基于图灵机的递归技术的实现陈晓亮[1] 卢朝辉[2] 宋文[1]

图灵机是通用的计算机模型,一般程序设计和以图灵机为机器模型的计算也是支持递归的。本文首先分析了递归的特征,利用多带图灵机作为计算模型,定义了递归技术转移 函数形式,提出了图灵机递归过程信息传递与保存的方法,给出了图灵机调用的实现,继而给出了图灵机递归技术的实现,同时证明了图灵机的调用与图灵机的递归调用是图灵可识别的。     

基于扩展通用图灵机的计算机病毒传染模型

在计算机基础理论模型——图灵机模型的基础上,提出了一种扩展的通用图灵机（EUTM）模型,这种模型突出了计算机病毒的传染特性,极大地简化了计算机病毒传染的形式描述,并根据EUTM模型给出了计算机病毒的形式定义,形式化描述了计算机病毒在单机和多机环境下的传染。最后指出了Fred Cohen关于病毒检测不可判定性定量证明的不足,并利用EUTM模型证明了病毒检测不可判定性定理。     

基于持久图灵机和细胞自动机的病毒Agent及其动力学建模

计算机病毒是这样一种程序,当它在一台计算机上执行时,能够利用该机器的资源生成自身的可能进化了的副本,每个副本可以通过某种方式传播到其他计算机中,并有机会得到执行.一个具体的病毒实例被称为病毒Agent,因为它在执行过程中能够在无用户干预的情况下自主进行动作选择,从而具有了基本的自治性.持久图灵机是一种描述顺序交互的正则模型,基于持久图灵机建立了一种病毒Agent的计算模型.该模型展现了计算机病毒的传染性本质,并且克服了传统图灵机病毒模型在表现交互性上的固有缺陷.在该模型的基础上,推导出一些与病毒Agent相关的有用定理.还讨论了利用细胞自动机进行病毒Agent动力学建模的问题,并得到了一些启发性的结论.     

图灵机

6.1 引言 在这章,我们研究第三类识别装置——图灵机。图灵机已被建议作为描述过程的数学模型。因为过程的直观概念被看成是能机械实现的指令的有穷序列,这在数学上是不精确的,所以我们决不能期望形式地证明它等价于图灵机的精确概念。但是,根据图灵机定义,不难     

从现代计算机视角看嵌入式系统（2）--从图灵机到现代计算机

现代计算机的源头是微处理器,微处理器的体系结构的源头是20世纪30年代图灵学者们的思想体系.1936年,图灵提出了人工智能的图灵机模型后,现代计算机经历了一个曲折的演变过程.冯.诺依曼沿着图灵机的思路,参与创建了世界上第一台电子计算机ENIAC.然而,从第1代电子管(1945~1956年)、第2代晶体管(1956~1963年)到第3代集成电路(1964~1971年),此后对电子计算机的探索便停滞不前.后来,1972年诞生的第一个8位微处理器8008,从另一个渠道完成了图灵机到人工智力内核的蜕变.从此,电子计算机便走上了微处理器智力内核基础上的现代计算机发展的阳光大道。     

交互计算模型概述

由于计算机技术的发展日新月异，以算法为核心，以图灵机和Church论题等为理论依据的计算模型已无力继续成为今天计算科学的理论范式，介绍了一个崭新的计算模型－－交互计算模型的基本思想，它是对算法的扩展，并比算法具有更强的描述能力，一系列基本概念被扩展到交互。     

图灵机模拟系统的设计与实现

图灵机模型是现代计算机科学的理论基础,学习图灵机理论对于研究计算机软件理论有重要意义。文章分析了在Von Neumann机上仿真图灵机存在的两个问题,并针对单带图灵机模型设计了一个完整的图灵机模拟系统。该模拟系统能够编译并执行图灵机程序,为基于图灵机模型或其扩展模型的理论研究提供实验平台,对构造基于中间语言解释执行的虚拟机提供实践指导。     

一种广义分子计算模型及其在 NP问题中的应用

目前各种分子计算模型多基于生物技术,求解一个问题的分子计算机算法很难不作修改地应用于其他类似的问题,尚不似传统计算机般通用。为此,提出一种基于图灵机的广义分子计算模型,其由一台单带图灵机、一条单向只写带和一条工作带组成,通过只写带与工作带之间特殊的映射函数实现并行的同时读、写操作。实验说明了该模型能够在多项式时间求解NP完全的满足性问题（SAT）,比现有分子计算模型在计算准确性和通用性上存在明显优势。     

图灵机及其构造研究

图灵机模型是现代计算机科学的理论基础,学习图灵机理论对于研究计算机理论有着重要的意义。本文介绍了图灵机的产生和用途,分析其基本模型以及扩展模型,并通过举例对图灵机的构造进行了探讨。     

