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arXiv 2020年

作者： Lopez, Ricard Durall Chatzimichailidis, Avraam Labus, Peter Keuper, Janis Fraunhofer ITWM Germany IWR University of Heidelberg Germany Chair for Scientific Computing TU Kaiserslautern Germany Fraunhofer Center Machine Learning Germany Institute for Machine Learning and Analytics Offenburg University Germany

Generative adversarial networks (GANs) provide state-of-the-art results in image generation. However, despite being so powerful, they still remain very challenging to train. This is in particular caused by their highly non-convex optimization space leading to a number of instabilities. Among them, mode collapse stands out as one of the most daunting ones. This undesirable event occurs when the model can only fit a few modes of the data distribution, while ignoring the majority of them. In this work, we combat mode collapse using second-order gradient information. To do so, we analyse the loss surface through its Hessian eigenvalues, and show that mode collapse is related to the convergence towards sharp minima. In particular, we observe how the eigenvalues of the G are directly correlated with the occurrence of mode collapse. Finally, motivated by these findings, we design a new optimization algorithm called nudged-Adam (NuGAN) that uses spectral information to overcome mode collapse, leading to empirically more stable convergence properties. © 2020, CC BY.

关键词： Eigenvalues and eigenfunctions

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On the influence of several factors on pathway enrichment analysis

arXiv

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arXiv 2022年

作者： Mubeen, Sarah Kodamullil, Alpha Tom Hofmann-Apitius, Martin Domingo-Fernández, Daniel Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin53757 Germany University of Bonn Bonn53115 Germany Fraunhofer Center for Machine Learning Germany Enveda Biosciences BoulderCO80301 United States

Pathway enrichment analysis has become a widely used knowledge-based approach for the interpretation of biomedical data. Its popularity has led to an explosion of both enrichment methods and pathway databases. While the elegance of pathway enrichment lies in its simplicity, multiple factors can impact the results of such an analysis which may not be accounted for. Researchers may fail to give influential aspects their due, resorting instead to popular methods and gene set collections, or default settings. Despite ongoing efforts to establish set guidelines, meaningful results are still hampered by a lack of consensus or gold standards around how enrichment analysis should be conducted. Nonetheless, such concerns have prompted a series of benchmark studies specifically focused on evaluating the influence of various factors on pathway enrichment results. In this review, we organize and summarize the findings of these benchmarks to provide a comprehensive overview on the influence of these factors. Our work covers a broad spectrum of factors, spanning from methodological assumptions to those related to prior biological knowledge, such as pathway definitions and database choice. In doing so, we aim to shed light on how these aspects can lead to insignificant, uninteresting, or even contradictory results. Finally, we conclude the review by proposing future benchmarks as well as solutions to overcome some of the challenges which originate from the outlined factors. © 2022, CC BY.

关键词： Database systems

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Predicting Properties of Oxide Glasses Using Informed Neural Networks

arXiv

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arXiv 2023年

作者： Maier, Gregor Hamaekers, Jan Martilotti, Dominik-Sergio Ziebarth, Benedikt Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin53757 Germany Institut für Numerische Simulation Universität Bonn Friedrich-Hirzebruch-Allee 7 Bonn53115 Germany Fraunhofer Center for Machine Learning Schloss Birlinghoven Sankt Augustin53757 Germany Schott AG Hattenbergstrasse 10 Mainz55122 Germany

Many modern-day applications require the development of new materials with specific properties. In particular, the design of new glass compositions is of great industrial interest. Current machine learning methods for learning the composition-property relationship of glasses promise to save on expensive trial-and-error approaches. Even though quite large datasets on the composition of glasses and their properties already exist (i.e., with more than 350,000 samples), they cover only a very small fraction of the space of all possible glass compositions. This limits the applicability of purely data-driven models for property prediction purposes and necessitates the development of models with high extrapolation power. In this paper, we propose a neural network model which incorporates prior scientific and expert knowledge in its learning pipeline. This informed learning approach leads to an improved extrapolation power compared to blind (uninformed) neural network models. To demonstrate this, we train our models to predict three different material properties, that is, the glass transition temperature, the Young’s modulus (at room temperature), and the shear modulus of binary oxide glasses which do not contain sodium. As representatives for conventional blind neural network approaches we use five different feed-forward neural networks of varying widths and depths. For each property, we set up model ensembles of multiple trained models and show that, on average, our proposed informed model performs better in extrapolating the three properties of previously unseen sodium borate glass samples than all five conventional blind models. © 2023, CC BY-SA.

