Digital Christmas Lunch 2020, Soft Matter Lab and Biological Physics Lab

A screenshot from the Soft Matter Lab and Biological Physics Lab’s Digital Christmas Lunch 2020.
On November 25, the members of the Soft Matter Lab and of the Biological Physics Lab joined for the Digital Christmas Lunch 2020.

This activity has been held in substitution of the traditional Physics Department Christmas Lunch, which this year cannot take place in the usual format because of the ongoing coronavirus epidemic.

After the usual group meeting, which is held online on Zoom since the beginning of March 2020, the two groups shared a common lunch, in respect of the current recommendations of the Folkhälsomyndigheten, which do not allow public gatherings with more than 8 people.

Several group members joined from their homes. The group members involved in experimental work, who, in any case, had to be present in the respective labs, joined the group lunch from various rooms in Soliden, to comply with the current rules of social distancing.

Screenshots:

Lecture by G. Volpe: Graph Theory Concepts, 25 November 2020

On 25 November 2020, Giovanni Volpe gave an online lecture on Graph Theory Concepts, in the scope of Karolinska Institute graduate course 3064: Imaging in Neuroscience: With a focus on structural MRI methods

The lecture is published online on youtube.

Link:
Imaging in Neuroscience: Graph Theory Concepts

Project “Active matter goes smart” featured on KAW Foundation website

Giovanni Volpe, photo of Johan Wingborg.
Giovanni Volpe’s new project “Active matter goes smart” has been featured on the website of the Knut and Alice Wallenberg (KAW) Foundation.

The feature article explains the project and its main aim of creating smart particles that react to their environment to a general audience.

The article is available both in English and in Swedish.

Links:
Skapar smarta partiklar med naturen som förebild (Swedish)
Creating smart particles modeled on nature (English)

Photos of Johan Wingborg, taken from Creating smart particles modeled on nature

Improving epidemic testing and containment strategies using machine learning on ArXiv

Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half).
Improving epidemic testing and containment strategies using machine learning
Laura Natali, Saga Helgadottir, Onofrio M. Maragò, Giovanni Volpe
arXiv: 2011.11717

Containment of epidemic outbreaks entails great societal and economic costs. Cost-effective containment strategies rely on efficiently identifying infected individuals, making the best possible use of the available testing resources. Therefore, quickly identifying the optimal testing strategy is of critical importance. Here, we demonstrate that machine learning can be used to identify which individuals are most beneficial to test, automatically and dynamically adapting the testing strategy to the characteristics of the disease outbreak. Specifically, we simulate an outbreak using the archetypal susceptible-infectious-recovered (SIR) model and we use data about the first confirmed cases to train a neural network that learns to make predictions about the rest of the population. Using these prediction, we manage to contain the outbreak more effectively and more quickly than with standard approaches. Furthermore, we demonstrate how this method can be used also when there is a possibility of reinfection (SIRS model) to efficiently eradicate an endemic disease.

Career Webinar with Christian Reimer: From electro-optics to a start-up company

Christian Reimer, Co-founder and Head of Product at HyperLight and OSA Ambassador

The OSA Student chapter together with FFF will host a career webinar again, this time with Christian Reimer, Co-founder and Head of Product at HyperLight and OSA Ambassador. 

Christian Reimer will give a talk with title: Electro-optics with thin film lithium niobite and what it is like to work at a start-up company.

In the scientific part of his talk, Christian will give an introduction to the field of integrated photonics with thin-film lithium niobate, with a  focus on electro-optic applications, as well as recent progress on transforming the field from chip-based proof-of-concept realizations for wafer-scale production.

In the professional development section, he will then share his experience transitioning from academia to a start-up company. He will talk about differences and similarities in the work environment, what to expect in terms of tasks and responsibilities, and explain how salaries at start-ups can include combinations of equity and incentives.

Christian Reimer´s mini bio:  Dr. Christian Reimer is a physicist and entrepreneur working in the fields of nonlinear optics, integrated photonics and quantum optics. He received graduate degrees from the Karlsruhe Institute of Technology in Germany, Heriot-Watt University in Scotland, and the National Institute of Scientific Research in Canada. He then worked as a postdoctoral fellow at Harvard University, before becoming Co-Founder and Head of Product of HyperLight Corporation. HyperLight, a Venture-Capital funded start-up out of Harvard University, is specialized on integrated lithium niobate technologies for ultra-high performance photonic solutions.

The webinar will be on the 9th of December at 16:30 via zoom.

Registration is required: https://tinyurl.com/reimer-OSA-FFF

Lucero nominated for “Best HealthTech Startup” in Sweden

The spinoff Lucero emerged a year ago as a joint effort between the Soft Matter Lab, the Biological Physics Group and the Chalmers School of Entrepreneurship. The idea of providing a non-invasive micromanipulation platform recently received initial support from the European Research Council (Proof of Concept) and Chalmers Ventures. Lucero has now been nominated for “Best HealthTech Startup” in the Swedish national final of the prestigious Nordic Startup Awards. National winners are partially determined by public vote and will go on to compete against the winners from Iceland, Finland, Norway, and Denmark in the Nordic Final.

