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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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Geospatial effects on phonological complexity in the world’s languages

Linguistic Typology. 2025.
Hartmann F., Nichols J.

Linguistic complexity has generally been seen as influenced by ecological, demographic, and sociolinguistic factors and has been approached by seeking correlations of increased complexity along one linguistic dimension with one or another extralinguistic factor. Here we use a multidimensional definition of phonological complexity and analyze its global patterning quantitatively across predefined continents or sets of continents. We use linear and nonlinear regression models to estimate the gradient of geographical distributions of various complexity measures. We found significantly lower phonological complexity levels in South America and Australasia, suggesting isolation by distance (from centers of demographic and economic influence in the Old World). An unexpected finding is that our models show a broad transitional zone between the different levels of complexity in Africa and Europe. This Euro-African Transition Zone (ETZ) has its core between the Equator and the African coastline of the Mediterranean Sea. It has not been regarded as a language area before and is not a convergence area, but it has a robust geospatial complexity profile and we propose it as an area. We also find reasons to regard phonological complexity as a coherent property in itself, with its several dimensions behaving as a whole, and not as adaptive but as geospatially patterned. This paper is an exercise in using synchronic analysis of geolinguistic patterns of complexity to draw inferences about long-term change and evolutionary trends in language. On a higher level, the paper employs methods of geospatial analysis novel to this particular problem with the goal of raising new questions and laying groundwork for wider research into the distribution of linguistic complexity.

Research target: Philology and Linguistics
Language: English
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Keywords: квантитативная типологияязыковая сложностьlinguistic complexityhistorical phonologyисторическая фонологияquantitative typologygeospatial distribution of phonological featuresпространственное распределение фонологических черт
Publication based on the results of:
Linguistic and cognitive diversity in formal models, computer tools, and educational resources (2025)
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