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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.
May 25, 2026
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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?
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The effect of spelling errors on reading tasks: A study on Russian

The Mental Lexicon. 2026. P. 1–31.
Slioussar N., Chernova D., Magomedova V., Maximova P., Gurkov I.

Many studies on different languages analyzed how spelling errors are produced and detected. Recently, a new generalization was made for several languages: frequently misspelled words are read more slowly, even when they are written correctly and one knows how to spell them. This is explained by the lower quality of their lexical representations diluted by the exposure to recurring errors. In this study, we confirm this generalization for Russian and report several novel findings. We conducted four experiments with different participants: two lexical decision tasks and two spelling error detection tasks, to compare a task that consciously focuses on spelling to the one that does not. Firstly, the accuracy rate in the error detection task was a better predictor of response times in the lexical decision task than other factors including spelling entropy and word frequency. This further confirms the low lexical quality hypothesis. Secondly, although Russian orthography is in the middle of the transparency scale, Russian patterned with languages having transparent orthographies in these experiments — this shows which properties may be relevant. Thirdly, we tested errors of different types and showed that this factor was important for the error detection task, but not for the lexical decision task, in which only the frequencies of different spellings matter. 

Research target: Philology and Linguistics
Language: English
DOI
Keywords: linguistics
Publication based on the results of:
Mental health research: behavioral and neurophysiological foundations (2025)
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