Limit on Br(b→sg)Br(b\to s g) in Two Higgs Doublet Models

Using the recent CLEO measurement of $Br(b\to s \gamma)$, we find that the branching ratio of $b\to s g$ cannot be larger than $10\%$ in two Higgs doublet models. The small experimental value of $Br(b\to e\bar{\nu}X)$ can no longer be explained by charged Higgs boson effects.Comment: 11 pages + 3 Figures (not included), ReVTeX, NTUTH-94-16 and NHCU-HEP-94-1

Generative Adversarial Positive-Unlabelled Learning

In this work, we consider the task of classifying binary positive-unlabeled (PU) data. The existing discriminative learning based PU models attempt to seek an optimal reweighting strategy for U data, so that a decent decision boundary can be found. However, given limited P data, the conventional PU models tend to suffer from overfitting when adapted to very flexible deep neural networks. In contrast, we are the first to innovate a totally new paradigm to attack the binary PU task, from perspective of generative learning by leveraging the powerful generative adversarial networks (GAN). Our generative positive-unlabeled (GenPU) framework incorporates an array of discriminators and generators that are endowed with different roles in simultaneously producing positive and negative realistic samples. We provide theoretical analysis to justify that, at equilibrium, GenPU is capable of recovering both positive and negative data distributions. Moreover, we show GenPU is generalizable and closely related to the semi-supervised classification. Given rather limited P data, experiments on both synthetic and real-world dataset demonstrate the effectiveness of our proposed framework. With infinite realistic and diverse sample streams generated from GenPU, a very flexible classifier can then be trained using deep neural networks.Comment: 8 page

Motives and location choice of OFDI: the case of China

The motives and location choice of OFDI: the case of ChinaThis research investigates the location choice of Chinese companies' outward foreign direct investment (OFDI). It addresses the question of why companies with similar motivations will invest in different types of locations. In more details, how a company's motivation affects their location for OFDI, and what it is about some Chinese companies that allows them to invest in certain countries. It also seeks to examine the extent to which classical theories can explain the OFDI from emerging markets in the case of China.Chinese official data has certain flaws, such as low quality at firm level and the omission of some important investments due to the data collection methods. Thus this research uses data collected from a private survey. Logistic regression analysis was applied to a sample of 129 companies, and the following conclusions made:Firstly, a company's investment motivation is the determining factor for their investment. Chinese MNEs motivated to acquire created assets, such as brand name, technology and managerial expertise are more likely to invest in developed countries rather than less developed countries. There is no clear evidence of efficiency seeking as a motivation for Chinese companies, but natural resource seeking FDIs are considerable from China.Meanwhile, this research finds evidence of capital seeking FDI. As a result of the Chinese government's capital control policy, some Chinese companies invest overseas to access the host countries' stock market. This kind of investment is generally performed using some unconventional method, such as 'reverse takeover' or 'round-tripping'.Secondly, the Chinese Government plays an important role in Chinese OFDI. The Government's foreign policy may influence a company's investment decision making. E.g. companies with a stronger relationship with the Government are more likely to invest in developed countries rather than Hong KongThis research also finds evidence that a closer government relationship enhances companies' competitive advantages when performing the investment. This research suggests that a strong connection with the Government acts as an ownership advantage in the case of Chinese firms.Thirdly, Internalisation of Internationalisation (i2) is suggested and developed in this research. This term encapsulates the systematic knowledge transfer that is hypothesised as occurring during the operation of a joint venture or other form of alliance with foreign investors in China. It is proposed that this knowledge transfer from foreign firms to the domestic company enables Chinese companies to acquire knowledge of international operation even before engaging with the overseas market. This research finds that companies with exposure to i2 are better prepared and therefore more likely to undertake OFDI. The thesis concludes that previous participation in international business collaboration increases the probability of OFDI by these Chinese firms.This research finds that classical OFDI theories are still reliable in explaining the emerging market OFDI in the case of China. However, IB theories should also draw more attention to the fact that asset augmenting can play a major role in this investment environment. Moreover, some new concepts such as capital seeking and i2 should be added into the theoretical framework