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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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Temperature-driven charge transport in annealed CdSe/CdS nanoplatelets film

Journal of Applied Physics. 2026. Vol. 139. No. 18. Article 185702.
Saitov S. R., Saidzhonov B. M., Vasiliev R. B., Musorin A. I., Fedyanin A. A., Mantsevich V. N., Smirnov A.

This work presents a comprehensive investigation into the temperature-dependent thermally stimulated conductivity and photoconductivity of vacuum-annealed thin films based on core–shell CdSe/CdS nanoplatelets passivated with oleic acid ligands. Controlled thermal annealing under vacuum conditions leads to a substantial increase in both dark and photoinduced conductivity, which is primarily attributed to partial desorption of the ligands. This process enhances interparticle electronic coupling while preserving the key optical properties of the nanostructures. Consequently, the fabricated single-layer photodetector demonstrates high responsivity, highlighting the potential of ligand-engineered nanoplatelet films for optoelectronic applications.

Detailed analysis of the temperature-dependent conductivity enabled the characterization of hole trapping states within the bandgap of the metamaterial. The extracted effective attempt-to-escape frequency is remarkably low, νeff ≈ 1.5 × 10⁴ s⁻¹, indicating strong carrier localization and large capturing cross section. The density of states can be characterized by Gaussian function with a pronounced maximum at an energy of approximately 0.345 eV above the transport level and a dispersion below 34 meV. These parameters were consistently derived from both thermally stimulated conductivity and photoconductivity measurements through rigorous statistical modeling of thermally released charge carriers. The excellent agreement between the two independent experimental techniques further validates the reliability of the obtained trap-state characteristics.

Research target: Physics
Language: English
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Keywords: annealingphotoconductivitythin filmTrap statesnanoplateletscadmium selenide
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