基于Shapley additive explanations(SHAP)分析乳腺X线检查假阴性乳腺癌的特征

目的:基于Shapley additive explanations(SHAP)分析乳腺X线摄影(MG)检查假阴性乳腺癌的特征。方法:回顾性收集我院2018年1月—2022年5月首诊乳腺癌患者,评估临床病理和影像学表现。通过logistic回归分析确定MG假阴性乳腺癌的独立危险因素。应用SHAP分析各独立危险因素的贡献价值。结果:1505个病灶纳入研究,MG假阴性乳腺癌373个。较小的病灶、致密或不均匀致密的MG密度、无可疑钙化、非肿块样强化(NME)、原位癌和腋窝淋巴结阴性与MG假阴性乳腺癌独立相关,上述参数构建的联合模型具有良好的预测效能(AUC=0.871)。根据SHAP分析,病灶大小、MG密度和病灶MRI强化类型是导致MG假阴性的主要因素;钙化状态、病理类型和腋窝淋巴结状态是重要补充。结论:MG假阴性乳腺癌具有一定的特征,此类患者需补充乳腺MRI检查以提高癌灶的检出率。

Multiple subtraction method based on a non-causal matching filter

The filter operator used in normal multichannel matching filter is physically realizable. This filter operator only delays seismic data in the filtering process. A non- causal multichannel matching filter based on a least squares criterion is proposed to resolve the problem in which predicted multiple model data is later than real data. The differences between causal and non-causal multichannel matching filters are compared using a synthetic shot gather, which demonstrates the validity of the non-causal matching filter. In addition, a variable length sliding window which changes with offset and layer velocity is proposed to solve the count of events increasing with increasing offset in a fixed length sliding window. This variable length sliding window is also introduced into the modified and expanded multichannel matching filter. This method is applied to the Pluto1.5 synthetic data set. The benefits of the non-causal filter operator and variable length sliding window are demonstrated by the good multiple attenuation result.

Investigation of feature contribution to shield tunneling-induced settlement using Shapley additive explanations method

Accurate prediction of shield tunneling-induced settlement is a complex problem that requires consideration of many influential parameters.Recent studies reveal that machine learning(ML)algorithms can predict the settlement caused by tunneling.However,well-performing ML models are usually less interpretable.Irrelevant input features decrease the performance and interpretability of an ML model.Nonetheless,feature selection,a critical step in the ML pipeline,is usually ignored in most studies that focused on predicting tunneling-induced settlement.This study applies four techniques,i.e.Pearson correlation method,sequential forward selection(SFS),sequential backward selection(SBS)and Boruta algorithm,to investigate the effect of feature selection on the model’s performance when predicting the tunneling-induced maximum surface settlement(S_(max)).The data set used in this study was compiled from two metro tunnel projects excavated in Hangzhou,China using earth pressure balance(EPB)shields and consists of 14 input features and a single output(i.e.S_(max)).The ML model that is trained on features selected from the Boruta algorithm demonstrates the best performance in both the training and testing phases.The relevant features chosen from the Boruta algorithm further indicate that tunneling-induced settlement is affected by parameters related to tunnel geometry,geological conditions and shield operation.The recently proposed Shapley additive explanations(SHAP)method explores how the input features contribute to the output of a complex ML model.It is observed that the larger settlements are induced during shield tunneling in silty clay.Moreover,the SHAP analysis reveals that the low magnitudes of face pressure at the top of the shield increase the model’s output。

Visualization for Explanation of Deep Learning-Based Defect Detection Model Using Class Activation Map

Recently,convolutional neural network(CNN)-based visual inspec-tion has been developed to detect defects on building surfaces automatically.The CNN model demonstrates remarkable accuracy in image data analysis;however,the predicted results have uncertainty in providing accurate informa-tion to users because of the“black box”problem in the deep learning model.Therefore,this study proposes a visual explanation method to overcome the uncertainty limitation of CNN-based defect identification.The visual repre-sentative gradient-weights class activation mapping(Grad-CAM)method is adopted to provide visually explainable information.A visualizing evaluation index is proposed to quantitatively analyze visual representations;this index reflects a rough estimate of the concordance rate between the visualized heat map and intended defects.In addition,an ablation study,adopting three-branch combinations with the VGG16,is implemented to identify perfor-mance variations by visualizing predicted results.Experiments reveal that the proposed model,combined with hybrid pooling,batch normalization,and multi-attention modules,achieves the best performance with an accuracy of 97.77%,corresponding to an improvement of 2.49%compared with the baseline model.Consequently,this study demonstrates that reliable results from an automatic defect classification model can be provided to an inspector through the visual representation of the predicted results using CNN models.

