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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
'The Humanities Serve as a Conscience'
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.
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Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Влияние легкости наименования пространственных признаков на научение новым правилам категоризации

Психология. Журнал Высшей школы экономики. 2020. Т. 17. № 1. С. 145–155.
Kotov A. A., Жердева М. П.

What is the relationship between the vocabulary of a person and the process of cognition? A lot of studies show that the nameability of labeling objects accelerates the category learning. We hypothesized that the presence of labels of the object’s features locations also helps learning new category rules. In the experiment the subjects learned to distinguish two fictitious illnesses with the images of symptoms, located in various places. We varied the location of the symptoms on a silhouette of a foot. In the condition of a high nameability of a location, the images of symptoms were located on those parts of foot, for which common labels exist (for example, a heel or a sole). In the condition of a low nameability, the images of symptoms were located on those parts of foot, for which the labels are rarely used (Achilles or an arch). The formation of the rule demanded finding a link between the location of the symptom and its image. According to the hypothesis, the location of category features in places, which have more convenient labels, will improve the success of learning the category rule, as opposed to location of the same features in places that do not have convenient labels. As a result, we’ve found that this hypothesis was confirmed: the subjects formed a rule in the condition of high nameability more successfully, than in the condition of low nameability. We explain this result with the following: the presence of convenient labels allows testing hypotheses while learning new categories more easily – matching the features with the feedback while determining the category rules. The results are discussed in the context of development in ontogenesis the ability to form categories.

Research target: Psychology
Priority areas: humanitarian
Language: Russian
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Keywords: категориальное научениеcategory learning
Publication based on the results of:
Neurocognitive foundations of mathematical cognition in children (2020)
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