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The integration of preferences into answer set programming constitutes an important practical device for distinguishing certain preferred answer sets from non-preferred ones. To this end, we elaborate upon rule dependency graphs and their colorings for characterizing different preference handling strategies found in the literature. We start from a characterization of (three types of) preferred answer sets in terms of totally colored dependency graphs. In particular, we demonstrate that this approach allows us to capture all three approaches to preferences in a uniform setting by means of the concept of a height function. In turn, we exemplarily develop an operational characterization of preferred answer sets in terms of operators on partial colorings for one particular strategy. In analogy to the notion of a derivation in proof theory, our operational characterization is expressed as a (non-deterministically formed) sequence of colorings, gradually turning an uncolored graph into a totally colored one
Antwortmengenprogrammierung
(2003)
In order to take full advantage of Grid environments, applications need to be able to run on various heterogeneous platforms. Distributed runs across several clusters or supercomputers for example, require matching binaries at each site. Thus, at some stage, each Grid enabled application needs to be recompiled for every platform. Up to now, creating matching binaries on different platforms was a manual, sequential, slow, and very error-prone process. Developers had to log into each machine, transfer source code, check consistency and recompile if necessary. This cumbersome procedure is surely one reason for the (still existing) lack of production Grid computing. Gridmake, a tool to automate and speed up this procedure is presented in this paper.
Characterizing Grids
(2003)
We present a new data model approach to describe the various objects that either represent the Grid infrastructure or make use of it. The data model is based on the experiences and experiments conducted in heterogeneous Grid environments. While very sophisticated data models exist to describe and characterize e.g. compute capacities or web services, we will show that a general description, which combines {em all} of these aspects, is needed to give an adequate representation of objects on a Grid. The Grid Object Description Language (GODsL)} is a generic and extensible approach to unify the various aspects that an object on a Grid can have. GODsL provides the content for the XML based communication in Grid migration scenarios, carried out in the GridLab project. We describe the data model architecture on a general level and focus on the Grid application scenarios.
We propose two methods that reduce the post-nonlinear blind source separation problem (PNL-BSS) to a linear BSS problem. The first method is based on the concept of maximal correlation: we apply the alternating conditional expectation (ACE) algorithm-a powerful technique from nonparametric statistics-to approximately invert the componentwise nonlinear functions. The second method is a Gaussianizing transformation, which is motivated by the fact that linearly mixed signals before nonlinear transformation are approximately Gaussian distributed. This heuristic, but simple and efficient procedure works as good as the ACE method. Using the framework provided by ACE, convergence can be proven. The optimal transformations obtained by ACE coincide with the sought-after inverse functions of the nonlinearitics. After equalizing the nonlinearities, temporal decorrelation separation (TDSEP) allows us to recover the source signals. Numerical simulations testing "ACE-TD" and "Gauss-TD" on realistic examples are performed with excellent results
A well-known result by Stein (1956) shows that in particular situations, biased estimators can yield better parameter estimates than their generally preferred unbiased counterparts. This letter follows the same spirit, as we will stabilize the unbiased generalization error estimates by regularization and finally obtain more robust model selection criteria for learning. We trade a small bias against a larger variance reduction, which has the beneficial effect of being more precise on a single training set. We focus on the subspace information criterion (SIC), which is an unbiased estimator of the expected generalization error measured by the reproducing kernel Hilbert space norm. SIC can be applied to the kernel regression, and it was shown in earlier experiments that a small regularization of SIC has a stabilization effect. However, it remained open how to appropriately determine the degree of regularization in SIC. In this article, we derive an unbiased estimator of the expected squared error, between SIC and the expected generalization error and propose determining the degree of regularization of SIC such that the estimator of the expected squared error is minimized. Computer simulations with artificial and real data sets illustrate that the proposed method works effectively for improving the precision of SIC, especially in the high-noise-level cases. We furthermore compare the proposed method to the original SIC, the cross-validation, and an empirical Bayesian method in ridge parameter selection, with good results