关键词： Extrapolation

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Predictive analytics in quality assurance for assembly processes: lessons learned from a case study at an industry 4.0 demonstration cell

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Procedia CIRP 2021年 104卷 641-646页

作者： Peter Burggräf Johannes Wagner Benjamin Heinbach Fabian Steinberg Alejandro R. Pérez M. Lennart Schmallenbach Jochen Garcke Daniela Steffes-lai Moritz Wolter Chair of International Production Engineering and Management (IPEM) Universität Siegen Paul-Bonatz-Straße 9-11 57076 Siegen Germany Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven 1 53757 Sankt Augustin Germany Fraunhofer Center for Machine Learning Schloss Birlinghoven 1 53757 Sankt Augustin Germany Institute for Numerical Simulation Universität Bonn Endenicher Allee 19b 53115 Bonn Germany Institute for Computer Science Universität Bonn Endenicher Allee 19a 53115 Bonn Germany

Quality assurance (QA) is an important task in manufacturing to assess whether products meet their specifications. However, QA might be expensive, time-consuming, or incomplete. This paper presents a solution for predictive analytics in QA based on machine sensor values during production while employing specialized machine-learning models for classification in a controlled environment. Furthermore, we present lessons learned while implementing this model, which helps to reduce complexity in further industrial applications. The paper’s outcome proves that the developed model was able to predict product quality, as well as to identify the correlation between machine-status and faulty product occurrence.

关键词： machine-learning Predictive Quality Production Quality Assurance Classification

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Assessing the landscape of Alzheimer’s disease cohort data

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Alzheimer's & Dementia 2020年第S4期16卷

作者： Colin Birkenbihl Yasamin Salimi Daniel Domingo-Fernández Holger Fröhlich Martin Hofmann-Apitius Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Sankt Augustin Germany Bonn-Aachen International Center for IT (b-it) Bonn Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany

Background Data collected in cohort studies lay the groundwork for a plethora of Alzheimer’s disease (AD) research endeavours. While there exist numerous cohort datasets in our field, few dedicated efforts have focused on comparing these datasets to each other. In recent years, large research consortia, such as EMIF and ROADMAP, were formed to investigate and evaluate the AD data landscape. To the best of our knowledge, such previous approaches have solely investigated cohorts at the metadata level without considering the cohort dataset itself. In this work, we explored eight major AD cohort datasets with the aim of 1) characterizing their underlying data, 2) assessing the quantity and availability of data, 3) evaluating the interoperability across distinct cohort datasets, and 4) presenting our findings in an interactive web application. Thus, we allow researchers to investigate the AD data landscape and find the most suited datasets for their research. Method We collected data from multiple major AD cohort studies. Through intensive manual curation of these datasets, we identified which data modalities can be found in the shared data. Using descriptive statistics and visualizations, we highlighted differences and similarities between cohorts focusing on key demographic characteristics and AD biomarkers. Finally, we assessed and compared the longitudinal follow-up data for each study. Result We show that demographics can vary substantially across cohorts (Table 1). The overlap between datasets is limited with regard to assessed measurements (Figure 1). Additionally, distributions of the same variables often differ across cohorts due to differences in processing and normalization; thus, hampering dataset interoperability. By investigating longitudinal follow-up data, we demonstrate that although there is reasonable longitudinal coverage on cognitive assessments, important biomarkers were only collected for a small fraction of study participants over time (Figure 2).

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A representer theorem for deep kernel learning

The Journal of Machine Learning Research

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The Journal of machine learning Research 2019年第1期20卷

作者： Bastian Bohn Christian Rieger Michael Griebel Institute for Numerical Simulation University of Bonn Bonn Germany Institute for Numerical Simulation University of Bonn Bonn Germany and Fraunhofer Center for Machine Learning Fraunhofer Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany

In this paper we provide a finite-sample and an infinite-sample representer theorem for the concatenation of (linear combinations of) kernel functions of reproducing kernel Hilbert spaces. These results serve as mathematical foundation for the analysis of machine learning algorithms based on compositions of functions. As a direct consequence in the finite-sample case, the corresponding infinite-dimensional minimization problems can be recast into (nonlinear) finite-dimensional minimization problems, which can be tackled with nonlinear optimization algorithms. Moreover, we show how concatenated machine learning problems can be reformulated as neural networks and how our representer theorem applies to a broad class of state-of-the-art deep learning methods.

