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69th Annual Meeting of the German Association of Medical Informatics, Biometry and Epidemiology, GMDS 2024

作者： Schneider, Julian Walter, Marvin Otte, Karen Meurers, Thierry Perrar, Ines Nöthlings, Ute Adams, Tim Fröhlich, Holger Prasser, Fabian Fluck, Juliane Kühnel, Lisa Knowledge Management ZB MED - Information Centre for Life Sciences Cologne Germany Medical Informatics Group Berlin Institute of Health at Charité - Universitätsmedizin Berlin Berlin Germany Institute of Nutritional and Food Sciences - Nutritional Epidemiology University of Bonn Bonn Germany Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany The Agricultural Faculty University of Bonn Bonn Germany Faculty of Technology Bielefeld University Bielefeld Germany

ISBN: (纸本)9781643685366

Introduction A modern approach to ensuring privacy when sharing datasets is the use of synthetic data generation methods, which often claim to outperform classic anonymization techniques in the trade-off between data utility and privacy. Recently, it was demonstrated that various deep learning-based approaches are able to generate useful synthesized datasets, often based on domain-specific analyses. However, evaluating the privacy implications of releasing synthetic data remains a challenging problem, especially when the goal is to conform with data protection guidelines. Methods Therefore, the recent privacy risk quantification framework Anonymeter has been built for evaluating multiple possible vulnerabilities, which are specifically based on privacy risks that are considered by the European Data Protection Board, i.e. singling out, linkability, and attribute inference. This framework was applied to a synthetic data generation study from the epidemiological domain, where the synthesization replicates time and age trends previously found in data collected during the DONALD cohort study (1312 participants, 16 time points). The conducted privacy analyses are presented, which place a focus on the vulnerability of outliers. Results The resulting privacy scores are discussed, which vary greatly between the different types of attacks. Conclusion Challenges encountered during their implementation and during the interpretation of their results are highlighted, and it is concluded that privacy risk assessment for synthetic data remains an open problem. © 2024 The Authors.

关键词： Differential privacy

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PERTURBATIONS OF EMBEDDED EIGENVALUES OF ASYMPTOTICALLY PERIODIC MAGNETIC SCHRÖDINGER OPERATORS ON A CYLINDER

arXiv

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arXiv 2025年

作者： Jansen, Jonas Sasane, Sara Maad Treschow, Wilhelm Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin53757 Germany Centre for Mathematical Sciences Lund University P.O. Box 118 Lund221 00 Sweden

We investigate the persistence of embedded eigenvalues for a class of magnetic Laplacians on an infinite cylindrical domain. The magnetic potential is assumed to be C2 and asymptotically periodic along the unbounded direction, with an algebraic decay rate towards a periodic background potential. Under the condition that the embedded eigenvalue of the unperturbed operator lies away from the thresholds of the continuous spectrum, we show that the set of nearby potentials for which the embedded eigenvalue persists forms a smooth manifold of finite and even codimension. The proof employs tools from Floquet theory, exponential dichotomies, and Lyapunov–Schmidt reduction. Additionally, we give an example of a potential which satisfies the assumptions of our main *** Codes 35P05, 47A55, 81Q15, 35B20, 35J10, 35J15, 81Q10 Copyright © 2025, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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FAST-MOVING PATTERN INTERFACES CLOSE TO A TURING INSTABILITY IN AN ASYMPTOTIC MODEL FOR THE THREE-DIMENSIONAL BÉNARD-MARANGONI PROBLEM

arXiv

引用

arXiv 2024年

作者： Hilder, Bastian Jansen, Jonas Department of Mathematics Technische Universität München Boltzmannstrasse 3 Garching B. München 85748 Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin53757 Germany

We study the bifurcation of planar patterns and fast-moving pattern interfaces in an asymptotic longwave model for the three-dimensional Bénard–Marangoni problem, which is close to a Turing instability. We derive the model from the full free-boundary Bénard–Marangoni problem for a thin liquid film on a heated substrate of low thermal conductivity via a lubrication approximation. This yields a quasilinear, fully coupled, mixed-order degenerate-parabolic system for the film height and temperature. As the Marangoni number M increases beyond a critical value M∗, the pure conduction state destabilises via a Turing(–Hopf) instability. Close to this critical value, we formally derive a system of amplitude equations which govern the slow modulation dynamics of square or hexagonal patterns. Using center manifold theory, we then study the bifurcation of square and hexagonal planar patterns. Finally, we construct planar fast-moving modulating travelling front solutions that model the transition between two planar patterns. The proof uses a spatial dynamics formulation and a center manifold reduction to a finite-dimensional invariant manifold, where modulating fronts appear as heteroclinic orbits. These modulating fronts facilitate a possible mechanism for pattern formation, as previously observed in *** Codes 35B36, 70K50, 35B32, 35Q35, 35K41, 35K59, 35K65, 35Q79, 76A20, 35B06, 34C37, 37L10 Copyright © 2024, The Authors. All rights reserved.

关键词： Thermal conductivity of liquids

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NFDI4Health Workflow and Service for Synthetic Data Generation, Assessment and Risk Management

NFDI4Health Workflow and Service for Synthetic Data Generati...

