Gramian angular fields for leveraging pretrained computer vision models with anomalous diffusion trajectories

Òscar Garibo-i-Orts, Nicolas Firbas, Laura Sebastiá, and J. Alberto Conejero
Phys. Rev. E 107, 034138 – Published 28 March 2023

Abstract

Anomalous diffusion is present at all scales, from atomic to large ones. Some exemplary systems are ultracold atoms, telomeres in the nucleus of cells, moisture transport in cement-based materials, arthropods' free movement, and birds' migration patterns. The characterization of the diffusion gives critical information about the dynamics of these systems and provides an interdisciplinary framework with which to study diffusive transport. Thus, the problem of identifying underlying diffusive regimes and inferring the anomalous diffusion exponent α with high confidence is critical to physics, chemistry, biology, and ecology. Classification and analysis of raw trajectories combining machine learning techniques with statistics extracted from them have widely been studied in the Anomalous Diffusion Challenge [Muñoz-Gil et al., Nat. Commun. 12, 6253 (2021)]. Here we present a new data-driven method for working with diffusive trajectories. This method utilizes Gramian angular fields (GAF) to encode one-dimensional trajectories as images (Gramian matrices), while preserving their spatiotemporal structure for input to computer-vision models. This allows us to leverage two well-established pretrained computer-vision models, ResNet and MobileNet, to characterize the underlying diffusive regime and infer the anomalous diffusion exponent α. Short raw trajectories of lengths between 10 and 50 are commonly encountered in single-particle tracking experiments and are the most difficult ones to characterize. We show that GAF images can outperform the current state-of-the-art while increasing accessibility to machine learning methods in an applied setting.

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  • Received 3 October 2022
  • Revised 26 December 2022
  • Accepted 28 February 2023

DOI:https://doi.org/10.1103/PhysRevE.107.034138

©2023 American Physical Society

Physics Subject Headings (PhySH)

Interdisciplinary PhysicsPhysics of Living SystemsStatistical Physics & Thermodynamics

Authors & Affiliations

Òscar Garibo-i-Orts*

  • GRID–Grupo de Investigacion en Ciencia de Datos Valencian International University–VIU, Carrer Pintor Sorolla 21, 46002 València, Spain

Nicolas Firbas

  • DBS–Department of Biological Sciences, National University of Singapore 16 Science Drive 4, Singapore 117558, Singapore

Laura Sebastiá

  • VRAIN–Valencian Research Institute for Artificial Intelligence Universitat Politècnica de València, Cami de Vera s/n, 46022 València, Spain

J. Alberto Conejero§

  • Instituto Universitario de Matemática Pura y Aplicada Universitat Politècnica de València, Cami de Vera s/n, 46022 València, Spain

  • *oscar.garibo@campusviu.es; also at VRAIN–Valencian Research Institute for Artificial Intelligence, Universitat Politècnica de València, Cami de Vera s/n, 46022 València, Spain.
  • Nicolas.Firbas@gmail.com
  • lsebastia@upv.es
  • §aconejero@upv.es

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Issue

Vol. 107, Iss. 3 — March 2023

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