A priori optimization for the probabilistic maximum independent set problem

We first propose a formal definition for the concept of probabilistic combinatorial optimization problem (under the a priori method). Next, we study the complexity of optimally solving probabilistic maximum independent set problem under several a priori optimization strategies as well as the complexity of approximating optimal solutions. For the different strategies studied, we present results about the restriction of probabilistic independent set on bipartite graphs.     

Local communities towards a sustainable energy future: needs and priorities

The local authorities demonstrate their willingness to implement sound local sustainable energy policies, especially through their participation in the Covenant of Mayors (CoM). However, in rural environments, namely areas outside of large cities and towns, fulfilling their CoM commitments, especially as regards the local energy planning at the medium- to long-term scale, can come with very different and sometimes challenging constraints. In this context, the main objective of this paper is the assessment of the local communities’ needs and priorities, so as to identify the key parameters that should be taken into consideration during the development of their Sustainable Energy Action Plan. The adopted approach was implemented in rural communities from four countries (Austria, Croatia, Greece and Portugal). From the results obtained, the need for a methodology, appropriately customised to the rural communities’ characteristics, was determined, addressing especially interested stakeholders who are not ‘experts’ in the field.     

Problem decomposition in distributed problem-solving systems

Distributed Problem Solving (DPS) is defined as the cooperative solution of problems by a decentralized and loosely coupled collection of problem solvers (agents), each of them knowing how to execute only some of the necessary tasks. This approach considers the problem-solving process as occurring in three phases: problem decomposition, subproblem solution, and answer synthesis. In the problem decomposition phase, one has to determine which tasks will be executed by each agent and when. One of the key research questions in the problem decomposition process is how to decompose a problem in order to minimize the cost of resources needed for its solution. In this article, we construct mathematical programming models in order to describe the decomposition process under the above criterion, study its complexity, and present exact and heuristic algorithms for its solution. Our work was motivated by the operation of an actual system that can be considered as a distributed problem solver for the assessment of irrigation projects design.     

Towards building a blog preservation platform

Social media content and user participation has increased dramatically since the advent of Web 2.0. Blogs have become relevant to every aspect of business and personal life. Nevertheless, we do not have the right tools to aggregate and preserve blog content correctly, as well as to manage blog archives effectively. Given the rising importance of blogs, it is crucial to build systems to facilitate blog preservation, safeguarding an essential part of our heritage that will prove valuable for current and future generations. In this paper, we present our work in progress towards building a novel blog preservation platform featuring robust digital preservation, management and dissemination facilities for blogs. This work is part of the BlogForever project which is aiming to make an impact to the theory and practice of blog preservation by creating guidelines and software that any individual or organization could use to preserve their blogs.     

Fast Algorithms for max independent set

We first propose a method, called “bottom-up method” that, informally, “propagates” improvement of the worst-case complexity for “sparse” instances to “denser” ones and we show an easy though non-trivial application of it to the min set cover problem. We then tackle max independent set. Here, we propagate improvements of worst-case complexity from graphs of average degree?d to graphs of average degree greater than?d. Indeed, using algorithms for max independent set in graphs of average degree 3, we successively solve max independent set in graphs of average degree 4, 5 and?6. Then, we combine the bottom-up technique with measure and conquer techniques to get improved running times for graphs of maximum degree?5 and?6 but also for general graphs. The computation bounds obtained for max independent set are?O ^?(1.1571 ⁿ ), O ^?(1.1895 ⁿ ) and?O ^?(1.2050 ⁿ ), for graphs of maximum (or more generally average) degree?4, 5 and?6 respectively, and?O ^?(1.2114 ⁿ ) for general graphs. These results improve upon the best known results for these cases for polynomial space algorithms.     

Boosted ranking models: a unifying framework for ranking predictions

Ranking is an important functionality in a diverse array of applications, including web search, similarity-based multimedia retrieval, nearest neighbor classification, and recommendation systems. In this paper, we propose a new method, called Boosted Ranking Model (BRM), for learning how to rank from training data. An important feature of the proposed method is that it is domain-independent and can thus be applied to a wide range of ranking domains. The main contribution of the new method is that it reduces the problem of learning how to rank to the much more simple, and well-studied problem of constructing an optimized binary classifier from simple, weak classifiers. Using that reduction, our method constructs an optimized ranking model using multiple simple, easy-to-define ranking models as building blocks. The new method is a unifying framework that includes, as special cases, specific methods that we have proposed in earlier publications for specific ranking applications, such as nearest neighbor retrieval and classification. In this paper, we reformulate those earlier methods as special cases of the proposed BRM method, and we also illustrate a novel application of BRM, on the problem of making movie recommendations to individual users.     

High-grade glioma diffusive modeling using statistical tissue information and diffusion tensors extracted from atlases

Glioma, especially glioblastoma, is a leading cause of brain cancer fatality involving highly invasive and neoplastic growth. Diffusive models of glioma growth use variations of the diffusion-reaction equation in order to simulate the invasive patterns of glioma cells by approximating the spatiotemporal change of glioma cell concentration. The most advanced diffusive models take into consideration the heterogeneous velocity of glioma in gray and white matter, by using two different discrete diffusion coefficients in these areas. Moreover, by using diffusion tensor imaging (DTI), they simulate the anisotropic migration of glioma cells, which is facilitated along white fibers, assuming diffusion tensors with different diffusion coefficients along each candidate direction of growth. Our study extends this concept by fully exploiting the proportions of white and gray matter extracted by normal brain atlases, rather than discretizing diffusion coefficients. Moreover, the proportions of white and gray matter, as well as the diffusion tensors, are extracted by the respective atlases; thus, no DTI processing is needed. Finally, we applied this novel glioma growth model on real data and the results indicate that prognostication rates can be improved.     

Sectoral linkages and industrial efficiency: a dilemma or a requisition in identifying development priorities?

This paper attempts to provide an empirical evaluation of the potential relationship between sectoral linkages (backward and forward) and technical efficiency using the 1995 input–output tables for 14 EU countries. Sectoral technical efficiency is obtained by the econometric estimation of a “multilateral” stochastic input distance function, while sectoral backward and forward linkage coefficients were computed using the noncomplete hypothetical extraction method suggested by Dietzenbacher and Van der Linden (J Reg Sci 37:235–257, 1997). The empirical results suggest that the relationship between industrial technical efficiency and sectoral interdependence is ambiguous. Although the majority of the countries, in the sample exhibit a negative relationship, for some countries, the opposite is revealed. This implies that policy makers should not be blindly based on sectoral interdependence in forming development plans, and they should take into consideration the efficiency of resource utilization of individual sectors. The combination of the information provided by both indices will assist in devising effective policy plans in stimulating internal economic growth.