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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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О прошлом, но в разное время: компьютерный анализ текстов учебников по истории СССР/России для шести поколений студентов

Вестник Томского государственного университета. Филология. 2024. № 89. С. 73–103.
Kolmogorova A., Колмогорова П. А., Куликова Е. Р.

In this article, we focus on the analysis of the texts of three history textbooks for university students published at different times: in 1946, in 1983 and in 2006. As a material, we use texts devoted in each of the textbooks to seven historical topics since the beginnings of Kiev principality till the Reforms of Peter I. In our research, we tried to move away from the tradition banalized in discursive research to analyze history textbooks as a kind of ideologically labeled discourse. Instead, we consider the analyzed texts as a form of manifestation of a certain generational narrative. The authors of textbooks, being not only institutional narrators, but also representatives of their generation, color the historiographical canvas, which remains, in principle, unchanged, with a certain emotional tone, and, when telling about the same events, shift the focus of thematic attention based on the spirit of their time. To solve this problem, we use computational linguistics methods: sentiment analysis, clusterization and topic modeling. 

Their use in combination with interpretive analysis allowed us to draw a number of conclusions. 

The measurement of similarity using the cosine distance between the vector representations of texts has shown that the most constant objects of narrativization in the pedagogical discourse are the themes about historical characters - Ivan the Terrible and Peter the Great: these texts have very similar vectors, identical for all textbooks tonality (Terrible - negative, Peter - balanced between negative and positive) and topical content. 

The biggest difference between the text vectors we observe between the texts of the section "Feudal fragmentation" and "Consolidation of lands". Although the latter fall into the same cluster, the distance between them is quite significant. The texts on feudal fragmentation are characterized by variability of emotional tone: from pronounced negativity in 1946, moderate positivity in 1983 to the predominance of positivity in 2006. As for the topic of the consolidation of lands under the aegis of the Moscow kingdom, compared to feudal fragmentation, the tone of these texts is more positive, with the highest percentage of positive evaluation in 1946. The leading topics of these texts do not coincide by year. 

Of all the sections, in general, the most positive in tone are the most mythologized and legendary time periods - "Kievan Rus'" and "Dokiev Rus'". At the same time, it is interesting how the focus of the origins of national identity shifts: from Arab contacts and the Asian trace in 1946 to the Pan-Slavic idea in 2006.

Research target: Philology and Linguistics
Language: Russian
Full text
DOI
Keywords: Russian historyроссийская историяsentiment analysistopic modelingтематическое моделированиесентимент-анализtextbook textsideological discourseтексты учебниковидеологический дискурс
Publication based on the results of:
Текст как Big Data: моделирование конвергентных процессов в языке и речи цифровыми методами (2023)
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