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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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Российский мониторинг экономического положения и здоровья населения НИУ ВШЭ: особенности методологии, принципы организации, исследовательские возможности

Мониторинг общественного мнения: Экономические и социальные перемены. 2026. № 1. С. 40–64.
Dorofeeva Z., Kozyreva P. M., Kosolapov M. S., Тонис Е. И.

The article examines the method-ology, organizational principles, and research potential of the «Russian Longitudinal Monitoring Survey — HSE (RLMS-HSE)». It describes the sample structure, its design features, and size dynamics, including expansion and reduction of the sample. The article focuses on the formation of the pure panel part of the sample, with special emphasis on respondent attrition across the 30-year survey period. It is shown that the mechanisms of this process are not random; certain groups systematically leave the panel more actively than others. For instance, the proportion of men leaving the study is consistently higher than that of women. Another significant factor is age; the age group most "retained" in the sample con-sists of respondents who were between 31 and 45 years old in 1994. Rural residents are more likely to be among those participating in the survey for 25 years or longer. Weighting as one of the most common and accessible methods is considered as an example for correcting sample bias. Meanwhile, the analysis of sampling errors in RLMS-HSE shows that bias in representative samples of each consecutive round is minor. The article presents examples of studies using the panel component of the data along with the broad set of socio-eco-nomic indicators and demonstrate the extensive potential of RLMS-HSE, including its applications in the field if interdisciplinary research.

Research target: Sociology (including Demography and Anthropology
Language: Russian
DOI
Text on another site
Keywords: панельные данныеRLMS-HSEРМЭЗ НИУ ВШЭlongitudinal surveysлонгитюдные обследования panel datadata representativespanel attritionsample biasрепрезентативность данных осыпание панелисмещение выборки
Publication based on the results of:
Monitoring the socio-economic conditions of households in Russia, 1994-2026 (2027)
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