New workshop series “Trustworthy AI”

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New workshop series “Trustworthy AI”

New workshop series “Trustworthy AI”

The AI for Good global summit is an all year digital event, featuring a weekly program of keynotes, workshops, interviews or Q&As. BIFOLD Fellow Dr. Wojciech Samek, head of department of Artificial Intelligence at Fraunhofer Heinrich Hertz Institute (HHI), is implementing a new online workshop series “Trustworthy AI” for this platform.

The AI for Good series is the leading action-oriented, global and inclusive United Nations platform on Artificial Intelligence (AI). The Summit is organized all year, always online, in Geneva by the International Telecommunication Union (ITU) – the United Nations specialized agency for information and communication technologies. The goal of the AI for Good series is to identify practical applications of AI and scale those solutions for global impact.

“AI systems have steadily grown in complexity, gaining predictivity often at the expense of interpretability, robustness and trustworthiness. Deep neural networks are a prime example of this development. While reaching ‘superhuman’ performances in various complex tasks, these models are susceptible to errors when confronted with tiny, adversarial variations of the input – variations which are either not noticeable or can be handled reliably by humans”

Dr. Wojciech Samek

The workshop series will discuss these challenges of current AI technology and will present new research aiming at overcoming these limitations and developing AI systems which can be certified to be trustworthy and robust.

The workshop series will cover the following topics:

  • Measuring Neural Network Robustness
  • Auditing AI Systems
  • Adversarial Attacks and Defences
  • Explainability & Trustworthiness
  • Poisoning Attacks on AI
  • Certified Robustness
  • Model and Data Uncertainty
  • AI Safety and Fairness

The first workshop is held by Nicholas Carlini, Research Scientist at Google AI, 25. March 2021, 5:00 pm CET: Trustworthy AI: Adversarially (non-)Robust Machine Learning.

Register here: https://itu.zoom.us/webinar/register/WN_md37GUoSTdiQTq92ZNVvDw

„European Data Sovereignty is a critical success factor“

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„European Data Sovereignty is a critical success factor“

„European Data Sovereignty is a critical success factor“

Prof. Markl invited to speak on “Artificial Intelligence and Competitiveness” at EU Committee “AIDA”

On Tuesday, March 23, 2021, 09:00-12:00 CET, the European Committee Artificial Intelligence in a Digital Age (AIDA) is organizing a hearing on “AI and Competitiveness”. AIDA is a standing committee, established by the European Parliament to analyze the future impact of artificial intelligence in the digital age on the EU economy.

The event will address the following questions: What are the choices for regulatory frameworks to enabling the potential of AI solutions for increasing EU enterprises competitiveness?; How to build a competitive and innovative AI sector?; What are the EU enterprises challenges in entering AI markets, by developing and adopting competitive AI solutions?

BIFOLD Co-Director Prof. Dr. Volker Markl

BIFOLD Co-Director Prof. Dr. Volker Markl is invited to give an initial intervention for the second panel on “How to build a competitive and innovative AI sector? What are EU enterprises challenges in entering AI markets, by developing and adopting competitive AI solutions?” 

Prof. Markl has been actively promoting European Data Sovereignty and a data analysis infrastructure for years. For the AIDA hearing, he strengthens three key aspects:

1. “Most of the novel AI applications today are due to advances in machine learning (ML) and big data (BD) systems and technologies. Due to international competition, the EU needs to make massive investments in research to develop next generation ML methods and BD systems as well as in education to train our workforce in their use. In particular, we need to provide basic training in data literacy and computer science competencies, such as data programming, data management, and data intensive algorithms. These subjects need to be taught throughout our educational studies (from elementary education, through middle and high-school and beyond at universities, across all academic study programs).”

2. “Data is the new production factor for our economy. Europe needs to be competitive. We need an independent, technical infrastructure and ecosystem that will enable us to create, share and use both data and algorithms as well as storage and compute resources in an open and inclusive way – moving beyond North American and Chinese solutions. If Europe intends to shape the future of the digital world through its industry, we have to: (i) maintain digital sovereignty in AI, (ii) retain technical talent, (iii) facilitate data-driven business opportunities and citizen science and (iv) compete globally.”

