阮大铖丛考(上)

阮大铖为南明罪魁,声名狼藉,故生平材料多遭删削。本文择要选取部分史传、别集、方志及家乘,勾稽考索,推证出阮大铖家世及其早年生活基本情况。阮大铖明万历十五年丁亥(1587)八月生于安徽桐城,自幼出继为长房伯父阮以鼎嗣子。约在万历二十六年(1598)十二岁时随嗣父迁入怀宁,成为一“怀宁籍桐城人”。后因大铖无子,不能担负承嗣重任,仍旧回归二房,而另以三房大铸为长房嗣子。大铖稚龄即深受嗣父阮以鼎影响,为人处事干练机警。又从其叔祖阮自华游,于诗文戏曲一道获益良多。大铖生父阮以巽赋性恬淡,奉持佛法至恭至谨,大铖学佛可能与此有一定关联。     

从刻板印象到理论探索：关于“中国学习者”研究的述评

“中国学习者”是国际上中国教育研究领域的新兴议题,旨在从国际和比较视野剖析中国学生群体的学习特点,为建构有中国特色的学生学习理论提供重要参考。本文通过对中国学习者研究文献的系统查阅和述评,认为本领域研究可划分为三个阶段：消极刻板评价（20世纪80年代）、破解“中国学习者悖论”（20世纪90年代）、理论探索和反思超越（21世纪初至今）。研究呈现出三个主要特点：价值取向从过于消极到过度积极再趋于客观,研究内容从学习方式向理论探索和影响因素扩展,解释视角从文化主义向教师教学等视角转变。中国研究者需要更多地参与到本领域研究中,持续加强研究的“中国立场”,展现更为真实而复杂的中国学生学习样态,提升在本领域的理论贡献水平和国际话语权,并完善研究思路以服务中国教育教学的改革。     

服务贸易开放、研发投入结构错配与工业绿色全要素生产率

本文研究了服务贸易开放影响工业绿色全要素生产率的阻滞和疏通机制。结果显示：在工业全行业层面,研发投入的技术促进传导机制存在阻滞,提升研发人员和研发经费投入的匹配度可以疏通这种阻滞的机制。分行业层面,在清洁行业和低技术密集型行业,研发投入的技术促进传导机制存在阻滞,可以通过提升研发投入匹配度来疏通阻滞的机制。但是在高技术密集型行业,通过提升研发投入匹配度带来的疏通作用不大。在高度污染行业,这种传导机制并没有受阻,相对比较通畅。     

英语听力理解中的语用推理

以Grice会话含义理论和关联理论为基础,探讨了英语听力理解中听者透过言者话语的表面意义分析、推导话语的真实含义的语用推理过程,及其对听力理解的影响,指出听者应结合语境寻求关联,并进行语用推理,从而正确地理解言者的话语含义。将语用推理知识引入英语听力教学和听力理解过程是对改进听力教学和提高学生听力理解水平的有益尝试。     

论世纪之交文学中的人文精神及其存在的几个悖论

本文在界定了人文精神的概念内涵之后,重点对世纪之交八九十年代中国文学尤其是精英文学的人文精神及其表现进行了考察。论者认为,这种人文精神在它最初的发韧阶段,是以人文与政治合一即人文政治化的形态出现;到了80年代,逐步向个人本位与社会本位统一的人文复归;至90年代,社会本位让位于经济本位,个人本位则变成了形而下的私人化话语;近几年在反思的基础上,又出现了人文精神与现实主义精神接轨的新机。文章最后,针对文学中的人文精神的客观现状,论者进而将当下及未来人文精神发展的难点归结为如下三个悖论1．人本与民本;2．拯人与拯己;3．解放与解构。     

实质蕴涵刻画推理的思考

用实质蕴涵刻画推理 ,尽管它不具有内容相关性和独立性 ,造成了反直观的“怪论” ,但它概括了“如果 ,那么”这类联结词各种用法中的共性 ,在日常用法中有其经验根据 ,具有广泛的适用性和使用的方便性。相对逻辑学家已提出的各种刻画推理的蕴涵理论 ,用实质蕴涵刻画推理是比较好的推理理论     

智能微波炉模糊控制器的设计

讨论了智能微波炉的系统组成,分析了应用模糊控制技术实现烹调时间控制的模糊控制器的设计,并对软件系统的设计进行了探讨。     

日语模糊表现试述

该文论述了日语中的一个突出语言现象－模糊表现。模糊表现主要通过推量、否定、被动、传闻等形式构成。模糊表现源于日本人的语言心理,他们不喜欢简单、清晰、直接和肯定的表达方式,喜欢给对方和自己都留下回旋的余地。     

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.     

Causal aware parameterized quantum stochastic gradient descent for analyzing marketing advertisements and sales forecasting

Nowadays, all marketing companies are focusing on advertisements to maximize their sales and profit. Media mix models are used by advertisers to measure how advertisements would effectively, attract and motivate the consumers to buy their brands. However, due to large number of media mix variables, it is difficult to use these media mix models for producing reliable approximation, extending these approximations to infer causation (not as correlation), and parameterizing these models to handle the complex marketing interactions between consumers. In this work, we develop a novel model integrating causal inference analysis with a parameterized quantum stochastic gradient descent model to assess the relation between the marketing advertisements and sales forecasting. The proposed causal aware PQSGD model is experimented on two different media mix variables datasets. The proposed model outperforms the existing quantum approaches (Quantum linear/lasso regression, Quantum decision tree, Quantum LightGBM, Quantum random forest regressor) by achieving the MSE values of 4.36 for Dataset 1 and 19.24 for Dataset 2.