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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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?

FDI Attractiveness: An Attempt to Compare Countries

Ecoforum. 2018. Vol. 7. No. 1. P. 54–65.
Demir A. O.

The aim of this research is to examine the reasons of the level of FDI inflow differences among countries
and try to understand why some countries are more attractive for investors. The research used a dataset of 103
countries for due to data availability and created a FDI Inflow Index for 2016. Principal Components Analysis
was used to analyze and construct the FDI Index. The results show that even there are substantial improvements
in the conditions of developing economies for higher FDI levels, still, the macroeconomic conditions, business
and legal environment and agglomeration effects are better situated in developed economies which make them
more attractive for investors.

Language: English
Full text
Text on another site
Keywords: composite indicatorsprincipal components analysisforeign direct investment (FDI)FDI
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