Abstract
Computer aided diagnosis systems with the capability of automatically decide if a patient has or not a pathology and to hold the decision on the dificult cases, are becoming more frequent. The latter are afterwards reviewed by an expert reducing therefore time consuption on behalf of the expert. The number of cases to review depends on the cost of erring the diagnosis. In this work we analyse the incorporation of the option to hold a decision on the diagnostic of pathologies on the vertebral column. A comparison with several state of the art techniques is performed. We conclude by showing that the use of the reject option techniques is an asset in line with the current view of the research community.

Abstract
This paper presents a model to solve the Generation Expansion Planning (GEP), problem in competitive electricity markets. The developed approach recognizes the presence of several generation agents aiming at maximizing their profits and that the planning environment is influenced by uncertainties affecting the demand, fuel prices, investment and maintenance costs and the electricity price. Several of these variables have interrelations between them turning it important to develop an approach that adequately captures the long-run behavior of electricity markets. In the developed approach we used System Dynamics to capture this behavior and to characterize the evolution of electricity prices and of the demand. Using this information, generation agents can then prepare their individual expansion plans. The resulting individual optimization problems have a mixed integer nature, justifying the use of Genetic Algorithms (GAs). Once individual plans are obtained, they are input once again on the System Dynamics model to update the evolution of the price, of the demand and of the capacity factors. This defines a feedback mechanism between the individual expansion planning problems and the long-term System Dynamics model. This approach can be used by a generation agent to build a robust expansion plan in the sense it can simulate different reactions of the other competitors and also by regulatory or state agencies to investigate the impact of regulatory decisions on the evolution of the generation system. Finally, the paper includes a Case Study to illustrate the use and the results of this approach.

Abstract
We propose a Hybrid Genetic Algorithm (HGA) for a combinatorial optimization problem, motivated by, and a simplification of, a TV Self-promotion Assignment Problem. Given the weekly self-promotion space (a set of TV breaks with known duration) and a set of products to promote, the problem consists of assigning the products to the breaks in the "best" possible way. The objective is to maximize contacts in the target audience for each product, whist satisfying all constraints. The HGA developed incorporates a greedy heuristic to initialize part of the population and uses a repair routine to guarantee feasibility of each member of the population. The HGA works on a simplified version of the problem that, nevertheless, maintains its essential features. The proposed simplified problem is a binary programming problem that has similarities with other known combinatorial optimization problems, such as the assignment problem or the multiple knapsack problem, but has some distinctive features that characterize it as a new problem. Although we are mainly interested in solving problems of large dimension (of about 200 breaks and 50 spots), the quality of the solution has been tested on smaller dimension problems for which we are able to find an exact global minimum using a branch-and-bound algorithm. For these smaller dimension problems we have obtained solutions, on average, within 1% of the optimal solution value.

Abstract
The World Wide Web (WWW) is a huge information network from which retrieving and organizing quality relevant content remains an open question for mostly all implicit temporal queries, i.e., queries without any date but with an underlying temporal intent. In this research, we aim at studying the temporal nature of any given query by means of web snippets or web query logs. For that purpose, we conducted a set of experiments, which goal is to assess the percentage of web snippets or queries (in query logs) having temporal features, thus checking whether they are a valuable source of data to help on inferring the temporal intent of queries, namely implicit ones. Our results show that web snippets, as opposed to web query logs, are an important source of concentrated information, where time clues often appear. As a consequence, they can be particularly useful to identify and understand "on-the-fly" the implicit temporal nature of queries in the context of ephemeral clustering.

Abstract
Image binarization is a common operation in the preprocessing stage in most Optical Music Recognition (OMR) systems. The choice of an appropriate binarization method for handwritten music scores is a difficult problem. Several works have already evaluated the performance of existing binarization processes in diverse applications. However, no goal-directed studies for music sheets documents were carried out. This paper presents a novel binarization method based in the content knowledge of the image. The method only needs the estimation of the staffline thickness and the vertical distance between two stafflines. This information is extracted directly from the gray level music score. The proposed binarization procedure is experimentally compared with several state of the art methods.

Abstract
In this work we address the Singe-Source Uncapacitated Minimum Cost Network Flow Problem with concave cost functions. Given that this problem is of a combinatorial nature and also that the total costs are nonlinear, we propose a hybrid heuristic to solve it. In this type of algorithms one usually tries to manage two conflicting aspects of searching behaviour: exploration, the algorithm's ability to search broadly through the search space; and exploitation, the algorithm ability to search locally around good solutions that have been found previously. In our case, we use an Ant Colony Optimization algorithm to mainly deal with the exploration, and a Local Search algorithm to cope with the exploitation of the search space. Our method proves to be very efficient while solving both small and large size problem instances. The problems we have used to test the algorithm were previously solved by other authors using other population based heuristics and our algorithm was able to improve upon their results, both in terms of computing time and solution quality.