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  • Разработка инструментов визуализации данных на основе предметно-ориентированного моделирования
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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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Разработка инструментов визуализации данных на основе предметно-ориентированного моделирования

С. 300–314.
Джейранян А. Д., Ермаков И. Д., Проскуряков К. А., Lyadova L. N.

An approach to the development of data visualization tools is described that provides the ability to customize to the needs of users and the specifics of the domains in which they work, based on domain-specific modeling. The results of the analysis of data visualization tools and the possibility of customizing them to subject area based on the needs of users and the tasks they solve are briefly presented. It is shown that existing tools require programming skills from users to customize the data visualization format or to develop new visualization models. It is proposed to use domain-specific modeling tools (language toolkits) to create domain-specific languages (DSL) designed to develop new data visualization models that reflect the specifics of the tasks solved by users. The use of the developed tools does not require users to have professional knowledge of programming languages. The architecture of a knowledge-driven software system is described. The core of the system is a multifaceted ontology, which includes descriptions of languages and domains, as well as rules for generating new languages and transforming constructed models. Languages are designed to describe different classes of diagrams. The system includes tools for automating the creation of new DSLs by mapping the domain ontology onto the metamodel of the base language according to user-specified rules, which are also stored in the ontology. The classification of different types of diagrams forms the basis for creating an ontology of data visualization languages. An example of a basic language for creating diagrams is described. The ability to customize the DSL and transform visualization models developed with its help and generate code that implements the model is demonstrated.

Language: Russian
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Keywords: грамматикаgrammarметамоделированиеmetamodelingdomain-specific modelingпредметно-ориентированное моделированиеdomain-specific languageпредметно-ориентированный языкdata visualizationвизуализация данныхmodel transformationтрансформация моделейmultifaceted ontologyмногоаспектная онтология

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GraphiCon 2024: Материалы 34-й Международной конференции по компьютерной графике и машинному зрению (Россия, Омск, 17–19 сент. 2024 г.)
Омск: Издательство ОмГТУ, 2024.
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