Supervised Collective Classification for Crowdsourcing

Crowdsourcing utilizes the wisdom of crowds for collective classification via information (e.g., labels of an item) provided by labelers. Current crowdsourcing algorithms are mainly unsupervised methods that are unaware of the quality of crowdsourced data. In this paper, we propose a supervised collective classification algorithm that aims to identify reliable labelers from the training data (e.g., items with known labels). The reliability (i.e., weighting factor) of each labeler is determined via a saddle point algorithm. The results on several crowdsourced data show that supervised methods can achieve better classification accuracy than unsupervised methods, and our proposed method outperforms other algorithms.Comment: to appear in IEEE Global Communications Conference (GLOBECOM) Workshop on Networking and Collaboration Issues for the Internet of Everythin

The Division of Temporary and Permanent Employment and Business Cycle Fluctuations

This paper investigates the fluctuations in temporary relative to aggregate employment over the business cycle, as well as the underlying driving forces. We develop a dynamic general equilibrium model to investigate the following stylized facts: (i) temporary employment is more volatile than permanent employment, (ii) the share of temporary employment (the ratio of temporary to aggregate employment) exhibits strong pro-cyclicality, (iii) permanent employment lags by two quarters on average, and (iv) the correlation between temporary employment and output is stronger than that involving the permanent counterpart. The quantitative analysis suggests that the proposed channels explain the main facts very well and the model provides a possible prediction based on the counter-factual exercises

Sampling Neural Radiance Fields for Refractive Objects

Recently, differentiable volume rendering in neural radiance fields (NeRF) has gained a lot of popularity, and its variants have attained many impressive results. However, existing methods usually assume the scene is a homogeneous volume so that a ray is cast along the straight path. In this work, the scene is instead a heterogeneous volume with a piecewise-constant refractive index, where the path will be curved if it intersects the different refractive indices. For novel view synthesis of refractive objects, our NeRF-based framework aims to optimize the radiance fields of bounded volume and boundary from multi-view posed images with refractive object silhouettes. To tackle this challenging problem, the refractive index of a scene is reconstructed from silhouettes. Given the refractive index, we extend the stratified and hierarchical sampling techniques in NeRF to allow drawing samples along a curved path tracked by the Eikonal equation. The results indicate that our framework outperforms the state-of-the-art method both quantitatively and qualitatively, demonstrating better performance on the perceptual similarity metric and an apparent improvement in the rendering quality on several synthetic and real scenes.Comment: SIGGRAPH Asia 2022 Technical Communications. 4 pages, 4 figures, 1 table. Project: https://alexkeroro86.github.io/SampleNeRFRO/ Code: https://github.com/alexkeroro86/SampleNeRFR

The Division of Temporary and Permanent Employment and Business Cycle Fluctuations

This paper investigates the fluctuations in temporary relative to aggregate employment over the business cycle, as well as the underlying driving forces. We develop a dynamic general equilibrium model to investigate the following stylized facts: (i) temporary employment is more volatile than permanent employment, (ii) the share of temporary employment (the ratio of temporary to aggregate employment) exhibits strong pro-cyclicality, (iii) permanent employment lags by two quarters on average, and (iv) the correlation between temporary employment and output is stronger than that involving the permanent counterpart. The quantitative analysis suggests that the proposed channels explain the main facts very well and the model provides a possible prediction based on the counter-factual exercises

Panax notoginseng Attenuates Bleomycin-Induced Pulmonary Fibrosis in Mice

Panax notoginseng (PN) is a traditional Chinese herb experimentally proven to have anti-inflammatory effects, and it is used clinically for the treatment of atherosclerosis, cerebral infarction, and cerebral ischemia. This study aimed to determine the anti-inflammatory effects of PN against bleomycin-induced pulmonary fibrosis in mice. First, in an in vitro study, culture media containing lipopolysaccharide (LPS) was used to stimulate macrophage cells (RAW 264.7 cell line). TNF-α and IL-6 levels were then determined before and after treatment with PN extract. In an animal model (C57BL/6 mice), a single dose of PN (0.5 mg/kg) was administered orally on Day 2 or Day 7 postbleomycin treatment. The results showed that TNF-α and IL-6 levels increased in the culture media of LPS-stimulated macrophage cells, and this effect was significantly inhibited in a concentration-dependent manner by PN extract. Histopathologic examination revealed that PN administered on Day 7 postbleomycin treatment significantly decreased inflammatory cell infiltrates, fibrosis scores, and TNF-α, TGF-β, IL-1β, and IL-6 levels in bronchoalveolar lavage fluid when compared with PN given on Day 2 postbleomycin treatment. These results suggest that PN administered in the early fibrotic stage can attenuate pulmonary fibrosis in an animal model of idiopathic pulmonary fibrosis