关键词： artificial neural networks deep kernel learning multilayer kernel regularized least-squares regression representer theorem

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Ten new insights in climate science 2024

One Earth

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One Earth 2025年

作者： Schaeffer, Roberto Schipper, E. Lisa F. Ospina, Daniel Mirazo, Paula Alencar, Ane Anvari, Mehrnaz Artaxo, Paulo Biresselioglu, Mehmet Efe Blome, Tanja Boeckmann, Melanie Brink, Ebba Broadgate, Wendy Bustamante, Mercedes Cai, Wenju Canadell, Josep G. Cardinale, Roberto Chidichimo, Maria Paz Ditlevsen, Peter Eicker, Ursula Feron, Sarah Fikru, Mahelet G. Fuss, Sabine Gaye, Amadou T. Gustafsson, Örjan Harring, Niklas He, Cheng Hebden, Sophie Heilemann, Adrian Hirota, Marina Janardhanan, Nandakumar Juhola, Sirkku Jung, Tae Yong Kejun, Jiang Kilkiș, Şiir Kumarasinghe, Nilushi Lapola, David Lee, June-Yi Levis, Carolina Lusambili, Adelaide Maasakkers, Joannes D. MacIntosh, Claire Mahmood, Jemilah Mankin, Justin S. Marchegiani, Pía Martin, Maria Mukherji, Aditi Muñoz-Erickson, Tischa A. Niazi, Zeenat Nyangon, Joseph Pandipati, Santosh Perera, Amarasinghage T.D. Persad, Geeta Persson, Åsa Redman, Aaron Riipinen, Ilona Rockström, Johan Roffe, Sarah Roy, Joyashree Sakschewski, Boris Samset, Bjørn H. Schlosser, Peter Sharifi, Ayyoob Shih, Wan-Yu Sioen, Giles B. Sokona, Youba Stammer, Detlef Suk, Sunhee Thiam, Djiby Thompson, Vikki Tullos, Erin van Westen, René M. Vargas Falla, Ana Maria Vecellio, Daniel J. Worden, John Wu, Henry C. Xu, Chi Yang, Yang Zachariah, Mariam Zhang, Zhen Ziervogel, Gina Federal University of Rio de Janeiro Rio de Janeiro Brazil University of Bonn Bonn Germany Future Earth Secretariat Stockholm Sweden Arizona State University Tempe AZ United States Amazon Environmental Research Institute (IPAM) Brasilia Brazil Fraunhofer Institute for Algorithms and Scientific Computing SCAI St. Augustin Germany Potsdam Institute for Climate Impact Research (PIK) Potsdam Germany University of São Paulo São Paulo Brazil İzmir University of Economics İzmir Turkey Climate Service Center Germany (GERICS) Helmholtz-Zentrum Hereon Hamburg Germany University of Bremen Bremen Germany Lund University Lund Sweden University of Brasilia Brasilia Brazil Ocean University of China Qingdao China Xiamen University Xiamen China Commonwealth Scientific and Industrial Research Organisation (CSIRO) Environment Canberra ACT Australia University College London London United Kingdom National Scientific and Technical Research Council (CONICET) Buenos Aires Argentina National University of San Martin Buenos Aires Argentina University of Copenhagen Copenhagen Denmark Concordia University Montreal QC Canada University of Groningen Groningen Netherlands Missouri University of Science and Technology Rolla MO United States Mercator Research Institute on Global Commons and Climate Change (MCC) Berlin Germany Humboldt-University of Berlin Berlin Germany Cheikh Anta Diop University Dakar Senegal Stockholm University Stockholm Sweden University of Gothenburg Gothenburg Sweden Institute of Epidemiology Helmholtz Zentrum München—German Research Center for Environmental Health (GmbH) Neuherberg Germany European Space Agency (ESA) - European Centre for Space Applications and Telecommunications (ECSAT) Oxford United Kingdom Federal University of Santa Catarina Florianopolis Brazil Institute for Global Environmental Strategies (IGES) Hayama Japan University of Helsinki Helsinki Finland Yonsei University Graduate Seoul South Korea Chinese Academy of Macroeconomic

