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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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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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A UNICORE Plugin for HPC-Enabled scientific Workflows in Taverna 2.2

A UNICORE Plugin for HPC-Enabled Scientific Workflows in Tav...

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IEEE World Congress on Services (SERVICES)

作者： Sonja Holl Olav Zimmermann Martin Hofmann-Apitius Juelich Supercomputing Center (JSC) Forschungszentrum Juelich GmbH Juelich Germany Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin Germany

As scientific workflows are becoming more complex and apply compute-intensive methods to increasingly large data volumes, access to HPC resources is becoming mandatory. We describe the development of a novel plug in for the Tavern a workflow system, which provides transparent and secure access to HPC/grid resources via the UNICORE grid middleware, while maintaining the ease of use that has been the main reason for the success of scientific workflow systems. A use case from the bioinformatics domain demonstrates the potential of the UNICORE plug in for Tavern a by creating a scientific workflow that executes the central parts in parallel on a cluster resource.

关键词： Middleware Servers Monitoring Databases Bioinformatics Support vector machines Computer architecture

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Life science application support in an interoperable e-science environment

Life science application support in an interoperable e-scien...

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Annual IEEE Symposium on Computer-Based Medical Systems

作者： Sonja Holl Morris Riedel Bastian Demuth Mathilde Romberg Achim Streit Vinod Kasam Forschungszentrum Juelich Juelich Supercomputing Center Juelich Germany Schloss Birlinghoven Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin Germany

In the last decade, life science applications have become more and more integrated into e-Science environments, hence they are typically very demanding, both in terms of computational capabilities and data capacities. Especially the access to life science applications, embedded in such environments via Grid clients still constitutes a major hurdle for scientists that do not have an IT background. Life science applications often comprise a whole set of small programs instead of a single executable. Many of the graphical Grid clients are not perfectly suited for these types of applications, as they often assume that Grid jobs will run a single executable instead of a set of chained executions (i.e. sequences). This means that in order to execute a sequence of multiple programs on a single Grid resource, piping data from one program to the next, the user would have to run a hand-written shell script. Otherwise each program is independently scheduled as a Grid job, which causes unnecessary file transfers between the jobs, even if they are scheduled on the same resource. We present a generic solution to this problem and provide a reference implementation, which seamlessly integrates with the Grid middleware UNICORE. Our approach focuses on a comfortable user interface for the creation of such program sequences, validated in UNICORE-driven HPC-based Grids. Thus, we applied our approach in order to provide support for the usage of the AMBER package (a widely-used collection of programs for molecular dynamics simulations) within Grid workflows. We finally provide a scientific use case of our approach leveraging the interoperability of two different scientific infrastructures that represents an instance of the infrastructure interoperability reference model.

关键词： Middleware Biology computing Sequences Biological system modeling Bioinformatics scientific computing User interfaces Packaging Collaborative work Chemistry

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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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Optimally rotated coordinate systems for adaptive least-squares regression on sparse grids_

arXiv

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arXiv 2018年

作者： Bohn, Bastian Griebel, Michael Oettershagen, Jens Institute for Numerical Simulation University of Bonn Endenicher Allee 19b Bonn53115 Fraunhofer Center for Machine Learning Fraunhofer Institute for Algorithms and Scien-tific Computing Scai Schloss Birlinghoven Sankt Augustin53754 Germany

For low-dimensional data sets with a large amount of data points, standard kernel methods are usually not feasible for regression anymore. Besides simple linear models or involved heuristic deep learning models, grid-based discretizations of larger (kernel) model classes lead to algo-rithms, which naturally scale linearly in the amount of data points. For moderate-dimensional or high-dimensional regression tasks, these grid-based discretizations suffer from the curse of dimensionality. Here, sparse grid methods have proven to circumvent this problem to a large extent. In this context, space-and dimension-Adaptive sparse grids, which can detect and exploit a given low effective dimensionality of nominally high-dimensional data, are particularly successful. They nevertheless rely on an axis-Aligned structure of the solution and exhibit issues for data with predominantly skewed and rotated coordinates. In this paper we propose a preprocessing approach for these adaptive sparse grid algorithms that determines an optimized, problem-dependent coordinate system and, thus, reduces the effective dimensionality of a given data set in the ANOVA sense. We provide numerical examples on synthetic data as well as real-world data to show how an adaptive sparse grid least squares algorithm benefits from our preprocessing *** Codes 68T05 Copyright © 2018, The Authors. All rights reserved.

关键词： Analysis of variance (ANOVA)

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Disease systems modeling for discovery of mechanistic biomarkers

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New Horizons in Translational Medicine 2015年第2期2卷 61-61页

作者： Erfan Younesi Fraunhofer Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany

The use of biomarkers is becoming increasingly integral to to the contemporary practice of medicine and continues to play a central role in preventive, predictive and personalized medicine. However, the limited number of FDA-approved, in use biomarkers, on one hand, and an increasing number of published potential biomarkers, on the other hand, calls for an accelerated approach to translating biomarker research to clinical application. In this talk, I introduce novel concept of "Biomarker-guided Mechanism Discovery" through an integrative disease modeling approach and present the successful application of this methodology to deciphering the network model of genomic hormone interactions underlying dementia and its translational validation through serendipitous off-target effect. Moreover, the current challenges in biomarker discovery is discussed and results of our proposed Biomarker Ontology in collaboration with pharma industry will be presented.

