Leibniz University of Hanover

About: Leibniz University of Hanover is a education organization based out in Hanover, Niedersachsen, Germany. It is known for research contribution in the topics: Finite element method & Computer science. The organization has 14283 authors who have published 29845 publications receiving 682152 citations.

Fluctuation-Induced Transitions in a Bistable Surface Reaction: Catalytic CO Oxidation on a Pt Field Emitter Tip

Abstract: Fluctuations which arise in catalytic CO oxidation on a Pt field emitter tip have been studied with field electron microscopy as the imaging method. Fluctuation-driven transitions between the active and the inactive branch of the reaction are found to occur sufficiently close to the bifurcation point, terminating the bistable range. The experimental results are modeled with Monte Carlo simulations of a lattice-gas reaction model incorporating rapid CO diffusion. {copyright} {ital 1999} {ital The American Physical Society}

All-sky search for periodic gravitational waves in LIGO S4 data

Abstract: We report on an all-sky search with the LIGO detectors for periodic gravitational waves in the frequency range 50–1000 Hz and with the frequency’s time derivative in the range −1×10^(−8) Hz s^(−1) to zero. Data from the fourth LIGO science run (S4) have been used in this search. Three different semicoherent methods of transforming and summing strain power from short Fourier transforms (SFTs) of the calibrated data have been used. The first, known as StackSlide, averages normalized power from each SFT. A “weighted Hough” scheme is also developed and used, which also allows for a multi-interferometer search. The third method, known as PowerFlux, is a variant of the StackSlide method in which the power is weighted before summing. In both the weighted Hough and PowerFlux methods, the weights are chosen according to the noise and detector antenna-pattern to maximize the signal-to-noise ratio. The respective advantages and disadvantages of these methods are discussed. Observing no evidence of periodic gravitational radiation, we report upper limits; we interpret these as limits on this radiation from isolated rotating neutron stars. The best population-based upper limit with 95% confidence on the gravitational-wave strain amplitude, found for simulated sources distributed isotropically across the sky and with isotropically distributed spin axes, is 4.28×10^(−24) (near 140 Hz). Strict upper limits are also obtained for small patches on the sky for best-case and worst-case inclinations of the spin axes.

The Schr\"odinger-Newton equation as non-relativistic limit of self-gravitating Klein-Gordon and Dirac fields

Abstract: In this paper we show that the Schr\"odinger-Newton equation for spherically symmetric gravitational fields can be derived in a WKB-like expansion in 1/c from the Einstein-Klein-Gordon and Einstein-Dirac system.

A Blocking Framework for Entity Resolution in Highly Heterogeneous Information Spaces

Abstract: In the context of entity resolution (ER) in highly heterogeneous, noisy, user-generated entity collections, practically all block building methods employ redundancy to achieve high effectiveness. This practice, however, results in a high number of pairwise comparisons, with a negative impact on efficiency. Existing block processing strategies aim at discarding unnecessary comparisons at no cost in effectiveness. In this paper, we systemize blocking methods for clean-clean ER (an inherently quadratic task) over highly heterogeneous information spaces (HHIS) through a novel framework that consists of two orthogonal layers: the effectiveness layer encompasses methods for building overlapping blocks with small likelihood of missed matches; the efficiency layer comprises a rich variety of techniques that significantly restrict the required number of pairwise comparisons, having a controllable impact on the number of detected duplicates. We map to our framework all relevant existing methods for creating and processing blocks in the context of HHIS, and additionally propose two novel techniques: attribute clustering blocking and comparison scheduling. We evaluate the performance of each layer and method on two large-scale, real-world data sets and validate the excellent balance between efficiency and effectiveness that they achieve.