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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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'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 a Citys Life Based on the Shape of Its Neighbourhoods?
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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Роль числа категорий и условий их освоения в формировании вероятностных правил категоризации

Вопросы психологии. 2021. Т. 67. № 6. С. 141–152.
А.А. Котов, Котова Т. Н., Афанасьева В. М.

In traditional studies of categorical learning, its conditions are usually limited to only two categories for which a categorization rule needs to be found. However, in practice, when learning any new category, people often use examples from other, additional categories as well. How does this affect the learning of the main category? In the present experiment, participants performed a task to form new categories. They had to learn a probabilistic rule, a prototype. In addition to the main task (distinguishing between examples from two main categories), participants were presented with examples from a third category. The conditions of presentation of this category were varied: existence or absence for the examples included in it of their own categorization rule, as well as feedback. After the formation of categories, participants performed a transfer test. It was found that the accuracy of learning basic categories was influenced by the existence of a categorization rule for additional category and was not influenced by the presence of feedback for it. However, when there was feedback, participants categorized new examples more quickly. The findings are discussed in the context of the multiple categorization systems model (COVIS) and studies of learning with partial feedback.

Research target: Psychology
Language: Russian
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Keywords: категориальное научениеcategory learning
Publication based on the results of:
Brain and math: Cognition, age, and environmental factors (2022)
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