Multi-dimensional classification with super-classes

The multi-dimensional classification problem is a generalisation of the recently-popularised task of multi-label classification, where each data instance is associated with multiple class variables. There has been relatively little research carried out specific to multi-dimensional classification and, although one of the core goals is similar (modelling dependencies among classes), there are important differences; namely a higher number of possible classifications. In this paper we present method for multi-dimensional classification, drawing from the most relevant multi-label research, and combining it with important novel developments. Using a fast method to model the conditional dependence between class variables, we form super-class partitions and use them to build multi-dimensional learners, learning each super-class as an ordinary class, and thus explicitly modelling class dependencies. Additionally, we present a mechanism to deal with the many class values inherent to super-classes, and thus make learning efficient. To investigate the effectiveness of this approach we carry out an empirical evaluation on a range of multi-dimensional datasets, under different evaluation metrics, and in comparison with high-performing existing multi-dimensional approaches from the literature. Analysis of results shows that our approach offers important performance gains over competing methods, while also exhibiting tractable running time.

Fusión de redes Bayesianas Gaussianas

Las redes Bayesianas constituyen un modelo ampliamente utilizado para la representación de relaciones de dependencia condicional en datos multivariantes. Su aprendizaje a partir de un conjunto de datos o expertos ha sido estudiado profundamente desde su concepción. Sin embargo, en determinados escenarios se demanda la obtención de un modelo común asociado a particiones de datos o conjuntos de expertos. En este caso, se trata el problema de fusión o agregación de modelos. Los trabajos y resultados en agregación de redes Bayesianas son de naturaleza variada, aunque escasos en comparación con aquellos de aprendizaje. En este documento, se proponen dos métodos para la agregación de redes Gaussianas, definidas como aquellas redes Bayesianas que modelan una distribución Gaussiana multivariante. Los métodos presentados son efectivos, precisos y producen redes con menor cantidad de parámetros en comparación con los modelos obtenidos individualmente. Además, constituyen un enfoque novedoso al incorporar nociones exploradas tradicionalmente por separado en el estado del arte. Futuras aplicaciones en entornos escalables hacen dichos métodos especialmente atractivos, dada su simplicidad y la ganancia en compacidad de la representación obtenida.---ABSTRACT---Bayesian networks are a widely used model for the representation of conditional dependence relationships among variables in multivariate data. The task of learning them from a data set or experts has been deeply studied since their conception. However, situations emerge where there is a need of obtaining a consensuated model from several data partitions or a set of experts. This situation is referred to as model fusion or aggregation. Results about Bayesian network aggregation, although rich in variety, have been scarce when compared to the learning task. In this context, two methods are proposed for the aggregation of Gaussian Bayesian networks, that is, Bayesian networks whose underlying modelled distribution is a multivariate Gaussian. Both methods are effective, precise and produce networks with fewer parameters in comparison with the models obtained by individual learning. They constitute a novel approach given that they incorporate notions traditionally explored separately in the state of the art. Future applications in scalable computer environments make such models specially attractive, given their simplicity and the gaining in sparsity of the produced model.

Formation energy of vacancies in FeCr alloys: Dependence on Cr concentration

A modified version of the concentration-dependent model (CDM) potential (A. Caro et al., Phys. Rev. Lett. 95 (2005) 075702) [1] has been developed to study defects in Fe–Cr for different Crconcentrations. A comparison between this new potential and DFT results for a variety of point defect configurations is performed in order to test its reliability for radiation damage studies. The effect of Crconcentration on the vacancyformationenergy in Fe–Cr alloys is analyzed in detail. This study shows a linear dependence of the vacancyformationenergy on Crconcentration for values above 6% of Cr. However, the formationenergy deviates from the linear interpolation in the region below 6% Crconcentration. In order to understand this behavior, the influence of the relative positions between Cr atoms and vacant sites on the vacancyformationenergy has been studied.

Thickness dependence of critical temperature in Mo/Au bilayers

We report on the sensitivity of the superconducting critical temperature (TC) to layer thickness, as well as on TC reproducibility in Mo/Au bilayers. Resistivity measurements on samples with a fixed Au thickness (dAu) and Mo thickness (dMo) ranging from 50 to 250 nm, and with a fixed dMo and different dAu thickness are shown. Experimental data are discussed in the framework of Martinis model, whose application to samples with dAu above their coherence length is analysed in detail. Results show a good coupling between normal and superconducting layers and excellent TC reproducibility, allowing to accurately correlate Mo layer thickness and bilayer TC.

