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In this paper, we address the problem of unsupervised domain transfer learning in which no labels are available in the target domain. We use a transformation matrix to transfer both the source and target data to a common subspace, where each target sample can be represented by a combination of source samples such that the samples from different domains can be well interlaced. In this way, the discrepancy of the source and target domains is reduced. By imposing joint low-rank and sparse constraints on the reconstruction coefficient matrix, the global and local structures of data can be preserved. To enlarge the margins between different classes as much as possible and provide more freedom to diminish the discrepancy, a flexible linear classifier (projection) is obtained by learning a non-negative label relaxation matrix that allows the strict binary label matrix to relax into a slack variable matrix. Our method can avoid a potentially negative transfer by using a sparse matrix to model the noise and, thus, is more robust to different types of noise. We formulate our problem as a constrained low-rankness and sparsity minimization problem and solve it by the inexact augmented Lagrange multiplier method. Extensive experiments on various visual domain adaptation tasks show the superiority of the proposed method over the state-of-the art methods. The MATLAB code of our method will be publicly available at http://www.yongxu.org/lunwen.html.

The effect of agricultural land use change on soil microbial community composition and biomass remains a widely debated topic. Here, we investigated soil microbial community composition and biomass [e.g., bacteria (B), fungi (F), Arbuscular mycorrhizal fungi (AMF) and Actinomycete (ACT)] using phospholipid fatty acids (PLFAs) analysis, and basal microbial respiration in afforested, cropland and adjacent uncultivated soils in central China. We also investigated soil organic carbon and nitrogen (SOC and SON), labile carbon and nitrogen (LC and LN), recalcitrant carbon and nitrogen (RC and RN), pH, moisture, and temperature. Afforestation averaged higher microbial PLFA biomass compared with cropland and uncultivated soils with higher values in top soils than deep soils. The microbial PLFA biomass was strongly correlated with SON and LC. Higher SOC, SON, LC, LN, moisture and lower pH in afforested soils could be explained approximately 87.3% of total variation of higher total PLFAs. Afforestation also enhanced the F: B ratios compared with cropland. The basal microbial respiration was higher while the basal microbial respiration on a per-unit-PLFA basis was lower in afforested land than adjacent cropland and uncultivated land, suggesting afforestation may increase soil C utilization efficiency and decrease respiration loss in afforested soils.

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A sign language recognition system translates signs performed by deaf individuals into text/speech in real time. Inertial measurement unit and surface electromyography (sEMG) are both useful modalities to detect hand/arm gestures. They are able to capture signs and the fusion of these two complementary sensor modalities will enhance system performance. In this paper, a wearable system for recognizing American Sign Language (ASL) in real time is proposed, fusing information from an inertial sensor and sEMG sensors. An information gain-based feature selection scheme is used to select the best subset of features from a broad range of well-established features. Four popular classification algorithms are evaluated for 80 commonly used ASL signs on four subjects. The experimental results show 96.16% and 85.24% average accuracies for intra-subject and intra-subject cross session evaluation, respectively, with the selected feature subset and a support vector machine classifier. The significance of adding sEMG for ASL recognition is explored and the best channel of sEMG is highlighted.

The article aims to provide an overview of China's agricultural development and its sustainability by focusing on the agriculture-environmental nexus. We first review literature regarding trends in agricultural development and driving forces. China has made impressive progress at providing food for 22% of the world's population. At the same time, severe environmental impacts have been incurred which not only affect future food security but also have impacts on other socioeconomic aspects. The agricultural policies that have been put into practice have direct or indirect impacts on such environmental outcomes. We review the impacts of agricultural policies as well as conservation policies, their effectiveness, some unintended consequences and conflicts. The article concludes that technology and institutional innovation in China should emphasize more integrated sustainable development considering the agriculture-environment nexus, instead of setting incoherent and sometimes incompatible policy goals for each separate side.

Abstract-In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zeroone matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of theses methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability. To this end, we present an elastic-net regularized linear regression (ENLR) framework, and develop two robust linear regression models which possess the following special characteristics. First, our methods exploit two particular strategies to enlarge the margins of different classes by relaxing the strict binary targets into a more feasible variable matrix. Second, a robust elastic-net regularization of singular values is introduced to enhance the compactness and effectiveness of the learned projection matrix. Third, the resulting optimization problem of ENLR has a closed-form solution in each iteration, which can be solved efficiently. Finally, rather than directly exploiting the projection matrix for recognition, our methods employ the transformed features as the new discriminate representations to make final image classification. Compared with the traditional linear regression model and some of its variants, our method is much more accurate in image classification. Extensive experiments conducted on publicly available datasets well demonstrate that the proposed framework can outperform the state-of-the-art methods. The MATLAB codes of our methods can be available at http://www.yongxu.org/lunwen.html.

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