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News
June 5, 2026
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.
June 5, 2026
‘In the Age of Technology, It Is Interesting to Look into the Past and Think about What We Can Take from It
Polina Tabakova decided to apply for a Philology degree at HSE in Nizhny Novgorod because she grew up in Mari El and did not want to move far away from the Russian forests. In an interview for the Young Scientists of HSE University project, she spoke about the genre of the campus novel, the existential drama of Kolobok, and a blackout version of Eugene Onegin.
June 5, 2026
HSE Scientists Develop Method to Compress Large Language Models Without Losing Quality
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a new compression method for large language models such as GPT and LLaMA that reduces their size by 25–36% without additional training or significant loss of accuracy. This is the first approach to use mathematical transformations—specifically, rotations of model weights—to make models more amenable to compression with structured matrices. The study results have been published in ACL Findings 2025. The code is available on GitHub.

 

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Причины и тенденции регионального неравенства в России

Корпоративное управление и инновационное развитие экономики Севера: Вестник Научно-исследовательского центра корпоративного права, управления и венчурного инвестирования Сыктывкарского государственного университета. 2024. Т. 4. № 2. С. 132–140.
Rastvortseva S.

Income inequality among regions in Russia gives rise to a number of social and economic problems. The causes of inequality are numerous and relatively well-studied. However, different studies may present directly opposing characteristics of factors and conditions contributing to inequality growth. This can be attributed to the geographic, historical, cultural, industrial, and even natural-climatic specificities of particular regions. This study assesses the causes of inequality in Russian regions, identifies trends, and proposes strategies for alignment. It reviews contemporary research on inequality in Russia and other regions worldwide. Regression models were constructed using the least squares method to determine the dependency of the Gini index on various indicators across Russian regions and different climate zones. The resulting Gini index for each region of the Russian Federation was utilized as the resulting indicator. The factor indicators include GDP per capita, density of paved roads, average wages, foreign direct investment, housing availability, and climate zone of the region. It was found that the level of inequality depends on GDP per capita, proximity to major cities reduces inequality, while colder and harsher climates tend to increase it. Specific features were identified for regions in different climate zones, and recommendations were provided. These findings contribute to the advancement of economic science and form an informational basis for the development and implementation of regional policies.

Research target: Economics and Management
Language: Russian
Full text
DOI
Keywords: регионы Россиисоциально-экономическое неравенствоregions of Russiaфакторы неравенстваSocio-Economic Inequalitylocal climate zonesfactors of inequalityклиматические пояса
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