Determination of variable dependence information through abstract interpretation

Traditional schemes for abstract interpretation-based global analysis of logic programs generally focus on obtaining procedure argument mode and type information. Variable sharing information is often given only the attention needed to preserve the correctness of the analysis. However, such sharing information can be very useful. In particular, it can be used for predicting run-time goal independence, which can eliminate costly run-time checks in and-parallel execution. In this paper, a new algorithm for doing abstract interpretation in logic programs is described which infers the dependencies of the terms bound to program variables with increased precisión and at all points in the execution of the program, rather than just at a procedure level. Algorithms are presented for computing abstract entry and success substitutions which extensively keep track of variable aliasing and term dependence information. The algorithms are illustrated with examples.

Conditional parallelization of nonStrict independence. Procedures and assessmen

This paper presents a conditional parallelization process for and-parallelism based on the notion of non-strict independence, a more relaxed notion than the traditional of strict independence. By using this notion, a parallelism annotator can extract more parallelism from programs. On the other hand, the intrinsic complexity of non-strict independence poses new challenges to this task. We report here on the implementation we have accomplished of an annotator for non-strict independence, capable of producing both static and dynamic execution graphs. This implementation, along with the also implemented independence checker and their integration in our system, have resulted what is, to the best of our knowledge, the first parallelizing compiler based on nonstrict independence which produces dynamic execution graphs. The paper also presents a preliminary assessment of the implemented tools, comparing them with the existing ones for strict independence, which shows encouraging results.

Stopping power dependence of nitrogen sputtering yields in copper nitride films under swift-ion irradiation: Exciton model approach

Nitrogen sputtering yields as high as 104 atoms/ion, are obtained by irradiating N-rich-Cu3N films (N concentration: 33 ± 2 at.%) with Cu ions at energies in the range 10?42 MeV. The kinetics of N sputtering as a function of ion fluence is determined at several energies (stopping powers) for films deposited on both, glass and silicon substrates. The kinetic curves show that the amount of nitrogen release strongly increases with rising irradiation fluence up to reaching a saturation level at a low remaining nitrogen fraction (5?10%), in which no further nitrogen reduction is observed. The sputtering rate for nitrogen depletion is found to be independent of the substrate and to linearly increase with electronic stopping power (Se). A stopping power (Sth) threshold of ?3.5 keV/nm for nitrogen depletion has been estimated from extrapolation of the data. Experimental kinetic data have been analyzed within a bulk molecular recombination model. The microscopic mechanisms of the nitrogen depletion process are discussed in terms of a non-radiative exciton decay model. In particular, the estimated threshold is related to a minimum exciton density which is required to achieve efficient sputtering rates.

Pounding force assessment in performance-based design of bridges

Bridges with deck supported on either sliding or elastomeric bearings are very common in mid-seismicity regions. Their main seismic vulnerabilities are related to the pounding of the deck against abutments or between the different deck elements. A simplified model of the longitudinal behavior of those bridges will allow to characterize the reaction forces developed during pounding using the Pacific Earthquake Engineering Research Center framework formula. In order to ensure the general applicability of the results obtained, a large number of system parameter combinations will be considered. The heart of the formula is the identification of suitable intermediate variables. First, the pseudo acceleration spectral value for the fundamental period of the system (Sa(Ts)) will be used as an intensity measure (IM). This IM will result in a very large non-explained variability of the engineering demand parameter. A portion of this variability will be proved to be related to the relative content of high-frequency energy in the input motion. Two vector-valued IMs including a second parameter taking this energy content into account will then be considered. For both of them, a suitable form for the conditional intensity dependence of the response will be obtained. The question of which one to choose will also be analyzed. Finally, additional issues related to the IM will be studied: its applicability to pulse-type records, the validity of scaling records and the sufficiency of the IM.

