Distributed Relay Protocol for Probabilistic Information-Theoretic Security in a Randomly-Compromised Network

We introduce a simple, practical approach with probabilistic information-theoretic security to mitigate one of quantum key distribution's major limitations: the short maximum transmission distance (~200 km) possible with present day technology. Our scheme uses classical secret sharing techniques to allow secure transmission over long distances through a network containing randomly-distributed compromised nodes. The protocol provides arbitrarily high confidence in the security of the protocol, with modest scaling of resource costs with improvement of the security parameter. Although some types of failure are undetectable, users can take preemptive measures to make the probability of such failures arbitrarily small.Comment: 12 pages, 2 figures; added proof of verification sub-protocol, minor correction

The ECMWF Ensemble Prediction System: Looking Back (more than) 25 Years and Projecting Forward 25 Years

This paper has been written to mark 25 years of operational medium-range ensemble forecasting. The origins of the ECMWF Ensemble Prediction System are outlined, including the development of the precursor real-time Met Office monthly ensemble forecast system. In particular, the reasons for the development of singular vectors and stochastic physics - particular features of the ECMWF Ensemble Prediction System - are discussed. The author speculates about the development and use of ensemble prediction in the next 25 years.Comment: Submitted to Special Issue of the Quarterly Journal of the Royal Meteorological Society: 25 years of ensemble predictio

Multi-facet determination for clustering with Bayesian networks

Real world applications of sectors like industry, healthcare or finance usually generate data of high complexity that can be interpreted from different viewpoints. When clustering this type of data, a single set of clusters may not suffice, hence the necessity of methods that generate multiple clusterings that represent different perspectives. In this paper, we present a novel multi-partition clustering method that returns several interesting and non-redundant solutions, where each of them is a data partition with an associated facet of data. Each of these facets represents a subset of the original attributes that is selected using our information-theoretic criterion UMRMR. Our approach is based on an optimization procedure that takes advantage of the Bayesian network factorization to provide high quality solutions in a fraction of the time