Modeling degrees of conceptual overlap in Semantic Web ontologies

《多模态翻译理论与实践研究》读书笔记目录一、内容综述 (2)1. 研究背景与意义 (2)2. 翻译学科的发展趋势 (4)二、多模态翻译理论 (5)1. 多模态翻译的定义 (6)2. 多模态翻译的理论框架 (7)a. 结构主义视角 (8)b. 功能主义视角 (9)c. 概念式翻译学视角 (11)3. 多模态翻译的理论模型 (12)a. 以翻译为中心的模型 (13)b. 以信息为中心的模型 (14)c. 综合性模型 (16)三、多模态翻译实践 (17)1. 多模态翻译的应用领域 (18)a. 文化交流与传播 (20)b. 语言教学 (20)c. 人工智能辅助翻译 (22)2. 多模态翻译的案例分析 (23)a. 跨文化交际中的多模态翻译 (25)b. 语言教学中的多模态翻译实践 (26)c. 人工智能辅助翻译系统的设计与应用 (27)四、多模态翻译的挑战与对策 (28)1. 技术挑战 (29)a. 自然语言处理技术 (31)b. 计算机辅助翻译工具 (32)2. 理论挑战 (33)a. 翻译理论的创新与发展 (34)b. 多模态翻译的哲学思考 (36)3. 实践挑战 (37)a. 教育培训的需求分析 (38)b. 企业跨文化交流与合作 (39)五、结论 (40)1. 研究成果总结 (41)2. 研究展望与建议 (42)一、内容综述《多模态翻译理论与实践研究》是一本深入探讨多模态翻译理论和实践的学术著作。

本书通过对多模态翻译的全面分析，揭示了其在跨文化交流中的重要作用和广阔的应用前景。

在内容综述部分，作者首先回顾了多模态翻译的基本概念和理论框架，包括多模态翻译的定义、特点、分类以及多模态翻译的理论基础等。

作者详细介绍了多模态翻译在不同领域的应用实践，如翻译学术文献、广告语、影视作品等，并分析了多模态翻译所面临的挑战和问题，如跨文化交际障碍、语言结构差异等。

作者还探讨了多模态翻译与语义翻译、交际翻译等翻译理论的关系，强调了多模态翻译在促进跨文化交流、增进不同文化理解和尊重方面的重要作用。

10 of 39
Work engagement PERSONNEL PSYCHOLOGY 2011, 64, 89–136
Process of the study
Discussing antecedents and consequences
Discriminant Validity With Job Attitudes
techniques to test our predictions.
5 of 39
Work engagement PERSONNEL PSYCHOLOGY 2011, 64, 89–136
Process of the study
personal engagement (Kahn 1990)
Defining work engagement
experiencing an emotional connection with their work. As such, work engagement is fundamentally a motivational concept that represents the active allocation of personal
Defining work engagement
The definition of engagement in this study
Based on the author’s review, they defined work engagement as a relatively enduring state of mind referring to the simultaneous investment of personal energies in the experience or performance of work.

Geometric ModelingGeometric modeling is a fundamental concept in computer graphics and design, playing a crucial role in various industries such as architecture, engineering, and entertainment. It involves creating digital representations of physical objects or environments using mathematical and computational techniques. Geometric modeling allows designers and engineers to visualize, analyze, and manipulate complex shapes and structures, leading to the development of innovative products and solutions. However, it also presents several challenges and limitations that need to be addressed to ensure its effectiveness and efficiency. One of the key challenges in geometric modeling is the accurate representation of real-world objects and environments. This requires the use of advanced mathematical algorithms and computational methods to capture the intricate details and complexities of physical entities. For example, creating a realistic 3D model of a human face or a natural landscape involves precise measurements, surface calculations, and texture mapping to achieve a lifelike appearance. This level of accuracy is essential in industries such as animation, virtual reality, and simulation, where visual realism is critical for creating immersive experiences. Another challenge in geometric modeling is the efficient manipulation and editing of geometric shapes. Designers and engineers often need to modify existing models or create new ones to meet specific requirements or constraints. This process can be time-consuming and labor-intensive, especially when dealing with large-scale or highly detailed models. As a result, there is a constant demand for more intuitive and user-friendly modeling tools that streamline the design process and enhance productivity. Additionally, the interoperability of geometric models across different software platforms and systems is a persistent issue that hinders seamless collaboration and data exchange. Moreover, geometric modeling also faces challenges in terms of computational resources and performance. Generating and rendering complex 3D models requires significant computing power and memory, which can limit the scalability and accessibility of geometric modeling applications. High-resolution models with intricate geometries may strain hardware capabilities and lead to slow processing times, making it difficult for designers and engineers to work efficiently. This is particularly relevant in industries such as gamingand virtual reality, where real-time rendering and interactive simulations are essential for delivering engaging and immersive experiences. Despite these challenges, geometric modeling continues to evolve and advance through technological innovations and research efforts. The development of advanced modeling techniques such as parametric modeling, procedural modeling, and non-uniform rational B-spline (NURBS) modeling has significantly improved the accuracy and flexibility of geometric representations. These techniques enable designersand engineers to create complex shapes and surfaces with greater precision and control, paving the way for more sophisticated and realistic virtual environments. Furthermore, the integration of geometric modeling with other disciplines such as physics-based simulation, material science, and machine learning has expanded its capabilities and applications. This interdisciplinary approach allows for the creation of interactive and dynamic models that accurately simulate physical behaviors and interactions, leading to more realistic and immersive experiences. For example, in the field of architecture and construction, geometric modeling combined with structural analysis and environmental simulation enables the design and evaluation of sustainable and resilient buildings and infrastructure. In conclusion, while geometric modeling presents several challenges and limitations, it remains an indispensable tool for innovation and creativity in various industries. The ongoing advancements in geometric modeling techniques and technologies continue to push the boundaries of what is possible, enabling designers and engineers to create increasingly realistic and complex digital representations of the physical world. As computational power and software capabilities continue to improve, the future of geometric modeling holds great promise for revolutionizing the way we design, visualize, and interact with the world around us.。

高等代数与解析几何陈跃的英语专业词汇Delving into the Realms of Higher Algebra and Analytic Geometry: Exploring the English Vocabulary LandscapeThe study of higher algebra and analytic geometry encompasses a vast and intricate tapestry of concepts, principles, and terminology that form the foundation of advanced mathematical reasoning. As an English language learner exploring these domains, navigating the richlexical landscape can present both exhilarating opportunities and formidable challenges.Let us embark on a journey to unravel the nuances and complexities of the English vocabulary associated with higher algebra and analytic geometry. This endeavor will not only broaden our understanding of these mathematical disciplines but also cultivate a deeper appreciation for the precision and elegance inherent in the language used to articulate them.At the heart of higher algebra lies the exploration ofabstract algebraic structures, such as groups, rings, and fields. The very nomenclature of these constructs, with terms like "homomorphism," "isomorphism," and "subgroup," demands a keen grasp of the English language to comprehend their intricate relationships and underlying principles. The fluent articulation of these concepts is paramount, as it enables seamless communication within the mathematical community and facilitates the exchange of ideas and advancements.Analytic geometry, on the other hand, seamlessly integrates the realms of algebra and geometry, giving rise to a rich tapestry of linguistic expressions. From the precise delineation of conic sections, including ellipses, parabolas, and hyperbolas, to the elegant formulations of vector spaces and transformations, the English vocabulary serves as a vital conduit for conveying the visual and abstract nature of these geometric constructs.Navigating the intricacies of this specialized discourse demands not only a solid grasp of mathematical concepts but also a nuanced understanding of the English language. Theability to fluidly transition between the symbolic representations of equations and the descriptive power of words is a hallmark of mathematical proficiency. This versatility allows for the clear and concise articulationof complex ideas, enabling effective collaboration,problem-solving, and the dissemination of knowledge.Moreover, the English vocabulary employed in higher algebra and analytic geometry often exhibits a unique blend of technical terminology and linguistic elegance. Terms like "eigenvalue," "eigenvector," and "orthogonal" seamlessly integrate Greek and Latin roots, showcasing the richheritage and global influences that have shaped thelanguage of mathematics. Mastering the pronunciation, spelling, and contextual usage of these specialized termsis essential for ensuring clear communication and avoiding misunderstandings.Beyond the technical lexicon, the English language also plays a pivotal role in the formulation of mathematical proofs, theorems, and conjectures. The precise and logical flow of ideas, the careful use of modifiers and quantifiers,and the ability to navigate the nuances of mathematical notation all contribute to the effective expression of mathematical reasoning. Developing a command of these linguistic skills is paramount for success in the realm of higher algebra and analytic geometry.As we delve deeper into the interplay between English and the realms of higher algebra and analytic geometry, we must also acknowledge the importance of maintaining conceptual clarity. The English language, with its inherentflexibility and contextual ambiguity, can sometimes present challenges in conveying the unambiguous and rigorous nature of mathematical thinking. Navigating this balance, where the precision of mathematical concepts is harmonized with the expressive power of the English language, is a hallmark of true mastery.In conclusion, the exploration of the English vocabulary associated with higher algebra and analytic geometry is a multifaceted endeavor that encompasses not only the technical terminology but also the broader linguisticskills required for effective communication and reasoningwithin the mathematical domain. By embracing this challenge, we not only deepen our understanding of these captivating mathematical disciplines but also cultivate a versatile command of the English language that transcends the boundaries of academia, ultimately empowering us to engage with the world of mathematics with confidence and clarity.。