Stability of vacancy clusters in FeCr alloys: A study of the Cr concentration dependence

Fe–Cr based alloys are the leading structural material candidates in the design of next generation reactors due to their high resistance to swelling and corrosion. Despite these good properties there are others, such as embrittlement, which require a higher level of understanding in order to improve aspects such as safety or lifetime of the reactors. The addition of Cr improves the behavior of the steels under irradiation, but not in a monotonic way. Therefore, understanding the changes in the Fe–Cr based alloys microstructure induced by irradiation and the role played by the alloying element (Cr) is needed in order to predict the response of these materials under the extreme conditions they are going to support. In this work we perform a study of the effect of Cr concentration in a bcc Fe–Cr matrix on formation and binding energies of vacancy clusters up to 5 units. The dependence of the calculated formation and binding energy is investigated with two empirical interatomic potentials specially developed to study radiation damage in Fe–Cr alloys. Results are very similar for both potentials showing an increase of the defect stability with the cluster size and no real dependence on Cr concentration for the binding energy.

The TRANSPLANTA collection of Arabidopsis lines: a resource for functional analysis of transcription factors based on their conditional overexpression.

Los factores de transcripción (FTs) son reguladores clave de la expresión génica en todos los organismos. En eucariotas los FTs con frecuencia están representados por miembros funcionalmente redundantes de familias génicas de gran tamaño. La sobreexpresión de FTs puede representar una herramienta para revelar las funciones biológicas de FTs redundantes en plantas; sin embargo, la sobreexpresión constitutiva de FTs con frecuencia conlleva diversos defectos en el desarrollo, impidiendo su caracterización funcional. Sin embargo, aproximaciones de sobreexpresión condicional podrían ayudar a solventar este problema. En el consorcio TRANSPLANTA, en el que participan varios laboratorios del CBGP, hemos generado una colección de líneas transgénicas de Arabidopsis, cada una de las cuales expresa un FT bajo el control de un promotor inducible por ?estradiol. Hasta el momento se han generado 1636 líneas homocigotas independientes que corresponden a 634 FTs diferentes, lo que representa una media de 2,6 líneas por cada FT. Como confirmación de la utilidad de esta herramienta, el tratamiento con ?estradiol de líneas que expresaban condicionalmente FTs provoca alteraciones fenotípicas tales como proliferación de pelos radiculares, senescencia inducida por oscuridad, acumulación de antocianinas y enanismo, y que corroboran fenotipos previamente descritos debidos a la sobreexpresión de dichos FTs. Rastreos realizados posteriormente con otras líneas TRANSPLANTA han permitido la identificación de FTs implicados en diferentes procesos biológicos de plantas, confirmando que la colección es una herramienta valiosa para la caracterización funcional de FTs. Las semillas de las líneas TRANSPLANTA han sido depositadas en el Nottingham Arabidopsis Stock Centre para su distribución posterior.

On the loading-rate dependence of the Al 7017-T73 fracture-initiation toughness

While static fracture toughness is a widely studied and standardised parameter, its dynamic counterpart has not been exhaustively examined. Therefore, in this research a series of quasi-static and different loading-rate dynamic tests were carried out to determine the evolution of fracture toughness with the velocity of the application of the load on aluminium 7017-T73 alloy. Three-point bending tests of pre-fatigued standard specimens (ASTM E399) at four loading-rates were carried out. The experiments were conducted by employing the subsequent apparatus ordered from lowest to highest load application velocity: a servo-hydraulic universal testing machine, a free-drop tower, a modified Split Hopkinson Pressure Bar and an explosive load testing device. In order to perform the dynamic fracture toughness tests, it was necessary to design and develop some experimental devices. The fracture-initiation toughness of the aluminium 7017-T73 alloy did not exhibit a significant variation for the studied cases. As a conclusion, the research showed that fracture-initiation toughness remained constant regardless of the velocity at which the load was applied.

Conditional density approximations with mixtures of polynomials

Mixtures of polynomials (MoPs) are a non-parametric density estimation technique especially designed for hybrid Bayesian networks with continuous and discrete variables. Algorithms to learn one- and multi-dimensional (marginal) MoPs from data have recently been proposed. In this paper we introduce two methods for learning MoP approximations of conditional densities from data. Both approaches are based on learning MoP approximations of the joint density and the marginal density of the conditioning variables, but they differ as to how the MoP approximation of the quotient of the two densities is found. We illustrate and study the methods using data sampled from known parametric distributions, and we demonstrate their applicability by learning models based on real neuroscience data. Finally, we compare the performance of the proposed methods with an approach for learning mixtures of truncated basis functions (MoTBFs). The empirical results show that the proposed methods generally yield models that are comparable to or significantly better than those found using the MoTBF-based method.

