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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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An Ontology Model to Facilitate Sharing Risk Information and Analysis in Construction Projects

P. 237–249.
Aldbs H., Jrad F., Saoud L., Saleh H.

Construction projects face a high level of dynamic and various risks. Risks may result in deviation from pre-determined construction project’ objectives. Systematic risk analysis is critical for sharing risk information related to decision making thus effective risk management. In this study, a Risk Analysis Ontology RA-Onto is proposed that may facilitates development of databases and information sharing for risk analysis. A detailed review of the literature on construction risks has been carried out to development of the RA-Onto that organizes risk knowledge into unified classes together with corresponding properties and relations. Ontology is evaluated theoretically and practically by using five case studies. RA-Onto could be used to support decision-making during the risk management. It enables companies to corporate memories, create databases, and develop a model to support the systematic risk analysis for better decision making.

Language: English
DOI
Text on another site
Keywords: risk analysis

In book

Proceedings of the 15th KES-IDT 2023 Conference. Intelligent Decision Technologies
Proceedings of the 15th KES-IDT 2023 Conference. Intelligent Decision Technologies
Issue 352. , Singapore: Springer, 2023.
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