图灵机的行为学习*

图灵机计算时读写头在带子上的活动,包括移动及改写字符，称为圈灵机的行为．本文证明，图灵机的行为是可学习的．即存在一个学习过程，它能根据一个图灵机的行为序列，学到另一个也能产生同样行为序列的图灵机．     

非经典参与介质的渲染方法研究综述

参与介质在自然界中广泛存在,也是包括影视特效、电子游戏、仿真系统等大多数图形应用中的主要场景元素之一,对其外观的模拟和再现,可以极大地提升场景的真实感和沉浸感.但是由于参与介质本身结构以及光线在参与介质中的传播过程都非常复杂,所以迄今为止,对参与介质渲染的研究都一直是图形领域的热点和难点.为了处理的方便和计算的高效,传统的参与介质渲染方法都基于两点假设:独立散射假设和局部连续假设.这两点假设也是经典的辐射传输方程成立的关键.但实际上,自然界中的很多参与介质都不满足这两点假设,因此导致现有的参与介质渲染方法生成的图片效果和真实世界的效果存在一定的差异.近年来,研究者们提出了各种非经典参与介质渲染方法,试图打破上述的两点假设,用更符合物理客观规律的方式来处理参与介质,从而进一步提升参与介质渲染的物理真实感.从相干介质渲染技术和离散介质渲染技术两方面展开对现有的面向非经典参与介质的渲染方法进行分析和讨论,重点阐述经典和非经典参与介质渲染方法的区别,以及现有非经典参与介质渲染方法的原理、优势和不足.最后,展望一些开放性问题并进行总结.本综述希望能启发相关领域的研究人员进一步攻克非经典参与介质渲...     

Evolution of cartesian genetic programs for development of learning neural architecture

Although artificial neural networks have taken their inspiration from natural neurological systems, they have largely ignored the genetic basis of neural functions. Indeed, evolutionary approaches have mainly assumed that neural learning is associated with the adjustment of synaptic weights. The goal of this paper is to use evolutionary approaches to find suitable computational functions that are analogous to natural sub-components of biological neurons and demonstrate that intelligent behavior can be produced as a result of this additional biological plausibility. Our model allows neurons, dendrites, and axon branches to grow or die so that synaptic morphology can change and affect information processing while solving a computational problem. The compartmental model of a neuron consists of a collection of seven chromosomes encoding distinct computational functions inside the neuron. Since the equivalent computational functions of neural components are very complex and in some cases unknown, we have used a form of genetic programming known as Cartesian genetic programming (CGP) to obtain these functions. We start with a small random network of soma, dendrites, and neurites that develops during problem solving by repeatedly executing the seven chromosomal programs that have been found by evolution. We have evaluated the learning potential of this system in the context of a well-known single agent learning problem, known as Wumpus World. We also examined the harder problem of learning in a competitive environment for two antagonistic agents, in which both agents are controlled by independent CGP computational networks (CGPCN). Our results show that the agents exhibit interesting learning capabilities.     

Application of statistical inference for partially ordered hypotheses

In quality control and decision support systems, ranking and selection are very important, because we often encounter situations in which we need to rank several objects and to select some out of them. We investigate a problem of ranking k( 2) independent normal populations and selecting superior or inferior m(≤ k) out of them. The purpose of this paper is to formulate the problem as that of statistical hypothesis test, and to give critical values for practical use. We present computational methods for the level probabilities, which are needed for finding the critical values.     

一种基于图像的非均匀单散射参与介质建模方法

针对非均匀单散射参与介质建模问题,提出一种能够保持密度场空间变化细节的建模方法.使用体数据、吸收和散射系数之比分别描述参与介质空间变化的密度分布及光线在其内部的传输特性,通过构建采集图像像素值与它们之间的表达式,将建模问题转化为非线性数值优化问题,求最优解.为了解决高分辨率体数据下大量体素密度值同时求解带来的时间开销大及数值不稳定问题,提出一种符合采集图像像素值明暗分布规律的密度场体数据初始化算法和一种多分辨率体数据逐步求精优化算法;为了快速计算大量体素密度值梯度,提出一种基于GPU的多体素并行梯度计算算法.实验结果表明,提出的方法能够根据采集图像重构烟模型,并保持其细节信息.     

Image-based modeling of inhomogeneous single-scattering participating media

We propose a method to reconstruct inhomogeneous single-scattering participating media, which could preserve fine high-frequency details of density field.Volumetric data and the ratio of absorption coefficient to scattering coefficient are used to describe the spatial-varying density distribution and optical properties of certain participating media, and a function between the above parameters and captured pixel values is built.Thus the problem of how to find solutions to these parameters is formulated into...     

