Large-x d/u Ratio in W-boson Production

Recent analysis of proton and deuteron deep-inelastic scattering data have suggested that the extracted d/u quark distribution ratio at large x may be significantly larger than previously believed, if the data are corrected for nuclear binding effects in the deuteron. We examine the sensitivity to the large-x d/u ratio of lepton asymmetries from W-boson production in p-pbar and p-p collisions at large rapidity, which do not suffer from nuclear contamination.Comment: 15 pages revtex, 5 postscript figures; new data on lepton asymmetry included, references added, version to be published in Phys. Lett.

Multi-Estimator Full Left Ventricle Quantification through Ensemble Learning

Cardiovascular disease accounts for 1 in every 4 deaths in United States. Accurate estimation of structural and functional cardiac parameters is crucial for both diagnosis and disease management. In this work, we develop an ensemble learning framework for more accurate and robust left ventricle (LV) quantification. The framework combines two 1st-level modules: direct estimation module and a segmentation module. The direct estimation module utilizes Convolutional Neural Network (CNN) to achieve end-to-end quantification. The CNN is trained by taking 2D cardiac images as input and cardiac parameters as output. The segmentation module utilizes a U-Net architecture for obtaining pixel-wise prediction of the epicardium and endocardium of LV from the background. The binary U-Net output is then analyzed by a separate CNN for estimating the cardiac parameters. We then employ linear regression between the 1st-level predictor and ground truth to learn a 2nd-level predictor that ensembles the results from 1st-level modules for the final estimation. Preliminary results by testing the proposed framework on the LVQuan18 dataset show superior performance of the ensemble learning model over the two base modules.Comment: Jiasha Liu, Xiang Li and Hui Ren contribute equally to this wor