3.“Member states need to bootstrap and create demand for such an ecosystem by enabling a holistic European solution. We must go beyond data exchange and multi-cloud considerations, like GAIA-X, but rather be centered around the development of an easy-to-use, integrated single platform to store data, host algorithms, and perform processing. The creation of such an ecosystem should avoid the complexity and cacophony of too many stakeholders. Instead, it should be developed by a single institution with a clear vision, mission and objectives. It should leverage economies of scale, follow software-hardware co-design principles, and take recent technological advances in networking, distributed systems, data management, and machine learning into consideration. Moreover, it should enable EU startups, companies, and EU citizens to share data and algorithms as well as compose, process, and offer AI applications in open and protected spaces.”

The hearing will be publicly available via webstream. More information is available at https://www.europarl.europa.eu/committees/en/aida-hearing-on-ai-and-competitiveness/product-details/20210210CAN59709.

Making the role of AI in Medicine explainable

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Making the role of AI in Medicine explainable

Making the role of AI in Medicine explainable

Analysis system for the diagnosis of breast cancer

Researchers at TU Berlin and Charité – Universitätsmedizin Berlin as well as the University of Oslo have developed a new tissue-section analysis system for diagnosing breast cancer based on artificial intelligence (AI). Two further developments make this system unique: For the first time, morphological, molecular and histological data are integrated in a single analysis. Secondly, the system provides a clarification of the AI decision process in the form of heatmaps. Pixel by pixel, these heatmaps show which visual information influenced the AI decision process and to what extent, thus enabling doctors to understand and assess the plausibility of the results of the AI analysis. This represents a decisive and essential step forward for the future regular use of AI systems in hospitals. The results of this research have now been published in Nature Machine Intelligence.

Cancer treatment is increasingly concerned with the molecular characterization of tumor tissue samples. Studies are conducted to determine whether and/or how the DNA has changed in the tumor tissue as well as the gene and protein expression in the tissue sample. At the same time, researchers are becoming increasingly aware that cancer progression is closely related to intercellular cross-talk and the interaction of neoplastic cells with the surrounding tissue – including the immune system.

Image data provide high spatial detail

Although microscopic techniques enable biological processes to be studied with high spatial detail, they only permit a limited measurement of molecular markers. These are rather determined using proteins or DNA taken from tissue. As a result, spatial detail is not possible and the relationship between these markers and the microscopic structures is typically unclear. “We know that in the case of breast cancer, the number of immigrated immune cells, known as lymphocytes, in tumor tissue has an influence on the patient’s prognosis. There are also discussions as to whether this number has a predictive value – in other words if it enables us to say how effective a particular therapy is,” says Professor Dr. Frederick Klauschen from the Institute of Pathology at the Charité.

“The problem we have is the following: We have good and reliable molecular data and we have good histological data with high spatial detail. What we don’t have as yet is the decisive link between imaging data and high-dimensional molecular data,” adds Professor Dr. Klaus-Robert Müller, professor of machine learning at TU Berlin. Both researchers have been working together for a number of years now at the national AI center of excellence the Berlin Institute for the Foundations of Learning and Data (BIFOLD) located at TU Berlin.

Missing link between molecular and histological data

Determination of tumor-infiltrating lymphocytes (TiLs) using Explainable AI technology. Histological preparation of a breast carcinoma.
(© Frederick Klauschen)

Result of the AI process shows a so-called heatmap, which highlights the TiLs in red. Other tissue/cells: blue and green.
(© Frederick Klauschen)

It is precisely this symbiosis which the newly published approach makes possible. “Our system facilitates the detection of pathological alterations in microscopic images. Parallel to this, we are able to provide precise heatmap visualizations showing which pixel in the microscopic image contributed to the diagnostic algorithm and to what extent,” explains Müller. The research team has also succeeded in significantly further developing this process: “Our analysis system has been trained using machine learning processes so that it can also predict various molecular characteristics, including the condition of the DNA, the gene expression as well as the protein expression in specific areas of the tissue, on the basis of the histological images.

