About mid-way through his PhD, Falko Schmidt presented his past research activities and gave an outlook on his future projects. The topics range from miniaturised machines to self-assembled active molecules activated by light to machine-learning techniques to better characterise dynamical behaviour of microscopic systems.
The seminar will be held at the Department of Physics at Gothenburg University, June 10th 2019 starting at 12:15 p.m.
Light driven colloidal micro swimmers Seminar by Juliane Simmchen
from TU Dresden, Germany
In the last decade the generation of motion on the microscale has evolved into a fascinating field of modern science. We have learned to activate and control Janus particles in a regime dominated by low Reynolds numbers, where motion is not influenced by inertia. This implements several principles to take into account for the engineering of artificial microswimmers and often meant that toxic fuels had to be used to achieve propulsion. To move one step further towards possible applications in the environmental or biomedical field, we are now using light sensitive materials to explore new propulsion strategies.
Place: Soliden 3rd floor Time: 11 June 2019, 10:00
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
Adrian Leidegren defended his Master thesis in Physics at the University of Gothenburg on 5 June 2019
Title: Estimating the validity of synthetic data using neural network ensembles
The use of synthetic data has the potential to yield unlimited amounts of resources for use in training neural networks. This is however contin- gent on finding the right parameters to use with the data-generating system. As a worst-case scenario this would be done by careful guesswork. Herein is presented an alternative that has the potential to automate this work. The Deeptrack system for particle tracking in digital video microscopy was used as a framework, due to its ability to generate synthetic data from a handful of parameters. An ensemble was trained according to one set of parameter values and tested against a set of test data generated by the same parameters except for one, which was made to vary over a wide range. To contrast this, using a new parameter set, another set of test data was generated alongside several ensembles where one parameter was varied for each ensemble’s training data.
It was found that the limiting density of discreet points as a function of the vary- ing parameter had a local minimum around the region where the variable matched the same parameter’s value in the other data set, be it training or testing. This shows the possibility of using ensembles of neural networks to identify the most suitable parameter values in Deeptrack to ensure that the synthetic training data is represen- tative of the laboratory test data. There may also be a wider use case to this technique as a means of estimating confidence in the networks’ predictions.
Name of the master programme: Physics Supervisor: Saga Helgadottir, Department of Physics, University of Gothenburg Examiner: Giovanni Volpe, Department of Physics, University of Gothenburg
Theo Berglin & Adam Liberda defended their Master thesis in Complex Adaptive Systems at Chalmers University of Technology on 31 May 2019
Title: Generation of random connectivity matrices for BRAPH
The proportion of the population suffering from neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease is increasing. Recent studies suggest that graph theory measures can be used as biomarkers in early stages of the diseases enabling researches to study spread and aiding inhibiting drug discovery. BRAPH is an object oriented and easy to use software for analyzing brain connectivity using graph theory. A bottleneck in the analyzis of brain connectivity using graph theory is a degree, strength and weight preserving randomization function. We have developed a new method with a speed improvement in the magnitude of 105 to 106 compared with the original method of Brain Connectivity Toolbox (BCT). The method is working for graphs consisting of up to 105 nodes and is verified to be equally random as the original method of BCT for sparse graphs. A speed improvement in the magnitude of 106 is equal to going from 1000 years to 8 hours.
Name of the master programme: MPCAS – Complex Adaptive Systems Supervisor: Giovanni Volpe, Department of Physics, University of Gothenburg Examiner: Giovanni Volpe, Department of Physics, University of Gothenburg Opponent: Jens Wilhelmsson, MP Complex Adaptive Systems, Department of Physics, Chalmers University of Technology
Kevin Andersson, Sofia Cvetkovic Destouni, Ebba Ekblom, Lilian Hee, Emil Jansson & Thomas Ottink defended their Bachelor Thesis at Chambers University of Technology on 29 May 2018.
Title: Agenters sökförmåga i komplexa miljöer
Abstract: Tendensen hos en aktiv Brownsk agent att svänga i en viss rikting benämns i denna studie som kiralitet och ger upphov till vad som kallas cirkulär Brownsk rörelse. Denna typ av rörelse förekommer i många både naturliga och tekniska sammanhang och har potentiella applikationer inom bland annat medicin, biologi och robotik. För att undersöka hur cirkulärt Brownska agenters sökförmåga påverkas av kiralitet utförs i detta kandidatarbete en kombination av simuleringar och experiment i olika miljöer. Simuleringarna baseras på en diskretisering av Langevins ekvationer i två rumsdimensioner och experimenten utförs med hjälp av vibrerande mikrorobotar. Studien visar att agenters sökförmåga minskar vid ökande kiralitet i homogen miljö och bestämmer kiralitetsoptimum för sökförmåga i cirkulärt begränsade miljöer. I ett fall där två agenter placeras i en cirkulärt begränsad miljö identifieras effektiva kiralitetskombinationer för korta mötestider samt en möjlig koppling mellan sökförmåga och mötestid.
