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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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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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Фальсификация бухгалтерской отчетности на предприятиях обрабатывающей промышленности в 2012–2019 гг.

Вестник Московского университета. Серия 6: Экономика. 2024. Т. 59. № 3. С. 236–258.
Ладыгина К. С.

This paper considers the falsification of financial reports at Russian manufacturing
enterprises in the period from 2012 to 2019. The factors are associated with the heterogeneity
of estimates of falsified financial statements. We examine the evolution of corporate fraud
during the period under review with a special focus on the correlation between false reporting
and sanctions crisis of 2014. We identify two main lines of corporate behavior in relation
to falsified corporate reporting: a consistently «honest» strategy characteristic of no more
than 30–60% of enterprises, or situational behavior, when an enterprise provides either
reliable or questionable data in certain years depending on the circumstances. For large
and medium-sized manufacturing enterprises, the quality of reporting provided has generally
improved in the sanction period of 2015–2019 compared to the pre-sanction period. Drawing
on econometric calculations, we demonstrate that the main factors associated with provision
of inaccurate reporting are the size of an enterprise, and the growth rate of accounts receivable
in previous years.

Language: Russian
DOI
Text on another site
Keywords: обрабатывающая промышленностьроссийские компанииcrisisRussian companiessanctionsмодель Бенишамодель Роксасфальсификация отчетностиBeneish modelRoxas modelсанкционный кризисreporting falsification
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