Martin Selin presented his half-time seminar on 2 September 2022

Martin Selin’s half-time seminar: Opponent Dag Hanstorp (left), Martin Selin (right). (Photo by H. P. Tanabalan.)
Martin Selin completed the first half of his doctoral studies and defended his half-time on the 2nd of September 2022.

The presentation was held in hybrid format, with part of the audience in the Von Bahr room and the rest connected through zoom. The half-time consisted of a presentation of Martins two main projects followed by a discussion and questions proposed by Martins opponent Dag Hanstorp.

The presentation started providing a background on optical tweezers and continued with the ongoing project of positioning quantum dots using optical tweezers. Thereafter the presentation continued with the Minitweezers project. Data on DNA stretching was presented and shown to be in good agreement with results found in literature. Lastly the future of the two projects were outlined. Specifically, how to address the challenging task of detecting moving quantum dots and how to improve on the Minitweezers system through automation.

Martin Selin during his half-time seminar. (Photo by L. Natali.)

Soft Matter Lab members present at SPIE Optics+Photonics conference in San Diego, 21-25 August 2022

The Soft Matter Lab participates to the SPIE Optics+Photonics conference in San Diego, CA, USA, 21-25 August 2022, with the presentations listed below.

Giovanni Volpe is also co-author of the presentations:

Presentation by L. Pérez García at OSA-OMA-2021

FORMA allows to identify and characterize all the equilibrium points in a force field generated by a speckle pattern.
FORMA and BEFORE: Expanding Applications of Optical Tweezers. Laura Pérez Garcia, Martin Selin, Alejandro V. Arzola, Giovanni Volpe, Alessandro Magazzù, Isaac Pérez Castillo.
Submitted to OSA-OMA 2021,  ATh1D.5
Date: 15 April
Time: 15:45 (CEST)

FORMA (force reconstruction via maximum-likelihood-estimator analysis) addresses the need to measure the force fields acting on microscopic particles. Compared to alternative established methods, FORMA is faster, simpler, more accurate, and more precise. Furthermore, FORMA can also measure non-conservative and out-of-equilibrium force fields. Here, after a brief introduction to FORMA, I will present its use, advantages, and limitations. I will conclude with the most recent work where we exploit Bayesian inference to expand FORMA’s scope of application.

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.

Influence of Sensorial Delay on Clustering and Swarming published in Phys. Rev. E

Influence of Sensorial Delay on Clustering and Swarming

Influence of Sensorial Delay on Clustering and Swarming
Rafal Piwowarczyk, Martin Selin, Thomas Ihle & Giovanni Volpe
Physical Review E 100(1), 012607 (2019)
doi: 10.1103/PhysRevE.100.012607
arXiv:  1803.06026

We show that sensorial delay alters the collective motion of self-propelling agents with aligning interactions: In a two-dimensional Vicsek model, short delays enhance the emergence of clusters and swarms, while long or negative delays prevent their formation. In order to quantify this phenomenon, we introduce a global clustering parameter based on the Voronoi tessellation, which permits us to efficiently measure the formation of clusters. Thanks to its simplicity, sensorial delay might already play a role in the organization of living organisms and can provide a powerful tool to engineer and dynamically tune the behavior of large ensembles of autonomous robots.

Martin Selin defended his Master Thesis. Congrats!

Martin Selin defended his Master thesis in Physics at Chalmers University of Technology on 5 June 2019

Title: Growing Artificial Neural Networks. Novel approaches to Deep Learning for Image Analysis and Particle Tracking

Deep-learning has recently emerged as one of the most successful methods for an- alyzing large amounts of data and constructing models from it. It has virtually revolutionized the field of image analysis and the algorithms are now being employed in research field outside of computer science. The methods do however suffer from several drawbacks such as large computational costs.

In this thesis alternative methods for training the networks underlying networks are evaluated based on gradually growing networks during training using layer-by- layer training as well as a method based on increasing network width dubbed breadth training.

These training methods lends themselves to easily implementing networks of tune- able size allowing for choice between high accuracy or fast execution or the construc- tion of modular network in which one can chose to execute only a small part of the network to get a very fast prediction at the cost of some accuracy. The layer-by-layer method is applied to multiple different image analysis tasks and the performance is evaluated and compared to that of regular training. Both the layer by layer training and the breadth training comparable to normal training in performance and in some cases slightly superior while in others slightly inferior. The modular nature of the networks make them suitable for applications within multi-particle tracking.

​Name of the master programme: MPPHYS – Physics
Supervisor: Giovanni Volpe, Department of Physics, University of Gothenburg
Examiner: Giovanni Volpe, Department of Physics, University of Gothenburg
Opponent: Henry Yang, MP Complex Adaptive Systems, Department of Physics, Chalmers University of Technology

Place: Raven & Fox room
Time: 5 June, 2019, 15:00

Lovisa Hagstöm, Erik Holmberg, Eliza Nordén, Teodor Norrestad, Martin Selin & Lisa Sjöblom defended their Bachelor Thesis. Congrats!

Lovisa Hagstöm, Erik Holmberg, Eliza Nordén, Teodor Norrestad, Martin Selin & Lisa Sjöblom defended their Bachelor Thesis at Chambers University of Technology on 23 May 2017.

Title: Autonoma agenter i komplexa miljöer — En studie av tidsfördröjningens inverkan på kollektiva beteenden

Abstract: Interagerande autonoma agenter är ett högintressant och relativt outforskat område. Syftet med detta arbete är att utforska grundläggande metoder för att simulera aktiva agenter som påverkas av ett intensitetsfält med en fördröjning. Fördröjningen mellan agentens indata och dess reaktion på denna visar sig vara väsentlig vad gäller styrandet av dess beteende. Efter att de grundläggande metoderna är etablerade ämnar återstoden av arbetet att fördjupa sig i tre olika aspekter av autonoma agenter. Den rotationella diffusionskoefficienten, DR, visar sig vara en parameter som likt farten kan användas för att styra agenternas beteende. Dock syns inga kvalitativa skillnader i beteendet om inte en fördröjning införs. Med en positiv fördröjning söker sig agenterna till områden med stort DR och med en negativ söker de sig till områden med litet DR. Intressanta beteenden framkallas också genom att låta en aktiv agent röra sig i en propagerande vågpotential, både i en och två dimensioner. För det endimensionella vågfallet kan man med hjälp av fördröjningen styra om agenten färdas mot eller från vågkällan. Agenter som interagerar via tvådimensionella vågpulser kan manipuleras till att samlas eller sprida sig, beroende på fördröjningens karaktär. Slutligen utreds möjligheterna att använda autonoma aktiva agenter för att simulera rovdjur och bytesdjur. För att realisera detta används fördröjningen som styrande parameter. Utöver detta utvecklas en enkel evolutionsalgoritm där byten och rovdjur visar sig kunna anpassa sig efter varandra. Fördröjningar visar sig överlag vara ett kraftfullt verktyg för att påverka beteendet hos aktiva agenter med stor potential i framtida applikationer.

Supervisor: Giovanni Volpe, Department of Physics, University of Gothenburg
Examiner: Lena Falk, Department of Physics, University of Gothenburg