Soft Matter Lab

Invited Talk by G. Volpe at MoLE Conference 2022, Donostia/San Sebastián, Spain, 27 July 2022

Artificial intelligence in microscopy, photonics, and active matter
Giovanni Volpe
27 July 2022, 12:40 (CEST)
MoLE Conference 2022
Donostia/San Sebastián, Spain, 25-29 July 2022

After a brief overview of artificial intelligence, machine learning and deep learning, I will present a series of recent works in which we have employed deep learning for applications in microscopy, optical tweezers, and active matter. In particular, I will explain how we employed deep learning to enhance digital video microscopy, to perform virtual staining of tissues, to estimate the properties of anomalous diffusion, to characterize microscopic force fields, to improve the calculation of optical forces, and to characterize nanoparticles. Finally, I will provide an outlook on the future for the application of deep learning in these fields.

Dynamic live/apoptotic cell assay using phase-contrast imaging and deep learning on bioRxiv

Phase-contrast image before virtual staining. *(Image by the Authors.)*

Dynamic live/apoptotic cell assay using phase-contrast imaging and deep learning
Zofia Korczak, Jesús Pineda, Saga Helgadottir, Benjamin Midtvedt, Mattias Goksör, Giovanni Volpe, Caroline B. Adiels
bioRxiv: 10.1101/2022.07.18.500422

Chemical live/dead assay has a long history of providing information about the viability of cells cultured in vitro. The standard methods rely on imaging chemically-stained cells using fluorescence microscopy and further analysis of the obtained images to retrieve the proportion of living cells in the sample. However, such a technique is not only time-consuming but also invasive. Due to the toxicity of chemical dyes, once a sample is stained, it is discarded, meaning that longitudinal studies are impossible using this approach. Further, information about when cells start programmed cell death (apoptosis) is more relevant for dynamic studies. Here, we present an alternative method where cell images from phase-contrast time-lapse microscopy are virtually-stained using deep learning. In this study, human endothelial cells are stained live or apoptotic and subsequently counted using the self-supervised single-shot deep-learning technique (LodeSTAR). Our approach is less labour-intensive than traditional chemical staining procedures and provides dynamic live/apoptotic cell ratios from a continuous cell population with minimal impact. Further, it can be used to extract data from dense cell samples, where manual counting is unfeasible.

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.

Invited Talk by G. Volpe at Active and Intelligent Living Matter Conference, Erice, 30 June 2022

Artificial intelligence in microscopy, photonics, and active matter
Giovanni Volpe
30 June 2022, 16:20 (CEST)
Active and Intelligent Living Matter Conference
Erice, Italy, 26 June-1 July 2022

Kunli Xiong joins the Soft Matter Lab

Kunli Xiong started his research position at the Physics Department of the University of Gothenburg on 16th June 2022.

Kunli received a Ph.D. degree in Material Science from the Chalmers University of Technology, Sweden. In his research, he focuses on developing novel plasmonic E-paper technology.

Jesper Boberg and Anders Segerlund will defend their Master thesis on 14 June 2022

Jesper Boberg and Anders Segerlund will defend their Master thesis in MPCAS at the Chalmers University of Technology on 14 June 2022 at 16:00.

Title: Early detection of rare evens: Predicting battery cell deviations.

Abstract:
Despite rigorous quality control in battery cell production, the production process is still subject to quality deviations. These quality deviations; known as “rare events” initially act as inherent passive quality deviations and may not affect the performance of the battery. Yet, a passive quality deviation can transition into an active quality deviation that give rise to behavioral deviations in the battery cell at some point during the battery’s lifetime. An active quality deviation may cause the entire battery to misbehave and eventually fail. This thesis investigates the possibility of predicting these cell deviations in car batteries. Better predictions of these events would avoid expensive and troublesome car failures and instead enable preventive car maintenance to solve the problem.

In this thesis different models have been created and evaluated with the aim of preventing these deviations. The dataset is supplied by Volvo Cars and contains a large amount of data collected from BEV cars where the arguably largest challenge comes from the imbalance of the dataset. In addition to the modelling, the thesis will include a thorough data analysis with the aim of improving both the dataset itself and the data collection process at Volvo Cars.

These deviations occur extremely rarely which also makes a relatively large amount of false positives difficult to avoid. The results show that a quite simple time series model can catch these deviations well but also brings along a large amount of false positives. A recurrent neural network was able to improve this significantly, still being able to catch the deviations while producing a lot fewer false positives.

