An automatic translation scheme from prolog to the andorra kernel language

The Andorra family of languages (which includes the Andorra Kernel Language -AKL) is aimed, in principie, at simultaneously supporting the programming styles of Prolog and committed choice languages. On the other hand, AKL requires a somewhat detailed specification of control by the user. This could be avoided by programming in Prolog to run on AKL. However, Prolog programs cannot be executed directly on AKL. This is due to a number of factors, from more or less trivial syntactic differences to more involved issues such as the treatment of cut and making the exploitation of certain types of parallelism possible. This paper provides basic guidelines for constructing an automatic compiler of Prolog programs into AKL, which can bridge those differences. In addition to supporting Prolog, our style of translation achieves independent and-parallel execution where possible, which is relevant since this type of parallel execution preserves, through the translation, the user-perceived "complexity" of the original Prolog program.

Regularization for sparsity in statistical analysis and machine learning

Pragmatism is the leading motivation of regularization. We can understand regularization as a modification of the maximum-likelihood estimator so that a reasonable answer could be given in an unstable or ill-posed situation. To mention some typical examples, this happens when fitting parametric or non-parametric models with more parameters than data or when estimating large covariance matrices. Regularization is usually used, in addition, to improve the bias-variance tradeoff of an estimation. Then, the definition of regularization is quite general, and, although the introduction of a penalty is probably the most popular type, it is just one out of multiple forms of regularization. In this dissertation, we focus on the applications of regularization for obtaining sparse or parsimonious representations, where only a subset of the inputs is used. A particular form of regularization, L1-regularization, plays a key role for reaching sparsity. Most of the contributions presented here revolve around L1-regularization, although other forms of regularization are explored (also pursuing sparsity in some sense). In addition to present a compact review of L1-regularization and its applications in statistical and machine learning, we devise methodology for regression, supervised classification and structure induction of graphical models. Within the regression paradigm, we focus on kernel smoothing learning, proposing techniques for kernel design that are suitable for high dimensional settings and sparse regression functions. We also present an application of regularized regression techniques for modeling the response of biological neurons. Supervised classification advances deal, on the one hand, with the application of regularization for obtaining a na¨ıve Bayes classifier and, on the other hand, with a novel algorithm for brain-computer interface design that uses group regularization in an efficient manner. Finally, we present a heuristic for inducing structures of Gaussian Bayesian networks using L1-regularization as a filter. El pragmatismo es la principal motivación de la regularización. Podemos entender la regularización como una modificación del estimador de máxima verosimilitud, de tal manera que se pueda dar una respuesta cuando la configuración del problema es inestable. A modo de ejemplo, podemos mencionar el ajuste de modelos paramétricos o no paramétricos cuando hay más parámetros que casos en el conjunto de datos, o la estimación de grandes matrices de covarianzas. Se suele recurrir a la regularización, además, para mejorar el compromiso sesgo-varianza en una estimación. Por tanto, la definición de regularización es muy general y, aunque la introducción de una función de penalización es probablemente el método más popular, éste es sólo uno de entre varias posibilidades. En esta tesis se ha trabajado en aplicaciones de regularización para obtener representaciones dispersas, donde sólo se usa un subconjunto de las entradas. En particular, la regularización L1 juega un papel clave en la búsqueda de dicha dispersión. La mayor parte de las contribuciones presentadas en la tesis giran alrededor de la regularización L1, aunque también se exploran otras formas de regularización (que igualmente persiguen un modelo disperso). Además de presentar una revisión de la regularización L1 y sus aplicaciones en estadística y aprendizaje de máquina, se ha desarrollado metodología para regresión, clasificación supervisada y aprendizaje de estructura en modelos gráficos. Dentro de la regresión, se ha trabajado principalmente en métodos de regresión local, proponiendo técnicas de diseño del kernel que sean adecuadas a configuraciones de alta dimensionalidad y funciones de regresión dispersas. También se presenta una aplicación de las técnicas de regresión regularizada para modelar la respuesta de neuronas reales. Los avances en clasificación supervisada tratan, por una parte, con el uso de regularización para obtener un clasificador naive Bayes y, por otra parte, con el desarrollo de un algoritmo que usa regularización por grupos de una manera eficiente y que se ha aplicado al diseño de interfaces cerebromáquina. Finalmente, se presenta una heurística para inducir la estructura de redes Bayesianas Gaussianas usando regularización L1 a modo de filtro.

Distributed static linear Gaussian models using consensus

Algorithms for distributed agreement are a powerful means for formulating distributed versions of existing centralized algorithms. We present a toolkit for this task and show how it can be used systematically to design fully distributed algorithms for static linear Gaussian models, including principal component analysis, factor analysis, and probabilistic principal component analysis. These algorithms do not rely on a fusion center, require only low-volume local (1-hop neighborhood) communications, and are thus efficient, scalable, and robust. We show how they are also guaranteed to asymptotically converge to the same solution as the corresponding existing centralized algorithms. Finally, we illustrate the functioning of our algorithms on two examples, and examine the inherent cost-performance tradeoff.

