Tag: Giovanni Volpe

Keynote talk by G. Volpe at the Online Conference Motile Active Matter, 26 October 2020

Active Matter Meets Machine Learning: Opportunities and Challenges
Giovanni Volpe
26 October 2020, 13:45 CEST
Keynote talk (Online) at the Online Conference Motile Active Matter, Jülich Förschungszentrum, 26 October 2020

Abstract: Machine-learning methods are starting to shape active-matter research. Which new trends will this start? Which new groundbreaking insight and applications can we expect? More fundamentally, what can this contribute to our understanding of active matter? Can this help us to identify unifying principles and systematise active matter? This presentation addresses some of these questions with some concrete examples, exploring how machine learning is steering active matter towards new directions, offering unprecedented opportunities and posing practical and fundamental challenges. I will illustrate some most successful recent applications of machine learning to active matter with a slight bias towards work done in my research group: enhancing data acquisition and analysis [1, 2]; providing new data-driven models; improving navigation and search strategies [3, 4]; offering insight into the emergent dynamics of active matter in crowded and complex environments. I will discuss the opportunities and challenges that are emerging: implementing feedback control; uncovering underlying principles to systematise active matter; understanding the behaviour, organisation and evolution of biological active matter; realising active matter with embodied intelligence. Finally, I will highlight how active matter and machine learning can work together for mutual benefit.

References
[1] S. Helgadottir, A. Argun, G. Volpe, Digital video microscopy enhanced by deep learning. Optica 6, 506–513 (2019)
[2] S. Bo, F. Schmidt, R. Eichhorn, G. Volpe, Measurement of anomalous diffusion using recurrent neural networks. Phys. Rev. E 100, 010102(R) (2019)
[3] G. Volpe, G. Volpe, The topography of the environment alters the optimal search strategy for active particles. Proc. Natl. Acad. Sci. 114, 11350–11355 (2017)
[4] S. Colabrese, K. Gustavsson, A. Celani, L. Biferale, Flow navigation by smart microswimmers via reinforcement learning. Phys. Rev. Lett. 118, 158004 (2017).

Online seminar by G. Volpe at DiSTAP, Singapore-MIT Alliance for Research and Technology (SMART) Centre

Quantitative Digital Microscopy with Deep Learning
Giovanni Volpe
22 October 2020, 14:00 CEST
Invited Seminar (Online) at Disruptive & Sustainable Technologies for Agricultural Precision (DiSTAP), Singapore-MIT Alliance for Research and Technology (SMART) Centre, Singapore & Boston (MA)

Abstract: Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions. To overcome this issue, we introduce a software, DeepTrack 2.0, to design, train and validate deep-learning solutions for digital microscopy. We use it to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking and characterization to cell counting and classification. Thanks to its user-friendly graphical interface, DeepTrack 2.0 can be easily customized for user-specific applications, and, thanks to its open-source object-oriented programming, it can be easily expanded to add features and functionalities, potentially introducing deep-learning-enhanced video microscopy to a far wider audience.

References:
Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, Giovanni Volpe, “Quantitative Digital Microscopy with Deep Learning”, arXiv:2010.08260 (2020)

Invited talk by G. Volpe at GSJP, 1 October 2020

Logo of GSJP2020 – First Global Symposium on Janus Particles.

Giovanni Volpe will give an online invited presentation at the First Global Symposium on Janus Particles (GSJP) 2020.

GSJP will bring together a collection of experts who are in the vanguard of scientific and engineering investigations on Janus particles all around the globe.

The contribution of Giovanni Volpe will be presented according to the following schedule:

Giovanni Volpe
Light-controlled Assembly of Active Colloidal Molecules

Activity and life have emerged from a primordial broth of simple building blocks when the presence of energy flows made these blocks come together and interact in non-trivial ways. Here, we use experiments and simulations demonstrating that active molecules can be created and controlled by light. Shining light on a primordial broth containing passive particles of two different species, we create active colloidal molecules of increasing complexity, which behave as migrators, spinners and rotators. This demonstrates a powerful new route for nonequilibrium self-assembly, which may help explaining the emergence of complex systems in living matter and may also proof useful as a design principle for the construction of flexible micromotors and cargo transport in health care applications.

