Lead-lag statistical analysis of simultaneous high frequency oceanographic parameters in moored coastal systems

<div>High-frequency sampling at fixed positions in oceanography are installed all over the World. These provide time series of different oceanographic parameters over large range of scales and can help obtain informations on the complex coupling existing between physical, biogeochemical and biological parameters.</div><div>Here we explore the lead-lag information existing between two quantities: this is done by extracting the dissymmetry in the cross-correlation, corresponding to the statistical lead or lag of one series with respect to the other one (it is not necessarily a causality information). This analysis is done for all available parameters, two by two, giving way to generate a network of lead-lag influences.</div><div>As example this new approach is applied to the MAREL buoy system installed in Boulogne-sur-mer (France) operated by Ifremer (https://www.seanoe.org/data/00286/39754/). It is a moored buoy equipped with physico-chemical and biological measuring devices working in continuous and autonomous conditions with measurement every 20 minutes. We consider here the measurements at high frequency of air temperature, sea temperature, salinity, dissolved oxygen, fluorescence and turbidity for all year from 2005 to 2013. The new method is applied to the whole data set and also to data every year, in order to see a time evolution of the lead-lag network of relations between all studies parameters.</div><div> </div><div> <div> <div> <div> </div> </div> </div> </div>

Information and disturbance in operational probabilistic theories

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.

Annotation and curation of the causality information in LncRNADisease

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.

Activation and Persistence of Implicit Causality Information in 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.

Communication Analysis of Distributed Programs

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.