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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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Ecosystem–Atmosphere Exchange of CO2 in Ombrotrophic and Mesotrophic Peatlands in the Taiga Zone of European Russia and West Siberia

Global Biogeochemical Cycles. 2026. Vol. 40. No. 1. Article e2025GB008592.
V. Mamkin, Avilov V., Dmitrichenko A., Dyukarev E., Emelianova E., Gorbarenko E., Gulyaev R., Ivanov D., Kurbatov E., Kuricheva O., Lapshina E., Miglovets M., Ogurtsov S., R. Sandlersky, Suvorov G., Trusova S., Zagirova S., J. Kurbatova

Northern peatlands (>50°N) account for approximately 70% of the global peatland area and play a key role in the global carbon cycle. However, their role as long‐term carbon sinks is vulnerable to modern climate warming at high latitudes. Future climate predictions require data on how peatlands respond to observed changes in global environmental parameters, particularly in the Northern Hemisphere taiga zones. Here, we compared CO2 net ecosystem exchange (NEE), ecosystem respiration (Reco), gross primary production (GPP), and their responses to changes in environmental parameters using direct Eddy covariance measurements in four representative peatlands: two southern and two middle taiga sites located in European Russia and West Siberia, respectively. Three sites were ombrotrophic bogs, and one was a transitional (between bog and fen) mesotrophic peatland. All studied peatlands functioned as CO2 sinks either annually or during the growing season. The largest net CO2 uptake was detected in the mesotrophic peatland in the middle taiga of European Russia. In the ombrotrophic bogs, elevated air temperatures (>25°C) and vapor pressure deficits (>2 kPa) negatively impacted GPP in the summer; however, at the mesotrophic peatland, these conditions corresponded to the highest GPP observed during the measurement period. Additionally, a reduction in net CO2 uptake was detected at the mesotrophic site during the anomalously wet summer. The study findings suggest that the differences in CO2 exchange processes and their responses to ambient conditions in ombrotrophic bogs and transitional peatland types are important to consider in modelling studies and flux predictions.

Research target: Earth Sciences Biology
Language: English
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Keywords: Eddy covarianceметод турбулентных пульсацийтемпературные аномалиичистый экосистемный обменpeatland ecosystemбаланс углеродаecosystem exchangegross primary productionecosystem respirationtemperature anomaliesторфяникваловая первичная продукцияэкосистемное дыхание
Publication based on the results of:
Structure and functioning of supercomplex natural-territorial systems (2025)
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