2,093,800 research outputs found
Extensible Automated Constraint Modelling
In constraint solving, a critical bottleneck is the formulationof an effective constraint model of a given problem. The CONJURE system described in this paper, a substantial step forward over prototype versions of CONJURE previously reported, makes a valuable contribution to the automation of constraint modelling by automatically producing constraint models from their specifications in the abstract constraint specification language ESSENCE. A set of rules is used to refine an abstract specification into a concrete constraint model. We demonstrate that this set of rules is readily extensible to increase the space of possible constraint models CONJURE can produce. Our empirical results confirm that CONJURE can reproduce successfully the kernels of the constraint models of 32 benchmark problems found in the literature
Induction of Word and Phrase Alignments for Automatic Document Summarization
Current research in automatic single document summarization is dominated by
two effective, yet naive approaches: summarization by sentence extraction, and
headline generation via bag-of-words models. While successful in some tasks,
neither of these models is able to adequately capture the large set of
linguistic devices utilized by humans when they produce summaries. One possible
explanation for the widespread use of these models is that good techniques have
been developed to extract appropriate training data for them from existing
document/abstract and document/headline corpora. We believe that future
progress in automatic summarization will be driven both by the development of
more sophisticated, linguistically informed models, as well as a more effective
leveraging of document/abstract corpora. In order to open the doors to
simultaneously achieving both of these goals, we have developed techniques for
automatically producing word-to-word and phrase-to-phrase alignments between
documents and their human-written abstracts. These alignments make explicit the
correspondences that exist in such document/abstract pairs, and create a
potentially rich data source from which complex summarization algorithms may
learn. This paper describes experiments we have carried out to analyze the
ability of humans to perform such alignments, and based on these analyses, we
describe experiments for creating them automatically. Our model for the
alignment task is based on an extension of the standard hidden Markov model,
and learns to create alignments in a completely unsupervised fashion. We
describe our model in detail and present experimental results that show that
our model is able to learn to reliably identify word- and phrase-level
alignments in a corpus of pairs
Effective temperature determinations of late-type stars based on 3D non-LTE Balmer line formation
Hydrogen Balmer lines are commonly used as spectroscopic effective
temperature diagnostics of late-type stars. However, the absolute accuracy of
classical methods that are based on one-dimensional (1D) hydrostatic model
atmospheres and local thermodynamic equilibrium (LTE) is still unclear. To
investigate this, we carry out 3D non-LTE calculations for the Balmer lines,
performed, for the first time, over an extensive grid of 3D hydrodynamic
STAGGER model atmospheres. For H, H, and H, we find
significant 1D non-LTE versus 3D non-LTE differences (3D effects): the outer
wings tend to be stronger in 3D models, particularly for H, while the
inner wings can be weaker in 3D models, particularly for H. For
H, we also find significant 3D LTE versus 3D non-LTE differences
(non-LTE effects): in warmer stars (K) the inner
wings tend to be weaker in non-LTE models, while at lower effective
temperatures (K) the inner wings can be stronger in
non-LTE models; the non-LTE effects are more severe at lower metallicities. We
test our 3D non-LTE models against observations of well-studied benchmark
stars. For the Sun, we infer concordant effective temperatures from H,
H, and H; however the value is too low by around 50K which could
signal residual modelling shortcomings. For other benchmark stars, our 3D
non-LTE models generally reproduce the effective temperatures to within
uncertainties. For H, the absolute 3D effects and non-LTE
effects can separately reach around 100K, in terms of inferred effective
temperatures. For metal-poor turn-off stars, 1D LTE models of H can
underestimate effective temperatures by around 150K. Our 3D non-LTE model
spectra are publicly available, and can be used for more reliable spectroscopic
effective temperature determinations.Comment: 19 pages, 10 figures, abstract abridged; accepted for publication in
Astronomy & Astrophysic
Cohomological analysis of bosonic D-strings and 2d sigma models coupled to abelian gauge fields
We analyse completely the BRST cohomology on local functionals for two
dimensional sigma models coupled to abelian world sheet gauge fields, including
effective bosonic D-string models described by Born-Infeld actions. In
particular we prove that the rigid symmetries of such models are exhausted by
the solutions to generalized Killing vector equations which we have presented
recently, and provide all the consistent first order deformations and candidate
gauge anomalies of the models under study. For appropriate target space
geometries we find nontrivial deformations both of the abelian gauge
transformations and of the world sheet diffeomorphisms, and antifield dependent
candidate anomalies for both types of symmetries separately, as well as mixed
ones.Comment: 41 pages, latex, no figures; change of title and abstract, some
comments added; to appear in Nucl. Phys.
Dynamic Model-based Management of Service-Oriented Infrastructure.
Models are an effective tool for systems and software design. They allow software architects to abstract from the non-relevant details. Those qualities are also useful for the technical management of networks, systems and software, such as those that compose service oriented architectures. Models can provide a set of well-defined abstractions over the distributed heterogeneous service infrastructure that enable its automated management. We propose to use the managed system as a source of dynamically generated runtime models, and decompose management processes into a composition of model transformations. We have created an autonomic service deployment and configuration architecture that obtains, analyzes, and transforms system models to apply the required actions, while being oblivious to the low-level details. An instrumentation layer automatically builds these models and interprets the planned management actions to the system. We illustrate these concepts with a distributed service update operation
Climate Assessment of Orientation Design in the Housing Master Plan Close to the Airport
Abstract— This study is a continuation research of Sustainable Master plan Design in Residential Area Near the Airport which was previously conducted. In that study, it has been known that the angles of 135° and 180° towards the runway are the most effective angles which can reduce noise emitted from the airport. For reviewing the climate aspects, models are fitted in those angles, and then the observation of climatic aspects such as: temperature, humidity and wind speed should be conducted to review their influence to noise received by inhabitants. The research methods used were polynomial regressions of goniometric. And the results are correlation models of noise level to temperature, humidity and wind speed
Resonant and nonresonant D+ -> K- pi+ l+ nu(l) semileptonic decays
We analyse the semileptonic decay D+ -> K- pi+ l+ nu(l) using an effective
Lagrangian developed previously to describe the decays D -> P l nu(l) and D ->
V l nu(l). Light vector mesons are included in the model which combines the
heavy quark effective Lagrangian and chiral perturbation theory approach. The
nonresonant and resonant contributions are compared. With no new parameters the
model correctly reproduces the measured ratio Gamma(nres)/Gamma(nres + res). We
also present useful nonresonant decay distributions. Finally, a similar model,
but with a modified current which satisfies the soft pion theorems at the
expense of introducing another parameter, is analyzed and the results of the
models are compared.Comment: 17 pages, 3 Postscript figures, standard Latex, extended revision,
title, abstract and text (especially Sec. IV) changed, results unchange
Transfer Learning for Speech and Language Processing
Transfer learning is a vital technique that generalizes models trained for
one setting or task to other settings or tasks. For example in speech
recognition, an acoustic model trained for one language can be used to
recognize speech in another language, with little or no re-training data.
Transfer learning is closely related to multi-task learning (cross-lingual vs.
multilingual), and is traditionally studied in the name of `model adaptation'.
Recent advance in deep learning shows that transfer learning becomes much
easier and more effective with high-level abstract features learned by deep
models, and the `transfer' can be conducted not only between data distributions
and data types, but also between model structures (e.g., shallow nets and deep
nets) or even model types (e.g., Bayesian models and neural models). This
review paper summarizes some recent prominent research towards this direction,
particularly for speech and language processing. We also report some results
from our group and highlight the potential of this very interesting research
field.Comment: 13 pages, APSIPA 201
- …