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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.
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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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Mermaid construction: a case of Kazym Khanty

Proceedings of ConSOLE. 2022. P. 89–101.
Шикунова А. С.

This paper investigates the syntax of mermaid constructions in Kazym Khanty. Mermaid constructions (MMCs) consist of the following parts: [[Clause] Noun Copula] (for a detailed description and criteria, see Tsunoda 2020). MMCs can express modal, aspectual, evidential and other meanings. MMCs have been argued by Tsunoda (2020) to be monoclausal, but data from Kazym Khanty suggests otherwise: Khanty MMCs are biclausal and exhibit control. Based on arguments involving clausal negation, passivisation of the embedded clause, scope of negative pronouns and partial control, I demonstrate that MMCs can be biclausal, thus potentially expanding Tsunoda’s classificaton of mermaid constructions.

Language: English
Text on another site
Keywords: controlkhanty languagenonfinite predicationmermaid construction
Publication based on the results of:
Languages of Russia: morphosyntax and its interaction with other modules (2022)
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