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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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Improving knowledge transfer through enterprise social media: the mediating role of transactive memory

Industrial Management and Data Systems. 2022. Vol. 122. No. 1. P. 272–291.
Yuan S., Rong-An S., Haiyang C., Boehnke K., Hongyu J., Jindi F.

Purpose – Enterprise socialmedia can be the organizational transactive memory in which the knowledge dialogue provides users with the metaknowledge to support knowledge transfer. The purpose of this study is to examine a mediation model to show how perceived critical mass, openness and affiliation climate affect organizational knowledge transfer through the mediation of improving the metaknowledge of who knows what and whom. Design/methodology/approach – To test the mediation model and corresponding hypotheses, this study employs structural equation modeling analysis using 264 valid questionnaires. Findings – The study found the two mediators fully explained the effects of the three preconditions on knowledge transfer. Originality/value – These results help us to better understand the benefits of enterprise social media and the functions of transactive memory in organizations.

Research target: Psychology Sociology (including Demography and Anthropology Economics and Management
Language: English
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Keywords: организационный климатпередача знанийknowledge transfer Critical massEnterprise social mediaMetaknowledgeOrganizational climateTransactive memory systemкорпоративные социальные медиаметазнаниясистема трансактивной памяти
Publication based on the results of:
Values, intercultural and socio-economic interaction in the context of ethnic diversity and the COVID-19 pandemic (2022)
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