made in terms of three levels -回复题目：三个层次的维度，解析现代社会的发展与变革导语：现代社会的发展与变革是一个复杂而又庞大的课题，我们可以从三个层次的维度来深入剖析。

这三个层次分别是个人层面、组织层面和全球层面。

通过对这三个层次的分析，我们可以更好地理解和解决现代社会发展中所面临的诸多问题。

第一层次：个人层面的发展与变革在个人层面，社会的发展与变革主要以个体的成长和变化为基础。

首先，个人的教育和培训是推动社会发展的重要因素。

现代社会对人才的需求日益增长，个人必须通过学习和不断提升自己的能力，才能适应社会的发展和变化。

其次，个人的意识和价值观的转变也是社会发展与变革的重要方面。

现代社会对公平、正义、环境保护等价值观念的追求，需要个人逐步转变其传统的观念和思维方式，以适应当代社会的需求。

第二层次：组织层面的发展与变革在组织层面，社会的发展与变革主要取决于企业和组织的发展和变革。

首先，企业的创新能力是推动社会发展的关键。

现代社会对科技、产业和管理等各个方面的创新能力有着越来越高的要求，只有不断创新才能在激烈的市场竞争中取得优势。

其次，企业的社会责任也是社会发展与变革的重要方面。

现代社会对企业的社会责任要求越来越高，企业需要认识到自己与社会的相互关系，积极履行社会责任，为社会做出积极的贡献。

第三层次：全球层面的发展与变革在全球层面，社会的发展与变革是全球化进程的产物。

首先，全球化使得不同国家和地区之间的联系更加紧密，推动了信息、人员和资金的流动，加速了社会的发展与变革。

其次，全球层面上的合作和治理也对社会的发展与变革起到重要作用。

在全球化的背景下，各国需要加强合作，共同应对全球性的挑战，制定并实施更加有效的全球治理措施。

结语：通过对个人层面、组织层面和全球层面的发展与变革进行分析，我们可以更加深入地理解现代社会的发展动力和变革机制。

只有站在不同层次的视角上，我们才能更好地应对现代社会发展中面临的挑战，推动社会的持续进步与繁荣。

相同点英文作文英文回答：When it comes to thinking about the similarities between two different topics, it is important to consider the context and the specific aspects being compared. In general, there are a few key areas where different topics can overlap and share common ground.Conceptual Overlap:One potential area of similarity is conceptual overlap. This occurs when two topics share underlying ideas or principles. For example, both psychology and sociology study human behavior, albeit from different perspectives. They may share concepts such as motivation, cognition, and social interactions.Methodological Similarities:Another area of similarity can be found in methodological approaches. Certain research methods and techniques can be applicable across different fields. For instance, statistical analysis is used in both economics and biology to draw inferences from data. This shared methodology allows researchers to employ similar tools and techniques to solve problems in their respective disciplines.Interdisciplinary Connections:Furthermore, interdisciplinary connections can lead to similarities between topics. When different fields collaborate and exchange ideas, they can identify commonalities and develop new insights. For example, the field of bioinformatics combines biology and computer science to analyze biological data. This interdisciplinary approach creates a new area of research that shares characteristics from both parent disciplines.Historical Influences:Historical influences can also contribute tosimilarities between topics. As knowledge and ideas evolve over time, they can spread across different disciplines and shape their development. For instance, the influence of Newtonian physics on engineering and astronomy has led to shared concepts and principles in these fields.Common Applications:Finally, topics may share similarities based on their practical applications. For example, both marketing and public relations aim to communicate messages to specific audiences. Despite their different purposes, they may employ similar strategies and techniques to achieve their goals.中文回答：共同点：概念重叠，不同学科可能共享底层思想或原理。

Research triangulation 研究三角测量 Ethical research 伦理研究
Writing a research proposal 撰写研究计划 Conclusion 结语 Summary 总结
Keywords:
Research problem 研究问题 Research statement 研究报告 Research question 研究问题 Hypothesis 假设 Literature review 文献评论 Glossary of key terms 关键术语表 Research Methods 研究方法 Research methodology 研究方法论
Review of the literature 文献复习 Glossary of key terms 关键术语表
ห้องสมุดไป่ตู้
目录
CONTENTS
Understanding research Methods and Methodologies 理解研究方法 Chossing a research approach 选择研究方法
3.2 Formulating a research statement 制定一个研究声明
Once you have identified what the research problem is , you will also need to articulate this problem in a way that is carefully phrased and represents the single goal of the total research effort （leedy and ormrod , 2010 : 48 ) . 一旦你确定了研究问题，就需要进一步阐述研究这个问题的方式。 这是精心设计出来的，代表了总的研究目标。