Next on the agenda are certification and further clinical validations – including tests in tumor routine diagnostics. However, Frederick Klauschen is already convinced of the value of the research: “The methods we have developed will make it possible in the future to make histopathological tumor diagnostics more precise, more standardized and qualitatively better.”

Publication:

Morphological and molecular breast cancer profiling through explainable machine learning, Nature Machine Intelligence

Further information can be obtained from:

Prof. Dr. Klaus-Robert Müller
TU Berlin
Maschinelles Lernen
Tel.: 030 314 78621
E-Mail: klaus-robert.mueller@tu-berlin.de

Prof. Dr. Frederick Klauschen
Charité – Universitätsmedizin Berlin
Institut für Pathologie
Tel.: 030 450 536 053
E-Mail: frederick.klauschen@charite.de

2020 Pattern Recognition Best Paper Award

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2020 Pattern Recognition Best Paper Award

2020 Pattern Recognition Best Paper Award

A team of scientists from TU Berlin, Fraunhofer Heinrich Hertz Institute (HHI) and University of Oslo has jointly received the 2020 “Pattern Recognition Best Paper Award” and “Pattern Recognition Medal” of the international scientific journal Pattern Recognition. The award committee honored the publication “Explaining Nonlinear Classification Decisions with Deep Taylor Decomposition” by Dr. Grégoire Montavon and Prof. Dr. Klaus-Robert Müller from TU Berlin, Prof. Dr. Alexander Binder from University of Oslo, as well as Dr. Wojciech Samek and Dr. Sebastian Lapuschkin from HHI.

Dr. Grégoire Montavon with the 2020 Pattern Recognition Best Paper Award in hand.

The publication addresses the so-called black box problem. Machine Learning methods, in particular Deep Learning, successfully solve a variety of tasks. However, in most cases they fail to provide the information that has led to a particular decision. The paper tackles this problem by using a pixel-by-pixel decomposition of nonlinear classifications and evaluates the procedure in different scenarios. This method provides a theoretical framework for Explainable Artificial Intelligence (XAI) that is generally applicable. XAI is a major research field of the Berlin Institute for the Foundations of Learning and Data (BIFOLD), of which the authors from TU Berlin and HHI are members.

The award was presented to Grégoire Montavon in January 2021, during the virtual International Conference on Pattern Recognition (ICPR). The “Pattern Recognition Best Paper Award” is granted every two years. It recognizes a highly cited paper in the area of pattern recognition and its application areas such as image processing, computer vision and biometrics.

“We are very proud to receive this award and for our work to be highlighted within the global scientific community.”

Dr. Grégoire Montavon.

BIFOLD Fellow Dr. Wojciech Samek heads newly established AI research department at Fraunhofer HHI

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BIFOLD Fellow Dr. Wojciech Samek heads newly established AI research department at Fraunhofer HHI

BIFOLD Fellow Dr. Wojciech Samek heads newly established AI research department at Fraunhofer HHI

Dr. Samek (l.) and Prof. Müller in front of an XAI demonstrator at Fraunhofer HHI. (Copyright: TU Berlin/Christian Kielmann)

The Fraunhofer Heinrich Hertz Institute (HHI) has established a new research department dedicated to “Artificial Intelligence”. The AI expert and BIFOLD Fellow Dr. Wojciech Samek, previously leading the research group “Machine Learning” at Fraunhofer HHI, will head the new department. With this move Fraunhofer HHI aims at expanding the transfer of its AI research on topics such as Explainable AI and neural network compression to the industry.

Dr. Wojciech Samek: “The mission of our newly founded department is to make today’s AI truly trustable and in all aspects practicable. To achieve this, we will very closely collaborate with BIFOLD in order to overcome the limitations of current deep learning models regarding explainability, reliability and efficiency.“

“Congratulations, I look forward to a continued successful teamwork with BIFOLD fellow Wojciech Samek, who is a true AI hot shot.”

BIFOLD Director Prof. Dr. Klaus-Robert Müller

The new department further strengthens the already existing close connection between basic AI research at BIFOLD and applied research at Fraunhofer HHI and is a valuable addition to the dynamic AI ecosystem in Berlin.

