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: 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.

A class of iterative methods for solving saddle point problems

Abstract: We consider the numerical solution of indefinite systems of linear equations arising in the calculation of saddle points. We are mainly concerned with sparse systems of this type resulting from certain discretizations of partial differential equations. We present an iterative method involving two levels of iteration, similar in some respects to the Uzawa algorithm. We relate the rates of convergence of the outer and inner iterations, proving that, under natural hypotheses, the outer iteration achieves the rate of convergence of the inner iteration. The technique is applied to finite element approximations of the Stokes equations.

Performance of missing transverse momentum reconstruction with the ATLAS detector using proton-proton collisions at s√ = 13 TeV

Abstract: The performance of the missing transverse momentum ( ETmiss ) reconstruction with the ATLAS detector is evaluated using data collected in proton-proton collisions at the LHC at a centre-of-mass energy of 13 TeV in 2015. To reconstruct ETmiss , fully calibrated electrons, muons, photons, hadronically decaying τ-leptons , and jets reconstructed from calorimeter energy deposits and charged-particle tracks are used. These are combined with the soft hadronic activity measured by reconstructed charged-particle tracks not associated with the hard objects. Possible double counting of contributions from reconstructed charged-particle tracks from the inner detector, energy deposits in the calorimeter, and reconstructed muons from the muon spectrometer is avoided by applying a signal ambiguity resolution procedure which rejects already used signals when combining the various ETmiss contributions. The individual terms as well as the overall reconstructed ETmiss are evaluated with various performance metrics for scale (linearity), resolution, and sensitivity to the data-taking conditions. The method developed to determine the systematic uncertainties of the ETmiss scale and resolution is discussed. Results are shown based on the full 2015 data sample corresponding to an integrated luminosity of 3.2fb-1 .

On Cultural and Macroeconomic Contingencies of the Entrepreneurial Orientation–Performance Relationship:

Abstract: The relationship between entrepreneurial orientation (EO) and firm performance is among the best-researched topics in entrepreneurship research. These studies have been conducted in various national contexts. While a first meta-analysis by Rauch et al. finds no significant difference between EO's effects based on the continent in which the firm is based, the present study considers how national cultural and macroeconomic drivers impact the EO–performance relationship. Building upon 177 studies with data from 41 countries, the meta-analysis consolidates this literature stream, contributing to the evidence-based entrepreneurship research.

Monitoring Structural Change in Dynamic Econometric Models

Abstract: The classical approach to testing for structural change employs retrospective tests using a historical data set of a given length. Here we consider a wide array of fluctuation-type tests in a monitoring situation - given a history period for which a regression relationship is known to be stable, we test whether incoming data are consistent with the previously established relationship. Procedures based on estimates of the regression coefficients are extended in three directions: we introduce (a) procedures based on OLS residuals, (b) rescaled statistics and (c) alternative asymptotic boundaries. Compared to the existing tests our extensions offer better power against certain alternatives, improved size in finite samples for dynamic models and ease of computation respectively. We apply our methods to two data sets, German M1 money demand and U.S. labor productivity.