小学上册英语第3单元期末试卷英语试题一、综合题(本题有100小题，每小题1分，共100分.每小题不选、错误，均不给分)1.What is the sound a dog makes?A. MeowB. BarkC. RoarD. Hiss2.Learning about plants can inspire ______ (环保) efforts.3.What is the term for a young fox?A. CubB. KitC. PupD. Calf答案:B Kit4.What do you call the person who writes books?A. AuthorB. EditorC. PublisherD. Journalist答案:A5.I can _____ my shoes by myself. (put on)6.What do we call a song that tells a story?A. PoemB. BalladC. NovelD. Play答案:B7.The _______ (The Harlem Renaissance) was a cultural movement in the 1920s.8.The movie is ______ (based) on a true story.9.The ________ is a famous painting by Leonardo da Vinci.10.What is the name of the ancient civilization that built pyramids in Egypt?A. MayansB. AztecsC. EgyptiansD. Greeks答案:C Egyptians11.What is the opposite of "hot"?A. WarmB. ColdC. CoolD. Spicy12.Which season comes after winter?A. SpringB. SummerC. FallD. Autumn13.The tree has green ________.14.The ________ chirps in the morning.15.The tropical fish in aquariums come in various ________________ (颜色) and patterns.16.My sister loves __________ (动物).17.I enjoy playing musical instruments. I play __________.18.What do we call a person who studies the weather?A. MeteorologistB. ClimatologistC. GeologistD. Astronomer19.I saw a ladybug on a ______.20.The clock says it is ________ (三点).21.I like eating ________ (chocolate) cake.22.ts are _____ (多年生) and come back every year. Some pla23.What is the capital of Brazil?A. Rio de JaneiroB. BrasíliaC. São PauloD. Salvador答案:B24.What do we call the process of writing down thoughts or ideas?A. DrawingB. PaintingC. WritingD. Sculpting答案:C25.The _____ (fish/bird) is swimming.26. A polymer is a large molecule made up of many ______ units.27.I saw a _______ (小猩猩) swinging from tree to tree.28. A plateau is an elevated flat ______.29.The bird builds a nest in the _______ (鸟在_______中筑巢).30. A ______ reaction releases energy, often as heat or light.31.What do we call the central part of an atom?A. ElectronB. ProtonC. NucleusD. Neutron答案:C32.What do plants need to grow?A. LightB. WaterC. SoilD. All of the above答案:D33.My dog wags its ______ (尾巴) when happy.34.The study of chemicals and their reactions is called ______.35.What is the name of the famous Greek philosopher who taught Alexander the Great?A. PlatoB. SocratesC. AristotleD. Epicurus答案:C36.I enjoy _______ (培养兴趣) in different subjects.37.The girl loves to ________.38.The main gas produced during respiration is __________.39.The ancient Greeks made significant advancements in ________ (医学).40.The process of condensation occurs when a gas turns into a __________.41.Which shape has four equal sides?A. RectangleB. TriangleC. SquareD. Circle42.What is the capital of Brunei?A. Bandar Seri BegawanB. Kuala BelaitC. TutongD. Seria答案:A43.What is the name of the famous wizarding school in Harry Potter?A. HogwartsB. NarniaC. Middle-earthD. Oz44. A __________ is a famous city for its festivals.45.The ______ is a measure of how far light travels in one year.46. A __________ is a famous site for camping.47.The _______ is where the roots grow.48.The ______ (植物生长) cycle is fascinating.49.I like to ride my ______ (horse).50.I saw a _______ (小鸟) building a nest.51. A __________ is the measure of how much matter is in an object.52.The _____ (peony) blooms in late spring.53.The ______ (植物的适应机制) is a subject of study.54.How do you say "thank you" in Japanese?A. GraciasB. MerciC. ArigatoD. Danke55.The _______ of a tree is called its trunk.56.The cake is _____ (delicious/yummy).57.My favorite toy is a ________ (拼图). I enjoy putting the pieces ________ (在一起).58.The Milky Way is just one of billions of _______ in the universe.59.I want to be a _______ when I grow up.60.My family goes camping in the ______.61.What is the opposite of "hot"?A. WarmB. ColdC. CoolD. Mild答案:B62.What do you call the female counterpart of a rooster?A. HenB. DuckC. GooseD. Chicken答案:A63.What is the capital of Kyrgyzstan?A. BishkekB. TashkentC. DushanbeD. Almaty答案:A64.I love ________ on the beach.65.The invention of ________ has revolutionized personal communication.66.The fish swims in the ______ (池塘). It is very ______ (活泼).67.My _____ (奶奶) makes delicious cookies.68.Shadows are formed when light is ______ (blocked).69.The Earth's crust is constantly being shaped by ______ forces.70.What is the main ingredient in popcorn?A. WheatB. CornC. RiceD. Barley答案:B71.In a polymerization reaction, monomers join together to form _____.72. A rabbit can hop across the ______ (草地).73.The peacock displays its feathers to attract ________________ (配偶).74.My dad takes us to ____.75.My dad is a __________ (商业分析师).76.The __________ (历史的说明) clarifies misconceptions.77.I have a special ________ that shines in the dark.78.The capital city of Djibouti is ________ (吉布提的首都城市是________).79.What is the color of an eggplant?A. RedB. PurpleC. GreenD. Yellow答案:B80.What is the name of the fairy tale character who had long hair?A. CinderellaB. RapunzelC. Snow WhiteD. Belle答案:B81. A _____ is a fun toy to ride.82. A molecule made of two identical atoms is called a _______ molecule.83.I saw a ________ flying over the lake.84.The puma is also called a _________ (美洲狮).85.How many colors are there in a rainbow?A. FiveB. SixC. SevenD. Eight86.The concept of "light years" helps us measure distances in _______.87.The capital city of Vietnam is ________ (河内).88.I like to ride my _______ (我喜欢骑我的_______).89.What do you call a baby cat?A. PuppyB. KittenC. CubD. Foal90.The ancient Romans spoke ________.91.My cat enjoys chasing ______ (小虫) in the sunlight.92.What is the name of the famous singer known as the "King of Pop"?A. Elvis PresleyB. Michael JacksonC. Freddie MercuryD. Prince答案:B93.What is the longest river in the world?A. AmazonB. NileC. MississippiD. Yangtze答案:B94.What do we call a baby dog?A. KittenB. PuppyC. CalfD. Chick95.An insulator does not allow ______ to pass through.96.Which animal can fly?A. ElephantB. DogC. BirdD. Fish答案:C97.What is the capital of Uganda?A. KampalaB. EntebbeC. JinjaD. Gulu98.Which fruit is yellow and curved?A. AppleB. BananaC. GrapeD. Orange答案:B99.How many days are in February during a leap year?A. 28B. 29C. 30D. 31100. A volcano that has not erupted in a long time is called a ______ volcano.。

I。

Put the following English terms into Chinese. (1'×10＝10’)所指对象referent所指论Referential theory专有名词 proper name普通名词 common nouns固定的指称记号 rigid designators指称词语deixical items确定性描述语definite descriptions编码时间 coding—time变异性variability表示反复的词语 iterative表述句 constative补救策略redressive strategies不可分离性 non—detachability不确定性indeterminacy不使用补救策略，赤裸裸地公开施行面子威胁行bald on record without redressive actions 阐述类言语行为 representatives承诺类言语行为 commissives指令类言语行为directives表达类言语行为expressives，宣告类言语行为declarations诚意条件 sincerity condition次要言外行为 secondary illocutionary act等级含义 scalar implicature等级划分法 rating scales副语言特征 paralinguistic features非公开施行面子威胁行为 off record非规约性non—conventionality非规约性意义 non-conventional implicature非论证性的 non—demonstrative非自然意义non—natural meaning (meaning—nn)否定测试法negation test符号学 semiotics构成性规则 constitutive rules古典格莱斯会话含义理论 Classical Gricean theory of conversational implicature关联论Relevance Theory关联原则Principle of Relevance归属性用法 attributive use规约性含义conventional implicature人际修辞 interpersonal rhetoric篇章修辞textual rhetoric含蓄动词 implicative verbs合适条件 felicity conditions呼语 vocatives互相显映 mutually manifest会话含义 conversational implicature话语层次策略 utterance-level strategy积极面子positive face间接言语行为 indirect speech acts间接指令 indirect directives结语 upshots交际意图communicative intention可撤销性 cancellability可废弃性 defeasibility可推导性 calculability跨文化语用失误cross—cultural pragmatic failure跨文化语用学cross—cultural pragmatics命题内容条件 propositional content condition面子保全论 Face-saving Theory面子论 Face Theory面子威胁行为 Face Threatening Acts （FTAs）蔑视 flouting明示 ostensive明示－推理模式ostensive—inferential model摹状词理论Descriptions粘合程度 scale of cohesion篇章指示 discourse deixis前提 presupposition前提语 presupposition trigger强加的绝对级别absolute ranking of imposition确定谈话目的 establishing the purpose of the interaction确定言语事件的性质 establishing the nature of the speech event 确定性描述语 definite descriptions认知语用学 cognitive pragmatics上下文 co—text社会语用迁移sociopragmatic transfer社交语用失误 sociopragmatic failure施为句 performative省力原则 the principle of least effort实情动词 factive verbs适从向 direction of fit手势型用法 gestural usage首要言外行为 primary illocutionary act双重或数重语义模糊 pragmatic bivalence/ plurivalence顺应的动态性 dynamics of adaptability顺应性adaptability语境关系的顺应(contextual correlates of adaptability)、语言结构的顺应(structural objects of adaptability)、顺应的动态性（dynamics of adaptability)和顺应过程的意识程度（salience of the adaptation processes）。

网络流行语“中国大妈”和“中国式X”解析--基于认知及社会文化维度徐敏【期刊名称】《铜陵学院学报》【年(卷),期】2014(000)004【摘要】伴随着新媒体的发展，“中国大妈”和与其结构类似的“中国式X”的网络流行语正在受到越来越多的关注。

