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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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Лесоторговое районирование России: разработка методики кластеризации

Известия РАН. Серия географическая. 2024. Т. 88. № 2. С. 214–230.
Rusakov D., Елютин М. С., Синицын Н. А., Коротков А. С., Меняльщиков Р. Р., Кушлевич А. О.

Network analysis of the Russian timber industry was based on the information about timber transactions between
Russian companies in 2020. The data were collected from the Unified State Automated Information System
Accounting Timber and Transactions with It. The method of graph clustering was developed. The first step is clus-
tering of the hole graph by Leiden algorithm. The second step is drawing of each Leiden created cluster in abstract
space using Fruchterman–Reingold layout and extraction of clusters from layout using meanshift algorithm.
Such approach helped us to divide many Leiden-created clusters into components. This algorithm has a problem
of resolution limit. It tends to combine some medium and small clusters into large ones. Also, a special method
was developed to limit the uncertainty of clustering – both Leiden algorithm and Fruchterman–Reingold layout
are non-deterministic algorithms. Wood trade clusters vary in size and content. They are rather autonomous.
The average share of intra-cluster trade is 89%. The modularity score of our partition is 0.863. Each cluster has
been described using open sources from the Internet. The content of the clusters does not contradict the logic
of the wood industry processes. The clusters create rather compact areas on the map – wood trade regions. Their
borders often overlap with existing administrative boundaries. Five types of network structure of clusters were de-
fined: vertical monocentric, vertical polycentric, horizontal, dendritic, simple. There are three factors of cluster-
ing in wood industry of Russia: production chains (4 types – pulp, plywood, lumber, chipboard chains), demand
chains (redistribution of raw material between manufacturers within the cluster, accumulation of wood volumes for some enterprises outside the cluster, wood trade between traders within the cluster), common holder. We have
shown that existing approaches to cluster detection based on location quotients often misrepresent economic
networks.

Research target: Earth Sciences Economics and Management
Language: Russian
Full text
DOI
Text on another site
Keywords: сетевой анализлесная промышленностьрайонированиеgraph clusteringэкономические кластерыeconomic clusterswood industrytrade regionsкластеризация графа
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