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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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Имитационное моделирование процесса выдачи кредита в региональном коммерческом банке

Цифровая экономика. 2022. № 2(18). С. 72–81.
Ramenskaya A., Яркова О. Н.

To form a strategy for managing the lending process and credit risk, analysts of a credit organization need to have tools for modeling the lending process, which allows not only to assess the effectiveness of the lending process in the current situation, but also to assess risks in a changing economic environment. The purpose of this study is to develop and test a simulation model of the process of issuing a loan to retail bank customers, which makes it possible to evaluate overdue debts in dynamics, to model the organization's credit risks. The paper proposes a simulation model of the process of issuing loans in a regional commercial bank, using the concepts of discrete-event and agent-based modeling in the AnyLogic environment. The simulation model of the process of issuing loans makes it possible to evaluate the following indicators: the number of received and approved applications, the number of new bank customers, the dynamics of the amount of loans issued and the amount of overdue debt, the share of overdue debt for various values of the parameters of the loan portfolio. The model was tested on data on consumer lending in a regional commercial bank in Orenburg: calculations were made for the basic scenario of loan portfolio management; analysis of the sensitivity of the key indicators of the loan portfolio to changes in the external and internal environment was carried out. 

Research target: Mathematics Economics and Management Computer Science
Language: Russian
DOI
Text on another site
Keywords: имитационное моделированиеоценка рисковкоммерческий банкsimulation modelingдискретно-событийное моделированиеагентное моделированиеcommercial banksAgent modelingrisc management
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