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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.
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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.
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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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Межличностная синхронизация в дуэтах профессиональных музыкантов: методы оценки и эксплораторный анализ

Психологические исследования: электронный научный журнал. 2024. Т. 17. № 95. Статья 4.
Vodneva A., Мачнев Е. Г., Занин Е. В., Лаишевцева Е. С., Григоренко Е. Л.

Research on interpersonal synchrony is actively developing abroad, yet the experimental condition of musical
interaction remains understudied, despite the fundamental importance of music in human social life. The pre-
sent study aims to investigate the phenomenon of interpersonal synchrony at the behavioral level during the
joint performance of a musical piece by professional musicians, and to evaluate the contribution of individual,
professional, and musical characteristics to this process. The study involved 16 participants (M = 30.7 years;
SD = 5.6; 3 women), including 8 musicians from the Russian National Youth Symphony Orchestra and 8
musicians from the Saint Petersburg Academic Philharmonia, performing on string or wind instruments.
Within the framework of the experimental interaction, audio and video data of the performance of a musical
piece consisting of two different scale parts were recorded. Motion Energy Analysis, rMEA, and Surrogate
Synchrony software and packages were used to assess interpersonal synchrony and social present. Empathy
was measured using the Questionnaire of Cognitive and Affective Empathy, personality traits — by the Big
Five Inventory-2. Information on professional experience, absolute pitch, and sight-reading skill was obtained
by means of a questionnaire. Individual tempo during joint performance was determined through sound track
analysis in FL Studio 21. The conducted exploratory analysis allowed the assessment of listed characteristics
and highlighted a significant relationship between the performance of a musical piece part and interpersonal
synchrony. Further research with larger sample sizes and alternative designs is needed to examine the relation-
ships between these characteristics more extensively.

Research target: Psychology
Language: Russian
DOI
Text on another site
Keywords: эмпатиясоциальное взаимодействиеличностные чертымежличностная синхронизацияневербальная синхронизациямузыканты
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