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  1. Abstract

    With increasing livestock production due to high demand for consumption, the planted area of green fodder, an essential livestock supplement, has grown rapidly and will continue to grow in China. However, the climate feedback of this rapid land cover conversion is still unclear. Using multisource data (e.g. remote sensing observation and meteorological data), we compared the land surface temperature of green fodder plantation areas and native grassland in the northeastern Tibetan Plateau. The green fodder area was detected to be cooler than the native grassland by −0.54 ± 0.98 °C in the daytime throughout the growing season. The highest magnitude (−1.20 ± 1.68 °C) of cooling was observed in August. A nonradiative process, indicated by the energy redistribution factor, dominated the cooling effects compared to the radiative process altered by albedo variation. The results indicate the potential cooling effects of increasing green fodder area on native grassland, highlighting the necessity of investigating climate feedback from anthropogenic land use change, including green fodder expansion.

     
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  2. Laplacian Embedding (LE) is a powerful method to reveal the intrinsic geometry of high-dimensional data by using graphs. Imposing the orthogonal and nonnegative constraints onto the LE objective has proved to be effective to avoid degenerate and negative solutions, which, though, are challenging to achieve simultaneously because they are nonlinear and nonconvex. In addition, recent studies have shown that using the p-th order of the L2-norm distances in LE can find the best solution for clustering and promote the robustness of the embedding model against outliers, although this makes the optimization objective nonsmooth and difficult to efficiently solve in general. In this work, we study LE that uses the p-th order of the L2-norm distances and satisfies both orthogonal and nonnegative constraints. We introduce a novel smoothed iterative reweighted method to tackle this challenging optimization problem and rigorously analyze its convergence. We demonstrate the effectiveness and potential of our proposed method by extensive empirical studies on both synthetic and real data sets.

     
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