《王昭君》的两难选择

《王昭君》的创作是特定时期的产物,充满了复杂的两难选择。主要表现为悲剧题材转为喜剧题材的悖论和两难,以及孙芙人、姜夫人的人生追求所形成的主体追求的悖论与两难。导致两难选择的原因是这部作品既要体现历史性,又要体现现代性;既要体现政治意图。又要体现作者的主体意识。     

论弗罗斯特诗歌中悖论的文化意蕴

悖论是罗伯特·弗罗斯特诗歌中的主要艺术手法之一。就其诗歌的表现形式来看,弗罗斯特诗歌中的悖论有同一诗行中的悖论、同一诗节之间的悖论、同一首诗中的悖论。就其表现的内容来看,弗罗斯特诗歌中的悖论呈现了生存与死亡、孤立与联系、无序与秩序以及欢欣与惆怅等文化意蕴。悖论使弗罗斯特的诗歌语言言简意赅,高度凝练精确。弗罗斯特的诗歌往往充满普遍真理的光辉,而悖论是展现弗罗斯特的真知灼见的有效途径之一。     

培养推理判断技能 提高阅读理解能力

理解能力是英语阅读能力的核心，是学生阅读能力的重要标志，而推理判断技能作为英语阅读的重要技能之一，直接影响到人们的阅读能力的强与弱，如何培养外语学习者的这种技能和如何提高理解能力是英语阅读课所面临的一个重要课题。     

基于多模块贝叶斯网络的恐怖袭击威胁评估

通过考察恐怖袭击事件可能造成的后果, 针对恐怖袭击事件评估信息的多样性、不确定性及模糊性, 提出用贝叶斯网络方法对恐怖袭击威胁进行评估, 从而为反恐决策者提供决策支持, 以减少恐怖袭击所造成的影响.针对恐怖袭击事件的复杂性, 提出多模块贝叶斯网络的恐怖袭击威胁评估模型, 并对多模块贝叶斯网络的结构学习、参数学习和推理进行研究, 提出多模块贝叶斯网络的推理算法.最后, 给出恐怖袭击威胁度的计算方法, 并对多模块贝叶斯网络的威胁评估模型进行实例分析.实例表明, 基于多模块贝叶斯网络的恐怖袭击威胁评估模型, 能有效评估恐怖袭击事件的威胁程度.     

逻辑悖论矛盾的误用与缺位

《韩非子.难一》描述的"自相矛盾"是逻辑悖论矛盾,将其用作"对立统一"意义上的矛盾是误用,这导致逻辑悖论矛盾在唯物辩证法中的缺位。误用和缺位的语言学方面的原因是将德语、俄语中的"对立统一"等同于矛盾,中国本土文化方面的原因是崇尚和谐的传统理念使得"自相矛盾"——逻辑悖论矛盾思想未入哲学和道德文化的主流,唯物辩证法"泊来"中国时只能以"自相矛盾"的语形来表达矛盾,而不能接受"自相矛盾"的语义。"自相矛盾"作为一种逻辑悖论矛盾其实是普遍存在的,将其引进唯物辩证法的范畴体系是很有必要的。这将使唯物辩证法的矛盾具有"对立统一"和"自相矛盾"的双层含义,赢得自身的丰富和发展。     

“美国治下的和平”的悖论

二战后的美国,成为世界上的头号强国,拥有无与伦比的军事经济实力。战后世界秩序的重建都是在美国一手主导下建立起来的。此时的美国人很乐意把自己比喻为新的罗马,认为美国是世界和平的保障,世界将进入“美国治下的和平”时期。虽然其中美国也经历了低潮期,但是总体上美国一直是世界头号强国。美国的外交政策始终体现着“美国治下的和平”的梦想。认真分析“美国治下的和平”的内容、策略及发展趋势具有很强的现实意义。     

试论回溯推理

回溯推理在逻辑上常常被人忽视，论及亦有分歧。应把回溯推理定义为“从已知的结论导出前提的推理”，它的一般形式是“q，所以P”，它的完整的逻辑形式是“q，并且p→q，所以P”，它的结论是或然的。     

An Analysis of Fictional Dialogues of Love in a Fallen City in the Light of Relevance Theory

Although literary works are imaginary,they are based on careful observations of the real world.The fictional world is an imitation of the real world.So there is a strong similarity between them.The character utterances in fictions may be artificial in reality,but they are true in fictions.Therefore,pragmatic theories also have validity for literary interaction in theory.The au thor analyzes the fictional dialogues in Eileen Chang’ s novel——Love in a Fallen City under the framework of the communica tion model based on Relevance Theory.In the academic field,Relevance Theory has been applied to explain a wide range of phenomena in translation,stylistics,phonology,and English teaching.However,the researches on mass communication discourse are still on the development.     

高校图书采访中的悖论现象及对策探究

当前图书市场日益繁荣,图书品种越来越多。但是,在专业性、学术性、知识性强,审美价值与情趣、阅读综合素质相对较高的高校却遭遇到了抵触,从高校图书馆大量图书零借阅率的不断上升就足以说明这一点。本文指出了高校图书采访中的问题和大量悖论现象,并对其产生的原因进行了认真独到的分析,相应给出了一些建议。     

Counterfactual can be strong in medical question and answering

Medical question and answering is a crucial aspect of medical artificial intelligence, as it aims to enhance the efficiency of clinical diagnosis and improve treatment outcomes. Despite the numerous methods available for medical question and answering, they tend to overlook the data generation mechanism’s imbalance and the pseudo-correlation caused by the task’s text characteristics. This pseudo-correlation is due to the fact that many words in the question and answering task are irrelevant to the answer but carry significant weight. These words can affect the feature representation and establish a false correlation with the final answer. Furthermore, the data imbalance mechanism can cause the model to blindly follow a large number of classes, leading to bias in the final answer. Confounding factors, including the data imbalance mechanism, bias due to textual characteristics, and other unknown factors, may also mislead the model and limit its performance.In this study, we propose a new counterfactual-based approach that includes a feature encoder and a counterfactual decoder. The feature encoder utilizes ChatGPT and label resetting techniques to create counterfactual data, compensating for distributional differences in the dataset and alleviating data imbalance issues. Moreover, the sampling prior to label resetting also helps us alleviate the data imbalance issue. Subsequently, label resetting can yield better and more balanced counterfactual data. Additionally, the construction of counterfactual data aids the subsequent counterfactual classifier in better learning causal features. The counterfactual decoder uses counterfactual data compared with real data to optimize the model and help it acquire the causal characteristics that genuinely influence the label to generate the final answer. The proposed method was tested on PubMedQA, a medical dataset, using machine learning and deep learning models. The comprehensive experiments demonstrate that this method achieves state-of-the-art results and effectively reduces the false correlation caused by confounders.