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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.
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Russian blues reveal the limits of language influencing colour discrimination

Cognition. 2020. Vol. 201. Article 104281.
Martinovic J., Paramei G., MacInnes W.

Chromatic stimuli across a boundary of basic color categories (BCCs; eg blue and green) are discriminated faster than colorimetrically equidistant colors within a given category. Russian has two BCCs for blue, sinij 'dark blue' and goluboj 'light blue'. These language-specific BCCs were reported to enable native Russian speakers to discriminate cross-boundary dark and light blues faster than English speakers (Winawer et al., 2007, PNAS, 4, 7780-7785). We re-evaluated this finding in two experiments that employed identical tasks as in the cited study. In Experiment 1, Russian and English speakers categorized colors as sinij / goluboj or dark blue / light blue respectively; this was followed by a color discrimination task. In experiment 2 Russian speakers initially performed the discrimination task on sinij / goluboj and goluboj / zelënyj 'green' sets. They then categorized these colors in three frequency contexts with each stimulus presented: (i) an equal number of times (unbiased); more frequent (ii) either sinij or goluboj; (iii) either goluboj or zelënyj. We observed a boundary response speed advantage for goluboj / zelënyj but not for sinij / goluboj. The frequency bias affected only the sinij / goluboj boundary such that in a lighter context, the boundary shifted towards lighter shades, and vice versa. Contrary to previous research, our results show that in Russian, stimulus discrimination at the lightnessdefined blue BCC boundary is not reflected in processing speed. The sinij / goluboj boundary did have a sharper categorical transition than the dark blue / light blue boundary,

Research target: Psychology
Language: English
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Keywords: Russian languagecolour terms
Publication based on the results of:
Interdisciplinary Studies of Vision (2019)
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