935 resultados para Separable Set


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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.

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Abstract is not available.

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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.

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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.

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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.

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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.

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The set agreement problem states that from n proposed values at most n-1 can be decided. Traditionally, this problem is solved using a failure detector in asynchronous systems where processes may crash but not recover, where processes have different identities, and where all processes initially know the membership. In this paper we study the set agreement problem and the weakest failure detector L used to solve it in asynchronous message passing systems where processes may crash and recover, with homonyms (i.e., processes may have equal identities) and without a complete initial knowledge of the membership.

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We present a quasi-monotone semi-Lagrangian particle level set (QMSL-PLS) method for moving interfaces. The QMSL method is a blend of first order monotone and second order semi-Lagrangian methods. The QMSL-PLS method is easy to implement, efficient, and well adapted for unstructured, either simplicial or hexahedral, meshes. We prove that it is unconditionally stable in the maximum discrete norm, � · �h,∞, and the error analysis shows that when the level set solution u(t) is in the Sobolev space Wr+1,∞(D), r ≥ 0, the convergence in the maximum norm is of the form (KT/Δt)min(1,Δt � v �h,∞ /h)((1 − α)hp + hq), p = min(2, r + 1), and q = min(3, r + 1),where v is a velocity. This means that at high CFL numbers, that is, when Δt > h, the error is O( (1−α)hp+hq) Δt ), whereas at CFL numbers less than 1, the error is O((1 − α)hp−1 + hq−1)). We have tested our method with satisfactory results in benchmark problems such as the Zalesak’s slotted disk, the single vortex flow, and the rising bubble.

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A formulation of the perturbed two-body problem that relies on a new set of orbital elements is presented. The proposed method represents a generalization of the special perturbation method published by Peláez et al. (Celest Mech Dyn Astron 97(2):131?150,2007) for the case of a perturbing force that is partially or totally derivable from a potential. We accomplish this result by employing a generalized Sundman time transformation in the framework of the projective decomposition, which is a known approach for transforming the two-body problem into a set of linear and regular differential equations of motion. Numerical tests, carried out with examples extensively used in the literature, show the remarkable improvement of the performance of the new method for different kinds of perturbations and eccentricities. In particular, one notable result is that the quadratic dependence of the position error on the time-like argument exhibited by Peláez?s method for near-circular motion under the J2 perturbation is transformed into linear.Moreover, themethod reveals to be competitive with two very popular elementmethods derived from theKustaanheimo-Stiefel and Sperling-Burdet regularizations.

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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.

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Fluid flow and fabric compaction during vacuum assisted resin infusion (VARI) of composite materials was simulated using a level set-based approach. Fluid infusion through the fiber preform was modeled using Darcy’s equations for the fluid flow through a porous media. The stress partition between the fluid and the fiber bed was included by means of Terzaghi’s effective stress theory. Tracking the fluid front during infusion was introduced by means of the level set method. The resulting partial differential equations for the fluid infusion and the evolution of flow front were discretized and solved approximately using the finite differences method with a uniform grid discretization of the spatial domain. The model results were validated against uniaxial VARI experiments through an [0]8 E-glass plain woven preform. The physical parameters of the model were also independently measured. The model results (in terms of the fabric thickness, pressure and fluid front evolution during filling) were in good agreement with the numerical simulations, showing the potential of the level set method to simulate resin infusion

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Hay un ejemplar encuadernado con: Individual noticia de todos los altares, arcos pinturas, adornos y lo mas exquisito y notable que havia en la carrera de la procession, y de las iluminaciones en general ... : (XVIII/1705).

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Hay un ejemplar encuadernado con: Romans, y coloqui nou, pera divertir el humor y desterrar la melancolia, yà que no tenim dinès ... (NP849.91/3085).

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Hay un ejemplar encuadernado con: Bandos divertidísimos contra los borrachos y borrachas, y gente aficionada al vino(NP849.91/3087).