Models for the Prediction of the Thermal

Five models and eq\1ationsJorthe.predictic;>nof the tbertnal conduc~"ities of powders in the literature are compared with the data obtainedill the experiments of the authors. Anew modified ntodel for the. correlation of the experimental data is presented. Key words: differential scanning calorimetry, porosity, solid content, specific heat, thermal conductivity.Mechanical Engineerin

Neutrino masses and mixings

We propose a novel theoretical understanding of neutrino masses and mixings, which is attributed to the intrinsic vector-like feature of the regularized Standard Model at short distances. We try to explain the smallness of Dirac neutrino masses and the decoupling of the right-handed neutrino as a free particle. Neutrino masses and mixing angles are completely related to each other in the Schwinger-Dyson equations for their self-energy functions. The solutions to these equations and a possible pattern of masses and mixings are discussed.Comment: LaTex 11 page

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

Deep Learning for Single Image Super-Resolution: A Brief Review

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions. To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is firstly established and several critical limitations of the baseline are summarized. Then representative works on overcoming these limitations are presented based on their original contents as well as our critical understandings and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally we conclude this review with some vital current challenges and future trends in SISR leveraging deep learning algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM