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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?
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Индикаторы инновационной деятельности: 2024 статистический сборник

M. : National Research University Higher School of Economics, 2024.
Gokhberg L., Vlasova V., Gracheva G., Ditkovsky K. A., Kuznetsova I. A., Martynova S. V., Ratay T. V., Rosovetskaya L. A., Streltsova E., Fridlyanova S.
Under the general editorship: Anisimov N., Gokhberg L., Kuzminov Y., Колесников М. А.

This data book presents the results of statistical surveys characterising innovation processes in the economy of the country. The methodology for the formation of the main indicators is based on the latest recommendations of international organizations in the field of statistical measurement of innovation (the 4th edition of the Oslo Manual) and the Unified Program of Innovation Surveys in the EU countries.

The book contains statistical data reflecting the state and prospects of innovation activity of enterprises, the development of product and process innovations, resource provision and the effectiveness of innovation activities, including the level of novelty and sales markets, the implementation of innovative products by order of users, are presented.

The publication also includes indicators of the intensity of cooperative relations with external and internal partners, characteristics and forms of open innovation, and assessments of factors that deter innovation. Separate chapters of the collection are devoted to the characteristics of innovation activity in the regions of the Russian Federation, environmental innovations and international comparisons covering a wide range of indicators.

The data book includes information of the Federal State Statistics Service, the OECD, the European Commission, Eurostat, National Statistical Agencies of foreign countries, as well as in-house methodological and analytical studies conducted at Institute for Statistical Studies and Economics of Knowledge at National Research University Higher School of Economics.

Research target: Economics and Management
Language: Russian
Text on another site
Keywords: инновационная деятельностьinnovationинновацияinnovation activityинновационная продукцияexpenditure on innovationssales of innovative goods and servicesзатраты на инновационную деятельность
Publication based on the results of:
Formation of the statistical basis for the study of economics of knowledge as a tool for strategic planning (2024)
Индикаторы инновационной деятельности: 2024 статистический сборник
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