An empirical study about catering theory of dividends: The proof from Chinese stock market

Purpose: This paper aims to study the remarkable effect of dividends catering in Chinese stock market, and its significance on dividend policy and investment decisions. Is there a significant difference in operation status of companies between issuing cash dividend and those without cash dividend, and which one is the better? Is there a significant difference in income level of stocks between issuing cash dividend and those without cash dividend, which one is the higher? Is the irrational preference of cash dividend detracting along with the development and improvement of securities market? All issues above need the market inspection. Design/methodology/approach: Based on related dividend theories and the empirical data of Chinese securities market, we construct three portfolios including stock dividend, cash dividend and non-dividend. The paper studies the returns from these three kinds of portfolios which is analyzed by the least significant difference method, co-integration model, Sharpe index model, and error correction model, and then finally comes to the conclusions. Findings: The main finding is that there is significant effect of dividends catering in Chinese stock market; the income level of cash dividend portfolio is significantly lower than that of other portfolios; the listed companies issuing stock dividend have a high investment value. Originality/value: Through collecting a lot of data from the year 2004 to 2009 and developing models to analyze, the paper deem that whether the earnings growth or not determines the dividend policy of listed companies, and that stock dividend is the natural choice for those listed companies which have sustainable development advantages.Peer Reviewe

Optimal measurements to access classical correlations of two-qubit states

We analyze the optimal measurements accessing classical correlations in arbitrary two-qubit states. Two-qubit states can be transformed into the canonical forms via local unitary operations. For the canonical forms, we investigate the probability distribution of the optimal measurements. The probability distribution of the optimal measurement is found to be centralized in the vicinity of a specific von Neumann measurement, which we call the maximal-correlation-direction measurement (MCDM). We prove that for the states with zero-discord and maximally mixed marginals, the MCDM is the very optimal measurement. Furthermore, we give an upper bound of quantum discord based on the MCDM, and investigate its performance for approximating the quantum discord.Comment: 8 pages, 3 figures, version accepted by Phys. Rev.

Integrating Range and Texture Information for 3D Face Recognition

The performance of face recognition systems that use two-dimensional images depends on consistent conditions w.r.t. lighting, pose, and facial appearance. We are developing a face recognition system that utilizes three-dimensional shape information to make the system more robust to arbitrary view, lighting, and facial appearance. For each subject, a 3D face model is constructed by integrating several 2.5D face scans from different viewpoints. A 2.5D scan is composed of one range image along with a registered 2D color image. The recognition engine consists of two components, surface matching and appearance-based matching. The surface matching component is based on a modified Iterative Closest Point (ICP) algorithm. The candidate list used for appearance matching is dynamically generated based on the output of the surface matching component, which reduces the complexity of the appearance-based matching stage. The 3D model in the gallery is used to synthesize new appearance samples with pose and illumination variations that are used for discriminant subspace analysis. The weighted sum rule is applied to combine the two matching components. A hierarchical matching structure is designed to further improve the system performance in both accuracy and efficiency. Experimental results are given for matching a database of 100 3D face models with 598 2.5D independent test scans acquired in different pose and lighting conditions, and with some smiling expression. The results show the feasibility of the proposed matching scheme. 1

Dynamic Perturbation-Adaptive Adversarial Training on Medical Image Classification

Remarkable successes were made in Medical Image Classification (MIC) recently, mainly due to wide applications of convolutional neural networks (CNNs). However, adversarial examples (AEs) exhibited imperceptible similarity with raw data, raising serious concerns on network robustness. Although adversarial training (AT), in responding to malevolent AEs, was recognized as an effective approach to improve robustness, it was challenging to overcome generalization decline of networks caused by the AT. In this paper, in order to reserve high generalization while improving robustness, we proposed a dynamic perturbation-adaptive adversarial training (DPAAT) method, which placed AT in a dynamic learning environment to generate adaptive data-level perturbations and provided a dynamically updated criterion by loss information collections to handle the disadvantage of fixed perturbation sizes in conventional AT methods and the dependence on external transference. Comprehensive testing on dermatology HAM10000 dataset showed that the DPAAT not only achieved better robustness improvement and generalization preservation but also significantly enhanced mean average precision and interpretability on various CNNs, indicating its great potential as a generic adversarial training method on the MIC.Comment: 9 pages, 4 figures, 2 table