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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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Centrality for Modeling Greedy Algorithms of Road Maintenance

P. 888–893.
Karpukhina D., Fedyanin D.

The authors focus on the maintenance of transportation systems. Its dependability is fundamental to the well-being, health and comfort of citizens. The model described in this article imitates two processes simultaneously: road deterioration and road maintenance. The main assumption is that the destruction of roads depends on the betweenness centrality of the city’s graph. The basis for road repair in the model are the assumptions that there is near-perfect real-time monitoring of the extent of destruction and that only nearly destroyed roads are repaired. Several intuitive assumptions are made about the importance of certain roads in different city structures. The results of the developed greedy algorithm and its modifications on real city graphs are presented and described. The chosen cities illustrate several possible characteristics of the urban topology. The similarities and differences between the work of the algorithm on different graphs are outlined, unusual tendencies are noted. A domino-like interaction between the elements of the system, once the algorithm is working, is observed. This work also helps to study and predict the reasoning of the citizens. It is possible that they anticipate improvements in their quality of life as a result of maintenance activities. However, a naive algorithm could cause annoyance, and it can be predicted to what extent, if we work under the assumption that the more time people spend in traffic, the more annoyed they are.

Language: English
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Keywords: cognitive approachbetweenness centralitygreedy algorithmsWiener indexTransportation systemnaive algorithmsroad deteriorationroad maintenance

In book

2023 5th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA)
Vol. 5. , IEEE, 2023.
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