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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.
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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.
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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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Who’s Bad? Attitudes Toward Resettlers From the Post-Soviet South Versus Other Nations in the Russian Blogosphere

International Journal of Communication. 2017. Vol. 11. P. 3242–3264.
Svetlana S. Bodrunova, Koltsova O., Sergey Koltcov, Sergey Nikolenko

Communication in social media is increasingly being found to reproduce or even reinforce ethnic prejudice and hostility toward migrants. In Russia of the 2010s, with its world’s second largest immigrant population, polls have detected high levels of hostility of the Russian population toward migranty (migrants), a label attached to resettlers from Central Asia and the Caucasus. We tested the online hostility hypothesis by using the data of 363,000 posts from the Russian-language LiveJournal. We applied data mining, regression analysis, and selective interpretative reading to map bloggers’ attitudes toward migranty, among other ethnicities and nations. Our findings significantly alter the picture drawn from the polls: Migranty neither provoke the biggest amount of discussion nor experience the worst treatment in Russian blogs, in which Americans take the lead. Furthermore, Central Asians and North Caucasians are treated very differently.

Research target: Sociology (including Demography and Anthropology Mathematics Philology and Linguistics
Priority areas: humanitarian IT and mathematics
Language: English
Full text
Text on another site
Keywords: Russiaethnic studiesmigrantsblogospheretopic modelingbig data
Publication based on the results of:
Разработка концепции и методологии многоуровневого мониторинга состояния межнациональных отношений по данным социальных сетей (2017)
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