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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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Метод увеличения пропускной способности сетевых каналов с использованием моделей транспортного равновесия

Защита информации. Инсайд. 2025. Т. 121. № 1. С. 1–7.
Zykov S. V., Маличенко С. В.

The article announces an analogy of using transport equilibrium models in the problems of distributing computer
network flows. The purpose of the study is to test the possibility of using transport models and algorithms to optimize routing in a software-defined network (SDN) and to build an optimal flow distribution method based on these models. To achieve this goal, integer optimization and computer modeling methods were used. The practical result of the study confirmed the improvement of traffic characteristics when using the proposed solutions. As a conclusion, the feasibility and effectiveness of using transport equilibrium models and algorithms for optimizing routing in SDN is confirmed. Practical results demonstrate clear prospects for further development and improvement of routing solutions in the field of computer networks.

Research target: Computer Science Electronics and Electrical Engineering
Language: Russian
Full text
Keywords: routingмаршрутизацияпрограммно-конфигурируемые сетиstable dynamics modelBeckmann modeltransport equilibrium model software-defined networkмодель Бекманамодель транспортного равновесиямодель стабильной динамики
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