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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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Волновая (де)проблематизация: освещение пандемии коронавируса в России на федеральном телеканале

Мониторинг общественного мнения: Экономические и социальные перемены. 2020. № 6. С. 284–306.
Kazun A. D., Kazun A.

The article investigates the coverage of the coronavirus pandemic on Channel One for the period from February 1, 2020 to July 30, 2020. Based on the analysis of more than 3,000 news stories, the authors divide the discussion into three major stages. Each stage was defined in terms of specific metaphors, meanings and methods of argumentation aimed at implementing a certain strategy (to soften, to problematize or to deproblematize the situation). The dynamics in the COVID-19 coverage is explained through the concept of cyclic (de)problematization which helps to interpret and analyze significant changes in the tone of the coverage of a particular topic by the same media outlet over a short period of time. By comparing the discussion dynamics and public opinion surveys conducted by the Public Opinion Foundation (FOM), the authors conclude that the way the COVID-19 topic was covered on Channel One may have substantially affected the public perceptions of the situation. However, cyclic deproblematization can increase public distrust in official information about the COVID-19; this, in turn, can make it difficult to reproblematize the situation if the epidemiological situation further deteriorates.

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: телевидениеconstructivismTVdeproblematizationдепроблематизацияcoronavirusкоронавирусCOVID-19health risk communication конструирование проблемCOVID-19
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