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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
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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.
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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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TOWARDS AN ‘IDEAL’ CLUSTER SUPPORT PROGRAM: BLENDING THE APPROACHES

NRU Higher School of Economics , 2020.
Kutsenko E., Islankina E., Abashkin V., Popova E.
Clusters have become a major element of innovation and industrial policies in many countries worldwide. Over the years, targeted cluster support programs have been designed and implemented, each featuring a variety of approaches to the selection of clusters, terms and prerequisites for funds allocation, the areas of support, etc. Such approaches have both advantages and drawbacks, which leads to a conception of an ‘ideal’ support program mix that could consider the best practices and ignore some unsuccessful solutions. The working paper aims at suggesting such an ‘ideal’ approach to designing a cluster support program, based on the synchronization of the most effective elements of various such programs in Russia. Over the past decade, cluster policy has occupied an important position in the agenda of the Russian Government. Two federal Ministries – the Ministry of Economic Development and the Ministry of Industry and Trade initiated several cluster support programs for innovative and industrial clusters. Nowadays, there are more than 118 clusters in Russia, and over half of them benefit from current public support measures – 27 innovative clusters, 42 industrial and 12 leading clusters. The comparative analysis of federal support programs revealed several benefits and limitations in both approaches in terms of subsidy allocation principles, areas of support and cluster selection criteria. In particular, among the key advantages of innovative clusters programs are the focus on cluster management development, and identifying the strongest clusters through the one-time selection procedure. The successful features of the industrial clusters program are the permanent application process, reduction of budget risk due to the compensation principle of funding, and stimulation of cooperation through special requirements for joint projects. The major disadvantages of innovative cluster support programs are budget risks caused by advanced financing of cluster activities, and a lack of project focus; the probability to support low-quality projects and neglecting the issue of cluster management development are the key weaknesses within the industrial clusters program. The paper suggests a ‘smart’ synchronization of approaches to cluster support, which blends the best practices of different ministries.  
Priority areas: management
Language: English
Full text
Keywords: cluster policyinnovative clustersindustrial clustersnational cluster support program
Publication based on the results of:
The impact of science, technology, and innovation on structural transformation (2020)
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