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Hillevi Wachtmeister to defend her Master Thesis on June 11, 2020

Hillevi Wachmeister will defend her Master Thesis in Physics at Chalmers University of Technology on 11 June 2020

Title: Tracking marine micro organisms using deep learning

The goal of this project is to develop a software that can be used to study swimming patterns of marine micro organisms. The software is based on a neural network, which is trained to recognize different types of plankton. The predictions from the network are then used to find the positions of the plankton, and then track their movements.

The project is divided into two parts. First, videos containing only one type of plankton, Lingulodinium polyedra and Alexandrium tamarense respectively, are analyzed. A type of neural network, called U-net, is trained to segment the input images into background and plankton sections. From the segmented images, positions can be obtained and then connected to form a trajectory for each plankton. The drift of the plankton movements is calculated and subtracted from the trajectories, and finally the speed and net displacement is calculated. The results from the single plankton experiments are compared to a previous analysis that was made using the algorithmic method TrackMate.

Secondly, videos containing two types of plankton are analyzed. Two experiments are conducted using Strombidium arenicola and Rhodomonas baltica in the first experiment, and Alexandrium tamarense and Rhodomonas baltica in the second. The segmented images, obtained from the U-net, consists of an additional plankton section for the second type of plankton present in the experiment.

The analysis of the single plankton experiments yields longer and fewer trajectories using the U-net method, compared to the previous TrackMate results. This shows that the TrackMate method is losing plankton at more positions, compared to the U-net method. The U-net method is therefore able to track each plankton for a longer time. The multi-plankton experiments proves the network’s ability to distinguish and track multiple plankton at the same time.

Master programme: MPPHYS – Physics
Supervisor: Daniel Midtvedt
Examiner: Giovanni Volpe
Opponent: Frida Eriksson

Date: 11 June 2020, 9:00
Place: Nexus + Online via Zoom

Sofia Lundborg to defend her Master Thesis on June 4, 2020

Sofia Lundborg will defend her Master Thesis in Complex Adaptive Systems at Chalmers University of Technology on 4 June 2020.

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

Tobias Sandström and Lars Jansson to defend their Master Thesis on 15 June, 2020

Tobias Sandström and Lars Jansson will defend their Master Thesis in Complex Adaptive Systems at Chalmers University of Technology on 15 June 2020.

Title: Graph Convolutional Neural Networks for Brain Connectivity Analysis​​

We explore the strengths and limitations of Graph Convolutional Neural Networks (GCNs) for classification of graph structured data. GCNs differs from regular Artificial Neural Networks (ANNs) in that they operate directly on graph structures by defining convolutional operators in a non-euclidean space. We show that GCNs perform well on graph structured data, where regular ANNs typically fail due to the arbitrary ordering of nodes. Different GCN architectures are examined and compared to simplistic ANNs. Tests are initially performed on simulated data sets with implicit class-dissimilarities in regards to graph structures. We demonstrate that GCNs is vital in accurately classifying the simulated data. Network performance is later evaluated on structured MRI-data, displaying cortical thicknesses for 68 regions in the brain of patients with Alzheimer’s disease and a healthy control group. On the structured MRI-data, both GCNs and regular ANNs are shown to be able classifiers. However, it is crucial for the performance of ANNs that an order of nodes can be imposed on the MRI-data from labeled brain regions.

Supervisors: Jonas Andersson & Alice Deimante Neimantaite, Syntronic AB
Examiner: Giovanni Volpe, Department of Physics, University of Gothenburg
Opponent: Jonathan Bergqvist

Time: 15 June, 2020, 14:00
Place: Online via Zoom

Benjamin Midtvedt to defend his Master Thesis on June 15, 2020

Benjamin Midtvedt will defend his Master Thesis in Engineering Mathematics and Computer Science at Chalmers University of Technology on 15 June 2020.

Title: DeepTrack: A comprehensive deep learning framework for digital microscopy

Despite the rapid advancement of deep-learning methods for image analysis, they remain underutilized for the analysis of microscopy images. State of the art methods require expertise in deep-learning to implement, disconnecting the development of new methods from end-users. The packages that are available are typically highly specialized, challenging to reappropriate, and almost impossible to interface with other methods. Finally, training deep-learning models often requires large datasets of manually annotated images, making it prohibitively difficult to procure training data that accurately represents the problem.

