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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: Large Hadron Collider & Neutrino. 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: A new classification of peptidomimetics (classes A–D) is introduced that enables a clear assignment of available approaches for the structure-based design of PPI inhibitors through stabilizing or mimicking turns, β-sheets, and helices.
Abstract: Protein–protein interactions (PPIs) are involved at all levels of cellular organization, thus making the development of PPI inhibitors extremely valuable The identification of selective inhibitors is challenging because of the shallow and extended nature of PPI interfaces Inhibitors can be obtained by mimicking peptide binding epitopes in their bioactive conformation For this purpose, several strategies have been evolved to enable a projection of side chain functionalities in analogy to peptide secondary structures, thereby yielding molecules that are generally referred to as peptidomimetics Herein, we introduce a new classification of peptidomimetics (classes A–D) that enables a clear assignment of available approaches Based on this classification, the Review summarizes strategies that have been applied for the structure-based design of PPI inhibitors through stabilizing or mimicking turns, β-sheets, and helices

491 citations

Posted Content
TL;DR: In this article, a generalization of GNNs, called $k$-dimensional GNN, was proposed, which can take higher-order graph structures at multiple scales into account.
Abstract: In recent years, graph neural networks (GNNs) have emerged as a powerful neural architecture to learn vector representations of nodes and graphs in a supervised, end-to-end fashion. Up to now, GNNs have only been evaluated empirically---showing promising results. The following work investigates GNNs from a theoretical point of view and relates them to the $1$-dimensional Weisfeiler-Leman graph isomorphism heuristic ($1$-WL). We show that GNNs have the same expressiveness as the $1$-WL in terms of distinguishing non-isomorphic (sub-)graphs. Hence, both algorithms also have the same shortcomings. Based on this, we propose a generalization of GNNs, so-called $k$-dimensional GNNs ($k$-GNNs), which can take higher-order graph structures at multiple scales into account. These higher-order structures play an essential role in the characterization of social networks and molecule graphs. Our experimental evaluation confirms our theoretical findings as well as confirms that higher-order information is useful in the task of graph classification and regression.

489 citations

Journal ArticleDOI
Morad Aaboud, Georges Aad1, Brad Abbott2, Jalal Abdallah3  +2845 moreInstitutions (197)
TL;DR: This paper presents a short overview of the changes to the trigger and data acquisition systems during the first long shutdown of the LHC and shows the performance of the trigger system and its components based on the 2015 proton–proton collision data.
Abstract: During 2015 the ATLAS experiment recorded 3.8 fb(-1) of proton-proton collision data at a centre-of-mass energy of 13 TeV. The ATLAS trigger system is a crucial component of the experiment, respons ...

488 citations

Proceedings Article
29 Jun 2000
TL;DR: A new method to recognize and handle concept changes with support vector machines that maintains a window on the training data and can eeectively select an appropriate window size in a robust way is proposed.
Abstract: For many learning tasks where data is collected over an extended period of time, its underlying distribution is likely to change. A typical example is information ltering, i.e. the adaptive classiication of documents with respect to a particular user interest. Both the interest of the user and the document content change over time. A ltering system should be able to adapt to such concept changes. This paper proposes a new method to recognize and handle concept changes with support vector machines. The method maintains a window on the training data. The key idea is to automatically adjust the window size so that the estimated generalization error is minimized. The new approach is both theoretically well-founded as well as eeective and eecient in practice. Since it does not require complicated parameterization, it is simpler to use and more robust than comparable heuristics. Experiments with simulated concept drift scenarios based on real-world text data compare the new method with other window management approaches. We show that it can eeectively select an appropriate window size in a robust way.

488 citations

Journal ArticleDOI
M. G. Aartsen1, K. Abraham2, Markus Ackermann, Jenni Adams3  +316 moreInstitutions (45)
TL;DR: In this article, the results from six different IceCube searches for astrophysical neutrinos in a maximum-likelihood analysis are combined, and the combined event sample features high-statistics samples of shower-like and track-like events.
Abstract: Evidence for an extraterrestrial flux of high-energy neutrinos has now been found in multiple searches with the IceCube detector. The first solid evidence was provided by a search for neutrino events with deposited energies greater than or similar to 30 TeV and interaction vertices inside the instrumented volume. Recent analyses suggest that the extraterrestrial flux extends to lower energies and is also visible with throughgoing, nu(mu)-induced tracks from the Northern Hemisphere. Here, we combine the results from six different IceCube searches for astrophysical neutrinos in a maximum-likelihood analysis. The combined event sample features high-statistics samples of shower-like and track-like events. The data are fit in up to three observables: energy, zenith angle, and event topology. Assuming the astrophysical neutrino flux to be isotropic and to consist of equal flavors at Earth, the all-flavor spectrum with neutrino energies between 25 TeV and 2.8 PeV is well described by an unbroken power law with best-fit spectral index -2.50 +/- 0.09 and a flux at 100 TeV of (6.7(-1.2)(+1.1)) x 10(-18) GeV-1 s(-1) sr(-1) cm(-2). Under the same assumptions, an unbroken power law with index -2 is disfavored with a significance of 3.8 sigma (p = 0.0066%) with respect to the best fit. This significance is reduced to 2.1 sigma (p = 1.7%) if instead we compare the best fit to a spectrum with index -2 that has an exponential cut-off at high energies. Allowing the electron-neutrino flux to deviate from the other two flavors, we find a nu(e) fraction of 0.18 +/- 0.11 at Earth. The sole production of electron neutrinos, which would be characteristic of neutron-decay-dominated sources, is rejected with a significance of 3.6 sigma ( p = 0.014%).

487 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