The effects of China’ s VAT enlargement reform on the income redistribution of urban households

Background: China's former goods and service tax (GST) system subjects sale of goods to VAT and provision of services to business tax. The VAT enlargement reform launched in 2012 aimed to replace the business tax with VAT step by step. This paper is intended to explore the redistribution effects of this reform. Methods: On basis of input-output model and statutory tax rates, this paper derives the measurement of full GST burden of households in China where both VAT and business tax are imposed. Using the 2012 urban household survey data, the redistribution effects of the VAT enlargement reform is estimated by comparing the Gini coefficient and general entropy indexes before and after the reform. Results: The VAT enlargement reform has improved the redistribution effects of China's GST system mainly through lowering the average tax burden and reducing the inequality within the lowest-income group, though the inequality among different income groups was not reduced considerably. Conclusions: Compared with overall rate reduction, greater relief to necessity items could improve the redistribution effects of the future VAT system more effectively

Giant magnetoimpedance in crystalline Mumetal

We studied giant magnetoimpedance (GMI) effect in commercial crystalline Mumetal, with the emphasis to sample thickness dependence and annealing effects. By using appropriate heat treatment one can achieve GMI ratios as high as 310%, and field sensitivity of about 20%/Oe, which is comparable to the best GMI characteristics obtained for amorphous and nanocrystalline soft magnetic materials.Comment: 8 pages, 3 figure

Optical spectroscopy study of Nd(O,F)BiS2 single crystals

We present an optical spectroscopy study on F-substituted NdOBiS$_2$ superconducting single crystals grown using KCl/LiCl flux method. The measurement reveals a simple metallic response with a relatively low screened plasma edge near 5000 \cm. The plasma frequency is estimated to be 2.1 eV, which is much smaller than the value expected from the first-principles calculations for an electron doping level of x=0.5, but very close to the value based on a doping level of 7$\%$ of itinerant electrons per Bi site as determined by ARPES experiment. The energy scales of the interband transitions are also well reproduced by the first-principles calculations. The results suggest an absence of correlation effect in the compound, which essentially rules out the exotic pairing mechanism for superconductivity or scenario based on the strong electronic correlation effect. The study also reveals that the system is far from a CDW instability as being widely discussed for a doping level of x=0.5.Comment: 5 pages, 5 figure

Pulse generation without gain-bandwidth limitation in a laser with self-similar evolution

With existing techniques for mode-locking, the bandwidth of ultrashort pulses from a laser is determined primarily by the spectrum of the gain medium. Lasers with self-similar evolution of the pulse in the gain medium can tolerate strong spectral breathing, which is stabilized by nonlinear attraction to the parabolic self-similar pulse. Here we show that this property can be exploited in a fiber laser to eliminate the gain-bandwidth limitation to the pulse duration. Broad (̃200 nm) spectra are generated through passive nonlinear propagation in a normal-dispersion laser, and these can be dechirped to ̃20-fs duration

Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks

Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique. Scalar measures, such as FA (fractional anisotropy) and MD (mean diffusivity), quantifying micro-structural tissue properties can be obtained using diffusion models and data processing pipelines. However, it is costly and time consuming to collect high quality diffusion data. Here, we therefore demonstrate how Generative Adversarial Networks (GANs) can be used to generate synthetic diffusion scalar measures from structural T1-weighted images in a single optimized step. Specifically, we train the popular CycleGAN model to learn to map a T1 image to FA or MD, and vice versa. As an application, we show that synthetic FA images can be used as a target for non-linear registration, to correct for geometric distortions common in diffusion MRI

Strawberry Verticillium Wilt Detection Network Based on Multi-Task Learning and Attention

© 2013 IEEE. Plant disease detection has an inestimable effect on plant cultivation. Accurate detection of plant disease can control the spread of disease early and prevent unnecessary loss. Strawberry verticillium wilt is a soil-borne, multi-symptomatic disease. To detect strawberry verticillium wilt accurately, we first propose a disease detection network based on Faster R-CNN and multi-task learning to detect strawberry verticillium wilt. Then, the strawberry verticillium wilt detection network (SVWDN), which uses attention mechanisms in the feature extraction of the disease detection network, is proposed. SVWDN detects verticillium wilt according to the symptoms of detected plant components (i.e.,young leaves and petioles). Compared with other existing methods for detecting disease from the whole plant appearance, the SVWDN automatically classifies the petioles and young leaves while determining whether the strawberry has verticillium wilt. To provide a dataset for evaluating and testing our method, we construct a large dataset that contains 3, 531 images with 4 categories (Healthy-leaf, Healthy-petiole, Verticillium-leaf and Verticillium-petiole). Each image also has a label to indicate whether the strawberry is suffering from verticillium wilt. With the proposed strawberry verticillium wilt detection network, we achieved a mAP of 77.54% on object detection of 4 categories and 99.95% accuracy for strawberry verticillium wilt detection

Genome-Wide Footprints of Pig Domestication and Selection Revealed through Massive Parallel Sequencing of Pooled DNA

Background Artificial selection has caused rapid evolution in domesticated species. The identification of selection footprints across domesticated genomes can contribute to uncover the genetic basis of phenotypic diversity. Methodology/Main Findings Genome wide footprints of pig domestication and selection were identified using massive parallel sequencing of pooled reduced representation libraries (RRL) representing ~2% of the genome from wild boar and four domestic pig breeds (Large White, Landrace, Duroc and Pietrain) which have been under strong selection for muscle development, growth, behavior and coat color. Using specifically developed statistical methods that account for DNA pooling, low mean sequencing depth, and sequencing errors, we provide genome-wide estimates of nucleotide diversity and genetic differentiation in pig. Widespread signals suggestive of positive and balancing selection were found and the strongest signals were observed in Pietrain, one of the breeds most intensively selected for muscle development. Most signals were population-specific but affected genomic regions which harbored genes for common biological categories including coat color, brain development, muscle development, growth, metabolism, olfaction and immunity. Genetic differentiation in regions harboring genes related to muscle development and growth was higher between breeds than between a given breed and the wild boar. Conclusions/Significance These results, suggest that although domesticated breeds have experienced similar selective pressures, selection has acted upon different genes. This might reflect the multiple domestication events of European breeds or could be the result of subsequent introgression of Asian alleles. Overall, it was estimated that approximately 7% of the porcine genome has been affected by selection events. This study illustrates that the massive parallel sequencing of genomic pools is a cost-effective approach to identify footprints of selection