驾驶员属性对信号灯路口交通流的影响研究

采用元胞自动机模型研究信号灯路口的交通流特性,系统地分析了与性别、驾驶经验、性格等驾驶员属性相关的驾驶行为对交通流的影响.数值模拟发现,技术生疏或紧张驾驶、急躁行驶等驾驶行为更易引发自由流到阻塞流的相变,是路口通行低效率及事故隐患的重要原因,信号周期是影响流量与个人通行时间公平的主要因素.     

因果代数及其在物理学中的应用

依据定量因果原理,给出了物理学中的一个因果代数的应用,当满足定量因果原理的互逆可消条件且又满足消去律的解时, 得到因果分解代数;由因果分解代数导出了结合律和单位元,进而导出了因果分解代数又具有群的结构特征,同时给出了这新代数系统在高能物理学中的应用.严格地给出了在高能物理中既不是群又不是环的反应,发现因果代数和因果分解代数是严格描述粒子物理反应的基本工具,得到了所有各种相加性、相乘性物理量和各种粒子反应都必须满足的统一恒等式,给出了因果代数和因果分解代数对高能物理的具体应用.利用因果代数的表示和超对称的R数,得到了含有超对称粒子反应中相乘性的超对称的P_R=(-1)^R对称性.还得到了一个关于电子自旋角动量的任意分量间的一个对称关系式,利用这对称关系式,可以化简多电子相互作用的计算.利用互逆可消条件定义了一般的逆元,可重新定义群,使群的公理减少一个,消除了重复定义. 关键词： 对称性 群论 因果原理 粒子物理     

A weighted causal theory for acquiring and utilizing open knowledge

Motivated by enabling intelligent robots/agents to take advantage of open-source knowledge resources to solve open-ended tasks, a weighted causal theory is introduced as the formal basis for the development of these robots/agents. The action model of a robot/agent is specified as a causal theory following McCain and Turner's nonmonotonic causal theories. New knowledge is needed when the robot/agent is given a user task that cannot be accomplished only with the action model. This problem is cast as a variant of abduction, that is, to find the most suitable set of causal rules from open-source knowledge resources, so that a plan for accomplishing the task can be computed using the action model together with the acquired knowledge. The core part of our theory is constructed based on credulous reasoning and the complexity of corresponding abductive reasoning is analyzed. The entire theory is established by adding weights to hypothetical causal rules and using them to compare competing explanations which induce causal models satisfying the task. Moreover, we sketch a model theoretic semantics for the weighted causal theory and present an algorithm for computing a weighted-abductive explanation. An application of the techniques proposed in this paper is illustrated in an example on our service robot, KeJia, in which the robot tries to acquire proper knowledge from OMICS, a large-scale open-source knowledge resource, and solve new tasks with the knowledge.     

On the Bohmian quantum friction

The problem of quantum friction in the framework of Bohmian quantum mechanics is studied. The appropriate equations for such a system is written and solved exactly for some cases. Also two approximate solutions are found which represent the transition of a system from an upper state to the ground state caused by the friction. The physical nature of these solutions are examined.     

一个虚拟事实模型中因果效应的Bayes估计

给出了一个虚拟事实模型中因果效应的Bayes估计和经验Bayes估计,提供了三种可替换性假设的先验分布的选择方法,并用实验说明,在不知道取哪个可替换性假设的情况下,经验Bayes估计要优于其他的估计.     

Meromorphic extension of the spherical functions on a class of ordered symmetric spaces

We discuss a conjecture of Ólafsson and Pasquale published in (J. Funct. Anal. 181 (2001) 346). This conjecture gives the Bernstein-Sato polynomial associated with the Poisson kernel of the ordered (or non-compactly causal) symmetric spaces. The Bernstein-Sato polynomials allow to locate the singularities of the spherical functions on the considered spaces. We prove that this conjecture does not hold in general, and propose a slight improvement of it. Finally, we prove that the new conjecture holds for a class of ordered symmetric spaces, called both the Makarevi? spaces of type I, and the satellite cones.     

Approximation of Surfaces by Fairness Bicubic Splines

In this paper we present an approximation method of surfaces by a new type of splines, which we call fairness bicubic splines, from a given Lagrangian data set. An approximating problem of surface is obtained by minimizing a quadratic functional in a parametric space of bicubic splines. The existence and uniqueness of this problem are shown as long as a convergence result of the method is established. We analyze some numerical and graphical examples in order to prove the validity of our method.     

