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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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Где пролегает «черта бедности» в современной России?

Вопросы экономики. 2018. № 1. С. 104–127.
Slobodenyuk E., Anikin V. A.

The present paper focuses on identification of relative “poverty line” and a threshold of high poverty risks. The paper also studies key poverty factors in contemporary Russia. It demonstrates that the relative approach to poverty which is widely used in Western countries is applicable in Russia too. However, the relative poverty thresholds set at 0.5 and 0.75 medians per capita family income identify quite different groups of the poor. The threshold of 0.5 median income indicates deep poverty happened mostly to the unemployed workforce. The rel­ ative poverty threshold equal to 0.75 median income identifies the poverty of the elderly who are not considered as the poor by the absolute approach because pensions of Russians has been recently equalized to the subsistence level. Above all, the paper provides econometric estimates of socio­economic determinants of both absolute and relative poverty. It was revealed that the relative deep poverty of the working population was primarily caused by the “bad jobs” rather than “bad” human capital. Absolute poverty of workers is more or less determined by both factors.

 

Research target: Sociology (including Demography and Anthropology Mathematics Economics and Management Education
Priority areas: economics sociology management state and public administration
Language: Russian
Full text
Keywords: социальная политикаsocial policyработающие бедныеуровень доходовабсолютная бедностьотносительная бедностьworking poorrelative povertyдоходная стратификацияabsolute povertyincome stratificationчерта бедности
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