We present a two-dimensional (2-D) fitting algorithm (GALFIT) designed to extract structural components from galaxy images, with emphasis on closely modeling light profiles of spatially well-resolved, nearby galaxies observed with the Hubble Space Telescope. Our algorithm improves on previous techniques in two areas, by being able to simultaneously fit a galaxy with an arbitrary number of compo...

Hypergraphs arise in a variety of applications and are commonly classiied as cyclic or acyclic. In this paper we develop a more reened classiication scheme for cyclic hypergraphs based on a natural decomposition strategy. The fundamental building blocks in our decompositions are subsets of edges known as k-hinges. For any hypergraph, a set of more than k of its edges is deened to be a k-hinge i...

A method for modular design of structural products such as automotive bodies is presented where two structural products are simultaneously decomposed to components considering the structural performances of each structure and the component sharing between two structures. The problem is posed as an optimization to minimize the reduction of structural strength due to the introduction of spot-weld...

The analysis and modeling of the response of parts of the body provides valuable insight into many features of the head-related transfer function (HRTF). In spatial sound simulations, partial models, such as the spherical head model, can also generate simple and effective approximate localization cues. In this paper, we consider the composition of an approximate HRTF from the responses of struc...

We consider the problem of selecting labels in vertices of the graph in order to maximize sum of weights, assigned to pairs of labels ( (max,+) problem, MAP MRF, minimization of Gibbs energy). We construct upper bounds on the goal function as sum of two auxiliary superand submodular problems. The tightest possible bound of this type can be found by solving a special case of LP. It's solution al...

Chain graphs present a broad class of graphical models for description of conditional independence structures, including both Markov networks and Bayesian networks as special cases. In this paper, we propose a computationally feasible method for the structural learning of chain graphs based on the idea of decomposing the learning problem into a set of smaller scale problems on its decomposed su...

Complex engineering systems require efficient fault diagnosis methodologies, but centralized approaches do not scale well, and this motivates the development of distributed solutions. This work presents an event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, by using the structural model decomposition capabilities provided by Possible Conflicts. We d...