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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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Роль частных трансфертов в поддержке семей с несовершеннолетними детьми как группы с повышенным риском бедности

Журнал исследований социальной политики. 2025. Т. 23. № 1. С. 79–98.
Mironova A. A.

Reducing poverty is one of the most important national tasks in modern Russia. One of the key factors that determine the risk of poverty in Russia is having minor children. Households with children under the age of 18 make up the majority of those living in poverty in Russia. This study examines the characteristics of Russian households with children involved in the exchange of private transfers. In addition, the paper assesses the relationship between public and private transfers for households with children. The study was based on data from 2023 Statistical Survey of Income and Participation in Social Programs (VNDN 2023), as well as data from the All-­Russian Censuses of 2010 and 2020, and the 1994 and 2015 micro-­censuses of the population of Russia. The study showed that households with children under 18 are the most frequent recipients of private transfers. For low-income households, the amount of private transfers received is negatively related to the amount of social transfers received. For higher income households, however, the direction of the relationship is reversed – the amount of private transfers received is positively related to the amount of social transfers received.

Research target: Sociology (including Demography and Anthropology
Language: Russian
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DOI
Text on another site
Keywords: бедностьpovertyсемьи с детьмичастные трансфертыдоход домохозяйстваprivate transfersсоциальные трансфертыsocial transfersfamilies with minor children
Publication based on the results of:
Recommendations on family policy and active ageing policy measures taking into account the dynamics of quality of life indicators (2025)
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