Accurate face segmentation strongly benefits the human face image analysis problem. In this paper we propose a unified framework for face image analysis through end-to-end semantic face segmentation. The proposed framework contains a set of stack components for face understanding, which includes head pose estimation, age classification, and gender recognition. A manually labeled face data-set is used for training the Conditional Random Fields (CRFs) based segmentation model. A multi-class face segmentation framework developed through CRFs segments a facial image into six parts. The probabilistic classification strategy is used, and probability maps are generated for each class. The probability maps are used as features descriptors and a Random Decision Forest (RDF) classifier is modeled for each task (head pose, age, and gender). We assess the performance of the proposed framework on several data-sets and report better results as compared to the previously reported results.
Labor productivity is important as it is the major factor determining nations' living standards. This study analyzes the factors affecting labor productivity in Pakistan using time series data. ARDL model is applied for estimation of the long run relationship of variables for the period 1981-2018. Data have been taken from the Handbook of Statistics of State Bank of Pakistan and various economic surveys of Pakistan. The findings show that wages, human capital investment, labor force participation, and inflation significantly affect labor productivity. The results indicate that wage rate has a positive effect on labor productivity, and human capital investment also is positively related to labor productivity. At the same time, labor force participation and inflation are negatively related to labor productivity. These findings imply that labor productivity can be raised by increasing the wage rate and investing more in human capital. Results are consistent with efficiency wage theory and human capital theory.
A simultaneous equation model is used to estimate export demand and
supply functions for US soybeans. Price, income, exchange rate and other
effects on exports to four world regions are estimated. Inclusion of
export supply relationships have very significant implications for
estimated price‐, income‐, and exchange‐rate elasticities of export
demand. Results fail to support the usual empirical assumption of
infinite supply price elasticity for soybeans.
Uncertainty of country elevator prices for corn, in terms of error‐variance and mean‐squared forecast errors, failed to increase in response to the deregulation in 1980 of railroad rates. Regression analysis was used to separate the uncertainty impacts of deregulation from those of other unanticipated changes in supply and demand. Factors influencing uncertainty included unexpected changes in storage costs, export demand, corn futures, and rail car utilization. Some of the factors creating price uncertainty prior to deregulation were replaced by new uncertainty factors occurring as a result of deregulation.
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