Electricity consumption and economic growth in seven South American countries

This paper attempts to investigate the causal relationship between electricity consumption and economic growth among seven South American countries, namely Argentina, Brazil, Chile, Columbia, Ecuador, Peru, and Venezuela using widely accepted time-series techniques for the period 1975–2006. The results indicate that the causal nexus between electricity consumption and economic growth varies across countries. There is a unidirectional, short-run causality from electricity consumption to real GDP for Argentina, Brazil, Chile, Columbia, and Ecuador. This means that an increase in electricity consumption directly affects economic growth in those countries. In Venezuela, there is a bidirectional causality between electricity consumption and economic growth. This implies that an increase in electricity consumption directly affects economic growth and that economic growth also stimulates further electricity consumption in that country. However, no causal relationships exist in Peru. The documented evidence from seven South American countries can provide useful information for each government with regard to energy and growth policy.     

新疆旅游业与经济增长关系浅析

采用现代计量经济学协整分析及Granger因果检验法,对新疆1990-2006年旅游业发展与经济增长的关系进行实证分析。结果发现：新疆国际、国内旅游收入与经济增长虽为非平稳的时间序列,却存在长期的协整关系;经济增长对旅游业的拉动效应在时间上有一个滞后期,短期内新疆旅游业发展与经济增长不存在相互因果关系,而长期内却具有均衡的因果关系;国际旅游收入对经济的推动作用在时间上超前于国内旅游收入。这一结论在一定程度上为制定新疆今后的旅游发展政策提供了一定的科学依据,避免盲目性。     

Granger因果关系检验在攻击检测中的应用研究

在时态数据挖掘框架下,对基于Granger因果关系检验的攻击检测方法进行了研究。通过计算多个前兆输入时间序列与给定异常输出时间序列之间的因果关联程度,可从描述网络系统安全状态的多元时间序列数据集中检测出网络攻击行为的前兆,进而形成可供实际检测和预警使用的高置信度前兆规则和因果规则。对所提方法的正确性和精度进行了验证,并在设计的攻击检测与预警原型系统中对其进行了应用分析。     

Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning

We describe an incremental learning algorithm, called theory-driven learning, that creates rules to predict the effect of actions. Theory-driven learning exploits knowledge of regularities among rules to constrain learning. We demonstrate that this knowledge enables the learning system to rapidly converge on accurate predictive rules and to tolerate more complex training data. An algorithm for incrementally learning these regularities is described and we provide evidence that the resulting regularities are sufficiently generally to facilitate learning in new domains. The results demonstrate that transfer from one domain to another can be achieved by deliberately overgeneralizing rules in one domain and biasing the learning algorithm to create new rules that specialize these overgeneralizations in other domains.     

基于改进收敛交叉映射的厂级振荡源诊断

由于噪声、周期性、非线性和非平稳性的干扰,现有的大多数因果分析方法在工业过程控制系统的应用中往往是不可靠和不准确的。为提高厂级振荡源定位的性能,提出了一种基于改进收敛交叉映射的因果关系检测方法。首先,指出噪声和周期性对因果关系检测的不利影响。其次,将经验模态分解和去趋势波动分析相结合,实现了振荡信号去噪。然后,通过奇异谱分析有效地去除信号的周期性。利用收敛交叉映射可以对去噪去周期后的信号进行分析,进而准确地定位厂级振荡的源头。仿真结果表明,所提方法能提高过程控制系统中厂级振荡源定位的准确性。     

Dark,Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense

Recent progress in deep learning is essentially based on a “big data for small tasks” paradigm, under which massive amounts of data are used to train a classifier for a single narrow task. In this paper, we call for a shift that flips this paradigm upside down. Specifically, we propose a “small data for big tasks” paradigm, wherein a single artificial intelligence (AI) system is challenged to develop “common sense,” enabling it to solve a wide range of tasks with little training data. We illustrate the potential power of this new paradigm by reviewing models of common sense that synthesize recent breakthroughs in both machine and human vision. We identify functionality, physics, intent, causality, and utility (FPICU) as the five core domains of cognitive AI with humanlike common sense. When taken as a unified concept, FPICU is concerned with the questions of “why” and “how,” beyond the dominant “what” and “where” framework for understanding vision. They are invisible in terms of pixels but nevertheless drive the creation, maintenance, and development of visual scenes. We therefore coin them the “dark matter” of vision. Just as our universe cannot be understood by merely studying observable matter, we argue that vision cannot be understood without studying FPICU. We demonstrate the power of this perspective to develop cognitive AI systems with humanlike common sense by showing how to observe and apply FPICU with little training data to solve a wide range of challenging tasks, including tool use, planning, utility inference, and social learning. In summary, we argue that the next generation of AI must embrace “dark” humanlike common sense for solving novel tasks.     

因果图向信度网转化的方法研究

在因果图理论中，采用了图形化和直接因果强度来表达知识和因果关系，它克服了贝叶斯网的一些不足，已经发展成了一个能够处理离散变量和连续变量的混合模型。但已有的因果图的推理算法还不能完全适应实际问题的需要，这大大地限制了因果图推广和使用，然而信度网研究已比较成熟，已有许多现成的算法和实用的推理软件。文中给出了从因果图向信度网转化的一般方法，包括因果图的连接强度向信度网的条件概率表转化和因果图的结构向信度网的结构转化，从而可以利用信度网的这些成果。     

Causality and atomicity in distributed computations

Summary. In a distributed system, high-level actions can be modeled by nonatomic events. This paper proposes causality relations between distributed nonatomic events and provides efficient testing conditions for the relations. The relations provide a fine-grained granularity to specify causality relations between distributed nonatomic events. The set of relations between nonatomic events is complete in first-order predicate logic, using only the causality relation between atomic events. For a pair of distributed nonatomic events X and Y, the evaluation of any of the causality relations requires integer comparisons, where and , respectively, are the number of nodes on which the two nonatomic events X and Y occur. In this paper, we show that this polynomial complexity of evaluation can by simplified to a linear complexity using properties of partial orders. Specifically, we show that most relations can be evaluated in integer comparisons, some in integer comparisons, and the others in integer comparisons. During the derivation of the efficient testing conditions, we also define special system execution prefixes associated with distributed nonatomic events and examine their knowledge-theoretic significance. Received: July 1997 / Accepted: May 1998     

State-space approach to discrete-time 2-optimal control with a causality constraint

Controller design with a causality constraint arises in periodic or multirate control systems. In this paper complete state-space solutions to the optimal and suboptimal ₂ control problems are developed with a causality constraint on controller feedthrough terms. Explicit formulas for the controllers are given in terms of solutions of two Riccati equations. The results are more implementable than existing frequency-domain solutions.     

An improved constraint-inference approach for causality exploration of power system transient stability

Transient stability is the key aspect of power system dynamic security assessment, and data-driven methods are becoming alternative measures of assessment. The current data-driven methods only construct correlations between variables while neglecting causal relationships. Therefore, they face problems such as poor robustness, which restrict their practical application. This paper introduces an improved constraint-inference approach for causality exploration of power system transient stability. Firstly, a causal structure discovery method of power system transient stability is proposed based on a PC-IGCI algorithm, which addresses the shortage caused by Markov equivalence and massive variables. Then, a relative average causal efect index is proposed to reveal the relationship between relative intervention strength and causal efects. The results of a case study verify that the proposed method can identify the causal structure between the transient stability variables entirely based on data. In addition, the causal efect sorting between“cause” and“outcome” of transient stability variables is revealed. This paper provides a new approach for data mining to uncover the causal mechanisms between variables in power systems and expand the capabilities of data-driven methods in power system application.