Gramian Angular Fields for leveraging pretrained computer vision models with anomalous diffusion trajectories. (arXiv:2310.01416v1 [eess.IV])

Anomalous diffusion is present at all scales, from atomic to large scales.
Some exemplary systems are; ultra-cold atoms, telomeres in the nucleus of
cells, moisture transport in cement-based materials, the free movement of
arthropods, and the migration patterns of birds. 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 {$alpha$} 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 ge (Munoz-Gil et al., 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 pre-trained computer-vision models, ResNet and MobileNet, to
characterize the underlying diffusive regime, and infer the anomalous diffusion
exponent {$alpha$}. Short raw trajectories, of lengths between 10 and 50, are
commonly encountered in single-particle tracking experiments and are the most
difficult to characterize. We show that by using GAF images, we can outperform
the current state-of-the-art while increasing accessibility to machine learning
methods in an applied setting.



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