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September 4, 2026
Time to Showcase Your Research: Applications Are Now Open for Student Research Paper Competition 2026
Taking part in the Student Research Paper Competition (SRPC) gives you an opportunity to present your research to experts, receive an independent assessment, and determine the future direction of your work. The competition is open to students graduating in 2026 not only from HSE University but from universities in Russia and abroad. Papers may be submitted in Russian and English, and in some fields also in French, German, and Spanish.
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.

 

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Simulation of Residential Real Estate Markets in the Largest Russian Cities

Economy of Regions. 2022. Vol. 18. No. 2. P. 609–622.
Alekseev A.

The existing mass appraisal models and mathematical tools for predicting the market value of residential property have a number of disadvantages, as they are developed for individual regions. Without considering the constantly changing economic environment, these models quickly become outdated and require constant updating. Thus, they are not suitable for construction business optimisation. The study aims to create a universally applicable real estate appraisal system for Russian cities, regardless of the constantly changing economic situation. This goal was achieved through the creation of a neural network, whose input parameters include construction and operational data, geographical factors, time effect, as well as a number of indicators characterising the economic situation in specific regions, Russia and the world. In order to examine the dynamics of real estate markets in the Russian Federation, statistical data for neural network training were collected over a long period from 2006 to 2020. Virtual computer experiments were performed for testing the developed system. They showed that minimum size one-room apartments of 16 square meters have the highest unit cost per square meter in Moscow. Two-room apartments with an area of 90 square meters have the maximum price, as well as 100 sq. m. three-room, 110 sq. m. four-room and 120 sq. m. five-room apartments. In Ekaterinburg, two-room apartments with a total area of 30 square meters have the highest cost per square meter; the same applies for 110 sq. m. three-room, 130 sq. m. four-room and 150 sq. m. five-room apartment. Thus, the proposed system can be used to optimise the construction business. It can be also be useful for government institutions concerned with urban real estate market management, property taxation, and housing market improvement.

Research target: Mathematics Computer Science Construction
Language: English
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Keywords: нейронная сетьreal estateпрогнозирование цен summary cost estimateartifitial neural networksгородская недвижимость, прогнозирование, стоимость, рынок, мак-роэкономические факторы, нейронная сеть Forecasting
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