We discuss a generic model of Bayesian inference with binary variables defined on edges of a planar graph. The Loop Calculus approach of [1, 2] is used to evaluate the resulting series expansion for the partition function. We show that, for planar graphs, truncating the series at single-connected loops reduces, via a map reminiscent of the Fisher transformation [3], to evaluating the partition function of the dimer matching model on an auxiliary planar graph. Thus, the truncated series can be easily re-summed, using the Pfaffian formula of Kasteleyn [4]. This allows to identify a big class of computationally tractable planar models reducible to a dimer model via the Belief Propagation (gauge) transformation. The Pfaffian representation can also be extended to the full Loop Series, in which case the expansion becomes a sum of Pfaffian contributions, each associated with dimer matchings on an extension to a subgraph of the original graph. Algorithmic consequences of the Pfaffian representation, as well as relations to quantum and non-planar models, are discussed.

This paper describes application of information granulation theory, on the analysis of "lugeon data". In this manner, using a combining of Self Organizing Map (SOM) and Neuro-Fuzzy Inference System (NFIS), crisp and fuzzy granules are obtained. Balancing of crisp granules and sub- fuzzy granules, within non fuzzy information (initial granulation), is rendered in open-close iteration. Using two criteria, "simplicity of rules "and "suitable adaptive threshold error level", stability of algorithm is guaranteed. In other part of paper, rough set theory (RST), to approximate analysis, has been employed >.Validation of the proposed methods, on the large data set of in-situ permeability in rock masses, in the Shivashan dam, Iran, has been highlighted. By the implementation of the proposed algorithm on the lugeon data set, was proved the suggested method, relating the approximate analysis on the permeability, could be applied.

This paper pursues some applications of Rough Set Theory (RST) and neural-fuzzy model to analysis of "lugeon data". In the manner, using Self Organizing Map (SOM) as a pre-processing the data are scaled and then the dominant rules by RST, are elicited. Based on these rules variations of permeability in the different levels of Shivashan dam, Iran has been highlighted. Then, via using a combining of SOM and an adaptive Neuro-Fuzzy Inference System (NFIS) another analysis on the data was carried out. Finally, a brief comparison between the obtained results of RST and SOM-NFIS (briefly SONFIS) has been rendered.

The eighteenth annual International Workshop on Principles of Diagnosis was held in Nashville, Tennessee, May 29–31, 2007. Papers presented at the workshop covered a variety of theories, principles, and computational techniques for diagnosis, monitoring, testing, reconfiguration, fault-adaptive control, and repair of complex systems. This year’s workshop emphasized inter-actions and exchange of ideas and experiences between researchers and practitioners whose backgrounds included AI, control theory, systems engineering, software engineering, and related areas.

We consider how a search engine should select advertisements to display with search results, in order to maximize its revenue. Under the standard "pay-per-click" arrangement, revenue depends on how well the displayed advertisements appeal to users. The main difficulty stems from new advertisements whosedegree of appeal has yet to be determined. Often the only reliable way of determining appeal is exploration via display to users, which detracts from exploitation of other advertisements known to have high appeal. Budget constraints and finite advertisement lifetimes make it necessary to explore as well as exploit. In this paper we study the tradeoff between exploration and exploitation, modeling advertisement placement as a multi-armed bandit problem. We extend traditional bandit formulations to account for budget constraints that occur in search engine advertising markets, and derive theoretical bounds on the performance of a family of algorithms.

We consider how a search engine should select advertisements to display with search results, in order to maximize its revenue. Under the standard "pay-per-click" arrangement, revenue depends on how well the displayed advertisements appeal to users. The main difficulty stems from new advertisements whose degree of appeal has yet to be determined. Often the only reliable way of determining appeal is exploration via display to users, which detracts from exploitation of other advertisements known to have high appeal. Budget constraints and finite advertisement lifetimes make it necessary to explore as well as exploit. In this paper we study the tradeoff between exploration and exploitation, modeling advertisement placement as a multi-armed bandit problem. We extend traditional bandit formulations to account for budget constraints that occur in search engine advertising markets, and derive theoretical bounds on the performance of a family of algorithms.

We consider how a search engine should select advertisements to display with search results, in order to maximize its revenue. Under the standard "pay-per-click" arrangement, revenue depends on how well the displayed advertisements appeal to users. The main difficulty stems from new advertisements whose degree of appeal has yet to be determined. Often the only reliable way of determining appeal is exploration via display to users, which detracts from exploitation of other advertisements known to have high appeal. Budget constraints and finite advertisement lifetimes make it necessary to explore as well as exploit. In this paper we study the tradeoff between exploration and exploitation, modeling advertisement placement as a multi-armed bandit problem. We extend traditional bandit formulations to account for budget constraints that occur in search engine advertising markets, and derive theoretical bounds on the performance of a family of algorithms.

Contributing to the rigorous understanding of BP, in this paper we relate the convergence of BP to spectral properties of the graph. This encompasses a result for random graphs with a ``planted'' solution; thus, we obtain the first rigorous result on BP for graph coloring in the case of a complex graphical structure (as opposed to trees). In particular, the analysis shows how Belief Propagation breaks the symmetry between the $3!$ possible permutations of the color classes.

Previous work in social network analysis (SNA) has modeled the existence of links from one entity to another, but not the attributes such as language content or topics on those links. We present the Author-Recipient-Topic (ART) model for social network analysis, which learns topic distributions based on the direction-sensitive messages sent between entities. The model builds on Latent Dirichlet Allocation (LDA) and the Author-Topic (AT) model, adding the key attribute that distribution over topics is conditioned distinctly on both the sender and recipient---steering the discovery of topics according to the relationships between people. We give results on both the Enron email corpus and a researcher's email archive, providing evidence not only that clearly relevant topics are discovered, but that the ART model better predicts people's roles and gives lower perplexity on previously unseen messages. We also present the Role-Author-Recipient-Topic (RART) model, an extension to ART that explicitly represents people's roles.

Message passing algorithms have proved surprisingly successful in solving hard constraint satisfaction problems on sparse random graphs. In such applications, variables are fixed sequentially to satisfy the constraints. Message passing is run after each step. Its outcome provides an heuristic to make choices at next step. This approach has been referred to as `decimation,' with reference to analogous procedures in statistical physics. The behavior of decimation procedures is poorly understood. Here we consider a simple randomized decimation algorithm based on belief propagation (BP), and analyze its behavior on random k-satisfiability formulae. In particular, we propose a tree model for its analysis and we conjecture that it provides asymptotically exact predictions in the limit of large instances. This conjecture is confirmed by numerical simulations.