  Anomalous diffusion occurs at very different scales in nature, from atomic
systems to motions in cell organelles, biological tissues or ecology, and also
in artificial materials, such as cement. Being able to accurately measure the
anomalous exponent associated with a given particle trajectory, thus
determining whether the particle subdiffuses, superdiffuses or performs normal
diffusion is of key importance to understand the diffusion process. Also, it is
often important to trustingly identify the model behind the trajectory, as this
gives a large amount of information on the system dynamics. Both aspects are
particularly difficult when the input data are short and noisy trajectories. It
is even more difficult if one cannot guarantee that the trajectories output in
experiments is homogeneous, hindering the statistical methods based on
ensembles of trajectories. We present a data-driven method able to infer the
anomalous exponent and to identify the type of anomalous diffusion process
behind single, noisy and short trajectories, with good accuracy. This model was
used in our participation in the Anomalous Diffusion (AnDi) Challenge. A
combination of convolutional and recurrent neural networks were used to achieve
state-of-the-art results when compared to methods participating in the AnDi
Challenge, ranking top 4 in both classification and diffusion exponent
regression.
