Causality information and proactive cancellation mechanisms

2009 ◽  
Vol 21 (11) ◽  
pp. 1483-1503 ◽  
Author(s):  
Malolan Chetlur ◽  
Philip A. Wilsey
Keyword(s):  
Causality Information

Activation and Persistence of Implicit Causality Information in Spoken Language Comprehension

2010 ◽  
Vol 57 (1) ◽  
pp. 5-16 ◽  
Author(s):  
Pirita Pyykkönen ◽  
Juhani Järvikivi
Keyword(s):  
Eye Tracking ◽  
Language Comprehension ◽  
Spoken Language ◽  
Visual World ◽  
Implicit Causality ◽  
Causality Information ◽  
Spoken Language Comprehension

A visual world eye-tracking study investigated the activation and persistence of implicit causality information in spoken language comprehension. We showed that people infer the implicit causality of verbs as soon as they encounter such verbs in discourse, as is predicted by proponents of the immediate focusing account ( Greene & McKoon, 1995 ; Koornneef & Van Berkum, 2006 ; Van Berkum, Koornneef, Otten, & Nieuwland, 2007 ). Interestingly, we observed activation of implicit causality information even before people encountered the causal conjunction. However, while implicit causality information was persistent as the discourse unfolded, it did not have a privileged role as a focusing cue immediately at the ambiguous pronoun when people were resolving its antecedent. Instead, our study indicated that implicit causality does not affect all referents to the same extent, rather it interacts with other cues in the discourse, especially when one of the referents is already prominently in focus.

scholarly journals Communication Analysis of Distributed Programs

2006 ◽  
Vol 14 (2) ◽  
pp. 151-170 ◽  
Author(s):  
Sharon Simmons ◽  
Dennis Edwards ◽  
Phil Kearns
Keyword(s):  
Distributed Systems ◽  
Partial Order ◽  
Distributed System ◽  
Source Code ◽  
Communication Analysis ◽  
Distributed Programs ◽  
Wide Range ◽  
Concurrent Relationships ◽  
Causality Information ◽  
Time Evaluation

Capturing and examining the causal and concurrent relationships of a distributed system is essential to a wide range of distributed systems applications. Many approaches to gathering this information rely on trace files of executions. The information obtained through tracing is limited to those executions observed. We present a methodology that analyzes the source code of the distributed system. Our analysis considers each process's source code and produces a single comprehensive graph of the system's possible behaviors. The graph, termed the partial order graph (POG), uniquely represents each possible partial order of the system. Causal and concurrent relationships can be extracted relative either to a particular partial order, which is synonymous to a single execution, or to a collection of partial orders. The graph provides a means of reasoning about the system in terms of relationships that will definitely occur, may possible occur, and will never occur. Distributed assert statements provide a means to monitor distributed system executions. By constructing thePOGprior to system execution, the causality information provided by thePOGenables run-time evaluation of the assert statement without relying on traces or addition messages.

scholarly journals Polymorphic Subtyping for Side Effects

1997 ◽  
Vol 26 (529) ◽  
Author(s):  
Torben Amtoft ◽  
Flemming Nielson ◽  
Hanne Riis Nielson
Keyword(s):  
Side Effects ◽  
Type System ◽  
Common Type ◽  
Prototype System ◽  
Inference Algorithm ◽  
Post Processing ◽  
Core Subset ◽  
Causality Information

<p>The integration of polymorphism (in the style of the ML let-construct), subtyping, and effects (modelling assignment or communication) into one common type system has proved remarkably difficult. This paper presents a type system for (a core subset of) Concurrent~ML that extends the ML type system in a conservative way and that employs all these features; and in addition causality information has been incorporated into the effects (which may therefore be termed "behaviours").</p><p>The semantic soundness of the system is established via a subject reduction result. An inference algorithm is presented; it is proved sound and (in a certain sense) also complete. A prototype system based on this algorithm has been implemented and can be experienced on the <a href="http://www.daimi.aau.dk/~bra8130/TBAcml/TBA_CML.html">WWW</a>; thanks to a special post-processing phase it produces quite readable and informative output.</p>

