Book “Simulation of Complex Systems” published at IOP

Book cover. (From the IOP website.)
The book Simulation of Complex Systems, authored by Aykut Argun, Agnese Callegari and Giovanni Volpe, has been published by IOP in December 2021.

The book is available for the students of Gothenburg University and Chalmers University of Technology through the library service of each institution.
The example codes presented in the book can be found on GitHub.

Links
@ IOP Publishing

@ Amazon.com

Citation 
Aykut Argun, Agnese Callegari & Giovanni Volpe. Simulation of Complex Systems. IOP Publishing, 2022.
ISBN: 9780750338417 (Hardback) 9780750338431 (Ebook).

Press release on Objective comparison of methods to decode anomalous diffusion

The article Objective comparison of methods to decode anomalous diffusion has been featured in the News of the University of Gothenburg.

The study, published in Nature Communications and co-written by researchers at the Soft Matter Lab of the Department of Physics at the University of Gothenburg, originates from the AnDi Challenge, a competition co-organised by Giovanni Volpe with researchers from University of Vic – Central University of Catalunya, Institute of Photonic Sciences in Barcelona, University of Potsdam, and Valencia Polytechnic University.

The challenge was held during March–November 2020 and consisted of three main tasks concerning anomalous exponent inference, model classification, and trajectory segmentation. The goal was to provide an objective assessment of the performance of methods to characterise anomalous diffusion from single trajectories.

Here the links to the press releases:
English: A scientific competition led to improved methods for analysing the diffusion of particles.
Swedish: En vetenskaplig tävling ledde till förbättrade metoder för att analysera diffusion av partiklar.

Objective comparison of methods to decode anomalous diffusion published in Nature Communications

An illustration of anomalous diffusion. (Image by Gorka Muñoz-Gil.)
Objective comparison of methods to decode anomalous diffusion
Gorka Muñoz-Gil, Giovanni Volpe, Miguel Angel Garcia-March, Erez Aghion, Aykut Argun, Chang Beom Hong, Tom Bland, Stefano Bo, J. Alberto Conejero, Nicolás 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, Janusz Szwabiński, Samudrajit Thapa, Hippolyte Verdier, Giorgio Volpe, Arthur Widera, Maciej Lewenstein, Ralf Metzler, and Carlo Manzo
Nat. Commun. 12, Article number: 6253 (2021)
doi: 10.1038/s41467-021-26320-w
arXiv: 2105.06766

Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers.

Aykut Argun joins as postdoc the Soft Matter Lab

Aykut Argun starts his postdoc at the Physics Department of the University of Gothenburg on 21st September 2021.

Aykut has a PhD degree in Physics from the University of Gothenburg, Sweden.

During his postdoc, Aykut will continue his work on analysing stochastic trajectories with machine learning as well as experimental active matter systems.

Classification, inference and segmentation of anomalous diffusion with recurrent neural networks published in Journal of Physics A: Mathematical and Theoretical

RANDI architecture to classify the model underlying anomalous diffusion.
Classification, inference and segmentation of anomalous diffusion with recurrent neural networks
Aykut Argun, Giovanni Volpe, Stefano Bo
J. Phys. A: Math. Theor. 54 294003 (2021)
doi: 10.1088/1751-8121/ac070a
arXiv: 2104.00553

Countless systems in biology, physics, and finance undergo diffusive dynamics. Many of these systems, including biomolecules inside cells, active matter systems and foraging animals, exhibit anomalous dynamics where the growth of the mean squared displacement with time follows a power law with an exponent that deviates from 1. When studying time series recording the evolution of these systems, it is crucial to precisely measure the anomalous exponent and confidently identify the mechanisms responsible for anomalous diffusion. These tasks can be overwhelmingly difficult when only few short trajectories are available, a situation that is common in the study of non-equilibrium and living systems. Here, we present a data-driven method to analyze single anomalous diffusion trajectories employing recurrent neural networks, which we name RANDI. We show that our method can successfully infer the anomalous exponent, identify the type of anomalous diffusion process, and segment the trajectories of systems switching between different behaviors. We benchmark our performance against the state-of-the art techniques for the study of single short trajectories that participated in the Anomalous Diffusion (AnDi) challenge. Our method proved to be the most versatile method, being the only one to consistently rank in the top 3 for all tasks proposed in the AnDi challenge.

Aykut Argun defended his PhD Thesis in Physics on 14 June 2021. Congrats!

(Image by Aykut Argun)
Aykut Argun defended his Ph.D. thesis on June 14, 2021, at 2 pm CEST. Congrats!

The details of the presentation can be found below. The link to the webinar is announced on the faculty website.

Title: Thermodynamics of microscopic environments: From anomalous diffusion to heat engines.

Abstract:
Unlike their macroscopic counterparts, microscopic systems do not evolve deterministically due to the thermal noise becoming prominent. Such systems are subject to fluctuations that can only be studied within the framework of stochastic thermodynamics. Within the last few decades, the development of stochastic thermodynamics has lead to microscopic heat engines, nonequilibrium relations and the study of anomalous diffusion and active Brownian motion. In this thesis, I experimentally show that the non-Boltzmann statistics emerge in systems that are coupled to an active bath. These non-Boltzmann statistics that result from correlated active noise also disturb the nonequilibrium relations. Nevertheless, I show that these relations can be recovered using an effective potential approach. Next, I demonstrate an experimental realization of a microscopic heat engine. This engine is referred to as the Brownian gyrator, which is coupled to two different heat baths along perpendicular directions. I show that when confined into an elliptical trap that is not aligned with the temperature anisotropy, the Brownian particle is subject to a torque due to the symmetry breaking. This torque creates an autonomous engine whose direction and amplitude can be controlled by tuning the alignment of the elliptical trap. Then, I show that the force fields acting on Brownian particles can be calibrated using a data-driven method that outperforms the existing calibration methods. More importantly, I show that this method, named DeepCalib, can calibrate non-conservative and time-varying force fields that no standard calibration methods exist. Finally, I show that a similar machine-learning-based approach can be used to characterize anomalous diffusion from single trajectories. This method, named RANDI, is very versatile and performs very well in various tasks including classification, inference and segmentation of anomalous diffusion. The work presented in this thesis presents novel experiments that advance microscopic thermodynamics as well as newly developed methods that open up new possibilities in analyzing stochastic trajectories. These findings increased the scientific knowledge at the nexus between microscopic thermodynamics, anomalous diffusion, active matter and machine learning.

Supervisor: Giovanni Volpe
Co-supervisors: Joakim Stenhammar, Mattias Goksör
Examiner: Bernhard Mehlig
Opponent: Juan M. R. Parrondo
Committee: Monika Ritsch-Marte, Sabine H. L. Klapp, Édgar Roldán

Screenshots from Aykut Argun’s PhD Thesis defense.

PhD Thesis Committee, Supervisor, Co-Supervisor, Opponent, and GU Physics Department Chair.
PhD Thesis Committee, Supervisor, Opponent, and GU Physics Department Chair.
PhD Opponent presentation.
PhD Thesis presentation starts.
PhD Thesis front slide.
PhD Thesis content slide.
PhD Thesis final acknowledgment slide.
PhD Thesis final acknowledgment slide.

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.