881 research outputs found
RDF Knowledge Graph Visualization From a Knowledge Extraction System
In this paper, we present a system to visualize RDF knowledge graphs. These
graphs are obtained from a knowledge extraction system designed by
GEOLSemantics. This extraction is performed using natural language processing
and trigger detection. The user can visualize subgraphs by selecting some
ontology features like concepts or individuals. The system is also
multilingual, with the use of the annotated ontology in English, French, Arabic
and Chinese
Slow Radiation-Driven Wind Solutions of A-Type Supergiants
The theory of radiation-driven winds succeeded in describing terminal
velocities and mass loss rates of massive stars. However, for A-type
supergiants the standard m-CAK solution predicts values of mass loss and
terminal velocity higher than the observed values. Based on the existence of a
slow wind solution in fast rotating massive stars, we explore numerically the
parameter space of radiation-driven flows to search for new wind solutions in
slowly rotating stars, that could explain the origin of these discrepancies. We
solve the 1-D hydrodynamical equation of rotating radiation-driven winds at
different stellar latitudes and explore the influence of ionization's changes
throughout the wind in the velocity profile. We have found that for particular
sets of stellar and line-force parameters, a new slow solution exists over the
entire star when the rotational speed is slow or even zero. In the case of slow
rotating A-type supergiant stars the presence of this novel slow solution at
all latitudes leads to mass losses and wind terminal velocities which are in
agreement with the observed values. The theoretical Wind Momentum-Luminosity
Relationship derived with these slow solutions shows very good agreement with
the empirical relationship. In addition, the ratio between the terminal and
escape velocities, which provides a simple way to predict stellar wind energy
and momentum input into the interstellar medium, is also properly traced.Comment: 7 Pages, 3 figures, Astrophysical Journal, Accepte
Improving prediction performance of stellar parameters using functional models
This paper investigates the problem of prediction of stellar parameters,
based on the star's electromagnetic spectrum. The knowledge of these parameters
permits to infer on the evolutionary state of the star. From a statistical
point of view, the spectra of different stars can be represented as functional
data. Therefore, a two-step procedure decomposing the spectra in a functional
basis combined with a regression method of prediction is proposed. We also use
a bootstrap methodology to build prediction intervals for the stellar
parameters. A practical application is also provided to illustrate the
numerical performance of our approach
Limb-Darkened Radiation-Driven Winds from Massive Stars
We calculated the influence of the limb-darkened finite disk correction
factor in the theory of radiation-driven winds from massive stars. We solved
the 1-D m-CAK hydrodynamical equation of rotating radiation-driven winds for
all three known solutions, i.e., fast, \Omega-slow and \delta-slow. We found
that for the fast solution, the mass loss rate is increased by a factor \sim
10%, while the terminal velocity is reduced about 10%, when compared with the
solution using a finite disk correction factor from a uniformly bright star.
For the other two slow solutions the changes are almost negligible. Although,
we found that the limb darkening has no effects on the wind momentum luminosity
relationship, it would affect the calculation of synthetic line profiles and
the derivation of accurate wind parameters.Comment: Accepted for publication in ApJ. 19 pages, 6 figure
LiteMat: a scalable, cost-efficient inference encoding scheme for large RDF graphs
The number of linked data sources and the size of the linked open data graph
keep growing every day. As a consequence, semantic RDF services are more and
more confronted with various "big data" problems. Query processing in the
presence of inferences is one them. For instance, to complete the answer set of
SPARQL queries, RDF database systems evaluate semantic RDFS relationships
(subPropertyOf, subClassOf) through time-consuming query rewriting algorithms
or space-consuming data materialization solutions. To reduce the memory
footprint and ease the exchange of large datasets, these systems generally
apply a dictionary approach for compressing triple data sizes by replacing
resource identifiers (IRIs), blank nodes and literals with integer values. In
this article, we present a structured resource identification scheme using a
clever encoding of concepts and property hierarchies for efficiently evaluating
the main common RDFS entailment rules while minimizing triple materialization
and query rewriting. We will show how this encoding can be computed by a
scalable parallel algorithm and directly be implemented over the Apache Spark
framework. The efficiency of our encoding scheme is emphasized by an evaluation
conducted over both synthetic and real world datasets.Comment: 8 pages, 1 figur
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