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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Вариативность окончаний глаголов в форме 3-го лица настоящего времени в говоре вокруг города Опочки (Псковская область, Опочецкий район)

Русский язык в научном освещении. 2023. № 1 (45). С. 180–201.
Заливина Е. А.

The article is devoted to the variability of third person verbal affixes in the present tense in the dialect around Opochka. This work considers spoken texts of speakers whose speech includes verb forms with dialectal affixes. For each informant the quantitative distribution of word forms based on the conjugation, number, and stress is provided. Attention was also paid to how various sociolinguistic factors affect the use of dialectal affixes. These factors include the speakers' age, level of education, occupation, and place of living. In order to determine how the use of dialect forms of the verb depends on these sociolinguistic factors, the article employs the chi-squared test. Additionally, the process of the loss of dialect variants of affixes was analyzed based on the material of four speakers in whose speech variability was observed. The paper explores the implications of the results obtained from this analysis, which suggest that certain dialect affixes may be more stable than others.

Research target: Philology and Linguistics
Language: Russian
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Keywords: русские диалектыпсковские говорыопочецкие говоры
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