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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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High-order semi-Lagrangian kinetic scheme for compressible turbulence

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Physical Review E 2021年第2期104卷 025301-025301页

作者： Dominik Wilde Andreas Krämer Dirk Reith Holger Foysi Department of Mechanical Engineering University of Siegen Paul-Bonatz-Straße 9-11 57076 Siegen-Weidenau Germany Institute of Technology Resource and Energy-efficient Engineering (TREE) Bonn-Rhein-Sieg University of Applied Sciences Grantham-Allee 20 53757 Sankt Augustin Germany Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 6 14195 Berlin Germany Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven 53754 Sankt Augustin Germany

Turbulent compressible flows are traditionally simulated using explicit time integrators applied to discretized versions of the Navier-Stokes equations. However, the associated Courant-Friedrichs-Lewy condition severely restricts the maximum time-step size. Exploiting the Lagrangian nature of the Boltzmann equation's material derivative, we now introduce a feasible three-dimensional semi-Lagrangian lattice Boltzmann method (SLLBM), which circumvents this restriction. While many lattice Boltzmann methods for compressible flows were restricted to two dimensions due to the enormous number of discrete velocities in three dimensions, the SLLBM uses only 45 discrete velocities. Based on compressible Taylor-Green vortex simulations we show that the new method accurately captures shocks or shocklets as well as turbulence in 3D without utilizing additional filtering or stabilizing techniques other than the filtering introduced by the interpolation, even when the time-step sizes are up to two orders of magnitude larger compared to simulations in the literature. Our new method therefore enables researchers to study compressible turbulent flows by a fully explicit scheme, whose range of admissible time-step sizes is dictated by physics rather than spatial discretization.

关键词： Compressible flows Kinetic theory Turbulence Lattice-Boltzmann methods Navier-Stokes equation

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SDA: Towards a novel knowledge discovery model for information systems 11

SDA: Towards a novel knowledge discovery model for informati...

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11th IADIS International Conference Information Systems 2018, IS 2018

作者： Jacobs, Marc Hodapp, Sven Dörpinghaus, Jens Fraunhofer Institute for Scientific Computing and Algorithms SCAI Sankt Augustin Germany

ISBN: (纸本)9789898533746

Here, we present a novel model for knowledge discovery in biomedical sciences which has already been used for analyzing millions of journal articles from PMC and PubMed. We decompose each scientific document into semantic digital assets (SDA) that help to build an efficient workflow to retrieve single statements and hypothesis from huge and heterogeneous collections of large documents instead of complete articles. This new approach is so general applicable, that it will also be useful for information systems in other domains. © 2018 IADIS. All rights reserved.

关键词： Knowledge representation

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Graph based discovery in biomedical information systems connecting scientific texts with structured expert knowledge 11

Graph based discovery in biomedical information systems conn...

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11th IADIS International Conference Information Systems 2018, IS 2018

作者： Dörpinghaus, Jens Jacobs, Marc Fluck, Juliane Fraunhofer Institute for Scientific Computing and Algorithms SCAI Sankt Augustin Germany

ISBN: (纸本)9789898533746

In this poster we describe a novel approach for knowledge discovery in biomedical information systems utilizing the Biological Expression Language (BEL). This language is widely used for network-based approaches in text mining for a better understanding of biological mechanisms. Storing all relevant data of decomposed documents, the ontologies and BEL statements in Semantic Digital Assets (SDA) leads to a knowledge graph capable of answering semantic questions and making more biomedical knowledge available for analyses. © 2018 IADIS. All rights reserved.

关键词： Knowledge management

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Sparse Grids and Applications - Miami 2016 1st ed. 2018

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丛书名： Lecture Notes in Computational Science and Engineering

2018年

作者： Jochen Garcke Dirk Pflüger Clayton G. Webster Guannan Zhang

ISBN: (数字)9783319754260

ISBN: (纸本)9783319754253

Sparse grids are a popular tool for the numerical treatment of high-dimensional problems. Where classical numerical discretization schemes fail in more than three or four dimensions, sparse grids, in their different flavors, are frequently the method of choice. This volume of LNCSE presents selected papers from the proceedings of the fourth workshop on sparse grids and applications, and demonstrates once again the importance of this numerical discretization scheme. The articles present recent advances in the numerical analysis of sparse grids in connection with a range of applications including computational chemistry, computational fluid dynamics, and big data analytics, to name but a few.

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Sparse Grids and Applications - Stuttgart 2014

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2018年

作者： Jochen Garcke Dirk Pflüger

ISBN: (纸本)9783319803098

This volume of LNCSE is a collection of the papers from the proceedings of the third workshop on sparse grids and applications. Sparse grids are a popular approach for the numerical treatment of high-dimensional problems. Where classical numerical discretization schemes fail in more than three or four dimensions, sparse grids, in their different guises, are frequently the method of choice, be it spatially adaptive in the hierarchical basis or via the dimensionally adaptive combination technique. Demonstrating once again the importance of this numerical discretization scheme, the selected articles present recent advances on the numerical analysis of sparse grids as well as efficient data structures. The book also discusses a range of applications, including uncertainty quantification and plasma physics.

