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July 20, 2026
Scientists Create Open Dataset for Studying Concentration
A team of Russian researchers, including scientists from HSE University–St Petersburg, has developed the first open multimodal dataset containing recordings of brain activity, heart function, and video observations to help researchers understand what happens in the human brain during deep concentration. In the future, the dataset could accelerate the development of neural interfaces, rehabilitation technologies, and AI systems. The article has been published in Scientific Data.
July 20, 2026
‘Science Is Universal-It Knows No Borders
Fuad Aleskerov, Tenured Professor and Director of the International Centre of Decision Choice and Analysis at HSE University, together with his colleagues, has developed methods of network analysis in bibliometrics that have made it possible to identify patterns in the appearance and citation of publications in academic journals, as well as their influence on each other. When one or a number of studies are frequently cited by a wide range of journals, this is an indicator that the research is of high quality. By contrast, extensive cross-citation within a limited group of journals increases the likelihood of identifying a network of predatory publications.
July 20, 2026
Scientists Propose Method for More Efficient Resource Use in Machine Learning
An international group of researchers, including mathematicians from the AI and Digital Science Institute at the HSE Faculty of Computer Science, has provided a theoretical justification for a simple and computationally efficient method of estimating uncertainty in Stochastic Gradient Descent (SGD). The paper has been published on the scientific preprint server arXiv.org and presented at AISTATS 2026.

 

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Modelling mobile robot navigation in 3D environments: Camera-based stairs recognition in Gazebo

P. 1–6.
Mustafin M., Tsoy T., Martinez-Garcia E., Meshcheryakov R., Magid E.

The task of sensory-based autonomous navigation of mobile robots requires data fusion from multiple sources in order to properly detect and recognize environmental obstacles. One of important issues mobile robots deal in a typical multi-level indoor environment is a stair well detection and negotiation. This paper presents ROS-based stairs detection implementation using onboard cameras of the Russian mobile crawler robot Servosila Engineer. Virtual experiments were performed in Gazebo environment with a single camera and a stereo camera. © 2022 IEEE.

Language: English
DOI
Text on another site
Keywords: мобильный роботкомпьютерное зрениеROScomputer visionobject recognitionGazeboMobile crawler robot

In book

Proceedings of 2022 IEEE Moscow Workshop on Electronic and Networking Technologies (MWENT)
Proceedings of 2022 IEEE Moscow Workshop on Electronic and Networking Technologies (MWENT)
M.: IEEE, 2022.
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