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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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Implicit-statistical learning in aphasia and its relation to lesion location

Neuropsychologia. 2020. Vol. 147. Article 107591.
Vadinova V., Buivolova O., Dragoy O., van Witteloostuijn M., Bos L.

Background

Implicit-statistical learning (ISL) research investigates whether domain-general mechanisms are recruited in the linguistic processes that require manipulation of patterned regularities (e.g. syntax). Aphasia is a language disorder caused by focal brain damage in the left fronto-temporal-parietal network. Research shows that people with aphasia (PWA) with frontal lobe lesions manifest convergent deficits in syntax and ISL mechanisms. So far, ISL mechanisms in PWA with temporal or parietal lobe lesions have not been systematically investigated.

Aims

We investigated two complementary hypotheses: 1) the anatomical hypothesis, that PWA with frontal lesions display more severely impaired ISL abilities than PWA with posterior lesions and 2) the behavioural hypothesis, that the magnitude of impairment in ISL mechanisms correlates to syntactic deficits in aphasia.

Methods

We tested 13 PWA, 5 with frontal lesions and 8 with posterior lesions, and 11 non-brain-damaged controls on a visual statistical learning (VSL) task. In addition, all PWA completed several linguistic tasks. Reaction times, obtained in the VSL task, were analyzed using linear mixed-effects model. Correlational statistics were used to assess the relationship between VSL task performance and linguistic measures.

Results and Discussion

We did not find support for the anatomical hypothesis as patients with spared frontal regions also manifested impaired ISL mechanisms. This is attributed to a) ISL mechanisms being vulnerable to other cognitive dysfunctions and/or b) ISL mechanisms anatomically extending to the posterior brain regions. Notably, ISL mechanisms were impaired, but not absent in aphasia. With regards to the behavioural hypothesis, we provide empirical evidence of correlation between ISL mechanisms and syntactic, but not lexical impairment in aphasia. We discuss both the theoretical contributions to the debate of domain-independence of ISL mechanisms and clinical implications for implicit language therapy.

Research target: Philology and Linguistics
Priority areas: humanitarian
Language: English
Full text
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Keywords: грамматикасинтаксисsyntaxgrammarAphasiaафазияинсультStatistical Learningstrokeимплицитное научениестатистическое обучение Implicit learning
Publication based on the results of:
Язык и мозг: предотвращение, диагностика и коррекция речевых расстройств (2020)
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