The thermodynamics of strange quark matter with density dependent bag constant are studied self-consistently in the framework of the general ensemble theory and the MIT bag model.In our treatment,an additional term is...The thermodynamics of strange quark matter with density dependent bag constant are studied self-consistently in the framework of the general ensemble theory and the MIT bag model.In our treatment,an additional term is found in the expression of pressure.With the additional term,the zero pressure locates exactly at the lowest energy state,indicating that our treatment is a self-consistently thermodynamic treatment.The self-consistent equations of state of strange quark matter in both the normal and color-flavor-locked phase are derived.They are both softer than the inconsistent ones.Strange stars in both the normal and color-flavor locked phase have smaller masses and radii in our treatment.It is also interesting to find that the energy density at a star surface in our treatment is much higher than that in the inconsistent treatment for both phases.Consequently,the surface properties and the corresponding observational properties of strange stars in our treatment are different from those in the inconsistent treatment.展开更多
【目的】分析文本相似度计算方法,了解该领域的发展态势。【文献范围】在CNKI和Web of Science中分别以检索式"篇名:文本相似度OR篇名:词汇相似度OR篇名:语义相似度"和"TI:‘text similarity’or‘semantic similarity...【目的】分析文本相似度计算方法,了解该领域的发展态势。【文献范围】在CNKI和Web of Science中分别以检索式"篇名:文本相似度OR篇名:词汇相似度OR篇名:语义相似度"和"TI:‘text similarity’or‘semantic similarity’or‘lexical similarity’"并限定文献类型进行检索,最终得到69篇重点文献。【方法】对文本相似度计算方法进行系统梳理,分析重点方法的基本思想、特点并总结未来发展方向。【结果】形成了较为全面的分类描述体系,文本相似度计算方法可分为4类:基于字符串的方法、基于语料库的方法、基于世界知识的方法和其他方法。其中,基于神经网络和基于世界知识的方法以及针对跨领域文本的相似度计算将成为该领域的发展趋势。【局限】仅将不同方法本身作为探讨的核心,未进一步分析方法的应用情况。【结论】有助于全面把握和深入了解文本相似度计算方法的研究现状和未来趋势。展开更多
本文利用Python语言,对25 000条英文影评数据进行文本分类。首先利用词袋模型对文本数据进行分类。在此基础上加入Word2Vec建立新的词向量特征,通过精准率和召回率对比前后2种模型的分类效果;最后通过逻辑回归和朴素贝叶斯分类模型的分...本文利用Python语言,对25 000条英文影评数据进行文本分类。首先利用词袋模型对文本数据进行分类。在此基础上加入Word2Vec建立新的词向量特征,通过精准率和召回率对比前后2种模型的分类效果;最后通过逻辑回归和朴素贝叶斯分类模型的分类效果对照得出研究结论。结果表明:对于英文影评文本分类,在同等条件下,使用Word2Vec构建词向量模型的精准率和召回率比使用bag of Word词袋模型分别高出0.02个百分点和0.026个百分点;在使用Word2Vec的基础上,朴素贝叶斯分类器的精准率和召回率分别高出逻辑回归分类0.027个百分点和0.028个百分点。展开更多
基金Supported by the National Natural Science Foundation of China (Grant Nos.10275029 and 10675024)the National Fundamental Fund Project Subsidiary Funds of Personnel Training (Grant No.J0730311)
文摘The thermodynamics of strange quark matter with density dependent bag constant are studied self-consistently in the framework of the general ensemble theory and the MIT bag model.In our treatment,an additional term is found in the expression of pressure.With the additional term,the zero pressure locates exactly at the lowest energy state,indicating that our treatment is a self-consistently thermodynamic treatment.The self-consistent equations of state of strange quark matter in both the normal and color-flavor-locked phase are derived.They are both softer than the inconsistent ones.Strange stars in both the normal and color-flavor locked phase have smaller masses and radii in our treatment.It is also interesting to find that the energy density at a star surface in our treatment is much higher than that in the inconsistent treatment for both phases.Consequently,the surface properties and the corresponding observational properties of strange stars in our treatment are different from those in the inconsistent treatment.
文摘本文利用Python语言,对25 000条英文影评数据进行文本分类。首先利用词袋模型对文本数据进行分类。在此基础上加入Word2Vec建立新的词向量特征,通过精准率和召回率对比前后2种模型的分类效果;最后通过逻辑回归和朴素贝叶斯分类模型的分类效果对照得出研究结论。结果表明:对于英文影评文本分类,在同等条件下,使用Word2Vec构建词向量模型的精准率和召回率比使用bag of Word词袋模型分别高出0.02个百分点和0.026个百分点;在使用Word2Vec的基础上,朴素贝叶斯分类器的精准率和召回率分别高出逻辑回归分类0.027个百分点和0.028个百分点。
