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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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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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Association between sleep patterns, somatization, and depressive symptoms among Russian university students

Chronobiology International. 2023. Vol. 40. No. 2. P. 215–221.
Dokuka S., Mikhaylova O., Krekhovets E.

In adolescents and young adults, physical and mental health are known to be associated with sleep. In this paper, we consider the connection between health indicators and sleep patterns. We collected and analyzed data of 267 master’s and bachelor’s degree students from 21 educational programs at one elite Russian university. Somatic health and mental health were measured using, respectively, the 8-item and the 15-item Patient Health Questionnaire, and sleep patterns were identified using the Munich Chronotype Questionnaire. The results indicated that shorter sleep time, later bedtime, and later rising time on workdays were associated with student somatic health. Shorter sleep on workdays was positively associated with the severity of depressive symptoms. Our findings highlight the importance of healthy sleep behavior for student health.

Research target: Social Sciences
Language: English
Full text
DOI
Keywords: university studentssleephigher educationdepressive symptomssomatization
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