Date: 1 October 2020
Time: 10:10 (EST)
Place: Online

Diagnosis of a genetic disease improves with machine learning, a summary in Swedish published in Fysikaktuellt

Neural networks consist of a series of connected layers of neurons, whose connection weights are adjusted to learn how to determine the diagnosis from the input data.

A summary in Swedish of our previously published article “Virtual genetic diagnosis for familial hypercholesterolemia powered by machine learning” has been published in Fysikaktuellt, the journal of the Swedish Physical Society (Svenska fysikersamfundet).

Article: “Diagnostisering av sjukdomar förbättras med maskininlärning”, Saga Helgadottir, Giovanni Volpe and Stefano Romeo (in Swedish)

Original article: Virtual genetic diagnosis for familial hypercholesterolemia powered by machine learning

Press release: 
Algoritm lär sig diagnostisera genetisk sjukdom (in Swedish)
An algorithm that learns to diagnose genetic disease (in English)

Invited talk by G. Volpe at SCOP2020, 25 September 2020

Student Conference on Optics and Photonics (SCOP), organized by the OSA student chapter of the Physical Research Laboratory, Ahmedabad, India.

Giovanni Volpe will give an online invited presentation at the Student Conference on Optics and Photonics (SCOP), organized by the OSA student chapter of Physical Research Laboratory, Ahmedabad, India.

The conference addresses various topics in optics with an emphasis on non linear optics and quantum optics, will be held during 23-25 September, 2020 at the Physical Research Laboratory (PRL), Ahmedabad, India.
The conference includes invited talks by eminent scientists from India and abroad, as well as posters and oral presentations by student participants and research fellows.

The contribution of Giovanni Volpe will be presented according to the following schedule:

Giovanni Volpe
Deep Learning for microscopy and optical trapping
Date: 25 September 2020
Time: 15:10 IST (GMT+5:30)
Place: Online

E-workshop: Novel Features and Applications of Optical Manipulation

The School of Nano Science, IPM, Tehran, Iran, with the support of IASBS, Zanjan, Iran, is organizing a one-day e-workshop on
Novel Features and Applications of Optical Manipulation
on September 8th, 2020.

The workshop will address the latest features of optical manipulation. Distinguished lecturers in the field will present exciting aspects and applications of optical manipulation along with providing educational outreach to students.

The workshop is open to all and it is free, but pre-registration is required. Registration dates: between 10 and 25 August.

Invited lecturers:
Prof. Kishan Dolakia
Prof. Giovanni Volpe
Prof. Onofrio Maragò
Dr. Valentina Emiliani
Dr. Samaneh Rezvani
Dr. Fatemeh Kalandarifard

Organizers:
Dr. Alireza Moradi
Prof. Reza Asgari

Date: 8 September 2020
Link: Workshop Homepage, Registration

Feedback-controlled active brownian colloids with space-dependent rotational dynamics published in Nature Communications

Active Colloids with Position-Dependent Rotational Diffusivity

Active Colloids with Position-Dependent Rotational Diffusivity
Miguel Angel Fernandez-Rodriguez, Fabio Grillo, Laura Alvarez, Marco Rathlef, Ivo Buttinoni, Giovanni Volpe & Lucio Isa
Nature Communications 11, 4223 (2020)
doi: 10.1038/s41467-020-17864-4
arXiv: 1911.02291

The non-thermal nature of self-propelling colloids offers new insights into non-equilibrium physics. The central mathematical model to describe their trajectories is active Brownian motion, where a particle moves with a constant speed, while randomly changing direction due to rotational diffusion. While several feedback strategies exist to achieve position-dependent velocity, the possibility of spatial and temporal control over rotational diffusion, which is inherently dictated by thermal fluctuations, remains untapped. Here, we decouple rotational diffusion from thermal fluctuations. Using external magnetic fields and discrete-time feedback loops, we tune the rotational diffusivity of active colloids above and below its thermal value at will and explore a rich range of phenomena including anomalous diffusion, directed transport, and localization. These findings add a new dimension to the control of active matter, with implications for a broad range of disciplines, from optimal transport to smart materials.

