Jointly Modeling Topics and Intents with Global Order Structure

Modeling document structure is of great importance for discourse analysis and related applications. The goal of this research is to capture the document intent structure by modeling documents as a mixture of topic words and rhetorical words. While the topics are relatively unchanged through one document, the rhetorical functions of sentences usually change following certain orders in discourse. We propose GMM-LDA, a topic modeling based Bayesian unsupervised model, to analyze the document intent structure cooperated with order information. Our model is flexible that has the ability to combine the annotations and do supervised learning. Additionally, entropic regularization can be introduced to model the significant divergence between topics and intents. We perform experiments in both unsupervised and supervised settings, results show the superiority of our model over several state-of-the-art baselines.Comment: Accepted by AAAI 201

The economics of military spending, conflict and growth

Includes bibliographical referencesThis dissertation is a collection of studies on the economics of peace and security. Chapter one introduces the roles military spending and conic play in affecting economic growth, while also considering the causes of civil conflict. Chapter two investigates the relationship between military expenditure and economic growth, considering group heterogeneity and non-linearity. Using an exogenous growth model and dynamic panel approach, the results suggest military burden to have a negative effect on growth. Breaking the overall panel down into various sub-samples shows estimates that are remark-ably consistent with the full panel. These results provide strong support for the argument that military spending has an adverse effect on growth. There are, however, some intriguing results suggesting that for certain types of countries military burden has no negative growth effect. Chapter three deals with the transnational spatial spillover effects of conflict on neighbouring countries. It moves beyond using geographical dis-tance as a spillover measurement and allows for economic and political distances. The initial empirical results suggest that conflict has a strong negative spillover effect on directly contiguous countries growth, but no significant impacts were observed for non-contiguous countries. When economic and political factors are considered, this result remains, but the spillover effect is smaller. While the impact of conflict remains devastating, it is important to take other factors into account as studies using only geographical distance may be overestimating the impact on neighbours. The fourth chapter examines the determinants of civil war, using a zero-inflated modelling approach to deal with excess zeroes in the dependent variable. Traditional probit and logit models have limited capacity in dealing with this issue and can create misleading results, which is illustrated through replicating published work. A general greed-grievance model is then estimated giving further support to using zero-inflated models. While the standard probit models tend to emphasise opportunity variables, consistent in other studies, the zero inflated model gives supports both opportunity and grievance variables. In particular, ethnicity, democracy and inequality are found to play a significant role in civil war prevalence. Finally, chapter five summarises the findings of the dissertation, providing some policy recommendations, concluding remarks and discusses future research opportunities

Charged basal stacking fault (BSF) scattering in nitride semiconductors

A theory of charge transport in semiconductors in the presence of basal stacking faults is developed. It is shown that the presence of basal stacking faults leads to anisotropy in carrier transport. The theory is applied to carrier transport in non-polar GaN films consisting of a large number BSFs, and the result is compared with experimental data.Comment: 4 pages, 3 figure

Enhanced CNN for image denoising

Owing to flexible architectures of deep convolutional neural networks (CNNs), CNNs are successfully used for image denoising. However, they suffer from the following drawbacks: (i) deep network architecture is very difficult to train. (ii) Deeper networks face the challenge of performance saturation. In this study, the authors propose a novel method called enhanced convolutional neural denoising network (ECNDNet). Specifically, they use residual learning and batch normalisation techniques to address the problem of training difficulties and accelerate the convergence of the network. In addition, dilated convolutions are used in the proposed network to enlarge the context information and reduce the computational cost. Extensive experiments demonstrate that the ECNDNet outperforms the state-of-the-art methods for image denoising.Comment: CAAI Transactions on Intelligence Technology[J], 201

Perturbative QCD study of BsB_s decays to a pseudoscalar meson and a tensor meson

We study two-body hadronic $B_s\to PT$ decays, with $P (T)$ being a light pseudoscalar (tensor) meson, in the perturbative QCD approach. The CP-averaged branching ratios and the direct CP asymmetries of the $\Delta S=0$ modes are predicted, where $\Delta S$ is the difference between the strange numbers of final and initial states. We also define and calculate experimental observables for the $\Delta S=1$ modes under the $B_s^0-\bar{B}_s^0$ mixing, including CP averaged branching ratios, time-integrated CP asymmetries, and the CP observables $C_{f}$, $D_{f}$ and $S_{f}$. Results are compared to the $B_s\to PV$ ones in the literature, and to the $B\to PT$ ones, which indicate considerable U-spin symmetry breaking. Our work provides theoretical predictions for the $B_s\to PT$ decays for the first time, some of which will be potentially measurable at future experiments.Comment: 6 pages, 1 figur