The Anomalous Diffusion (AnDi) challenge aims at assessing the performance of published and unpublished methods for characterizing anomalous diffusion from single trajectories.
The organization of the challenge was triggered by the appearance of several articles describing methods to assess anomalous exponents and aims at bringing together a vibrating and multidisciplinary community of scientists around this problem.
The challenge is now open! Register here to participate.
Tasks
The AnDi challenge is based on three main competition modalities:
- Inference of the anomalous exponent.
- Classification of the underlying model.
- Segmentation of trajectories switching between diffusion modes.
Each problem will further include sub-modalities for the different number of dimensions, different noise and/or nonuniform sampling.
Details for the participation and benchmarking datasets will be made available soon.
Organizers
- Gorka Muñoz-Gil & Maciej Lewenstein, Quantum Optics Theory @ ICFO
- Carlo Manzo, the QuBI lab @ FCT, UVic-UCC
- Giovanni Volpe, Soft Matter Lab @ University of Gothenburg
- Miguel A. Garcia-March @ UPV
- Ralf Metzler, Theoretical Physics @ UniPotsdam
Recent relevant literature
-
Bo et al – Measurement of Anomalous Diffusion Using Recurrent Neural Networks (6 May 2019)
-
Granik et al – Single particle diffusion characterization by deep learning (4 Apr 2019)
-
Muñoz-Gil et al – Machine learning method for single trajectory characterization (7 Mar 2019)
-
Kowalek et al – Classification of diffusion modes in single-particle tracking data… (21 Feb 2019)
-
Thapa et al – Bayesian analysis of single-particle tracking data using the nested-sampling algorithm...(28 Nov 2018)
- Diego Krapf et al – Power spectral density of a single Brownian trajectory: what one can and cannot learn from it (09 Feb 2018)