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Bel networks derived from qualitative translations of BioNLP shared task annotations

Bel networks derived from qualitative translations of BioNLP...

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2013 Workshop on Biomedical Natural Language Processing, BioNLP 2013 at the 51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

作者： Fluck, Juliane Klenner, Alexander Madan, Sumit Ansari, Sam Bobic, Tamara Hoeng, Julia Hofmann-Apitius, Martin Peitsch, Manuel C. Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin Germany Philip Morris International RandD Philip Morris Products S.A. Quai Jeanrenaud 5 Neuchâtel2000 Switzerland Bonn-Aachen International Centre for Information Technology Dahlmannstr. 2 Bonn Germany

ISBN: (纸本)9781937284541

Interpreting the rapidly increasing amount of experimental data requires the availability and representation of biological knowledge in a computable form. The Biological expression language (BEL) encodes the data in form of causal relationships, which describe the association between biological events. BEL can successfully be applied to large data and support causal reasoning and hypothesis generation. With the rapid growth of biomedical literature, automated methods are a crucial prerequisite for handling and encoding the available knowledge. The BioNLP shared tasks support the development of such tools and provide a linguistically motivated format for the annotation of relations. On the other hand, BEL statements and the corresponding evidence sentences might be a valuable resource for future BioNLP shared task training data generation. In this paper, we briefly introduce BEL and investigate how far BioNLP-shared task annotations could be converted to BEL statements and in such a way directly support BEL statement generation. We present the first results of the automatic BEL statement generation and emphasize the need for more training data that captures the underlying biological meaning. © 2013 Association for Computational Linguistics

关键词： Encoding (symbols)

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An Automatic Multi-Level Solver Switching Strategy for PEEC-Based EMC Simulation

An Automatic Multi-Level Solver Switching Strategy for PEEC-...

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EMC Zurich 2007 - 18th International Zurich Symposium on Electromagnetic Compatibility

作者： Tanja Clees Tatiana Samrowski Martin Ludwig Zitzmann Robert Weigel Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin Germany BMW Group Munich Germany Institute for Electronics Engineering University of Erlangen Nuremberg Erlangen Germany

In this paper, we propose a new, efficient and robust automatic solver switching strategy, called alpha-SAMG, tailored to linear systems of equations arising in PEEC-based EMC simulation. The PEEC method (partial element equivalent circuit) is an approach for transforming conducting objects into linear networks with basic electrical elements. Such equivalent circuit models can be simulated by conventional circuit solvers such as SPICE (simulation program for integrated circuit emphasis) based on the MNA (modified nodal analysis) approach. By applying appropriate sparsification techniques, sparse PEEC matrices can be obtained, adequate for iterative solvers. By using multi-level approaches, linear complexity w.r.t. time and memory consumptions can be achieved in the best case. Due to the fact that properties of PEEC matrices can differ drastically, we developed the automatic solver switching strategy alpha-SAMG. Its efficiency for PEEC matrices is demonstrated for seven practically relevant benchmark cases by comparison against standard solvers. In particular, it is shown that alpha-SAMG provides a robust and fast solution strategy.

关键词： Electromagnetic compatibility Circuit simulation Integrated circuit modeling Robustness Equivalent circuits Analytical models Sparse matrices Linear systems Equations SPICE

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ProtSTonKGs: A Sophisticated Transformer Trained on Protein Sequences, Text, and Knowledge Graphs 13

ProtSTonKGs: A Sophisticated Transformer Trained on Protein ...

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13th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences, SWAT4HCLS 2022

作者： Balabin, Helena Hoyt, Charles Tapley Gyori, Benjamin M. Bachman, John Kodamullil, Alpha Tom Hofmann-Apitius, Martin Domingo-Fernández, Daniel Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin53757 Germany Bonn-Rhein-Sieg University of Applied Sciences Sankt Augustin53757 Germany Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Fraunhofer Center for Machine Learning Germany Enveda Biosciences BoulderCO80301 United States

While most approaches individually exploit unstructured data from the biomedical literature or structured data from biomedical knowl- edge graphs, their union can better exploit the advantages of such ap- proaches, ultimately improving representations of biology. Using mul- timodal transformers for such purposes can improve performance on context dependent classification tasks, as demonstrated by our previ- ous model, the Sophisticated Transformer Trained on Biomedical Text and Knowledge Graphs (STonKGs). In this work, we introduce Prot- STonKGs, a transformer aimed at learning all-encompassing representa- tions of protein-protein interactions. ProtSTonKGs presents an extension to our previous work by adding textual protein descriptions and amino acid sequences (i.e., structural information) to the text- and knowledge graph-based input sequence used in STonKGs. We benchmark Prot- STonKGs against STonKGs, resulting in improved F1 scores by up to 0.066 (i.e., from 0.204 to 0.270) in several tasks such as predicting protein interactions in several contexts. Our work demonstrates how multimodal transformers can be used to integrate heterogeneous sources of infor- mation, paving the foundation for future approaches that use multiple modalities for biomedical applications. Copyright © 2022 for this paper by its authors.

关键词： Natural language processing systems

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