Resource abundance and regional development in China:

"Over the past several decades, China has made tremendous progress in market integration and infrastructure development. Demand for natural resources has increased from the booming coastal economies, causing the terms of trade to favor the resource sector, which is predominantly based in the interior regions of the country. However, the gap in economic development level between the coastal and inland regions has widened significantly. In this paper, using a panel data set at the provincial level, we show that Chinese provinces with abundant resources perform worse than their resource-poor counterparts in terms of per capita consumption growth. This trend that resource-poor areas are better off than resource-rich areas is particularly prominent in rural areas. Because of the institutional arrangements regarding property rights of natural resources, most gains from the resource boom have been captured either by the government or state owned enterprises. Thus, the windfall of natural resources has more to do with government consumption than household consumption. Moreover, in resource-rich areas, greater revenues accrued from natural resources bid up the price of non-tradable goods and hurt the competitiveness of the local economy." from Authors' AbstractRegional inequality, Resource curse, Dutch disease, Property rights, Rural-urban linkages,

Village inequality in Western China: implications for development strategy in lagging regions

"Increased regional inequality has been a major concern in many emerging economies like China, India, Vietnam and Thailand. However, even a large inequality is observed within the lagging regions. The objective of this paper is to look into what are the sources of within region inequality using the community surveys and a census type of households in Western China. This snapshot view of inequality within and between rural villages in western China is based on a census-type household survey in three administrative villages and a sampling survey of 286 natural villages in the poor province of Guizhou in 2004. In contrast to coastal regions, nonfarm income is distributed unevenly in this inland western region. This accounts for the largest share of overall income inequality. But agriculture is still the rural people's major source of livelihood in this particular location. On the expenditure side, health care is one of the most important sources of inequality. Because rural income is strongly related to human capital, the uneven access to health care will translate into a larger income gap in the long run. The analysis based on the natural village survey indicates that income varies widely across villages. Access to infrastructure and markets, education, and political participation explain most of this variation. These findings have important implications on the future development strategy in promoting lagging regions development and poverty reduction. While the overall economic development will be the main instrument to bring the majority poor out of poverty, a targeted approach has become increasingly crucial in helping the poor villages and households. It is critical to understand why these villages and households can not participate in the growth process and how development programs and various transfer programs help them to overcome the constraints they face." Authors' AbstractRural development, Poverty reduction, Inequality, Public investment, China, Asia, Household surveys, Agriculture, Income Rural areas,

Village Inequality in Western China

Increased regional inequality has been a major concern in many emerging economies like China, India, Vietnam and Thailand. However, even a large inequality is observed within the lagging regions. The objective of this paper is to look into what are the sources of within region inequality using the community surveys and a census type of households in Western China. This snapshot view of inequality within and between rural villages in western China is based on a census-type household survey in three administrative villages and a sampling survey of 286 natural villages in the poor province of Guizhou in 2004. In contrast to coastal regions, nonfarm income is distributed unevenly in this inland western region. This acco unts for the largest share of overall income inequality. But agriculture is still the rural peoples major source of livelihood in this particular location. On the expenditure side, health care is one of the most important sources of inequality. Because rural income is strongly related to human capital, the uneven access to health care will translate into a larger income gap in the long run. The analysis based on the natural village survey indicates that income varies widely across villages. Access to infrastructure and markets, education, and political participation explain most of this variation. These findings have important implications on the future development strategy in promoting lagging regions development and poverty reduction. While the overall economic development will be the main instrument to bring the majority poor out of poverty, a targeted approach has become increasingly crucial in helping the poor villages and households. It is critical to understand why these villages and households can not particulate in the growth process and how development programs and various transfer programs help them to overcome the constraints they face.Rural Development, Poverty, Inequality, Public investment, H54, O47, O53, R11, Community/Rural/Urban Development,

High Quality Image Interpolation via Local Autoregressive and Nonlocal 3-D Sparse Regularization

In this paper, we propose a novel image interpolation algorithm, which is formulated via combining both the local autoregressive (AR) model and the nonlocal adaptive 3-D sparse model as regularized constraints under the regularization framework. Estimating the high-resolution image by the local AR regularization is different from these conventional AR models, which weighted calculates the interpolation coefficients without considering the rough structural similarity between the low-resolution (LR) and high-resolution (HR) images. Then the nonlocal adaptive 3-D sparse model is formulated to regularize the interpolated HR image, which provides a way to modify these pixels with the problem of numerical stability caused by AR model. In addition, a new Split-Bregman based iterative algorithm is developed to solve the above optimization problem iteratively. Experiment results demonstrate that the proposed algorithm achieves significant performance improvements over the traditional algorithms in terms of both objective quality and visual perceptionComment: 4 pages, 5 figures, 2 tables, to be published at IEEE Visual Communications and Image Processing (VCIP) 201

SIFT Saliency Analysis for Matching Repetitive Structures

The ambiguity resulting from repetitive structures in a scene presents a major challenge for image matching. This paper proposes a matching method based on SIFT feature saliency analysis to achieve robust feature matching between images with repetitive structures. The feature saliency within the reference image is estimated by analyzing feature stability and dissimilarity via Monte-Carlo simulation. In the proposed method, feature matching is performed only within the region of interest to reduce the ambiguity caused by repetitive structures. The experimental results demonstrate the efficiency and robustness of the proposed method, especially in the presence of respective structures

Human Emotion Recognition Based On Galvanic Skin Response signal Feature Selection and SVM

A novel human emotion recognition method based on automatically selected Galvanic Skin Response (GSR) signal features and SVM is proposed in this paper. GSR signals were acquired by e-Health Sensor Platform V2.0. Then, the data is de-noised by wavelet function and normalized to get rid of the individual difference. 30 features are extracted from the normalized data, however, directly using of these features will lead to a low recognition rate. In order to gain the optimized features, a covariance based feature selection is employed in our method. Finally, a SVM with input of the optimized features is utilized to achieve the human emotion recognition. The experimental results indicate that the proposed method leads to good human emotion recognition, and the recognition accuracy is more than 66.67%

Spiking Semantic Communication for Feature Transmission with HARQ

In Collaborative Intelligence (CI), the Artificial Intelligence (AI) model is divided between the edge and the cloud, with intermediate features being sent from the edge to the cloud for inference. Several deep learning-based Semantic Communication (SC) models have been proposed to reduce feature transmission overhead and mitigate channel noise interference. Previous research has demonstrated that Spiking Neural Network (SNN)-based SC models exhibit greater robustness on digital channels compared to Deep Neural Network (DNN)-based SC models. However, the existing SNN-based SC models require fixed time steps, resulting in fixed transmission bandwidths that cannot be adaptively adjusted based on channel conditions. To address this issue, this paper introduces a novel SC model called SNN-SC-HARQ, which combines the SNN-based SC model with the Hybrid Automatic Repeat Request (HARQ) mechanism. SNN-SC-HARQ comprises an SNN-based SC model that supports the transmission of features at varying bandwidths, along with a policy model that determines the appropriate bandwidth. Experimental results show that SNN-SC-HARQ can dynamically adjust the bandwidth according to the channel conditions without performance loss