  Anomalous diffusion, which shows a deviation of transport dynamics from the
framework of standard Brownian motion, is involved in the evolution of various
physical, chemical, biological, and economic systems. The study of such random
processes is of fundamental importance in unveiling the physical properties of
random walkers and complex systems. However, classical methods to characterize
anomalous diffusion are often disqualified for individual short trajectories,
leading to the launch of the Anomalous Diffusion (AnDi) Challenge. This
challenge aims at objectively assessing and comparing new approaches for single
trajectory characterization, with respect to three different aspects: the
inference of the anomalous diffusion exponent; the classification of the
diffusion model; and the segmentation of trajectories. In this article, to
address the inference and classification tasks in the challenge, we develop a
WaveNet-based deep neural network (WADNet) by combining a modified WaveNet
encoder with long short-term memory networks, without any prior knowledge of
anomalous diffusion. As the performance of our model has surpassed the current
1st places in the challenge leaderboard on both two tasks for all dimensions (6
subtasks), WADNet could be the part of state-of-the-art techniques to decode
the AnDi database. Our method presents a benchmark for future research, and
could accelerate the development of a versatile tool for the characterization
of anomalous diffusion.
