A key question in cooperative game theory is that of coalitional stability, usually captured by the notion of the \emph{core}--the set of outcomes such that no subgroup of players has an incentive to deviate. However, some coalitional games have empty cores, and any outcome in such a game is unstable. In this paper, we investigate the possibility of stabilizing a coalitional game by using external payments. We consider a scenario where an external party, which is interested in having the players work together, offers a supplemental payment to the grand coalition (or, more generally, a particular coalition structure). This payment is conditional on players not deviating from their coalition(s). The sum of this payment plus the actual gains of the coalition(s) may then be divided among the agents so as to promote stability. We define the \emph{cost of stability (CoS)} as the minimal external payment that stabilizes the game. We provide general bounds on the cost of stability in several classes of games, and explore its algorithmic properties. To develop a better intuition for the concepts we introduce, we provide a detailed algorithmic study of the cost of stability in weighted voting games, a simple but expressive class of games which can model decision-making in political bodies, and cooperation in multiagent settings. Finally, we extend our model and results to games with coalition structures.

Turing [8] introduced the concept of "computing machines" which subsequently were called Turing machines. He proved that Hilbert's decision problem(Entscheidungsproblem) is unsolvable, that is, there is no Turing machine determining whether or not a given statement in first-order predicate calculus (a mathematical proposition in number theory) can be proved. Wegner [11] writes that Turing's precise characterization of what can be computed established the respectability of computer science as a discipline. He argues that Turing machines cannot capture the intuitive notion of what computers compute when computing is extended to include interaction. His interaction machines have been criticized as an unnecessary Kuhnian paradigm shift [12]. Prasse and Rittgen [7] write that Wegner's "interaction machines cannot compute non-recursive functions, so Church's thesis still holds". This implies that interaction machines cannot "compute" functions not computable by Turing machines. This work is licensed under the Creative Commons Attribution-No Derivative Works 3.0 Unported License (see http://creativecommons.org/licenses/by-nd/3.0/).

Probability Density Estimation (PDE) is a multivariate discrimination technique based on sampling signal and background densities defined by event samples from data or Monte-Carlo (MC) simulations in a multi-dimensional phase space. In this paper, we present a modification of the PDE method that uses a self-adapting binning method to divide the multi-dimensional phase space in a finite number of hyper-rectangles (cells). The binning algorithm adjusts the size and position of a predefined number of cells inside the multi-dimensional phase space, minimising the variance of the signal and background densities inside the cells. The implementation of the binning algorithm PDE-Foam is based on the MC event-generation package Foam. We present performance results for representative examples (toy models) and discuss the dependence of the obtained results on the choice of parameters. The new PDE-Foam shows improved classification capability for small training samples and reduced classification time compared to the original PDE method based on range searching.

We present a procedure for effective estimation of entropy and mutual information from small-sample data, and apply it to the problem of inferring high-dimensional gene association networks. Specifically, we develop a James-Stein-type shrinkage estimator, resulting in a procedure that is highly efficient statistically as well as computationally. Despite its simplicity, we show that it outperforms eight other entropy estimation procedures across a diverse range of sampling scenarios and data-generating models, even in cases of severe undersampling. We illustrate the approach by analyzing E. coli gene expression data and computing an entropy-based gene-association network from gene expression data. A computer program is available that implements the proposed shrinkage estimator.

W e give improved constants for data dependent and variance sensitive confidence bounds, called empirical Bernstein bounds, and extend these inequalities to hold uniformly over classes of functions whose growth function is polynomial in the sample size n . The bounds lead us to consider sample variance penalization, a novel learning method which takes into account the empirical variance of the loss function. W e give conditions under which sample variance penalization is effective. In particular, we present a bound on the excess risk incurred by the method. Using this, we argue that there are situations in which the excess risk of our method is of order 1 /n, while the excess risk of empirical risk minimization is of order 1 / n . W e show some experimental results, which confirm the theory. Finally, we discuss the potential application of our results to sample compression schemes.

Music genre classification is an essential tool for music information retrieval systems and it has been finding critical applications in various media platforms. Two important problems of the automatic music genre classification are feature extraction and classifier design. This paper investigates inter-genre similarity modelling (IGS) to improve the performance of automatic music genre classification. Inter-genre similarity information is extracted over the mis-classified feature population. Once the inter-genre similarity is modelled, elimination of the inter-genre similarity reduces the inter-genre confusion and improves the identification rates. Inter-genre similarity modelling is further improved with iterative IGS modelling(IIGS) and score modelling for IGS elimination(SMIGS). Experimental results with promising classification improvements are provided.

Graph theory provides a primary tool for analyzing and designing computer communication networks. In the past few decades, Graph theory has been used to study various types of networks, including the Internet, wide Area Networks, Local Area Networks, and networking protocols such as border Gateway Protocol, Open shortest Path Protocol, and Networking Networks. In this paper, we present some key graph theory concepts used to represent different types of networks. Then we describe how networks are modeled to investigate problems related to network protocols. Finally, we present some of the tools used to generate graph for representing practical networks.

The article describes the proposition and application of a local search metaheuristic for multi-objective optimization problems. It is based on two main principles of heuristic search, intensification through variable neighborhoods, and diversification through perturbations and successive iterations in favorable regions of the search space. The concept is successfully tested on permutation flow shop scheduling problems under multiple objectives and compared to other local search approaches. While the obtained results are encouraging in terms of their quality, another positive attribute of the approach is its simplicity as it does require the setting of only very few parameters.

The article presents a study of rather simple local search heuristics for the single machine total weighted tardiness problem (SMTWTP), namely hillclimbing and Variable Neighborhood Search. In particular, we revisit these approaches for the SMTWTP as there appears to be a lack of appropriate/challenging benchmark instances in this case. The obtained results are impressive indeed. Only few instances remain unsolved, and even those are approximated within 1% of the optimal/best known solutions. Our experiments support the claim that metaheuristics for the SMTWTP are very likely to lead to good results, and that, before refining search strategies, more work must be done with regard to the proposition of benchmark data. Some recommendations for the construction of such data sets are derived from our investigations.

In this paper, we propose a first-order ontology for generalized stratified order structure. We then classify the models of the theory using model-theoretic techniques. An ontology mapping from this ontology to the core theory of Process Specification Language is also discussed.