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Understanding the Impact of Training Set Size on Animal Re-identification

Autor: Algasov, Aleksandr, Nepovinnykh, Ekaterina, Eerola, Tuomas, Kälviäinen, Heikki, Stewart, Charles V., Otarashvili, Lasha, Holmberg, Jason A.

Recent advancements in the automatic re-identification of animal individuals from images have opened up new possibilities for studying wildlife through camera traps and citizen science projects. Existing methods leverage distinct and permanent visual

Externí odkaz: http://arxiv.org/abs/2405.15976

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Understanding X-ray absorption spectra by means of descriptors and machine learning algorithms

Autor: A. A. Guda, S. A. Guda, A. Martini, A. N. Kravtsova, A. Algasov, A. Bugaev, S. P. Kubrin, L. V. Guda, P. Šot, J. A. van Bokhoven, C. Copéret, A. V. Soldatov

Publikováno v: npj Computational Materials, Vol 7, Iss 1, Pp 1-13 (2021)

Abstract X-ray absorption near-edge structure (XANES) spectra are the fingerprint of the local atomic and electronic structures around the absorbing atom. However, the quantitative analysis of these spectra is not straightforward. Even with the most

Externí odkaz: https://doaj.org/article/08b96f9931b64ca9a7c59fb2cb51b471

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PyFitit: The software for quantitative analysis of XANES spectra using machine-learning algorithms

Autor: Martini, A., Guda, S.A., Guda, A.A., Smolentsev, G., Algasov, A., Usoltsev, O., Soldatov, M.A., Bugaev, A., Rusalev, Yu., Lamberti, C., Soldatov, A.V.

Publikováno v: In Computer Physics Communications May 2020 250

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Search for Analytical Relations between X-Ray Absorption Spectra Descriptors and the Local Atomic Structure Using Machine Learning

Autor: Alexander Algasov, Alexander V. Soldatov, Andrea Martini, Antonina N. Kravtsova, Sergey A. Guda, Aram L. Bugaev, Alexander A. Guda, L. V. Guda

Publikováno v: Journal of Surface Investigation: X-ray, Synchrotron and Neutron Techniques. 15:934-938

In this paper, we develop a new technique for quantitative analysis of the near region of X-ray absorption spectra that is based on the extraction of spectrum descriptors and machine learning. The use of descriptors (edge position, intensity and curv

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f7c16c132544218a88136199e1fce3d8
https://doi.org/10.1134/s1027451021050050

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Quantitative Analysis of X-Ray Spectral Data for a Mixture of Compounds Using Machine-Learning Algorithms

Autor: Sergey A. Guda, Alexander Algasov, Yu. V. Rusalev, Alexander A. Guda, Alexander V. Soldatov

Publikováno v: Journal of Surface Investigation: X-ray, Synchrotron and Neutron Techniques. 15:495-501

Based on machine-learning algorithms, a method is developed for determining the structural parameters of the components of a mixture from X-ray absorption spectra. For each component, a database of spectra is constructed for all possible deformations

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::03cfa0c521fa5f87c511dedd9e6d642c
https://doi.org/10.1134/s1027451021030034

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Deciphering the Phillips Catalyst by Orbital Analysis and Supervised Machine Learning from Cr Pre-edge XANES of Molecular Libraries

Autor: Alexander V. Soldatov, Alexander A. Guda, Harry Ramanantoanina, Olga V. Safonova, Christophe Copéret, David Trummer, Keith Searles, Sergey A. Guda, Alexander Algasov

Publikováno v: Journal of the American Chemical Society, 143 (19)

Unveiling the nature and the distribution of surface sites in heterogeneous catalysts, and for the Phillips catalyst (CrO3/SiO2) in particular, is still a grand challenge despite more than 60 years of research. Commonly used references in Cr K-edge X

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cda20128e436517ef6df4e55af0ba564
https://pubmed.ncbi.nlm.nih.gov/33974429

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