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arXiv 2021年

作者： Nikitin, Igor 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. © 2021, CC BY.

关键词： Galaxies

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

arXiv

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arXiv 2021年

作者： Nikitin, Igor 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, CC BY.

关键词： Galaxies

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Wormholes and Teleporters

arXiv

引用

arXiv 2021年

作者： Nikitin, Igor Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin53757 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, which can be considered as a model of a teleportation event. © 2021, CC BY.

关键词： Topology

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

ProtSTonKGs: A Sophisticated Transformer Trained on Protein ...

引用

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

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

引用

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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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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On dark matter, dark energy and Bose-Einstein condensate

引用

AIP Conference Proceedings 2023年第1期2872卷

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

Equation of state for Bose-Einstein condensate is evaluated in two cases: non-interacting particles and particles interacting via potential possessing a non-trivial minimum. In astrophysical context, the first case becomes equivalent to cold dark matter, while the second case – to dark energy. A particular cosmological scenario with dark matter injection from quasi-black holes to galactic halo, followed by Bose-Einstein condensation in the intergalactic medium is considered.

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Efficient solution of ill-posed integral equations through averaging

arXiv

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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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Sustainability nexus AID: infrastructure resilience

引用

Sustainability Nexus Forum 2024年第1期32卷 1-7页

作者： Erfani, Tohid Naeini, Mohammad Mortazavi Borgomeo, Edoardo Anvari, Mehrnaz Hurford, Anthony Erfani, Rasool Zarei, Azin Matin, Mir A. Madani, Kaveh Department of Civil Environmental and Geomatic Engineering University College London London UK Department of Planning Industry and Environment Parramatta Australia Department of Engineering University of Cambridge Cambridge UK Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin Germany Environment Agency Wales UK Department of Engineering Manchester Metropolitan University Manchester UK United Nations University Institute for Integrated Management of Material Fluxes and of Resources (UNU-FLORES) Dresden Germany United Nations University Institute for Water Environment and Health (UNU-INWEH) Richmond Hill Canada

Infrastructure resilience advanced through nexus thinking is pivotal for societies to handle disruptions and ensure sustainable functionality. This interconnected approach understands infrastructure as an interdependent complex system and enables cooperative planning to achieve resilience. However, challenges like data inadequacy, financial limitations and governance issues impede its adoption, especially in developing regions. The United Nations University (UNU) Sustainability Nexus Analytics, Informatics and Data (AID) Programme strives to promote integrated resource management for sustainable development and fulfilling the UN 2030 Agenda. Through its Infrastructure Resilience Module, the initiative provides tools, data platforms and localised capacity building to empower professionals and communities for evidence-based, collaborative decision-making accounting for intersectoral relationships. By supporting context-specific analytical capabilities, bridging data gaps, and governance silos, the programme aims to pave the way for resilient and sustainable infrastructure development, particularly across vulnerable regions in the Global South, which face disproportionate infrastructure service disruptions.

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