一种基于高阶累积量的因果结构学习算法

从观测数据中学习因果结构具有重要的应用价值。目前,一类学习因果结构的方法是基于函数因果模型假设,通过检验噪声与原因变量的独立性来学习因果结构。然而,该类方法涉及高计算复杂度的独立性检验过程,影响结构学习算法的实用性和鲁棒性。为此,提出了一种在线性非高斯模型下,利用高阶累积量作为独立性评估的因果结构学习算法。该算法主要分为两个步骤,第一个步骤是利用基于条件独立性约束的方法学习到因果结构的马尔可夫等价类,第二个步骤是定义了一种基于高阶累积量的得分,该得分可以判别两个随机变量的独立性,从而可以从马尔可夫等价类中搜索到最佳独立性得分的因果结构作为算法的输出。该算法的优势在于：a)相比基于核方法的独立性检验,该方法有较低的计算复杂度;b)基于得分搜索的方法,可以得到一个最匹配数据生成过程的模型,提高学习方法的鲁棒性。实验结果表明,基于高阶累积量的因果结构学习方法在合成数据中F₁得分提高了5%,并在真实数据中学习到更多的因果方向。     

基于多组典型相关变量的因果关系发现算法

现有的因果关系发现算法主要基于单个观察变量本身之间的因果关系,无法适用于多组观察变量,为此提出了一种多组典型相关变量的因果关系发现算法。首先,引入多组典型相关变量建立多组典型相关变量的线性非高斯无环模型并提出对应的目标函数;然后,采用梯度上升的方法求解目标函数,构建多组典型相关变量的因果关系网络。模拟实验验证了该算法的有效性,并在移动基站数据上发现了一批有价值的多组无线网络性能指标间的因果关系。     

串行程序的任务DAG图构造算法

任务DAG图是刻画程序中各任务间依赖关系的一种手段,DAG图上除了标有任务间的依赖关系,还记录了各任务的计算量和任务之间的通信量,这些信息共同构成了任务调度的依据,国内外有许多基于任务DAG图的调度算法研究,但通过分析串行程序的相关性来构造任务DAG图的研究却不多见.分析了串行程序中存在的数据相关性和控制相关性,就程序中的顺序,分支,循环三种基本结构进行分别讨论,提出了一种串行程序任务DAG图的构造算法.     

一种任意分布下的隐变量因果结构学习算法

因果发现旨在通过观测数据挖掘变量间的因果关系,在实际应用中需要从观测数据中学习隐变量间的因果结构。现有方法主要利用观测变量间的协方差信息（如四分体约束）或引入非高斯假设（如三分体约束）来解决线性因果模型下的隐变量结构学习问题,但大多限定于分布明确的情况,而实际应用环境往往并不满足这种假设。给出任意分布下隐变量结构的识别性证明,指出在没有混淆因子影响的情况下,两个隐变量的因果方向可识别所需要的最小条件是仅需要其中一个隐变量的噪声服从非高斯分布。在此基础上,针对线性隐变量模型提出一种在任意分布下学习隐变量因果结构的算法,先利用四分体约束方法学习得到隐变量骨架图,再通过枚举骨架图的等价类并测量每一个等价类中的三分体约束来学习因果方向,同时将非高斯约束放宽到尽可能最小的变量子集,从而扩展线性隐变量模型的应用范围。实验结果表明,与MIMBuild和三分体约束方法相比,该算法得到了最佳的F1值,能够在任意分布下学习更多的隐变量因果结构信息,且具有更强的鲁棒性。     

异步DVE系统中生命周期约束下的因果一致性控制方法研究

分布式虚拟环境是模拟现实世界的虚拟空间,对因果一致性控制具有实时性要求,必须在事件生命周期结束前得到维护.然而,在大规模网络条件下,网络传输高延迟和动态性会导致部分事件不能及时到达,使已传到事件间因果关系无法在生命周期限制内有效传递.在现有方法中,部分方法基于所有事件一定能及时传到的假设,没有考虑生命周期对因果关系的制约;而另一部分方法虽然考虑了生命周期的约束,但其因果关系传递要求仿真时钟精确同步,且因果控制效率随系统规模的扩大而快速降低,限制了虚拟环境的普适性和实时性.提出了生命周期约束下的因果一致性控制方法LCO,突破了异步时钟间的时间值比较、多路径因果控制信息选择的终止条件、网络状况敏感的因果控制信息动态调节等关键技术,能够在事件无法及时传到时,仍可以根据已传到的事件计算出因果传递关系.实验证明,LCO既能维护生命周期内的因果一致性,又使因果控制信息量与系统规模无关,降低网络传输和计算开销.     

