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News
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.’
August 21, 2026
Social Integration: At the Crossroads of Knowledge and Values
The International Laboratory for Social Integration Research (ILSIR) at HSE University studies the challenges faced by vulnerable groups and explores ways to help them participate fully in everyday life. To develop effective solutions, the laboratory’s researchers combine cutting-edge methods with practical fieldwork. In this interview with the HSE News Service, Laboratory Head Elena Iarskaia-Smirnova discusses the laboratory’s work.

 

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Оценивание вероятностей, предельных эффектов и эффектов воздействия в иерархических системах бинарных уравнений

Вестник Московского университета. Серия 6: Экономика. 2023. Т. 58. № 4. С. 23–49.
Kossova E. V., Slabolitskiy I. S., Potanin B.

This article addresses the interpreting results of recursive system of binary equations estimation in the case when the system does not satisfy exclusion restriction conditions. It means that the equation defining the endogenous variable does not contain a unique covariate. This article extends the analysis of previous studies on the identifiability of parameters of recursive binary systems by analyzing the conditions for the identifiability of probabilities, marginal effects and treatment effects. We provide a reasonable consideration suggesting that even if parameters of the model are unidentifiable, it is still possible to estimate accurately the conditional probabilities and marginal effects, but not the treatment effects. The problem of identifiability discussed in the paper is also considered on real data. We estimate the probability of purchasing medicine depending on individuals’ characteristics and the fact of visiting a doctor. An important practical contribution of the work is the recommendation for researchers to interpret the result recursive binary system estimation via marginal effects in the case when it is not possible to include at least one unique variable in the equation for the binary covariate.

Research target: Economics and Management Mathematics
Language: Russian
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Text on another site
Keywords: предельные эффектыtreatment effectsmarginal effectsиерархическая система бинарных уравненийбинарный эндогенный регрессорограничения исключенияэффекты воздействийдвумерная пробит модельrecursive bivariate probit modelbinary endogenous covariateexclusion restrictions
Publication based on the results of:
Nonstationary random environments and their applications (2023)
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