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News
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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Innovation Development: Review and Estimation of Heterogeneity

P. 22.1–22.10.
Myachin A. L.

The paper describes the assessment of data on the innovation development of individual countries and regions, taking into account a large number of different indicators. An overview of international and national approaches to the assessment of innovative development is presented: Global Innovation Index (Cornell University, INSEAD, and the World Intellectual Property Organization); Portfolio Innovation Index (USA). The mathematical apparatus for calculating each of them is given. The generalized groups of indicators included in each index are described. Designations and methods of calculation are given to a uniform form of presentation. A methodology for assessing the heterogeneity of the innovative development of countries/economic regions of innovation systems based on the use of clustering methods is proposed. The methodology of the compilation of three indices is given, taking into account the results of clustering and evaluating the qualitative proximity of the studied territorial entities.

Language: English
Full text
Text on another site
Keywords: innovationclusteringheterogeneitypattern analysisindices of innovation development
Publication based on the results of:
Decision making and data analysis in socio-economic and political systems (2020)

In book

Proceedings of the 20th International Conference on Group Decision and Negotiation
Ryerson University, 2020.
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