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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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Факторы экспортной диверсификации: эконометрический анализ промышленных регионов России

Экономика региона. 2022. Т. 18. № 3. С. 895–909.
Vasilyeva R., Мариев О. С., Voytenkov V., Urazbaeva A.

Oil and gas dependence and the volatility of their prices is currently a serious challenge for the Russian economy. Coincidently, export revenues from oil and gas products are the main source of the federal budget. Export diversification can contribute to risk reduction for the Russian economy by increasing the share of products from other industries in the export structure. In this regard, the present study examines the determinants of export diversification in Russian industrial regions using econometric modelling methods. To this end, the Herfindahl and Theil indices for 97 export groups were calculated. It is hypothesised that the development of small and medium-sized enterprises, as well as the sanctions imposed by Western states against Russia are the main factors of export diversification in industrial regions. Simultaneously, natural resource extraction is assumed to significantly increase the concentration of exports in the regions. To test this hypothesis, panel data of 50 Russian industrial regions for the period 2001-2019 were analysed. The quantile regression approach was applied to solve the heteroscedasticity problem. Three groups of regions were distinguished according to their level of diversification: regions with a high level of export diversification (Q10-Q30), with an average level of diversification (Q40-Q60), with a low level of export diversification (Q70-Q90). The research findings show that the development of small and medium-sized enterprises contributes to export diversification in Russian industrial regions. While the sanctions did not have a significant impact on export diversification, regional potential and natural resource extraction increase the concentration of exports. The obtained study results complement the existing literature on export diversification in Russia, and contribute to the development of policy implications.

Research target: Economics and Management
Language: Russian
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Keywords: малый и средний бизнесприродные ресурсысанкциирегионы Россииквантильная регрессияиндекс Тейлабизнес-потенциалэкспортная диверсификацияпромышленные регионыиндекс Херфиндалябизнес-риск
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