Factors Affecting the Development of Land Rental Markets in China A Case Study for Puding County, Guizhou Province

The development of land rental markets can enhance agricultural productivity and equity by facilitating transfers of land to more productive farmers and facilitating the participation in the non-farm economy of less productive farmers. In recent years there has been a rapid increase in the incidence of land rental activities in China. Large differences exist, however, both between regions and within regions in the share of households participating in land renting activities. The purpose of this study is to analyze the factors affecting the development of land rental markets in one of the poorest regions within China, namely Puding County in Guizhou Province. Data from 792 households in three villages are used to analyze the participation in land rental markets. For renting out of land, a binary probit model is used that corrects for missing observation caused by migrated households. We find that the land rental market is mainly driven by off-farm employment; land-labor ratios do not play a significant role in land renting out. Other important findings are that households belonging to minority groups are significantly more inactive in the land rental market, and that the age of the household head shows an inverted U-shaped relationship with land renting in. Participation in off-farm employment is relatively low in the research area. With further increases in off-farm work, the land rental market is expected to develop further. Households belonging to minority groups, however, are unlikely to participate much. Appropriate measures taken by local governments to stimulate land rental participation by minority groups can be an important way to stimulate agricultural productivity and total household incomes of such minority groups.land rental markets, off-farm, China, binary probit, data correction, Land Economics/Use,

Zener Tunneling in Semiconducting Nanotube and Graphene Nanoribbon p-n Junctions

A theory is developed for interband tunneling in semiconducting carbon nanotube and graphene nanoribbon p-n junction diodes. Characteristic length and energy scales that dictate the tunneling probabilities and currents are evaluated. By comparing the Zener tunneling processes in these structures to traditional group IV and III-V semiconductors, it is proved that for identical bandgaps, carbon based 1D structures have higher tunneling probabilities. The high tunneling current magnitudes for 1D carbon structures suggest the distinct feasibility of high-performance tunneling-based field-effect transistors.Comment: 4 Pages, 2 Figure

Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification

Implicit discourse relation classification is of great challenge due to the lack of connectives as strong linguistic cues, which motivates the use of annotated implicit connectives to improve the recognition. We propose a feature imitation framework in which an implicit relation network is driven to learn from another neural network with access to connectives, and thus encouraged to extract similarly salient features for accurate classification. We develop an adversarial model to enable an adaptive imitation scheme through competition between the implicit network and a rival feature discriminator. Our method effectively transfers discriminability of connectives to the implicit features, and achieves state-of-the-art performance on the PDTB benchmark.Comment: To appear in ACL201

Scalability of Atomic-Thin-Body (ATB) Transistors Based on Graphene Nanoribbons

A general solution for the electrostatic potential in an atomic-thin-body (ATB) field-effect transistor geometry is presented. The effective electrostatic scaling length, {\lambda}eff, is extracted from the analytical model, which cannot be approximated by the lowest order eigenmode as traditionally done in SOI-MOSFETs. An empirical equation for the scaling length that depends on the geometry parameters is proposed. It is shown that even for a thick SiO2 back oxide {\lambda}eff can be improved efficiently by thinner top oxide thickness, and to some extent, with high-k dielectrics. The model is then applied to self-consistent simulation of graphene nanoribbon (GNR) Schottky-barrier field-effect transistors (SB-FETs) at the ballistic limit. In the case of GNR SB-FETs, for large {\lambda}eff, the scaling is limited by the conventional electrostatic short channel effects (SCEs). On the other hand, for small {\lambda}eff, the scaling is limited by direct source-to-drain tunneling. A subthreshold swing below 100mV/dec is still possible with a sub-10nm gate length in GNR SB-FETs.Comment: 4 figures, accepted by ED