Objective comparison of methods to decode anomalous diffusion
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2021
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918128573743104 |
|---|---|
| author | Muñoz-Gil, Gorka Volpe, Giovanni Garcia-March, Miguel Angel Aghion, Erez Argun, Aykut Hong, Chang Beom Bland, Tom Bo, Stefano Conejero, J. Alberto Firbas, Nicolás Orts, Òscar Garibo i Gentili, Alessia Huang, Zihan Jeon, Jae-Hyung Kabbech, Hélène Kim, Yeongjin Kowalek, Patrycja Krapf, Diego Loch-Olszewska, Hanna Lomholt, Michael A. Masson, Jean-Baptiste Meyer, Philipp G. Park, Seongyu Requena, Borja Smal, Ihor Song, Taegeun Szwabiński, Janusz Thapa, Samudrajit Verdier, Hippolyte Volpe, Giorgio Widera, Artur Lewenstein, Maciej Metzler, Ralf Manzo, Carlo |
| author_facet | Muñoz-Gil, Gorka Volpe, Giovanni Garcia-March, Miguel Angel Aghion, Erez Argun, Aykut Hong, Chang Beom Bland, Tom Bo, Stefano Conejero, J. Alberto Firbas, Nicolás Orts, Òscar Garibo i Gentili, Alessia Huang, Zihan Jeon, Jae-Hyung Kabbech, Hélène Kim, Yeongjin Kowalek, Patrycja Krapf, Diego Loch-Olszewska, Hanna Lomholt, Michael A. Masson, Jean-Baptiste Meyer, Philipp G. Park, Seongyu Requena, Borja Smal, Ihor Song, Taegeun Szwabiński, Janusz Thapa, Samudrajit Verdier, Hippolyte Volpe, Giorgio Widera, Artur Lewenstein, Maciej Metzler, Ralf Manzo, Carlo |
| contents | Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2105_06766 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Objective comparison of methods to decode anomalous diffusion Muñoz-Gil, Gorka Volpe, Giovanni Garcia-March, Miguel Angel Aghion, Erez Argun, Aykut Hong, Chang Beom Bland, Tom Bo, Stefano Conejero, J. Alberto Firbas, Nicolás Orts, Òscar Garibo i Gentili, Alessia Huang, Zihan Jeon, Jae-Hyung Kabbech, Hélène Kim, Yeongjin Kowalek, Patrycja Krapf, Diego Loch-Olszewska, Hanna Lomholt, Michael A. Masson, Jean-Baptiste Meyer, Philipp G. Park, Seongyu Requena, Borja Smal, Ihor Song, Taegeun Szwabiński, Janusz Thapa, Samudrajit Verdier, Hippolyte Volpe, Giorgio Widera, Artur Lewenstein, Maciej Metzler, Ralf Manzo, Carlo Data Analysis, Statistics and Probability Soft Condensed Matter Biological Physics Quantitative Methods Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers. |
| title | Objective comparison of methods to decode anomalous diffusion |
| topic | Data Analysis, Statistics and Probability Soft Condensed Matter Biological Physics Quantitative Methods |
| url | https://arxiv.org/abs/2105.06766 |