The years 2023 and 2024 were characterized by unprecedented warming across the globe, underscoring the urgency of climate action. Robust science advice for decision makers on subjects as complex as climate change requires deep cross- and interdisciplinary understanding. However, navigating the ever-expanding and diverse peer-reviewed literature on climate change is enormously challenging for individual researchers. We elicited expert input through an online questionnaire (188 respondents from 45 countries) and prioritized 10 key advances in climate-change research with high policy relevance. The insights span a wide range of areas, from changes in methane and aerosol emissions to the factors shaping citizens’ acceptance of climate policies. This synthesis and communications effort forms the basis for a science-policy report distributed to party delegations ahead of the 29th session of the Conference of the Parties (COP29) to inform their positions and arguments on critical issues, including heat-adaptation planning, comprehensive mitigation strategies, and strengthened governance in energy-transition minerals value chains. © 2025 The Authors

关键词： adaptation climate policy climate science governance just transition mitigation resilience science policy

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A knowledge-based surrogate modeling approach for cup drawing with limited data

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IOP Conference Series: Materials Science and Engineering 2019年第1期651卷

作者： L Morand D Helm R Iza-Teran J Garcke Fraunhofer Institute for Mechanics of Materials IWM Wöhlerstraße 11 Freiburg 79108 Germany Fraunhofer Center for Machine Learning and Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin 53754 Germany Institute for Numerical Simulations University of Bonn Endenicher Allee 19b Bonn 53115 Germany

To predict the quality of a process outcome with given process parameters in real-time, surrogate models are often adopted. A surrogate model is a statistical model that interpolates between data points obtained either by process measurements or deterministic models of the process. However, in manufacturing processes the amount of useful data is often limited, and therefore setting up a sufficiently accurate surrogate model is challenging. The present contribution shows how to handle limited data in a surrogate modeling approach using the example of a cup drawing process. The purpose of the surrogate model is to classify the quality of the drawn cup and to predict its final geometry. These classification and regression tasks are solved via machine learning methods. The training data is sampled on a relatively wide range varying three parameters of a finite element simulation, namely sheet metal thickness, blank holder force, and friction. The geometrical features of the cup are extracted using domain knowledge. Besides this knowledge-based approach, an outlook is given for a data-driven surrogate modeling approach.

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From integrative disease modeling to predictive, preventive, personalized and participatory (P4) medicine

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EPMA Journal 2014年第1期5卷 1-1页

作者： Erfan Younesi Martin Hofmann-Apitius Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven Sankt Augustin Germany Rheinische Friedrich-Wilhelms-Universität Bonn Bonn-Aachen International Center for IT Bonn Germany

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SYNTHETIC DATA GENERATION FOR A LONGITUDINAL COHORT STUDY - EVALUATION, METHOD EXTENSION AND REPRODUCTION OF PUBLISHED DATA ANALYSIS RESULTS

arXiv

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arXiv 2023年

作者： Kühnel, Lisa Schneider, Julian Perrar, Ines Adams, Tim Prasser, Fabian Nöthlings, Ute Fröhlich, Holger Fluck, Juliane Knowledge Management Zb Med Information Centre for Life Sciences Cologne50931 Germany Faculty of Technology Bielefeld University Bielefeld33615 Germany Institute of Nutritional and Food Sciences - Nutritional Epidemiology University of Bonn Bonn53115 Germany Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing Scai Sankt Augustin53757 Germany Medical Informatics Group Berlin Institute of Health Charité - Universitätsmedizin Berlin Berlin10117 Germany Bonn-Aachen International Center for It University of Bonn Friedrich Hirzebruch-Allee 6 Bonn53115 Germany The Agricultural Faculty University of Bonn Bonn53115 Germany

Access to individual-level health data is essential for gaining new insights and advancing science. In particular, modern methods based on artificial intelligence rely on the availability of and access to large datasets. In the health sector, access to individual-level data is often challenging due to privacy concerns. A promising alternative is the generation of fully synthetic data, i.e. data generated through a randomised process that have similar statistical properties as the original data, but do not have a one-to-one correspondence with the original individual-level records. In this study, we use a state-of-the-art synthetic data generation method and perform in-depth quality analyses of the generated data for a specific use case in the field of nutrition. We demonstrate the need for careful analyses of synthetic data that go beyond descriptive statistics and provide valuable insights into how to realise the full potential of synthetic datasets. By extending the methods, but also by thoroughly analysing the effects of sampling from a trained model, we are able to largely reproduce significant real-world analysis results in the chosen use case. © 2023, CC BY.

关键词： Large dataset

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