Private states, quantum data hiding and the swapping of perfect secrecy

We derive a formal connection between quantum data hiding and quantum privacy, confirming the intuition behind the construction of bound entangled states from which secret bits can be extracted. We present three main results. First, we show how to simplify the class of private states and related states via reversible local operation and one-way communication. Second, we obtain a bound on the one-way distillable entanglement of private states in terms of restricted relative entropy measures, which is tight in many cases and shows that protocols for one-way distillation of key out of states with low distillable entanglement lead to the distillation of data hiding states. Third, we consider the problem of extending the distance of quantum key distribution with help of intermediate stations. In analogy to the quantum repeater, this paradigm has been called the quantum key repeater. We show that when extending private states with one-way communication, the resulting rate is bounded by the one-way distillable entanglement. In order to swap perfect secrecy it is thus essentially optimal to use entanglement swapping.Comment: v3 published version, some details of the main proofs have been moved to the appendix, 21 pages. v2 claims changed from LOCC to one-way LOCC in the process of correcting a mistake found in v1 (in proof of Lemma 3). v1: 15 pages, 9 figure

Asteroseismology of the solar analogs 16 Cyg A & B from Kepler observations

The evolved solar-type stars 16 Cyg A & B have long been studied as solar analogs, yielding a glimpse into the future of our own Sun. The orbital period of the binary system is too long to provide meaningful dynamical constraints on the stellar properties, but asteroseismology can help because the stars are among the brightest in the Kepler field. We present an analysis of three months of nearly uninterrupted photometry of 16 Cyg A & B from the Kepler space telescope. We extract a total of 46 and 41 oscillation frequencies for the two components respectively, including a clear detection of octupole (l=3) modes in both stars. We derive the properties of each star independently using the Asteroseismic Modeling Portal, fitting the individual oscillation frequencies and other observational constraints simultaneously. We evaluate the systematic uncertainties from an ensemble of results generated by a variety of stellar evolution codes and fitting methods. The optimal models derived by fitting each component individually yield a common age (t=6.8+/-0.4 Gyr) and initial composition (Z_i=0.024+/-0.002, Y_i=0.25+/-0.01) within the uncertainties, as expected for the components of a binary system, bolstering our confidence in the reliability of asteroseismic techniques. The longer data sets that will ultimately become available will allow future studies of differential rotation, convection zone depths, and long-term changes due to stellar activity cycles.Comment: 6 pages, 2 figures, 2 tables, ApJ Letters (accepted

Analysis of a circular code model

A circular code has been identified in the protein (coding) genes of both eukaryotes and prokaryotes by using a statistical method called Trinucleotide Frequency method (TF method) [Arquès & Michel, (1996) J.Theor. Biol. 182, 45-58]. Recently, a probabilistic model based on the nucleotide frequencies with a hypothesis of absence of correlation between successive bases on a DNA strand, has been proposed by Koch & Lehmann [(1997) J.Theor. Biol. 189, 171-174] for constructing some particular circular codes. Their interesting method which we call here Nucleotide Frequency method (NF method), reveals several limits for constructing the circular code observed with protein genes

A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning

This paper presents a general vector-valued reproducing kernel Hilbert spaces (RKHS) framework for the problem of learning an unknown functional dependency between a structured input space and a structured output space. Our formulation encompasses both Vector-valued Manifold Regularization and Co-regularized Multi-view Learning, providing in particular a unifying framework linking these two important learning approaches. In the case of the least square loss function, we provide a closed form solution, which is obtained by solving a system of linear equations. In the case of Support Vector Machine (SVM) classification, our formulation generalizes in particular both the binary Laplacian SVM to the multi-class, multi-view settings and the multi-class Simplex Cone SVM to the semi-supervised, multi-view settings. The solution is obtained by solving a single quadratic optimization problem, as in standard SVM, via the Sequential Minimal Optimization (SMO) approach. Empirical results obtained on the task of object recognition, using several challenging datasets, demonstrate the competitiveness of our algorithms compared with other state-of-the-art methods.Comment: 72 page