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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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Automated Identification of Current Sheets—A New Tool to Study Turbulence and Intermittency in the Solar Wind

JOURNAL OF GEOPHYSICAL RESEARCH-SPACE PHYSICS. 2021. Vol. 126. No. 8. P. 1–25.
Khabarova O., Timothy Sagitov, Kislov R., Gang L.

We propose a new method of the automated identification of current sheets (CSs) that represents a formalization of the visual inspection approach employed in case studies. CSs are often identified by eye via the analysis of characteristic changes in the interplanetary magnetic field (IMF) and plasma parameters. Known visual and semi-automated empirical methods of CS identification are exact but do not allow a comprehensive statistical analysis of CS properties. Existing automated methods partially solve this problem. Meanwhile, these methods suggest an analysis of variations of the IMF and its direction only. In our three-parameter empirical method, we employ both the solar wind plasma and IMF parameters to identify CSs of various types. Derivatives of the IMF strength, the plasma beta and the ratio of the Alfvén speed VA to the solar wind speed V taken with the one-second cadence are used. We find that the CS daily rate R correlates with the solar wind temperature T rather than with V and is proportional to the sum of the kinetic and thermal energy density ∼V2(N+5N′)+10T(N + N′), where N′ = 2 cm−3 is the background level of the solar wind density N. Maxima of R are associated with stream/corotating interaction regions and interplanetary mass ejection sheaths. A multiyear list of CSs identified at 1 AU can be found at https://csdb.izmiran.ru.

Research target: Earth Sciences Physics Computer Science
Priority areas: IT and mathematics
Language: English
DOI
Text on another site
Keywords: Intermittencysolar windturbulenceCurrent Sheetsplasma heating
Publication based on the results of:
Data Analysis and Modeling of Heterogeneous dynamics of the magnetic fields of the Earth and the Sun (2021)
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