A Realist Interpretation Of U.S.Relations With China

Realism theory provides the most powerful explanation for the state of war and the rise and fall of great powers. It expounds the important concepts and themes like national sovereignty, security, survival, interests, balance of power, balance of terror, alliance, dominance, hegemony and polarity. Realism can be classified as classical realism, structural realism and neoclassical realism. In recent years, liberalism, globalism and constructivism also have greatly influenced academics and policy-makers under the new phenomena of globalization and terrorism. This paper explores how classical realism theory has been applied to and revealed in the issue of American policy towards China. The past years of U.S. relations with China have been marked by many wars and diplomatic issues that bear important messages for contemporary policy-makers. Based upon the most representative incidents in the chronicles, this paper categorizes American relations with China into three periods: period one, from commercialism in 1784 to imperialism in 1899; period two, from dominance in 1900 to confrontation in 1949; Period three, from enemies in 1950 to competitors in 2009. From a brief retrospective of major events that occurred, it is concluded that most incidents are related to national interest and power issues, while only several cases are about ideological disputes. The emergence of China as an economic power within the last few years will shape the world as much as the United States in the late 19th century. As America is the world\u27s greatest power and China is the world\u27s greatest emerging power, the relationship between these two countries will largely determine the history of the twenty-first century. History teaches that such power transitions are inherently fraught with dangers and opportunities. Thus, it would serve the interests of the United States to rethink its relationship with China and make its policies more global and focused on the long term. No matter what happens in China, American policy towards that country should be guided by a clear and firm sense of American national interests

Automated Segmentation of Pulmonary Lobes using Coordination-Guided Deep Neural Networks

The identification of pulmonary lobes is of great importance in disease diagnosis and treatment. A few lung diseases have regional disorders at lobar level. Thus, an accurate segmentation of pulmonary lobes is necessary. In this work, we propose an automated segmentation of pulmonary lobes using coordination-guided deep neural networks from chest CT images. We first employ an automated lung segmentation to extract the lung area from CT image, then exploit volumetric convolutional neural network (V-net) for segmenting the pulmonary lobes. To reduce the misclassification of different lobes, we therefore adopt coordination-guided convolutional layers (CoordConvs) that generate additional feature maps of the positional information of pulmonary lobes. The proposed model is trained and evaluated on a few publicly available datasets and has achieved the state-of-the-art accuracy with a mean Dice coefficient index of 0.947 $\pm$ 0.044.Comment: ISBI 2019 (Oral

Equipments for Crop Protection:Standardization Development in China

 The history of standardization for crop protection equipments was reviewed to analyze the trends of standards preparation in this paper. The currently active standards were firstly reviewed by their attributes to present the general state of art. The trends of standard preparation, through which the overall development of crop protection equipments are reflected, were interpreted by descriptive items. Finally the future development was predicted as suggestions for decision-making in policy constitution

Box-supervised Instance Segmentation with Level Set Evolution

In contrast to the fully supervised methods using pixel-wise mask labels, box-supervised instance segmentation takes advantage of the simple box annotations, which has recently attracted a lot of research attentions. In this paper, we propose a novel single-shot box-supervised instance segmentation approach, which integrates the classical level set model with deep neural network delicately. Specifically, our proposed method iteratively learns a series of level sets through a continuous Chan-Vese energy-based function in an end-to-end fashion. A simple mask supervised SOLOv2 model is adapted to predict the instance-aware mask map as the level set for each instance. Both the input image and its deep features are employed as the input data to evolve the level set curves, where a box projection function is employed to obtain the initial boundary. By minimizing the fully differentiable energy function, the level set for each instance is iteratively optimized within its corresponding bounding box annotation. The experimental results on four challenging benchmarks demonstrate the leading performance of our proposed approach to robust instance segmentation in various scenarios. The code is available at: https://github.com/LiWentomng/boxlevelset.Comment: 17 page, 4figures, ECCV202