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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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Reassessing Racial Differences in Authoritarianism in the United States: Measurement Invariance and Group Comparisons using the ANES Child-Rearing Values Scale

Public Opinion Quarterly. 2026. P. 1–18.
Fabrício M Fialho

Measurement invariance is a necessary condition for meaningful comparisons across populations. Previous studies argue that authoritarianism between Blacks and Whites in the United States cannot be compared due to the lack of measurement invariance in the American National Election Studies child-rearing values index. This paper challenges this claim. I analyze ANES data from 1992 to 2016 and identify partial measurement invariance in the 1992–2008 surveys (only one item, respect for elders, displays differential item functioning across groups) and full invariance in the 2012 and 2016 data. It is demonstrated that the child-rearing scale is comparable across Black and White respondents once partial measurement invariance is modeled accordingly, and that group comparisons are methodologically sound. I advanced a hypothesis for the non-invariance of respect for elders and suggest that future work should use measurement models that accommodate differential item functioning and further develop the child-rearing scale.

Research target: Social Sciences Sociology (including Demography and Anthropology Political Science, International Relations, and Public Administration Psychology Other social sciences
Language: English
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Keywords: race relationsmeasurement invarianceUnited States
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