An Analysis of Britain and American Poverty and Poverty Alleviation and Development Pattern

This article focuses on the subject of both Britain and America, studies the poverty in Britain and United States after World War II in the 19th and 20th century, and illustrates the historical roots of the British and American poverty and their concrete manifestation. In the process, Both Britain and America have been in a critical period of continuous transformation, where poverty alleviation and development played a very important role in the social harmonious development, which becomes the touchstone of two countries, measuring every government achievements since the 20th century. The paper has reviewed, summarized and combed the two countries specific patterns and experiences formed in terms of area and population of poverty alleviation and development, which will have no doubt be the certain enlightenment to the battle for poverty alleviation in the new period of China

CD-CNN: A Partially Supervised Cross-Domain Deep Learning Model for Urban Resident Recognition

Driven by the wave of urbanization in recent decades, the research topic about migrant behavior analysis draws great attention from both academia and the government. Nevertheless, subject to the cost of data collection and the lack of modeling methods, most of existing studies use only questionnaire surveys with sparse samples and non-individual level statistical data to achieve coarse-grained studies of migrant behaviors. In this paper, a partially supervised cross-domain deep learning model named CD-CNN is proposed for migrant/native recognition using mobile phone signaling data as behavioral features and questionnaire survey data as incomplete labels. Specifically, CD-CNN features in decomposing the mobile data into location domain and communication domain, and adopts a joint learning framework that combines two convolutional neural networks with a feature balancing scheme. Moreover, CD-CNN employs a three-step algorithm for training, in which the co-training step is of great value to partially supervised cross-domain learning. Comparative experiments on the city Wuxi demonstrate the high predictive power of CD-CNN. Two interesting applications further highlight the ability of CD-CNN for in-depth migrant behavioral analysis.Comment: 8 pages, 5 figures, conferenc

Characterization of the Semi-Permeable Layer in Seed of \u3cem\u3eElymus nutans\u3c/em\u3e

A semi-permeable layer of the seed coat exists in many species which allows controlled water uptake and gas ex-change, while preventing solute transport (Beresniewicz et al. 1995). This layer could act as a barrier to apoplastic permeability and radicle emergence (Salanenka et al. 2009). It could also restrict the tetrazolium viability test and the electrical conductivity vigour test applied to evaluate seed quality (Yan and Wang 2008; Zhou and Wang 2012). An earlier study reported that semi-permeable layers exist in grass species, but recently research has shown that the layer was not found in seeds of oat (He 2011) or tall fescue (Yan 2008). This paper reports the location and chemical composition of a semi-permeable layer and its relationship to the electrical conductivity vigour tests in seeds of Elymus nutans, an important forage grass species widely distributed in Northwestern alpine grasslands China