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69th Annual Meeting of the German Association of Medical Informatics, Biometry and Epidemiology, GMDS 2024

作者： Moazemi, Sobhan Adams, Tim Ng, Hwei Geok Kühnel, Lisa Schneider, Julian Näher, Anatol-Fiete Fluck, Juliane Fröhlich, Holger Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany Knowledge Management ZB MED - Information Centre for Life Sciences Cologne Germany Faculty of Technology Bielefeld University Bielefeld Germany Digital Global Public Health Hasso Plattner Institute for Digital Engineering University of Potsdam Potsdam Germany Institute of Medical Informatics Charité-Universitätsmedizin Germany Method Development Research Infrastructure and Information Technology Robert Koch Institute Berlin Germany The Agricultural Faculty University of Bonn Bonn Germany Institute for Geodesy and Geoinformation University of Bonn Bonn Germany University of Bonn Bonn Germany

ISBN: (纸本)9781643685366

Individual health data is crucial for scientific advancements, particularly in developing Artificial Intelligence (AI);however, sharing real patient information is often restricted due to privacy concerns. A promising solution to this challenge is synthetic data generation. This technique creates entirely new datasets that mimic the statistical properties of real data, while preserving confidential patient information. In this paper, we present the workflow and different services developed in the context of Germany's National Data Infrastructure project NFDI4Health. First, two state-of-the-art AI tools (namely, VAMBN and MultiNODEs) for generating synthetic health data are outlined. Further, we introduce SYNDAT (a public web-based tool) which allows users to visualize and assess the quality and risk of synthetic data provided by desired generative models. Additionally, the utility of the proposed methods and the web-based tool is showcased using data from Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Center for Cancer Registry Data of the Robert Koch institute (RKI). © 2024 The Authors.

关键词： Electronic health record

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Integrating Cloud computing, Bayesian Optimization, and Neural-Additive Modeling for Enhanced CAM Systems in 5-Axis Milling

引用

Procedia CIRP 2024年 128卷 532-537页

作者： Viktor Rudel Georg Vinogradov Philipp Ganser Thomas Bergs Christopher Vahl Markus Frings Valentina König Maximilian Schambach Stefan Dietzel Michael Königs Fraunhofer Institute for Production Technology IPT Steinbachstraße 17 52074 Aachen Germany Laboratory for Machine Tools and Production Engineering (WZL) RWTH Aachen University Campus-Boulevard 30 52074 Aachen Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven 1 53757 Sankt Augustin Germany ModuleWorks GmbH Henricistraße 50 52072 Aachen Germany Merantix Momentum Merantix AI Campus Max-Urich-Straße 3 13355 Berlin Germany EXAPT Systemtechnik GmbH Theaterstraße 12 52062 Aachen Germany

This publication introduces the development and application of an advanced CAD/CAM/CAE system that leverages the computational capabilities of an edge-cloud infrastructure. The developed system consists of containerized technology models for various complex process planning simulation tasks in 5-axis-milling, such as toolpath calculation, cutter-workpiece-engagement, uncut chip geometry or cutting force simulations. Moreover, a Bayesian optimization (BO) algorithm is coupled with the system enabling multi-objective optimizations of the considered machining operations by varying predefined CAM-parameters. The result of the optimization consists of a set of Pareto-efficient solutions. Each solution realizes a different tradeoff between the technological objectives of the process planner. Since mastering the complexity of the design space is a major challenge in today’s CAM programming workflow, a Neural-Additive Model (NAM) is coupled with the system improving guided search through the configuration/result space. This reduces the convergence time to an optimal CAM parameter set. The coupling of the cloud computing, multi-objective optimization and artificial intelligence method with a CAM-kernel is demonstrated based on the process design of 5-axis milling operations for a blade-integrated disk (blisk).

关键词： Computer-aided Manufacturing systems (CAM-Systems) Multi-objective optimization Design methodology tools technologies

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A Dimension-adaptive Combination Technique for Uncertainty Quantification

arXiv

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arXiv 2022年

作者： Seidler, Uta Griebel, Michael Institute for Numerical Simulation University of Bonn Friedrich-Hirzebruch-Allee 7 Bonn53115 Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin53754 Germany

We present an adaptive algorithm for the computation of quantities of interest involving the solution of a stochastic elliptic PDE where the diffusion coefficient is parametrized by means of a Karhunen-Loève expansion. The approximation of the equivalent parametric problem requires a restriction of the countably infinite-dimensional parameter space to a finite-dimensional parameter set, a spatial discretization and an approximation in the parametric variables. We consider a sparse grid approach between these approximation directions in order to reduce the computational effort and propose a dimension-adaptive combination technique. In addition, a sparse grid quadrature for the high-dimensional parametric approximation is employed and simultaneously balanced with the spatial and stochastic approximation. Our adaptive algorithm constructs a sparse grid approximation based on the benefit-cost ratio such that the regularity and thus the decay of the Karhunen-Loève coefficients is not required beforehand. The decay is detected and exploited as the algorithm adjusts to the anisotropy in the parametric variables. We include numerical examples for the Darcy problem with a lognormal permeability field, which illustrate a good performance of the algorithm: For sufficiently smooth random fields, we essentially recover the spatial order of convergence as asymptotic convergence rate with respect to the computational *** Codes 65N35, 65C20, 65D30, 65N30, 60H35 © 2022, CC BY.

