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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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Образовательные риски в представлениях родителей московских школьников

Социологические исследования. 2016. № 11. С. 144–149.
Pishnyak A.

This article analyses the schoolchildren`s parents subjective perceptions of risks related with receiving education by children in Moscow. The study examines the prevalence of different opinions on this process and attempts to implement a classification of subjective risks. Basing on the data of survey of 1-11th grade pupils` parent conducted in 2013 in Moscow by Institute of Megalopolis Human Development (IMHD), three different types of risks are identified. The first type is system risks which includes dissatisfaction with the characteristics of schools (educational programs and teaching stuff as well as ethnic composition of school classes). According to the data, this sort of risks is spread to 67% of families with schoolchildren in Russian capital. The second type – intra-household risks – covers risks of lack of households` resources. Approximately 39% of respondents declare at list a component of the intra-household risks. The third type – external risks – concerns with indicators of child-friendliness of urban space. About 83% of pupils` parents are preoccupied with fear of street crime on the way to school or the lack of reciprocity among citizens. A special attention is paid to the intersection of different risks and concentration of system, Intra-household and external risks in the minds of respondents. It should be point out that more than half of schoolchildren`s parents do not find a risk in the context of education. However a considerable quantity of respondents – 44% – presumes that at list one type of risk occurs and almost 23% notice at once system, intra-household and external risks.

Research target: Sociology (including Demography and Anthropology Economics and Management Education
Priority areas: economics sociology
Language: Russian
Full text
Keywords: качество образованияшкольное образованиеquality of educationschool educationродители школьниковeducation at schoolisks of educationsubjective risk assessmenturban environment riskparents of pupilsобразование школьниковриски в образованиисубъективные оценки рисковриски городской среды
Publication based on the results of:
Analysis of the demographic and socio-economic behavior of households at different stages of the life cycle and assessment of social and tax policies impact on the dynamics of the standard of living and its differentiation (2016)
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