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News
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.
May 25, 2026
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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Object and Spatial Working Memory in Visual Search for Multiple Targets

NRU Higher School of Economics , 2017.
Gorbunova E. S., Kozlov K., Le S. T., Makarov I.
Visual search for multiple targets is especially error prone. One of these errors is called subsequent search misses (SSM) and represents a decrease in accuracy at detecting a second target after a first target has been found. One of the possible explanations of SSM errors is working memory resource depletion. Four experiments investigated the role of different kinds of working memory in SSM errors. The first experiment investigated the role of object working memory using a classical color change detection task. In the second and the third experiments, a modified change detection task was applied, using shape as the relevant feature. In the fourth experiment, a spatial working memory task was used to reveal the role of spatial working memory in SSM. The second and the third experiments revealed interference between working memory and visual search tasks, whereas in the first and the fourth experiments interference was not found. The results are discussed in terms of specific working memory deficit in SSM errors.
Research target: Psychology
Priority areas: engineering science
Language: English
Full text
Text on another site
Keywords: working memoryрабочая памятьзрительный поискзрительное вниманиеvisual attentionvisual search
Publication based on the results of:
Роль объектной рабочей памяти в зрительном поиске множественных стимулов (2017)
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