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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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University Students and Teachers’ Experiences with Distance Education in Uzbekistan

Journal of Eastern European and Central Asian Research. 2024. Vol. 11. No. 1. P. 156–175.
Akhmedjanova D., Kerimova I.

This study has examined the experiences of students and teachers with distance education in Uzbekistan during the COVID-19 pandemic. Distance education is a new field in Uzbekistan, and evidence-based practices should be examined for its successful implementation. A convergent mixed-methods design was used, employing surveys and focus group interviews. The survey data was analyzed using descriptive and inferential statistics, and the focus group interview data was analyzed using thematic analysis. The results revealed that students and faculty had both positive and negative experiences with distance education, but also suggested that teachers were more self-efficacious in teaching online rather than students learning online. This exploratory study indicates that students need targeted instruction and support on studying online and self-regulate their learning. Teachers should participate in intensive professional learning workshops on materials design and pedagogical practices in online classes. Practical applications and ideas for future research are discussed.

Research target: Education
Language: English
Full text
DOI
Keywords: distance educationself-efficacyLearning management system (LMS)TPACK
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