论文标题
安迪：异常扩散挑战
AnDi: The Anomalous Diffusion Challenge
论文作者
论文摘要
通常被称为异常扩散的纯布朗运动的偏差在科学文献中引起了很大的关注，以描述许多物理情况。已经开发出基于经典统计和机器学习方法的几种方法来表征从实验数据中的异常扩散，通常将其作为粒子轨迹获取。为了评估和比较表征异常扩散的可用方法，我们组织了异常扩散（ANDI）挑战（\ url {http://www.andi-challenge.org/}）。具体而言，ANDI挑战将解决异常扩散表征的三个不同方面，即：（i）推断异常扩散指数。 （ii）鉴定基础扩散模型。 （iii）轨迹的分割。每个问题包括不同数量的维度（1D，2D和3D）的子任务。为了比较各种方法，我们开发了一个专用的开源框架，以模拟用于训练和测试数据集的异常扩散轨迹。挑战于2020年3月1日发起，由三个阶段组成。目前，第一阶段的参与是开放的。将自动评估提交内容，并将在即将到来的文章中进行彻底分析和比较。
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 (\url{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.