代表一种独特的文化现象的“中国式X”，本身就是一个语言模因，它的各种次生语言形式在模因的作用下得以在社会大众之间广泛繁殖和衍生。

我们既可以从认知语言学的概念重叠和概念整合角度对“中国大妈”的形成及其所隐含的调侃语义进行解读，也可以从社会文化学领域的模因论视角对“中国式X”的传播进行探讨。

%With the development of new med ia, more attention is being paid to the network buzzwords. This paper focuses on“Zhongguo Dama”and similar structure “Zhongguoshi X”, and argues that “Zhongguo”in “Zhongguo Dama” is not just a noun represent-ing the country, but possesses the modification function like adjectives, which can be understood as “Zhongguo style” that refers to a special Chinese cultural phenomenon. Besides, this paper makes an explanation on the formation of “Zhongguo Dama” and its implied teasing meaning using conceptual overlap and conceptual integration proposed in cognitive linguistics. Finally, this paper discusses the spread of “Zhongguoshi X” from the perspective of “meme” in the domain of socio-culture. “Zhongguoshi X” is a linguistic meme, and its varioussecondary language forms can be reproduced and derived among the social public under the effect of meme.【总页数】4页(P83-86)【作者】徐敏【作者单位】澳门大学，澳门 999078【正文语种】中文【中图分类】H136【相关文献】1.中国互联网公益捐赠监管体制的多维度解析r——基于整体性治理视角 [J], 朱虹;吴楠2.中国式财政分权与地区收入差距——基于不同分权维度的分析 [J], 焦健;罗鸣令3.基于文化语言学的中国式英语现象多维度研究 [J], 杨永青4.基于文化语言学的中国式英语现象多维度研究 [J], 杨永青5.霍夫斯泰德文化维度理论视角下的中国式教育观——基于《中国式家长》游戏的分析 [J], 康健秋;禹娜因版权原因，仅展示原文概要，查看原文内容请购买。

Fig. 2: Representation on T of the interval [a, b] Fig. 1: Global fusion scheme formation, provided by the past observations and the evolution model of the vehicle may be compared at this stage to individual inferred data. After a study of the conflict (section 4) between these pieces of information and a discounting of unreliable sources, discounted masses are fused (section 5) and pignistic transformation is made to take a decision. Section 6 gives the evolution model and the observations models, which constitute a six-multisensor system. Finally, in section 7 are examined the contribution of the modeling of the positioning problem under the TBM angle of view. m([a, b)] is the part of the belief holder that supports [a, b] i.e., that the actual world is in [a, b] and that, due to lack of information, does not support any strict subset of [a, b]. The degree of belief of [a, b], bel([a, b]), quantifies the total amount of justified support given to [a, b]. bel([a, b]) is the sum of the masses given to the subsets of [a, b]. The degree of plausibility of [a, b], pl([a, b]), quantifies the maximum amount of potential specific support that could be given to [a, b]. It is defined as the sum of the masses given to intervals [ai , bi ] such that [ai , bi ] [a, b] = ∅. The commonality q ([a, b]) is another measure of belief which is useful for calculus in belief combination. It is defined as the sum of the masses given to the intervals [ai , bi ] ⊇ [a, b]. Graphical representations on T of belief, commonality and plausibility are represented in the figure 3. Each one is the integral of the bbds allocated to the gray areas.

化学工程chemical engineering化学工程学chemical engineering science化工热力学chemical engineering thermodynamics化学反应工程chemical reaction engineering 过程系统工程process system engineering又称“化工系统工程”。

生化工程biochemical engineering单元操作unit operation单元过程unit process传递现象transport phenomenon物料衡算material balance能量衡算energy balance稳态stable state非稳态unstable state亚稳态metastable state暂态transient state又称“瞬态”，曾用名“过渡状态”。

定态steady state曾用名“稳态”，“定常态”。

非定态unsteady state曾用名“非稳态”。

定态近似steady state approximation[微]元element过程process连续过程continuous process 半连续过程semi-continuous process间歇过程batch process又称“分批过程”。

流程图flow sheet,flow diagram物流stream循环recycle途径path又称“路径”。

原料feedstock,raw material进料feed关键组分key component副产物by-product中间产物intermediate product介质medium梯度gradient推动力driving force又称”驱动力”。

安全系数safety factor模型model理论模型theoretical model经验模型empirical model 半经验模型semi-empirical model统计模型statistical model建模modeling全称“建立模型”。

模拟ai英文面试题目及答案模拟AI英文面试题目及答案1. 题目: What is the difference between a neural network anda deep learning model?答案: A neural network is a set of algorithms modeled loosely after the human brain that are designed to recognize patterns. A deep learning model is a neural network with multiple layers, allowing it to learn more complex patterns and features from data.2. 题目: Explain the concept of 'overfitting' in machine learning.答案: Overfitting occurs when a machine learning model learns the training data too well, including its noise and outliers, resulting in poor generalization to new, unseen data.3. 题目: What is the role of a 'bias' in an AI model?答案: Bias in an AI model refers to the systematic errors introduced by the model during the learning process. It can be due to the choice of model, the training data, or the algorithm's assumptions, and it can lead to unfair or inaccurate predictions.4. 题目: Describe the importance of data preprocessing in AI.答案: Data preprocessing is crucial in AI as it involves cleaning, transforming, and reducing the data to a suitableformat for the model to learn effectively. Proper preprocessing can significantly improve the performance of AI models by ensuring that the input data is relevant, accurate, and free from noise.5. 题目: How does reinforcement learning differ from supervised learning?答案: Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize a reward signal. It differs from supervised learning, where the model learns from labeled data to predict outcomes based on input features.6. 题目: What is the purpose of a 'convolutional neural network' (CNN)?答案: A convolutional neural network (CNN) is a type of deep learning model that is particularly effective for processing data with a grid-like topology, such as images. CNNs use convolutional layers to automatically and adaptively learn spatial hierarchies of features from input images.7. 题目: Explain the concept of 'feature extraction' in AI.答案: Feature extraction in AI is the process of identifying and extracting relevant pieces of information from the raw data. It is a crucial step in many machine learning algorithms, as it helps to reduce the dimensionality of the data and to focus on the most informative aspects that can be used to make predictions or classifications.8. 题目: What is the significance of 'gradient descent' in training AI models?答案: Gradient descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In the context of AI, it is used to minimize the loss function of a model, thus refining the model's parameters to improve its accuracy.9. 题目: How does 'transfer learning' work in AI?答案: Transfer learning is a technique where a pre-trained model is used as the starting point for learning a new task. It leverages the knowledge gained from one problem to improve performance on a different but related problem, reducing the need for large amounts of labeled data and computational resources.10. 题目: What is the role of 'regularization' in preventing overfitting?答案: Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function, which discourages overly complex models. It helps to control the model's capacity, forcing it to generalize better to new data by not fitting too closely to the training data.。

