Randomised Mutual Search

We study the efficiency of randomised solutions to the mutual search problem of finding k agents distributed over n nodes. For a restricted class of so-called linear randomised mutual search algorithms we derive a lower bound of k−1 k+1 (n+1) expected calls in the worst case. A randomised algorithm in the shared-coins model matching this bound is also presented. Finally we show that in general more adaptive randomized mutual algorithms perform better (using k−1+k−1k+1− k−2n(n−k) worst case expected calls in the shared coins model) than the lower bound for the restricted case, even when given only private coins. A lower bound of k − 1 + n−k k+1 for this case is also derived

Detection thresholding using mutual information

In this paper, we introduce a novel non-parametric thresholding method that we term Mutual-Information Thresholding. In our approach, we choose the two detection thresholds for two input signals such that the mutual information between the thresholded signals is maximised. Two efficient algorithms implementing our idea are presented: one using dynamic programming to fully explore the quantised search space and the other method using the Simplex algorithm to perform gradient ascent to significantly speed up the search, under the assumption of surface convexity. We demonstrate the effectiveness of our approach in foreground detection (using multi-modal data) and as a component in a person detection system

Feature selection for chemical sensor arrays using mutual information

We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays

Credible Threats in a Wage Bargaining Model with on-the-job Search

In standard equilibrium search models with strategic wage bargaining and on-the-job search, renegotiation is permitted without requirement of a credible threat. Workers trigger renegotiation whenever they have a new outside option that could raise their wages. In this note I modify the model to be consistent with renegotiation by mutual agreement and I show that estimating the model without imposing credible threats for renegotiation generates downward bias in the estimates of the bargaining power.Credible Threats; On-the-job search; Wage bargaining