Technical University of Dortmund

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.

Handling of the generation of primary events in Gauss, the LHCb simulation framework

Abstract: The LHCb simulation application, Gauss, consists of two independent phases, the generation of the primary event and the tracking of particles produced in the experimental setup. For the LHCb experimental program it is particularly important to model B meson decays: the EvtGen code developed in CLEO and BABAR has been chosen and customized for non-coherent B production as occuring in pp collisions at the LHC. The initial proton-proton collision is provided by a different generator engine, currently PYTHIA 6 for massive production of signal and generic pp collisions events. Beam gas events, background events originating from proton halo, cosmics and calibration events for different detectors can be generated in addition to pp collisions. Different generator packages as available in the physics community or specifically developed in LHCb are used for the different purposes. Running conditions affecting the generated events such as the size of the luminous region, the number of collisions occuring in a bunch crossing and the number of spill-over events from neighbouring bunches are modeled via dedicated algorithms appropriately configured. The design of the generator phase of Gauss will be described: a modular structure with well defined interfaces specific to the various tasks, e.g. pp collisions, particle decays, selections, etc. has been chosen. Different implementations are available for the various tasks allowing selecting and combining them as most appropriate at run time as in the case of PYTHIA 6 for pp collisions or HIJING for beam gas. The advantages of such structure, allowing for example to adopt transparently new generators packages, will be discussed.

Antimicrobial Polymers in Solution and on Surfaces: Overview and Functional Principles

Abstract: The control of microbial infections is a very important issue in modern society. In general there are two ways to stop microbes from infecting humans or deteriorating materials—disinfection and antimicrobial surfaces. The first is usually realized by disinfectants, which are a considerable environmental pollution problem and also support the development of resistant microbial strains. Antimicrobial surfaces are usually designed by impregnation of materials with biocides that are released into the surroundings whereupon microbes are killed. Antimicrobial polymers are the up and coming new class of disinfectants, which can be used even as an alternative to antibiotics in some cases. Interestingly, antimicrobial polymers can be tethered to surfaces without losing their biological activity, which enables the design of surfaces that kill microbes without releasing biocides. The present review considers the working mechanisms of antimicrobial polymers and of contact-active antimicrobial surfaces based on examples of recent research as well as on multifunctional antimicrobial materials.

The identification of multiple outliers

Abstract: One approach to identifying outliers is to assume that the outliers have a different distribution from the remaining observations. In this article we define outliers in terms of their position relative to the model for the good observations. The outlier identification problem is then the problem of identifying those observations that lie in a so-called outlier region. Methods based on robust statistics and outward testing are shown to have the highest possible breakdown points in a sense derived from Donoho and Huber. But a more detailed analysis shows that methods based on robust statistics perform better with respect to worst-case behavior. A concrete outlier identifier based on a suggestion of Hampel is given.

Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks

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.

Combined Measurement and QCD Analysis of the Inclusive e± p Scattering Cross Sections at HERA

Abstract: A combination is presented of the inclusive deep inelastic cross sections measured by the H1 and ZEUS Collaborations in neutral and charged current unpolarised e(+/-)p scattering at HERA during the period 1994-2000. The data span six orders of magnitude in negative four-momentum-transfer squared, Q(2), and in Bjorken x. The combination method used takes the correlations of systematic uncertainties into account, resulting in an improved accuracy. The combined data are the sole input in a NLO QCD analysis which determines a new set of parton distributions, HERAPDF1.0, with small experimental uncertainties. This set includes an estimate of the model and parametrisation uncertainties of the fit result.