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June 5, 2026
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.
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Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a new compression method for large language models such as GPT and LLaMA that reduces their size by 25–36% without additional training or significant loss of accuracy. This is the first approach to use mathematical transformations—specifically, rotations of model weights—to make models more amenable to compression with structured matrices. The study results have been published in ACL Findings 2025. The code is available on GitHub.

 

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Track Reconstruction in the Upgraded Tracking System of MPD/NICA

Physics of Particles and Nuclei. 2022. Vol. 53. No. 2. P. 519 –523.
Zinchenko D., Zinchenko A., E. Nikonov

Although the start-up of the MPD experiment is still ahead, the work on the preparation and physics validation of the future detector upgrade program has already been initiated. As one of the possible MPD upgrade steps, an Inner Tracking System (ITS) based on the next generation silicon pixel detectors is being considered to be installed between the beam pipe and the Time Projection Chamber (TPC). It is expected that such a detector will increase the research potential of the experiment for both the proton-proton (high luminosity) and nucleus-nucleus (high particle multiplicity) interactions. The existing in the MPD track reconstruction method is based on the Kalman filter in the TPC. Its simple extension to the ITS is not considered as the most efficient approach. Therefore, another method, based on the “vector finder” approach, was developed. This paper describes the proposed algorithm and presents its performance results for primary and secondary track reconstruction in the MPD ITS obtained on Monte-Carlo generated data of nucleus-nucleus collisions. 

Research target: Physics
Language: English
DOI
Keywords: Kalman filterTrack Reconstruction
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