13,931 research outputs found
Temporalized logics and automata for time granularity
Suitable extensions of the monadic second-order theory of k successors have
been proposed in the literature to capture the notion of time granularity. In
this paper, we provide the monadic second-order theories of downward unbounded
layered structures, which are infinitely refinable structures consisting of a
coarsest domain and an infinite number of finer and finer domains, and of
upward unbounded layered structures, which consist of a finest domain and an
infinite number of coarser and coarser domains, with expressively complete and
elementarily decidable temporal logic counterparts.
We obtain such a result in two steps. First, we define a new class of
combined automata, called temporalized automata, which can be proved to be the
automata-theoretic counterpart of temporalized logics, and show that relevant
properties, such as closure under Boolean operations, decidability, and
expressive equivalence with respect to temporal logics, transfer from component
automata to temporalized ones. Then, we exploit the correspondence between
temporalized logics and automata to reduce the task of finding the temporal
logic counterparts of the given theories of time granularity to the easier one
of finding temporalized automata counterparts of them.Comment: Journal: Theory and Practice of Logic Programming Journal Acronym:
TPLP Category: Paper for Special Issue (Verification and Computational Logic)
Submitted: 18 March 2002, revised: 14 Januari 2003, accepted: 5 September
200
Structural Material Property Tailoring Using Deep Neural Networks
Advances in robotics, artificial intelligence, and machine learning are
ushering in a new age of automation, as machines match or outperform human
performance. Machine intelligence can enable businesses to improve performance
by reducing errors, improving sensitivity, quality and speed, and in some cases
achieving outcomes that go beyond current resource capabilities. Relevant
applications include new product architecture design, rapid material
characterization, and life-cycle management tied with a digital strategy that
will enable efficient development of products from cradle to grave. In
addition, there are also challenges to overcome that must be addressed through
a major, sustained research effort that is based solidly on both inferential
and computational principles applied to design tailoring of functionally
optimized structures. Current applications of structural materials in the
aerospace industry demand the highest quality control of material
microstructure, especially for advanced rotational turbomachinery in aircraft
engines in order to have the best tailored material property. In this paper,
deep convolutional neural networks were developed to accurately predict
processing-structure-property relations from materials microstructures images,
surpassing current best practices and modeling efforts. The models
automatically learn critical features, without the need for manual
specification and/or subjective and expensive image analysis. Further, in
combination with generative deep learning models, a framework is proposed to
enable rapid material design space exploration and property identification and
optimization. The implementation must take account of real-time decision cycles
and the trade-offs between speed and accuracy
BlogForever D2.6: Data Extraction Methodology
This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform
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