We investigate the problem of learning a classification task on data represented in terms of their pairwise proximities. This representation does not refer to an explicit feature representation of the data items and is thus more general than the standard approach of using Euclidean feature vectors, from which pairwise proximities can always be calculated. Our first approach is based on a combin...

A reparametrization of the Generalized Pareto Distribution is here proposed. It is suitable to parsimoniously check trend assumptions within a Point-OverThreshold model of hazardous events. This is based on considerations about the scale of both the excesses of the event magnitudes and the distribution parameters. The usefulness of this approach is illustrated with a data set from two buoys, wh...

We prove that all the translation invariant Gibbs states of the Ising model are a linear combination of the pure phases μ+β , μ − β for any β 6= βc. This implies that the average magnetization is continuous for β > βc. Furthermore, combined with previous results on the slab percolation threshold [B2] this shows the validity of Pisztora’s coarse graining [Pi] up to the critical temperature.

Causal independence modelling is a well-known method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. Many Bayesian network models incorporate causal independence assumptions; however, only the noisy OR and noisy AND, two examples of causal independence models, are used in practice. Their underlying assumption that either at lea...

Message-passing methods provide a powerful approach for calculating the expected size of cascades either on random networks (e.g., drawn from a configuration-model ensemble or its generalizations) asymptotically as the number N of nodes becomes infinite or on specific finite-size networks. We review the message-passing approach and show how to derive it for configurationmodel networks using the...

How will a virus propagate in a real network? Does an epidemic threshold exist for a finite powerlaw graph, or any finite graph? How long does it take to disinfect a network given particular values of infection rate and virus death rate? We answer the first question by providing equations that accurately model virus propagation in any network including real and synthesized network graphs. We pr...

In this paper we study the simultaneous problems of food waste and hunger in the context of the possible solution of food (waste) rescue and redistribution. To this end, we develop an empirical model that can be used in Monte Carlo simulations to study the dynamics of the underlying problem. Our model’s parameters are derived from a unique data set provided by a large food bank and food rescue ...

Economic lifting has made email spam a scathing threat to the society due to its related exploits. Many spam detection schemes have been proposed employing the tendency of spam to alter the normal statistical behavior of mail traffic. Threshold tuning of these detectors is still a challenging task. Since, shooting down benign emails as spam (false positive), in pursuit of higher detection rates...

Assume individuals are treated if a latent variable, containing a continuous instrument, lies between two thresholds. We place no functional form restrictions on the latent errors. Here unconfoundedness does not hold and identification at infinity is not possible. Yet we still show nonparametric point identification of the average treatment effect. We provide an associated root-n consistent est...

This paper explores nonlinear dynamics for the time series of the short term interest rate in the United States. The proposed model is an autoregressive threshold model augmented by conditional heteroskedasticity. The performance of the model is evaluated by considering its implications for the term structure of interest rates. The nonlinear dynamics imply a form of nonlin-earity in the levels ...