Invited talk by G. Volpe at MPI-PKS Workshop, Dresden, Germany, 23 July 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
https://www.pks.mpg.de/mml19/

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

Sophia Simon visits the Soft Matter Lab. Welcome!

Sophia Simon is a bachelor student at the Freie Universität of Berlin. She will do her summer internship at the Soft Matter Lab from July 21 to September 27, 2019, with a grant from DAAD (Deutscher Akademischer Austauschdienst). She will work on the tunability of critical Casimir forces in critical mixtures.

Anomalous Diffusion Measurement with Neural Networks published in Phys Rev E

Measurement of Anomalous Diffusion Using Recurrent Neural Networks

Measurement of Anomalous Diffusion Using Recurrent Neural Networks
Stefano Bo, Falko Schmidt, Ralf Eichborn & Giovanni Volpe
Physical Review E 100(1), 010102(R) (2019)
doi: 10.1103/PhysRevE.100.010102
arXiv: 1905.02038

Anomalous diffusion occurs in many physical and biological phenomena, when the growth of the mean squared displacement (MSD) with time has an exponent different from one. We show that recurrent neural networks (RNN) can efficiently characterize anomalous diffusion by determining the exponent from a single short trajectory, outperforming the standard estimation based on the MSD when the available data points are limited, as is often the case in experiments. Furthermore, the RNN can handle more complex tasks where there are no standard approaches, such as determining the anomalous diffusion exponent from a trajectory sampled at irregular times, and estimating the switching time and anomalous diffusion exponents of an intermittent system that switches between different kinds of anomalous diffusion. We validate our method on experimental data obtained from sub-diffusive colloids trapped in speckle light fields and super-diffusive microswimmers.

Influence of Sensorial Delay on Clustering and Swarming published in Phys. Rev. E

Influence of Sensorial Delay on Clustering and Swarming

Influence of Sensorial Delay on Clustering and Swarming
Rafal Piwowarczyk, Martin Selin, Thomas Ihle & Giovanni Volpe
Physical Review E 100(1), 012607 (2019)
doi: 10.1103/PhysRevE.100.012607
arXiv:  1803.06026

We show that sensorial delay alters the collective motion of self-propelling agents with aligning interactions: In a two-dimensional Vicsek model, short delays enhance the emergence of clusters and swarms, while long or negative delays prevent their formation. In order to quantify this phenomenon, we introduce a global clustering parameter based on the Voronoi tessellation, which permits us to efficiently measure the formation of clusters. Thanks to its simplicity, sensorial delay might already play a role in the organization of living organisms and can provide a powerful tool to engineer and dynamically tune the behavior of large ensembles of autonomous robots.

Falko Schmidt attends the 69th Lindau Nobel laureate meeting

Picture from the open discussion with Steven Chu (Nobel Prize Physics 1997) on the left. 69th Lindau Nobel Laureate Meeting 02.07.2019 Photo/Credit: Patrick Kunkel/ Lindau Nobel Laureate Meetings Open Exhange
Picture of the boat ride to Mainau Island with Donna Strickland (Nobel Prize Physics 2018) on the left. 69th Lindau Nobel Laureate Meeting, 04.07.2019, Lindau, Germany
Picture/Credit: Julia Nimke/Lindau Nobel Laureate Meetings
Picture of the open discussion with David Gross (Nobel Prize Physics 2004) on the left. 69th Lindau Nobel Laureate Meeting 03.07.2019 Photo/Credit: Patrick Kunkel/ Lindau Nobel Laureate Meetings Open Exchange David J. Gross

Falko Schmidt, and Jalpa Soni have been selected to attain the 69th Lindau Nobel Laureate meeting in Lindau, Germany from the 30th June till 5th July 2019.

The Lindau meeting is a platform where 600 young scientists around the world meet former Nobel laureates (as well as Turing-award winners). There they can exchange scientific ideas and experiences, inspire each other and connect for a more interdisciplinary scientific community. These are the three incentives that make this meeting a unique experience.

Falko Schmidt had the privilege to attend it and shares the following insight:

“For me, the Lindau meeting was a unique experience where I was able to meet peers across many disciplines, share ideas and experiences beyond my field of active matter and received much feedback on career choices and daily life as a PhD. Especially fruitful were the many possibilities to engage with senior scientists such as the Nobel laureates which with their humour, insight and advice deepened my passion about science. Personally, I would consider my best encounters with Steven Chu and William Phillips (Nobel Prize in Physics in 1997 on laser cooling),  Donna Strickland (Nobel Prize in Physics in 2018 on ultra-fast lasers), and Stefan Hell (Nobel Prize in Chemistry in 2014 on super-resolution microscopy). I am very grateful for the possibility of attending this meeting and would like to thank the Lindau Nobel committee and Söderbergs Foundation who  were selecting and sponsoring me.
From now on, in times of struggle, I will always look back to this meeting and remember why we all love doing science.”