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23rd Workshop What Comes Beyond the Standard Models

作者： Nikitin, I. Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin53757 Germany

Dark stars are compact massive objects, described by Einstein gravitational field equations with matter. The type we consider possesses no event horizon, instead, there is a deep gravitational well with a very strong redshift factor. Observationally, dark stars can be identified with black holes. Inside dark stars, Planck density of matter is reached, Planck cores are formed, where the equations are modified by quantum gravity. In the paper, several models of dark stars with Planck cores are considered, resulting in the following hypothesis on the composition of dark matter. The galaxies are flooded with low-energetic radiation from the dark stars. The particle type can be photons and gravitons from the Standard Model, can also be a new type of massless particles. The model estimations show that the extremely large redshift factor z ∼ 1049 and the emission wavelength λ0 ∼ 1014m can be reached. The particles are not registered directly in the existing dark matter experiments. They come in a density sufficient to explain the observable rotation curves. The emission has a geometric dependence of density on radius ρ ∼ r−2, producing flat rotation curves. The distribution of sources also describes the deviations from the flat shape. The model provides a good fit of experimental rotation curves. Outbreaks caused by a fall of an external object on a dark star lead to emission wavelength shifted towards smaller values. The model estimations give the outbreak wavelength λ ∼ 1m compatible with fast radio bursts. The paper raises several principal questions. White holes with Planck core appear to be stable. Galactic rotation curves in the considered setup do not depend on the matter type. Inside the galaxy, dark matter can be of hot radial type. At cosmological distances, it can behave like the cold uniform type. © 2020 DMFA zaloznistvo. All rights reserved.

关键词： Stars

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Galactic model with a phase transition from dark matter to dark energy 24

Galactic model with a phase transition from dark matter to d...

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24th Workshop What Comes Beyond the Standard Models

作者： Nikitin, I. Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin53757 Germany

This work continues the construction of a recently proposed model of dark matter stars. In this model, dark matter quanta are sterile massless particles that are emitted from the central regions of the galaxy in the radial direction. As a result, at large distances r from the center of the galaxy, the mass density of dark matter has the form ρ ∼ r-2, in contrast to the homogeneous model ρ = Const. In the cosmological context, the homogeneous model with massless particles corresponds to the radiation epoch of the expansion of the universe, while the proposed inhomogeneous model turns out to be equivalent to ΛCDM. In this paper, scenarios will be considered in which the radial emission of dark matter is brought into hydrostatic equilibrium with a uniform background. It is shown that solutions exist if the uniform background has an equation of state typical for dark energy. Thus, this model describes a phase transition from dark matter inside the galaxy to dark energy outside of it. The specific mechanism for such a transition could be Bose-Einstein condensation. In addition, the question of what happens if dark matter particles are not sterile, for example, are photons of the Standard Model, is considered. © 2021 DMFA zaloznistvo. All rights reserved.

关键词： Galaxies

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MYNTS: Multi-physics network simulator 6

MYNTS: Multi-physics network simulator

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6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2016

作者： Clees, Tanja Cassirer, Kläre Hornung, Nils Klaassen, Bernhard Nikitin, Igor Nikitina, Lialia Suter, Robin Torgovitskaia, Inna Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin53754 Germany

ISBN: (纸本)9789897581991

We present a generic approach for the simulation of transport networks, where the steps of physical modeling and numerical simulation are effectively separated. The model is described by a list of physical equations and inequalities as problem constraints for non-linear programming (NLP). This list is translated to the language of expression trees and is made accessible for the numerical solution by standard NLP solvers. Various problem types can be solved in this way, including stationary and transient network simulation, feasibility analysis and energy-saving optimization. The simulation is provided for different disciplines, such as gas transport, water supply and electric power networks. We demonstrate the implementation of this approach in our multiphysics network simulator. © Copyright 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

关键词： Linear systems

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Using LAMA for efficient AMG on hybrid clusters

Using LAMA for efficient AMG on hybrid clusters

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作者： Kraus, Jiri Förster, Malte Brandes, Thomas Soddemann, Thomas Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven 53754 Sankt Augustin Germany

In this paper, we describe the implementation of an AMG solver for a hybrid cluster that exploits distributed and shared memory parallelization and uses the available GPU accelerators on each node. This solver has been written by using LAMA (Library for Accelerated Math Applications). This library does not only provide an easy-to-use framework for solvers that might run on different devices with different matrix formats, but also comes with features to optimize and hide communication and memory transfers between CPUs and GPUs. These features are explained and their impact on the efficiency of the AMG solver is shown in this paper. The benchmark results show that an efficient use of hybrid clusters is even possible for multi-level methods like AMG where fast solutions are needed on all levels for multiple problem sizes. © 2012 Springer-Verlag.

关键词： Program processors

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Knowledge Detection and Discovery using Semantic Graph Embeddings on Large Knowledge Graphs generated on Text Mining Results 15

Knowledge Detection and Discovery using Semantic Graph Embed...

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15th Federated Conference on Computer Science and Information Systems, FedCSIS 2020

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

ISBN: (纸本)9788395541674

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. © 2020 Polish Information Processing Society - as it is since 2011.

关键词： Knowledge graph

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Optimization of Retrieval algorithms on Large Scale Knowledge Graphs 15

Optimization of Retrieval Algorithms on Large Scale Knowledg...

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15th Federated Conference on Computer Science and Information Systems, FedCSIS 2020

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

ISBN: (纸本)9788395541674

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. © 2020 Polish Information Processing Society - as it is since 2011.

