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News
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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Мета-анализ гендерного разрыва в оплате труда в России

Экономический журнал Высшей школы экономики. 2022. Т. 26. № 2. С. 213–239.
Roshchin S., Yemelina N.

The paper summarizes the estimates of the gender wage gap in the Russian labour market from 1996 to 2021 based on a meta-analysis. Parenthood is the most important factor that impacts the variation in the unexplained portion of the gap. Ignoring parenthood leads to a significant underestimation of the gender wage gap. In contrast, lack of controls for industry and profession leads to an overestimation of the unexplained portion of the gap. The explanatory power of the traditional factors, such as human capital or job characteristics, tends to decline over time. Including personality traits of men and women into analysis reduces the unexplained portion of the gap. Our analysis contradicts the hypothesis of the gender bias among researchers. Moreover, measure of pay is a major determinant of the gap, which is higher for monthly than for hourly wages. We found no significant differences in the gender gap estimates obtained with various assessment methods or data bases. Finally, Heckman's correction for self-selection does not affect the estimates of gender inequality.

Research target: Economics and Management
Language: Russian
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Text on another site
Keywords: рынок трудаРоссияlabour marketRussiameta-analysisмета-анализуравнение заработной платыwage equationgender pay gapгендерный разрыв в оплате трудаdecomposition methodsметоды декомпозиции
Publication based on the results of:
Transformation of personnel policy of enterprises in the context of the Covid-19 (2022)
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