The Pump-Priming Effect of Regulatory Reform on Stock Repurchases : Evidence from Lifting the Ban on Treasury Stocks in Japan

This study investigates corporate reactions to the deregulation of stock repurchases set forth on 1 October 2001, in Japan, by looking at the motivations for stock repurchases. We found that stock repurchases increased significantly after the ban on treasury stocks was lifted. Our results show that firms with free-cash flow problems initiated a repurchase plan to distribute excess cash to shareholders and reduce agency costs over the sample period. In addition, firms who wanted to signal undervaluation also undertook stock repurchases over the sample period. These firms were affected by the deregulation, unlike firms that repurchase to reduce agency costs. We determined that firms with weak incentives to signal undervaluation increased stock repurchases significantly in order to respond to the deregulation, since these firms had the ability to take advantage of treasury stock purchases.Treasury stocks, Undervaluation, Takeover deterrence, Capital structure, Cash distribution

k-Same-Siamese-GAN: k-Same Algorithm with Generative Adversarial Network for Facial Image De-identification with Hyperparameter Tuning and Mixed Precision Training

For a data holder, such as a hospital or a government entity, who has a privately held collection of personal data, in which the revealing and/or processing of the personal identifiable data is restricted and prohibited by law. Then, "how can we ensure the data holder does conceal the identity of each individual in the imagery of personal data while still preserving certain useful aspects of the data after de-identification?" becomes a challenge issue. In this work, we propose an approach towards high-resolution facial image de-identification, called k-Same-Siamese-GAN, which leverages the k-Same-Anonymity mechanism, the Generative Adversarial Network, and the hyperparameter tuning methods. Moreover, to speed up model training and reduce memory consumption, the mixed precision training technique is also applied to make kSS-GAN provide guarantees regarding privacy protection on close-form identities and be trained much more efficiently as well. Finally, to validate its applicability, the proposed work has been applied to actual datasets - RafD and CelebA for performance testing. Besides protecting privacy of high-resolution facial images, the proposed system is also justified for its ability in automating parameter tuning and breaking through the limitation of the number of adjustable parameters

A review of audit quality and joint provision of audit and non-audit services (2013)

Report prepared for Institute of Chartered Accountants in England and Wales (ICAEW)The primary aim of the proposed research is to investigate and analyse changes in recent years in the market for the provision of non-audit services (NAS) - with a particular focus on the joint provision of audit and non-audit services and the potential effects on independence and the quality of audit. To reflect the need for a better understanding of the importance of non-audit services provision, and in order to further quantify, qualify and categorize these, we investigate how NAS are perceived by the public and investors, and why companies request joint provision from firms. Against a background in which issues of audit competition, efficiency, pricing and value added are on the front stage of the audit debate and audit regulation, this research is designed to provide input relevant to these issues, - input which will inform and be of use to firms, as well as the professional bodies and regulators more widely. Such an investigation provides additional insights on the magnitude and type of NAS provision to a broad spectrum of companies and stakeholders and also develops an understanding how NAS may enhance and/or overlap with their use of audit services.Institute of Chartered Accountants in England and Wales (ICAEW

How does joint provision of audit and non-audit services affect audit quality and independence? A review

The primary aim of this report is to analyse changes in recent years in the market for the provision of non-audit services (NAS), with a particular focus on the joint provision of audit and non-audit services and the potential effects on independence and the quality of audit. This report is relevant to the ongoing debate at both national and European level on issues of competition, liability and regulation in the audit market. and it seeks to contextualise the issue and provide a résumé of the arguments that have beenadvanced for and against allowing such joint provision

Optimization of ultrasound-assisted enzymatic hydrolysis extraction of tea polyphenols from green tea and their antioxidant activities

Production of natural extracts requires suitable processing conditions to facilitate the accumulation and preservation of bioactive ingredients. This study aimed to optimize the conditions for extracting tea polyphenols (TPs) from green tea using ultrasound-assisted compound enzymatic extraction (UACEE) technology with response surface methodology (RSM), based on a three-level, four-variable central composite rotatable design (CCRD). Extracted TPs yields were in the range of 16.48% to 28.77%; the experimental results were fitted to a second-order quadratic polynomial model and showed a good fit to the proposed model (R2 > 0.90). Compared with other ex-traction methods, UACEE exhibited significant advantages in the TPs extraction rate and preservation of catechins composition. The antioxidant activities of these extracts were also analyzed using reducing power and DPPH radical scavenging activity; all extracts showed excellent antioxidant activity in a dose-dependent manner, and UACEE extracts showed the strongest antioxidant activity in vitro

Dialogue State Induction Using Neural Latent Variable Models

Dialogue state modules are a useful component in a task-oriented dialogue system. Traditional methods find dialogue states by manually labeling training corpora, upon which neural models are trained. However, the labeling process can be costly, slow, error-prone, and more importantly, cannot cover the vast range of domains in real-world dialogues for customer service. We propose the task of dialogue state induction, building two neural latent variable models that mine dialogue states automatically from unlabeled customer service dialogue records. Results show that the models can effectively find meaningful slots. In addition, equipped with induced dialogue states, a state-of-the-art dialogue system gives better performance compared with not using a dialogue state module.Comment: IJCAI 202

Singular layer Physics Informed Neural Network method for Plane Parallel Flows

We construct in this article the semi-analytic Physics Informed Neural Networks (PINNs), called {\em singular layer PINNs} (or {\em sl-PINNs}), that are suitable to predict the stiff solutions of plane-parallel flows at a small viscosity. Recalling the boundary layer analysis, we first find the corrector for the problem which describes the singular behavior of the viscous flow inside boundary layers. Then, using the components of the corrector and its curl, we build our new {\em sl-PINN} predictions for the velocity and the vorticity by either embedding the explicit expression of the corrector (or its curl) in the structure of PINNs or by training the implicit parts of the corrector (or its curl) together with the PINN predictions. Numerical experiments confirm that our new {\em sl-PINNs} produce stable and accurate predicted solutions for the plane-parallel flows at a small viscosity