On Tuesday 1 November 2022 the event called “Ämnets dag” took place at the university of Gothenburg.
The event is aimed to physics and science teachers at different school levels working in the Gothenburg area. Laura Natali joined the initiative and organised one of the workshops available. The activity prepared was an introductory class to simulations modelling active matter.
The workshop addressed the basic aspects of active matter and some examples of its relevant applications nowadays. The focus was on a hands-on workshop, to try out simulations and give a qualitative idea of active behaviour and the effect of different parameters on it. Next to the simulated active particles, it was also possible to play with Hexbugs a simple robotic example of active matter.
Laura Natali and David Bronte Ciriza during the presentation on the fundamentals of effective communication.(Photo by Alireza Khoshzaban.)During the ActiveMatter meeting in Lisbon, Laura Natali and David Bronte Ciriza proposed a two hours activity on the fundamentals of effective communication. The activity was structured in an interactive way, and it began with a open discussion about the importance of communication, especially in science.
Then, the ESRs briefly described their research in a popular science style, so addressed to a broader public. The first hour concluded with a presentation about rules to keep in mind while communicating both in oral and written form.
Afterwards, a few examples among the written texts were selected and discussed with all the participants. The aim was to exchange feedback and suggestions on how to make the communication more effective. The feedback was the inspiration for everyone to review their communication example, and the final versions are being uploaded on the official twitter account @ActiveMatterITN.
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.
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.
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.
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.
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 .
