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Sparse Grids and Applications - Stuttgart 2014

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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Knowledge Detection and Discovery using Semantic Graph Embeddings on Large Knowledge Graphs generated on Text Mining Results

Knowledge Detection and Discovery using Semantic Graph Embed...

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Jens Dörpinghaus Marc Jacobs German Center for Neurodegenerative Diseases (DZNE) Bonn Germany Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin Germany

ISBN: (数字)9788395541674

ISBN: (纸本)9781728141473

Knowledge graphs play a central role in big data integration, especially for connecting data from different domains. Bringing unstructured texts, e.g. from scientific literature, into a structured, comparable format is one of the key assets. Here, we use knowledge graphs in the biomedical domain working together with text mining based document data for knowledge extraction and retrieval from text and natural language structures. For example cause and effect models, can potentially facilitate clinical decision making or help to drive research towards precision medicine. However, the power of knowledge graphs critically depends on context information. Here we provide a novel semantic approach towards a context enriched biomedical knowledge graph utilizing data integration with linked data applied to language technologies and text mining. This graph concept can be used for graph embedding applied in different approaches, e.g with focus on topic detection, document clustering and knowledge discovery. We discuss algorithmic approaches to tackle these challenges and show results for several applications like search query finding and knowledge discovery. The presented remarkable approaches lead to valuable results on large knowledge graphs.

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optimization of Retrieval algorithms on Large Scale Knowledge Graphs

optimization of Retrieval Algorithms on Large Scale Knowledg...

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Jens Dörpinghaus Andreas Stefan German Center for Neurodegenerative Diseases (DZNE) Bonn Germany Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin Germany

ISBN: (数字)9788395541674

ISBN: (纸本)9781728141473

Knowledge graphs have been shown to play an important role in recent knowledge mining and discovery, for example in the field of life sciences or bioinformatics. Although a lot of research has been done on the field of query optimization, query transformation and of course in storing and retrieving large scale knowledge graphs the field of algorithmic optimization is still a major challenge and a vital factor in using graph databases. Few researchers have addressed the problem of optimizing algorithms on large scale labeled property graphs. Here, we present two optimization approaches and compare them with a naive approach of directly querying the graph database. The aim of our work is to determine limiting factors of graph databases like Neo4j and we describe a novel solution to tackle these challenges. For this, we suggest a classification schema to differ between the complexity of a problem on a graph database. We evaluate our optimization approaches on a test system containing a knowledge graph derived biomedical publication data enriched with text mining data. This dense graph has more than 71M nodes and 850M relationships. The results are very encouraging and- depending on the problem-we were able to show a speedup of a factor between 44 and 3839.

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INTERATOMIC-POTENTIAL-FREE, DATA-DRIVEN MOLECULAR DYNAMICS

arXiv

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arXiv 2022年

作者： Bulin, J. Hamaekers, J. Ariza, M. Pilar Ortiz, M. Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin53757 Germany Escuela Técnica Superior de Ingeniería Universidad de Sevilla Sevilla41092 Spain Hausdorff Center for Mathematics Universität Bonn Endenicher Allee 60 Bonn53115 Germany Division of Engineering and Applied Science California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States

We present a Data-Driven (DD) paradigm that enables molecular dynamics calculations to be performed directly from sampled force-field data such as obtained, e. g., from ab initio calculations, thereby eschewing the conventional step of modeling the data by empirical interatomic potentials entirely. The data required by the DD solvers consists of local atomic configurations and corresponding atomic forces and is, therefore, fundamental, i. e., it is not beholden to any particular model. The resulting DD solvers, including a fully explicit DD-Verlet algorithm, are provably convergent and exhibit robust convergence with respect to the data in selected test cases. We present an example of application to C60 buckminsterfullerenes that showcases the feasibility, range and scope of the DD molecular dynamics paradigm. Copyright © 2022, The Authors. All rights reserved.

