Round Table Discussion on Introduction to Theoretical Active Matter

A screenshot taken during the round table discussion of 7 September 20201.

The first round table in the theoretical training gave a chance to start an interesting discussion which will continue in the following meetings.

The organizing ESRs were Ayten Gülce Bayram, Laura Natali, Liam Ruske, Jérémie Bertrand, Davide Breoni and Audrey Nsamela. They welcomed and introduced the three guests of the session: Nuno Araújo from the University of Lisbon, Jan Wehr from the University of Arizona and Denis Bartolo from École normale supérieure de Lyon.

The round table started with a personal question to the speakers about their interests and motivations for working in theoretical active matter. Having different backgrounds, the answers were very different, Nuno was attracted by non-intuitive behaviors observed in active matter experiments, while Jan started from a purely mathematical point of view and then moved towards physics of active systems. Denis provided another motivation, being head of a lab that deals with both theory and experiments.

The following discussion focused on the interaction and hierarchy between theory, simulations, and experiments. They all agree that establishing a constructive collaboration with experimental groups is not easy, but at the same time, it can have many benefits for both sides. However, none of the three elements is necessary for the others: a good paper can be presenting a theory not connected with experiments, even if its possible applications are not foreseeable yet. Denis firmly pointed out the difference between the observations and the tools (theoretical, numerical, and experimental) employed to explain it.

We also had a few more specific questions for the speakers, such as the distinctions in thinking between mathematicians and theoretical physicists, the possible applications to financial markets, and the differences in modeling artificial flocks and human crowds, which are often controlled by non-hydrodynamic variables.

We concluded the meeting by asking every one of our guests their tips for communicating the theory of active matter to a larger public. Here the answers were more relaxed and can be summed up as: trying to avoid technical and mathematical details while explaining the importance of the research problems, also using more familiar examples such as simulations employed in animation movies.

Press release on Machine learning can help slow down future pandemics

Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half). Image by L. Natali.
The article Improving epidemic testing and containment strategies using machine learning has been featured in the News of the Faculty of Science of Gothenburg University.

Here the links to the press releases:
Swedish: Maskininlärning kan bidra till att bromsa framtida pandemier
English: Machine learning can help slow down future pandemics

Improving epidemic testing and containment strategies using machine learning accepted in Machine Learning: Science and Technology

Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half). Image by L. Natali.
Improving epidemic testing and containment strategies using machine learning
Laura Natali, Saga Helgadottir, Onofrio M. Maragò, Giovanni Volpe
Machine Learning: Science and Technology (2021)
doi: 10.1088/2632-2153/abf0f7
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.

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). Image by L. Natali.
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.

Laura Natali presents her PhD project at the ActiveMatter online meeting, 10 September 2020

The ActiveMatter ESRs + PIs Online Meeting (if you want to know more read the main news here) took place on the 10th of last month. For the first time, the members of the ActiveMatter were in the “same place” also if just online. All the Early Stage Researchers were asked to introduce themselves in a short presentation video.

Laura Natali, ESR at the University of Gothenburg, also presented herself during the meeting.

The video lasts only five minutes and introduces Laura, her current project and the area of research she will study during the PhD. You can find the presentation below, and it is also on the ActiveMatter youtube channel .