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

A Latent Space Model for HLA Compatibility Networks in Kidney Transplantation

Kidney transplantation is the preferred treatment for people suffering from end-stage renal disease. Successful kidney transplants still fail over time, known as graft failure; however, the time to graft failure, or graft survival time, can vary significantly between different recipients. A significant biological factor affecting graft survival times is the compatibility between the human leukocyte antigens (HLAs) of the donor and recipient. We propose to model HLA compatibility using a network, where the nodes denote different HLAs of the donor and recipient, and edge weights denote compatibilities of the HLAs, which can be positive or negative. The network is indirectly observed, as the edge weights are estimated from transplant outcomes rather than directly observed. We propose a latent space model for such indirectly-observed weighted and signed networks. We demonstrate that our latent space model can not only result in more accurate estimates of HLA compatibilities, but can also be incorporated into survival analysis models to improve accuracy for the downstream task of predicting graft survival times.Comment: This work has been accepted to BIBM 202

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