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November 6, 2026
Study Reveals Why Experienced Readers Sometimes Make Mistakes in Comprehension
Researchers at the HSE Centre for Language and Brain examined how people comprehend sentences in which the grammatical structure conveys a meaning that conflicts with assumptions based on their life experience. In an experiment, participants were asked to read sentences describing two characters and then identify which character had performed the action. The results showed that people with greater reading experience performed better when a character's actions matched their expectations but were more likely to make mistakes when the actions contradicted those expectations. The paper has been published in the Quarterly Journal of Experimental Psychology. The study was supported by a grant from the Russian Science Foundation.
November 6, 2026
Fall into ML 2026: From Fundamental Research to Discussions on the Future of AI
On October 23–24, HSE University hosted Fall into ML 2026, the fifth conference on machine learning and AI. Over the past five years, the event has grown from a small gathering of researchers into a flagship platform that brings together leading machine learning specialists, early-career researchers, students, and representatives of technology companies. The fifth edition marked a new stage in the development of the conference, with a programme that included discussions with international speakers, presentations of joint research projects, and tutorials on the latest trends in AI.
November 5, 2026
Masters Programmes and AI: Shaping the Future of Higher Education
From October 28 to 30, the 17th International Conference on Higher Education, ‘The (Non)Homogeneous Landscape of Higher Education: Institutions, Trajectories, Effects,’ took place at HSE University. Among the key topics discussed at the plenary sessions on the second and third days were the development of master's programmes and the application of AI in the learning process.

 

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On Regularized Systems of Equations for Gas Mixture Dynamics with New Regularizing Velocities and Diffusion Fluxes

Entropy. 2023. Vol. 25. No. 1. Article 158.
Zlotnik A., Lomonosov T.

We deal with multidimensional regularized systems of equations for the one-velocity and one-temperature inert gas mixture dynamics consisting of the balance equations for the mass of components and the momentum and total energy of the mixture, with diffusion fluxes between the components as well as the viscosity and heat conductivity terms. The regularizations are kinetically motivated and aimed at constructing conditionally stable symmetric in space discretizations without limiters. We consider a new combined form of regularizing velocities containing the total pressure of the mixture. To confirm the physical correctness of the regularized systems, we derive the balance
equation for the mixture entropy with the nonnegative entropy production, under generalized assumptions on the diffusion fluxes. To confirm nice regularizing properties, we derive the systems of equations linearized at constant solutions and provide the existence, uniqueness and L2-dissipativity of weak solutions to an initial-boundary problem for them. For the original systems, we also discuss the related Petrovskii parabolicity property and its important corollaries. In addition, in the one-dimensional case, we also present the special three-point and symmetric finite-difference discretization in space of the regularized systems and prove that it inherits the entropy correctness
property. We also give results of numerical experiments confirming that the discretization is able to simulate well various dynamic problems of contact between two different gases.

Research target: Mathematics Computer Science
Language: English
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DOI
Text on another site
Keywords: linearizationentropy balance equationsemi-discrete entropy balance equationregularized equations for one-velocity and one-temperature gas mixture dynamicsthree-point symmetric spatial discretization
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