2024年研究生考试考研英语(一201)复习试题与参考答案一、完型填空（10分）1.In order to be admitted into a top university, many students choose to take thepostgraduate entrance examination (or GRE). This exam is designed to test astudent’s knowledge in various subjects, including math, science, and English.However, some students may struggle with certain sections of the exam, such as the GRE verbal section. To prepare for this section, it is important to practice reading and comprehension skills regularly. Additionally, memorizing vocabulary words andpracticing answering sample questions can also help improve performance on theverbal section of the GRE.二、传统阅读理解（本部分有4大题，每大题10分，共40分）第一题The article below discusses the impact of the digital revolution on traditional retail businesses. After reading the article, please answer the following questions:1.What does the author mainly discuss in the first paragraph?2.How does the author illustrate the convenience of online shopping in thesecond paragraph?3.What is the author’s main argument in the third paragraph?4.What evidence does the author provide to support the claim that onlineshopping has a negative impact on traditional retail businesses?5.What is the author’s view on the future of traditional retail businessesin the conclusion?答案1.The author mainly discusses the impact of the digital revolution ontraditional retail businesses.2.The author illustrates the convenience of online shopping by comparingthe experience of shopping online with that of shopping in a physical store.3.The author’s main argument in the third paragraph is that while onlineshopping provides convenience and a wider selection of products,traditional retail businesses are struggling to compete and mayeventually be forced out of business.4.The author provides evidence such as the decline in sales for certaintraditional retail stores due to the rise of online shopping and the fact that many consumers now start their shopping journey online.5.The author’s view on the future of traditional retail businesses is thatthey will continue to struggle unless they adapt to the changing market and find ways to integrate online shopping into their business models.第二题A new study conducted by researchers at the University of Oxford has found that the traditional reading habits of people are significantly declining. The study, wh ich was published in the journal “Reading Habits and Trends,” analyzed the reading patterns of over 2,000 individuals from various age groups and backgrounds. The results showed that while only 35% of respondents still engage in traditional reading activities, such as reading books or newspapers, 65% prefer digital forms of content, including e-books and online articles.The researchers argue that this shift is due to the increasing popularity of digital devices and the convenience of reading on a screen. However, they also noted that this change may have detrimental effects on literacy and critical thinking skills. The study found that traditional readers tend to exhibit better reading comprehension and critical thinking skills compared to their digital reading counterparts.The implications of this shift are profound. Reading, in its traditional form, has been an essential part of intellectual development for centuries. It has been used to entertain, inform, and educate for generations. As the world moves further into the digital age, the question remains: Will this shift away from traditional reading diminish our collective intellectual capabilities?1、What percentage of the respondents still engage in traditional reading activities?A) 35%B) 65%C) 50%D) 25%答案：A) 35%2、According to the study, what are some traditional reading activities mentioned?A) Watching televisionB) Reading books or newspapersC) Listening to audiobooksD) Playing video games答案：B) Reading books or newspapers3、What is the argument made by the researchers about the decline in traditional reading habits?A) The increase in e-book sales is directly correlated with the decline.B) The public library is no longer a significant influence.C) Traditional reading activities are now considered outdated.D) Television and social media are to blame.答案：A) The increase in e-book sales is directly correlated with the decline.4、Which of the following skills do traditional readers tend to exhibit better than digital readers, according to the study?A) Playing video gamesB) Critical thinking skillsC) Reading comprehensionD) Writing skills答案：C) Reading comprehension5、What is the main concern about the shift towards digital reading that the passage discusses?A) Lack of time to readB) Loss of cultural identityC) Diminished intellectual capabilitiesD) Inability to comprehend dense texts答案：C) Diminished intellectual capabilitiesThird QuestionReading PassageThe American poet Walt Whitman was known for his radical approach to poetry. He broke away from traditional forms and embraced themes of democracy, equality, and the individual. Born in 1819, Whitman’s early years were sp ent in rural New York, experiencing the simple joys and struggles of working-class life. This early exposure to the multifaceted experiences of ordinary people heavily influenced his writing.Whitman’s ground-breaking work, Leaves of Grass, first published in 1855, was met with both praise and condemnation. It challenged the conventions of Victorian poetry with its free verse style, bold imagery, and frank explorationof sexuality and the human body. Some critics were outraged by its perceived vulgarity, while others hailed it as a revolutionary manifesto for a changing America.Despite the initial controversy, Leaves of Grass ultimately became a seminal work of American literature. Whitman’s innovative style and his celebration of the common man resonated with readers who were yearning for a new voice in poetry. He became known as the “Bard of Democracy” and his influence on generations of poets, both American and international, is undeniable.Whitman’s legacy endures not only through his poetry but also thr ough his activism. He was a passionate advocate for social justice and equality, and his writings frequently addressed issues such as slavery, poverty, and women’s rights. He believed that poetry had the power to bring about social change, and he used his platform to challenge the status quo and speak out for the marginalized.Questions:1.What is the passage primarily about?•Answer: The passage is primarily about the life, work, and legacy of the American poet Walt Whitman.2.Which of the following is NOT a characteristic of Whitman’s poetry mentioned in thepassage?A. Free verse styleB. Formal structure with strict rhyme schemeC. Bold imageryD. Exploration of sexuality•Answer: B3.Why was Leaves of Grass initially met with both praise and condemnation?•Answer: It challenged the conventions of Victorian poetry with its free verse style, bold imagery, and frank exploration of sexuality and the human body.4.What does the passage suggest about Whitman’s impact on future generations ofpoets?•Answer: Whitman’s innovative style and his celebration of the common man had a profound influence on generations of poets, both in America and internationally.5.According to the passage, what was one of Whitman’s primary beliefs about thepower of poetry?•Answer: Whitman believed that poetry had the power to bring about social change.第四题passage：The Role of Technology in Modern EducationWith the advent of the digital age, technology has become an integral part of modern education. Its influence is pervasive and has transformed the way students learn and teachers teach.1.Introduction of technology in education:In the past decade, technology has significantly entered the educational system. From online courses to e-learning platforms, it has made education accessible to a wider audience.2.Benefits of technology in education:The integration of technology in education has numerous advantages. It improves accessibility, as students can learn from anywhere. It also enhances learning efficiency by providing varied learning tools and resources. Additionally, technology aids in personalized learning, allowing students to learn at their own pace and style.3.Challenges of technology in education:Despite its benefits, technology in education also brings challenges.Over-dependency on technology can lead to a decline in critical thinking skills as students may not need to analyze information deeply if it is readily available online. Moreover, the lack of face-to-face interaction can affect social skills and emotional intelligence.4.Future prospects:Despite these challenges, the future of technology in education remains promising. With advancements in AI and other technologies, personalized learning will become more prevalent, and learning experiences will be further enriched. The integration of VR/AR technologies can create immersive learning environments that simulate real-world scenarios for better understanding and application of knowledge.Questions：1.How has technology transformed modern education?A. It has made education more accessible to a wider audience.B. It has replaced traditional teaching methods with digital ones entirely.C. It has increased the speed of academic progression for students.D. It has ensured equal educational opportunities for all students.答案：A2.Which of the following is NOT a benefit of technology in education?A. Improving accessibility of education.B. Reducing the need for critical thinking skills.C. Providing varied learning tools and resources.D. Personalizing the learning process for students.答案：B3.What is one challenge posed by the integration of technology in education?A. The cost of purchasing educational technology is high.B. Teachers may not be skilled in using educational technology effectively.C. Students may become overly dependent on technology for information retrieval.D. The lack of face-to-face interaction can affect social skills and emotional intelligence.答案：D4.What does the passage say about the future prospects of technology in education?A. It remains uncertain whether technology will improve education in the future.B. Technology will revolutionize education, bringing more personalized learning experiences and enriched learning experiences for students through advancements in AI and other technologies, as well as VR/AR technologies creating immersive learning environments.C. Technology will replace all traditional teaching methods in the comingyears due to its overwhelming benefits in education system today..D. There will be a reduction in usage of technology in classrooms as it will be perceived as harmful for children’s development..答案：B5 . Which statement best summarizes the overall view of this passage about the role of technology in modern education? 4 technology plays an integral part in modern education that contributes positively with numerous benefits but also brings challenges which need to be tackled properly along with further advancements in AI, VR/AR etc.. 正确答案：Technology plays an integral part in modern education that contributes positively with numerous benefits but also brings challenges which need to be tackled properly along with further advancements in AI，VR/AR etc.。

knowledge fusion of large languagemodelsKnowledge Fusion of Large Language ModelsLarge language models (LLMs) have emerged as a transformative technology in the field of artificial intelligence. These models, such as GPT-3 from OpenAI or BERT from Google, are capable of generating human-like text and understanding complex language patterns. However, one of the key challenges in their development is the integration and fusion of knowledge from various sources.Knowledge fusion, in the context of LLMs, refers to the process of combining and整合不同来源的知识 into a single, coherent representation. This involves integrating information from structured databases, unstructured text documents, and even expert knowledge. The goal is to create a model that can draw upon a rich and diverse knowledge base to generate more accurate and informative responses.To achieve this, LLMs rely on a range of techniques and methods. One approach is the use of transfer learning, where a model trained on a large corpus of text is fine-tuned on a specific task or domain. This allows the model to acquire knowledge from a general-purpose dataset and then adapt it to a more focused context.Another approach is the integration of external knowledge sources, such as knowledge graphs or ontologies. These structured representations of knowledge can provide valuable information to the model, enabling it to understand relationships and connections between different concepts and entities.The fusion of knowledge in LLMs is also aided by the use of advanced architectures and algorithms. For example, transformer-based models like GPT-3 employ self-attention mechanisms that allow them to capture complex dependencies between words and phrases. This enables the model to understand context more effectively and generate more coherent and informative text.In summary, knowledge fusion is a crucial aspect of large language modeldevelopment. By integrating diverse sources of knowledge and leveraging advanced techniques and algorithms, we can create models that are not only capable of generating human-like text but also possess a rich and comprehensive understanding of the world. This has the potential to transform a wide range of applications, from language understanding and generation to question answering and knowledge reasoning.。

A Survey of Statistical Network ModelsAnna Goldenberg University of Toronto Alice X.Zheng Microsoft Research Stephen E.Fienberg Carnegie Mellon UniversityEdoardo M.Airoldi Harvard UniversityDecember 2009a r X i v :0912.5410v 1 [s t a t .M E ] 29 D e c 2009这篇文章从机器学习的角度讲网络，非常有意思。

