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September 4, 2026
‘Hedgehog Versus ‘Relatives: Researchers Measure How the Brain Responds to Unexpected Words During Natural Speech
Russian neurophysiologists, including researchers from HSE University, have demonstrated the feasibility of using event-related fields (ERFs) to study brain activity during natural speech perception. The researchers showed that this approach can be applied not only to individual words but also to continuous speech. Their findings indicate that words whose meanings differ significantly from the preceding context require longer processing times. The study also reveals that the brain processes function words in two stages: first, it identifies their grammatical role and then uses this information to predict the next word. The study has been published in Frontiers in Human Neuroscience.
August 25, 2026
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
August 24, 2026
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’

 

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Эконометрическое моделирование потенциала региона по привлечению прямых иностранных инвестиций

Экономика региона. 2020. Т. 16. № 1. С. 310–324.
Drapkin I. M., Дубинина Е. О.

In the Russian economy, foreign direct investments (FDI) are distributed very unevenly between the regions. This inequality is caused by differences in the regions’ economic characteristics, as well as by varying effectiveness of regional policies to attract FDI. The assessment of a region’s potential to attract FDI could serve as a guide for analysing the effectiveness of local authorities in terms of creating a positive environment for foreign investors. The research focuses on constructing and assessing an econometric model of FDI determinants at the regional level. The theoretical foundation for this model is a gravity approach. We used estimates obtained with the help of Poisson’s method of quasi-maximum likelihood estimation for calculating the potential of foreign direct investments. Using the data for the period from 2015 to 2017, we identified the crucial factors influencing the inflow of foreign direct investment in Russia at the regional level: availability of the workforce, level of bureaucracy, level of income, population density, and the financial results of the regional companies. Based on the calculated potentials, we identified successful and lagging Russian regions in terms of FDI inflows. We analysed and systemised the volume of FDI inflows as well as the ratio of actual/potential FDI of the Russian regions. The largest Russian regions in terms of FDI inflows have limitations in growth because they either exceed their potential level already (Moscow oblast, Krasnodar krai, Samara oblast) or are close to it (Moscow city, Leningrad oblast, Saint-Petersburg city). At the same time, more than a half of the Russian regions (41 out of analysed 82) not only have very low potential to attract FDI inflows, but also operate at a level lower than potential. A significant increase in FDI inflows across the country may be provided by a small group of regions with medium and high levels of FDI inflows but with considerable unrealized potential. This group includes such regions as Republic of Bashkortostan, Republic of Tatarstan, Sverdlovsk oblast, Udmurt republic, KhMAO–Yugra, Orenburg oblast, and Belgorod oblast. The suggested methodology for assessing a region’s FDI potential and comparing it with actual FDI inflows can be used for goal setting for relevant regional authorities and subsequent control of their efforts to attract FDI.

Research target: Economics and Management
Language: Russian
Full text
DOI
Keywords: foreign direct investmentFDI gravity modelFDI determinantsгравитационная модель ПИИдетерминанты ПИИFDI in Russiaattracting FDI in the Russian economyPoisson quasi-maximum likelihood estimationпрямые иностранные инвестиции в Россиипривлечение ПИИ в российскую экономикурегиональные факторы привлечения ПИИметод псевдомакси- мального правдоподобия Пуассона
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