【latex版】水贴

德国国防部长冯·德莱恩（Ursula von der Leyen）周四（11月26日）晚间在接受德国媒体采访时表示，联邦ZF当天作出的关于德国参与军事打击在叙利亚的"伊斯兰国"武装是必要的，是从这一圣战者组织的所做所为中得出的教训。

这位德国基民盟籍政治家在德国电视一台上强调，这是过去数月"我们得到的痛苦教训，即：如果我们继续不一致，获益的就只是'伊斯兰国'"。她就西方国家和俄罗斯目前正协调军事打击在叙"伊斯兰国"的外交努力时表示，大家现在一起坐到谈判桌旁来，这是 "外交的胜利"。

谢谢楼主分享

http://web.stanford.edu/class/ee364a/lectures.html

曾经被论文格式恶心过。。

水水更健康

好吧，我只是想试下自己的运气

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Recent, highly advanced artifact systems make it more complex and unpredictable to understand how they are used by human under a particular situation.

Three dimensional reconstruction of lung and vessel tree has great significance to 3D observation and quantitative analysis for lung diseases. This paper presents non-sheltered 3D models of lung and vessel tree based on a supervised semi-3D lung tissues segmentation method. A recursive strategy based on geometric active contour is proposed instead of the “coarse-to-fine” framework in existing literature to extract lung tissues from the volumetric CT slices. In this model, the segmentation of the current slice is supervised by the result of the previous one slice due to the slight changes between adjacent slice of lung tissues. Through this mechanism, lung tissues in all the slices are segmented fast and accurately. The serious problems of left and right lungs fusion, caused by partial volume effects, and segmentation of pleural nodules can be settled meanwhile during the semi-3D process. The proposed scheme is evaluated by fifteen scans, from eight healthy participants and seven participants suffering from early-stage lung tumors. The results validate the good performance of the proposed method compared with the “coarse-to-fine” framework. The segmented datasets are utilized to reconstruct the non-sheltered 3D models of lung and vessel tree.