Text Generation Based on Generative Adversarial Nets with Latent Variable

In this paper, we propose a model using generative adversarial net (GAN) to generate realistic text. Instead of using standard GAN, we combine variational autoencoder (VAE) with generative adversarial net. The use of high-level latent random variables is helpful to learn the data distribution and solve the problem that generative adversarial net always emits the similar data. We propose the VGAN model where the generative model is composed of recurrent neural network and VAE. The discriminative model is a convolutional neural network. We train the model via policy gradient. We apply the proposed model to the task of text generation and compare it to other recent neural network based models, such as recurrent neural network language model and SeqGAN. We evaluate the performance of the model by calculating negative log-likelihood and the BLEU score. We conduct experiments on three benchmark datasets, and results show that our model outperforms other previous models

Model Selection for Gaussian Mixture Models

This paper is concerned with an important issue in finite mixture modelling, the selection of the number of mixing components. We propose a new penalized likelihood method for model selection of finite multivariate Gaussian mixture models. The proposed method is shown to be statistically consistent in determining of the number of components. A modified EM algorithm is developed to simultaneously select the number of components and to estimate the mixing weights, i.e. the mixing probabilities, and unknown parameters of Gaussian distributions. Simulations and a real data analysis are presented to illustrate the performance of the proposed method

The growth impact of intersectoral and intergovernmental allocation of public expenditure: With applications to China and India

The negative association between fiscal decentralization and provincial economic growth has been found to be consistently significant and robust in China. For India, however, we have found that fiscal decentralization is positively, and even statistically significantly, associated with state economic growth. The state allocation of public spending in various sectors is broadly consistent with ¡°growth maximizing,¡± whereas increases in the central allocation of its budget among development projects, nondevelopment projects, and social and community services by cutting the center's spending on all other functions can promote regional growth.Fiscal decentralization, Public spending, Growth, Chinese economy, Indian economy

Fiscal Decentralization, the Composition of Public Spending, and Regional Growth in India

In this paper, we present an analytical model for examining the growth impact of intergovernmental and intersectoral allocation of public expenditure. The model helps us quantify the role of fiscal decentralization in regional economic growth and identify whether central and local allocation of public spending among various sectors are growth-enhancing. Applying our analytical framework to a panel data set of 16 major states in India, we have found that, in many cases of our regressions, fiscal decentralization is positively, and even statistically significantly, associated with state economic growth. The state allocation of public spending in various sectors is broadly consistent with "growth-maximizing", whereas increases in the central allocation of its budget among development projects, nondevelopment projects, and social and community services by cutting the center¡¯s spending on all other functions can promote regional growth. Furthermore, the distortionary effect of the state tax in India is dominated by the productive effect of tax-financed public spending, whereas the reverse holds for the central tax.

Fiscal decentralization, public spending, and economic growth in China

The authors of this report use data on China to demonstrate how the allocation of fiscal revenue and expenditures between central and local governments has affected economic growth since reforms that began in the late 1970s. They find a higher degree of fiscal decentralization associated with lower provincial economic growth over the past 15 years in China. This implies that fiscal reforms begun in China in the early 1980s have probably failed to promote the country's economic growth. This result is consistently significant and robust in their empirical examinations, and is surprising in light of the argument that fiscal decentralization usually contributes positively to provincial or local economic growth.Public&Municipal Finance,Public Sector Economics&Finance,Urban Economics,Economic Theory&Research,Banks&Banking Reform,Urban Economics,Economic Theory&Research,Public Sector Economics&Finance,National Governance,Public&Municipal Finance

Hashing for Similarity Search: A Survey

Similarity search (nearest neighbor search) is a problem of pursuing the data items whose distances to a query item are the smallest from a large database. Various methods have been developed to address this problem, and recently a lot of efforts have been devoted to approximate search. In this paper, we present a survey on one of the main solutions, hashing, which has been widely studied since the pioneering work locality sensitive hashing. We divide the hashing algorithms two main categories: locality sensitive hashing, which designs hash functions without exploring the data distribution and learning to hash, which learns hash functions according the data distribution, and review them from various aspects, including hash function design and distance measure and search scheme in the hash coding space