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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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Technology upgrading of middle income economies: A new approach and results

Technological Forecasting and Social Change. 2018. No. 129. P. 56–75.
Radosevic S., Yoruk E.

We explore issues of measurement for technology upgrading of the economies moving from middle to highincome status. In exploring this issue, we apply theoretically relevant and empirically grounded middle level conceptual and statistical framework based on three dimensions: (i) Intensity (ii) breadth of technological upgrading, and (iii) technology and knowledge exchange. As an outcome, we construct a three-pronged composite indicator of technology upgrading based on 35 indicators which reflect different drivers and patterns of technology upgrading of countries at different income levels. We show that technology upgrading of middle-income economies is distinctively different from that of low and high-income economies. Our results suggest the existence of middle-income trap in technology upgrading – i.e. countries' technology upgrading activities are not reflected in their income levels. Based on the simple statistical analysis we show that the middle-income trap is present in all three aspects of technology upgrading, but their importance varies across different aspects. A trap seems to be higher for ‘breadth’ of technology upgrading than for ‘intensity’ of technology upgrading and is by far the highest for the dimension of knowledge and technology interaction with the global economy. Finally, our research shows that technology upgrading is a multidimensional process and that it would be methodologically wrong to aim for an aggregate index.

Language: English
DOI
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Keywords: structural changecomposite indexTechnology upgradingMiddle-income trapR&D and innovation
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