Resumen:
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[EN] In order to study transport in complex environments, it is extremely important to determine the physical mechanism underlying diffusion and precisely characterize its nature and parameters. Often, this task is strongly ...[+]
[EN] In order to study transport in complex environments, it is extremely important to determine the physical mechanism underlying diffusion and precisely characterize its nature and parameters. Often, this task is strongly impacted by data consisting of trajectories with short length (either due to brief recordings or previous trajectory segmentation) and limited localization precision. In this paper, we propose a machine learning method based on a random forest architecture, which is able to associate single trajectories to the underlying diffusion mechanism with high accuracy. In addition, the algorithm is able to determine the anomalous exponent with a small error, thus inherently providing a classification of the motion as normal or anomalous (sub- or super-diffusion). The method provides highly accurate outputs even when working with very short trajectories and in the presence of experimental noise. We further demonstrate the application of transfer learning to experimental and simulated data not included in the training/test dataset. This allows for a full, high-accuracy characterization of experimental trajectories without the need of any prior information.
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Código del Proyecto:
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/BFU2017-85693-R/ES/ESTUDIO DEL PAPEL DE LAS INTEGRINAS EN LOS MECANISMOS MOLECULARES DE LA CURACION DE LAS HERIDAS A LA ESCALA NANOMETRICA/
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/BFU2017-85693-R/ES/ESTUDIO DEL PAPEL DE LAS INTEGRINAS EN LOS MECANISMOS MOLECULARES DE LA CURACION DE LAS HERIDAS A LA ESCALA NANOMETRICA/
info:eu-repo/grantAgreement/GC//2017 SGR 1341/
info:eu-repo/grantAgreement/MINECO//RYC-2015-17896/ES/RYC-2015-17896/
info:eu-repo/grantAgreement/GC//2017SGR940/
info:eu-repo/grantAgreement/MINECO//SEV-2015-0522/ES/AGR-INSTITUTO DE CIENCIAS FOTONICAS/
info:eu-repo/grantAgreement/MICINN//FIS2016-79508-P//FRONTERAS DE LA FISICA TEORICA ATOMICA, MOLECULAR y OPTICA/
info:eu-repo/grantAgreement/NCN//2016%2F20%2FW%2FST4%2F00314//Symfonia/
info:eu-repo/grantAgreement/ //BEAGAL18%2F00203//AYUDA BEATRIZ GALINDO MODALIDAD JUNIOR-GARCIA MARCH/
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Agradecimientos:
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This work has been funded by the Spanish Ministry MINECO (National Plan 15 Grant: FISICATEAMO No. FIS2016-79508-P, SEVEROOCHOA No. SEV-2015-0522, FPI), European Social Fund, Fundacio Cellex, Generalitat de Catalunya (AGAUR ...[+]
This work has been funded by the Spanish Ministry MINECO (National Plan 15 Grant: FISICATEAMO No. FIS2016-79508-P, SEVEROOCHOA No. SEV-2015-0522, FPI), European Social Fund, Fundacio Cellex, Generalitat de Catalunya (AGAUR Grant No. 2017 SGR 1341 and CERCA/Program), ERC AdG OSYRIS, EU FETPRO QUIC, and the National Science Centre, Poland-Symfonia Grant No. 2016/20/W/ST4/00314. CM acknowledges funding from the Spanish Ministry of Economy and Competitiveness and the European Social Fund through the Ramon y Cajal program 2015 (RYC-2015-17896) and the BFU2017-85693-R and from the Generalitat de Catalunya (AGAUR Grant No. 2017SGR940). GM acknowledges financial support from Fundacio Social La Caixa. MAGM acknowledges funding from the Spanish Ministry of Education and Vocational Training (MEFP) through the Beatriz Galindo program 2018 (BEAGAL18/00203). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU.
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