统计代写|机器学习作业代写machine learning代考| Characteristics: Meta-Convergence

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  • Statistical Inference 统计推断
  • Statistical Computing 统计计算
  • Advanced Probability Theory 高等概率论
  • Advanced Mathematical Statistics 高等数理统计学
  • (Generalized) Linear Models 广义线性模型
  • Statistical Machine Learning 统计机器学习
  • Longitudinal Data Analysis 纵向数据分析
  • Foundations of Data Science 数据科学基础
Cybernetics in the 20th Century | SpringerLink
统计代写|机器学习作业代写machine learning代考| Characteristics: Meta-Convergence

统计代写|机器学习作业代写machine learning代考|Characteristics: Meta-Convergence

In Biology, the term evolutionary convergence is used to describe the process by which distinctly different animals develop similar traits and functional characteristics over time, without these traits being present in their last common ancestor. By exploring cities as living, autopoietic systems, we can introduce a new thinking/approach to urban functions, development and user experience that is based on a new convergent design and planning methodology grounded in the core theories presented in the previous section. The evolution of traditional cities to smart cities is a natural evolutionary progression driven by rapid advancements in our technological capabilities, accelerated by the increased global awareness and urgent mandate to reverse the challenges and effects due to climate change.

Having identified and presented several of the critical theories of systems organization and behavior in the previous section, we now turn our focus to defining the specific characteristics exhibited by autopoietic systems to determine to what extent such characteristics can be incorporated in the design, planning, and operations of future smart cities. Building on the essential attributes of autopoietic systems defined by Maturana and Varela, we propose the addition of several autopoietic properties. The convergence of these properties will enable the complexity and scalability we envision smart cities will require to manage diverse urban functions and dynamic urban growth as part of a gestalt operating system.

Table 1 represents the five characteristics and properties we have identified as key enablers of smart cities and the core attributes of autopoietic smart city operating systems. These properties work together to form a convergent composite we define as meta-convergence.

统计代写|机器学习作业代写machine learning代考|Sentience and Cognition

Maturana and Varela treat sentience and cognition as inseparable elements of how autopoietic systems operate. Such systems are structurally coupled with their environment and the ability to maintain recursive interactions with their environment, defined as medium, is key to their survival. The system function remains constant, while the structure adapts to their environment to maintain self-reproduction, organization, and reorganization. Through continuous recurring interactions, autopoietic systems gain knowledge. This is a key element to Maturana and Varela’s autopoiesis theory-from the most basic organism to the most complex system, the process of cognition is a key property of living systems.

Perception and cognition occur through the operation of the nervous system, which is realized through the autopoiesis of the organism. As we have seen, autopoietic systems operate in a medium to which they are structurally coupled. Their survival is dependent on certain recurrent interactions continuing. For Maturana, this itself means that the organism has knowledge, even if only implicitly. The notion of cognition is extended to cover all the effective interactions that an organism has. A cognitive system is a system whose organization defines a domain of interactions in which it can act with relevance to the maintenance of itself, and the process of cognition is the actual (inductive) acting or behaving in this domain. Living systems are cognitive systems and living as a process is a process of cognition. This statement is valid for all organisms, with and without a nervous system (Maturana and Varela 1980, p. 13).

As defined above, sentience and cognition are key characteristics of intelligent living systems. Through the rapid advancements in technology, machines are developing biomimetic capabilities that increasingly simulate behaviors observed in humans and other natural organisms. These capabilities will converge as a form of collective intelligence (Fig. 2) combining both natural and artificial characteristics propagating a new stage in the evolution of the relationship and communication of humans, machines and nature. As a result of these relationships, new communication capabilities and hybrid languages will form to support diverse combinations of human-machine-nature sentience and cognition.

统计代写|机器学习作业代写machine learning代考|Structural Coupling

As different components and systems become increasingly interconnected, new possibilities for training machine learning algorithms become available, giving rise to convergence driven by artificial intelligence. With advancements in technological innovation and increasingly technological convergence, coupled with the adoption of data-driven methodologies, systems are becoming increasingly smarter and autonomous. In the process of achieving convergence in systems, the underlying dynamics are the inherent forces in nature and evolution itself to develop parallel traits in systems and system behaviors. The diverse functions of systems and subsystems within smart cities, in principle, should move towards converging states given the same level of technological advancement and enablers, infrastructure/platforms and operational mechanisms. For example, if all smart cities’ functions are developed on top of big data analytics, the behavior response mechanisms should, over time, develop convergence characteristics, including realtime data response, data filtering and processing. Applied to the difference between human and machine information processing, the convergence characteristics of autonomous (AI) data analytics will eventually lessen the disparities between human and machine data processing. Within this integrated domain, human and machine intelligence will be co-dependent and co-creative, allowing the generation of convergent responses across all smart city functions. Equally, with other AIdriven functions, human and machine intelligence will converge with the formation of a new hybrid intelligence that we have defined as collective intelligence. In their book on Smart Cities and AI, Kirwan and Fu (2020) expand the definition of collective intelligence to include a human-machine-nature convergence, ultimately integrating human and machine intelligence as part of a broader unified operating system that is the extension of the natural environment. This stage of urban evolution will enable autopoiesis to occur as a comprehensive, unified ecosystem, where smart city functions are harmoniously aligned to their physical environment while further expanded to interface with broader interplanetary systems.