Biokerosene from coconut, babassu, camelina and palm kernel oils: production and properties of their blends with fossil kerosene

On December 20th 2006 the European Commission approved a law proposal to include the civil aviation sector in the European market of carbon dioxide emission rights [European Union Emissions Trading System, EUETS). On July 8th 2009, the European Parliament and Conseil agreed that all flights leaving or landing in the EU airports starting from January 1st 2012 should be included in the EUETS. On November 19th 2008, the EU Directive 2008/101/CE [1] included the civil aviation activities in the EUETS, and this directive was transposed by the Spanish law 13/2010 of July 5th 2010 [2]. Thus, in 2012 the aviation sector should reduce their emissions to 97 % of the mean values registered in the period 2004-2006, and for 2013 these emission reductions should reach 95 % of the mean values for that same period. Trying to face this situation, the aviation companies are planning seriously the use of alternative jet fuels to reduce their greenhouse gas emissions and to lower their costs. However, some US airlines have issued a lawsuit before the European Court of Justice based in that this EU action violates a long standing worldwide aviation treaty, the Chicago convention of 1944, and also the Chinese aviation companies have rejected to pay any EU carbon dioxide tax [3]. Moreover, the USA Departments of Agriculture and Energy and the Navy will invest a total of up to $150 million over three years to spur production of aviation and marine biofuels for commercial and military applications [4]. However, the jet fuels should fulfill a set of extraordinarily sensitive properties to guarantee the safety of planes and passengers during all the flights.

Seismic hazards of the Iberian Peninsula - evaluation with Kernel functions

The seismic hazard of the Iberian Peninsula is analysed using a nonparametric methodology based on statistical kernel functions; the activity rate is derived from the catalogue data, both its spatial dependence (without a seismogenetic zonation) and its magnitude dependence (without using Gutenberg–Richter's law). The catalogue is that of the Instituto Geográfico Nacional, supplemented with other catalogues around the periphery; the quantification of events has been homogenised and spatially or temporally interrelated events have been suppressed to assume a Poisson process. The activity rate is determined by the kernel function, the bandwidth and the effective periods. The resulting rate is compared with that produced using Gutenberg–Richter statistics and a zoned approach. Three attenuation laws have been employed, one for deep sources and two for shallower events, depending on whether their magnitude was above or below 5. The results are presented as seismic hazard maps for different spectral frequencies and for return periods of 475 and 2475 yr, which allows constructing uniform hazard spectra.

Airplane materials compatibility with blends of fossil kerosene JET A1 with biokerosenes from babassu, palm kernel and coconut oils

The aviation companies are facing some problems that argue in favor of biofuels: Rising cost of traditional fuel: from 0.71 USD/gallon in May 2003 to 3.09 USD/gallon in January 2012. Environmental concerns: direct emissions from aviation account for about 3 % of the EU’s total greenhouse gas emissions. The International Civil Aviation Organization (ICAO) forecasts that by 2050 they could grow by a further 300-700 %. On December 20th 2006 the European Commission approved a law proposal to include the civil aviation sector in the European market of carbon dioxide emission rights (European Union Emissions Trading System, EUETS)

Blends of babassu, palm kernel and coconut fame with fossil kerosene. Technical aspects of low carbon number methyl esters as a possible source for renewable jet fuel

State of the Art. Process and Distillation. Fuel Characterization. Fuel Compatibility Tests

Fully adaptive gaussian mixture metropolis-hastings algorithm

Markov Chain Monte Carlo methods are widely used in signal processing and communications for statistical inference and stochastic optimization. In this work, we introduce an efficient adaptive Metropolis-Hastings algorithm to draw samples from generic multimodal and multidimensional target distributions. The proposal density is a mixture of Gaussian densities with all parameters (weights, mean vectors and covariance matrices) updated using all the previously generated samples applying simple recursive rules. Numerical results for the one and two-dimensional cases are provided.

Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices

The diversity of bibliometric indices today poses the challenge of exploiting the relationships among them. Our research uncovers the best core set of relevant indices for predicting other bibliometric indices. An added difficulty is to select the role of each variable, that is, which bibliometric indices are predictive variables and which are response variables. This results in a novel multioutput regression problem where the role of each variable (predictor or response) is unknown beforehand. We use Gaussian Bayesian networks to solve the this problem and discover multivariate relationships among bibliometric indices. These networks are learnt by a genetic algorithm that looks for the optimal models that best predict bibliometric data. Results show that the optimal induced Gaussian Bayesian networks corroborate previous relationships between several indices, but also suggest new, previously unreported interactions. An extended analysis of the best model illustrates that a set of 12 bibliometric indices can be accurately predicted using only a smaller predictive core subset composed of citations, g-index, q2-index, and hr-index. This research is performed using bibliometric data on Spanish full professors associated with the computer science area.

