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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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Quarterly financial statements Data for Russian firms

Data in Brief. 2026. Vol. 65. Article 112508.
Maria Marakueva, Popova P.

Our objective is to create the most comprehensive database of Russian firms with the highest data frequency. We created the unique comprehensive database of quarterly financial statements for Russian firms, covering the period from 2014 to 2024. The dataset is assembled from Federal Tax Service of Russia (FNS), SPARK Interfax, Cbonds, and the Moscow Exchange. It comprises over 100,000 firm-quarter observations from 4,568 companies, representing more than 30% of total corporate assets in Russia. The data have been standardized, industry (OKVED) and regional identifiers are included, along with firm characteristics such as registration codes and legal status. This dataset provides researchers with high-frequency financial information suitable for studying credit risk, corporate behavior, monetary policy transmission, and other microeconomic questions in the Russian context.

Research target: Economics and Management
Language: English
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DOI
Text on another site
Keywords: бухгалтерская отчетностьбаза данных financial accountingaccounting and reportingФинансовая отчетность организацийquarterly reports databasefirms financial statementsотчетность российский компаний
Publication based on the results of:
Новые аспекты измерения справедливой рисковой премии на кредитных рынках с использованием моделей точечных процессов (2025)
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