Reasoning about non-immediate triggers in biological networks

Modeling molecular interactions in signalling networks is important from various perspectives such as predicting side effects of drugs, explaining unusual cellular behavior and drug and therapy design. Various formal languages have been proposed for representing and reasoning about molecular interactions. The interactions are modeled as triggered events in most of the approaches. The triggering of events is assumed to be immediate: once an interaction is triggered, it should occur immediately. Although working well for engineering systems, this assumption poses a serious problem in modeling biological systems. Our knowledge about biological systems is inherently incomplete, thus molecular interactions are constantly elaborated and refined at different granularity of abstraction. The model of immediate triggers can not consistently deal with this refinement. In this paper we propose an action language to address this problem. We show that the language allows for refinements of biological knowledge, although at a higher cost in terms of complexity.      

Repeated detection of conjunctive predicates in distributed executions

Given a conjunctive predicate ? over a distributed execution, this paper gives an algorithm to detect all interval sets, each interval set containing one interval per process, in which the local values satisfy the Definitely(?) modality. The time complexity of the algorithm is O(n³p), where n is the number of processes and p is the bound on the number of times a local predicate becomes true at any process. The paper also proves that unlike the Possibly(?) modality which admits O(pn) solution interval sets, the Definitely(?) modality admits O(np) solution interval sets. The paper also gives an on-line test to determine whether all solution interval sets can be detected in polynomial time under arbitrary fine-grained causality-based modality specifications.     

基于抽样的因果图仿真推理算法研究

由于因果图的经典推理的计算复杂度是NP难的,因此其不便于推广和使用。基于因果图理论和MonteCarlo思想,提出了基于抽样的A-R Sampling和重要性抽样的因果图仿真推理算法。在故障诊断中的有效应用验证了方法是可行的。     

Statistical Data Analysis in the Era of Big Data

Big data is on everyone's lips and often raises emotions. On the one hand, the notion is a basis for much technological optimism, mostly directed towards new business models, or simplifications and optimizations in professional and private life. On the other hand, it is a basis for dystopic perspectives, which are targeted, e.g., at profiling of the individual and their privacy space, overarching optimization in daily life and intransparency of decision making. In this article, after a short historical prolog, it is discussed what distinguishes big data from traditional data analysis. The underlying mathematical methods are introduced and scientific successes are reported. Additionally, the risks and limits – especially regarding the derivation of causal relationships – of data analysis are discussed.     

Disaggregate energy consumption and industrial production in South Africa

This paper tries to assess the relationship between disaggregate energy consumption and industrial output in South Africa by undertaking a cointegration analysis using annual data from 1980 to 2005. We also investigate the causal relationships between the various disaggregate forms of energy consumption and industrial production. Our results imply that industrial production and employment are long-run forcing variables for electricity consumption. Applying the [Toda, H.Y., Yamamoto, T., 1995. Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics 66, 225–250] technique to Granger-causality, we find bi-directional causality between oil consumption and industrial production. For the other forms of energy consumption, there is evidence in support of the energy neutrality hypothesis. There is also evidence of causality between employment and electricity consumption as well as coal consumption causing employment.     

Positive Dedalus programs tolerate non-causality

Declarative networking is a recent approach to programming distributed applications with languages inspired by Datalog. A recent conjecture posits that the delivery of messages should respect causality if and only if they are used in non-monotone derivations. We present our results about this conjecture in the context of Dedalus, a Datalog-variant for distributed programming. We show that both directions of the conjecture fail under a strong semantical interpretation. But on a more syntactical level, we show that positive Dedalus programs can tolerate non-causal messages, in the sense that they compute the correct answer even when messages can be sent into the past.     

