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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.
June 4, 2026
Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns
The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.

 

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Агрессия и тревожность до и во время COVID-19 (на основе опроса московских и казанских студентов)

Вопросы психологии. 2021. Т. 67. № 6. С. 65–77.
Burkova V., Butovskaya M., Зинурова Р. И.

This paper presents the results of a longitude study conducted on samples of Moscow and Kazan students before (2016-2019) and during (2020-2021) the COVID-19 pandemic. The sample of Kazan is represented by pre-pandemic data and responses during the first wave COVID-19. The Moscow data was collected before the pandemic and during the three waves of the COVID-19. The total sample of the study is 2194 people. The goal is to assess changes in the level of anxiety and aggression before and during the COVID-19 pandemic among students from the two regions of Russia - Moscow and Kazan. The analysis revealed significant changes in the stress level of young people: the level of anxiety before the pandemic was the lowest in both samples, while the dynamics of changes in the level of anxiety in the Moscow sample during COVID-19 (data for all waves) showed significant decrease of the stress level from the1st to the 3rd wave. Hence, people adapt to a stressful situation. The level of aggressive behavior on all scales (except for anger among Kazan students) have been decreased during pandemic, compared to the pre-pandemic level. Our data may help to assess the psychological consequences of global stress in populations from two large cities from Russia and contribute to the fight against COVID-19.

Research target: Sociology (including Demography and Anthropology Psychology
Language: Russian
Full text
Keywords: тревожностьагрессияanxietystressстрессaggressionCOVID-19COVID-19
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