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Theo Berglin & Adam Liberda defended their Master Thesis. Congrats!

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

Place: Faraday room
Time: 31 May, 2019, 13:00

Kevin Andersson, Sofia Cvetkovic Destouni, Ebba Ekblom, Lilian Hee, Emil Jansson & Thomas Otting defended their Bachelor Thesis. Congrats!

Kevin Andersson, Sofia Cvetkovic Destouni, Ebba Ekblom, Lilian Hee, Emil Jansson & Thomas Ottink defended their Bachelor Thesis at Chalmers University of Technology on 29 May 2019.

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. Congrats!

Matilda Hanes & Kevin Rylander defended their Bachelor Thesis at Chalmers University of Technology on 29 May 2019.

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 Brain Atrophy in Alzheimer’s Disease published in Front. Neurol.

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)
DOI: 10.3389/fneur.2019.00524

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.

Jan Wehr visits the Soft Matter Lab. Welcome!

Jan Wehr from the University of Arizona, Tucson (AZ), USA, will visit the Soft Matter Lab from 27 May 2019 till 4 June 2019.

Invited talk by G. Volpe at Interface Dynamics and Dissipation Across the Time and Length-Scales, Tel Aviv, 22 May 2019

Emergent Complex Behaviour in Active Matter across Time- and Length Scales
Giovanni Volpe
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.

https://www3.tau.ac.il/cecam/index.php/events/eventdetail/28/-/interface-dynamics-and-dissipation-across-the-time-and-length-scales#Program

Seminar on reinforcement learning in photonics networks by Daniel Brunner from FEMTO-ST, France, Nexus, 13 May 2019

Reinforcement Learning in a Large Scale Photonic Network
Seminar by Daniel Brunner
from FEMTO-ST Institute/Optics Department, CNRS & University Bourgogne Franche-Comté, Besançon Cedex, France

We experimentally create a neural network via a spatial light modulator, implementing connections between 2025 in parallel based on diffractive coupling. We numerically validate the scheme for at least 34.000 photonic neurons. Based on a digital micro-mirror array we demonstrate photonic reinforcement learning and predict a chaotic time-series via our optical neural network. The prediction error efficiently converges. Finally, we give insight based on the first investigation of effects to be encountered in neural networks physically implemented in analogue substrates.

Place: Nexus
Time: 13 May 2019, 14:00

Invited talk by G. Volpe at DINAMO 2019, San Crístobal, Ecuador, 22-26 Apr 2019

Deep Learning Applications in Photonics and Active Matter
Giovanni Volpe
Discussions on Nano & Mesoscopic Optics (DINAMO-2019), San Crístobal, Galápagos Islands, Ecuador, 22-26 April 2019.

After a brief overview of artificial intelligence, machine learning and deep learning, I will present a series of recent works in which we have employed deep learning for applications in photonics and active matter. In particular, I will explain how we employed deep learning to enhance digital video microscopy [1], to estimate the properties of anomalous diffusion, and to improve the calculation of optical forces. Finally, I will provide an outlook for the application of deep learning in photonics and active matter.

References

[1] S. Helgadottir, A. Argun and G. Volpe, Digital video microscopy enhanced by deep learning. arXiv 1812.02653 (2018).

Digital Video Microscopy Enhanced by Deep Learning published in Optica

Digital video microscopy enhanced by deep learning
(Cover article)
Saga Helgadottir, Aykut Argun & Giovanni Volpe
Optica 6(4), 506—513 (2019)
doi: 10.1364/OPTICA.6.000506
arXiv: 1812.02653
GitHub: DeepTrack

Single particle tracking is essential in many branches of science and technology, from the measurement of biomolecular forces to the study of colloidal crystals. Standard methods rely on algorithmic approaches; by fine-tuning several user-defined parameters, these methods can be highly successful at tracking a well-defined kind of particle under low-noise conditions with constant and homogenous illumination. Here, we introduce an alternative data-driven approach based on a convolutional neural network, which we name DeepTrack. We show that DeepTrack outperforms algorithmic approaches, especially in the presence of noise and under poor illumination conditions. We use DeepTrack to track an optically trapped particle under very noisy and unsteady illumination conditions, where standard algorithmic approaches fail. We then demonstrate how DeepTrack can also be used to track multiple particles and non-spherical objects such as bacteria, also at very low signal-to-noise ratios. In order to make DeepTrack readily available for other users, we provide a Python software package, which can be easily personalized and optimized for specific applications.

Featured in :
“Deep Learning for Particle Tracking”, Optics & Photonics News (December 1, 2019)

Funding: