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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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arXiv

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arXiv 2021年

作者： Bulin, Johannes Hamaekers, Jan Department of Virtual Material Design Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin53757 Germany Fraunhofer Center for Machine Learning Schloss Birlinghoven Sankt Augustin53757 Germany

Determining whether two particle systems are similar is a common problem in particle simulations. When the comparison should be invariant under permutations, orthogonal transformations, and translations of the systems, special techniques are needed. We present an algorithm that can test particle systems of finite size for similarity and, if they are similar, can find the optimal alignment between them. Our approach is based on an invariant version of the root mean square deviation (RMSD) measure and is capable of finding the globally optimal solution in O(n3) operations where n is the number of three-dimensional particles. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Semantic knowledge graph embeddings for biomedical research: data integration using linked open data 15

Semantic knowledge graph embeddings for biomedical research:...

引用

15th International Conference on Semantic Systems, SEMPDS 2019

作者： Dörpinghaus, Jens Jacobs, Marc Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin Germany

Knowledge Graphs are becoming a key instrument for biomedical knowledge discovery and modeling. These approaches rely on structured data, e.g. about related proteins or genes, and form cause-and-effect networks or - if enriched with literature data and other linked data sources - knowledge graphs. A key aspect of analysis on these graphs is the missing context. Here we present a novel semantic approach towards a context enriched Knowledge Graph for biomedical research utilizing data integration with linked data. The result is a general graph concept that can be used for graph embeddings in different contexts or layers. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Knowledge graph

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On the Trade-Off between Fidelity, Utility and Privacy of Synthetic Patient Data

SSRN

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SSRN 2024年

作者： Adams, Tim Birkenbihl, Colin Otte, Karen Ng, Hwei Geok Rieling, Jonas Adrian Näher, Anatol-Fiete Sax, Ulrich Prasser, Fabian Fröhlich, Holger Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin Germany Berlin Germany Department of Medical Informatics University Medical Center Goettingen Goettingen Germany Digital Global Public Health Hasso Plattner Institute for Digital Engineering University of Potsdam Potsdam Germany Method Development Research Infrastructure and Information Technology Robert Koch Institute Berlin Germany Bonn-Aachen International Center for IT Rheinische Friedrich-Wilhelms-Universität Bonn Bonn Germany Department of Neurology Massachusetts General Hospital Harvard Medical School Boston United States

The advancement of medical research and healthcare is increasingly dependent on the analysis of patient-level data, but privacy concerns and legal constraints often hinder data sharing. Synthetic data mimicking real patient data offers a widely discussed potential solution. According to the literature, synthetic data may, however, not fully guarantee patient privacy and can vary greatly in terms of fidelity and utility. In this study, we aim to systematically investigate the trade-off between privacy, fidelity and utility of synthetic patient data. We assess synthetic data fidelity in terms of statistical similarity to real data, and utility via the performance of machine learning models trained on synthetic and tested on real data. Regarding data privacy we focus on membership inference via shadow model attacks as well as singling out and attribute inference risks. In this regard, we also consider differential privacy (DP) as a possible mechanism to probabilistically guarantee a certain level of data privacy, and we compare against classical anonymization techniques. We evaluate the fidelity, utility and privacy of synthetic data generated by five different models for three distinctive patient-level datasets. Our results show that our implementations of DP have a strongly detrimental effect on the fidelity of synthetic data, specifically its correlation structure, and therefore emphasize the need to improve techniques that effectively balance privacy, fidelity and utility in synthetic patient data generation. © 2024, The Authors. All rights reserved.

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

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

引用

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...

引用

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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Resonant Solitary States in Complex Networks

arXiv

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arXiv 2024年

作者： Niehues, Jakob Yanchuk, Serhiy Berner, Rico Kurths, Jürgen Hellmann, Frank Anvari, Mehrnaz Member of the Leibniz Association P.O. Box 60 12 03 PotsdamD-14412 Germany Humboldt-Universität zu Berlin Department of Physics Newtonstraße 15 Berlin12489 Germany Technische Universität Berlin ER 3-2 Hardenbergstrasse 36a Berlin10623 Germany University College Cork School of Mathematical Sciences Western Road CorkT12 XF62 Ireland Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin53757 Germany

Partially synchronized solitary states occur frequently when a synchronized system of networked oscillators with inertia is perturbed locally. Several asymptotic states of different frequencies can coexist at the same node. Here, we reveal the mechanism behind this multistability: additional solitary frequencies arise from the coupling between network modes and the solitary oscillator’s frequency, leading to significant energy transfer. This can cause the solitary node’s frequency to resonate with a Laplacian eigenvalue. We analyze which network structures enable this resonance and explain longstanding numerical observations. Another solitary state that is known in the literature is characterized by the effective decoupling of the synchronized network and the solitary node at the natural frequency. Our framework unifies the description of solitary states near and far from resonance, allowing to predict the behavior of complex networks from their topology. © 2024, CC BY.

关键词： Synchronization

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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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