Gibbs Max-margin Topic Models with Data Augmentation

Max-margin learning is a powerful approach to building classifiers and structured output predictors. Recent work on max-margin supervised topic models has successfully integrated it with Bayesian topic models to discover discriminative latent semantic structures and make accurate predictions for unseen testing data. However, the resulting learning problems are usually hard to solve because of the non-smoothness of the margin loss. Existing approaches to building max-margin supervised topic models rely on an iterative procedure to solve multiple latent SVM subproblems with additional mean-field assumptions on the desired posterior distributions. This paper presents an alternative approach by defining a new max-margin loss. Namely, we present Gibbs max-margin supervised topic models, a latent variable Gibbs classifier to discover hidden topic representations for various tasks, including classification, regression and multi-task learning. Gibbs max-margin supervised topic models minimize an expected margin loss, which is an upper bound of the existing margin loss derived from an expected prediction rule. By introducing augmented variables and integrating out the Dirichlet variables analytically by conjugacy, we develop simple Gibbs sampling algorithms with no restricting assumptions and no need to solve SVM subproblems. Furthermore, each step of the "augment-and-collapse" Gibbs sampling algorithms has an analytical conditional distribution, from which samples can be easily drawn. Experimental results demonstrate significant improvements on time efficiency. The classification performance is also significantly improved over competitors on binary, multi-class and multi-label classification tasks.Comment: 35 page

IS Per Capita Real GDP Stationary in China¡H Evidence Based on A Panel SURADF Approach

[[abstract]]In this study we use newly developed Panel SURADF tests of the Breuer et al., (2001) to investigate the time-series properties of 25 Chinese provinces¡¦ per capita real GDP for the 1952-1998 period. While the other Panel-based unit root tests are joint tests of a unit root for all members of the panel and are incapable of determining the mix of I(0) and I(1) series in the panel setting, the Panel SURADF tests a separate unit-root null hypothesis for each individual panel member and, therefore identifies how many and which series in the panel are stationary processes. The empirical results indicate that for all the provinces studied per capita real GDP are non-stationary, except Hebei, Jeilongjiang, Qinghai and Shaanxi when Breuer et al.¡¦s (2001) Panel SURADF tests are conducted.[[journaltype]]國外[[booktype]]紙本[[countrycodes]]US

Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation

We present a discriminative nonparametric latent feature relational model (LFRM) for link prediction to automatically infer the dimensionality of latent features. Under the generic RegBayes (regularized Bayesian inference) framework, we handily incorporate the prediction loss with probabilistic inference of a Bayesian model; set distinct regularization parameters for different types of links to handle the imbalance issue in real networks; and unify the analysis of both the smooth logistic log-loss and the piecewise linear hinge loss. For the nonconjugate posterior inference, we present a simple Gibbs sampler via data augmentation, without making restricting assumptions as done in variational methods. We further develop an approximate sampler using stochastic gradient Langevin dynamics to handle large networks with hundreds of thousands of entities and millions of links, orders of magnitude larger than what existing LFRM models can process. Extensive studies on various real networks show promising performance.Comment: Accepted by AAAI 201

IS Per Capita Real GDP Stationary in China¡H Evidence Based on A Panel SURADF Approach

In this study we use newly developed Panel SURADF tests of the Breuer et al., (2001) to investigate the time-series properties of 25 Chinese provinces¡¦ per capita real GDP for the 1952-1998 period. While the other Panel-based unit root tests are joint tests of a unit root for all members of the panel and are incapable of determining the mix of I(0) and I(1) series in the panel setting, the Panel SURADF tests a separate unit-root null hypothesis for each individual panel member and, therefore identifies how many and which series in the panel are stationary processes. The empirical results indicate that for all the provinces studied per capita real GDP are non-stationary, except Hebei, Jeilongjiang, Qinghai and Shaanxi when Breuer et al.¡¦s (2001) Panel SURADF tests are conducted.Per Capita Real GDP Panel Unit Root Tests

A coupled Particle-In-Cell (PIC)-Discrete Element Method (DEM) solver for fluid-solid mixture flow simulations

In this paper, a coupled Particle-In-Cell (PIC)-Discrete Element Method (DEM) model is developed for numerical simulations of complex fluid–solid mixture flows. The fluid–solid interaction part is solved using the hybrid Eulerian–Lagrangian PIC model, and the solid–solid interaction part is simulated using the Lagrangian DEM model. The PIC model gives the coupled PIC-DEM model both Eulerian efficiency and Lagrangian flexibility, compared to purely Lagrangian methods such as Smoothed Particle Hydrodynamics (SPH). The time step difference between the PIC model and the DEM model is handled using the idea of subcycles. In addition, a straightforward method is proposed for mitigating the issue of unphysical gaps between solids during collision due to the use of the Cartesian cut cell method for fluid–solid interaction. The PIC-DEM model is validated by physical experiments of the collapse of solid cylinder layers with and without water. Following that, the capability of the numerical model is further demonstrated through a more complex problem of solid dumping through fall pipes. The results show great potential of the PIC-DEM model being a useful tool for simulating complex fluid–solid mixture flows