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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
‘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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Концепция предотвратимой смертности как основа для разработки мер по её снижению: пример болезней системы кровообращения

Российский кардиологический журнал. 2025. Т. 30. № 10. С. 103–110.
Кобякова О. С., Стародубов В. И., Бойцов С. А., Шляхто Е. В., Деев И. А., Marychev G., Шибалков И. П., Логненко М. М., Перфильева Д. Ю., Соломатников И. А.

This article demonstrates how the concept of preventable mortality can be used to develop mortality reduction measures using cardiovascular disease (CVD) as an example. The measures are structured according to Donabedian model. A Russian methodology for determining preventable mortality, based on international approaches and adapted to Russian healthcare system, was used. In collaboration with federal experts, a list of measures to reduce preventable cardiovascular mortality was developed. To systematize these measures, Donabedian’s triad was used, identifying resources, processes, and performance indicators for each measure. Following activity examples are presented: "health schools" using telemedicine technologies, individual patient information, and stress echocardiography. Their structuring according to the Donabedian model makes it possible to identify the causes of potentially unsatisfactory results and improve their implementation. Integrating the concept of preventable mortality with the Donabedian model ensures a rational selection and evaluation of measures to reduce cardiovascular mortality, as well as the effective allocation of resources. The list of activities requires regular updating, taking into account changes in medical technologies and healthcare system capabilities.

Language: Russian
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Keywords: здравоохранениеpreventionhealthcareболезни системы кровообращенияtreatmentлечениепредотвратимая смертностьпрофилактикаcardiovascular diseasesPreventable mortalityDonabedian modelмодель Донабедиана
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