“The large Berlin innovation network centered around BIFOLD is unique in Germany. This ensures that the latest research results will find their way into business, science and society.”

BIFOLD Director Prof. Dr. Volker Markl

BIFOLD Co-Director Prof. Volker Markl named 2020 ACM Fellow

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BIFOLD Co-Director Prof. Volker Markl named 2020 ACM Fellow

BIFOLD Co-Director Prof. Volker Markl named 2020 ACM Fellow

The Association for Computing Machinery (ACM), the largest and oldest international association of computer scientists, has named Prof. Dr. Volker Markl, Co-Director of the Berlin Institute for the Foundations of Learning and Data (BIFOLD), Head of the Database Systems and Information Management (DIMA) Group at TU Berlin and Head of Intelligent Analytics for Massive Data (IAM) at the German Research Center for Artificial Intelligence as an ACM Fellow.

Less than one percent of all worldwide ACM members are recognized as ACM Fellows for their outstanding achievements each year since 1993. For 2020, the ACM awarded 95 fellowships, only three of which went to Germany.
Volker Markl received this distinction for his contributions to query optimization, scalable data processing and data programmability. He is one of 22 German scientists who have been honored by the ACM so far.

Prof. Dr. Volker Markl
(Copyright: TU Berlin /PR/Simon)

“I am very happy about this rare honor and international recognition of my work.”

Prof. Dr. Volker Markl

Approximately 100,000 members belong to the ACM worldwide. The prestigious ACM Fellowship recognizes those members who have made outstanding contributions to computing and information technology and/or outstanding service to ACM and the computational science community.

To learn more about the 2020 ACM Fellows, please visit https://www.acm.org/media-center/2021/january/fellows-2020.

For information in German please read the official press release of TU Berlin.

BIFOLD Research into Machine Learning for Molecular Simulation is among the Top 10 Most Downloaded Physical Science Articles in the Annual Reviews 2020

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BIFOLD Research into Machine Learning for Molecular Simulation is among the Top 10 Most Downloaded Physical Science Articles in the Annual Reviews 2020

BIFOLD Research into Machine Learnig for Molecular Simulation is among the Top 10 Most Downloaded Physical Science Articles in the Annual Reviews 2020

The paper “Machine Learning for Molecular Simulation” by BIFOLD Co-Director Prof. Dr. Klaus-Robert Müller, Principal Investigator Prof. Dr. Frank Noé and colleagues was among the top 10 most downloaded physical science articles in the Annual Reviews in 2020.

Machine Learning has a growing influence in the physical sciences. In 2020 BIFOLD researchers contributed to major scientific advances, especially in the field of Machine Learning for quantum chemistry. As a result of cooperations on a national and international level BIFOLD researchers achieved i.e. a scientific breakthrough by proposing a reinforced learning method to separate and move single molecules out of a structure, developed a Deep Learning method to solve Schroedingers equation more accurately and leveraged Machine Learning to achieve high quantum chemical accuracy from density functional approximations.

The paper “Machine Learning for Molecular Simulation” by Frank Noé Alexandre Tkatchenko, Klaus-Robert Müller and Cecilia Clementi is another example of a high impact publication in quantum mechanics. The authors review Machine Learning methods for molecular simulation with particular focus on (deep) neural networks for the prediction of quantum-mechanical energies and forces, on coarse-grained molecular dynamics, on the extraction of free energy surfaces and kinetics, and on generative network approaches to sample molecular equilibrium structures and compute thermodynamics.
The paper is among the most downloaded Annual Reviews articles of 2020, specifically one of the ten most downloaded physical science articles. Both Prof. Dr. Klaus-Robert Müller and Prof. Dr. Frank Noé were recently also featured in the Clarivate™ 2020 Highly Cited Researchers™ list, emphasizing their leading role in the international research community in the interdisciplinary area of computer science and chemistry.

