写一篇关于The similarities of affixation in English and Chinese的2000单词论文

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首先，语言学中的词缀是一种形式，可以通过在词根或基础词上添加前缀或后缀来构建新词。英语和汉语都是两种大量使用词缀的语言。

在英语中，词缀通常用于改变词的词性，例如-ness表示名词性，而-ly表示副词性。此外，英语还使用词缀来表示某种语法关系，例如un-表示否定，dis-表示分离。

与英语相似，汉语中的词缀也是一种常用的语言形式，用于表示词性、语法关系等。汉语中的词缀通常是汉字，例如“的”可以用来表示名词性，而“地”可以用来表示副词性。此外，汉语还使用词缀来表示语气和语法关系，例如“不”表示否定，“得”表示方式。

因此，可以发现英语和汉语的词缀存在着很多相似的特点。例如，两种语言都使用词缀来改变词的词性，以及表示语气和语法关系等。

在总结上，英语和汉语的词缀存在着相似性，因为它们都是两种大量使用

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请以On the Criterion of Substantial Similarity of Artistic Works in American Law and the Controversy of Its Infringement Boundary为题用英语写一篇论文

On the Criterion of Substantial Similarity of Artistic Works in American Law and the Controversy of Its Infringement BoundaryAmerican law has long recognized that copyright protects creators from the unauthorized use of their artistic works. The law requires that there be a substantial similarity between the original work and any potential infringing work before the infringing work can be held liable for copyright infringement. In this paper, I will discuss the criteria used to determine substantial similarity in American law, the controversy surrounding its infringement boundary, and the implications of these issues for creators.To determine substantial similarity in American law, courts consider a variety of factors, including the purpose and character of the work, the similarity of the works, and the amount of copying that has taken place. The purpose and character of the work is generally determined by examining the source material, the original expression of ideas, and the similarities between the two works. The similarity of the works is assessed by considering the amount of copying that has occurred, the similarities in the subject matter, and the degree of similarity. Finally, the amount of copying is weighed by considering whether the work's substantial elements were copied, and how much of the work was copied.The controversy surrounding the substantial similarity of artistic works in American law is largely due to the fact that it is difficult to determine where to draw the line between legal and illegal copying. This is especially problematic for creators as it is often difficult to prove that their work has been copied by another artist. Additionally, the amount of copying that is considered to be infringing can vary from case to case, resulting in inconsistencies in the law.The implications of the substantial similarity of artistic works in American law are far-reaching. On one hand, it allows creators to protect their works by preventing others from using their ideas without permission. On the other hand, it can be used to stifle creativity by preventing new works from being created. Additionally, it can be used to prevent the dissemination of information, which can have a negative impact on the public's right to access knowledge and information.In conclusion, the substantial similarity of artistic works in American law is an important and complex issue. It is important for creators to be aware of the criteria used to determine substantial similarity and the controversy surrounding its infringement boundary. Additionally, they should be mindful of the implications of the substantial similarity of artistic works in American law and take steps to protect their works from potential infringement.

精简下面表达：Existing protein function prediction methods integrate PPI networks and multivariate bioinformatics data to improve the performance of function prediction. By combining multivariate information, the interactions between proteins become diverse. Different interactions’ functions in functional prediction are various. Combining multiple interactions simply between two proteins can effectively reduce the effect of false negatives and increase the number of predicted functions, but it can also increase the number of false positive functions, which contribute to nonobvious enhancement for the overall functional prediction performance. In this article, we have presented a framework for protein function prediction algorithms based on PPI network and semantic similarity with the addition of protein hierarchical functions to them. The framework relies on diverse clustering algorithms and the calculation of protein semantic similarity for protein function prediction. Classification and similarity calculations for protein pairs clustered by the functional feature are more accurate and reliable, allowing for the prediction of protein function at different functional levels from different proteomes, and giving biological applications greater flexibility.The method proposed in this paper performs well on protein data from wine yeast cells, but how well it matches other data remains to be verified. Yet until now, most unknown proteins have only been able to predict protein function by calculating similarities to their homologues. The predictions result of those unknown proteins without homologues are unstable because they are relatively isolated in the protein interaction network. It is difficult to find one protein with high similarity. In the framework proposed in this article, the number of features selected after clustering and the number of protein features selected for each functional layer has a significant impact on the accuracy of subsequent functional predictions. Therefore, when making feature selection, it is necessary to select as many functional features as possible that are important for the whole interaction network. When an incorrect feature was selected, the prediction results will be somewhat different from the actual function. Thus as a whole, the method proposed in this article has improved the accuracy of protein function prediction based on the PPI network method to a certain extent and reduces the probability of false positive prediction results.

本文提出了一种基于PPI网络和语义相似性，加上蛋白质分层功能的蛋白质功能预测算法框架，对酒葡萄酵母细胞的蛋白质数据表现出良好的效果，但其他数据的效果如何仍有待验证。此外，该框架中的功能特征选择的数量以及每个功能层中蛋白质特征的选择量，对于后续功能预测的准确度有很大影响。因此，在进行特征选择时，必须尽可能选择对整个交互网络重要的多种功能特征，从而提高蛋白质功能预测的准确性，减少错误预测的概率。