Cybernetics --- What?
统计代写|机器学习作业代写machine learning代考| Characteristics: Meta-Convergence


统计代写|机器学习作业代写machine learning代考|Characteristics: Meta-Convergence


在上一节中确定并介绍了系统组织和行为的几个关键理论之后,我们现在将重点转向定义自创生系统所表现出的具体特征,以确定这些特征在多大程度上可以纳入设计、规划和未来智慧城市的运营。基于 Maturana 和 Varela 定义的自创生系统的基本属性,我们建议添加几个自创生属性。这些属性的融合将实现我们设想的智能城市所需的复杂性和可扩展性,以作为完形操作系统的一部分来管理多样化的城市功能和动态的城市增长。

表 1 代表了我们确定为智慧城市关键推动力的五个特征和属性以及自创生智慧城市操作系统的核心属性。这些属性共同作用形成我们定义为元收敛的收敛组合。

统计代写|机器学习作业代写machine learning代考|Sentience and Cognition

Maturana 和 Varela 将感知和认知视为自创生系统如何运作的不可分割的元素。此类系统在结构上与其环境耦合,并且与环境(定义为介质)保持递归交互的能力是其生存的关键。系统功能保持不变,而结构适应其环境以保持自我复制、组织和重组。通过不断重复的相互作用,自创生系统获得知识。这是 Maturana 和 Varela 的自创生理论的关键要素——从最基本的有机体到最复杂的系统,认知过程是生命系统的关键属性。


如上所述,感知和认知是智能生命系统的关键特征。通过技术的快速进步,机器正在开发仿生能力,越来越多地模拟在人类和其他自然生物中观察到的行为。这些能力将融合为一种集体智慧(图 2),结合自然和人工特征,在人类、机器和自然的关系和交流的演变中传播一个新阶段。由于这些关系,将形成新的通信能力和混合语言,以支持人机自然感知和认知的多样化组合。

统计代写|机器学习作业代写machine learning代考|Structural Coupling

随着不同的组件和系统变得越来越相互关联,训练机器学习算法的新可能性变得可用,从而产生了由人工智能驱动的融合。随着技术创新的进步和越来越多的技术融合,再加上数据驱动方法的采用,系统正变得越来越智能和自主。在实现系统收敛的过程中,潜在的动力是自然界和进化本身的内在力量,以发展系统和系统行为的平行特征。原则上,考虑到相同水平的技术进步和促成因素、基础设施/平台和运营机制,智慧城市内系统和子系统的多样化功能应朝着融合状态发展。例如,如果所有智慧城市的功能都建立在大数据分析之上,那么行为响应机制应该会随着时间的推移而发展出收敛特性,包括实时数据响应、数据过滤和处理。应用于人和机器信息处理之间的差异,自主(AI)数据分析的融合特性最终将减少人和机器数据处理之间的差异。在这个集成的领域内,人类和机器智能将相互依赖和共同创造,允许在所有智慧城市功能中产生聚合响应。同样,与其他人工智能驱动的功能一起,人类和机器智能将融合形成一种新的混合智能,我们将其定义为集体智能。在他们关于智慧城市和人工智能的书中,Kirwan 和 Fu (2020) 将集体智能的定义扩展为包括人机自然融合,最终将人类和机器智能整合为更广泛的统一操作系统的一部分,该操作系统是自然环境的延伸。这一阶段的城市进化将使自创生成为一个全面、统一的生态系统,其中智慧城市的功能与其物理环境和谐地结合在一起,同时进一步扩展为与更广泛的行星际系统交互。

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术语 广义线性模型(GLM)通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归,以及方差分析和方差分析(仅含固定效应)。



有限元是一种通用的数值方法,用于解决两个或三个空间变量的偏微分方程(即一些边界值问题)。为了解决一个问题,有限元将一个大系统细分为更小、更简单的部分,称为有限元。这是通过在空间维度上的特定空间离散化来实现的,它是通过构建对象的网格来实现的:用于求解的数值域,它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统,以模拟整个问题。然后,有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。





随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值(如1秒,5分钟,12小时,7天,1年),因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中,往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录,以得到其自身发展的规律。


多元回归分析渐进(Multiple Regression Analysis Asymptotics)属于计量经济学领域,主要是一种数学上的统计分析方法,可以分析复杂情况下各影响因素的数学关系,在自然科学、社会和经济学等多个领域内应用广泛。


MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中,其中问题和解决方案以熟悉的数学符号表示。典型用途包括:数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发,包括图形用户界面构建MATLAB 是一个交互式系统,其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题,尤其是那些具有矩阵和向量公式的问题,而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问,这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展,得到了许多用户的投入。在大学环境中,它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域,MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要,工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数(M 文件)的综合集合,可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。



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