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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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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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Производительность труда и фондовооруженность в обеспечении экономического роста российских регионов

Социальное пространство. 2018. Т. 13. № 1. С. 1–9.
Rastvortseva S.

Labor productivity is an important factor in economic growth. It is important to understand how employees’ provision of basic funds will improve the efficiency of their labor activity, to what extent it will affect the socio-economic indicators of the territory’s development, and whether the region’s specific features is of any significance. In this article, we want to demonstrate the opportunities of using production functions tools to distinguish territories whose contribution to improving productivity through provision of fixed assets will contribute to the economic growth, or regions where it is necessary to pay attention to other factors. The purpose for the article is to assess the impact of labor productivity on the economic growth in the region through construction of production functions. Production function tools are used to estimate the size of agglomeration economies and spatial effects, determine convergence/divergence rate of regional efficiency using the Solow growth model. For the purposes of the study, we modified the Cobb-Douglas production function. The study was performed among 83 Russian regions during 1995–2015. Official data from the Federal State Statistics Service of Russia (www.gks.ru), including statistical books “Russian Regions. Socio-economic indicators” for 2002–2017”, are used as initial information. The analysis results indicate that the profile of each region in different economic sectors largely determines labor productivity. Differences in labor productivity are explained by the development of high-tech and knowledge-intensive industries. In research has revealed that only in two out of 83 Russian regions capital to labor ratio does not affect labor productivity. The highest capital to labor ratio is demonstrated by Moscow, the Moscow, Belgorod and Orenburg oblasts, Republic of Bashkortostan, Yamalo-Nenets Autonomous district, and in Saint Petersburg. When implementing economic policies aimed at improving labor productivity in these regions it is advisable to take into account the high significance of investment in fixed assets and the need for increased capital to labor ratio for the development. The research results can be useful for the development and implementation of regional policies as they demonstrate the mechanism of influence of key determinants of economic growth, help determine whether the increment of fixed assets is the key to improving labor productivity and ensuring high economic growth rates.

Priority areas: economics
Language: Russian
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Keywords: производительность трударегиональная экономикаregional economyрегионы РоссииRussian regionsпроизводственные функцииProduction Functionslabor productivitycapital to labor ratio
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