182 research outputs found

    Finite size corrections to random Boolean networks

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    Since their introduction, Boolean networks have been traditionally studied in view of their rich dynamical behavior under different update protocols and for their qualitative analogy with cell regulatory networks. More recently, tools borrowed from statistical physics of disordered systems and from computer science have provided a more complete characterization of their equilibrium behavior. However, the largest part of the results have been obtained in the thermodynamic limit, which is often far from being reached when dealing with realistic instances of the problem. The numerical analysis presented here aims at comparing - for a specific family of models - the outcomes given by the heuristic belief propagation algorithm with those given by exhaustive enumeration. In the second part of the paper some analytical considerations on the validity of the annealed approximation are discussed.Comment: Minor correction

    Exhaustive enumeration unveils clustering and freezing in random 3-SAT

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    We study geometrical properties of the complete set of solutions of the random 3-satisfiability problem. We show that even for moderate system sizes the number of clusters corresponds surprisingly well with the theoretic asymptotic prediction. We locate the freezing transition in the space of solutions which has been conjectured to be relevant in explaining the onset of computational hardness in random constraint satisfaction problems.Comment: 4 pages, 3 figure

    Efficiently mining long patterns from databases

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    An experiment with association rules and classification: post-bagging and conviction

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    In this paper we study a new technique we call post-bagging, which consists in resampling parts of a classification model rather then the data. We do this with a particular kind of model: large sets of classification association rules, and in combination with ordinary best rule and weighted voting approaches. We empirically evaluate the effects of the technique in terms of classification accuracy. We also discuss the predictive power of different metrics used for association rule mining, such as confidence, lift, conviction and X². We conclude that, for the described experimental conditions, post-bagging improves classification results and that the best metric is conviction.Programa de Financiamento Plurianual de Unidades de I & D.Comunidade Europeia (CE). Fundo Europeu de Desenvolvimento Regional (FEDER).Fundação para a Ciência e a Tecnologia (FCT) - POSI/SRI/39630/2001/Class Project

    On the complexity of strongly connected components in directed hypergraphs

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    We study the complexity of some algorithmic problems on directed hypergraphs and their strongly connected components (SCCs). The main contribution is an almost linear time algorithm computing the terminal strongly connected components (i.e. SCCs which do not reach any components but themselves). "Almost linear" here means that the complexity of the algorithm is linear in the size of the hypergraph up to a factor alpha(n), where alpha is the inverse of Ackermann function, and n is the number of vertices. Our motivation to study this problem arises from a recent application of directed hypergraphs to computational tropical geometry. We also discuss the problem of computing all SCCs. We establish a superlinear lower bound on the size of the transitive reduction of the reachability relation in directed hypergraphs, showing that it is combinatorially more complex than in directed graphs. Besides, we prove a linear time reduction from the well-studied problem of finding all minimal sets among a given family to the problem of computing the SCCs. Only subquadratic time algorithms are known for the former problem. These results strongly suggest that the problem of computing the SCCs is harder in directed hypergraphs than in directed graphs.Comment: v1: 32 pages, 7 figures; v2: revised version, 34 pages, 7 figure

    Mining association rules for label ranking

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    Lecture Notes in Computer Science Volume 6635, 2011.Recently, a number of learning algorithms have been adapted for label ranking, including instance-based and tree-based methods. In this paper, we continue this line of work by proposing an adaptation of association rules for label ranking based on the APRIORI algorithm. Given that the original APRIORI algorithm does not aim to obtain predictive models, two changes were needed for this achievement. The adaptation essentially consists of using variations of the support and confidence measures based on ranking similarity functions that are suitable for label ranking. Additionally we propose a simple greedy method to select the parameters of the algorithm. We also adapt the method to make a prediction from the possibly con icting consequents of the rules that apply to an example. Despite having made our adaptation from a very simple variant of association rules for classification, partial results clearly show that the method is making valid predictions. Additionally, they show that it competes well with state-of-the-art label ranking algorithms.This work was partially supported by project Rank! (PTDC/EIA/81178/2006) from FCT and Palco AdI project Palco3.0 financed by QREN and Fundo Europeu de Desenvolvimento Regional (FEDER). We thank the anonymous referees for useful comments

    Introducing Autonomic Behaviour in Semantic Web Agents

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    Medios de comunicación y derecho a la información en Jalisco, 2015

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    El octavo informe Q ITESO: Análisis Crítico de Medios revisa el funcionamiento del sistema de comunicación política durante el proceso electoral de 2015, así como diversos aspectos relevantes de unas elecciones que culminaron con un cambio radical en el panorama político en Jalisco. En el universo de los medios de comunicación, se analizan los cambios experimentados por estos en el marco de la coyuntura electoral local, la equidad y profundidad en la cobertura por parte de los periódicos y la difusión que hicieron de las encuestas, así como el discurso e impacto de la propaganda difundida a través de la televisión y la Internet, a lo que se suma los debates registrados en redes sociales como Twitter, y la percepción sobre las campañas por parte de la audiencia tapatía. El examen político se enfoca en la campaña realizada por los candidatos independientes, el planteamiento socioeconómico de los contendientes por la capital del estado y el impacto electoral de un personaje incómodo como el papá del gobernador, para culminar este informe con una reflexión general y un balance de quiénes perdieron y quiénes ganaron al término de las elecciones de 2015.ITESO, A.C
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