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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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Институты как драйвер развития инноваций в российском регионе: опыт Республики Татарстан

НИУ ВШЭ , 2018.
Makarov S., Абзалилова Л. Р.
Innovations are becoming critical for the sustainable development of the Russian Federation (RF). Most of the articles on innovative development concern a country level missing some of the essential regional-scale factors. The subject of the analysis in this article is the functioning of the regional innovation system and the specifi c features of the instruments and institutions of innovation policy that provide incentives for innovation processes at the regional level. Within the framework of this study, the main characteristics, stages of evolution and SWOT-analysis of the innovation system of one of the leading regions in innovation development in the RF – the Republic of Tatarstan (RT) – are highlighted. The main historical and political reasons for the formation of key features of the regional innovation system are analyzed. Particular attention is paid to assessing the effectiveness of the regional innovation policy and identifying risks associated with its implementation. The main conclusion of the study is the thesis that subnational institutions that use indirect (soft) tools to stimulate innovation activity, to a signifi cant extent, form the innovative environment and become decisive in interregional competition. In our opinion, such institutional innovations were defi ning at the analyzed period. Among the key factors for the successful development of innovations in the Russian regions, the important role is played by coordinated activities of government brunches at all levels and the effectiveness of the organization of a dialogue between government and business. The fi ndings of the study are based on the analysis of federal and regional statistics, various Russian and international ratings, the results of the previous studies and a series of informal interviews with experts and participants in innovative processes in Tatarstan.
Language: Russian
Keywords: ТатарстанинновацииinnovationTatarstanрегиональная инновационная системаregional innovation systemregional innovation policyinnovation instrumentsinnovation institutionsрегиональная инновационная политикаинновационные инструментыинновационные институты
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