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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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Measuring Excellence in Russia: Highly Cited Papers, Leading Institutions, Patterns of National and International Collaboration

P. 651–662.
Pislyakov V., Shukshina E.

In this study, we aim to discover Russian “centers of excellence” and explore patterns of their collaboration with each other and with foreign partners. Highly cited papers serve as a proxy for “excellence” and co-authored papers as a measure of collaborative efforts. We find that at the moment research institutes (of the Russian Academy of Sciences as well as others) remain the key players, in spite of recent government initiatives to stimulate university science. The contribution of commercial sector to high-impact research is negligible. More than 90% of Russian highly cited papers involve international collaboration, Russian institutions often not playing a dominant role there. Patterns of national (“intra-national”) collaboration differ significantly across different types of organizations, the strongest ties are between three nuclear/particle physics centers. We draw a co-authorship map to visualize collaboration between Russian centers of excellence.

 

Language: English
Full text
Keywords: collaborationвизуализациянаучное сотрудничествоhighly cited paperscenters of excellencevisualizationвысокоцитируемые статьицентры превосходства

In book

Proceedings of STI 2012 Montréal. 17th International Conference on Science and Technology Indicators
Vol. 2. , Montréal: Science-Metrix; Observatoire des sciences et des technologies, 2012.
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