Tag: Aykut Argun

Soft Matter Lab’s presentations at OSA-OMA 2021

The Soft Matter Lab is involved in six presentations at the OSA Biophotonic Congress: Optics in the Life Sciences 2021, topical meeting of Optical Manipulation and its Applications.
Moreover, three of the presentations were selected as finalists for the best student paper in the topical meeting of Optical Manipulation and its Applications.

You can find the details below:

12 April

15 April

16 April

  • 16:15 CEST
    Calibration of Force Fields Using Recurrent Neural Networks (AF2D.4)
    Aykut Argun, University of Gothenburg

Quantitative Digital Microscopy with Deep Learning published in Applied Physics Reviews

Particle tracking and characterization in terms of radius and refractive index.

Quantitative Digital Microscopy with Deep Learning
Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, Giovanni Volpe
Applied Physics Reviews 8, 011310 (2021)
doi: 10.1063/5.0034891
arXiv: 2010.08260

Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions. To overcome this issue, we introduce a software, DeepTrack 2.0, to design, train and validate deep-learning solutions for digital microscopy. We use it to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking and characterization to cell counting and classification. Thanks to its user-friendly graphical interface, DeepTrack 2.0 can be easily customized for user-specific applications, and, thanks to its open-source object-oriented programming, it can be easily expanded to add features and functionalities, potentially introducing deep-learning-enhanced video microscopy to a far wider audience.

Aykut Argun’s team wins in four categories of the ANDI challenge

Aykut Argun (Soft Matter Lab) and Stefano Bo (MPI Dresden) participated in the AnDi Challenge, the Anomalous Diffusion challenge, in all the nine categories.

The challenge consisted of different tasks, specifically:

  • Task 1 – Inference of the anomalous diffusion exponent α.
  • Task 2 – Classification of the diffusion model.
  • Task 3 – Segmentation of trajectories.

Each task included modalities for different number of dimensions (1D, 2D and 3D), for a total of 9 subtasks.

Approximately 20 teams from all the world participated in the challenge.

Aykut’s and Stefano’s team, eduN, ranked in the first three positions in all the categories. EduN won the 1st place in 4 of the categories, i.e., Task 2 (1D and 2D), and Task 3 (1D and 3D), the 2nd place in another 4 categories, and 3rd in the remaining category.

The details and the information about the final results can be found on ANDI Challenge final results page: http://www.andi-challenge.org/ (select: Learn the Details and then Final Results)

Here the link to the video of the announcement.

Enhanced force-field calibration via machine learning featured in AIP SciLight

The article Enhanced force-field calibration via machine learning
has been featured in: “Machine Learning Outperforms Standard Force-Field Calibration Techniques”, AIP SciLight (November 6, 2020).

Scilight showcases the most interesting research across the physical sciences published in AIP Publishing Journals.

Scilight is published weekly (52 issues per year) by AIP Publishing.

Enhanced force-field calibration via machine learning published in Applied Physics Reviews

Representation of a particle in a force field
Enhanced force-field calibration via machine learning
Aykut Argun, Tobias Thalheim, Stefano Bo, Frank Cichos, Giovanni Volpe
Applied Physics Reviews 7, 041404 (2020)
doi: 10.1063/5.0019105
arXiv: 2006.08963

The influence of microscopic force fields on the motion of Brownian particles plays a fundamental role in a broad range of fields, including soft matter, biophysics, and active matter. Often, the experimental calibration of these force fields relies on the analysis of the trajectories of these Brownian particles. However, such an analysis is not always straightforward, especially if the underlying force fields are non-conservative or time-varying, driving the system out of thermodynamic equilibrium. Here, we introduce a toolbox to calibrate microscopic force fields by analyzing the trajectories of a Brownian particle using machine learning, namely recurrent neural networks. We demonstrate that this machine-learning approach outperforms standard methods when characterizing the force fields generated by harmonic potentials if the available data are limited. More importantly, it provides a tool to calibrate force fields in situations for which there are no standard methods, such as non-conservative and time-varying force fields. In order to make this method readily available for other users, we provide a Python software package named DeepCalib, which can be easily personalized and optimized for specific applications.

Funding:

ERC-founder H2020 European Research Council (ERC) Starting Grant ComplexSwimmers (677511).

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

Seven members of the Soft Matter Lab (Saga Helgadottir, Benjamin Midtvedt, Aykut Argun, Laura Pérez-Garcia, Daniel Midtvedt, Harshith Bachimanchi, Emiliano 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)

Enhanced force-field calibration via machine learning

Enhanced force-field calibration via machine learning
Aykut Argun, Tobias Thalheim, Stefano Bo, Frank Cichos, Giovanni Volpe

Click here to see the slides.
Twitter Link: here.

