Interjurisdictional tax competition for domestic and foreign capital

This paper examines the efficient provision of local public goods when jurisdictions compete for both domestic and foreign capital. Capital is freely mobile between jurisdictions in the home country, but capital owners will incur migration costs if investing abroad. Since the supply of foreign capital is not completely elastic, the traditional result of under-provision of local public goods found in the literature on tax competition may not hold. Furthermore, the less mobile that foreign capital is, the more likely it is that foreign capital will be taxed more heavily than domestic capital. If both types of capital are complementary to the locally untaxed labor, then jurisdictions will always tax foreign capital, and they may even subsidize domestic capital if it is sufficiently difficult to move the capital abroad.Tax competition, local public goods, migration costs, capital taxes

Shadow Banking and Systemic Risk in Europe and China

We compare the European and Chinese shadow banking systems. While the European shadow banking system is better developed than the Chinese shadow banking system, herd behavior and other factors in European markets create systemic risk, which contributed in part to the financial crisis. Dispersion of risk across the "under-developed" shadow banking system in China has led to some cases of localized, concentrated risk, but not to systemic risk. We discuss proposed European shadow banking regulation and its implications for systemic risk, and discuss what lessons China might glean from such policies. We also discuss what lessons China's diverse and systemically uncoordinated shadow banking sector might provide for Europe

To improve model soil moisture estimation in arid/semi-arid region using in situ and remote sensing information

Soil moisture plays a key role in water and energy exchange in the land hydrologic process. Effective soil moisture information can be used for many applications in weather and hydrological forecasting, water resources, and irrigation system management and planning. However, to accurate modeling of soil moisture variation in the soil layer is still very challenging. In this study, in situ and remote sensing information of near-surface soil moisture is assimilated into the Noah land surface model (LSM) to estimate deep-layer soil moisture variation. The sequential Monte Carlo-Particle Filter technique, being well known for capability of modeling high nonlinear and non-Gaussian processes, is applied to assimilate surface soil moisture measurement to the deep layers. The experiments were carried out over several locations over the semi-arid region of the US. Comparing with in situ observations, the assimilation runs show much improved from the control (non-assimilation) runs for estimating both soil moisture and temperature at 5-, 20-, and 50-cm soil depths in the Noah LSM. © 2012 Springer-Verlag

Image mining: issues, frameworks and techniques

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. Despite the development of many applications and algorithms in the individual research fields cited above, research in image mining is still in its infancy. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining at the end of this paper

Image mining: trends and developments

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining