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
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 (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:
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.
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.
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.
Funding:
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H2020 European Research Council (ERC) Starting Grant ComplexSwimmers (677511). |
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)
Optical Tweezers Go Smart
Giovanni Volpe
28 October 2020, 15:00
Invited Talk (Online) at OSA-EPN Student Chapter, Escuela Politécnica Nacional, Ecuador
Active Matter Meets Machine Learning: Opportunities and Challenges
Giovanni Volpe
26 October 2020, 13:45 CEST
Keynote talk (Online) at the Online Conference Motile Active Matter, Jülich Förschungszentrum, 26 October 2020
Abstract: Machine-learning methods are starting to shape active-matter research. Which new trends will this start? Which new groundbreaking insight and applications can we expect? More fundamentally, what can this contribute to our understanding of active matter? Can this help us to identify unifying principles and systematise active matter? This presentation addresses some of these questions with some concrete examples, exploring how machine learning is steering active matter towards new directions, offering unprecedented opportunities and posing practical and fundamental challenges. I will illustrate some most successful recent applications of machine learning to active matter with a slight bias towards work done in my research group: enhancing data acquisition and analysis [1, 2]; providing new data-driven models; improving navigation and search strategies [3, 4]; offering insight into the emergent dynamics of active matter in crowded and complex environments. I will discuss the opportunities and challenges that are emerging: implementing feedback control; uncovering underlying principles to systematise active matter; understanding the behaviour, organisation and evolution of biological active matter; realising active matter with embodied intelligence. Finally, I will highlight how active matter and machine learning can work together for mutual benefit.
References
[1] S. Helgadottir, A. Argun, G. Volpe, Digital video microscopy enhanced by deep learning. Optica 6, 506–513 (2019)
[2] S. Bo, F. Schmidt, R. Eichhorn, G. Volpe, Measurement of anomalous diffusion using recurrent neural networks. Phys. Rev. E 100, 010102(R) (2019)
[3] G. Volpe, G. Volpe, The topography of the environment alters the optimal search strategy for active particles. Proc. Natl. Acad. Sci. 114, 11350–11355 (2017)
[4] S. Colabrese, K. Gustavsson, A. Celani, L. Biferale, Flow navigation by smart microswimmers via reinforcement learning. Phys. Rev. Lett. 118, 158004 (2017).
Quantitative Digital Microscopy with Deep Learning
Giovanni Volpe
22 October 2020, 14:00 CEST
Invited Seminar (Online) at Disruptive & Sustainable Technologies for Agricultural Precision (DiSTAP), Singapore-MIT Alliance for Research and Technology (SMART) Centre, Singapore & Boston (MA)
Abstract: Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions. To overcome this issue, we introduce a software, DeepTrack 2.0, to design, train and validate deep-learning solutions for digital microscopy. We use it to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking and characterization to cell counting and classification. Thanks to its user-friendly graphical interface, DeepTrack 2.0 can be easily customized for user-specific applications, and, thanks to its open-source object-oriented programming, it can be easily expanded to add features and functionalities, potentially introducing deep-learning-enhanced video microscopy to a far wider audience.
References:
Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, Giovanni Volpe, “Quantitative Digital Microscopy with Deep Learning”, arXiv:2010.08260 (2020)
Gan Wang starts his PhD at the Physics Department of the University of Gothenburg on 9th October 2020.
Gan has a Master degree in Chemical Engineering from Xiamen University, Xiamen, China.
In his PhD, he will focus on smart particles and machine learning.
