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$\alpha$, H$\beta$, and H$\gamma$, 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$\gamma$, while the inner wings can be weaker in 3D models, particularly for H$\alpha$. For H$\alpha$, we also find significant 3D LTE versus 3D non-LTE differences (non-LTE effects): in warmer stars ($T_{\text{eff}}\approx6500$K) the inner wings tend to be weaker in non-LTE models, while at lower effective temperatures ($T_{\text{eff}}\approx4500$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$\alpha$, H$\beta$, and H$\gamma$; 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 $1\sigma$ uncertainties. For H$\alpha$, 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$\alpha$ 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