Soft Matter Lab

Sofia Lundborg defended her Master Thesis on June 4, 2020. Congrats!

Sofia Lundborg defended her Master Thesis in Complex Adaptive Systems at Chalmers University of Technology on 4 June 2020. Congrats!

Screenshot of Sofia Lundborg’s Master Thesis defence.

Title: Training Binary Deep Neural Networks Using Knowledge Distillation

Binary networks can be used to speed up inference time and make image analysis possible on less powerful devices. When binarizing a network the accuracy drops.
The thesis aimed to investigate how the accuracy of a binary network can be improved by using knowledge distillation.
Three different knowledge distillation methods were tested for various network types. Additionally, different architectures of a residual block in ResNet were suggested and tested. Test on CIFAR10 showed an 1.5% increase in accuracy when using knowledge distillation and an increase of 1.1% when testing on ImageNet dataset. The results indicate that the suggested knowledge distillation method can improve the accuracy of a binary network. Further testing needs to be done to verify the results, especially longer training. However, there is great potential that knowledge distillation can be used to boost the accuracy of binary networks.

Master programme: MPCAS – Complex Adaptive Systems
Supervisor: Giovanni Volpe
Supervisors @ Bit Addict: Karl Svensson, Fredrik Ring and Niclas Wikström
Examiner: Giovanni Volpe
Opponent: Viktor Olsson, Wilhelm Tranheden

Time: June 4, 2020 at 15:00
Place: Online via Zoom

Dennis Kristiansson, Adrian Lundell, Fredrik Meisingseth, David Tonderski defended their Bachelor Thesis. Congrats!

Dennis Kristiansson, Adrian Lundell, Fredrik Meisingseth and David Tonderski defended their Bachelor Thesis at Chalmers University of Technology on 27 May 2020. Congrats!

Title: Deep learning for particle tracking

Abstract: The use of machine learning for classication has in recent years spread into a wide range of disciplines, amongst them the detection of particles for particle tracking on microscopy data. We modified the Python package DeepTrack, which makes use of deep learning to detect particles, creating a package called U-Track. By using a new network architecture based on a U-Net, better performance and higher computational efficiency than DeepTrack was achieved on images with multiple particles. Furthermore, functionality to track detected particles over series of frames was developed. The application of U-Track on experimental data from two-dimensional flow nanometry produced tracks consistent with theory, as well as tracking larger quantities of particles over longer periods of time compared to a digital filter based benchmark algorithm.

Supervisors: Daniel Midtvedt, Department of Physics, University of Gothenburg
Examiner: Lena Falk, Department of Physics, University of Gothenburg
Opponents: Patrik Wallin, Isak Pettersson, Alexei Orekhov, Anna Wisakanto

Place: Online Meeting
Time: 27 May, 2020, 9:00

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.

Giovanni Volpe awarded with the ERC Proof of Concept Grant

Giovanni Volpe has been awarded with the ERC Proof of Concept Grant for the research project LUCERO: Smart Optofluidic micromanipulation of Biological Samples.

The grant, consisting of 150k EUR, is meant to commercialize the research project LUCERO, providing an innovative method that combines artificial intelligence and optical tweezers to analyze cells easily and inexpensively.

The current technologies for cell analysis have many limitations: they require access to a large number of cells and considerable expertise. The available methods are also labor-intensive and in some cases the cells are destroyed.

The new method developed in LUCERO simplifies the work and lowers the costs of biomedical research by allowing ordinary standard microscopes, which are already in use in biomedical laboratories, to be used to perform the cell analysis.

The method of LUCERO can be used in several areas, from artificial insemination to forensic medicine. It has potentially a large commercial market.

Giovanni Volpe expects that LUCERO will provide around 20 jobs for university-trained experts and researchers within the next five years.

The project LUCERO has already received initial funding and support from two different organizations (Venture Cup and SPIE). Two doctoral students, Falko Schmidt and Martin B. Mojica, are part of LUCERO’s contributors team.