Runup variability due to time dependence and stochasticity in the beach profiles: two extreme cases of the Spanish coast

Equations for extreme runup worked out from several experimental studies are compared. Infragraviatory oscillations dominate the swash in a dissipative state but not in intermediate - reflective states. Therefore two kinds of equation depending on either significant wave height, H-0, or the Iribarren number, xi(0), should be used. Through a sand bed physical model with a uniform sand bed slope, equations are proposed for both beach states, and results are compared with precedent field and physical model experiments. Once the equations are chosen, the time-longshore variability in a medium - long term time scale of the foreshore slope is evaluated in two extreme cases relating to the Spanish coast. The Salinas beach on the North coast (Bay of Biscay) displayed a permanent dissipative beach state with small variations in the beach foreshore slope both along the shore and in time, so foreshore slope deviations in a medium-long term period were irrelevant and extreme runup is predicted with the wave height worked out from the design return period. Peniscola beach on the East coast (Mediterranean sea) displayed an intermediate state. If only time variations are analysed, variations in determining extreme runup are irrelevant. In contrast, significant differences were found when the longshore variations were studied in this Mediterranean beach.

The TRANSPLANTA collection of Arabidopsis lines: a resource for functional analysis of transcription factors based on their conditional over-expression

Transcription factors (TFs) are key regulators of gene expression in all organisms. In eukaryotes, TFs are often represented by functionally redundant members of large gene families. Overexpression might prove a means to unveil the biological functions of redundant TFs; however, constitutive overexpression of TFs frequently causes severe developmental defects, preventing their functional characterization. Conditional overexpression strategies help to overcome this problem. Here, we report on the TRANSPLANTA collection of Arabidopsis lines, each expressing one of 949 TFs under the control of a β–estradiol-inducible promoter. Thus far, 1636 independent homozygous lines, representing an average of 2.6 lines for every TF, have been produced for the inducible expression of 634 TFs. Along with a GUS-GFP reporter, randomly selected TRANSPLANTA lines were tested and confirmed for conditional transgene expression upon β–estradiol treatment. As a proof of concept for the exploitation of this resource, β–estradiol-induced proliferation of root hairs, dark-induced senescence, anthocyanin accumulation and dwarfism were observed in lines conditionally expressing full-length cDNAs encoding RHD6, WRKY22, MYB123/TT2 and MYB26, respectively, in agreement with previously reported phenotypes conferred by these TFs. Further screening performed with other TRANSPLANTA lines allowed the identification of TFs involved in different plant biological processes, illustrating that the collection is a powerful resource for the functional characterization of TFs. For instance, ANAC058 and a TINY/AP2 TF were identified as modulators of ABA-mediated germination potential, and RAP2.10/DEAR4 was identified as a regulator of cell death in the hypocotyl–root transition zone. Seeds of TRANSPLANTA lines have been deposited at the Nottingham Arabidopsis Stock Centre for further distribution.

Negative density dependence and environmental heterogeneity effects on tree ferns across succession in a tropical montane forest.

Although tree ferns are an important component of temperate and tropical forests, very little is known about their ecology. Their peculiar biology (e.g., dispersal by spores and two-phase life cycle) makes it difficult to extrapolate current knowledge on the ecology of other tree species to tree ferns. In this paper, we studied the effects of negative density dependence (NDD) and environmental heterogeneity on populations of two abundant tree fern species, Cyathea caracasana and Alsophila engelii, and how these effects change across a successional gradient. Species patterns harbor information on processes such as competition that can be easily revealed using point pattern analysis techniques. However, its detection may be difficult due to the confounded effects of habitat heterogeneity. Here, we mapped three forest plots along a successional gradient in the montane forests of Southern Ecuador. We employed homogeneous and inhomogeneous K and pair correlation functions to quantify the change in the spatial pattern of different size classes and a case-control design to study associations between juvenile and adult tree ferns. Using spatial estimates of the biomass of four functional tree types (short- and long-lived pioneer, shade- and partial shade-tolerant) as covariates, we fitted heterogeneous Poisson models to the point pattern of juvenile and adult tree ferns and explored the existence of habitat dependencies on these patterns. Our study revealed NDD effects for C. caracasana and strong environmental filtering underlying the pattern of A. engelii. We found that adult and juvenile populations of both species responded differently to habitat heterogeneity and in most cases this heterogeneity was associated with the spatial distribution of biomass of the four functional tree types. These findings show the effectiveness of factoring out environmental heterogeneity to avoid confounding factors when studying NDD and demonstrate the usefulness of covariate maps derived from mapped communities.