Supervisors: Alessandro Magazzù & Giovanni Volpe, Department of Physics, University of Gothenburg Examiner: Lena Falk, Department of Physics, University of Gothenburg Opponent: Matilda Hanes & Kevin Rylander Place: FL72 Time: 29 May, 2018, 9:35-10:20
Matilda Hanes & Kevin Rylander defended their Bachelor Thesis at Chambers University of Technology on 29 May 2018.
Title: Påvisande av kritiska Casimir-krafter i ett två-partikel-system
Abstract: 1948 upptäckte Hendrik Casimir en kraft som skulle uppkomma mellan två stycken perfekt ledande plattor. Efter detta har foskare påvisat Casimir krafter och dess analogier som kritska Casimir-krafter. Syftet med denna rapport är att påvisa kritiska Casimir-krafter för ett två-partikel-system i vatten 2,6-lutidine lösning med hjälp av optiska pincetter. I teoridelen av rapporten förklaras kritiska Casimir-krafter och Brownsk rörelse samt potentialen för en optisk fångad partikel. Simuleringar för en optisk fångad partikel används för att få en djupare förståelse för rapporten. Med metoder som ekvipartitionsmetoden, mean squared displacement method och autokorrelationsmetoden kalibreras de optiska fällorna och MATLAB används för samtliga beräkningar. Resultatet för rapporten påvisar kritiska Casimir-krafter och kalibrerar de optiska pincetterna.
Supervisors: Alessandro Magazzù & Giovanni Volpe, Department of Physics, University of Gothenburg Examiner: Lena Falk, Department of Physics, University of Gothenburg Opponent: Kevin Andersson, Sofia Cvetkovic Destouni, Ebba Ekblom, Lilian Hee, Emil Jansson & Thomas Ottink Place: FL72 Time: 29 May, 2018, 9:35-10:20
Subtypes of Alzheimer’s disease display distinct network abnormalities extending beyond their pattern of brain atrophy
Daniel Ferreira, Joana B. Pereira, Giovanni Volpe & Eric Westman
Frontiers in Neurology 10, 524 (2019)
Different subtypes of Alzheimer’s disease (AD) with characteristic distributions of neurofibrillary tangles and corresponding brain atrophy patterns have been identified using structural magnetic resonance imaging (MRI). However, the underlying biological mechanisms that determine this differential expression of neurofibrillary tangles are still unknown. Here, we applied graph theoretical analysis to structural MRI data to test the hypothesis that differential network disruption is at the basis of the emergence of these AD subtypes. We studied a total of 175 AD patients and 81 controls. Subtyping was done using the Scheltens’ scale for medial temporal lobe atrophy, the Koedam’s scale for posterior atrophy, and the Pasquier’s global cortical atrophy scale for frontal atrophy. A total of 89 AD patients showed a brain atrophy pattern consistent with typical AD; 30 patients showed a limbic-predominant pattern; 29 patients showed a hippocampal-sparing pattern; and 27 showed minimal atrophy. We built brain structural networks from 68 cortical regions and 14 subcortical gray matter structures for each AD subtype and for the controls, and we compared between-group measures of integration, segregation, and modular organization. At the global level, modularity was increased and differential modular reorganization was detected in the four subtypes. We also found a decrease of transitivity in the typical and hippocampal-sparing subtypes, as well as an increase of average local efficiency in the minimal atrophy and hippocampal-sparing subtypes. We conclude that the AD subtypes have a distinct signature of network disruption associated with their atrophy patterns and further extending to other brain regions, presumably reflecting the differential spread of neurofibrillary tangles. We discuss the hypothetical emergence of these subtypes and possible clinical implications.
Emergent Complex Behaviour in Active Matter across Time- and Length Scales
Invited talk at “Interface Dynamics and Dissipation Across the Time- and Length-Scales”
CECAM Israel Workshop
Tel Aviv University, Tel Aviv, Israel
21-23 May 2019
After a brief introduction of active particles, I’ll present some recent advances on the study of active particles in complex and crowded environments. First, I’ll show that active particles can work as microswimmers and microengines powered by critical fluctuations and controlled by light. Then, I’ll discuss some examples of behavior of active particles in crowded environments: a few active particles alter the overall dynamics of a system; active particles create metastable clusters and channels; active matter leads to non-Boltzmann distributions and alternative non-equilibrium relations; and active colloidal molecules can be created and controlled by light. Finally, I’ll present some examples of the behavior of active particles in complex environments: active particles often perform 2D active Brownian motion; active particles at liquid-liquid interfaces behave as active interstitials or as active atoms; and the environment alters the optimal search strategy for active particles in complex topologies.