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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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Not all coexpressions are syncretisms: Limiting Nanosyntax

Glossa: a journal of general linguistics. 2026. Vol. 11. Article 25626.
Bubnov G.

This paper revises the findings of Dekier (2021) concerning the syncretism and containment of indefinites in light of their semantic implausibility and empirical inadequacy, compared to the alternative semantic approach of Degano & Aloni (2025), and argues against the omnipotence of a nanosyntactic approach to coexpression phenomena. The paper also addresses the diachronic predictions of Nanosyntax and discusses the structure of lexical entries more generally.

Research target: Philology and Linguistics
Language: English
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Text on another site
Keywords: синкретизмsyncretismнеопределенные местоимениясемантические картыsemantic mapsIndefinitesнаносинтаксисnanosyntax
Publication based on the results of:
Linguistic and cognitive diversity in formal models, computer tools, and educational resources (2025)
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