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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.
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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.
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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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Capitalization of Professional Knowledge in Economies with Different Level of Innovation Development

P. 628–637.
Sirotin V., Arkhipova M., Egorov A.

This paper is focused on the problem of aggregate return to education. We consider this factor as an indicator of the professional knowledge capitalization that can be used as a proxy for human capital stock in extended Cobb-Douglas type equation for modeling the economic growth. Identification of the indicator is based on time-series data on countries and regional economies. To divide the objects into groups with respect to the aggregate return to education, we employ the fuzzy classification methodology instead of the traditional clustering procedure. This approach provides more relevant dividing the whole sample into three homogeneous groups. The first group includes the economies with low level of innovation development. These economies have close to zero or negative rate of return to education. The second group is most numerous. It mostly consists of developed economies with moderate aggregate rate of return to education. The third group includes objects with high return to education. It turned out that they are predominantly emerging economies developing on the base of new technologies. Such a typology provides the opportunities to make some suggestions concerning the relation between innovation development and quality of human capital stock. In particular, high rate of return to education in emerging economies we explain by the excess demand on high-qualified staff on the labor market that cannot be fully satisfied by existing educational system in the economies with higher level of innovation potential. Conversely, in developed economies, educational system meets the demand of the labor market, so the aggregate return to education is not so high. So it is necessary to be more specific about the structure of educational system for preparing high qualified staff for the most promising directions of innovation development. Comprehensive research of the considered problem at the regional level requires more elaborated data sources.

Language: English
Full text
Keywords: economic growthreturn to educationinnovation developmenthuman capitallabor market

In book

Proceedings of the 12th European Conference on Innovation and Entrepreneurship
P.: ACPI, 2017.
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