A new set of relativistic screening constants for the screened hydrogenic model

AnewRelativisticScreenedHydrogenicModel has been developed to calculate atomic data needed to compute the optical and thermodynamic properties of high energy density plasmas. The model is based on anewset of universal screeningconstants, including nlj-splitting that has been obtained by fitting to a large database of ionization potentials and excitation energies. This database was built with energies compiled from the National Institute of Standards and Technology (NIST) database of experimental atomic energy levels, and energies calculated with the Flexible Atomic Code (FAC). The screeningconstants have been computed up to the 5p3/2 subshell using a Genetic Algorithm technique with an objective function designed to minimize both the relative error and the maximum error. To select the best set of screeningconstants some additional physical criteria has been applied, which are based on the reproduction of the filling order of the shells and on obtaining the best ground state configuration. A statistical error analysis has been performed to test the model, which indicated that approximately 88% of the data lie within a ±10% error interval. We validate the model by comparing the results with ionization energies, transition energies, and wave functions computed using sophisticated self-consistent codes and experimental data.

A set of canonical problems in sloshing. Part 0: Experimental setup and data processing

In a series of attempts to research and document relevant sloshing type phenomena, a series of experiments have been conducted. The aim of this paper is to describe the setup and data processing of such experiments. A sloshing tank is subjected to angular motion. As a result pressure registers are obtained at several locations, together with the motion data, torque and a collection of image and video information. The experimental rig and the data acquisition systems are described. Useful information for experimental sloshing research practitioners is provided. This information is related to the liquids used in the experiments, the dying techniques, tank building processes, synchronization of acquisition systems, etc. A new procedure for reconstructing experimental data, that takes into account experimental uncertainties, is presented. This procedure is based on a least squares spline approximation of the data. Based on a deterministic approach to the first sloshing wave impact event in a sloshing experiment, an uncertainty analysis procedure of the associated first pressure peak value is described.

Optimal Multiresolution 3D Level-Set Method for Liver Segmentation incorporating Local Curvature Constraints

Advanced liver surgery requires a precise pre-operative planning, where liver segmentation and remnant liver volume are key elements to avoid post-operative liver failure. In that context, level-set algorithms have achieved better results than others, especially with altered liver parenchyma or in cases with previous surgery. In order to improve functional liver parenchyma volume measurements, in this work we propose two strategies to enhance previous level-set algorithms: an optimal multi-resolution strategy with fine details correction and adaptive curvature, as well as an additional semiautomatic step imposing local curvature constraints. Results show more accurate segmentations, especially in elongated structures, detecting internal lesions and avoiding leakages to close structures

Level Set Segmentation with Shape and Appearance Models Using Affine Moment Descriptors

We propose a level set based variational approach that incorporates shape priors into edge-based and region-based models. The evolution of the active contour depends on local and global information. It has been implemented using an efficient narrow band technique. For each boundary pixel we calculate its dynamic according to its gray level, the neighborhood and geometric properties established by training shapes. We also propose a criterion for shape aligning based on affine transformation using an image normalization procedure. Finally, we illustrate the benefits of the our approach on the liver segmentation from CT images.

Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers

The main purpose of a gene interaction network is to map the relationships of the genes that are out of sight when a genomic study is tackled. DNA microarrays allow the measure of gene expression of thousands of genes at the same time. These data constitute the numeric seed for the induction of the gene networks. In this paper, we propose a new approach to build gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling. The interactions induced by the Bayesian classifiers are based both on the expression levels and on the phenotype information of the supervised variable. Feature selection and bootstrap resampling add reliability and robustness to the overall process removing the false positive findings. The consensus among all the induced models produces a hierarchy of dependences and, thus, of variables. Biologists can define the depth level of the model hierarchy so the set of interactions and genes involved can vary from a sparse to a dense set. Experimental results show how these networks perform well on classification tasks. The biological validation matches previous biological findings and opens new hypothesis for future studies

