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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.
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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?

Benchmarking and Data Synthesis for Colorization of Manga Sequential Pages for Augmented Reality

P. 608–611.
Golyadkin M., Saraev S., Makarov I.

This paper introduces an innovative approach to manga colorization within augmented reality (AR) environments, focusing on the unique challenges posed by colorizing photos of manga books. We present a novel method using diffusion models to generate a synthetic dataset that accurately replicates photographed manga pages. Additionally, we have compiled a dataset of real manga photographs, capturing diverse environmental conditions. Integrating these datasets, we established a comprehensive benchmark to evaluate colorization models in scenarios that simulate AR applications. This benchmark was validated through a human study, confirming the accuracy of our metrics across both datasets. We also showed that domain adaptation may improve model performance. Paving the way for practical applications, our framework enables the creation of an AR application designed to execute manga colorization effectively.

Language: English
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Keywords: бенчмаркDomain adaptationДоменная адаптацияbenchmark dataset

In book

2024 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)
IEEE, 2024.
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