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Institution

Technical University of Dortmund

EducationDortmund, Nordrhein-Westfalen, Germany
About: Technical University of Dortmund is a education organization based out in Dortmund, Nordrhein-Westfalen, Germany. It is known for research contribution in the topics: Context (language use) & Large Hadron Collider. The organization has 13028 authors who have published 27666 publications receiving 615557 citations. The organization is also known as: Dortmund University & University of Dortmund.


Papers
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Journal ArticleDOI
TL;DR: This study shows that the extruded magnesium alloy LAE442 provides low corrosion rates and reacts in vivo with an acceptable host response and the in vivo corrosion rate can be further reduced by additional MgF(2) coating.

389 citations

Journal ArticleDOI
TL;DR: It appears well established that the aromatic amine components from azo pigments based on 3,3'-dichlorobenzidine are practically not bioavailable, and it is very unlikely that occupational exposure to insoluble azo Pigments would be associated with a substantial risk of (bladder) cancer in man.

388 citations

Proceedings ArticleDOI
01 Jan 2018
TL;DR: This work presents Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e.g., graphs or meshes, that is a generalization of the traditional CNN convolution operator by using continuous kernel functions parametrized by a fixed number of trainable weights.
Abstract: We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e.g., graphs or meshes. Our main contribution is a novel convolution operator based on B-splines, that makes the computation time independent from the kernel size due to the local support property of the B-spline basis functions. As a result, we obtain a generalization of the traditional CNN convolution operator by using continuous kernel functions parametrized by a fixed number of trainable weights. In contrast to related approaches that filter in the spectral domain, the proposed method aggregates features purely in the spatial domain. In addition, SplineCNN allows entire end-to-end training of deep architectures, using only the geometric structure as input, instead of handcrafted feature descriptors. For validation, we apply our method on tasks from the fields of image graph classification, shape correspondence and graph node classification, and show that it outperforms or pars state-of-the-art approaches while being significantly faster and having favorable properties like domain-independence. Our source code is available on GitHub1.

388 citations

Journal ArticleDOI
Roel Aaij, Bernardo Adeva1, Marco Adinolfi2, A. A. Affolder3  +710 moreInstitutions (63)
TL;DR: In this article, the authors measured the isospin asymmetries of the B (0) -> K ( 0) mu (+) mu (-), B (1) → K (1)-m (+) m mu (-) and B (2)→ K (2)-m (-) m (-), respectively.
Abstract: The isospin asymmetries of B -> K mu (+) mu (-) and B -> K (*) mu (+) mu (-) decays and the partial branching fractions of the B (0) -> K (0) mu (+) mu (-), B (+) -> K (+) mu (+) mu (-) and B (+) -> K (*+) mu (+) mu (-) decays are measured as functions of the dimuon mass squared, q (2). The data used correspond to an integrated luminosity of 3 fb(-1) from proton-proton collisions collected with the LHCb detector at centre-of-mass energies of 7 TeV and 8 TeV in 2011 and 2012, respectively. The isospin asymmetries are both consistent with the Standard Model expectations. The three measured branching fractions favour lower values than their respective theoretical predictions, however they are all individually consistent with the Standard Model.

386 citations

Journal ArticleDOI
TL;DR: In this paper, a systematic analysis of B meson decays into pions has been performed for decay modes with 2−7 pions in the final state, and the upper limits obtained on various branching ratios are consistent with the current model predictions.

386 citations


Authors

Showing all 13240 results

NameH-indexPapersCitations
Hermann Kolanoski145127996152
Marc Besancon1431799106869
Kerstin Borras133134192173
Emmerich Kneringer129102180898
Achim Geiser129133184136
Valerio Vercesi12993779519
Jens Weingarten12889674667
Giuseppe Mornacchi12789475830
Kevin Kroeninger12683670010
Daniel Muenstermann12688570855
Reiner Klingenberg12673370069
Claus Gössling12677571975
Diane Cinca12682270126
Frank Meier12467764889
Daniel Dobos12467967434
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023131
2022306
20211,694
20201,773
20191,653
20181,579