关键词： Knowledge graph

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Scalable parallel AMG on ccNUMA machines with OpenMP

Scalable parallel AMG on ccNUMA machines with OpenMP

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作者： Förster, Malte Kraus, Jiri Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven 53754 Sankt Augustin Germany

In many numerical simulation codes the backbone of the application covers the solution of linear systems of equations. Often, being created via a discretization of differential equations, the corresponding matrices are very sparse. One popular way to solve these sparse linear systems are multigrid methods-in particular AMG- because of their numerical scalability. But looking at modern multi-core architectures, also the parallel scalability has to be taken into account. With the memory bandwidth usually being the bottleneck of sparse matrix operations these linear solvers can't always benefit from increasing numbers of cores. To exploit the available aggregated memory bandwidth on larger scale NUMA machines evenly distributed data is often more an issue than load balancing. Additionally, using a threading model like OpenMP, one has to ensure the data locality manually by explicit placement of memory pages. On non uniform data it is always a tradeoff between these three principles, while the ideal strategy is strongly machine- and application dependent. In this paper we want to present some benchmarks of an AMG implementation based on a new performance library. Main focus is on the comparability to state-of-the-art solver packages regarding sequential performance as well as parallel scalability on common NUMA machines. To maximize throughput on standard model problems, several thread and memory configurations have been evaluated. We will show that even on large scale multi-core architectures easy parallel programming models, like OpenMP, can achieve a competitive performance compared to more complex programming models. © Springer-Verlag 2011.

关键词： Matrix algebra

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Predicting dementia in people with Parkinson's disease

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NPJ Parkinson's disease 2025年第1期11卷 126页

作者： Mohamed Aborageh Tom Hähnel Patricia Martins Conde Jochen Klucken Holger Fröhlich Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) 53757 Sankt Augustin Germany. mohamed.aborageh@scai.fraunhofer.de. Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) 53757 Sankt Augustin Germany. Department of Neurology University Hospital and Faculty of Medicine Carl Gustav Carus TUD Dresden University of Technology Dresden Germany. Luxembourg Centre for Systems Biomedicine (LCSB) University of Luxembourg Esch-sur-Alzette Luxembourg. Centre Hospitalier de Luxembourg (CHL) Luxembourg Luxembourg. Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) 53757 Sankt Augustin Germany. holger.froehlich@scai.fraunhofer.de. Bonn-Aachen International Center for Information Technology (B-IT) Rheinische Friedrich-Wilhelms-Universität Bonn 53115 Bonn Germany. holger.froehlich@scai.fraunhofer.de.

Parkinson's disease (PD) exhibits a variety of symptoms, with approximately 25% of patients experiencing mild cognitive impairment and 45% developing dementia within ten years of diagnosis. Predicting this progression and identifying its causes remains challenging. Our study utilizes machine learning and multimodal data from the UK Biobank to explore the predictability of Parkinson's dementia (PDD) post-diagnosis, further validated by data from the Parkinson's Progression Markers Initiative (PPMI) cohort. Using Shapley Additive Explanation (SHAP) and Bayesian Network structure learning, we analyzed interactions among genetic predisposition, comorbidities, lifestyle, and environmental factors. We concluded that genetic predisposition is the dominant factor, with significant influence from comorbidities. Additionally, we employed Mendelian randomization (MR) to establish potential causal links between hypertension, type 2 diabetes, and PDD, suggesting that managing blood pressure and glucose levels in Parkinson's patients may serve as a preventive strategy. This study identifies risk factors for PDD and proposes avenues for prevention.

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Carl Adam Petri 1

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1000年

作者： Einar Smith

ISBN: (数字)9783662480939

ISBN: (纸本)9783662480922;9783662516836

The book presents the life and works of one of Germany's most famous computer scientists, Carl Adam Petri. It is written in a vivid and entertaining manner, providing an in-depth discussion of the background behind Petri's best-known contribution to computer science, the Petri net. In this way the book can be read as a first introduction to nets, but it also covers the theoretical, physical and philosophical foundations behind nets, thus facilitating a comprehensive understanding of the wider range of Petri's works. The book is intended for readers with a previous knowledge in computer science, as well as for “interested non-professionals”, who want to get to know a remarkable personality of contemporary science.

关键词： History of computing Mathematics, general Philosophy, general Theory of Computation

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On the fidelity versus privacy and utility trade-off of synthetic patient data

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iScience 2025年第5期28卷 112382页

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 Institute of Health (BIH) 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 02115 MA United States

The use of synthetic data is a widely discussed and promising solution for privacy-preserving medical research. Synthetic data may, however, not always rule out the risk of re-identifying characteristics of real patients and can vary greatly in terms of data fidelity and utility. We systematically evaluate the trade-offs between privacy, fidelity, and utility across five synthetic data models and three patient-level datasets. We evaluate fidelity based on statistical similarity to the real data, utility on three machine learning use cases, and privacy via membership inference, singling out, and attribute inference risks. Synthetic data without differential privacy (DP) maintained fidelity and utility without evident privacy breaches, whereas DP-enforced models significantly disrupted correlation structures. K-anonymity-based data sanitization of demographic features, while preserving fidelity, introduced notable privacy risks. Our findings emphasize the need to advance methods that effectively balance privacy, fidelity, and utility in synthetic patient data generation. © 2025 The Author(s)

关键词： Bioinformatics Health informatics

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