Goldenberg, A., Zheng, A.X., Fienberg, S.E., & Airoldi E.M. (2009). A survey of statistical network models.Foundations and Trends in Machine Learning, 2, 129-233.2ContentsPreface1 1Introduction31.1Overview of Modeling Approaches (4)1.2What This Survey Does Not Cover (7)2Motivation and Dataset Examples92.1Motivations for Network Analysis (9)2.2Sample Datasets (10)2.2.1Sampson’s“Monastery”Study (11)2.2.2The Enron Email Corpus (12)2.2.3The Protein Interaction Network in Budding Yeast (14)2.2.4The Add Health Adolescent Relationship and HIV Transmission Study142.2.5The Framingham“Obesity”Study (16)2.2.6The NIPS Paper Co-Authorship Dataset (17)3Static Network Models213.1Basic Notation and Terminology (21)3.2The Erd¨o s-R´e nyi-Gilbert Random Graph Model (22)3.3The Exchangeable Graph Model (23)3.4The p1Model for Social Networks (27)3.5p2Models for Social Networks and Their Bayesian Relatives (29)3.6Exponential Random Graph Models (30)3.7Random Graph Models with Fixed Degree Distribution (32)3.8Blockmodels,Stochastic Blockmodels and Community Discovery (33)3.9Latent Space Models (36)3.9.1Comparison with Stochastic Blockmodels (38)4Dynamic Models for Longitudinal Data414.1Random Graphs and the Preferential Attachment Model (41)4.2Small-World Models (44)4.3Duplication-Attachment Models (46)4.4Continuous Time Markov Chain Models (47)i4.5Discrete Time Markov Models (50)4.5.1Discrete Markov ERGM Model (51)4.5.2Dynamic Latent Space Model (52)4.5.3Dynamic Contextual Friendship Model(DCFM) (53)5Issues in Network Modeling57 6Summary61 Bibliography65iiPrefaceNetworks are ubiquitous in science and have become a focal point for discussion in everyday life.Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study,and most of these involve a form of graphical rep-resentation.Probability models on graphs date back to1959.Along with empirical studies in social psychology and sociology from the1960s,these early works generated an active “network community”and a substantial literature in the1970s.This effort moved into the statistical literature in the late1970s and1980s,and the past decade has seen a burgeoning network literature in statistical physics and computer science.The growth of the World Wide Web and the emergence of online“networking communities”such as Facebook,MyS-pace,and LinkedIn,and a host of more specialized professional network communities has intensified interest in the study of networks and network data.Our goal in this review is to provide the reader with an entry point to this burgeoning literature.We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature.Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections.We emphasize formal model descriptions,and pay special attention to the interpretation of parameters and their estimation.We end with a description of some open problems and challenges for machine learning and statistics.12Chapter1IntroductionMany scientificfields involve the study of networks in some works have been used to analyze interpersonal social relationships,communication networks,academic paper coauthorships and citations,protein interaction patterns,and much more.Popular books on networks and their analysis began to appear a decade ago,[see,e.g.,24;50;318;319;68] and online“networking communities”such as Facebook,MySpace,and LinkedIn are an even more recent phenomenon.In this work,we survey selective aspects of the literature on statistical modeling and analysis of networks in social sciences,computer science,physics,and biology.Given the volume of books,papers,and conference proceedings published on the subject in these differentfields,a single comprehensive survey would be impossible.Our goal is far more modest.We attempt to chart the progress of statistical modeling of network data over the past seventy years and to outline succinctly the major schools of thought and approaches to network modeling and to describe some of their interconnections.We also attempt to identify major statistical gaps in these modeling efforts.From this overview one might then synthesize and deduce promising future research directions.Kolaczyk[177]provides a complementary statistical overview.The existing set of statistical network models may be organized along several major axes.For this article,we choose the axis of static vs.dynamic models.Static network models concentrate on explaining the observed set of links based on a single snapshot of the network,whereas dynamic network models are often concerned with the mechanisms that govern changes in the network over time.Most early examples of networks were single static snapshots.Hence static network models have been the main focus of research for many years.However,with the emergence of online networks,more data is available for dynamic analysis,and in recent years there has been growing interest in dynamic modeling.In the remainder of this chapter we provide a brief historical overview of network modeling approaches.In subsequent chapters we introduce some examples studied in the network literature and give a more detailed comparative description of select modeling approaches.31.1Overview of Modeling ApproachesAlmost all of the“statistically”oriented literature on the analysis of networks derives from a handful of seminal papers.In social psychology and sociology there is the early work of Simmel and Wolff[268]at the turn of the last century and Moreno[221]in the1930s as well as the empirical studies of Stanley Milgram[215;298]in the1960s;in mathematics/probability there is the Erd¨o s-R´e nyi paper on random graph models[94].There are other papers that dealt with these topics contemporaneously or even earlier.But these are the ones that appear to have had lasting impact.Moreno[221]invented the sociogram—a diagram of points and lines used to represent relations among persons,a precursor to the graph representation for networks.Luce and others developed a mathematical structure to go with Moreno’s sociograms using incidence matrices and graphs(see,e.g.,[202;200;201;203;244;282;11]),but the structure they explored was essentially gram gave the name to what is now referred to as the”Small World”phenomenon—short paths of connections linking most people in social spheres—and his experiments had provocative results:the shortest path between any two people for completed chains has a median length of around6;however,the majority of chains initiated in his experiments were never completed!(His studies provided the title for the play and movie Six Degrees of Separation,ignoring the compleity of his results due to the censoring.)White[321]and Fienberg and Lee[100]gave a formal Markov-chain like model and analysis of the Milgram experimental data,including information on the uncompleted gram’s data were gathered in batches of transmission,and thus these models can be thought of as representing early examples of generative descriptions of dynamic network evolution.Recently,Dodds et al.[86]studied a global“replication”variation on the Milgram study in which more than60,000e-mail users attempted to reach one of18target persons in13countries by forwarding messages to acquaintances.Only384of24,163chains reached their targets but they estimate the median length for completions to be7,by assuming that attrition occurs at random.The social science network research community that arose in the1970s was built upon these earlier efforts,in particular the Erd¨o s-R´e nyi-Gilbert model.Research on the Erd¨o s-R´e nyi-Gilbert model(along with works by Katz et al.[166;168;167])engendered thefield of random graph theory.In their papers,Erd¨o s and R´e nyi worked withfixed number of vertices, N,and number of edges,E,and studied the properties of this model as E increases.Gilbert studied a related two-parameter version of the model,with N as the number of vertices and p thefixed probability for choosing edges.Although their descriptions might atfirst appear to be static in nature,we could think in terms of adding edges sequentially and thus turn the model into a dynamic one.In this alternative binomial version of the Erd¨o s-R´e nyi-Gilbert model,the key to asymptotic behavior is the valueλ=pN.There is a“phase change”associated with the value ofλ=1,at which point we shift from seeing many small connected components in the form of trees to the emergence of a single“giant connected component.”Probabilists such as Pittel[243]imported ideas and results from stochastic processes into the random graph literature.Holland and Leinhardt[149]’s p1model extended the Erd¨o s-R´e nyi-Gilbert model to allow4for differential attraction(popularity)and expansiveness,as well as an additional effect due to reciprocation.The p1model was log-linear in form,which allowed for easy computation of maximum likelihood estimates using a contingency table formulation of the model[101;102]. It also allowed for various generalizations to multidimensional network structures[103]and stochastic blockmodels.This approach to modeling network data quickly evolved into the class of p∗or exponential random graph models(ERGM)originating in the work of Frank and Strauss[110]and Strauss and Ikeda[287].A trio of papers demonstrating procedures for using ERGMs[316;241;254]led to the wide-spread use of ERGMs in a descriptive form for cross sectional network structures or cumulative links for networks—what we refer to here as static models.Full maximum likelihood approaches for ERGMs appeared in the work of Snijders and Handcock and their collaborators,some of which we describe in chapter3.Most of the early examples of networks in the social science literature were relatively small (in terms of the number of nodes)and involved the study of the network at afixed point in time or cumulatively over time.Only a few studies(e.g.,Sampson’s1968data on novice monks in the monastery[259])collected,reported,and analyzed network data at multiple points in time so that one could truly study the evolution of the network,i.e.,network dynamics.The focus on relatively small networks reflected the state-of-art of computation but it was sufficient to trigger the discussion of how one might assess thefit of a network model.Should one focus on“small sample”properties and exact distributions given some form of minimal sufficient statistic,as one often did in other areas of statistics,or should one look at asymptotic properties,where there is a sequence of networks of increasing size? Even if we have“repeated cross-sections”of the network,if the network is truly evolving in continuous time we need to ask how to ensure that the continuous time parameters are estimable.We return to many of these question in subsequent chapters.In the late1990s,physicists began to work on network models and study their properties in a form similar to the macro-level descriptions of statistical physics.Barab´a si,Newman, and Watts,among others,produced what we can think of as variations on the Erd¨o s-R´e nyi-Gilbert model which either controlled the growth of the network or allowed for differential probabilities for edge addition and/or deletion.These variations were intended to produce phenomena such as“hubs,”“local clustering,”and“triadic closures.”The resulting models gave usfixed degree distribution limits in the form of power laws—variations on preferential attachment models(“the rich get richer”)that date back to Yule[329]and Simon[269](see also[218])—as well as what became known as“small world”models.The small-world phenomenon,which harks back to Milgram’s1960s studies,usually refers to two distinct properties:(1)small average distance and(2)the“clustering”effect,where two nodes with a common neighbor are more likely to be adjacent.Many of these authors claim that these properties are ubiquitous in realistic networks.To model networks with the small-world phenomenon,it is natural to utilize randomly generated graphs with a power law degree distribution,where the fraction of nodes with degree k is proportional to k−a for some positive exponent a.Many of the most relevant papers are included in an edited collection by Newman et al.[231].More recently this style of statistical physics models have been used to detect community structure in networks,e.g.,see Girvan and Newman[122]and5Backstrom et al.[20],a phenomenon which has its counterpart description in the social science network modeling literature.The probabilistic literature on random graph models from the1990s made the link with epidemics and other evolving stochastic phenomena.Picking up on this idea,Watts and Strogatz[320]and others used epidemic models to capture general characteristics of the evolution of these new variations on random networks.Durrett[91]has provided us with a book-length treatment on the topic with a number of interesting variations on the theme. The appeal of stochastic processes as descriptions of dynamic network models comes from being able to exploit the extensive literature already developed,including the existence and the form of stationary distributions and other model features or properties.Chung and Lu [69]provide a complementary treatment of these models and their probabilistic properties.One of the principal problems with this diverse network literature that we see is that, with some notable exceptions,the statistical tools for estimation and assessing thefit of “statistical physics”or stochastic process models is lacking.Consequently,no attention is paid to the fact that real data may often be biased and noisy.What authors in the network literature have often relied upon is the extraction of key features of the related graphical network representation,e.g.,the use of power laws to represent degree distributions or mea-sures of centrality and clustering,without any indication that they are either necessary or sufficient as descriptors for the actual network data.Moreover,these summary quantities can often be highly misleading as the critique by Stouffer et al.[285,286]of methods used by Barab´a si[25]and V´a zquez et al.[304]suggest.Barab´a si claimed that the dynamics of a number of human activities are scale-free,i.e.,he specifically reported that the probability distribution of time intervals between consecutive e-mails sent by a single user and time delays for e-mail replies follow a power-law with exponent−1,and he proposed a priority-queuing process as an explanation of the bursty nature of human activity.Stouffer et al. [286]demonstrated that the reported power-law distribution was solely an artifact of the analysis of the empirical data and used Bayes factors to show that the proposed model is not representative of e-mail communication patterns.See a related discussion of the poorfit of power laws in Clauset et al.