Intracavity Optical Trapping published in Nature Commun.

Intracavity Optical Trapping

Intracavity optical trapping of microscopic particles in a ring-cavity fiber laser
Fatemeh Kalantarifard, Parviz Elahi, Ghaith Makey, Onofrio M. Maragò, F. Ömer Ilday & Giovanni Volpe
Nature Communications 10, 2683 (2019)
doi: 10.1038/s41467-019-10662-7
arXiv: 1808.07831

Standard optical tweezers rely on optical forces arising when a focused laser beam interacts with a microscopic particle: scattering forces, pushing the particle along the beam direction, and gradient forces, attracting it towards the high-intensity focal spot. Importantly, the incoming laser beam is not affected by the particle position because the particle is outside the laser cavity. Here, we demonstrate that intracavity nonlinear feedback forces emerge when the particle is placed inside the optical cavity, resulting in orders-of-magnitude higher confinement along the three axes per unit laser intensity on the sample. This scheme allows trapping at very low numerical apertures and reduces the laser intensity to which the particle is exposed by two orders of magnitude compared to a standard 3D optical tweezers. These results are highly relevant for many applications requiring manipulation of samples that are subject to photodamage, such as in biophysics and nanosciences.

Invited talk by G. Volpe at MPI-PKS Workshop, Dresden, Germany, 22-26 Jul 2019

Deep Learning Applications in Photonics and Active Matter
Giovanni Volpe
Invited talk at the “
Microscale Motion and Light” MPI-PKS Workshop, Dresden, Germany, 22-26 July 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 [2], 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. Optica 6(4), 506—513 (2019)
doi: 10.1364/OPTICA.6.000506

[2] S. Bo, F Schmidt, R Eichborn and G. Volpe, Measurement of Anomalous Diffusion Using Recurrent Neural Networks. arXiv: 1905.02038

Seminar by G. Volpe at the Department of Chemistry, University of Gothenburg, 19 Sep 2019

Soft Matter Meets Deep Learning
Giovanni Volpe
Department of Chemistry, University of Gothenburg, Sweden
19 September 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 [2], 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. Optica 6(4), 506—513 (2019)
doi: 10.1364/OPTICA.6.000506

[2] S. Bo, F Schmidt, R Eichborn and G. Volpe, Measurement of Anomalous Diffusion Using Recurrent Neural Networks. arXiv: 1905.02038

Plenary Presentation by G. Volpe at SPIE Nanoscience + Engineering, San Diego, 12 Aug 2019

Optical forces go smart
Giovanni Volpe
Plenary Presentation
SPIE Nanoscience + Engineering, San Diego (CA), USA
11-15 August 2019

Optical forces have revolutionized nanotechnology. In particular, optical forces have been used to measure and exert femtonewton forces on nanoscopic objects. This has provided the essential tools to develop nanothermodynamics, to explore nanoscopic interactions such as critical Casimir forces, and to realize microscopic devices capable of autonomous operation. The future of optical forces now lies in the development of smarter experimental setups and data-analysis algorithms, partially empowered by the machine-learning revolution. This will open unprecedented possibilities, such as the study of the energy and information flows in nanothermodynamics systems, the design of novel forms of interactions between nanoparticles, and the realization of smart microscopic devices.

Talk by G. Volpe at SPIE OTOM XVI, San Diego, 14 Aug 2019

FORMA: a high-performance algorithm for the calibration of optical tweezers
Laura Pérez-García, Alejandro V. Arzola, Jaime Donlucas Pérez, Giorgio Volpe  & Giovanni Volpe
SPIE Nanoscience + Engineering, Optical trapping and Optical Manipulation XV, San Diego (CA), USA
11-15 August 2019

We introduce a powerful algorithm (FORMA) for the calibration of optical tweezers. FORMA estimates accurately the conservative and non-conservative components of the force field with important advantages over established techniques, being parameter-free, requiring ten-fold less data and executing orders-of-magnitude faster. We demonstrate FORMA performance using optical tweezers, showing how, outperforming other available techniques, it can identify and characterise stable and unstable equilibrium points in generic force fields.

Reference: Pérez-García et al., Nature Communications 9, 5166 (2018)
doi: 10.1038/s41467-018-07437-x

Seminar on light driven colloidal micro swimmers by Juliane Simmchen from TU Dresden, Soliden 3rd floor, 11 June 2019

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.

From left to right, AgCl microstars, Cu@TiO2 Janus particles, BiVO4 crystals.

Place: Soliden 3rd floor
Time: 11 June 2019, 10:00

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

Adrian Leidegren defended his Master Thesis. Congrats!

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

Place: Faraday room
Time: 5 June, 2019, 15:00

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