关键词： Diffusion

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algorithms in the Orchard: An Embedding-Based Expert Answering System for Apple Rust

SSRN

引用

SSRN 2025年

作者： Anand, Astha Shen, Jian Cremers, Armin Bernd Jacobs, Marc Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven 1 North Rhine-Westphalia Sankt Augustin53757 Germany RPU Rhenish Plant Power Envirotech Co. Ltd. Bonn-Aachen International Center for Information Technology Friedrich-Hirzebruch-Allee 5 North Rhine-Westphalia Bonn53115 Germany

As sustainable agricultural practices gain importance, the need for intelligent pest control decision-making has grown. This paper introduces SEEDS: Similarity-based Expert Embedding Decision System, a Retrieval-Augmented Generation (RAG) based agricultural question-answering (QA) system. It is built upon a domain-specific knowledge graph (KG), representing Cedar Apple Rust disease, its host and causative agents, plant defense molecules against apple rust infection, and various pesticides. Utilizing the OpenAI embedding model, the system generates embeddings for user queries and KG data, employing similarity metrics to rank KG entries, facilitating accurate and relevant pest control recommendations. SEEDS is a promising niche AI tool in plant protection, setting the stage for scalable, extensible QA frameworks in precision agriculture. The results signify not only a step forward in agricultural expert systems but also highlight the potential for expanding this approach to other crops and pests, marking a substantial advancement in the use of AI for agricultural pest control. © 2025, The Authors. All rights reserved.

关键词： Fruits

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Proposal of an achitecture for terminology management in a research project 30

Proposal of an achitecture for terminology management in a r...

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30th Medical Informatics Europe Conference, MIE 2020

作者： Mora, Sara Madan, Sumit Gebel, Stephan Giacomini, Mauro University of Genoa Italy Fraunhofer Institute for Algorithms and Scientific Computing SCAI Department of Bioinformatics Schloss Birlinghoven Sankt Augustin Germany

ISBN: (纸本)9781643680828

Clinical and medical knowledge evolve and this causes changes in concepts and terms that describe them. The objective of this work is to formally present an ontology-based standard architecture that will be used in the scenario of neurodegeneration research to maintain terminologies and their relations updated and coherent over the time. The proposed structure is composed by three elements that will allow the user to do a list of operations on the terminology resources explicitly contemplated by the Common Terminology Service Release 2 (CTS2). © 2020 European Federation for Medical Informatics (EFMI) and IOS Press.

关键词： Ontology

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Efficient solution of ill-posed integral equations through averaging

arXiv

引用

arXiv 2024年

作者： Griebel, Michael Jahn, Tim Institut für Numerische Simulation Universität Bonn Friedrich-Hirzebruch-Allee 7 Bonn53115 Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin53754 Germany Institut für Mathematik TU Berlin Straße des 17. Juni 135 Berlin10623 Germany

This paper discusses the error and cost aspects of ill-posed integral equations when given discrete noisy point evaluations on a fine grid. Standard solution methods usually employ discretization schemes that are directly induced by the measurement points. Thus, they may scale unfavorably with the number of evaluation points, which can result in computational inefficiency. To address this issue, we propose an algorithm that achieves the same level of accuracy while significantly reducing computational costs. Our approach involves an initial averaging procedure to sparsify the underlying grid. To keep the exposition simple, we focus on regularization via the truncated singular value decomposition of one-dimensional ill-posed integral equations that have sufficient smoothness. However, the approach can be generalized to other popular regularization methods and more complicated two- and three-dimensional problems with appropriate *** Codes 65, 65R32, 65R20 © 2024, CC BY.

关键词： Integral equations

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Sizing optimization of hybrid hydrogen energy storage systems: A metamodel-based approach

引用

International Journal of Hydrogen Energy 2025年

作者： Bareev-Rudy, Michael Schedler, Steffen Clees, Tanja Grantham-Allee 20 Sankt Augustin53757 Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin53757 Germany

Hybrid energy storage systems (HESS), consisting of a battery, hydrogen storage, electrolyzer and fuel cell, have received increasing attention from the scientific community in recent years as they can help increase the use of renewable energy. In this paper, a novel metamodel-based sizing optimization workflow is developed. The workflow is demonstrated on residential building scenarios with real solar power data. Sobol sequences are utilized to vary the component size in simulation, while radial basis functions are employed to approximate simulation results, which are then used for the optimization. The utilization of waste heat from Power-to-Gas and Gas-to-Power processes is evaluated using thermal equations by extending an existing coupled electrochemical and thermodynamic HESS model built in the multiphysical energy system simulator MEgy. The results show a functional workflow that optimizes sizing for a given scenario and outputs the component's daily energy balance and state of charge. © 2025 The Authors

关键词： Waste heat utilization

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