The End of a Classical Ontology for Quantum Mechanics?

In this paper, I argue that the Shrapnel–Costa no-go theorem undermines the last remaining viability of the view that the fundamental ontology of quantum mechanics is essentially classical: that is, the view that physical reality is underpinned by objectively real, counterfactually definite, uniquely spatiotemporally defined, local, dynamical entities with determinate valued properties, and where typically ‘quantum’ behaviour emerges as a function of our own in-principle ignorance of such entities. Call this view Einstein–Bell realism. One can show that the causally symmetric local hidden variable approach to interpreting quantum theory is the most natural interpretation that follows from Einstein–Bell realism, where causal symmetry plays a significant role in circumventing the nonclassical consequences of the traditional no-go theorems. However, Shrapnel and Costa argue that exotic causal structures, such as causal symmetry, are incapable of explaining quantum behaviour as arising as a result of noncontextual ontological properties of the world. This is particularly worrying for Einstein–Bell realism and classical ontology. In the first instance, the obvious consequence of the theorem is a straightforward rejection of Einstein–Bell realism. However, more than this, I argue that, even where there looks to be a possibility of accounting for contextual ontic variables within a causally symmetric framework, the cost of such an account undermines a key advantage of causal symmetry: that accepting causal symmetry is more economical than rejecting a classical ontology. Either way, it looks like we should give up on classical ontology.     

Normalized Multivariate Time Series Causality Analysis and Causal Graph Reconstruction

Causality analysis is an important problem lying at the heart of science, and is of particular importance in data science and machine learning. An endeavor during the past 16 years viewing causality as a real physical notion so as to formulate it from first principles, however, seems to have gone unnoticed. This study introduces to the community this line of work, with a long-due generalization of the information flow-based bivariate time series causal inference to multivariate series, based on the recent advance in theoretical development. The resulting formula is transparent, and can be implemented as a computationally very efficient algorithm for application. It can be normalized and tested for statistical significance. Different from the previous work along this line where only information flows are estimated, here an algorithm is also implemented to quantify the influence of a unit to itself. While this forms a challenge in some causal inferences, here it comes naturally, and hence the identification of self-loops in a causal graph is fulfilled automatically as the causalities along edges are inferred. To demonstrate the power of the approach, presented here are two applications in extreme situations. The first is a network of multivariate processes buried in heavy noises (with the noise-to-signal ratio exceeding 100), and the second a network with nearly synchronized chaotic oscillators. In both graphs, confounding processes exist. While it seems to be a challenge to reconstruct from given series these causal graphs, an easy application of the algorithm immediately reveals the desideratum. Particularly, the confounding processes have been accurately differentiated. Considering the surge of interest in the community, this study is very timely.     

Multiscale and megavariate monitoring of the process networked structure: M2NET

We present a process monitoring scheme aimed at detecting changes in the networked structure of process data that is able to handle, simultaneously, three pervasive aspects of industrial systems: (i) their multivariate nature, with strong cross‐correlations linking the variables; (ii) the dynamic behavior of processes, as a consequence of the presence of inertial elements coupled with the high sampling rates of industrial acquisition systems; and (iii) the multiscale nature of systems, resulting from the superposition of multiple phenomena spanning different regions of the time‐frequency domain. Contrary to current approaches, the multivariate structure will be described through a local measure of association, the partial correlation, in order to improve the diagnosis features without compromising detection speed. It will also be used to infer the relevant causal structure active at each scale, providing a fine map for the complex behavior of the system. The scale‐dependent causal networks will be incorporated in multiscale monitoring through data‐driven sensitivity enhancing transformations (SETs). The results obtained demonstrate that the use of SET is a major factor in detecting process upsets. In fact, it was observed that even single‐scale monitoring methodologies can achieve comparable detection capabilities as their multiscale counterparts as long as a proper SET is employed. However, the multiscale approach still proved to be useful because it led to good results using a much simpler SET model of the system. Therefore, the application of wavelet transforms is advantageous for systems that are difficult to model, providing a good compromise between modeling complexity and monitoring performance. Copyright © 2015 John Wiley & Sons, Ltd.