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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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Декомпозиция факторов неравенства доходов в современной России

Журнал Новой экономической ассоциации. 2016. №  3(31). С. 170–186.
Ovcharova L., Popova D., Rudberg A.

Over the last 25 years, Russia has passed from the group of countries distinguished by low levels of income inequality to the group distinguished by high ones. The macroeconomic analysis shows that this process is due to the differences in salaries, the weak equalizing effect of social transfers and new sources of income such as the income from property and of business activity. We have a poor understanding of the factors determining this inequality at the household level so far. Decomposition of equivalised household expenditure inequality into inter- and intra-groups using cross-sectional data (RLMS–HSE, 1994–2014) shows that during the whole research period one of the most significant factors of intergroup inequality is region of residence. During periods of high economic growth the significance of education and intensity of employment increased in intergroup inequality; however, during the period of economic stagnation the intergroup differentiation of educational potential began to decrease. Despite the important population policy aimed to increase the birth rate, children in families have become the most significant factor of intergroup inequality for the last years. A regression analysis of the determinants of inequality, which allows us to estimate the contribution of each factor to the model dispersion of per capita expenditure allows us to draw the conclusion that meritocratic factors explain a majority of observed inequality. The contribution of the child-burden factor to total inequality did not change during the past ten years, and at the same time, the inequality-decreasing effect of the presence of pensioners in households has continuously increased.

Research target: Sociology (including Demography and Anthropology Mathematics Economics and Management
Priority areas: economics sociology
Language: Russian
Text on another site
Keywords: человеческий капиталбедностьpovertywealthнеравенство по доходамбогатство income and wages inequalityhuman capital
Publication based on the results of:
Analysis of the demographic and socio-economic behavior of households at different stages of the life cycle and assessment of social and tax policies impact on the dynamics of the standard of living and its differentiation (2016)
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