Hadron tomography for pion and its gravitational form factors

Generalized parton distributions (GPDs) are three-dimensional structure functions of hadrons, and they can reveal the orbital-angular-momentum contributions to the nucleon spin. Therefore, GPDs are important for solving the proton spin puzzle. The generalized distribution amplitudes (GDAs) are the $s$-$t$ crossed quantities of the GPDs, and the GDAs can be investigated in two-photon process ($\gamma^* \gamma \to h\bar h$) which is accessible at KEKB. The pion GDAs were obtained by analyzing the Belle measurements for $\pi^0 \pi^0$ production in the $e^+ e^-$ collision. From the obtained GDAs, the form factors of energy-momentum tenor were calculated for pion in the timelike region. In order to study the gravitational radii for the pion, the form factors of energy-momentum tenor were obtained in the spacelike region by using the dispersion relation. Then, the mass radius was calculated as 0.32 $\sim$ 0.39 fm and the mechanical radius was also estimated for the pion as 0.82 $\sim$ 0.88 fm by using the spacelike form factors. This is the first finding on gravitational form factors and radii of hadrons from actual experimental measurements. In the near future we can expect more precise measurements of $\gamma^* \gamma \to h\bar h$ as the Belle II started data taking by the higher luminosity Super KEKB, so that the GDAs of other hadrons could be studied as well.Comment: 4 pages, 4 figures, Proceedings of Eighth International Conference on Quarks and Nuclear Physics (QNP2018), November 13-17, 2018, Tsukuba, Japa

How Much Intraregional Exchange Rate Variability Could a Currency Union Remove? The Case of ASEAN+3

A multilateral currency union removes the intraregional exchange rates but not the union rate variability with the rest of the world. The intraregional exchange rate variability is thus latent. A two-step procedure is developed to measure the variability. The measured variables are used to model inflation and intraregional trade growth of individual union members. The resulting models form the base for counterfactual simulations of the union impact. Application to ASEAN+3 shows that the intraregional variability consists of mainly short-run shocks, which have significantly affected the inflation and trade growth of major ASEAN+3 members, and that a union would reduce inflation and promote intraregional trade on the whole but the benefits facing each member vary and may not be significant enough to warrant a vote for the union.Currency union, Latent variables, Dynamic factor model, Simulation

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

Maximum Smoothed Likelihood Component Density Estimation in Mixture Models with Known Mixing Proportions

In this paper, we propose a maximum smoothed likelihood method to estimate the component density functions of mixture models, in which the mixing proportions are known and may differ among observations. The proposed estimates maximize a smoothed log likelihood function and inherit all the important properties of probability density functions. A majorization-minimization algorithm is suggested to compute the proposed estimates numerically. In theory, we show that starting from any initial value, this algorithm increases the smoothed likelihood function and further leads to estimates that maximize the smoothed likelihood function. This indicates the convergence of the algorithm. Furthermore, we theoretically establish the asymptotic convergence rate of our proposed estimators. An adaptive procedure is suggested to choose the bandwidths in our estimation procedure. Simulation studies show that the proposed method is more efficient than the existing method in terms of integrated squared errors. A real data example is further analyzed