图像多尺度统计模型综述

基于小波变换的图像处理方法是目前的主流方法,而对图像特征的多尺度统计建模则是图像压缩、去噪、分割、纹理分析与合成等统计应用的关键问题。本文综述了图像的多尺度统计模型,包括边缘分布模型以及层内、层间和混合相关模型,分析了各模型的优缺点,给出了对各种相关模型捕捉系数间相关性能力的归一化量测。最后,简单介绍了基于多尺度几何分析的统计图像模型,并对多尺度统计建模的前景进行了展望。     

基于因果语义定向的贝叶斯网络结构学习

基于变量之间基本依赖关系、基本结构、d-separation标准、依赖分析思想和混合定向策略,给出了一种有效实用的贝叶斯网络结构学习方法,不需要结点有序,并能避免打分-搜索方法存在的指数复杂性,以及现有依赖分析方法的大量高维条件概率计算等问题。     

基于模型的软件安全预测与分析

为了有效表示和分析软件中存在的安全缺陷和隐患,基于模型的软件安全分析技术采用多层次建模技术实现安全特征的描述,在评价软件及软件组件间安全性的过程中提出软件安全预测技术.通过分析软件组成成分之间的关联度获得相关的安全距离,在此基础之上生成安全依赖图,最后根据安全依赖图进行安全预测和分析.基于模型的安全分析技术能够针对可能存在的安全隐患给出预测和分析,为软件的测试和维护提供依据和手段.     

An Extended Relational Data Model For Probabilistic Reasoning

Probabilistic methods provide a formalism for reasoning aboutpartial beliefs under conditions of uncertainty. This paper suggests a newrepresentation of probabilistic knowledge. This representation encompassesthe traditional relational database model. In particular, it is shown thatprobabilistic conditional independence is equivalent to the notion of generalized multivalued dependency. More importantly,a Markov network can be viewed as a generalized acyclic joindependency. This linkage between these two apparently different butclosely related knowledge representations provides a foundation fordeveloping a unified model for probabilistic reasoning and relationaldatabase systems.     

Constructing structural VAR models with conditional independence graphs

In this paper graphical modelling is used to select a sparse structure for a multivariate time series model of New Zealand interest rates. In particular, we consider a recursive structural vector autoregressions that can subsequently be described parsimoniously by a directed acyclic graph, which could be given a causal interpretation. A comparison between competing models is then made by considering likelihood and economic theory.     

Fodorian semantics,pathologies, and “Block's Problem”

In two recent books, Jerry Fodor has developed a set of sufficient conditions for an object X to non-naturally and non-derivatively mean X. In an earlier paper we presented three reasons for thinking Fodor's theory to be inadequate. One of these problems we have dubbed the Pathologies Problem. In response to queries concerning the relationship between the Pathologies Problem and what Fodor calls Block's Problem, we argue that, while Block's Problem does not threatenFodor's view, the Pathologies Problem does.We would like to thank Ray Elugardo, Pat Manfredi, and Donna Summerfield for helpful comments on an earlier paper on Fodorian Semantics, X means X: Semantics Fodor-Style. We would especially like to thank Ned Block for extended e-mail conversations about Block's Problem. Block agrees that his problem is not the same as our pathologies problem. Contrary to what we say here, he still maintains that his objection can ultimately be made to work to defeat Fodor's theory of meaning. His elaboration of Block's Problem is different than the one we present here. Versions of a related paper were presented at the 1991 Annual Meeting of the Southern Society for Philosophy and Psychology as well as the Canadian Society for History and Philosophy of Science.     

“X” means X: Semantics Fodor-style

InPsychosemantics Jerry Fodor offered a list of sufficient conditions for a symbol X to mean something X. The conditions are designed to reduce meaning to purely non-intentional natural relations. They are also designed to solve what Fodor has dubbed the disjunction problem. More recently, inA Theory of Content and Other Essays, Fodor has modified his list of sufficient conditions for naturalized meaning in light of objections to his earlier list. We look at his new set of conditions and give his motivation for them-tracing them to problems in the literature. Then we argue that Fodor's conditions still do not work. They are open to objections of two different varieties: they are too strong and too weak. We develop these objections and indicate why Fodor's new, improved list of conditions still do not work to naturalize meaning.We wish to thank Ray Elugardo, Pat Manfredi, and Donna Summerfield for comments on a draft of this paper, as well as other members of the CMU Mind Group (David Drebushenko, Gary Fuller, and Naomi Reshotko) for helpful discussion and support. A version of this paper was presented at the Southern Society for Philosophy and Psychology in Atlanta, March, 1991, and at the Canadian Society for History and Philosophy of Science in Kingston, Ontario, in May, 1991.     