Negative ternary set-sharing

The Set-Sharing domain has been widely used to infer at compiletime interesting properties of logic programs such as occurs-check reduction, automatic parallelization, and flnite-tree analysis. However, performing abstract uniflcation in this domain requires a closure operation that increases the number of sharing groups exponentially. Much attention has been given to mitigating this key inefflciency in this otherwise very useful domain. In this paper we present a novel approach to Set-Sharing: we define a new representation that leverages the complement (or negative) sharing relationships of the original sharing set, without loss of accuracy. Intuitively, given an abstract state sh\> over the finite set of variables of interest V, its negative representation is p(V) \ shy. Using this encoding during analysis dramatically reduces the number of elements that need to be represented in the abstract states and during abstract uniflcation as the cardinality of the original set grows toward 2 . To further compress the number of elements, we express the set-sharing relationships through a set of ternary strings that compacts the representation by eliminating redundancies among the sharing sets. Our experiments show that our approach can compress the number of relationships, reducing signiflcantly the memory usage and running time of all abstract operations, including abstract uniflcation.

Efficient top-down set-sharing analysis using cliques

Abstract. We study the problem of efficient, scalable set-sharing analysis of logic programs. We use the idea of representing sharing information as a pair of abstract substitutions, one of which is a worst-case sharing representation called a clique set, which was previously proposed for the case of inferring pair-sharing. We use the clique-set representation for (1) inferring actual set-sharing information, and (2) analysis within a top-down framework. In particular, we define the new abstract functions required by standard top-down analyses, both for sharing alone and also for the case of including freeness in addition to sharing. We use cliques both as an alternative representation and as widening, defining several widening operators. Our experimental evaluation supports the conclusión that, for inferring set-sharing, as it was the case for inferring pair-sharing, precisión losses are limited, while useful efficieney gains are obtained. We also derive useful conclusions regarding the interactions between thresholds, precisión, efficieney and cost of widening. At the limit, the clique-set representation allowed analyzing some programs that exceeded memory capacity using classical sharing representations.

Two efficient representations for set-sharing analysis in logic programs

Set-Sharing analysis, the classic Jacobs and Langen's domain, has been widely used to infer several interesting properties of programs at compile-time such as occurs-check reduction, automatic parallelization, flnite-tree analysis, etc. However, performing abstract uniflcation over this domain implies the use of a closure operation which makes the number of sharing groups grow exponentially. Much attention has been given in the literature to mitígate this key inefficiency in this otherwise very useful domain. In this paper we present two novel alternative representations for the traditional set-sharing domain, tSH and tNSH. which compress efficiently the number of elements into fewer elements enabling more efficient abstract operations, including abstract uniflcation, without any loss of accuracy. Our experimental evaluation supports that both representations can reduce dramatically the number of sharing groups showing they can be more practical solutions towards scalable set-sharing.

A study of set-sharing analysis via cliques

We study the problem of efñcient, scalable set-sharing analysis of logic programs. We use the idea of representing sharing information as a pair of abstract substitutions, one of which is a worst-case sharing representation called a clique set, which was previously proposed for the case of inferring pair-sharing. We use the clique-set representation for (1) inferring actual set-sharing information, and (2) analysis within a topdown framework. In particular, we define the abstract functions required by standard top-down analyses, both for sharing alone and also for the case of including freeness in addition to sharing. Our experimental evaluation supports the conclusión that, for inferring set-sharing, as it was the case for inferring pair-sharing, precisión losses are limited, while useful efñciency gains are obtained. At the limit, the clique-set representation allowed analyzing some programs that exceeded memory capacity using classical sharing representations.

Efficient representations for set-sharing analysis

Abstract is not available.

Precise set sharin analysis for java-style programs (and proofs).

