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Deviations from Brownian motion leading to anomalous diffusion control transport mechanisms in many fields, from ecology to quantum physics. The detection of anomalous diffusion from an individual trajectory is a challenging task, which traditionally relies on calculating the mean square displacement. This approach finds its limits for cases of practical interest, e.g. short/noisy trajectories or ensembles of heterogeneous trajectories. Recently, new approaches have been proposed, mostly building on the ongoing machine-learning revolution. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition. Participants applied their own algorithms independently to a commonly defined data set including diverse scenarios. Although no single method performed best across all conditions, the results revealed clear differences between the various approaches, providing practical advice for users and a benchmark for developers.
The deviation from pure Brownian motion, generally referred to as anomalous diffusion, has received large attention in the scientific literature to describe many physical scenarios. Several methods, based on classical statistics and machine learning approaches, have been developed to characterize anomalous diffusion from experimental data, which are usually acquired as particle trajectories. With the aim to assess and compare the available methods to characterize anomalous diffusion, we have organized the Anomalous Diffusion (AnDi) Challenge (http://www.andi-challenge.org/). Specifically, the AnDi Challenge will address three different aspects of anomalous diffusion characterization, namely: (i) Inference of the anomalous diffusion exponent. (ii) Identification of the underlying diffusion model. (iii) Segmentation of trajectories. Each problem includes sub-tasks for different number of dimensions (1D, 2D and 3D). In order to compare the various methods, we have developed a dedicated open-source framework for the simulation of the anomalous diffusion trajectories that are used for the training and test datasets. The challenge was launched on March 1, 2020, and consists of three phases. Currently, the participation to the first phase is open. Submissions will be automatically evaluated and the performance of the top-scoring methods will be thoroughly analyzed and compared in an upcoming article.
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