基于有限状态机的产品设计依赖动态建模及更新方法

针对产品设计依赖模型仅表达静态依赖关系,模型结构难以实现自动更新这一问题,首先提出基于有限状态机的设计依赖建模方法,通过对依赖关系适用性和依赖模式进行显式表征,可实现依赖关系的动态表征,支持多层级依赖关系建模;通过对设计参数及依赖关系的特性进行类别划分,定义相应的拓扑层次,实现基于拓扑层次的依赖模型参数更新;并在固有依赖关系基础上定义依赖模式,实现基于依赖模式的依赖模型结构更新.最后以二连杆机器人为实例进行实验的结果表明,该方法能有效地支持多层次动态依赖关系建模,所构建的建模平台成熟,便于工程应用.     

基于DPCNN模型与语句特征融合的汉语因果类复句关系自动识别

汉语复句关系识别是对复句语义关系的识别,复句关系类别的自动识别对促进语言学和中文信息处理的研究有重要的价值。因果类复句是使用频率最高的复句,文中以二句式有标广义因果复句为研究对象, 使用语言技术平台LTP 进行依存句法分析, 获得词性、依存父节点的词序、与父节点的依存关系等特征,将特征的不同组合与预训练的词向量拼接,得到新的向量,将新的向量输入到 DPCNN 模型中来进行关系类别识别。通过实验对提出的方法进行检验,实验结果显示: 与未融合任何特征相比,DPCNN模型中融合语句特征使实验结果的指标均有提升,表明融合语句特征能取得更好的识别效果。在各种特征组合中,融合POS特征组合得到的准确度和F₁值最高, 分别为98.41%, 98.28%。     

一种DVE中通用的因果消息序时间管理分布式中间件

对于遵循高层体系结构(HLA)标准构建的分布式虚拟环境(DVE),时间管理服务的性能将直接影响分布式仿真的效率和正确性。目前的时间管理服务实现了两种消息排序机制:接收序和时戳序。接收序对消息按其接收顺序进行处理,实时性高,但无法正确处理接收顺序与产生顺序相违背的消息;时戳序通常指保守时戳序机制,能对消息按其产生的时间顺序进行处理,但时间开销较大,难以到达DVE的实时性要求。因此,本文以分布式中间件的方式为DVE设计实现了一种因果消息序时间管理服务,提供符合HLA标准的程序接口,使分布式仿真应用在不改变原有调用接口的情况下,能够通过中间件对消息实现因果序时间管理。实验表明,使用该中间件可以有效维护消息的处理顺序,同时时间开销小,能够较好地满足DVE的实时性要求。     

Multistrategy Learning and Theory Revision

This article presents the system WHY, which learns and updates a diagnostic knowledge base using domain knowledge and a set of examples. The a priori knowledge consists of a causal model of the domain that states the relationships among basic phenomena, and a body of phenomenological theory that describes the links between abstract concepts and their possible manifestations in the world. The phenomenological knowledge is used deductively, the causal model is used abductively, and the examples are used inductively. The problems of imperfection and intractability of the theory are handled by allowing the system to make assumptions during its reasoning. In this way, robust knowledge can be learned with limited complexity and a small number of examples. The system works in a first-order logic environment and has been applied in a real domain.     

A syntactically informed reordering model for statistical machine translation

Word reordering is one of the challengeable problems of machine translation. It is an important factor of quality and efficiency of machine translation systems. In this paper, we introduce a novel reordering model based on an innovative structure, named, phrasal dependency tree. The phrasal dependency tree is a modern syntactic structure which is based on dependency relationships between contiguous non-syntactic phrases. The proposed model integrates syntactical and statistical information in the context of log-linear model aimed at dealing with the reordering problems. It benefits from phrase dependencies, translation directions (orientations) and translation discontinuity between translated phrases. In comparison with well-known and popular reordering models such as distortion, lexicalised and hierarchical models, the experimental study demonstrates the superiority of our model in terms of translation quality. Performance is evaluated for Persian → English and English → German translation tasks using Tehran parallel corpus and WMT07 benchmarks, respectively. The results report 1.54/1.7 and 1.98/3.01 point improvements over the baseline in terms of BLEU/TER metrics on Persian → English and German → English translation tasks, respectively. On average our model retrieved a significant impact on precision with comparable recall value with respect to the lexicalised and distortion models.