[74].There are several works,however,that try to address modelfitting and model comparison.For example,the work of Williams and Martinez[323] showed how a simple two-parameter model predicted“key structural properties of the most complex and comprehensive food webs in the primary literature”.Another good example is the work of Middendorf et al.[214]where the authors used network motif counts as input to a discriminative systematic classification for deciding which configuration model the actual observed network came from;they looked at power law,small-world,duplication-mutation and duplication-mutation-complementation and other models(seven in total)and concluded that the duplication-mutation-complementation model described the protein-protein inter-action data in Drosophila melanogaster species best.Machine learning approaches emerged in several forms over the past decade with the empirical studies of Faloutsos et al.[97]and Kleinberg[173,172,174],who introduced a model for which the underlying graph is a grid—the graphs generated do not have a power law degree distribution,and each vertex has the same expected degree.The strict6requirement that the underlying graph be a cycle or grid renders the model inapplicable to webgraphs or biological networks.Durrett[91]treats variations on this model as well. More recently,a number of authors have looked to combine the stochastic blockmodel ideas from the1980s with latent space models,model-based clustering[137]or mixed-membership models[9],to provide generative models that scale in reasonable ways to substantial-sized networks.The class of mixed membership models resembles a form of soft clustering[95] and includes the latent Dirichlet allocation model[41]from machine learning as a special case.This class of models offers much promise for the kinds of network dynamical processes we discuss here.1.2What This Survey Does Not CoverThis survey focuses primarily on statistical network models and their applications.As a consequence there are a number of topics that we touch upon only briefly or essentially not at all,such as•Probability theory associated with random graph models.The probabilistic literature on random graph models is now truly extensive and the bulk of the theorems and proofs,while interesting in their own right,are largely unconnected with the present exposition.For excellent introductions to this literature,see Chung and Lu[69]and Durrett[91].For related results on the mathematics of graph theory,see Bollob´a s[43].•Efficient computation on networks.There is a substantial computer science litera-ture dealing with efficient calculation of quantities associated with network structures, such as shortest paths,network diameter,and other measures of connectivity,central-ity,clustering,etc.The edited volume by Brandes and Erlebach[48]contains good overviews of a number of these topics as well as other computational issues associated with the study of graphs.•Use of the network as a tool for sampling.Adaptive sampling strategies modify the sampling probabilities of selection based on observed values in a network structure.This strategy is beneficial when searching for rare or clustered populations.Thompson and Seber[296]and Thompson[293]discuss adaptive sampling in detail.There is also related work on target sampling[294]and respondent-driven sampling[258;305].•Neural networks.Neural networks originated as simple models for connections in the brain but have more recently been used as a computational tool for pattern recognition(e.g.,Bishop[38]),machine learning(e.g.,Neal[228]),and models of cognition(e.g.,Rogers and McClelland[257]).•Networks and economic theory.A relatively new area of study is the link between network problems,economic theory,and game theory.Some useful entrees to this literature are Even-Dar and Kearns[96],Goyal[131],Kearns et al.[169],and Jackson7[160],whose book contains an excellent semi-technical introduction to network concepts and structures.•Relational networks.This is a very popular area in machine learning.It uses proba-bilistic graphical models to represent uncertainty in the data.The types of“networks”in this area,such as Bayes nets,dependency diagrams,etc.,have a different meaning than the networks we consider in this review.The main difference is that the net-works in our work are considered to“be given”or arising directly from properties of the network under study,rather than being representative of the uncertainty of the relationships between nodes and node attributes.There is a multitude of literature on relational networks,e.g.,see Friedman et al.[112],Getoor et al.[117],Neville and Jensen[229];Neville et al.[230],and Getoor and Taskar[116].•Bi-partite graphs.These are graphs that represent measurement on two populations of objects,such as individuals and features.The graphs in this context are seldom the best representation of the data,with exception perhaps of binary measurements or when the true populations have comparable sizes.Recent work on exchangeable Rasch matrices is related to to this topic and potentially relevant for network analysis. Lauritzen[186,187];Bassetti et al.[29]suggest applications to bipartite graphs.•Agent-based modeling.Building on older ideas such as cellular automata,agent-based modeling attempts to simulate the simultaneous operations of multiple agents,in an effort to re-create and predict the actions of complex phenomena.Because the inter-est is often on the interaction among the agents,this domain of research has been linked with network ideas.With the recent advances in high-performance computing, simulations of large-scale social systems have become an active area of research,e.g., see[46].In particular,there is a strong interest in areas that revolve around national security and the military,with studies on the effects of catastrophic events and bio-logical warfare,as well as computational explorations of possible recovery strategies [57;59].These works are the contemporary counterparts of more classical work at the interface between artificial intelligence and the social sciences[54;56;55].8Chapter2Motivation and Dataset Examples2.1Motivations for Network AnalysisWhy do we analyze networks?The motivation behind network analysis is as diverse as the origin of network problems within differing academicfields.Before we delve into details of the“how”of statistical network modeling,we start with some examples of the“why.”This chapter also includes descriptions of popular datasets for interested readers who may wish to exercise their modeling muscles.Social scientists are often interested in questions of interpretation such as the meanings of edges in a social network[181].Do they arise out of friendliness,strategic alliance,obligation, or something else?When the meaning of edges are known,the object is often to characterize the structure of those relations(e.g.,whether friendships or strategic alliances are hierarchical or transitive).A large volume of statistically-oriented social science literature is dedicated to modeling the mechanisms and relations of network properties and testing hypotheses about network structure,see,e.g.,[280].Physicists,on the other hand,tend to be interested in understanding parsimonious mech-anisms for network formation[28;235].For example,a common modeling goal is to explain how a given network comes to have its particular degree distribution or diameter at time t.Several network analysis concepts have found niches in computational biology.For ex-ample,work on protein function classification can be thought of asfinding hidden groups in the protein-protein interaction network[7;8]to gain better understanding of underlying bi-ological bel propagation(node similarity)in networks can be harnessed to help with functional gene annotation[226].Graph alignment can be used to locate subgraphs that are common among species,thus advancing our understanding of evolution[105].Mo-tiffinding,or more generally the search for subgraph patterns,also has many applications [17].Combining networks from heterogeneous data sources helps to improve the accuracy of predicted genetic interactions[327].Heterogeneity of network data sources in biology introduces a lot of noise into the global network structure,especially when networks created for different purposes(such as protein co-regulation and gene co-expression)are combined. [225]addresses network de-noising via degree-based structure priors on graphs.For a review9of biological applications of networks,please see[332].The task offinding hidden groups is also relevant in analyzing communication networks, e.g.,in detecting possible latent terrorist cells[30].The related task of discovering the“roles”of individual nodes is useful for identity disambiguation[36]and for business organization analysis[207].These applications often take the machine learning approach of graph parti-tioning,a topic previously known in social science and statistics literature as blockmodeling [199;89].A related question is functional clustering,where the goal is not to statistically cluster the network,but to discover members of dynamic communities with similar functions based on existing network connectivity[122;232;234;266].In the machine learning community,networks are often used to predict missing informa-tion,which can be edge related,e.g.,predicting missing links in the network[238;73;198], or attribute related,e.g.,predicting how likely a movie is to be a box office hit[229].Other applications include locating the crucial missing link in a business or a terrorist network,or calculating the probability that a customer will purchase a new product,given the pattern of purchases of his friends[142].The latter question can more generally be stated as predict-ing individual’s preferences given the preferences of her“friends”.This research direction has evolved into an area of its own under the name of recommender systems,which has recently received a lot of media attention due to the competition by the largest online movie rental company Netflix.The company has awarded a prize of one million dollars to a team of researchers that were able to predict customer ratings of movies with higher than10% accuracy than their own in-house system[290].The concept of information propagation alsofinds many applications in the network domain,such as virus propagation in computer networks[310],HIV infection networks[222; 163;164],viral marketing[87]and more generally gossiping[170].Here some work focuses onfinding network configurations optimal for routing,while other research assumes that the network structure is given and focus on suitable models for disease or information spread.2.2Sample DatasetsA plethora of data sets are available for network analysis,and more are emerging every year. We provide a quick guided tour of the most popular datasets and applications in eachfield.In his ground-breaking paper,Milgram[215]experimented with the construction of in-terpersonal social networks.His result that the median length of completed chains was approximately6led to the pop-culture coining of the phrase“six degrees of separation.”Subjects of subsequent studies ranged from social interactions of monks[259],to hierar-chies of elephants[209;303],to sexual relationships between adults of Colorado[176],to friendships amongst elementary school students[141;299].While a lot of biological applications focus on the study of protein-protein interaction networks[114;115;184;248;328],metabolic networks[158],functional and co-expression gene similarity networks and gene regulatory networks[111;309],computer science applica-tions revolve around e-mail[207],the internet[97;63;151],the web[152;13],academic paper co-authorship[127]and citation networks[204;216].Citation networks have a long history10of modeling in different areas of research starting with the seminal paper of de Solla Price [83]and more recently in physics[190].With the recent rise of online networks,computer science and social science researchers are also starting to examine blogger networks such as LiveJournal,social networks found on Friendster,Facebook,Orkut,and dating networks such as .Terrorist networks(often simulated)and telecommunication networks have come under similar scrutiny,especially since the events of September11,2001(e.g.,see[182;250;249; 62]).There has also been work on ecological networks such as foodwebs[323;16],neuronal networks[188],network epidemiology[306],economic trading networks[123],transporta-tion networks(roads,railways,airplanes;e.g.,[113]),resource distribution networks,mobile phone networks[92]and many others.Several network data repositories are available on public websites and as part of packages. For example,UCINet1includes a lot of well known smaller scale datasets such as the Davis Southern Club Women dataset[80],Zachary’s karate club dataset[330],and Sampson’s monk data[259]described below.Pajek2contains a larger set of small and large networks from domains such as biology,linguistics,and food-web.Additional datasets in a variety of domains include power grid networks,US politics,cellular and protein networks and others3.A collection of large and very large directed and undirected networks in the areas of communication,citation,internet and others are available as part of Stanford Network Analysis Package(SNAP)4.We now introduce six examples of networks studied in the literature,describing the data in reasonable detail and including graphs depicting the networks wherever feasible.For each network example we articulate specific questions of interest.2.2.1Sampson’s“Monastery”StudyA classic example of a social network is the one derived from the survey administered by Samspon and published in his doctoral dissertation[259].Figure2.1displays the network derived from the“whom do you like”sociometric relations in this dataset.Sampson spent several months in a monastery in New England,where a number of novices were preparing to join a monastic order.Sampson’s original analysis was rooted in direct anthropological ob-servations.He strongly suggested the existence of tight factions among the novices:the loyal opposition(whose members joined the monasteryfirst),the young turks(whose members joined later on),the outcasts(who were not accepted in either of the two main factions),and the waverers(who did not take sides).The events that took place during Sampson’s stay at the monastery supported his observations.For instance,John and Gregory,two members of the young turks,were expelled over religious differences,and other members resigned 1/ucinet/2http://vlado.fmf.uni-lj.si/pub/networks/data/3/~mejn/netdata//cdg/datasets/~networks/resources.htm4/data/11。

