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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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«Чужие выборы»: новости соседнего государства о выборах президента РФ в 2018 г.

Экономическая социология. 2021. Т. 22. № 1. С. 71–91.
Kazun A. D., Pashakhin S.

News media tend to reflect voices in the political establishment while cov-ering international events. Is it still true when almost half of the national audience speak the language of the country featured in the coverage? In this paper, we present an analysis of 19.5k news messages collected from Russian-language Ukrainian news outlets covering the 2018 presidential elections in Russia. Using a mixed-method approach (topic modeling and qualitative reading), we identify key topics and stories and evaluate the ex-tent of personalization in the election coverage. We find three central angles: the focus on polls and election results, election preparations in Crimea, and Vladimir Putin’s victory. The elections are linked predominantly to Crimean issues through the date of the elections, each candidate’s stance on the sub-ject, the election management in the region, and other countries’ reactions to the results. Such coverage has an accusatory bias; it stresses the legal status of the Crimean referendum and the Russian authorities’ actions and reports the pressures on locals by authorities, especially the Crimean Tatars. Not linked directly to Crimea, other angles are less emotionally charged. Political personalization of the discussion has a contradictory nature. On one hand, the overwhelming majority of the messages mention public figures. On the other hand, the coverage of the figures is limited and omits their traits. More-over, at times, public figures are replaced by non-personalized symbols (e.g., Kremlin, Russian invaders). However, if the former’s coverage is predomi-nantly neutral, the latter’s coverage is more prone to negative and loaded statements.

Research target: Media and Communications Political Science, International Relations, and Public Administration Sociology (including Demography and Anthropology
Priority areas: sociology
Language: Russian
Full text
DOI
Text on another site
Keywords: медиаmedianewsновостиtopic modelingвыборы Президента РФтематическое моделированиеCrimea2018 Russian presidential electionsполитическая персонализацияpolitical personalization
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