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The effects of a new shape-memory alloy interspinous process device on the distribution of intervertebral disc pressures in vitro 被引量:2
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作者 Shengnai Zheng Qingqiang Yao +5 位作者 Li Cheng Yan Xu Peng Yuan Dongsheng Zhang xiangwen liao Liming Wang 《The Journal of Biomedical Research》 CAS 2010年第2期115-123,共9页
This study was designed to measure the pressure distribution of the intervertebral disc under different degrees of distraction of the interspinous process, because of a suspicion that the degree of distraction of the ... This study was designed to measure the pressure distribution of the intervertebral disc under different degrees of distraction of the interspinous process, because of a suspicion that the degree of distraction of the spinous process may have a close relationship with the disc load share. Six human cadaver lumbar spine L2-L5 segments were loaded in flexion, neutral position, and extension. The L3-L4 disc load was measured at each position using pressure measuring films. Shape-memory interspinous process implants (SMID) with different spacer heights, ranging in size from 10 to 20 mm at 2 mm increments, were used. It was found that a SMID with a spacer height equal to the distance of the interspinous process in the neutral position can share the biomechanical disc load without a significant change of load in the anterior annulus. An interspinous process stabilizing device (IPD) would not be appropriate to use in those cases with serious spinal stenosis because the over-distraction of the interspinous process by the SMID would lead to overloading the anterior annulus which is a recognized cause of disc degeneration. 展开更多
关键词 Lumbar spine disc pressure interspinous process device BIOMECHANICS
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基于分层注意力网络的社交媒体谣言检测 被引量:15
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作者 廖祥文 黄知 +2 位作者 杨定达 程学旗 陈国龙 《中国科学:信息科学》 CSCD 北大核心 2018年第11期1558-1574,共17页
在社交媒体谣言检测问题上,现有的基于特征表示学习的研究工作大多数先把微博事件划分为若干个时间段,再对每个时间段提取文本向量表示、全局用户特征等,忽略了时间段内各微博间的时序信息,且未利用到在传统机器学习方法中已取得较好效... 在社交媒体谣言检测问题上,现有的基于特征表示学习的研究工作大多数先把微博事件划分为若干个时间段,再对每个时间段提取文本向量表示、全局用户特征等,忽略了时间段内各微博间的时序信息,且未利用到在传统机器学习方法中已取得较好效果的文本潜在信息和局部用户信息,导致性能较低.因此,本文提出了一种基于分层注意力网络的社交媒体谣言检测方法.该方法首先将微博事件按照时间段进行分割,并输入带有注意力机制的双向GRU网络,获取时间段内微博序列的隐层表示,以刻画时间段内微博间的时序信息;然后将每个时间段内的微博视为一个整体,提取文本潜在特征和局部用户特征,并与微博序列的隐层表示相连接,以融入文本潜在信息和局部用户信息;最后通过带有注意力机制的双向GRU网络,得到时间段序列的隐层表示,进而对微博事件进行分类.实验采用了新浪微博数据集和Twitter数据集,实验结果表明,与目前最好的基准方法相比,该方法在新浪微博数据集和Twitter数据集上正确率分别提高了1.5%和1.4%,很好地验证了该方法在社交媒体谣言检测问题上的有效性. 展开更多
关键词 谣言检测 分层注意力网络 社交媒体 时序信息 深度学习
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Learning from context: a mutual reinforcement model for Chinese microblog opinion retrieval
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作者 Jingjing WEI xiangwen liao +2 位作者 Houdong ZHENG Guolong CHEN Xueqi CHENG 《Frontiers of Computer Science》 SCIE EI CSCD 2018年第4期714-724,共11页
This study addresses the problem of Chinese microblog opinion retrieval, which aims to retrieve opinionated Chinese microblog posts relevant to a target specified by a user query. Existing studies have shown that lexi... This study addresses the problem of Chinese microblog opinion retrieval, which aims to retrieve opinionated Chinese microblog posts relevant to a target specified by a user query. Existing studies have shown that lexicon-based approaches employed online public sentiment resources to rank sentiment words relying on the document features. However, this approach could not be effectively applied to mi- croblogs that have typical user-generated content with valu- able contextual information: "user-user" interpersonal interactions and "user-post/comment" intrapersonal interactions. This contextual information is very helpful in estimating the strength of sentiment words more accurately. In this study, we integrate the social contextual relationships among users, posts/comments, and sentiment words into a mutual reinforcement model and propose a unified three-layer heterogeneous graph, on which a random walk sentiment word weighting algorithm is presented to measure the strength of opinion of the sentiment words. Furthermore, the weights of sentiment words are incorporated into a lexicon-based model for Chinese microblog opinion retrieval. Comparative experiments are conducted on a Chinese microblog corpus, and the results show that our proposed mutual reinforcement model achieves significant improvement over previous methods. 展开更多
关键词 opinion retrieval sentiment words lexiconweighting mutual reinforcement model
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