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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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Социальная укорененность технологий: перспективные направления исследований

Форсайт. 2015. Т. 9. № 1. С. 6–19.
Добрякова М. С., Kotelnikova Z.

Strategic documents that reflect future S&T priorities are often formulated without sufficiently taking into account the social context of S&T developments. The paper discusses the capabilities of social sciences for a deeper contextual analysis when setting priorities and, consequently, for helping to make the diffusion of advanced technologies more efficient. The methodological basis of the analysis is the concept of the social construction of technology (SCOT).   The list of critical technologies of the Russian Federation serves as an illustrative example of a strategic document defining S&T priorities. The authors point out developments with the highest potential for social embeddedness, which could be fully used only if coupled with an understanding of related social matters. These developments are divided into four groups (clusters): biomedicine and health, energy, environment, and transport. We identify for each cluster the social groups that would be affected by the relevant technologies, the potential for multiple interpretations of a technology and a framework of interaction between members of relevant social groups. The paper proposes prospective areas of sociological research, allowing a deeper understanding of the real context in which new technologies might be developed and implemented, and thus may help optimize efforts for the diffusion of these technologies.

We conclude that many prospective technologies, which by nature belong to the 'physical' world, would be more efficient if their implementation, and possibly also development, were accompanied (and in some cases preceded) by the outputs of relevant social science and humanities studies. In this sense, we propose the use of the 'social embeddedness of technology' concept. We argue that this is an important factor affecting the success of technology implementation, and sometimes, technology configuration

 

Research target: Sociology (including Demography and Anthropology
Language: Russian
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Keywords: гуманитарные наукисоциальные наукиhumanitiesдиффузия инновацийсоциальная укорененностьdiffusion of innovationssocial sciencessocial embeddedness социальное конструирование технологий (SCOT)перспективные направления исследованийsocial construction of technology (SCOT)prospective research areas
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