Explaining How： The Intelligibility of Mechanical Explanations in Boyle

In this paper I examine the following claims by William Eaton in his monograph Boyle on Fire： （i） that Boyle＇s religious convictions led him to believe that the world was not completely explicable, and this shows that there is a shortcoming in the power of mechanical explanations; （ii） that mechanical explanations offer only sufficient, not necessary explanations, and this too was taken by Boyle to be a limit in the explanatory power of mechanical explanations; （iii） that the mature Boyle thought that there could be more intelligible explanatory models than mechanism; and （iv） that what Boyle says at any point in his career is incompatible with the statement of Maria Boas-Hall, i.e., that the mechanical hypothesis can explicate all natural phenomena. Since all four of these claims are part of Eaton＇s developmental argument, my rejection of them will not only show how the particular developmental story Eaton diagnoses is inaccurate, but will also explain what limits there actually are in Boyle＇s account of the intelligibility of mechanical explanations. My account will also show why important philosophers like Locke and Leibniz should be interested in Boyle＇s philosophical work.

Review on Gesture and Speech in the Vocabulary Explanations of One ESL Teacher： A Microanalytic Inquiry＂ by Anne Lazaraton

This paper takes a microanalytic perspective on the speech and gestures used by one teacher of ESL （English as a Second Language） in an intensive English program classroom. Videotaped excerpts from her intermediate-level grammar course were transcribed to represent the speech, gesture and other non-verbal behavior that accompanied unplanned explanations of vocabulary that arose during three focus-on-form lessons. The gesture classification system of McNeill （1992）, which delineates different types of hand movements （iconics metaphorics, deictics, beats）, was used to understand the role the gestures played in these explanations. Results suggest that gestures and other non-verbal behavior are forms of input to classroom second language learners that must be considered a salient factor in classroom-based SLA （Second Language Acquisition） research

On fine-grained visual explanation in convolutional neural networks

Existing explanation methods for Convolutional Neural Networks(CNNs)lack the pixel-level visualization explanations to generate the reliable fine-grained decision features.Since there are inconsistencies between the explanation and the actual behavior of the model to be interpreted,we propose a Fine-Grained Visual Explanation for CNN,namely F-GVE,which produces a fine-grained explanation with higher consistency to the decision of the original model.The exact backward class-specific gradients with respect to the input image is obtained to highlight the object-related pixels the model used to make prediction.In addition,for better visualization and less noise,F-GVE selects an appropriate threshold to filter the gradient during the calculation and the explanation map is obtained by element-wise multiplying the gradient and the input image to show fine-grained classification decision features.Experimental results demonstrate that F-GVE has good visual performances and highlights the importance of fine-grained decision features.Moreover,the faithfulness of the explanation in this paper is high and it is effective and practical on troubleshooting and debugging detection.

Gas liquid cylindrical cyclone flow regime identification using machine learning combined with experimental mechanism explanation

The flow regimes of GLCC with horizon inlet and a vertical pipe are investigated in experiments,and the velocities and pressure drops data labeled by the corresponding flow regimes are collected.Combined with the flow regimes data of other GLCC positions from other literatures in existence,the gas and liquid superficial velocities and pressure drops are used as the input of the machine learning algorithms respectively which are applied to identify the flow regimes.The choosing of input data types takes the availability of data for practical industry fields into consideration,and the twelve machine learning algorithms are chosen from the classical and popular algorithms in the area of classification,including the typical ensemble models,SVM,KNN,Bayesian Model and MLP.The results of flow regimes identification show that gas and liquid superficial velocities are the ideal type of input data for the flow regimes identification by machine learning.Most of the ensemble models can identify the flow regimes of GLCC by gas and liquid velocities with the accuracy of 0.99 and more.For the pressure drops as the input of each algorithm,it is not the suitable as gas and liquid velocities,and only XGBoost and Bagging Tree can identify the GLCC flow regimes accurately.The success and confusion of each algorithm are analyzed and explained based on the experimental phenomena of flow regimes evolution processes,the flow regimes map,and the principles of algorithms.The applicability and feasibility of each algorithm according to different types of data for GLCC flow regimes identification are proposed.

