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

Invited talks by G. Volpe and A. Magazzù at “SPACE Tweezers” Kick-off Meeting, Messina, Italy, 18-19 February 2020

Alessandro Magazzù and Giovanni Volpe will give invited presentations at the Kick-off meeting of SPACE Tweezers (Spectroscopy of Planetary and AtmospheriC particulatE by optical Tweezers).

SPACE Tweezers proposes research activities to trap and characterise spectroscopically extraterrestrial particles and their analogs. The opportunity to apply optical tweezers to planetary particulate matter can pave the way for space applications for in situ analyses and/or for sample return of particles in pristine conditions, i.e. preventing contamination and alteration, unlike collection methods so far used in space exploration.

The meeting, organised by Maria Grazia Donato, Pietro Guicciardi, Maria Antonia Iatì, and Onofrio M. Maragò, will take place at CNR-IPCF, Messina, on 18-19 February 2020.

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

Giovanni Volpe
Optical Tweezers Activities in Gothenburg
Date: 19 February 2020
Time: 10:55 CET

Alessandro Magazzù
Controlling the Dynamics of Colloidal Particles by Critical Casimir Forces using Blinking Optical Tweezers
Date: 19 February 2020
Time: 11:20 CET

 

 

 

Poster presentation by F. Schmidt at the Light at the Nanoscale conference, 5 December 2019

Light-induced phase separation power novel micro machines
Falko Schmidt, and Giovanni Volpe
Light at the Nanoscale Conference, Chalmers University, Gothenburg, Sweden
5 December 2019, 16:30-18:30

Focused laser light is used to trap a micronsized absorbing particle around its beam center where it performs constant orbital rotation. The light-induced local phase separations create a concentration gradient on which the particle moves along. Large patches of absorbing material on the particle’s surface give rise to a torque required for steady rotation1

Phase separation is a phenomena that commonly exists in nature, from the freezing of ice to the intrinsic mechanism of the cell to order matter. We are exploiting phase separations to produce new types of miniaturised machines, in particular micron and nano sized engines1as well as to form self-assembled colloidal molecules2. We control their behaviour using only light and varying its ambient temperature making this a simple tool to study complex matter3. This will enhance the development of future medicine where nano robots deliver drugs specifically to the local infection side.

References:1. F. Schmidt et al. Microscopic engine powered by critical demixingPhys Rev Lett 120, 068004, 2018

2. F. Schmidt et al. Light-controlled assembly of active colloidal molecules, J Chem Phys150, 094905, 2019

3. S. Bo et al. Measurement of anomalous diffusion using recurrent neural networksPhys Rev E 100, 010102(R), 2019

Invited talk by G. Volpe at BRC Day “Biomaterials meets AI”, Gothenburg, Sweden, 12 November 2019

Machine learning as a tool for the natural sciences: Opportunities and challenges
Giovanni Volpe
Invited Talk at BRC Day “Biomaterials meets AI”, University of Gothenburg, Gothenburg, Sweden, 12 November 2019

Abstract: Data-driven machine-learning methods are more and more widely used in the natural sciences. Machine learning offers unprecedented opportunities, but it also poses unexpected practical and fundamental challenges. Most importantly, machine-learning methods often work as black boxes, and therefore it can be difficult to understand and interpret their results. Here, we present an overview of the current state of the art of the adoption of machine learning in active-matter research. Finally, we discuss the opportunities and challenges that are emerging, highlighting how active matter and machine learning can work together for mutual benefit.

Bio: Giovanni Volpe is Associate Professor at the University of Gothenburg, where he leads the Soft Matter Lab (http://www.softmatterlab.org/).
He has published more than 80 articles on diverse topics including optical trapping, active matter, neurosciences, and machine learning.
He has co-authored the book “Optical Tweezers: Principles and Applications” (Cambridge University Press, 2015).
He is the recipient the ERC Starting Grant ComplexSwimmers, coordinator of the MSCA Innovative Training Networks ActiveMatter, and the KAW research grant “Active Matter Goes Smart”.
He is one of the chairs of the Conference Emerging Topics in Artificial Intelligence at the SPIE Optics & Photonics Meeting held annually in San Diego (CA).

Introductory Talk at CECAM Workshop “Active Matter and Artificial Intelligence” by G. Volpe, Lausanne, Switzerland, 1 October 2019

Machine learning for active matter
Giovanni Volpe
Introductory Talk at CECAM Workshop “Active Matter and Artificial Intelligence”
CECAM-HQ-EPFL, Lausanne, Switzerland
30 September – 2 October, 2019

Data-driven machine-learning methods are more and more widely used in the natural sciences. Active-matter research is no exception and has recently started experiment- ing machine-learning approaches. Machine learning offers unprecedented opportunities, but it also poses unexpected practical and fundamental challenges. Most importantly, machine-learning methods often work as black boxes, and therefore it can be difficult to understand and interpret their results. Here, we present an overview of the current state of the art of the adoption of machine learning in active-matter research. Finally, we discuss the opportunities and challenges that are emerging, highlighting how active matter and machine learning can work together for mutual benefit.

Invited talk by G. Volpe at RIAO/Optilas 2019, Cancun, Mexico, 23 Sep 2019

Deep Learning Applications in Digital Video Microscopy and Optical Micromanipulation
Saga Helgadottir, Aykut Argun, Giovanni Volpe
Invited talk at RIAO/Optilas 2019, Cancun, Mexico, 23-27 September 2019

Since its introduction in the mid 90s, digital video microscopy has become a staple for the analysis of data in optical trapping and optical manipulation experiments [1]. Current methods are able to predict the location of the center of a particle in ideal condition with high accuracy. However, these methods fail as the signal-to-noise ratio (SNR) of the images decreases or if there are non-uniform distortions present in the images. Both these conditions are commonly encountered in experiments. In addition, all these methods require considerable user input in terms of analysis parameters, which introduces user bias. In order to automatize the tracking process algorithms using deep learning have been successfully introduced but have not proved to be usable for practical applications.

Here, we provide a fully automated deep learning tracking algorithm with sub-pixel precision in localizing single particle and multiple particles’ positions from image data [2]. We have developed a convolutional neural network that is pre-trained on simulated single particle images in varying conditions of, for example, particle intensity, image contrast and SNR.

We test the pre-trained network on an optically trapped particle both in ideal condition and challenged condition with low SNR and non-uniform distortions [3]. This pre-trained network accurately predicts the location the trapped particle and a comparison of detected trajectories, the distribution of the particle position and the power spectral density of the particle trajectory clearly shows that our algorithm outperforms tracking by radial symmetry [4]. Our algorithm is also able to track non-ideal images with multiple Brownian particles as well as swimming bacteria that are problematic for traditional methods.

In conclusion, our algorithm outperforms current methods in precision and speed of tracking non-ideal images, while eliminating the need for user supervision and therefore the introduction of user biases. 

References

[1] John C Crocker, David G Grier, Journal of Colloid and Interface Science 179, 298–310 (1996).

[2] Saga Helgadottir, Aykut Argun, Giovanni Volpe, Optica 6, 506–513 (2019).

[3] Philip H Jones, Onofrio M Maragò, Giovanni Volpe, Optical tweezers: Principles and applications. Cambridge University Press, 2015.

[4] Raghuveer Parthasarathy. Nature Methods 9724 (2012).

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