Links:
Press release of the Swedish Research Council: in English, in Swedish.
News on Gothenburg University website: in Swedish.

Gain-Assisted Optomechanical Position Locking of Metal/Dielectric Nanoshells in Optical Potentials published on ACS Photonics

Counter-propagating laser beam intensity, represented and projected on the yz plane.

Gain-Assisted Optomechanical Position Locking of Metal/Dielectric Nanoshells in Optical Potentials
Paolo Polimeno, Francesco Patti, Melissa Infusino, Jonathan Sánchez, Maria A. Iatì, Rosalba Saija, Giovanni Volpe, Onofrio M. Maragò & Alessandro Veltri
ACS Photonics 7(5), 1262–1270 (2020)
doi: https://doi.org/10.1021/acsphotonics.0c00213

We investigate gain-assisted optical forces on dye-enriched silver nanoshell in the quasi-static limit by means of a theoretical/numerical approach. We demonstrate the onset of nonlinear optical trapping of these resonant nanostructures in a counter-propagating Gaussian beam configuration. We study the optical forces and trapping behavior as a function of wavelength, particle gain level, and laser power. We support the theoretical analysis with Brownian dynamics simulations that show how particle position locking is achieved at high gains in extended optical trapping potentials. Finally, for wavelengths blue-detuned with respect to the plasmon-enhanced resonance, we observe particle channeling by the standing wave antinodes due to gradient force reversal. This work opens perspectives for gain-assisted optomechanics where nonlinear optical forces are finely tuned to efficiently trap, manipulate, channel, and deliver an externally controlled nanophotonic system.

Ordering of Binary Colloidal Crystals by Random Potentials published on Soft Matter

Ordering of Binary Colloidal Crystals by Random Potentials
André S. Nunes, Sabareesh K. P. Velu, Iryna Kasianiuk, Denys Kasyanyuk, Agnese Callegari, Giorgio Volpe, Margarida M. Telo da Gama, Giovanni Volpe & Nuno A. M. Araújo
Soft Matter 16, 4267-4273 (2020)
doi: https://doi.org/10.1039/D0SM00208A
arXiv: 1903.01579

Structural defects are ubiquitous in condensed matter, and not always a nuisance. For example, they underlie phenomena such as Anderson localization and hyperuniformity, and they are now being exploited to engineer novel materials. Here, we show experimentally that the density of structural defects in a 2D binary colloidal crystal can be engineered with a random potential. We generate the random potential using an optical speckle pattern, whose induced forces act strongly on one species of particles (strong particles) and weakly on the other (weak particles). Thus, the strong particles are more attracted to the randomly distributed local minima of the optical potential, leaving a trail of defects in the crystalline structure of the colloidal crystal. While, as expected, the crystalline ordering initially decreases with an increasing fraction of strong particles, the crystalline order is surprisingly recovered for sufficiently large fractions. We confirm our experimental results with particle-based simulations, which permit us to elucidate how this non-monotonic behavior results from the competition between the particle-potential and particle-particle interactions.

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.

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.

Invited talk by G. Volpe at Nanolight, Benasque, Spain, 8-14 March 2020

Giovanni Volpe will give an invited presentation at Nanolight 2020.

The conference, organized by Luis Martín Moreno (ICMA, CSIC – U. Zaragoza) and Niek van Hulst (ICFO, Barcelona), aims at the exploration of the frontiers in the field of subwavelength optics. It is meant to facilitate the interaction between worldwide researchers working in the field, with a special emphasis on interaction between young and more experienced researchers.
The conference is held in Benasque, Spain, from 8 to 14 March 2020.

The contributions of Giovanni Volpe will be presented according to the following schedule:

Giovanni Volpe
Deep Learning for Microscopy
Date: 12 March 2020
Time: 15:35 CET

Link: Nanolight 2020 program

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