The public voting period is now open and the winner of each category will be announced on November 26th.

To vote, click here.

“The first prototype is on its way and we hope to start the initial tests with biological samples pretty soon, all thanks to the support from Chalmers Ventures and Prof. Giovanni Volpe.” Alejandro Diaz, co-founder of Lucero.

Lucero is joined by four other up-and-coming Swedish startups in the HealthTech category, including Spermosens, tendo, Flow Neuroscience, and Deversify.

Other categories include: Startup of the Year, Best Newcomer, Founder of the Year, Investor of the Year, Best Co-working Space, Best Accelerator/Incubator Program, Ecosystem Hero of the Year, Best Virtual Teamwork Solution, People’s Choice, and Best Climate Impact Startup.

The Nordic Startup Awards is part of the Global Startup Awards, which is a large startup competition that aims to recognize and connect entrepreneurs, investors, accelerator/incubator programs, and government initiatives from all around the world.

Follow Lucero’s updates on Lucerobio.com/, LinkedIn, and Instagram.

Links:
LinkedIn: https://www.linkedin.com/company/lucero/
Instagram: https://www.instagram.com/lucero_bio/
Lucerobio: https://www.lucerobio.com/

Aykut Argun’s team wins in four categories of the ANDI challenge

Aykut Argun (Soft Matter Lab) and Stefano Bo (MPI Dresden) participated in the AnDi Challenge, the Anomalous Diffusion challenge, in all the nine categories.

The challenge consisted of different tasks, specifically:

  • Task 1 – Inference of the anomalous diffusion exponent α.
  • Task 2 – Classification of the diffusion model.
  • Task 3 – Segmentation of trajectories.

Each task included modalities for different number of dimensions (1D, 2D and 3D), for a total of 9 subtasks.

Approximately 20 teams from all the world participated in the challenge.

Aykut’s and Stefano’s team, eduN, ranked in the first three positions in all the categories. EduN won the 1st place in 4 of the categories, i.e., Task 2 (1D and 2D), and Task 3 (1D and 3D), the 2nd place in another 4 categories, and 3rd in the remaining category.

The details and the information about the final results can be found on ANDI Challenge final results page: http://www.andi-challenge.org/ (select: Learn the Details and then Final Results)

Here the link to the video of the announcement.

Enhanced force-field calibration via machine learning featured in AIP SciLight

The article Enhanced force-field calibration via machine learning
has been featured in: “Machine Learning Outperforms Standard Force-Field Calibration Techniques”, AIP SciLight (November 6, 2020).

Scilight showcases the most interesting research across the physical sciences published in AIP Publishing Journals.

Scilight is published weekly (52 issues per year) by AIP Publishing.

Enhanced force-field calibration via machine learning published in Applied Physics Reviews

Representation of a particle in a force field
Enhanced force-field calibration via machine learning
Aykut Argun, Tobias Thalheim, Stefano Bo, Frank Cichos, Giovanni Volpe
Applied Physics Reviews 7, 041404 (2020)
doi: 10.1063/5.0019105
arXiv: 2006.08963

The influence of microscopic force fields on the motion of Brownian particles plays a fundamental role in a broad range of fields, including soft matter, biophysics, and active matter. Often, the experimental calibration of these force fields relies on the analysis of the trajectories of these Brownian particles. However, such an analysis is not always straightforward, especially if the underlying force fields are non-conservative or time-varying, driving the system out of thermodynamic equilibrium. Here, we introduce a toolbox to calibrate microscopic force fields by analyzing the trajectories of a Brownian particle using machine learning, namely recurrent neural networks. We demonstrate that this machine-learning approach outperforms standard methods when characterizing the force fields generated by harmonic potentials if the available data are limited. More importantly, it provides a tool to calibrate force fields in situations for which there are no standard methods, such as non-conservative and time-varying force fields. In order to make this method readily available for other users, we provide a Python software package named DeepCalib, which can be easily personalized and optimized for specific applications.

Alessandro Magazzù and David Bronte Ciriza visit the Soft Matter Lab

Alessandro Magazzù and David Bronte Ciriza are visiting the Soft Matter Lab from 2 to 9 November 2020.
Alessandro is currently Post Doc at the IPCF-CNR Messina, Italy, and David is a PhD student at the same institution, and he is one of the ESRs (Early Stage Researchers) of the ActiveMatter MSCA-ITN-ETN.
They will be working on a neural network approach to the calculation of optical forces and torques on dielectric particles in the geometrical optics approximation.

(Foto by Aykut Argun)