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,

Translating Phrases in Neural Machine Translation

Phrases play an important role in natural language understanding and machine translation (Sag et al., 2002; Villavicencio et al., 2005). However, it is difficult to integrate them into current neural machine translation (NMT) which reads and generates sentences word by word. In this work, we propose a method to translate phrases in NMT by integrating a phrase memory storing target phrases from a phrase-based statistical machine translation (SMT) system into the encoder-decoder architecture of NMT. At each decoding step, the phrase memory is first re-written by the SMT model, which dynamically generates relevant target phrases with contextual information provided by the NMT model. Then the proposed model reads the phrase memory to make probability estimations for all phrases in the phrase memory. If phrase generation is carried on, the NMT decoder selects an appropriate phrase from the memory to perform phrase translation and updates its decoding state by consuming the words in the selected phrase. Otherwise, the NMT decoder generates a word from the vocabulary as the general NMT decoder does. Experiment results on the Chinese to English translation show that the proposed model achieves significant improvements over the baseline on various test sets.Comment: Accepted by EMNLP 201

Covariant Formulation of Non-linear Langevin Theory with Multiplicative Gaussian White Noises

The multi-dimensional non-linear Langevin equation with multiplicative Gaussian white noises in Ito's sense is made covariant with respect to non-linear transform of variables. The formalism involves no metric or affine connection, works for systems with or without detailed balance, and is substantially simpler than previous theories. Its relation with deterministic theory is clarified. The unitary limit and Hermitian limit of the theory are examined. Some implications on the choices of stochastic calculus are also discussed.Comment: 12 pages, no figure

Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration

State-of-the-art convolutional neural networks are enormously costly in both compute and memory, demanding massively parallel GPUs for execution. Such networks strain the computational capabilities and energy available to embedded and mobile processing platforms, restricting their use in many important applications. In this paper, we push the boundaries of hardware-effective CNN design by proposing BCNN with Separable Filters (BCNNw/SF), which applies Singular Value Decomposition (SVD) on BCNN kernels to further reduce computational and storage complexity. To enable its implementation, we provide a closed form of the gradient over SVD to calculate the exact gradient with respect to every binarized weight in backward propagation. We verify BCNNw/SF on the MNIST, CIFAR-10, and SVHN datasets, and implement an accelerator for CIFAR-10 on FPGA hardware. Our BCNNw/SF accelerator realizes memory savings of 17% and execution time reduction of 31.3% compared to BCNN with only minor accuracy sacrifices.Comment: 9 pages, 6 figures, accepted for Embedded Vision Workshop (CVPRW