DeepTrack is a deep-learning framework targeting optical microscopy, designed to account for each of these issues. Firstly, it is packaged with an easy-to-use graphical user interface, solving standard microscopy problems with no required programming experience. Secondly, it bypasses the need for manually annotated experimental data by providing a comprehensive programming API for creating representative synthetic data, designed to exactly suit the problem. DeepTrack creates physical simulations of samples described by refractive index or fluorophore distributions, using fully customizable optical systems. To accurately represent the data to be analyzed, DeepTrack supports arbitrary optical aberration and experimental noise. Thirdly, many standard deep-learning methods are packaged with DeepTrack, including architectures such as U-NET, and regularization techniques such as augmentations, decreasing the barrier to entry. Finally, the framework is fully modular and easily extendable to implement new methods, providing both longevity and a centralized foundation to deploy new deep-learning solutions.

We demonstrate the versatility of DeepTrack by training networks to solve a broad range of common microscopy problems, including particle tracking, cell-counting in dense biological samples, multi-particle 3-dimensional tracking, and cell segmentation and classification.

Master Programme: Engineering Mathematics and Computer Science
Supervisor: Giovanni Volpe
Examiner: Giovanni Volpe
Opponents: Aykut Argun and Saga Helgadóttir

Time: 15 June 2020, 16:00
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.

Machine learning reveals complex behaviours in optically trapped particles on ArXiv

Illustration of a fully connected neural network with three inputs, three outputs, and three hidden layers.

Machine learning reveals complex behaviours in optically trapped particles
Isaac C. D. Lenton, Giovanni Volpe, Alexander B. Stilgoe, Timo A. Nieminen & Halina Rubinsztein-Dunlop
arXiv: 2004.08264

Since their invention in the 1980s, optical tweezers have found a wide range of applications, from biophotonics and mechanobiology to microscopy and optomechanics. Simulations of the motion of microscopic particles held by optical tweezers are often required to explore complex phenomena and to interpret experimental data. For the sake of computational efficiency, these simulations usually model the optical tweezers as an harmonic potential. However, more physically-accurate optical-scattering models are required to accurately model more onerous systems; this is especially true for optical traps generated with complex fields. Although accurate, these models tend to be prohibitively slow for problems with more than one or two degrees of freedom (DoF), which has limited their broad adoption. Here, we demonstrate that machine learning permits one to combine the speed of the harmonic model with the accuracy of optical-scattering models. Specifically, we show that a neural network can be trained to rapidly and accurately predict the optical forces acting on a microscopic particle. We demonstrate the utility of this approach on two phenomena that are prohibitively slow to accurately simulate otherwise: the escape dynamics of swelling microparticles in an optical trap, and the rotation rates of particles in a superposition of beams with opposite orbital angular momenta. Thanks to its high speed and accuracy, this method can greatly enhance the range of phenomena that can be efficiently simulated and studied.

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 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.

Optical Tweezers: A Comprehensive Tutorial from Calibration to Applications on ArXiv

Schematic of a bistable potential generated with a double-beam optical tweezers.

Optical Tweezers: A Comprehensive Tutorial from Calibration to Applications
Jan Gieseler, Juan Ruben Gomez-Solano, Alessandro Magazzù, Isaac Pérez Castillo, Laura Pérez García, Marta Gironella-Torrent, Xavier Viader-Godoy, Felix Ritort, Giuseppe Pesce, Alejandro V. Arzola, Karen Volke-Sepulveda & Giovanni Volpe
arXiv: 2004.05246

Since their invention in 1986 by Arthur Ashkin and colleagues, optical tweezers have become an essential tool in several fields of physics, spectroscopy, biology, nanotechnology, and thermodynamics. In this Tutorial, we provide a primer on how to calibrate optical tweezers and how to use them for advanced applications. After a brief general introduction on optical tweezers, we focus on describing and comparing the various available calibration techniques. Then, we discuss some cutting-edge applications of optical tweezers in a liquid medium, namely to study single-molecule and single-cell mechanics, microrheology, colloidal interactions, statistical physics, and transport phenomena. Finally, we consider optical tweezers in vacuum, where the absence of a viscous medium offers vastly different dynamics and presents new challenges. We conclude with some perspectives for the field and the future application of optical tweezers. This Tutorial provides both a step-by-step guide ideal for non-specialists entering the field and a comprehensive manual of advanced techniques useful for expert practitioners. All the examples are complemented by the sample data and software necessary to reproduce them.