人力资本结构高级化、技术进步与地区经济高质量发展作者：张红霞王天慧来源：《商业研究》2021年第02期内容提要：中国经济高质量发展需要高级人力资本的支撑，人力资本结构的升级任重而道远。

本文采用空间杜宾模型对人力资本结构高级化、技术进步与经济高质量发展之间的关系进行研究分析。

结果表明：（1）采用ArcGIS软件绘制的人力资本结构高级化与经济高质量发展的空间分布呈现出显著的空间集聚与非均衡特征，Moran指数显示人力资本结构高级化与经济高质量发展存在较强的时空相关性。

（2）全局层面看，人力资本结构高级化与经济高质量发展之间存在倒U型关系;技术进步抑制经济高质量发展，但人力资本结构高级化有助于扭转这一负向作用，继而强化了技术进步的经济增长效应。

（3）分区层面看，人力资本结构高级化与技术进步对经济高质量发展的空间溢出效应存在显著地区异质性。

其中，东部地区的溢出效应作用效果最佳，而东北地区表现出内卷化的溢出效应。

关键词：人力资本结构高级化;技术进步;经济高质量发展;空间杜宾模型中图分类号：F0615文献标识码：A文章编号：1001-148X（2021）02-0030-10收稿日期：2020-08-27作者简介：张红霞（1973-），女，山东潍坊人，山东理工大学经济学院教授，理学博士，研究方向：区域经济;王天慧（1995-），本文通讯作者，女，山东淄博人，山东理工大学经济学院硕士研究生，研究方向：区域经济。

基金项目：国家社会科学基金一般项目，项目编号：14BJL081;山东省社会科学规划研究重点项目，项目编号：18BJJJ01。

一、引言改革开放40多年来，中国经济凭借政策红利和要素价格优势获得飞速发展。

在此过程中，人力资本作为关键的生产要素，逐步经历了由简单劳动力向高级人力资本的内生转化、升级，其中高等教育是最主要的驱动力。

1949-2018年，中国高等教育毛入学率从026%增至481%，高校总本专科招生规模从31万人增加到791万人，高等教育人数占比由2000年的38%升至2018年的194%。