Impacts of natural factors and farming practices on greenhouse gas emissions in the North China Plain : A meta-analysis

This work received support from the National Science and Technology Support Program (No. 2012BAD14B01), the National 948 Project (No. 2011-G30), and the Non-profit Research Foundation for Agriculture (201103039). Thanks are expressed to the anonymous reviewers for their helpful comments and suggestions that greatly improved the manuscript. The authors declare that they have no competing interests.Peer reviewedPublisher PD

Self Sparse Generative Adversarial Networks

Generative Adversarial Networks (GANs) are an unsupervised generative model that learns data distribution through adversarial training. However, recent experiments indicated that GANs are difficult to train due to the requirement of optimization in the high dimensional parameter space and the zero gradient problem. In this work, we propose a Self Sparse Generative Adversarial Network (Self-Sparse GAN) that reduces the parameter space and alleviates the zero gradient problem. In the Self-Sparse GAN, we design a Self-Adaptive Sparse Transform Module (SASTM) comprising the sparsity decomposition and feature-map recombination, which can be applied on multi-channel feature maps to obtain sparse feature maps. The key idea of Self-Sparse GAN is to add the SASTM following every deconvolution layer in the generator, which can adaptively reduce the parameter space by utilizing the sparsity in multi-channel feature maps. We theoretically prove that the SASTM can not only reduce the search space of the convolution kernel weight of the generator but also alleviate the zero gradient problem by maintaining meaningful features in the Batch Normalization layer and driving the weight of deconvolution layers away from being negative. The experimental results show that our method achieves the best FID scores for image generation compared with WGAN-GP on MNIST, Fashion-MNIST, CIFAR-10, STL-10, mini-ImageNet, CELEBA-HQ, and LSUN bedrooms, and the relative decrease of FID is 4.76% ~ 21.84%

Content-sensitive superpixel generation with boundary adjustment.

Superpixel segmentation has become a crucial tool in many image processing and computer vision applications. In this paper, a novel content-sensitive superpixel generation algorithm with boundary adjustment is proposed. First, the image local entropy was used to measure the amount of information in the image, and the amount of information was evenly distributed to each seed. It placed more seeds to achieve the lower under-segmentation in content-dense regions, and placed the fewer seeds to increase computational efficiency in content-sparse regions. Second, the Prim algorithm was adopted to generate uniform superpixels efficiently. Third, a boundary adjustment strategy with the adaptive distance further optimized the superpixels to improve the performance of the superpixel. Experimental results on the Berkeley Segmentation Database show that our method outperforms competing methods under evaluation metrics

Association between High-Sensitivity C-Reactive Protein and N-Terminal Pro-B-Type Natriuretic Peptide in Patients with Hepatitis C Virus Infection

Background. Prior study showed HCV-infected patients have increased serum N-Terminal Pro-B-Type Natriuretic Peptide (NT-proBNP) and a possible left ventricular diastolic dysfunction. The objectives of the present paper were to investigate the characteristics of hs-CRP and its correlation with clinical profiles including NT-proBNP and echocardiographic variables in HCV-infected patients. Methods and Results. A total of 106 HCV-infected patients and 106 control healthy individuals were enrolled. The level of serum hs-CRP (median 1.023 mg/L, range 0.03∼5.379 mg/L) was significantly lower in all 106 patients than that in controls (median 3.147 mg/L, range 0.08~7.36 mg/L, P = 0.012). Although hs-CRP did not correlate significantly with NT-proBNP when all patients and controls were included (r = 0.169, P = 0.121), simple regression analysis demonstrated a statistically significant linear correlation between hs-CRP and NT-proBNP in HCV-infected patients group (r = 0.392, P = 0.017). Independent correlates of hs-CRP levels (R2 = 0.13) were older age (β′ = 0.031, P = 0.025) and NT proBNP (β′ = 0.024, P = 0.017). Conclusions. Although the level of serum hs-CRP decreased significantly, there was a significant association between hs-CRP and NT-proBNP in HCV-infected patients

Archimedes:The First Modern Type of Physicist in Ancient Time

Archimedes is the greatest natural scientist before the modern scientific revolution and he introduced the mathematical methods to physics and is also a rare ancient scientist who can skillfully manipulate the experimentation confirmation method in physics, and used physics to technology firstly. These features are the basic characteristics of the development of modern physics.Therefore, he is the first modern type of physics in ancient world and we call him the physics pioneer. Key words: Archimedes; Modern type; Physicist; ancient Gree

Effects of hyperbaric oxygen on vascular endothelial function in patients with slow coronary flow

   Background: To improve therapy for slow coronary flow (SCF), the effects of hyperbaric oxygen (HBO) therapy on vascular endothelial function in SCF patients is the focus of this investigation. Methods: Ninety-eight patients who exhibited chest discomfort were retrospectively analyzed, and di­agnosed with SCF by coronary artery angiography at the Third Hospital of Hebei Medical University, Shijiazhuang, China from 2014 to 2016. The patients were divided into two groups according to the following treatment: HBO group (n = 48) and the control group (n = 50). Patients in the control group were administrated with conventional treatment, while those in the HBO group were administrated HBO therapy for 4 weeks in addition to conventional treatment. To evaluate the effects of HBO on vas­cular endothelial functions, plasma levels of nitric oxide (NO), calcitonin gene-related peptide (CGRP), endothelin-1 (ET-1), high sensitivity C-reactive protein (hsCRP) as well as endothelial-dependent flow-mediated vasodilation (FMD) of the brachial artery were measured in both groups before and after their respective treatments. Results: There were no significant differences in plasma levels of NO, ET-1, CGRP, hsCRP nor in FMD measurements between the two groups before treatment (p > 0.05). Moreover, the levels of all the parameters measured showed no significant changes before and after treatment in the control group. However, when comparing the control group, FMD and plasma NO and CGRP levels were significantly increased in the HBO group after treatment (p < 0.01), whereas hsCRP and ET-1 levels decreased dramatically (p < 0.001). Conclusions: The HBO treatment in addition to conventional therapy may significantly improve the vascular endothelial function in SCF patients. (Cardiol J 2018; 25, 1: 106–112

CrossNER: Evaluating Cross-Domain Named Entity Recognition

Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains. However, most of the existing NER benchmarks lack domain-specialized entity types or do not focus on a certain domain, leading to a less effective cross-domain evaluation. To address these obstacles, we introduce a cross-domain NER dataset (CrossNER), a fully-labeled collection of NER data spanning over five diverse domains with specialized entity categories for different domains. Additionally, we also provide a domain-related corpus since using it to continue pre-training language models (domain-adaptive pre-training) is effective for the domain adaptation. We then conduct comprehensive experiments to explore the effectiveness of leveraging different levels of the domain corpus and pre-training strategies to do domain-adaptive pre-training for the cross-domain task. Results show that focusing on the fractional corpus containing domain-specialized entities and utilizing a more challenging pre-training strategy in domain-adaptive pre-training are beneficial for the NER domain adaptation, and our proposed method can consistently outperform existing cross-domain NER baselines. Nevertheless, experiments also illustrate the challenge of this cross-domain NER task. We hope that our dataset and baselines will catalyze research in the NER domain adaptation area. The code and data are available at https://github.com/zliucr/CrossNER.Comment: Accepted in AAAI-202