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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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Анализ динамики оказания медицинской помощи при отказе от табака в наркологических организациях Российской Федерации в 2013-2023 гг.

Вопросы наркологии. 2025. Т. 37. № 1. С. 27–43.
Салагай О. О., Сахарова Г. М., Антонов Н. С., Stadnik N.

In order to study the prevalence of signs of nicotine dependence among tobacco/nicotine users and the level of medical care for tobacco/nicotine use in addiction treatment organizations in the Russian Federation in 2013-2023, we have analyzed microdata from a sample observation of the health status of the population conducted in 2023. The analysis of the dynamics of the number of people who visited addiction treatment organizations in 2013-2023 for nicotine addiction, tobacco use, and smoking was carried out using data from the Form # 37, titled “Information on patients suffering from alcoholism, drug addiction, and substance abuse.” This analysis showed that in 2023, among all adults who used tobacco/nicotine, the largest proportion (about 40%) were people with a high degree of nicotine dependence. Among consumers over the age of 30, the proportion of people with signs of high and very high nicotine dependence was about 60%. In 2013-2023, there was an increase in the number of people who visited addiction treatment organizations for nicotine addiction (a relative increase of 35.1%). Each year, the proportion of patients who completed treatment did not exceed 50%, while the proportion who refused, or interrupted their treatment ranged between 32.3% and 50.1%. Among those who completed treatment, the proportion of patients in remission ranged between 33.0% and 56.3%, and among all those who sought treatment - from 15.9% to 26.4%. In general, in 2023 only 0.3% of tobacco/nicotine users sought treatment at addiction treatment organizations. We arrive at the conclusion, that it is necessary to further develop the interaction of primary health care institutions and addiction treatment services, in order to provide care to patients with nicotine addiction. It would be important to develop the appropriate referral framework, in which the addiction medical service would receive patients with the most severe nicotine dependence, the treatment of which could hardly be carried out by other specialists.

Research target: Clinical Medicine
Language: Russian
Full text
Keywords: табакзависимостьaddictiondependencenicotineникотинtobacco
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