Shielding method for polycrystalline and epitaxy growths

Technique prevents silicon wafers from adhering to susceptor following silicon epitaxial deposition. Annular ring of refractory material goes around wafer during epitaxial deposit. Silicon is deposited on ring, susceptor, and portions of wafer. Ring breaks away from susceptor and wafer and no silicon undergrowth occurs

The China A shares follow random walk but the B shares do not

The China A-Share stocks and the China B-Share stocks are common stocks issued by companies incorporated in China. These two classes of common stocks differ in the nationality of the investors each is restricted to by law. For the most part, the A shares, quoted in the Chinese yuan, or renminbi, are for Chinese nationals while the B shares, quoted in foreign currencies, are for non-Chinese nationals and residents of Macau, Hong Kong and Taiwan. This paper identified eighty-six companies issuing both the A and B shares and tested if these shares weekly returns follow a random walk. Employing the Lo and MacKinlay variance ratio test statistics, it is discovered that five times more B shares rejected the random walk as did the A shares. Moreover, both the Shenzhen and Shanghai B-Share indexes reject the random walk while neither the Shenzhen nor Shanghai A-Share index reject the random walk.

The Taiwan stock market does follow a random walk

Applying the Lo and MacKinlay variance ratio test on the weekly returns from the Taiwan stock market from 1990 to mid 2006, I obtained results strongly indicative of the fact that not only does the Taiwan composite stock index move in a random walk fashion, returns for the individual stocks do so as we. Previous authors employing the same methodology obtained opposite results, namely, that the movements of the Taiwan stock composite index do not follow a random walk.random walk

Joint models for noise annoyance and WTP for road noise reduction

Previous CV studies of the WTP for road noise reduction have used stated annoyance as an independent variable. We argue that this may be inappropriate due to potential endogeneity bias. Instead, an alternative model is proposed that treats both WTP and annoyance as endogenous variables in a simultaneous equation model as a combination of a linear regression with an ordered probit with correlated error terms and possibly common parameters. Thus, information on stated annoyance is utilised to estimate WTP without bias. Application of the model to a dataset from Copenhagen indicates a potential for improving the precision of the estimate of WTP for noise reduction with CV data.Road noise, annoyance, WTP, hurdle model, qualitative responses

The Recent Performance of the Traditional Measure of Core Inflation in G7 Countries

In this paper we undertake an empirical investigation concerning the performance of the traditional measure of core inflation in recent years. We consider the group of G7 countries and explore both the high-frequency and the low-frequency relations between overall inflation and core inflation. We find that the traditional core measure, obtained by subtracting from the overall index those components which exhibit high volatility and which are responsible for the short-run variability of inflation, is a reliable indicator of trend inflation for a group of countries including the USA, Canada and Japan. The innovation accounting shows that for the three countries the transitory shock, i.e. the total inflation shock, has limited persistence and hence there is a relatively quick convergence of overall inflation to its trend component.Core Inflation Indicator; Structural Cointegrated VARs; Permanent-transitory Decompositions

Manifold Constrained Low-Rank Decomposition

Low-rank decomposition (LRD) is a state-of-the-art method for visual data reconstruction and modelling. However, it is a very challenging problem when the image data contains significant occlusion, noise, illumination variation, and misalignment from rotation or viewpoint changes. We leverage the specific structure of data in order to improve the performance of LRD when the data are not ideal. To this end, we propose a new framework that embeds manifold priors into LRD. To implement the framework, we design an alternating direction method of multipliers (ADMM) method which efficiently integrates the manifold constraints during the optimization process. The proposed approach is successfully used to calculate low-rank models from face images, hand-written digits and planar surface images. The results show a consistent increase of performance when compared to the state-of-the-art over a wide range of realistic image misalignments and corruptions

Neonates(BIRTH – 1 MONTH)

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