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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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Репрезентация гендера в российском дискурсе трудоустройства (на материале объявлений о работе и резюме)

Вопросы психолингвистики. 2021. № 3(49). С. 30–45.
Аликина А. В.

The article describes gender representation peculiarities in the Russian discourse of employment, revealing the role of gender stereotypes in responding to a job advertisement and the ways employers inform candidates about the gender of the employee desired. We used the methods of content analysis and semantic interpretation, as well as ethnographic and quantitative methods. The corpus for analysis includes job advertisements and resumes that reflect the peculiarities of the interaction of languages and cultures in the context of globalization. The subject of the current research is gender representation ways in job advertisements and resumes in the Russian employment discourse. 

The results show that masculine gender predominance in the position names in job advertisements does not influence female candidates’ desire to apply for a job. Moreover, the research suggests that female candidates tend to apply for the professional spheres that were conventionally concerned as male ones like IT or telecom. At the same time, the resistance of Russian employment discourse to the global feminist one was reviled as no so-called feminitives were found in its corpus. The study demonstrates the growing influence of English-based patterns on Russian employment discourse conventions, expressed by informal, personalized, and individual-focused interaction with a candidate in job advertisements. The findings also indicate that professional skills and gender specificity of a professional sphere influence the desire of a candidate to apply for a job rather than a masculine gender in the name of a vacancy. Moreover, to implicit the gender of a candidate, employers can use either visual or text opportunities of a job advertisement. Besides, some cases of gender specificity blurring were detected in the professional spheres that were conventionally considered as the ones for females (domestic staff or beauty sphere) where male candidates now tend to be of demand. 

Research target: Philology and Linguistics
Language: Russian
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Keywords: гендерные стереотипыgender stereotypes gender representationдискурс трудоустройстваemployment discourseglobal and localрепрезентация гендераглобальное и локальное
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