Soft Matter Lab presentations at the SPIE Optics+Photonics Digital Forum

Seven members of the Soft Matter Lab (Saga HelgadottirBenjamin Midtvedt, Aykut Argun, Laura Pérez-GarciaDaniel MidtvedtHarshith BachimanchiEmiliano Gómez) were selected for oral and poster presentations at the SPIE Optics+Photonics Digital Forum, August 24-28, 2020.

The SPIE digital forum is a free, online only event.
The registration for the Digital Forum includes access to all presentations and proceedings.

The Soft Matter Lab contributions are part of the SPIE Nanoscience + Engineering conferences, namely the conference on Emerging Topics in Artificial Intelligence 2020 and the conference on Optical Trapping and Optical Micromanipulation XVII.

The contributions being presented are listed below, including also the presentations co-authored by Giovanni Volpe.

Note: the presentation times are indicated according to PDT (Pacific Daylight Time) (GMT-7)

Emerging Topics in Artificial Intelligence 2020

Saga Helgadottir
Digital video microscopy with deep learning (Invited Paper)
26 August 2020, 10:30 AM
SPIE Link: here.

Aykut Argun
Calibration of force fields using recurrent neural networks
26 August 2020, 8:30 AM
SPIE Link: here.

Laura Pérez-García
Deep-learning enhanced light-sheet microscopy
25 August 2020, 9:10 AM
SPIE Link: here.

Daniel Midtvedt
Holographic characterization of subwavelength particles enhanced by deep learning
24 August 2020, 2:40 PM
SPIE Link: here.

Benjamin Midtvedt
DeepTrack: A comprehensive deep learning framework for digital microscopy
26 August 2020, 11:40 AM
SPIE Link: here.

Gorka Muñoz-Gil
The anomalous diffusion challenge: Single trajectory characterisation as a competition
26 August 2020, 12:00 PM
SPIE Link: here.

Meera Srikrishna
Brain tissue segmentation using U-Nets in cranial CT scans
25 August 2020, 2:00 PM
SPIE Link: here.

Juan S. Sierra
Automated corneal endothelium image segmentation in the presence of cornea guttata via convolutional neural networks
26 August 2020, 11:50 AM
SPIE Link: here.

Harshith Bachimanchi
Digital holographic microscopy driven by deep learning: A study on marine planktons (Poster)
24 August 2020, 5:30 PM
SPIE Link: here.

Emiliano Gómez
BRAPH 2.0: Software for the analysis of brain connectivity with graph theory (Poster)
24 August 2020, 5:30 PM
SPIE Link: here.

Optical Trapping and Optical Micromanipulation XVII

Laura Pérez-García
Reconstructing complex force fields with optical tweezers
24 August 2020, 5:00 PM
SPIE Link: here.

Alejandro V. Arzola
Direct visualization of the spin-orbit angular momentum conversion in optical trapping
25 August 2020, 10:40 AM
SPIE Link: here.

Isaac Lenton
Illuminating the complex behaviour of particles in optical traps with machine learning
26 August 2020, 9:10 AM
SPIE Link: here.

Fatemeh Kalantarifard
Optical trapping of microparticles and yeast cells at ultra-low intensity by intracavity nonlinear feedback forces
24 August 2020, 11:10 AM
SPIE Link: here.

Note: the presentation times are indicated according to PDT (Pacific Daylight Time) (GMT-7)

Characterisation of Physical Processes from Anomalous Diffusion Data, special issue on Journal of Physics A

Logo of the AnDi challenge.

Characterisation of Physical Processes from Anomalous Diffusion Data
Guest Editors
Miguel A Garcia-March, Maciej Lewenstein, Carlo Manzo, Ralf Metzler, Gorka Muñoz-Gil, Giovanni Volpe
Journal of Physics A: Mathematical and Theoretical
URL: Special Issue on Characterisation of Physical Processes from Anomalous Diffusion Data

In many systems, stochastic transport deviates from the standard laws of Brownian motion. Determining the exponent α characterising anomalous diffusion and identifying the physical origin of this behaviour are crucial steps to understanding the nature of the systems under observation. However, the determination of these properties from the analysis of the measured trajectories is often difficult, especially when these trajectories are short, irregularly sampled, or switching between different behaviours.

Over the last years, several methods have been proposed to quantify anomalous diffusion and the underlying physical process, going beyond the classical calculation of the mean squared displacement. More recently, the advent of machine learning has produced a boost in the methods to quantify anomalous diffusion.

The AnDi challenge aims at bringing together a vibrating and multidisciplinary community of scientists working on this problem. The use of the same reference datasets will allow an unbiased assessment of the performance of methods for characterising anomalous diffusion from single trajectories. This Special Issue will report on these approaches and their performance.

The deadline for submissions will be 30th June 2021 and you can submit manuscripts through ScholarOne Manuscripts. All papers will be refereed according to the usual high standards of the journal.

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.

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