Generalized Dirichlet priors for Naïve Bayesian classifiers with multinomial models in document classification

The generalized Dirichlet distribution has been shown to be a more appropriate prior than the Dirichlet distribution for naïve Bayesian classifiers. When the dimension of a generalized Dirichlet random vector is large, the computational effort for calculating the expected value of a random variable can be high. In document classification, the number of distinct words that is the dimension of a prior for naïve Bayesian classifiers is generally more than ten thousand. Generalized Dirichlet priors can therefore be inapplicable for document classification from the viewpoint of computational efficiency. In this paper, some properties of the generalized Dirichlet distribution are established to accelerate the calculation of the expected values of random variables. Those properties are then used to construct noninformative generalized Dirichlet priors for naïve Bayesian classifiers with multinomial models. Our experimental results on two document sets show that generalized Dirichlet priors can achieve a significantly higher prediction accuracy and that the computational efficiency of naïve Bayesian classifiers is preserved.     

Models of parallel computation :a survey and classification

In this paper, the state-of-the-art parallel computational model research is reviewed. We will introduce various models that were developed during the past decades. According to their targeting architecture features, especially memory organization, we classify these parallel computational models into three generations. These models and their characteristics are discussed based on three generations classification. We believe that with the ever increasing speed gap between the CPU and memory systems, incorporating non-uniform memory hierarchy into computational models will become unavoidable. With the emergence of multi-core CPUs, the parallelism hierarchy of current computing platforms becomes more and more complicated. Describing this complicated parallelism hierarchy in future computational models becomes more and more important. A semi-automatic toolkit that can extract model parameters and their values on real computers can reduce the model analysis complexity, thus allowing more complicated models with more parameters to be adopted. Hierarchical memory and hierarchical parallelism will be two very important features that should be considered in future model design and research.     

Models of parallel computation: a survey and classification

In this paper, the state-of-the-art parallel computational model research is reviewed. We will introduce various models that were developed during the past decades. According to their targeting architecture features, especially memory organization, we classify these parallel computational models into three generations. These models and their characteristics are discussed based on three generations classification. We believe that with the ever increasing speed gap between the CPU and memory systems, incorporating non-uniform memory hierarchy into computational models will become unavoidable. With the emergence of multi-core CPUs, the parallelism hierarchy of current computing platforms becomes more and more complicated. Describing this complicated parallelism hierarchy in future computational models becomes more and more important. A semi-automatic toolkit that can extract model parameters and their values on real computers can reduce the model analysis complexity, thus allowing more complicated models with more parameters to be adopted. Hierarchical memory and hierarchical parallelism will be two very important features that should be considered in future model design and research.     

Is Weak Emergence Just in the Mind?

Weak emergence is the view that a system’s macro properties can be explained by its micro properties but only in an especially complicated way. This paper explains a version of weak emergence based on the notion of explanatory incompressibility and “crawling the causal web.” Then it examines three reasons why weak emergence might be thought to be just in the mind. The first reason is based on contrasting mere epistemological emergence with a form of ontological emergence that involves irreducible downward causation. The second reason is based on the idea that attributions of emergence are always a reflection of our ignorance of non-emergent explanations. The third reason is based on the charge that complex explanations are anthropocentric. Rather than being just in the mind, weak emergence is seen to involve a distinctive kind of complex, macro-pattern in the mind-independent objective micro-causal structure that exists in nature. The paper ends by addressing two further questions. One concerns whether weak emergence applies only or mainly to computer simulations and computational systems. The other concerns the respect in which weak emergence is dynamic rather than static.

Reasoning with Intensional Negative Adjectivals: Semantics, Pragmatics, and Context

Intensional negative adjectives alleged , artificial , fake , false , former , and toy are unusual adjectives that depending on context may or may not be restricting functions. A formal theory of their semantics, pragmatics, and context that uniformly accounts for their complex mathematical and computational characteristics and captures some peculiarities of individual adjectives is presented.
Such adjectives are formalized as new concept builders, negation‐like functions that operate on the values of intensional properties of the concepts denoted by their arguments and yield new concepts whose intensional properties have values consistent with the negation of the old values. Understanding these new concepts involves semantics, pragmatics and context‐dependency of natural language. It is argued that intensional negative adjectives can be viewed as a special‐purpose, weaker, conntext‐dependent negationin natural language. The theory explains and predicts many inferences licensed by expressions involving such adjectives. Implementation of sample examples demonstrates its computational feasibility. Computation of context‐dependent interpretation is discussed.
The theory allows one to enhance a knowledge representation system with similar concept building, negation‐like, context‐dependent functions, the availability of which appears to be a distinct characteristic of natural languages.