Finding useful sharing information between instances in object- oriented programs has recently been the focus of much research. The applications of such static analysis are multiple: by knowing which variables definitely do not share in memory we can apply conventional compiler optimizations, find coarse-grained parallelism opportunities, or, more importantly, verify certain correctness aspects of programs even in the absence of annotations. In this paper we introduce a framework for deriving precise sharing information based on abstract interpretation for a Java-like language. Our analysis achieves precision in various ways, including supporting multivariance, which allows separating different contexts. We propose a combined Set Sharing + Nullity + Classes domain which captures which instances do not share and which ones are definitively null, and which uses the classes to refine the static information when inheritance is present. The use of a set sharing abstraction allows a more precise representation of the existing sharings and is crucial in achieving precision during interprocedural analysis. Carrying the domains in a combined way facilitates the interaction among them in the presence of multivariance in the analysis. We show through examples and experimentally that both the set sharing part of the domain as well as the combined domain provide more accurate information than previous work based on pair sharing domains, at reasonable cost.

Precise set sharing and nullity analysis for java-style program

Finding useful sharing information between instances in object- oriented programs has been recently the focus of much research. The applications of such static analysis are multiple: by knowing which variables share in memory we can apply conventional compiler optimizations, find coarse-grained parallelism opportunities, or, more importantly,erify certain correctness aspects of programs even in the absence of annotations In this paper we introduce a framework for deriving precise sharing information based on abstract interpretation for a Java-like language. Our analysis achieves precision in various ways. The analysis is multivariant, which allows separating different contexts. We propose a combined Set Sharing + Nullity + Classes domain which captures which instances share and which ones do not or are definitively null, and which uses the classes to refine the static information when inheritance is present. Carrying the domains in a combined way facilitates the interaction among the domains in the presence of mutivariance in the analysis. We show that both the set sharing part of the domain as well as the combined domain provide more accurate information than previous work based on pair sharing domains, at reasonable cost.

Shadow Analysis of Soil Surface Roughness Compared to the Chain Set Method and Direct Measurement of Micro-relief.

Erosion potential and the effects of tillage can be evaluated from quantitative descriptions of soil surface roughness. The present study therefore aimed to fill the need for a reliable, low-cost and convenient method to measure that parameter. Based on the interpretation of micro-topographic shadows, this new procedure is primarily designed for use in the field after tillage. The principle underlying shadow analysis is the direct relationship between soil surface roughness and the shadows cast by soil structures under fixed sunlight conditions. The results obtained with this method were compared to the statistical indexes used to interpret field readings recorded by a pin meter. The tests were conducted on 4-m2 sandy loam and sandy clay loam plots divided into 1-m2 subplots tilled with three different tools: chisel, tiller and roller. The highly significant correlation between the statistical indexes and shadow analysis results obtained in the laboratory as well as in the field for all the soil?tool combinations proved that both variability (CV) and dispersion (SD) are accommodated by the new method. This procedure simplifies the interpretation of soil surface roughness and shortens the time involved in field operations by a factor ranging from 12 to 20.

Lineability of the set of holomorphic mappings with dense range

Let U be an open subset of a separable Banach space. Let F be the collection of all holomorphic mappings f from the open unit disc D � C into U such that f(D) is dense in U. We prove the lineability and density of F in appropriate spaces for diferent choices of U. RESUMEN. Sea U un subconjunto abierto de un espacio de Banach separable. Sea F el conjunto de funciones holomorfas f definidas en el disco unidad D del plano complejo con valores en U tales que f(D) es denso en U. En el artículo se demuestra la lineabilidad y densidad del conjunto F para diferentes elecciones de U.

Image analysis by moment invariants using a set of step-like basis functions

Moment invariants have been thoroughly studied and repeatedly proposed as one of the most powerful tools for 2D shape identification. In this paper a set of such descriptors is proposed, being the basis functions discontinuous in a finite number of points. The goal of using discontinuous functions is to avoid the Gibbs phenomenon, and therefore to yield a better approximation capability for discontinuous signals, as images. Moreover, the proposed set of moments allows the definition of rotation invariants, being this the other main design concern. Translation and scale invariance are achieved by means of standard image normalization. Tests are conducted to evaluate the behavior of these descriptors in noisy environments, where images are corrupted with Gaussian noise up to different SNR values. Results are compared to those obtained using Zernike moments, showing that the proposed descriptor has the same performance in image retrieval tasks in noisy environments, but demanding much less computational power for every stage in the query chain.