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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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Semi-Lagrangian lattice Boltzmann method for compressible flows

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Physical Review E 2020年第5期101卷 053306-053306页

作者： Dominik Wilde Andreas Krämer Dirk Reith Holger Foysi Department of Mechanical Engineering University of Siegen Paul-Bonatz-Straße 9-11 D-57076 Siegen-Weidenau Germany Institute of Technology Resource and Energy-efficient Engineering (TREE) Bonn-Rhein-Sieg University of Applied Sciences Grantham-Allee 20 D-53757 Sankt Augustin Germany National Heart Lung and Blood Institute National Institutes of Health Bethesda Maryland 20892 USA Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven D-53754 Sankt Augustin Germany

This work thoroughly investigates a semi-Lagrangian lattice Boltzmann (SLLBM) solver for compressible flows. In contrast to other LBM for compressible flows, the vertices are organized in cells, and interpolation polynomials up to fourth order are used to attain the off-vertex distribution function values. Differing from the recently introduced Particles on Demand (PoD) method [Dorschner, Bösch, and Karlin, Phys. Rev. Lett. 121, 130602 (2018)], the method operates in a static, nonmoving reference frame. Yet the SLLBM in the present formulation grants supersonic flows and exhibits a high degree of Galilean invariance. The SLLBM solver allows for an independent time step size due to the integration along characteristics and for the use of unusual velocity sets, like the D2Q25, which is constructed by the roots of the fifth-order Hermite polynomial. The properties of the present model are shown in diverse example simulations of a two-dimensional Taylor-Green vortex, a Sod shock tube, a two-dimensional Riemann problem, and a shock-vortex interaction. It is shown that the cell-based interpolation and the use of Gauss-Lobatto-Chebyshev support points allow for spatially high-order solutions and minimize the mass loss caused by the interpolation. Transformed grids in the shock-vortex interaction show the general applicability to nonuniform grids.

关键词： Kinetic theory Lattice-Boltzmann methods

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A multimodal digital biomarker of functional deficits in early-stage Alzheimer’s disease: results of the RADAR-AD study

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Alzheimer's & Dementia 2023年第S15期19卷

作者： Srinivasan Vairavan Manuel Lentzen Marijn Muurling Casper de Boer Alankar Atreya Jelena Curcic Chris Hinds Pauline Conde Margarita Grammatikopoulou Gaetano Scebba Ioulietta Lazarou Spiros Nikolopoulos Anna-Katharine Brem Neva Coello Pieter Jelle Visser Holger Fröhlich Vaibhav A Narayan Gayle Wittenberg Dag Aarsland Janssen Research and Development Titusville NJ USA Correspondece Fraunhofer Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany VU University Medical Center Amsterdam Netherlands University of Oxford Oxford United Kingdom Novartis Institutes for BioMedical Research Basel Switzerland King’s College London London United Kingdom Centre for Research & Technology Hellas Thessaloniki Greece Davos Alzheimers Collaborative Wayne PA USA Janssen Research and Development LLC Titusville NJ USA

Background Remote monitoring technologies (RMTs), such as smartphone apps, smartwatches, and in-home sensors, are rapidly changing the way functional and cognitive performance is measured in Alzheimer’s disease (AD) patients. Here, we present results from the European RADAR-AD study on the use of multimodal data streams for the identification of functional deficits across all syndromic stages of AD. Method Four study groups (Healthy controls (HC), preclinical AD (pre. AD), prodromal AD (pro. AD), and mild AD) were included in this cross-sectional study. The RMT features (gait measures from Timed up and Go (TUG), Dual Task Effect (DTE) using physilog, acoustic features from Speech task in Mezurio, neurocognitive function using Altoida, managing finances with Banking app) with in-clinic neuropsychological (NP) tests, activities of daily living with Amsterdam IADL and demographics (age, gender, education years) were analyzed for different combinations of the multimodal digital biomarker of disease stage in AD across different pairwise comparisons. The analysis includes data from 175 participants (HC = 67, *** = 26, *** = 50, Mild AD = 32) collected for 8 weeks. An extreme gradient boosting (XGBoost) machine learning model was trained to obtain a multimodal biomarker of AD disease stage with repeated cross-validation (5-fold with 4 repeats) (Figure 1). This investigation is part of the ongoing RADAR-AD study. Result The multimodal combination of RMTs achieved a mean AUC of > 0.60 in all pairwise comparisons with a mean AUC > 0.65 for HC vs (***, *** and Mild) (Figures 2 and 3). The addition of NP tests increases the performance considerably across all pairwise comparisons except HC vs ***. Conclusion Our results highlight the advantage of combining RMTs to identify functional deficits in the early stage of AD. In particular, in prodromal and mild AD patients, a combined signal shows much more strength compared to individual tests. This work has received s

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