Disclosure and Cross-listing: Evidence from Asia-Pacific Firms

Purpose – The purpose of this paper is to examine whether both country disclosure environment and firm-level disclosures are associated with cross-listing in the USA or London or otherwise. Design/methodology/approach – The authors test the association using a sample of Asia-Pacific firms covered in the Standard and Poor\u27s, 2001/2002 disclosure survey, capturing the country-level disclosure using the Center for International Financial Analysis and Research (CIFAR) score. The firm-level disclosure is measured using the S&P disclosure score. The authors conduct a logistic regression analysis and a two-stage least squares analysis to examine whether the outcome, cross-listing or not, is associated with the country disclosure environment and firm-level disclosures. Findings – The authors find that Asia-Pacific firms from weak disclosure environments and having higher firm-level disclosure scores are more likely to seek listing in the USA. Further, the paper provides initial evidence that these Asia-Pacific firms are as likely to seek listing in London as in the USA. No significant difference was found in S&P scores between US and London cross-listings after controlling for the effects of other variables. This suggests that firms that cross-list in London present similar disclosure levels to firms that cross-list in the USA. Originality/value – The paper\u27s findings contribute to the cross-listing literature on disclosure by showing that the interaction between firm-level disclosure and country-level disclosure has an impact on whether a firm cross-lists in the USA/London or not. The authors\u27 comparison of US cross-listings versus London cross-listings provides the first evidence that disclosures of US and London cross-listings are not significantly different

Perceptron learning with random coordinate descent

A perceptron is a linear threshold classifier that separates examples with a hyperplane. It is perhaps the simplest learning model that is used standalone. In this paper, we propose a family of random coordinate descent algorithms for perceptron learning on binary classification problems. Unlike most perceptron learning algorithms which require smooth cost functions, our algorithms directly minimize the training error, and usually achieve the lowest training error compared with other algorithms. The algorithms are also computational efficient. Such advantages make them favorable for both standalone use and ensemble learning, on problems that are not linearly separable. Experiments show that our algorithms work very well with AdaBoost, and achieve the lowest test errors for half of the datasets

On Vector Goldstone Boson

The possibility that higher dimensional field theories are broken spontaneously, through the usual Nambu-Goldstone mechanism, to 4-dimension is explored. As a consequence, vector Goldstone bosons can arise in this breaking of Lorentzian symmetry from higher dimension to 4-dimension. This can provide a simple mechanism for reduction to 4-dimension in theories with extra dimensions.Comment: 6 pages, equations, submitted for publicatio

Spontaneous Symmetry Breaking and Chiral Symmetry

In this introductory lecture, some basic features of the spontaneous symmetry breaking are discussed. More specifically, $\sigma$-model, non-linear realization, and some examples of spontaneous symmetry breaking in the non-relativistic system are discussed in details. The approach here is more pedagogical than rigorous and the purpose is to get some simple explanation of some useful topics in this rather wide area. .Comment: Lecture Delivered at VII Mexico Workshop on Paritcles and Fields, Merida, Yucatan Mexico, Nov 10-17,199

Strain engineering and photocatalytic application of single-layer ReS2_2

We present a theoretical study on the electronic, dynamical, and photocatalytic properties of single-layer ReS$_2$ under uniaxial and shear strains. The single-layer ReS$_2$ shows strong anisotropic responses to straining. It remains dynamically stable for a wide range of $x$-axial strain, but becomes unstable for 2\% $y$-axial compressive strain. The single-layer ReS$_2$ is calculated to be an indirect bandgap semiconductor, and there is an indirect$-$direct bandgap transition under 1$-$5\% $x$-axial tensile straining. The single-layer ReS$_2$ is predicted incapable of catalyzing the water oxidation reaction. However, 1$-$5\% $y$-axial tensile strain can enable the single-layer ReS$_2$ for overall photocatalytic water splitting. Besides, the single-layer ReS$_2$ can also catalyze the overall water splitting and be most efficient under acidic water solutions with pH=3.8

Heterogeneous data fusion for brain psychology applications

This thesis aims to apply Empirical Mode Decomposition (EMD), Multiscale Entropy (MSE), and collaborative adaptive filters for the monitoring of different brain consciousness states. Both block based and online approaches are investigated, and a possible extension to the monitoring and identification of Electromyograph (EMG) states is provided. Firstly, EMD is employed as a multiscale time-frequency data driven tool to decompose a signal into a number of band-limited oscillatory components; its data driven nature makes EMD an ideal candidate for the analysis of nonlinear and non-stationary data. This methodology is further extended to process multichannel real world data, by making use of recent theoretical advances in complex and multivariate EMD. It is shown that this can be used to robustly measure higher order features in multichannel recordings to robustly indicate ‘QBD’. In the next stage, analysis is performed in an information theory setting on multiple scales in time, using MSE. This enables an insight into the complexity of real world recordings. The results of the MSE analysis and the corresponding statistical analysis show a clear difference in MSE between the patients in different brain consciousness states. Finally, an online method for the assessment of the underlying signal nature is studied. This method is based on a collaborative adaptive filtering approach, and is shown to be able to approximately quantify the degree of signal nonlinearity, sparsity, and non-circularity relative to the constituent subfilters. To further illustrate the usefulness of the proposed data driven multiscale signal processing methodology, the final case study considers a human-robot interface based on a multichannel EMG analysis. A preliminary analysis shows that the same methodology as that applied to the analysis of brain cognitive states gives robust and accurate results. The analysis, simulations, and the scope of applications presented suggest great potential of the proposed multiscale data processing framework for feature extraction in multichannel data analysis. Directions for future work include further development of real-time feature map approaches and their use across brain-computer and brain-machine interface applications

Quantile correlations and quantile autoregressive modeling

In this paper, we propose two important measures, quantile correlation (QCOR) and quantile partial correlation (QPCOR). We then apply them to quantile autoregressive (QAR) models, and introduce two valuable quantities, the quantile autocorrelation function (QACF) and the quantile partial autocorrelation function (QPACF). This allows us to extend the classical Box-Jenkins approach to quantile autoregressive models. Specifically, the QPACF of an observed time series can be employed to identify the autoregressive order, while the QACF of residuals obtained from the fitted model can be used to assess the model adequacy. We not only demonstrate the asymptotic properties of QCOR, QPCOR, QACF, and PQACF, but also show the large sample results of the QAR estimates and the quantile version of the Ljung-Box test. Simulation studies indicate that the proposed methods perform well in finite samples, and an empirical example is presented to illustrate usefulness