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News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Parallel Computational Technologies, 19th International Conference, PCT 2025, Moscow, Russia, April 8–10, 2025, Revised Selected Papers. (CCIS, volume 2891)

Vol. 2891. Springer, 2026.
Under the general editorship: L. Sokolinsky, M. Zymbler, V. Voevodin, J. Dongarra
Chapters
Superconductivity and Trimer Formation in Attractive Hubbard Ladders
Pilé I., Burovskiy E., , in: Parallel Computational Technologies, 19th International Conference, PCT 2025, Moscow, Russia, April 8–10, 2025, Revised Selected Papers. (CCIS, volume 2891)Vol. 2891.: Springer, 2026. P. 546–560.
We investigate the interplay between superconducting correlations and trimer formation in polarized two-component Fermi gases confined to multileg attractive Hubbard ladders. Using density-matrix renormalization group (DMRG) simulations, we examine the effects of spin-dependent tunneling amplitudes on these systems. Specifically, we analyze how bound states of three fermions (trimers) influence Fulde–Ferrell–Larkin–Ovchinnikov (FFLO) superconducting correlations at commensurate ...
Added: May 12, 2026
Using a Cascade of Supervised Machine Learning Models to Discover Causality in Pairs of Variables
Zelenkov Y., , in: Parallel Computational Technologies, 19th International Conference, PCT 2025, Moscow, Russia, April 8–10, 2025, Revised Selected Papers. (CCIS, volume 2891)Vol. 2891.: Springer, 2026. P. 187–205.
Discovering causality between two variables is challenging, especially in the nonlinear case. Many causation coefficients exist to identify cause and effect, but most only distinguish two types of relationships,  and , treating all other cases as unidentifiable. Additionally, these models often rely on assumptions (e.g., linearity, non-Gaussian noise) that limit their practical applicability. To address these limitations, ...
Added: May 18, 2026
High-Performance Computing at HSE University
Kostenetskiy P., Vyacheslav Kozyrev, Chulkevich R. et al., , in: Parallel Computational Technologies, 19th International Conference, PCT 2025, Moscow, Russia, April 8–10, 2025, Revised Selected Papers. (CCIS, volume 2891)Vol. 2891.: Springer, 2026. Ch. 2 P. 17–29.
High-performance computing (HPC) has emerged as a critical tool for accelerating research across diverse scientific domains, enabling the efficient processing of large datasets and complex simulations. This article offers a comprehensive overview of the HPC resources available at HSE University in Moscow. We outline the university’s current HPC infrastructure, detailing its computational capabilities, software environments, ...
Added: May 19, 2026
A High-Level Framework for Distributed Systems with Simulation-Based Testing Support
Yakovlev S., Oleg S., , in: Parallel Computational Technologies, 19th International Conference, PCT 2025, Moscow, Russia, April 8–10, 2025, Revised Selected Papers. (CCIS, volume 2891)Vol. 2891.: Springer, 2026. P. 80–97.
This paper introduces DSBuild, a high-level framework designed to simplify the development of distributed systems with built-in support for simulation-based testing. The framework enables the implementation and execution of production-ready distributed applications that can be tested in deterministic simulations without code translation. By eliminating the need for code translation, our approach avoids common pitfalls such ...
Added: May 19, 2026
Optimizing Computational Infrastructure for Large Language Models in Bioinformatics: A Case Study
Beknazarov N., , in: Parallel Computational Technologies, 19th International Conference, PCT 2025, Moscow, Russia, April 8–10, 2025, Revised Selected Papers. (CCIS, volume 2891)Vol. 2891.: Springer, 2026. P. 3–16.
This paper addresses the challenge of efficiently training Large Language Models (LLMs) on large-scale, sparse omics datasets in high-performance computing (HPC) environments. Using over 1000 BED tracks as a representative data source, we propose a method combining interval-based chunked storage, sparse matrix transformation, and parallel data loading, integrated within a PyTorch Lightning training framework. Our ...
Added: May 19, 2026
Parallel Implementation of Voronoi Tessellation in the Julia Programming Language
Pisarev V., Locon Amezquita R. H., , in: Parallel Computational Technologies, 19th International Conference, PCT 2025, Moscow, Russia, April 8–10, 2025, Revised Selected Papers. (CCIS, volume 2891)Vol. 2891.: Springer, 2026. P. 264–279.
Voronoi tessellation is an important concept for analyzing the distribution of particles in physical systems. In this paper, we present an implementation of Voronoi tessellation in a Julia programming language library. The core engine is Voro++, a C++ library for threedimensional Voronoi tessellations. The C++ interface is made available in Julia through the CxxWrap.jl package. ...
Added: May 19, 2026
Performance Analysis of Computational Devices in Quantum Chemistry Tasks
Nedomolkin I., Konikov M., Fedorov I. et al., , in: Parallel Computational Technologies, 19th International Conference, PCT 2025, Moscow, Russia, April 8–10, 2025, Revised Selected Papers. (CCIS, volume 2891)Vol. 2891.: Springer, 2026. P. 513–531.
Modern supercomputer systems play a crucial role in scientific and engineering research. To ensure their effectiveness, these fields require reliable methods for evaluating supercomputer performance. Although benchmarking is a fundamental tool, current ranking systems often inadequately represent real-world performance in high-performance computing (HPC) applications. As a result, employing actual scientific software packages provides a more ...
Added: May 19, 2026
Scaling Up Molecular Hydrodynamics of Non-Laminar Flows with GPU-Aware MPI
Khnkoian G., Galigerov V., Grishichkin Y. et al., , in: Parallel Computational Technologies, 19th International Conference, PCT 2025, Moscow, Russia, April 8–10, 2025, Revised Selected Papers. (CCIS, volume 2891)Vol. 2891.: Springer, 2026. P. 532–545.
This work presents atomic-scale modeling of the perturbed flow of a Lennard-Jones fluid in a quasi-two-dimensional system containing one billion atoms. A statistically stationary flow regime corresponding to a Reynolds number of Re ≈ 1000 has been achieved, the flow structure has been analyzed, and the energy spectrum of velocities has been calculated. The results ...
Added: May 19, 2026
Research target: Computer Science
Language: English
DOI
Text on another site
Keywords: High Performance ComputingComputer Science
Parallel Computational Technologies, 19th International Conference, PCT 2025, Moscow, Russia, April 8–10, 2025, Revised Selected Papers. (CCIS, volume 2891)
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Рефакторинг исходного кода на основе LLM и расширения UML
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