WaveNet-based deep neural networks for the characterization of anomalous diffusion (WADNet)
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TLDR
Wang et al. as discussed by the authors developed 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.Abstract:
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.read more
Citations
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Journal ArticleDOI
Objective comparison of methods to decode anomalous diffusion.
Gorka Muñoz-Gil,Giovanni Volpe,Miguel Ángel García-March,Erez Aghion,Aykut Argun,Chang Beom Hong,Tom Bland,Stefano Bo,J. Alberto Conejero,Nicolas Firbas,Òscar Garibo i Orts,Alessia Gentili,Zihan Huang,Jae-Hyung Jeon,Hélène Kabbech,Yeongjin Kim,Patrycja Kowalek,Diego Krapf,Hanna Loch-Olszewska,Michael A. Lomholt,Jean-Baptiste Masson,Philipp G. Meyer,Seongyu Park,Borja Requena,Ihor Smal,Taegeun Song,Taegeun Song,Taegeun Song,Janusz Szwabiński,Samudrajit Thapa,Samudrajit Thapa,Hippolyte Verdier,Giorgio Volpe,Artur Widera,Maciej Lewenstein,Ralf Metzler,Carlo Manzo,Carlo Manzo +37 more
TL;DR: The Anomalous Diffusion Challenge (AnDi) as mentioned in this paper was an open competition for the characterization of anomalous diffusion from the measurement of an individual trajectory, which traditionally relies on calculating the trajectory mean squared displacement.
Journal ArticleDOI
Bayesian deep learning for error estimation in the analysis of anomalous diffusion
Henrik Seckler,Ralf Metzler +1 more
TL;DR: In this article , a Bayesian-Deep-Learning technique is used to train models for both the classification of the diffusion model and the regression of the anomalous diffusion exponent of single-particle-trajectories.
Journal ArticleDOI
Decomposing the effect of anomalous diffusion enables direct calculation of the Hurst exponent and model classification for single random paths
TL;DR: In this article , the authors present a questionnaire for model selection based on feature analysis of a set of known stochastic processes given as candidates, and present the theoretical background of the automated algorithm which they put for these tasks in the diffusion challenge.
Journal ArticleDOI
Characterization of anomalous diffusion through convolutional transformers
TL;DR: The Convolutional Transformer is able to outperform the previous state of the art at determining the underlying diffusive regime (Task2) in short trajectories, which are the most important for experimental researchers.
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