Airplane materials compatibility with blends of fossil kerosene jet a1 with biokerosenes from babassu, palm kernel and coconut oils

El desarrollo de bioqueroseno de diferentes orígenes y su uso creciente, hacen necesario el estudio de la compatibilidad estos nuevos combustibles con los materiales y recubrimientos con los que se encuentra en contacto. Por tanto, el presente proyecto estudia la compatibilidad de los bioquerosenos mezclados en diferentes proporciones con queroseno mineral, para evaluar posteriormente su compatibilidad con diferentes polímeros y composites presentes en la estructura de un avión.Currently there is a big interest to increase the sources of alternative fuels for aviation to get a reduction of their carbon footprint and the deep energetic dependence from fossil fuels of different countries. Although there are studies about how to produce this alternative fuel and how to accomplish the standards for a good performance in the aircraft turbines, there are no studies about how these fuels could affect the different materials of airplanes. In this context this work describes the compatibility of biokerosene blends of coconut, babassu and palm kernel with commercial Jet A-1 testing airplane polymeric materials, metals and composites. As a conclusion, all material samples show a good compatibility with the fuel blends tested.

Seismic hazard of the Iberian Peninsula: evaluation with Kernel functions

The seismic hazard of the Iberian Peninsula is analysed using a nonparametric methodology based on statistical kernel functions; the activity rate is derived from the catalogue data, both its spatial dependence (without a seismogenic zonation) and its magnitude dependence (without using Gutenberg–Richter's relationship). The catalogue is that of the Instituto Geográfico Nacional, supplemented with other catalogues around the periphery; the quantification of events has been homogenised and spatially or temporally interrelated events have been suppressed to assume a Poisson process. The activity rate is determined by the kernel function, the bandwidth and the effective periods. The resulting rate is compared with that produced using Gutenberg–Richter statistics and a zoned approach. Three attenuation relationships have been employed, one for deep sources and two for shallower events, depending on whether their magnitude was above or below 5. The results are presented as seismic hazard maps for different spectral frequencies and for return periods of 475 and 2475 yr, which allows constructing uniform hazard spectra

Analysis of the behavior of multijunction solar cells under high irradiance Gaussian light profiles showing chromatic aberration with emphasis on tunnel junction performance

In this work, we explain the behavior of multijunction solar cells under non-uniform (spatially and in spectral content) light profiles in general and in particular when Gaussian light profiles cause a photo-generated current density, which exceeds locally the peak current density of the tunnel junction. We have analyzed the implications on the tunnel junction's limitation, that is, in the loss of efficiency due to the appearance of a dip in the I–V curve. For that, we have carried out simulations with our three-dimensional distributed model for multijunction solar cells, which contemplates a full description of the tunnel junction and also takes into account the lateral resistances in the tunnel junction. The main findings are that the current density photo-generated spreads out through the lateral resistances of the device, mainly through the tunnel junction layers and the back contact. Therefore, under non-uniform light profiles these resistances are determinant not only to avoid the tunnel junction's limitation but also for mitigating losses in the fill factor. Therefore, taking into account these lateral resistances could be the key for jointly optimizing the concentrator photovoltaic system (concentrator optics, front grid layout and semiconductor structure)

Análise da aderência de práticas ágeis na cultura de startups de software: o mapeamento de práticas através do SEMAT Kernel.

Este trabalho analisa os principais métodos ágeis utilizados em empresas startup, como scrum, extreme programming, kanban e lean, isolando suas práticas e mapeando-as no Kernel do SEMAT para escolher os elementos essenciais da engenharia de software que estão relacionados a cada prática de forma independente. Foram identificadas 34 práticas que foram reduzidas a um conjunto de 26 pelas similaridades. Um questionário foi desenvolvido e aplicado no ambiente de startups de software para a avaliação do grau de utilização de cada determinada prática. Através das respostas obtidas foi possível a identificação de um subconjunto de práticas com utilização acima de 60% onde todos os elementos essenciais da engenharia de software são atendidos, formando um conjunto mínimo de práticas capazes de sustentar este tipo específico de ambiente.

An integral security kernel

As the user base of the Internet has grown tremendously, the need for secure services has increased accordingly. Most secure protocols, in digital business and other fields, use a combination of symmetric and asymmetric cryptography, random generators and hash functions in order to achieve confidentiality, integrity, and authentication. Our proposal is an integral security kernel based on a powerful mathematical scheme from which all of these cryptographic facilities can be derived. The kernel requires very little resources and has the flexibility of being able to trade off speed, memory or security; therefore, it can be efficiently implemented in a wide spectrum of platforms and applications, either software, hardware or low cost devices. Additionally, the primitives are comparable in security and speed to well known standards.

KDENS: Stata module for univariate kernel density estimation

kdens produces univariate kernel density estimates and graphs the result. kdens supplements official Stata's kdensity. Important additions are: adaptive (i.e. variable bandwidth) kernel density estimation, several automatic bandwidth selectors including the Sheather-Jones plug-in estimator, pointwise variability bands and confidence intervals, boundary correction for variables with bounded domain, fast binned approximation estimation. Note that the moremata package, also available from SSC, is required.