The influence of microscopic force fields on the motion of Brownian particles plays a fundamental role in a broad range of fields, including soft matter, biophysics, and active matter. Often, the experimental calibration of these force fields relies on the analysis of the trajectories of these Brownian particles. However, such an analysis is not always straightforward, especially if the underlying force fields are non-conservative or time-varying, driving the system out of thermodynamic equilibrium. Here, we introduce a toolbox to calibrate microscopic force fields by analyzing the trajectories of a Brownian particle using machine learning, namely recurrent neural networks. We demonstrate that this machine-learning approach outperforms standard methods when characterizing the force fields generated by harmonic potentials if the available data are limited. More importantly, it provides a tool to calibrate force fields in situations for which there are no standard methods, such as non-conservative and time-varying force fields. In order to make this method readily available for other users, we provide a Python software package named DeepCalib, which can be easily personalized and optimized for specific applications.

Poster Session
Time: June 22nd 2020
Place: Twitter

POM Conference
Link: POM
Time: June 25th 2020
Place: Online

Poster Slides

Aykut Argun – POM Poster – Page 1
Aykut Argun – POM Poster – Page 2
Aykut Argun – POM Poster – Page 3
Aykut Argun – POM Poster – Page 4

Soft Matter Lab presentations at the Photonics Online Meet-up, 22 June 2020

Six members of the Soft Matter Lab (Aykut Argun, Falko Schmidt, Laura Pérez-Garcia, Saga Helgadottir, Alessandro Magazzù, Daniel Midtvedt) were selected for poster presentations at the Photonics Online Meet-up (POM).

POM is an entirely free virtual conference. It aims to bring together a community of early career and established researchers from universities, industry, and government working in optics and photonics.

The meeting, at its second edition, will be held on June 25th 2020, 9-14.30 Central European Time. The virtual poster session will take place on June 22nd, on Twitter and virtual reality.

The poster contributions being presented are:

Aykut Argun
Enhanced force-field calibration via machine learning
Twitter Link: here.

Falko Schmidt
Dynamics of an active nanoparticle in an optical trap
Twitter Link: here.

Laura Pérez-García
Optical force field reconstruction using Brownian trajectories
Twitter Link: here.

Saga Helgadottir
DeepTrack: A comprehensive deep learning framework for digital microscopy
Twitter Link: here.

Alessandro Magazzù
Controlling the dynamics of colloidal particles by critical Casimir forces
Twitter Link: here.

Daniel Midtvedt
Holographic characterisation of subwavelength particles enhanced by deep learning
Twitter Link: here.

Link: Photonics Online Meet-up (POM)

DeepTrack selected by Optics & Photonics News as one of the most exciting optics discoveries in 2019

Optics & Photonics News has picked Saga Helgadóttir and Aykut Argun’s work on deep learning for particle tracking (DeepTrack) as a top break-through of the year.

“This has been a really good year for me, research-wise. My publication, presenting a new AI method, garnered a lot of attention,” says Saga Helgadóttir, PhD at the Department of Physics.

The research article in question, which is now included in Optics & Photonics News’ best-of-2019 list, identifies a new way of implementing neural networks and machine learning in order to track particle motion and study surrounding microenvironments.

After the publication in mid-April, Saga Helgadóttir was contacted by both national and international press to talk about her discoveries. She has also been invited to visit research groups abroad and was a speaker at the AI in Health and Health in AI conference held in Gothenburg in August.

Currently, Saga Helgadottir is collaborating with a group of scientists at Sahlgrenska’s Wallenberg Laboratory. They are working on new ways of using deep learning in the medical field.

“I started my PhD research studying bio-hybrid microswimmers, but ended up more within the area of artificial intelligence and optics. I like this work a lot, and the positive response to my publication earlier this year has allowed me to establish myself in the AI-field.”

Text: Carolina Svensson

List of highlighted research from 2019: Optics in 2019

Saga Helgadottir’s featured summary: Deep Learning for Particle Tracking

Original press release about the research: She has discovered a new method of using AI

Presentation by A. Argun at OSA Life Sciences Conference, Tucson, 14-17 April 2019

Statistics of Brownian particles held in non-harmonic potentials in an active bath

Aykut Argun and Giovanni Volpe
OSA Life Sciences Conference,
Tucson, 14-17 April 2019

Abstract: 

Active systems are subject to persistent noise that arise from biological media or artificial activity like self-propelled particles. Therefore, these systems are  intrinsically out of equilibrium and can only be studied within the framework of non-equilibrium physics. So far, steady-state behavior and dynamical fluctuations of Brownian particles in active baths have been investigated both theoretically and experimentally. While some of the equilibrium properties can be retained by using an effective temperature, for most systems this generalization is not possible. Here, we extend the existing studies to non-harmonic potential cases, where other qualitative distinctions of the active matter emerge.