Soft Matter Lab presentations at the SPIE Optics+Photonics Digital Forum

Seven members of the Soft Matter Lab (Saga Helgadottir, Benjamin Midtvedt, Aykut Argun, Laura Pérez-Garcia, Daniel Midtvedt, Harshith Bachimanchi, Emiliano Gómez) were selected for oral and poster presentations at the SPIE Optics+Photonics Digital Forum, August 24-28, 2020.

The SPIE digital forum is a free, online only event.
The registration for the Digital Forum includes access to all presentations and proceedings.

The Soft Matter Lab contributions are part of the SPIE Nanoscience + Engineering conferences, namely the conference on Emerging Topics in Artificial Intelligence 2020 and the conference on Optical Trapping and Optical Micromanipulation XVII.

The contributions being presented are listed below, including also the presentations co-authored by Giovanni Volpe.

Note: the presentation times are indicated according to PDT (Pacific Daylight Time) (GMT-7)

Emerging Topics in Artificial Intelligence 2020

Saga Helgadottir
Digital video microscopy with deep learning (Invited Paper)
26 August 2020, 10:30 AM
SPIE Link: here.

Aykut Argun
Calibration of force fields using recurrent neural networks
26 August 2020, 8:30 AM
SPIE Link: here.

Laura Pérez-García
Deep-learning enhanced light-sheet microscopy
25 August 2020, 9:10 AM
SPIE Link: here.

Daniel Midtvedt
Holographic characterization of subwavelength particles enhanced by deep learning
24 August 2020, 2:40 PM
SPIE Link: here.

Benjamin Midtvedt
DeepTrack: A comprehensive deep learning framework for digital microscopy
26 August 2020, 11:40 AM
SPIE Link: here.

Gorka Muñoz-Gil
The anomalous diffusion challenge: Single trajectory characterisation as a competition
26 August 2020, 12:00 PM
SPIE Link: here.

Meera Srikrishna
Brain tissue segmentation using U-Nets in cranial CT scans
25 August 2020, 2:00 PM
SPIE Link: here.

Juan S. Sierra
Automated corneal endothelium image segmentation in the presence of cornea guttata via convolutional neural networks
26 August 2020, 11:50 AM
SPIE Link: here.

Harshith Bachimanchi
Digital holographic microscopy driven by deep learning: A study on marine planktons (Poster)
24 August 2020, 5:30 PM
SPIE Link: here.

Emiliano Gómez
BRAPH 2.0: Software for the analysis of brain connectivity with graph theory (Poster)
24 August 2020, 5:30 PM
SPIE Link: here.

Optical Trapping and Optical Micromanipulation XVII

Laura Pérez-García
Reconstructing complex force fields with optical tweezers
24 August 2020, 5:00 PM
SPIE Link: here.

Alejandro V. Arzola
Direct visualization of the spin-orbit angular momentum conversion in optical trapping
25 August 2020, 10:40 AM
SPIE Link: here.

Isaac Lenton
Illuminating the complex behaviour of particles in optical traps with machine learning
26 August 2020, 9:10 AM
SPIE Link: here.

Fatemeh Kalantarifard
Optical trapping of microparticles and yeast cells at ultra-low intensity by intracavity nonlinear feedback forces
24 August 2020, 11:10 AM
SPIE Link: here.

Note: the presentation times are indicated according to PDT (Pacific Daylight Time) (GMT-7)

Machine learning reveals complex behaviours in optically trapped particles published in Machine Learning: Science and Technology

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
Machine Learning: Science and Technology, 1 045009 (2020)
doi: 10.1088/2632-2153/abae76
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.

Seminar by G. Volpe at ICFO, 16 June 2020

Lucky Encounters: From Optical Tweezers to deep Learning
Giovanni Volpe
ICFO Alumni Seminar (Online)
16 June 2020

In this semi-autobiographical talk, I will look back at my career and its evolution. It all started at ICFO with a PhD on optical tweezers in 2008. It then continued with a series of diverse research projects on different fields: active matter, stochastic thermodynamics, neurosciences and, finally, deep learning. I will emphasize how my career has been shaped by lucky encounters. Encounters that have taken me to places and topics I’d never have imagined beforehand. But it all makes sense, in insight.

Date: 16 June 2020
Time: 15:00
Place: Online