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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
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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.
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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Меры по поддержке рабочих мест в период коронакризиса: Россия versus страны ОЭСР

Журнал исследований социальной политики. 2023. Т. 21. № 4. С. 735–746.
Lukyanova A.

This paper analyses the Job Retention (JR) programs implemented in Russia in response to the COVID‑19 crisis and compares them with those undertaken in other, mostly OECD, countries. JR schemes allow eligible employers to access financial support to fund labor costs and preserve jobs that might have been terminated during the pandemic. Governments introduced this initiative to protect the income of workers during reduced working time or complete shut-down. We describe the original design of the JR schemes used in Russia and the subsequent adjustments made during the crisis. We discuss the eligibility criteria, the level of support for employees and employers, and the potential risks associated with the programs. Russian JR programs took a special form of lump-sum wage subsidies: funds were provided as loans, but the loans were fully forgiven for most borrowers at the end of the program period. The coverage was limited to a narrow set of the 'most affected' industries while the government-­imposed restrictions on business activity affected companies in all sectors. The amount of the loan was not tied to a reduction in earnings or working hours as has been the case in other countries that have used wage subsidies. The key support package was introduced late and did not allow for retroactive claims. In terms of financial rigidity, the Russian JR schemes resemble those implemented in emerging economies with severe fiscal pressures. The maximum amount of support was low and did not provide basic social guarantees for workers. Firms were required to maintain the number of employees, but not the level of compensation. Design flaws may have undermined the effectiveness of the program. On the positive side, we document intensive institutional learning in the implementation phase: the turn towards more free services and the increasing role of the Federal Tax Service as a program operator.

Research target: Economics and Management Sociology (including Demography and Anthropology
Language: Russian
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Text on another site
Keywords: неполная занятостькоронавирусCOVID-19job retention schemesshort-time workwage subsidiesподдержка рабочих местФОТ 2.0
Publication based on the results of:
Crisis Adjustment in the Labor Market: Structural Changes and Institutional Mechanisms (2023)
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