Lattice QCD Calculation of the Momentum Fraction Carried by Quarks in the Nucleon, and the Roper Puzzle

This thesis is concerned with the lattice QCD calculation of the momentum fraction carried by quarks in the nucleon. Particularly, the strange quark contribution, (x)s, is calculated, as well as the ratio of the strange (x) to that of u/d in the disconnected insertion which will be useful in constraining the global fit of parton distribution functions at small x. The disconnected insertion is known to be hard to calculate on the lattice. We adopt the overlap fermion action on several 2 + 1 flavor domain-wall fermion ensembles with a light sea quark mass which corresponds to pion mass of 330 MeV and 139 MeV. Smeared grid sources with Z3 noise are deployed to calculate the nucleon propagator with low-mode substitution. Even-odd grid sources and the time-dilution technique with stochastic noises are used to calculate the high mode contribution to the quark loop. Low mode averaging (LMA) for the quark loop is applied to reduce the statistical error of the disconnected insertion calculation. We also address the puzzle on the mass of the Roper resonance. Using overlap fermion on top of domain-wall fermion configurations, as well as using the clover fermion action, we explore various smeared sources, and use the variational approach to isolate the Roper. We explain why chiral symmetry is important in resolving the discrepancies between lattice calculations and experiment

Testing the Weak-Form Market Efficiency Hypothesis for Canadian and Chinese Stock

The main empirical test methods for Weak-form efficiency market hypothesis can be divided into two categories: one is to test the randomness of stock prices; the other is to test the invalidity of technical analysis, which testing the predictability of earnings. This study mainly focused on the first category.To examining the hypothesis whether Canadian and Chinese stock markets are efficient in the weak form, two types of test are conducted. They are parametric and non-parametric tests. For Non-parametric test, we implement the Runs test and Kolmogrov–Smirnov goodness of fit test. For parametric test, autocorrelation (LBQ test), variance ratio and ARMA model have been chosen. The empirical analysis in this study uses daily closing prices of indices from Shanghai Stock Exchange (SSE) and Toronto Stock Exchange (TSX). To avoiding the biases of choosing testing period, we implemented the same tests among different sample periods for each market.The overall testing results are mixed from sample period to sample period for both markets. In general, for the early testing period, almost all testing techniques generate unfavoured results against the weak-form efficient market hypothesis (EMH) for both TSX and SSE. Several testing results based on more recent sample periods align with the assumption under the EMH, but it is still early to claim that either the Canadian or the Chinese stock market hasbecome the weak form efficient. More comprehensive testing results and analysis can be found under section 5 and 6

Online Bearing Remaining Useful Life Prediction Based on a Novel Degradation Indicator and Convolutional Neural Networks

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are often limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct health index. In this paper, a novel online data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RUL of bearings. More concretely, the raw vibrations of training bearings are first processed using the Hilbert-Huang transform (HHT) and a novel nonlinear degradation indicator is constructed as the label for learning. The CNN is then employed to identify the hidden pattern between the extracted degradation indicator and the vibration of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings' RULs are predicted by using a $\epsilon$-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by transferring to bearings undergoing different operating conditions