Presentation by L. Natali at SPIE-ETAI, San Diego, 22 August 2023

Robot prototype wirelessly powered in a conductive arena. (Photo by L. Natali.)
Experimental realization of supervised learning in a swarm of autonomous robots
Laura Natali
Date: 22 August 2023
Time: 5:30 PM PDT

Artificial neural networks have limitations compared to biological counterparts as the latter can dynamically change connections and recover from damage. To simplify the study of connections evolving over time, we propose using programmable robots as a swarm to perform supervised learning and autonomously restructure the network. This experimental setup offers a way to study the evolution of connections in a simplified system while addressing the complexity of biological neurons. It has the potential to yield insights into the functioning of biological neural networks while providing a practical application in solving tasks.

Soft Matter Lab members present at SPIE Optics+Photonics conference in San Diego, 20-24 August 2023

The Soft Matter Lab participates to the SPIE Optics+Photonics conference in San Diego, CA, USA, 20-24 August 2023, with the presentations listed below.

Giovanni Volpe is also co-author of the presentations:

  • Jiawei Sun (KI): (Poster) Assessment of nonlinear changes in functional brain connectivity during aging using deep learning
    21 August 2023 • 5:30 PM – 7:00 PM PDT | Conv. Ctr. Exhibit Hall A
  • Blanca Zufiria Gerbolés (KI): (Poster) Exploring age-related changes in anatomical brain connectivity using deep learning analysis in cognitively healthy individuals
    21 August 2023 • 5:30 PM – 7:00 PM PDT | Conv. Ctr. Exhibit Hall A
  • Mite Mijalkov (KI): Uncovering vulnerable connections in the aging brain using reservoir computing
    22 August 2023 • 9:15 AM – 9:30 AM PDT | Conv. Ctr. Room 6C

“Coffee Rings” presented at Gothenburg Science Festival 2023

Coffee Ring exposition at science festival Gothenburg. (Photo by C. Beck Adiels.)
Our recent work on “coffee rings” was presented at the Gothenburg Science Festival, which, with about 100 000 visitors each year, is one of the largest popular science events in Europe.

On Wednesday 19th April 2023, Marcel Rey, Laura Natali, Daniela Pérez Guerrero and Caroline Adiels set up a stand in Nordstan.

In this guided exhibition, visitors were able to observe the flow inside a drying droplet using optical microscopes. They learned how the suspended solid coffee particles flow from the inside towards the edge of the coffee droplet, where they accumulate and cause the characteristic coffee ring pattern after drying.

Nowadays, the coffee ring effect presents still a major challenge in ink-jet printing or coating technologies, where a uniform drying is required. We thus shared our recently developed strategies to overcome the coffee ring effect and obtain a uniform deposit of drying droplets.

And finally, visitors were also offered a freshly-brewed espresso to not only drink but also to experience the “coffee ring effect” hands on.

Laura Natali participated in the Ämnets dag at the University of Gothenburg

Exemplary simulations of Active Brownian Particles proposed during the Ämnets Dag. (Figure by L. Natali.)
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.

Stay tuned for more activities like this!

Neural Network Training with Highly Incomplete Datasets published in Machine Learning: Science and Technology

Working principles for training neural networks with highly incomplete dataset: vanilla (upper panel) vs GapNet (lower panel) (Image by Yu-Wei Chang.)
Neural Network Training with Highly Incomplete Datasets
Yu-Wei Chang, Laura Natali, Oveis Jamialahmadi, Stefano Romeo, Joana B. Pereira, Giovanni Volpe
Machine Learning: Science and Technology 3, 035001 (2022)
arXiV: 2107.00429
doi: 10.1088/2632-2153/ac7b69

Neural network training and validation rely on the availability of large high-quality datasets. However, in many cases only incomplete datasets are available, particularly in health care applications, where each patient typically undergoes different clinical procedures or can drop out of a study. Since the data to train the neural networks need to be complete, most studies discard the incomplete datapoints, which reduces the size of the training data, or impute the missing features, which can lead to artefacts. Alas, both approaches are inadequate when a large portion of the data is missing. Here, we introduce GapNet, an alternative deep-learning training approach that can use highly incomplete datasets. First, the dataset is split into subsets of samples containing all values for a certain cluster of features. Then, these subsets are used to train individual neural networks. Finally, this ensemble of neural networks is combined into a single neural network whose training is fine-tuned using all complete datapoints. Using two highly incomplete real-world medical datasets, we show that GapNet improves the identification of patients with underlying Alzheimer’s disease pathology and of patients at risk of hospitalization due to Covid-19. By distilling the information available in incomplete datasets without having to reduce their size or to impute missing values, GapNet will permit to extract valuable information from a wide range of datasets, benefiting diverse fields from medicine to engineering.

Laura Natali and David Bronte Ciriza presented an effective communication activity in Lisbon

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.

Laura Natali presented her half-time seminar on 1 April 2022

Opponent Bernhard Mehlig (left), Laura Natali (center), and PhD supervisor Giovanni Volpe (right). (Photo by L. Perez.)
Laura Natali completed the first half of her doctoral studies and she defended her half-time on the 1st of April 2022.

The presentation was held in hybrid format, with part of the audience in the Von Bahr room and the rest connected through zoom. The half-time consisted in a presentation about her past and planned projects and it was followed by a discussion and questions proposed by her opponent Bernhard Mehlig.

The presentation started with a description of her concluded projects about employing neural networks in an epidemic agent-based model, published in Improving epidemic testing and containment strategies using machine learning accepted in Machine Learning: Science and Technology. It continued with her second project, about handling incomplete medical datasets with neural networks, available online as a preprint Neural Network Training with Highly Incomplete Datasets on ArXiv. In the last section, she outlined the proposed continuation of her PhD, with an ongoing project for combining artificial active matter with neural networks.

Presentation by L. Natali at Spatial Data Science 2020, 11 June 2021

Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half). (Image by Laura Natali.)
Improving epidemic testing and containment strategies using machine learning. 
Laura Natali, Saga Helgadottir, Onofrio M. Maragò, Giovanni Volpe.
Submitted to SDS2020
Date: 11 June
Time: 16:15 (CEST)

Abstract: 
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 published 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).
Improving epidemic testing and containment strategies using machine learning
Laura Natali, Saga Helgadottir, Onofrio M. Maragò, Giovanni Volpe
Machine Learning: Science and Technology, 2 035007 (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.

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 Laura 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

The articles was also featured in:
AI ska bromsa framtidens pandemier Metal Supply (23/04/2021)
El papel de la inteligencia artificial para frenar futuras pandemias El Nacional.cat. (16/04/2021)
AI could be critical to preventing future pandemics – study Health Tech World. (16/04/2021)
Machine Learning Slows Down Future Pandemics MedIndia. (15/04/2021)
Machine Learning May Be Key to Avoiding the Next Possible Pandemic News18.com (15/04/2021)
Så kan AI bromsa nästa pandemi – svensk forskningförfinar testningen Computer Sweden (15/04/2021)
AI could prevent future pandemics Electronics360 (14/04/2021)
L’IA peut contribuer à limiter la propagation des infections lors des futures épidémies (étude) Ecofin Telecom. (14/04/2021)
Machine Learning can help slow down future pandemics:Study SocialNews.xyz (14/04/2021)
Machine learning can help slow down future pandemics —ScienceDaily Sortiwa Trending Viral News Portal (14/04/2021)
AI mot smittspridning Sveriges Radio Vetenskapsradion. (14/04/2021)