Abstract
A linear system is a fundamental building block for several mesh-based computer graphics applications such as simulation, shape deformation, virtual surgery, and fluid/smoke animation, among others. Nevertheless, such a system is most of the times seen as a black box and algorithms do not deal with its optimization. Depending on the number of unknowns, the linear system is often considered as an obstacle for real time application and as a building block for offline computations. We present in this paper, a neighboring-based methodology for representing a linear system. This new representation enables a compact storage of the set of equation, flexibility for ordering the unknowns and a rapid iterative solution, by means of an optimized matrix-vector multiplication. In addition, this representation facilitates the modification of part of the linear system without affecting its unchanged part and avoiding the complete rebuild of the system. This specially benefits applications dealing with dynamic meshes, where the geometry, the topology or both are constantly changed. We present the capabilities of our methodology in models with different sizes and for different operations, highlighting the dynamic characteristic of the mesh. We believe that several applications in computer graphics could benefit from our methodology, in order to improve their convergence and their performance, reducing the number of iterations and the computation time. © The Eurographics Association 2009.

Abstract
The current exponential growth of data calls for massive-scale capabilities of storage and processing. Such large volumes of data tend to disallow their centralized storage and processing making extensive and flexible data partitioning unavoidable. This is being acknowledged by several major Internet players embracing the Cloud computing model and offering first generation remote storage services with simple processing capabilities. In this position paper we present preliminary ideas for the architecture of a flexible, efficient and dependable fully decentralized object store able to manage very large sets of variable size objects and to coordinate in place processing. Our target are local area large computing facilities composed of tens of thousands of nodes under the same administrative domain. The system should be capable of leveraging massive replication of data to balance read scalability and fault tolerance. Copyright 2009 ACM.

Abstract
A fiber-optic Fabry-Perot sensing structure based on the utilization of a suspended-core fiber is presented. The interferometric structure is formed when a small length of the suspended-core fiber is spliced to the end of a standard single-mode fiber. The interfering waves are generated by the refractive-index mismatches between the two fibers in the splice region and at the end of the suspended-core fiber. Thermal and strain responses of two different sensing heads associated with suspended-core fibers with three and four holes are characterized.

Abstract
This paper describes the methodology developed to build estimates of electricity prices having the horizon of one week. This approach uses artificial neural networks and includes a particular treatment of weekends and national holidays as a way to improve the quality of the results. The developed approach was tested using data obtained from the Spanish market operator for the time period of 2006 to 2008. The obtained value of MAPE - Mean Absolute Percentage Error - was 12,62% for workdays and 10,73% for holidays and weekends. The obtained results show that this study has interest to the market agents in question, since realistic forecasting was achieved.

Abstract
In this work, we discuss normal forms of necessary conditions of optimality (NCO) for optimal control problems subject to pathwise state constraints and for problems in the calculus of variations with inequality constraints. It is known that standard forms of the NCO may fail to provide information that is useful to identify optimal solutions, namely when the multiplier associated with the objective function takes the value zero. The normal forms of the NCO guarantee that the conditions remain always informative, which is of importance in critical applications where decisions based on optimization are taken, such as autonomous systems. Based on a previous nondegenerate maximum principle for optimal control problems, we extend the strengthness of these conditions to normality while applying them to the particular case of calculus of variations problems. We compare our results with existent normal forms of NCO for dynamic optimization problems and conclude that, when applied to calculus of variations problems, we may say that, under similar conditions, we can apply such result to a wider class of problems, having less regularity on the data.

Abstract