Causality in constraint propagation

1997 ◽  
Vol 11 (5) ◽  
pp. 419-433 ◽  
Author(s):  
Walid E. Habib ◽  
Allen C. Ward
Keyword(s):  
Physical System ◽  
Predicate Logic ◽  
Constraint Propagation ◽  
Design Rules ◽  
System Parameters ◽  
Causality Information ◽  
Propagation Rules ◽  
Account Variation

AbstractThis paper defines, for use in design, rules for propagating “distribution constraints” through relationships such as algebraic or vector equations. Distribution constraints are predicate logic statements about the values that physical system parameters may assume. The propagation rules take into account “variation source causality”: information about when and how the values are assigned during the design, manufacturing, and operation of the system.

scholarly journals Annotation and curation of the causality information in LncRNADisease

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Kaiwen Jia ◽  
Yuanxu Gao ◽  
Jiangcheng Shi ◽  
Yuan Zhou ◽  
Yong Zhou ◽  
...  
Keyword(s):  
Circular Rnas ◽  
Non Coding Rna ◽  
Disease Associations ◽  
Non Coding Rnas ◽  
Long Non Coding Rna ◽  
Causality Information

Abstract Disease causative non-coding RNAs (ncRNAs) are of great importance in understanding a disease, for they directly contribute to the development or progress of a disease. Identifying the causative ncRNAs can provide vital implications for biomedical researches. In this work, we updated the long non-coding RNA disease database (LncRNADisease) with long non-coding RNA (lncRNA) causality information with manual annotations of the causal associations between lncRNAs/circular RNAs (circRNAs) and diseases by reviewing related publications. Of the total 11 568 experimental associations, 2297 out of 10 564 lncRNA-disease associations and 198 out of 1004 circRNA-disease associations were identified to be causal, whereas 635 lncRNAs and 126 circRNAs were identified to be causative for the development or progress of at least one disease. The updated information and functions of the database can offer great help to future researches involving lncRNA/circRNA-disease relationship. The latest LncRNADisease database is available at http://www.rnanut.net/lncrnadisease.

The Time Course of the Influence of Implicit Causality Information: Focusing versus Integration Accounts

2000 ◽  
Vol 42 (3) ◽  
pp. 423-443 ◽  
Author(s):  
Andrew J Stewart ◽  
Martin J Pickering ◽  
Anthony J Sanford
Keyword(s):  
Time Course ◽  
Implicit Causality ◽  
Causality Information

scholarly journals Information and disturbance in operational probabilistic theories

Quantum ◽  
2020 ◽  
Vol 4 ◽  
pp. 363
Author(s):  
Giacomo Mauro D'Ariano ◽  
Paolo Perinotti ◽  
Alessandro Tosini
Keyword(s):  
Structure Theorem ◽  
General Theorem ◽  
Coarse Graining ◽  
Equivalent Condition ◽  
System State ◽  
System Composition ◽  
Definition Of ◽  
Causality Information ◽  
Probabilistic Theories ◽  
Extract Information

Any measurement is intended to provide information on a system, namely knowledge about its state. However, we learn from quantum theory that it is generally impossible to extract information without disturbing the state of the system or its correlations with other systems. In this paper we address the issue of the interplay between information and disturbance for a general operational probabilistic theory. The traditional notion of disturbance considers the fate of the system state after the measurement. However, the fact that the system state is left untouched ensures that also correlations are preserved only in the presence of local discriminability. Here we provide the definition of disturbance that is appropriate for a general theory. Moreover, since in a theory without causality information can be gathered also on the effect, we generalise the notion of no-information test. We then prove an equivalent condition for no-information without disturbance---atomicity of the identity---namely the impossibility of achieving the trivial evolution---the identity---as the coarse-graining of a set of non trivial ones. We prove a general theorem showing that information that can be retrieved without disturbance corresponds to perfectly repeatable and discriminating tests. Based on this, we prove a structure theorem for operational probabilistic theories, showing that the set of states of any system decomposes as a direct sum of perfectly discriminable sets, and such decomposition is preserved under system composition. As a consequence, a theory is such that any information can be extracted without disturbance only if all its systems are classical. Finally, we show via concrete examples that no-information without disturbance is independent of both local discriminability and purification.

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