新媒介文学阅读的文化逻辑及其阐释路径

自网络文学诞生以来,学术界基于社会状况、媒介特征和文本内容等阐释途径,建构了现代性、后现代和数字现代主义三种新媒介文学阅读的文化逻辑。现代性文化逻辑是由走向现代化国家的社会现实状况决定的,但网络文学中明显可见其阅读的后现代性;网络媒介带来了读写的后现代文化逻辑,却遭受到现代性立场的批判;数字现代主义将网络媒介与网络文学区隔,主张文本内容及其主题的新现代性,陷入媒介文艺学的内在矛盾中;它们存在着文学内容和主题的困境、媒介作为中介的片面性以及语言文本的过分重视等不足。要把握新媒介文学阅读的文化逻辑,必须回到后现代的原初含义,认清其是建立在可操作、多元呈现和融合生成的界面上,且在读者、阅读文本和审美效果等方面具有“虚拟后现代”特性。新媒介文学阅读体现出阅读文化的范式转型和阐释学意义。

津沽脏腑推拿理论发微

脏腑推拿是推拿学的重要分支,津沽脏腑推拿作为发源于天津地区的推拿技术,具有鲜明的地区特征,同时在国内推拿领域享有盛誉。津沽脏腑推拿发展至今已有百年,传承已有5代。近年来,传承人积极开展了津沽脏腑推拿的理论挖掘与整理工作,明确了津沽脏腑推拿调整脏腑、疏通经络、行气活血的中医作用原理,同时总结了其“通脉调气”的理论核心并对其进行了系统阐释与发微。希望对于后人理解和掌握津沽脏腑推拿,应用津沽脏腑推拿开展治病规律的临床及实验研究提供帮助,促进了中国脏腑推拿学科的发展。

经济法的立法路径审视及其改革

立法是法治的基础,这对以立法主体主动建构规则为重要特征的经济法尤为重要。经过多年改革,当前我国的经济立法在立法动因上体现出政策驱动性和实践回应性,在立法体系上体现出横向协同性和纵向续造性,在立法模式上体现出授权性和试验性,促进了经济立法的发展和进步。但同时,也存在对多元立法目标平衡不足、立法权配置的整合与统筹不力、对立法合理性和立法效果评估不够重视等问题。对此,应以规则合理性为标准,进一步深化经济法立法路径的改革,重点加强立法主体的说明解释机制,促进经济立法权的合理分配与整合,致力于立法合理性最大化,并构建和完善立法后评估制度。

加强新时代人文经济学的学理化阐释和体系化表达

研究阐释新时代人文经济学,是习近平总书记亲自布置的一项重大理论任务,我们要站在深入学习领会习近平经济思想和习近平文化思想的迫切需要、构建中国自主知识体系的重要增长点、为中国式现代化行稳致远提供方向指引的理论和实践高度来把握其重要意义,并以学理化阐释和体系化表达来高质量完成。加强新时代人文经济学的学理化阐释,必须对新时代人文经济学的本质内涵和基本特征、新时代人文经济学与西方经济学尤其是西方人文经济学的根本差异以及新时代人文经济学的唯物史观基础、社会现实根源、传统文化基因等一系列前提性、基础性问题展开深度追问和解答。加强新时代人文经济学的体系化表达,关键在于构建与之相应的学科体系、学术体系和话语体系。在“两个大局”交织激荡的时代背景下,加强新时代人文经济学的学理化阐释和体系化表达,不仅具有鲜明的中国意义,也具有深远的世界意义。

读《太极图说》对人文医学的意义

宋代新儒家周敦颐改绘了当时流传的“太极图”,并撰著《太极图说》,诠释“太极图”。文章虽然是周敦颐研究易学哲理思想的心得体会,但是蕴含有深刻的宇宙自然科学哲理和广博的人文哲学精神内涵,对于当今医学工作者仍然有重要的启迪意义。

基于反事实的非因果解释研究

反事实情形下的非因果解释理论能够在解释实践中更好地实现解释力,在解释的反事实理论、结构模型解释、最优模型解释等非因果解释理论中均存在反事实。但反事实与非因果解释的结合并不充分,反事实在应用于非因果解释的过程中,引发了非因果解释的不对称问题、过分覆盖问题,以及基于反事实无法涵盖所有类型非因果解释的问题。解决这些问题的关键在于坚持多元干预自由主义的解释立场,解释多元论与结构方程模型的结合为上述解释案例所出现的问题提供了可能的解决方案,从而实现对非因果解释的合理性辩护。

