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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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Распространенность буллинга: возрастные и гендерные различия, значимость размера и типа школы

Вопросы образования. 2021. № 4. С. 220–242.
Ivaniushina V. A., Khodorenko D. K., Alexandrov D. A.

This article looks into methodological issues in the assessment of bullying, providing cross-national bullying statistics and discussing the possible causes of essential variation in prevalence rate estimates. Individual- and school-level characteristics of bullying are described based on the results of a large-scale representative survey of school students (201 schools, 18 433 students) in Kaluga Oblast (Russia). Our findings show that 15.3% of all students in grades six through nine become victims of bullying during the school year, which is in line with the data obtained in the Health Behavior in School-Aged Children (HBSC), a WHO cross-national study, carried out on a nationally representative sample in Russia. In the age cohort analyzed, prevalence of bullying is the highest (19.4%) among sixth-graders and
the lowest (11.1%) among ninth-graders. Girls and boys are bullied at approximately the same frequency, but boys are exposed more to physical abuse while girls are more likely to be victimized verbally and socially. Prevalence rates of bullying behavior vary dramatically across schools, from 0 to 40% of students in a school being exposed to bullying during the school year, yet the prevalence of bullying is unrelated to schools’ structural characteristics (type, urban/rural, size, socioeconomic status). The relationship between school climate and bullying is discussed in the article, and further avenues of research are outline

Research target: Sociology (including Demography and Anthropology Education Psychology
Language: Russian
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DOI
Text on another site
Keywords: school climateбуллингbullyingшкольный климатраспространенность буллингаprevalence of bullyingbullying assessment instrumentsметоды измерения буллинга
Publication based on the results of:
Safe school: characteristics and factors of formation (2021)
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Added: October 2, 2025
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