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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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Processing Polarity: ERP Evidence for Differences between Positive and Negative Polarity

Neuropsychologia. 2013. Vol. 51. No. 1. P. 132–141.
Yurchenko A., Den Ouden D., Hoeksema J., Dragoy O., Hoeks J., Stowe L.

The goal of the present study was to investigate event-related potential (ERP) responses to Dutch negative and positive polarity adverbs of degree presented in licensed and unlicensed contexts with negative and affirmative particles directly preceding the polarity item. To control for effects of the processing of negation as such, neutral adverbs were also presented in negative and affirmative contexts. The results did not show any significant effect of negation for the non-polar adverbs, allowing context effects for polarity items to be interpreted as being due to the appropriateness of the context. Negative polarity violations elicited an N400 response that might reflect the lack of semantic congruity of the negative polarity item in an affirmative context. In contrast, processing positive polarity items in context of negation resulted in a positive effect resembling the P600, which may be considered as a marker of a different sort of integration difficulty caused by violation of licensing conditions and/or a search for a licensor in the wider discourse context. The study presented here is the first to show an unambiguous dissociation between responses to negative and positive polarity violations. This dissociation argues for different mechanisms underlying the processing of these two types of polarity; we propose that positive polarity items are sensitive to wider discourse context, while negative polarity items are more sensitive to local lexical context.

Research target: Philology and Linguistics
Priority areas: humanitarian
Language: English
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Text on another site
Keywords: language comprehensionвызванные потенциалыERPsnegative polarity positive polarityprocessing of negationпонимание речиотрицательная и положительная полярностьобработка отрицания
Publication based on the results of:
­­­­­­Language norm and pathology: continuum of linguistic effects (2013)
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