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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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Optimization of Network Interaction on a High-Performance Cluster Using Graph Scheduling

P. 141–148.
Timokhin I., Shaikhislamov D., Teplov A.

Data access time become large problem for data processing in distributed environments with a growth of system scale and data size and source of software optimization. In this paper, we research the problem of optimizing I/O and processing operations for a distributed Hadoop cluster that process data in HPC paradigm framework. To increase the efficiency of the framework that processes queries in HDFS, a graph algorithm were developed  with respect to optimal use of resources using HDFS data locality on a processing graph. The graph uses data file blocks and replicas, hosts and workers as a nodes, and links between them as edges. The results of reading stage optimization phase was implemented and performance improvements measured comparing baseline and Spark as Industry standard framework.

Language: English
Text on another site
Keywords: High-Performance Computing (HPC)distance graphMultiprocessing systems

In book

Материалы V Международного семинара по информационным, вычислительным и управляющим системам для распределенных сред (ICCS-DE 2023)
ИДСТУ СО РАН, 2023.
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