The paper in detail:

Machine Learning for Molecular Simulation

Authors:
Frank Noé Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi

Abstract:
Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for an ML revolution and have already been profoundly affected by the application of existing ML methods. Here we review recent ML methods for molecular simulation, with particular focus on (deep) neural networks for the prediction of quantum-mechanical energies and forces, on coarse-grained molecular dynamics, on the extraction of free energy surfaces and kinetics, and on generative network approaches to sample molecular equilibrium structures and compute thermodynamics. To explain these methods and illustrate open methodological problems, we review some important principles of molecular physics and describe how they can be incorporated into ML structures. Finally, we identify and describe a list of open challenges for the interface between ML and molecular simulation

Publication:
Frank Noé Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi: Machine Learning for Molecular Simulation.Annual Review of Physical Chemistry, Vol. 71: 361-390
https://doi.org/10.1146/annurev-physchem-042018-052331

Resilient Data Management for the Internet of Moving Things: TU Berlin and DFKI Paper was Accepted at BTW 2021

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Resilient Data Management for the Internet of Moving Things: TU Berlin and DFKI Paper was Accepted at BTW 2021

Resilient Data Management for the Internet of Moving Things: TU Berlin and DFKI Paper was Accepted at BTW 2021

The paper “Towards Resilient Data Management for the Internet of Moving Things” by Elena Beatriz Ouro Paz, Eleni Tzirita Zacharatou and Volker Markl was accepted for presentation at the 19. Fachtagung für Datenbanksysteme für Business, Technologie und Web (BTW 2021) on September 20 – 24, 2021. Following the acceptance of a paper on fast CSV loading using GPUS, this is the second paper by researchers from the Database Systems and Information Management (DIMA) group at TU Berlin and the Intelligent Analytics for Massive Data (IAM) group at DFKI that will be presented at BTW 2021.
BTW is the leading database conference in the german-speaking area. For more Information on the conference, please visit https://sites.google.com/view/btw-2021-tud/.

Abstract:
Mobile devices have become ubiquitous; smartphones, tablets and wearables are essential commodities for many people. The ubiquity of mobile devices combined with their ever increasing capabilities, open new possibilities for Internet-of-Things (IoT) applications where mobile devices act as both data generators as well as processing nodes. However, deploying a stream processing system (SPS) over mobile devices is particularly challenging as mobile devices change their position within the network very frequently and are notoriously prone to transient disconnections. To deal with faults arising from disconnections and mobility, existing fault tolerance strategies in SPS are either checkpointing-based or replication-based. Checkpointing-based strategies are too heavyweight for mobile devices, as they save and broadcast state periodically, even when there are no failures. On the other hand, replication-based strategies cannot provide fault tolerance at the level of the data source, as the data source itself cannot be always replicated. Finally, existing systems exclude mobile devices from data processing upon a disconnection even when the duration of the disconnection is very short, thus failing to exploit the computing capabilities of the offline devices. This paper proposes a buffering-based reactive fault tolerance strategy to handle transient disconnections of mobile devices that both generate and process data, even in cases where the devices move through the network during the disconnection. The main components of our strategy are: (a) a circular buffer that stores the data which are generated and processed locally during a device disconnection, (b) a query-aware buffer replacement policy, and (c) a query restart process that ensures the correct forwarding of the buffered data upon re-connection, taking into account the new network topology. We integrate our fault tolerance strategy with NebulaStream, a novel stream processing system specifically designed for the IoT. We evaluate our strategy using a custom benchmark based on real data, exhibiting reduction in data loss and query latency compared to the baseline NebulaStream.

A preprint version of the paper (PDF) is available for download.

TU Berlin, DFKI and NUS Paper on Parallelizing Intra-Window Join on Multicores was Accepted at SIGMOD 2021

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TU Berlin, DFKI and NUS Paper on Parallelizing Intra-Window Join on Multicores was Accepted at SIGMOD 2021

TU Berlin, DFKI and NUS Paper on Parallelizing Intra-Window Join on Multicores was Accepted at SIGMOD 2021

