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July 20, 2026
Scientists Create Open Dataset for Studying Concentration
A team of Russian researchers, including scientists from HSE University–St Petersburg, has developed the first open multimodal dataset containing recordings of brain activity, heart function, and video observations to help researchers understand what happens in the human brain during deep concentration. In the future, the dataset could accelerate the development of neural interfaces, rehabilitation technologies, and AI systems. The article has been published in Scientific Data.
July 20, 2026
‘Science Is Universal-It Knows No Borders
Fuad Aleskerov, Tenured Professor and Director of the International Centre of Decision Choice and Analysis at HSE University, together with his colleagues, has developed methods of network analysis in bibliometrics that have made it possible to identify patterns in the appearance and citation of publications in academic journals, as well as their influence on each other. When one or a number of studies are frequently cited by a wide range of journals, this is an indicator that the research is of high quality. By contrast, extensive cross-citation within a limited group of journals increases the likelihood of identifying a network of predatory publications.
July 20, 2026
Scientists Propose Method for More Efficient Resource Use in Machine Learning
An international group of researchers, including mathematicians from the AI and Digital Science Institute at the HSE Faculty of Computer Science, has provided a theoretical justification for a simple and computationally efficient method of estimating uncertainty in Stochastic Gradient Descent (SGD). The paper has been published on the scientific preprint server arXiv.org and presented at AISTATS 2026.

 

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Между культурой и экономикой: факторы, мотивирующие регионы поддерживать креативные индустрии

Экономика региона. 2024. Т. 20. № 4. С. 1041–1057.
Boos V., Shubina V., Kutsenko E.

Due to the lack of a unified approach to supporting creative industries and to the differentiation of initial socioeconomic conditions in Russian regions, different government participation models in the development of creative industries are emerging. Understanding the balance of factors influencing the creative industries is key to more effective goal-setting and support for the creative sector in Russian regions. Using the polynomial logistic regression method, this study defines objective factors that influence the quality of policies in the field of creative industries in the regions of Russia and, using k-means clustering, determines groups of regions with similar profiles of implemented creative policies. As a result, three groups of policies were identified: “organized”, “independent” and “unmanifested”. It was revealed that eight “organized” regions were ahead of other groups in terms of the level of organizational support (by 5 and 7 times), by federal support measures attracted (by 1.5 and 1.8 times), regulatory legal and strategic support (by 4 % and 8 %) and were approximately on the same level as twenty “independent” regions in terms of the scale of regional support measures. “Organized” regions were 1.4 times ahead of “independent” regions in terms of the number of students, and 1.9 times ahead of “unmanifested” regions. In terms of the number of patent applications “organized” regions were 2.2 and 2.5 times ahead, respectively, and in terms of attendance at cultural institutions 22% and 18%. Modelling showed that government authorities’ support for creative industries at the regional level is facilitated by a combination of innovative and cultural development factors.

Research target: Economics and Management Political Science, International Relations, and Public Administration
Language: Russian
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Keywords: региональная политикаинновационный потенциалregional policycreative industriesкреативные индустрииStrategic planningcultural industrieshuman potentialcreative clustersкультурный потенциалкультурный секторкреативные кластерыкреативный сектор
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