This paper presents a method for the decomposition of HML formulas. It can be used to decide whether a process algebra term satisfies a HML formula, by checking whether subterms satisfy certain formulas, obtained by decomposing the original formula. The method uses the structural operational semantics of the process algebra. The main contribution of this paper is the extension of an earlier dec...

It is a challenging task of learning a large Bayesian network from a small data set. Most conventional structural learning approaches run into the computational as well as the statistical problems. We propose a decomposition algorithm for the structure construction without having to learn the complete network. The new learning algorithm firstly finds local components from the data, and then rec...

Chemical structures of photocured acrylic ester resins were characterized using specific decomposition techniques such as pyrolysis-gas chromatography in the presence of organic alkali and matrixassisted laser desorption/ionization mass spectrometry combined with super critical methanolysis. The resultant products formed through specific cleavage at the ester linkages in the cured resin samples...

The paper addresses structural decomposition of images by using a family of non-linear and non-convex objective functions. These functions rely on `p quasi-norm estimation costs in a piecewise constant regularization framework. These objectives make image decomposition into constant cartoon levels and rich textural patterns possible. The paper shows that these regularizing objectives yield imag...

This paper presents a reexamination of a multiscale computational strategy with homogenization in space and time for the resolution of highly heterogeneous structural problems, focusing on its suitability for parallel computing. Spatially, this strategy can be viewed as a mixed, multilevel domain decomposition method (or, more accurately, as a “structure decomposition” method). Regarding time, ...

We discuss features extracted from a wavelet packet decomposition for image classification. Statistical features computed from wavelet packet coefficients are compared to structural features which are derived from an image dependent wavelet packet decomposition subband structure. Primary application area is the classification of pit pattern structures in zoom-endoscopic colon imagery, while res...

Constraint satisfaction problems (CSPs) are NP-complete in general, therefore it is important to identify tractable subclasses. A possible way to find such subclasses is to associate a hypergraph to the problem and impose restrictions on its structure. In this thesis we follow this direction. Among such structural properties, particularly important is acyclicity: it is well known that CSPs whos...