Destructive effect of fluctuations on the performance of a Brownian gyrator on ArXiv

Angular velocity in the steady-state. (Excerpt from Fig. 2 of the manuscript.)
Destructive effect of fluctuations on the performance of a Brownian gyrator
Pascal Viot, Aykut Argun, Giovanni Volpe, Alberto Imparato, Lamberto Rondoni, Gleb Oshanin
arxiv: 2307.05248

The Brownian gyrator (BG) is a minimal model of a nano-engine performing a rotational motion, judging solely upon the fact that in non-equilibrium conditions its torque, angular momentum L and angular velocity W have non-zero mean values. For a time-discretized model, we calculate the previously unknown probability density functions (PDFs) of L and W. We find that when the time-step δt → 0, both PDFs converge to uniform distributions with diverging variances. For finite δt, the PDF of L has exponential tails and all moments, but its noise-to-signal ratio is generically much bigger than 1. The PDF of W exhibits heavy power-law tails and its mean W is the only existing moment. The BG is therefore not an engine in common sense: it does not exhibit regular rotations on each run and its fluctuations are not only a minor nuisance.
Our theoretical predictions are confirmed by numerical simulations and experimental data. We discuss some improvements of the model which may result in a more systematic behavior.

Roadmap for Optical Tweezers published in Journal of Physics: Photonics

Illustration of an optical tweezers holding a particle. (Image by A. Magazzù.)
Roadmap for optical tweezers
Giovanni Volpe, Onofrio M Maragò, Halina Rubinsztein-Dunlop, Giuseppe Pesce, Alexander B Stilgoe, Giorgio Volpe, Georgiy Tkachenko, Viet Giang Truong, Síle Nic Chormaic, Fatemeh Kalantarifard, Parviz Elahi, Mikael Käll, Agnese Callegari, Manuel I Marqués, Antonio A R Neves, Wendel L Moreira, Adriana Fontes, Carlos L Cesar, Rosalba Saija, Abir Saidi, Paul Beck, Jörg S Eismann, Peter Banzer, Thales F D Fernandes, Francesco Pedaci, Warwick P Bowen, Rahul Vaippully, Muruga Lokesh, Basudev Roy, Gregor Thalhammer-Thurner, Monika Ritsch-Marte, Laura Pérez García, Alejandro V Arzola, Isaac Pérez Castillo, Aykut Argun, Till M Muenker, Bart E Vos, Timo Betz, Ilaria Cristiani, Paolo Minzioni, Peter J Reece, Fan Wang, David McGloin, Justus C Ndukaife, Romain Quidant, Reece P Roberts, Cyril Laplane, Thomas Volz, Reuven Gordon, Dag Hanstorp, Javier Tello Marmolejo, Graham D Bruce, Kishan Dholakia, Tongcang Li, Oto Brzobohatý, Stephen H Simpson, Pavel Zemánek, Felix Ritort, Yael Roichman, Valeriia Bobkova, Raphael Wittkowski, Cornelia Denz, G V Pavan Kumar, Antonino Foti, Maria Grazia Donato, Pietro G Gucciardi, Lucia Gardini, Giulio Bianchi, Anatolii V Kashchuk, Marco Capitanio, Lynn Paterson, Philip H Jones, Kirstine Berg-Sørensen, Younes F Barooji, Lene B Oddershede, Pegah Pouladian, Daryl Preece, Caroline Beck Adiels, Anna Chiara De Luca, Alessandro Magazzù, David Bronte Ciriza, Maria Antonia Iatì, Grover A Swartzlander Jr
Journal of Physics: Photonics 2(2), 022501 (2023)
arXiv: 2206.13789
doi: 110.1088/2515-7647/acb57b

Optical tweezers are tools made of light that enable contactless pushing, trapping, and manipulation of objects, ranging from atoms to space light sails. Since the pioneering work by Arthur Ashkin in the 1970s, optical tweezers have evolved into sophisticated instruments and have been employed in a broad range of applications in the life sciences, physics, and engineering. These include accurate force and torque measurement at the femtonewton level, microrheology of complex fluids, single micro- and nano-particle spectroscopy, single-cell analysis, and statistical-physics experiments. This roadmap provides insights into current investigations involving optical forces and optical tweezers from their theoretical foundations to designs and setups. It also offers perspectives for applications to a wide range of research fields, from biophysics to space exploration.

Roadmap on Deep Learning for Microscopy on ArXiv

Spatio-temporal spectrum diagram of microscopy techniques and their applications. (Image by the Authors of the manuscript.)
Roadmap on Deep Learning for Microscopy
Giovanni Volpe, Carolina Wählby, Lei Tian, Michael Hecht, Artur Yakimovich, Kristina Monakhova, Laura Waller, Ivo F. Sbalzarini, Christopher A. Metzler, Mingyang Xie, Kevin Zhang, Isaac C.D. Lenton, Halina Rubinsztein-Dunlop, Daniel Brunner, Bijie Bai, Aydogan Ozcan, Daniel Midtvedt, Hao Wang, Nataša Sladoje, Joakim Lindblad, Jason T. Smith, Marien Ochoa, Margarida Barroso, Xavier Intes, Tong Qiu, Li-Yu Yu, Sixian You, Yongtao Liu, Maxim A. Ziatdinov, Sergei V. Kalinin, Arlo Sheridan, Uri Manor, Elias Nehme, Ofri Goldenberg, Yoav Shechtman, Henrik K. Moberg, Christoph Langhammer, Barbora Špačková, Saga Helgadottir, Benjamin Midtvedt, Aykut Argun, Tobias Thalheim, Frank Cichos, Stefano Bo, Lars Hubatsch, Jesus Pineda, Carlo Manzo, Harshith Bachimanchi, Erik Selander, Antoni Homs-Corbera, Martin Fränzl, Kevin de Haan, Yair Rivenson, Zofia Korczak, Caroline Beck Adiels, Mite Mijalkov, Dániel Veréb, Yu-Wei Chang, Joana B. Pereira, Damian Matuszewski, Gustaf Kylberg, Ida-Maria Sintorn, Juan C. Caicedo, Beth A Cimini, Muyinatu A. Lediju Bell, Bruno M. Saraiva, Guillaume Jacquemet, Ricardo Henriques, Wei Ouyang, Trang Le, Estibaliz Gómez-de-Mariscal, Daniel Sage, Arrate Muñoz-Barrutia, Ebba Josefson Lindqvist, Johanna Bergman
arXiv: 2303.03793

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.

Playing with Active Matter on ArXiv

One exemplar of the HEXBUGS used in the experiment. (Image by the Authors of the manuscript.)
Playing with Active Matter
Angelo Barona Balda, Aykut Argun, Agnese Callegari, Giovanni Volpe
arXiv: 2209.04168

In the last 20 years, active matter has been a very successful research field, bridging the fundamental physics of nonequilibrium thermodynamics with applications in robotics, biology, and medicine. This field deals with active particles, which, differently from passive Brownian particles, can harness energy to generate complex motions and emerging behaviors. Most active-matter experiments are performed with microscopic particles and require advanced microfabrication and microscopy techniques. Here, we propose some macroscopic experiments with active matter employing commercially available toy robots, i.e., the Hexbugs. We demonstrate how they can be easily modified to perform regular and chiral active Brownian motion. We also show that Hexbugs can interact with passive objects present in their environment and, depending on their shape, set them in motion and rotation. Furthermore, we show that, by introducing obstacles in the environment, we can sort the robots based on their motility and chirality. Finally, we demonstrate the emergence of Casimir-like activity-induced attraction between planar objects in the presence of active particles in the environment.

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.