Name of the master programme: MPALG – Computer Science: Algorithms, Languages, and Logic, MPCAS – Complex Adaptive Systems
Examiner: Giovanni Volpe
Supervisor: Herman Johnsson (Volvo Cars)
Opponent: Jonathan Stålberg and Josef Gullholm5

Place: Nexus
Time: 14 June, 2022, 16:00

Deep learning in light–matter interactions published in Nanophotonics

Artificial neurons can be combined in a dense neural network (DNN), where the input layer is connected to the output layer via a set of hidden layers. *(Image by the Authors.)*

Deep learning in light–matter interactions
Daniel Midtvedt, Vasilii Mylnikov, Alexander Stilgoe, Mikael Käll, Halina Rubinsztein-Dunlop and Giovanni Volpe
Nanophotonics, 11(14), 3189-3214 (2022)
doi: 10.1515/nanoph-2022-0197

The deep-learning revolution is providing enticing new opportunities to manipulate and harness light at all scales. By building models of light–matter interactions from large experimental or simulated datasets, deep learning has already improved the design of nanophotonic devices and the acquisition and analysis of experimental data, even in situations where the underlying theory is not sufficiently established or too complex to be of practical use. Beyond these early success stories, deep learning also poses several challenges. Most importantly, deep learning works as a black box, making it difficult to understand and interpret its results and reliability, especially when training on incomplete datasets or dealing with data generated by adversarial approaches. Here, after an overview of how deep learning is currently employed in photonics, we discuss the emerging opportunities and challenges, shining light on how deep learning advances photonics.

David Rinman will defended his Master thesis on 13 June 2022

David Rinman will defended his Master thesis in MPCAS at the Chalmers University of Technology on 13 June 2022 at 13:00.

Title:
Monitoring Monitors; ML-based Anomaly Detection in Loudspeakers

Abstract:
Measuring the input voltage and current passing through a loudspeker and comparing the results to a parametric model is a way to monitor the condition of a loudspeaker in amplifiers. Prior research has shown that this can be done using music as input signal and can therefore work in commercial audio applications during normal operation. However, this solution requires modelling the specific loudspeaker setup which can be impractical in real-world scenarios. The aim of this project is to attempt to overcome these limitations by applying machine learning to the problem of anomaly detection in loudspeakers. Data is collected while playing music through two real functioning speakers where anomalies are simulated by disturbing the movement of their diaphragms. Three models are proposed and evaluated, two of which are based on deep neural networks. The results show that all three models are capable of learning a representation of one of the loudspeakers and detect deviances in these representations, however not for all of the simulated anomalies. Furthermore, the robustness of the models in prescence of nonlinear loudspeaker behavior is examined, and the limitations and benefits of the models are discussed. Finally, suggestions for future research directions are proposed.

Name of the master programme: MPCAS – Complex Adaptive Systems
Examiner: Giovanni Volpe
Supervisor: Jesper Pedersen (MusicTribe)
Opponent: TBA

Place: van Bahr
Time: 13 June, 2022, 13:00

Presentation by Vide Ramsten, 10 June 2022

Observer, Target Generation and Control Design in Robotics
Vide Ramsten
10 June 2022, 15:00 CET

Abstract
In this presentation, three topics related to Control Theory will be discussed together with practical examples from my Bachelor and Master thesis projects. First, the concept of state observers will be presented, where internal system states are estimated based on the measurable outputs of the system. Second, target generation will be discussed, in which the particular output or state trajectory of the system that is desired, is created. Lastly, we consider controller design, where we specify how to create the input given the previously defined parts such as target reference, measurable output and estimated system states. The theory will be applied to two projects. One in which a wheeled robot is developed for guiding purposes, so that the robot can show users the way to certain locations specified by the user. The project gives examples of state observers by localization algorithms, as well as target generation by path planning algorithms. The other example is a robotic testing system for passive prosthesis, where target generation through a motion-capture system is used as a reference for robot motion. A control strategy has been implemented in order to track this reference signal.

Short Bio
Vide Ramsten got his Bachelor degree in Automation and Mechatronic at the Chalmers University of Technology. After that, he continued his studies in a master programme in Systems, Control and Mechatronics at Chalmers. During his master, he did a double degree exchange with the University of Stuttgart, Germany in Engineering Cybernetics. While in Germany, he did a six-month internship at the robotics company BEC Gmbh focused on applications of control in robotics, as well as his master thesis at the Fraunhofer Institute of Manufacturing Engineering and Automation IPA.

Date: 10 June 2022
Time: 11:00
Place: Faraday

Kasper Hall and Noell Hall defended their Master thesis on 10 June 2022. Congrats!

Kasper Hall and Noell Hall defended their Master thesis in MPCAS at the Chalmers University of Technology on 10 June 2022 at 14:00. Congrats!

Title: Interference Object Detection using TensorFlow Lite and Transfer Learning for Android Devices

With the rapid evolution of machine learning and artificial intelligence faster and more robust network architectures are developed. This is possible due to the increase in computational power, improved algorithms and the creation of large scale annotated datasets. Re-purposing these state of the art networks using transfer learning allows for customized models to be created and applied to niche problems. In this paper, we create an object detection application able to detect interference points in anechoic testing chambers. The application runs detection on a mobile device using networks created with TensorFlow Lite. Utilizing the detection result the application can give advice on how to improve the installation in the testing chamber and can thus enforce a baseline for how installations are conducted increasing the repeatability of tests.

Name of the master programme: MPCAS – Complex Adaptive Systems
Examiner: Giovanni Volpe
Supervisor: Giovanni Volpe and Christian Heina, Ericsson
Opponent: Angelo Barona Balda

Place: Origo 5.102
Time: 10 June, 2022, 14:00