Social capital and cigarette smoking among Latinos in the United States

This paper presents the results of analyses conducted to examine if social capital indicators were associated with current cigarette smoking and with quitting smoking among a national representative sample of Latinos living in the United States. Data are from 2540 Mexican Americans, Puerto Ricans, Cuban Americans, and Other Latinos who participated in the National Latino and Asian American Survey. A significant inverse association between neighborhood cohesion and current smoking, and a positive association with quitting smoking, were found only among Mexican Americans. No other significant associations were found except for family conflict being associated with higher odds of current smoking with Cuban Americans. Implications of these findings are discussed to unravel the differences in social capital and smoking behaviors among Latino populations.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3374601/Published versio

Hierarchy Composition GAN for High-fidelity Image Synthesis

Despite the rapid progress of generative adversarial networks (GANs) in image synthesis in recent years, the existing image synthesis approaches work in either geometry domain or appearance domain alone which often introduces various synthesis artifacts. This paper presents an innovative Hierarchical Composition GAN (HIC-GAN) that incorporates image synthesis in geometry and appearance domains into an end-to-end trainable network and achieves superior synthesis realism in both domains simultaneously. We design an innovative hierarchical composition mechanism that is capable of learning realistic composition geometry and handling occlusions while multiple foreground objects are involved in image composition. In addition, we introduce a novel attention mask mechanism that guides to adapt the appearance of foreground objects which also helps to provide better training reference for learning in geometry domain. Extensive experiments on scene text image synthesis, portrait editing and indoor rendering tasks show that the proposed HIC-GAN achieves superior synthesis performance qualitatively and quantitatively.Comment: 11 pages, 8 figure