The paper “Parallelizing Intra-Window Join on Multicores: An Experimental Study” by Shuhao Zhang, Yancan Mao, Jiong He, Philipp Grulich, Steffen Zeuch, Bingsheng He, Richard Ma and Volker Markl was accepted for presentation at the ACM SIGMOD/PODS International Conference on Management of Data (SIGMOD/PODS 2021), which will take place from June 20 – 25, 2021 in Xi’an, China. This work is the result of a collaboration between researchers from the Database Systems and Information Management (DIMA) group at TU Berlin, the Intelligent Analytics for Massive Data (IAM) group at DFKI, the Department of Computer Science at the National University of Singapore and ByteDance.
The annual ACM SIGMOD/PODS Conference is a leading international forum for database researchers, practitioners, developers, and users to explore cutting-edge ideas and results, and to exchange techniques, tools, and experiences in all aspects of data management. To learn more about SIGMOD/PODS, please visit https://2021.sigmod.org/.

Abstract:
The intra-window join (IaWJ), i.e., joining two input streams over a single window, is a core operation in modern stream processing applications. This paper presents the first comprehensive study on parallelizing the IaWJ on modern multicore architectures. In particular, we classify IaWJ algorithms into lazy and eager execution approaches. For each approach, there are further design aspects to consider, including different join methods and partitioning schemes, leading to a large design space. Our results show that none of the algorithms always performs the best, and the choice of the most performant algorithm depends on: (i) workload characteristics, (ii) application requirements, and (iii) hardware architectures. Based on the evaluation results, we propose a decision tree that can guide the selection of an appropriate algorithm.

A preprint version of the paper is available here.

Researchers at FU Berlin Solve Schroedingers Equation with new Deep Learning Method

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Researchers at FU Berlin Solve Schroedingers Equation with new Deep Learning Method

Researchers at FU Berlin Solve Schroedingers Equation with new Deep Learning Method

BIFOLD Principal Investigator Prof. Dr. Frank Noé and Senior Researcher Dr. Jan Hermann of the Artificial Intelligence for the Sciences group at Freie Universität Berlin developed a new, exceptionally accurate and efficient method to solve the electronic Schroedinger equation. Their approach could have a significant impact on the future of quantum chemistry.

Prof. Noé’s and Dr. Hermann’s method, PauliNet, avoids the limitation of previous approaches. It is not only a more accurate way of representing the electronic wave function, but also includes physical properties into the deep neural network.

“Escaping the usual trade-off between accuracy and computational cost is the highest achievement in quantum chemistry. As yet, the most popular such outlier is the extremely cost-effective density functional theory. We believe that deep “Quantum Monte Carlo,” the approach we are proposing, could be equally, if not more successful. It offers unprecedented accuracy at a still acceptable computational cost.”

Dr. Jan Hermann

“Building the fundamental physics into the AI is essential for its ability to make meaningful predictions in the field. This is really where scientists can make a substantial contribution to AI, and exactly what my group is focused on.”

Prof. Dr. Frank Noé

For more information, please visit the official press release of FU Berlin (available also at phys.org).

The paper in detail:
Deep-neural-network solution of the electronic Schrödinger equation

Authors:
Jan Hermann, Zeno Schätzle, Frank Noé

Abstract:
The electronic Schrödinger equation can only be solved analytically for the hydrogen atom, and the numerically exact full configuration-interaction method is exponentially expensive in the number of electrons. Quantum Monte Carlo methods are a possible way out: they scale well for large molecules, they can be parallelized and their accuracy has, as yet, been only limited by the flexibility of the wavefunction ansatz used. Here we propose PauliNet, a deep-learning wavefunction ansatz that achieves nearly exact solutions of the electronic Schrödinger equation for molecules with up to 30 electrons. PauliNet has a multireference Hartree–Fock solution built in as a baseline, incorporates the physics of valid wavefunctions and is trained using variational quantum Monte Carlo. PauliNet outperforms previous state-of-the-art variational ansatzes for atoms, diatomic molecules and a strongly correlated linear H10, and matches the accuracy of highly specialized quantum chemistry methods on the transition-state energy of cyclobutadiene, while being computationally efficient.

Publication:
Hermann, J., Schätzle, Z. & Noé, F. Deep-neural-network solution of the electronic Schrödinger equation. Nat. Chem. 12, 891–897 (2020). https://doi.org/10.1038/s41557-020-0544-y