Ruiming Zhu, Zekun Ren +7
Machine learning interatomic potentials (MLIAPs) have emerged as powerful tools for accelerating materials simulations with near-density functional theory (DFT) accuracy. However,…
Engineering Point Defects in MoS2 for Tailored Material Properties using Large Language Models
Abdalaziz Al-Maeeni, Denis Derkach, Andrey Ustyuzhanin
The tunability of physical properties in transition metal dichalcogenides (TMDCs) through point defect engineering offers significant potential for the development of next-generati…
EagleEye: Attention to Unveil Malicious Event Sequences from Provenance Graphs
Philipp Gysel, Candid Wüest, Kenneth Nwafor +3
Securing endpoints is challenging due to the evolving nature of threats and attacks. With endpoint logging systems becoming mature, provenance-graph representations enable the crea…
AI Competitions and Benchmarks: Competition platforms
Andrey Ustyuzhanin, Harald Carlens
The ecosystem of artificial intelligence competitions is a diverse and multifaceted landscape, encompassing a variety of platforms that each host numerous competitions annually, al…
The DL Advocate: Playing the devil's advocate with hidden systematic uncertainties
Andrei Golutvin, Aleksandr Iniukhin, Andrea Mauri +3
We propose a new method based on machine learning to \emph{play the devil's advocate} and investigate the impact of unknown systematic effects in a quantitative way. This method pr…
Symbolic expression generation via Variational Auto-Encoder
Sergei Popov, Mikhail Lazarev, Vladislav Belavin +2
There are many problems in physics, biology, and other natural sciences in which symbolic regression can provide valuable insights and discover new laws of nature. A widespread Dee…
Unveiling the complex structure-property correlation of defects in 2D materials based on high throughput datasets
Pengru Huang, Ruslan luckin, Maxim Faleev +7
Modification of physical properties of materials and design of materials with on-demand characteristics is at the heart of modern technology. Rare application relies on pure materi…
Code4ML: a Large-scale Dataset of annotated Machine Learning Code
Anastasia Drozdova, Polina Guseva, Ekaterina Trofimova +2
Program code as a data source is gaining popularity in the data science community. Possible applications for models trained on such assets range from classification for data dimens…
SND@LHC: The Scattering and Neutrino Detector at the LHC
LHC Collaboration
SND@LHC is a compact and stand-alone experiment designed to perform measurements with neutrinos produced at the LHC in the pseudo-rapidity region of . The experimen…
Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper
Tommaso Dorigo, Andrea Giammanco, Pietro Vischia +33
The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, given the large dimensi…
Track reconstruction and matching between emulsion and silicon pixel detectors for the SHiP-charm experiment
SHiP Collaboration
In July 2018 an optimization run for the proposed charm cross section measurement for SHiP was performed at the CERN SPS. A heavy, moving target instrumented with nuclear emulsion…
The SHiP experiment at the proposed CERN SPS Beam Dump Facility
SHIP Collaboration
The Search for Hidden Particles (SHiP) Collaboration has proposed a general-purpose experimental facility operating in beam-dump mode at the CERN SPS accelerator to search for ligh…
Learning velocity model for complex media with deep convolutional neural networks
A. Stankevich, I. Nechepurenko, A. Shevchenko +3
The paper considers the problem of velocity model acquisition for a complex media based on boundary measurements. The acoustic model is used to describe the media. We used an open-…
Deep Learning for direct Dark Matter search with nuclear emulsions
Artem Golovatiuk, Andrey Ustyuzhanin, Andrey Alexandrov +1
We propose a new method for the discrimination of sub-micron nuclear recoil tracks from an instrumental background in fine-grain nuclear emulsions used in the directional dark matt…
Segmentation of EM showers for neutrino experiments with deep graph neural networks
Vladislav Belavin, Ekaterina Trofimova, Andrey Ustyuzhanin
We introduce a first-ever algorithm for the reconstruction of multiple showers from the data collected with electromagnetic (EM) sampling calorimeters. Such detectors are widely us…
Online detection of failures generated by storage simulator
Kenenbek Arzymatov, Mikhail Hushchyn, Andrey Sapronov +4
Modern large-scale data-farms consist of hundreds of thousands of storage devices that span distributed infrastructure. Devices used in modern data centers (such as controllers, li…
Vertex and Energy Reconstruction in JUNO with Machine Learning Methods
Zhen Qian, Vladislav Belavin, Vasily Bokov +20
The Jiangmen Underground Neutrino Observatory (JUNO) is an experiment designed to study neutrino oscillations. Determination of neutrino mass ordering and precise measurement of ne…
Machine Learning scientific competitions and datasets
David Rousseau, Andrey Ustyuzhanin
A number of scientific competitions have been organised in the last few years with the objective of discovering innovative techniques to perform typical High Energy Physics tasks,…
A study of Neural networks point source extraction on simulated Fermi/LAT Telescope images
Mariia Drozdova, Anton Broilovskiy, Andrey Ustyuzhanin +1
Astrophysical images in the GeV band are challenging to analyze due to the strong contribution of the background and foreground astrophysical diffuse emission and relatively broad…