A Study on the Impact of Industrial Production Index (IPI) to Beverage,Food and Tobacco Sector Index with Special Reference to Colombo Stock Exchange

This study attempts to explore the impact of Industrial Production Index to sector performance of Beverage, Food and Tobacco in Colombo Stock Exchange Sri Lanka. Secondary data were used for the analysis. Sector index was taken from the Data Library of Colombo Stock Exchange and Industrial Production Index was taken from Annual Reports of Central Bank Sri Lanka. Monthly data were gathered from January 2002 to December 2014. For the time series data set, first, the stationary was checked using Augmented Dickey-Fuller and Phillips Perron Tests of E-views software. The results of stationary check show that the sector index is stationary at 1^st difference in both ADF and PP tests. Industrial Production Index is stationary at 2^nd difference in ADF test but 1^st difference is stationary in PP test. Because of the seasonal trend in IPI 12^th difference also considered and it is stationary in both ADF and PP tests. Granger Causality test was used to find out the causal relationship between variables. Results show the one way causality that the changes occurring in IPI will have an effect on changes in sector index. But changes in sector index will not have any effect on changes in IPI. Finally a regression was used to find out the relationship between variables. A Pearson Correlation coefficient was checked to find the correlation among variables before moving to the regression. Result of correlation test shows 84% higher correlation between variables and regression result shows a significant positive relationship amongvariables. The study concludes that Industrial Production Index will positively impact on Beverage, Food and Tobacco sector Index in Sri Lanka. It can be recommended that the changes occurring in the IPI be considered by Investors when they buy and sell stocks in BFT sector.     

多值因果图的一种推理算法

该文针对多值因果图存在的两个困难:不严格满足概率论;将其用于实际问题时,推理结果可能出现错误。提出了一种基于因果影响可能性分配的推理算法。该算法对多值因果图进行了补充定义,使多值因果图能够兼容单值因果图;定义了事件变量状态可能性,及其精确计算方法和近似计算方法;给出了将多值因果图转化为单值因果图的方法和步骤,并推导了相应的计算公式;给出了收到证据后,感兴趣变量状态的后验概率计算方法。以核电站二回路系统中蒸汽发生器故障诊断因果图为例,展示了该算法推理计算的全过程。实例表明,该算法能够有效地克服多值因果图存在的困难,其推理过程严谨,计算结果符合实际情况。     

Complexity results for structure-based causality

We give a precise picture of the computational complexity of causal relationships in Pearl's structural models, where we focus on causality between variables, event causality, and probabilistic causality. As for causality between variables, we consider the notions of causal irrelevance, cause, cause in a context, direct cause, and indirect cause. As for event causality, we analyze the complexity of the notions of necessary and possible cause, and of the sophisticated notions of weak and actual cause by Halpern and Pearl. In the course of this, we also prove an open conjecture by Halpern and Pearl, and establish other semantic results. We then analyze the complexity of the probabilistic notions of probabilistic causal irrelevance, likely causes of events, and occurrences of events despite other events. Moreover, we consider decision and optimization problems involving counterfactual formulas. To our knowledge, no complexity aspects of causal relationships in the structural-model approach have been considered so far, and our results shed light on this issue.     

Causality tracking in causal message-logging protocols

Casual message-logging protocols have several attractive properties: they introduce no blocking, send no additional messages over those sent by the application, and never create orphans. Causal message logging, however, does require the casual effects of the deliveries of messages to be tracked. The information concerning causality tracking is piggybacked on application messages, and the amount of such information can become large. In this paper we study the cost of tracking causality in causal message-logging protocols. One can track causality as accurately as possible, but to do so requires piggybacking a considerable amount of additional information. One can reduce the amount of piggybacked information on each message by reducing the accuracy of causality tracking. But then, causal message logging may piggyback the reduced amount of information on more messages. We specify six different methods of tracking causality, each representing a natural choice based on the specification of causal message logging. We describe how these six methods can be implemented and compare them in terms of how large of a piggyback load they impose. This load depends on the application that is using causal message logging. We characterize some applications for which a given method has the smallest piggyback load, and study using simulation the size of the piggyback load for two different models of applications. Received: July 1999 / Accepted: July 2001