T-Crowd: Effective Crowdsourcing for Tabular Data

Crowdsourcing employs human workers to solve computer-hard problems, such as data cleaning, entity resolution, and sentiment analysis. When crowdsourcing tabular data, e.g., the attribute values of an entity set, a worker's answers on the different attributes (e.g., the nationality and age of a celebrity star) are often treated independently. This assumption is not always true and can lead to suboptimal crowdsourcing performance. In this paper, we present the T-Crowd system, which takes into consideration the intricate relationships among tasks, in order to converge faster to their true values. Particularly, T-Crowd integrates each worker's answers on different attributes to effectively learn his/her trustworthiness and the true data values. The attribute relationship information is also used to guide task allocation to workers. Finally, T-Crowd seamlessly supports categorical and continuous attributes, which are the two main datatypes found in typical databases. Our extensive experiments on real and synthetic datasets show that T-Crowd outperforms state-of-the-art methods in terms of truth inference and reducing the cost of crowdsourcing

Development and characterization of a laser-induced acoustic desorption source

A laser-induced acoustic desorption source, developed for use at central facilities, such as free-electron lasers, is presented. It features prolonged measurement times and a fixed interaction point. A novel sample deposition method using aerosol spraying provides a uniform sample coverage and hence stable signal intensity. Utilizing strong-field ionization as a universal detection scheme, the produced molecular plume is characterized in terms of number density, spatial extend, fragmentation, temporal distribution, translational velocity, and translational temperature. The effect of desorption laser intensity on these plume properties is evaluated. While translational velocity is invariant for different desorption laser intensities, pointing to a non-thermal desorption mechanism, the translational temperature increases significantly and higher fragmentation is observed with increased desorption laser fluence.Comment: 8 pages, 7 figure

Bayesian Varying-Coefficient Model with Missing Data

Ph.DDOCTOR OF PHILOSOPH

Strong gravitational lensing of rotating regular black holes in non-minimally coupled Einstein-Yang-Mills theory

The strong gravitational lensing of a regular and rotating magnetic black hole in non-minimally coupled Einstein-Yang-Mills theory is studied. We find that, with the increase of any characteristic parameters of this black hole, such as the rotating parameter, magnetic charge and EYM parameter, the angular image position and relative magnification decrease while deflection angle and image separation increase. The results will degenerate to that of the Kerr case, R-N case with magnetic charge and Schwarzschild case when we take some specific values for the black hole parameters. The results also show that, due to the small influence of magnetic charge and Einstein-Yang-Mills parameters, it is difficult for current astronomical instruments to tell this black hole apart from a General Relativity one