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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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Компьютерные симуляции и численные методы в двухвидовой модели пространственных сообществ

International Journal of Open Information Technologies. 2019. Т. 7. № 12. С. 18–23.
Галкин Е. Г., Зеленков В. К., Nikitin A. A.

This publication begins a series of works devoted to the comparison of the results of numerical methods for solving integral equations and the results of computer simulations using Poisson processes. The pros and cons of these two approaches are highlighted. The main subject of study is the model of two-species communities proposed in the works of W. Dickman and R. Lowe. The mathematical formulation of this model for an infinite domain is described. A modification of this model for work in a limited area is given. In the future, this is required for stochastic modeling. Further, the most important moments of the stochastic model are briefly described, and the features of the computer simulations built for it are noted – the algorithm used is described and the data structures used are noted. At the end of the work is a number of pictures, with the results of the above comparisons. The comparison is made within the framework of two classical scenarios of two-species models — Competition-colonization trade-off (Bay-Run scenario) and heteromyopia (People-and-Mosquitoes scenario). These drawings show very similar results, as well as some discrepancies. All this allows us to hope that the work in this direction will be very fruitful in the future.

Priority areas: IT and mathematics mathematics
Language: Russian
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Keywords: математическое моделированиеmathematical modelingинтегро-дифференциальные уравненияматематическая биологияmathematical biologyIntegro-Differential Equations
Publication based on the results of:
Математические модели. Дифференциальные уравнения и большие массивы информации. Аналитические и вычислительные методы. Практические приложения (2018)
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