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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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Measuring strategic technological strength: Patent Portfolio Model

Technological Forecasting and Social Change. 2020. Vol. 157. No. article 120119. P. 1–12.
Li S., Zhang X., Xu H., Fang S., Garces E., Daim T.

As technological innovation plays an important role in today's knowledge economy, the most important output of technology development is intellectual property, which is highly valued for generating a monopoly position in providing payoffs to innovation. In this context, this paper considers Intellectual Property Management (IPM) efficiency based on the Patent Portfolio Model (PPM) to help organizations identify, enhance, and evaluate their technological strength. The Patent Portfolio Model (PPM) is built to assess the advantages and disadvantages of an organization, to identify the opportunities of development potentials and optimal distribution, to support the decision-making for optimizing resource allocation, and to develop a layout for the technical field. The case study of the Research Institute of China shows that this method is feasible and fulfills the needs of different institutions to provide suggestions for R&D technology management. The main finding of the paper is that PPM is an effective tool to be used in strategic planning because it identifies the technology advantages to define offensive and defensive strategies against competitors. The use of IPM and PPM helps decision-makers to visualize and simplify complex decision-making problems.

Research target: Economics and Management
Priority areas: management
Language: English
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DOI
Text on another site
Keywords: интеллектуальная собственностьтехнологические инновацииintellectual propertyуправление интеллектуальной собственностьюintellectual property managementtechnological innovationPatent Portfolio Modelпатентный портфель
Publication based on the results of:
The impact of science, technology, and innovation on structural transformation (2020)
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