基于知识图谱的可解释学习路径推荐

学习路径推荐是解决信息超载、学习迷航等问题的关键,但当前的学习路径推荐相关研究存在推荐方法脱离学习场景、推荐结果缺乏解释等问题。为此,文章构建了基于知识图谱的可解释学习路径推荐模型:首先利用邻域标定的图注意力网络表征知识图谱语义信息并生成候选学习路径集,然后计算不同学习场景下学习者与候选学习路径之间的契合度和匹配度,最终实现可解释的学习路径推荐。之后,文章通过对基于知识图谱的可解释学习路径推荐模型与学习路径推荐基线模型进行对照实验和可解释案例分析,发现基于知识图谱的可解释学习路径推荐模型不仅提高了推荐结果的准确度,而且提升了推荐结果的可解释性。文章的研究有助于学习者获得准确、可解释的学习路径,从而提升个性化学习效果。

基于双流残差融合的多模态讽刺解释研究

针对现有多模态讽刺解释模型在融合过程中仅关注图像中的细粒度特征信息,使得模型存在解释效果不佳、多模态特征难以融合等问题,本文设计了一种基于双流残差注意力的多模态融合机制.首先,本文采用了BART和VGG19模型分别提取文本和图像两种模态特征.其次,模型经过两路多头注意力引导,分别关注图像和文本的细粒度信息,考虑到单纯的多头自注意力不能很好学习图文间的关联信息,采用二次注意力模块(AOA)合理分配特征权重.最后,本文将多模态特征拼接融合后输入BART解码器中进行讽刺解释.模型在公开的数据集MORE上的实验结果表明,相较于ExMore模型,本文模型在METEOR和ROUGE-L评价指标上分别提升了4.35%、3.39%.实验结果表明本文模型能更好融合模态特征,从而显著地提升模型解释的效果.

锚杆支护吸能调控效应理论解析与数值模拟

为揭示锚杆的支护吸能调控效应及巷道围岩能量场分布规律,采用理论分析、数值模拟方法研究了锚杆支护巷道围岩能量分布特征。首先,采用经典的弹塑性理论及能量守恒定律,分析了支护巷道围岩弹性变形能、耗散能和外力功之间的关系,给出了锚杆吸能表达式;其次,基于有限差分FLAC3D软件及内嵌的Fish语言编程,开发了能量单元模块,模拟揭示了锚固体的预应力扩散和释能特性;再次,通过单元网格遍历循环的方式计算了巷道围岩中各岩体单元的能量,获得了巷道围岩总变形能、弹性变形能和耗散能的数值及分布特征;最后,揭示了开挖与锚杆支护条件下巷道围岩能量场分布规律,从能量调控角度确定了锚杆合理的长度、间排距等支护参数。研究结果表明:锚杆主要以提高围岩体力学参数及转化吸收能量的方式来调控巷道围岩能量场的分布和耗散特征;随着锚杆间排距缩小和锚杆长度增大,巷道围岩最大耗散能密度呈降低趋势。

蒙药乌兰—13味汤方源考证及研究概况

蒙药乌兰—13味汤(WL—13)最早记载于《通瓦嘎吉德》,之后收录于1998年版《中华人民共和国卫生部药品标准》(蒙药分册)中。WL—13由栀子、诃子、川楝子、土木香、山柰、悬钩木、苦参、茜草、紫草、紫草茸、枇杷叶、金莲花、橡子等13种单味药材配伍组成,具有清血热的功效,主治血热盛行、头痛、目赤、高血压、成熟热与未成熟热、感冒、白脉病、肺热、鼻出血、皮疹、中暑、风湿、类风湿等诸疾,具有良好的潜在研究价值。本综述探析了WL—13的历史沿革、方解、临床应用及实验研究进展,以期为该药的现代化研究提供理论支撑。

《民法典》第193条(诉讼时效之职权禁用规则)评注

《民法典》第193条规定了诉讼时效之职权禁用规则,该规则与抗辩权发生主义的精神一致,但涉及的诉讼时效规定比抗辩权发生主义更为宽泛。本条适用于诉讼、执行、仲裁等法律程序已经启动的场合。“不得主动适用”包括不得主动审查诉讼时效事项和不得主动将诉讼时效事项作为裁判依据。法院不得积极释明诉讼时效,但在一定条件下可以消极释明。本条中的“诉讼时效的规定”是指当事人用以抗辩或者反抗辩的相关规定,不包括与当事人权利及意思无关的规定。法院遵守本条未主动适用诉讼时效规定的,当事人不得以此作为上诉或者申请再审的理由;法院违反本条主动适用诉讼时效规定的,构成实体审理中的适用法律错误。