AnDi: The Anomalous Diffusion Challenge on ArXiv

Logo of the AnDi challenge

AnDi: The Anomalous Diffusion Challenge
Gorka Muñoz-Gil, Giovanni Volpe, Miguel Angel Garcia-March, Ralf Metzler, Maciej Lewenstein & Carlo Manzo
arXiv: 2003.12036

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 (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.

Martin Selin joins the Soft Matter Lab

Martin Selin starts his PhD at the Physics Department of the University of Gothenburg on 16th March 2020.

Martin has a Master degree in Applied Physics at Chalmers University of Technology, Gothenburg, Sweden.

In his PhD, he will focus on automating particle trapping using optical tweezers and machine learning.

Invited talk by G. Volpe at Nanolight, Benasque, Spain, 8-14 March 2020

Giovanni Volpe will give an invited presentation at Nanolight 2020.

The conference, organized by Luis Martín Moreno (ICMA, CSIC – U. Zaragoza) and Niek van Hulst (ICFO, Barcelona), aims at the exploration of the frontiers in the field of subwavelength optics. It is meant to facilitate the interaction between worldwide researchers working in the field, with a special emphasis on interaction between young and more experienced researchers.
The conference is held in Benasque, Spain, from 8 to 14 March 2020.

The contributions of Giovanni Volpe will be presented according to the following schedule:

Giovanni Volpe
Deep Learning for Microscopy
Date: 12 March 2020
Time: 15:35 CET

Link: Nanolight 2020 program

Seminar on Machine Learning and Physics: a long standing relation? by Gorka Muñoz-Gil from ICFO, Nexus, 3 March 2020

Machine Learning and Physics: a long standing relation?
Seminar by Gorka Muñoz-Gil from ICFO, Barcelona, Spain.

In this talk, I will review the recent advances in single trajectory characterization via Machine Learning methods. Then, I will introduce the AnDi challenge, a competition which aims at bringing together a vibrating and multidisciplinary community of scientists working on the problem of anomalous diffusion.

Place: Nexus room, Fysik Origo, Fysik
Time: 03 March, 2020, 16:00

Seminar on Robust automated reading of the skin prick test via 3D imaging and parametric surface fitting by Jesús Pineda from Universidad Tecnologica de Bolivar, Nexus, 3 March 2020

Robust automated reading of the skin prick test via 3D imaging and parametric surface fitting.
Seminar by Jesús Pineda from the Universidad Tecnologica de Bolivar, Cartagena, Colombia.

The conventional reading of the skin prick test (SPT) for diagnosing allergies is prone to inter- and intra-observer variations. Drawing the contours of the skin wheals from the SPT and scanning them for computer processing is cumbersome. However, 3D scanning technology promises the best results in terms of accuracy, fast acquisition, and processing. In this work, we present a wide-field 3D imaging system for the 3D reconstruction of the SPT, and we propose an automated method for the measurement of the skin wheals. The automated measurement is based on pyramidal decomposition and parametric 3D surface fitting for estimating the sizes of the wheals directly. We proposed two parametric models for the diameter estimation. Model 1 is based on an inverted Elliptical Paraboloid function, and model 2 on a super-Gaussian function. The accuracy of the 3D imaging system was evaluated with validation objects obtaining transversal and depth accuracies within ± 0.1 mm and ± 0.01 mm, respectively. We tested the method on 80 SPTs conducted in volunteer subjects, which resulted in 61 detected wheals. We analyzed the accuracy of the models against manual reference measurements from a physician and obtained that the parametric model 2 on average yields diameters closer to the reference measurements (model 1: -0.398 mm vs. model 2: -0.339 mm) with narrower 95% limits of agreement (model 1: [-1.58, 0.78] mm vs. model 2: [-1.39, 0.71] mm) in a Bland-Altman analysis. In one subject, we tested the reproducibility of the method by registering the forearm under five different poses obtaining a maximum coefficient of variation of 5.24% in the estimated wheal diameters. The proposed method delivers accurate and reproducible measurements of the SPT [1].

References:

  1. Jesus Pineda, Raul Vargas, Lenny A. Romero, Javier Marrugo, Jaime Meneses & Andres G. Marrugo (2019) Robust automated reading of the skin prick test via 3D imaging and parametric surface fitting. PLOS ONE 14(10), e0223623.

Place: Nexus room, Fysik Origo, Fysik
Time: 03 March, 2020, 11:00

The AnDi challenge: an “anomalous” competition

Logo of the AnDi challenge.

Researchers from ICFO, UVic, Gothenborg University, Politecnica de Valencia and Potsdam University organize the AnDi challenge, a physics challenge to address Brownian motion and Anomalous diffusion.

Brownian motion was first observed in 1827 by Robert Brown: pollen grains suspended in water show a characteristic erratic motion. Almost 80 years after, Albert Einstein provided a theoretical foundation for the Brownian motion. Though the Brownian motion is observed in many different systems, significant deviations from it have also been observed, starting from biological systems to economics.

The deviation from Brownian motion is indicated with the term Anomalous diffusion. It is connected to non-equilibrium phenomena, complex environments, flows of energy and information, and transport in living systems. To understand the nature of such systems one must correctly identify the physical origin of the anomalous diffusion, and correctly characterize it, through the calculation of its properties. A simple data analysis of trajectories, though, often provides limited information, in particular when the trajectories are either short, or noisy, or irregularly sampled, or featuring mixed behaviors. Several methods going beyond the calculation of classical estimators have been proposed, in the last years, to quantify anomalous diffusion.

The AnDi challenge has been thought as a competition to test these methods as well as other alternative approaches, by bringing together the scientific community currently working on the quantification of the anomalous diffusion.

The use of the same reference datasets will allow an unbiased assessment of the performance of published and unpublished methods for characterizing anomalous diffusion from single trajectories. Participants can submit the results of their analysis on the internet until November 1st, 2020. These results will be then automatically scored and ranked among all competitors.

In addition to the main objective of the AnDi Challenge, the top-ranked participants will be invited to present their results in a workshop held at ICFO, in Barcelona, on February 17-20, 2021.

Organizers:

Website: www.andi-challenge.org

Codalab: https://competitions.codalab.org/competitions/23601

e-mail: andi.challenge@gmail.com

twitter: @AndiChallenge

Laura Natali joins the Soft Matter Lab

Laura Natali starts her PhD at the Physics Department of the University of Gothenburg on 1st March 2020.

Laura has a Master degree in Physics, curriculum in Physics of Biosystems, from the University of Rome “La Sapienza”, where he submitted a Master thesis whose results can be found here.

In her PhD, she will focus on microswimmers and active polymers employing machine learning techniques.