关键词： Molecular dynamics

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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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Mathematical modeling and visualization of topologically non-trivial solutions in general relativity

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Journal of Physics: Conference Series 2021年第1期1730卷

作者： Igor Nikitin Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven 53757 Sankt Augustin Germany

In general relativity, there is a class of solutions that currently do not have observed analogues, but on which the theory is shaped, giving an understanding what is fundamentally possible within its framework. Such solutions include wormholes, tunnels that connect distant regions in spacetime. Although not a single wormhole has yet been discovered, there is a large number of works devoted to their study, thanks to which wormholes as a class of solutions become firmly established in modern science. In this paper, we consider two topologically nontrivial types of solutions related to wormholes. First: wormholes that can open and close. In this relation, we will discuss topological censorship theorems, which under certain conditions prohibit changing topology. We will also discuss known ways to circumvent these theorems. Using analytical and numerical methods, as well as visualization, we will construct an example of an opening and closing wormhole with the dimensions of the central black hole in the Milky Way galaxy. Our construction continues the work by Kardashev, Novikov and Shatskiy, in which a static wormhole with the same parameters was considered. The second type is a modification of Visser's dihedral wormhole solution for a dynamic case.

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On dark stars, galactic rotation curves and fast radio bursts

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Journal of Physics: Conference Series 2021年第1期1730卷

作者： Igor Nikitin Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven 53757 Sankt Augustin Germany

This paper is a continuation of our recent work on Radial Dark Matter stars (RDM-stars), black holes, coupled with radial flows of dark matter. As a galaxy model, it produces flat rotation curves, approximately valid for many galaxies far from the center. In this paper, more detailed modeling is carried out, including the vicinity of the galactic center. Assuming that the distribution of stellar black holes repeats the distribution of luminous matter, we get a perfect match between the model rotation curves and the observed ones. Further, using numerical integration, we examine the gravitational field of an individual RDM-star. The computation shows the event horizon being erased and rapidly increasing mass density arising instead (mass inflation). In this regime, we apply the previously constructed Planck star model, where at high densities a repulsive force occurs (quantum bounce). In our stationary model, the evolution of a Planck star has stopped under the pressure of dark matter flows. This system is considered as a possible source of Fast Radio Bursts (FRBs). In a scenario involving an asteroid falling onto an RDM-star, the model reproduces the correct frequency range of FRBs. Their total energy, coherence and short duration are explained as well.

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Predicting missing links using PyKEEN

Predicting missing links using PyKEEN

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2019 ISWC Satellite Tracks (Posters and Demonstrations, Industry, and Outrageous Ideas), ISWC 2019-Satellites

作者： Ali, Mehdi Hoyt, Charles Tapley Domingo-Fernández, Daniel Lehmann, Jens Smart Data Analytics Group University of Bonn Germany Department of Enterprise Information Systems Fraunhofer Institute for Intelligent Analysis and Information Systems Sankt Augustin and Dresden Germany Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin Germany

PyKEEN is a framework, which integrates several approaches to compute knowledge graph embeddings (KGEs). We demonstrate the usage of PyKEEN in an biomedical use case, i.e. we trained and evaluated several KGE models on a biological knowledge graph containing genes’ annotations to pathways and pathway hierarchies from well-known databases. We used the best performing model to predict new links and present an evaluation in collaboration with a domain expert*. Copyright 2019 for this paper by its authors.

关键词： Knowledge graph

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Knowledge Extraction and Applications utilizing Context Data in Knowledge Graphs

Knowledge Extraction and Applications utilizing Context Data...

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Jens Dörpinghaus Andreas Stefan Schloss Birlinghoven Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin Germany

Context is widely considered for NLP and knowledge discovery since it highly influences the exact meaning of natural language. The scientific challenge is not only to extract such context data, but also to store this data for further NLP approaches. Here, we propose a multiple step knowledge graphbased approach to utilize context data for NLP and knowledge expression and extraction. We introduce the graph-theoretic foundation for a general context concept within semantic networks and show a proof-of-concept-based on biomedical literature and text mining. We discuss the impact of this novel approach on text analysis, various forms of text recognition and knowledge extraction and retrieval.

关键词： Text mining Ontologies Databases Semantics Diseases Knowledge discovery

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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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