Remote preparation of arbitrary ensembles and quantum bit commitment

The Hughston-Jozsa-Wootters theorem shows that any finite ensemble of quantum states can be prepared "at a distance", and it has been used to demonstrate the insecurity of all bit commitment protocols based on finite quantum systems without superselection rules. In this paper, we prove a generalized HJW theorem for arbitrary ensembles of states on a C*-algebra. We then use this result to demonstrate the insecurity of bit commitment protocols based on infinite quantum systems, and quantum systems with Abelian superselection rules.Comment: 21 pages, LaTeX. Version 2: Proofs expanded and made more self-contained; added an example of a bit commitment protocol with continuous ensemble

k-Color Multi-Robot Motion Planning

We present a simple and natural extension of the multi-robot motion planning problem where the robots are partitioned into groups (colors), such that in each group the robots are interchangeable. Every robot is no longer required to move to a specific target, but rather to some target placement that is assigned to its group. We call this problem k-color multi-robot motion planning and provide a sampling-based algorithm specifically designed for solving it. At the heart of the algorithm is a novel technique where the k-color problem is reduced to several discrete multi-robot motion planning problems. These reductions amplify basic samples into massive collections of free placements and paths for the robots. We demonstrate the performance of the algorithm by an implementation for the case of disc robots and polygonal robots translating in the plane. We show that the algorithm successfully and efficiently copes with a variety of challenging scenarios, involving many robots, while a simplified version of this algorithm, that can be viewed as an extension of a prevalent sampling-based algorithm for the k-color case, fails even on simple scenarios. Interestingly, our algorithm outperforms a well established implementation of PRM for the standard multi-robot problem, in which each robot has a distinct color.Comment: 2

Virial Masses of Black Holes from Single Epoch Spectra of AGN

We describe the general problem of estimating black hole masses of AGN by calculating the conditional probability distribution of M_BH given some set of observables. Special attention is given to the case where one uses the AGN continuum luminosity and emission line widths to estimate M_BH, and we outline how to set up the conditional probability distribution of M_BH given the observed luminosity, line width, and redshift. We show how to combine the broad line estimates of M_BH with information from an intrinsic correlation between M_BH and L, and from the intrinsic distribution of M_BH, in a manner that improves the estimates of M_BH. Simulation was used to assess how the distribution of M_BH inferred from the broad line mass estimates differs from the intrinsic distribution, and we find that this can lead to an inferred distribution that is too broad. We use these results and a sample of 25 sources that have recent reverberation mapping estimates of AGN black hole masses to investigate the effectiveness of using the C IV emission line to estimate M_BH and to indirectly probe the C IV region size--luminosity (R--L) relationship. We estimated M_BH from both C IV and H-Beta for a sample of 100 sources, including new spectra of 29 quasars. We find that the two emission lines give consistent estimates if one assumes R \propto L^{1/2}_{UV} for both lines.Comment: 38 pages, 6 figures, accepted by Ap

Electron Self Energy for Higher Excited S Levels

A nonperturbative numerical evaluation of the one-photon electron self energy for the 3S and 4S states with charge numbers Z=1 to 5 is described. The numerical results are in agreement with known terms in the expansion of the self energy in powers of Zalpha.Comment: 3 pages, RevTeX, to appear in Phys. Rev.

Variable cavity volume tooling for high-performance resin infusion moulding

This article describes the research carried out by Warwick under the BAE Systems/EPSRC programme ‘Flapless Aerial Vehicles Integrated Interdisciplinary Research – FLAVIIR’. Warwick's aim in FLAVIIR was to develop low-cost innovative tooling technologies to enable the affordable manufacture of complex composite aerospace structures and to help realize the aim of the Grand Challenge of maintenance-free, low-cost unmanned aerial vehicle manufacture. This article focuses on the evaluation of a novel tooling process (variable cavity tooling) to enable the complete infusion of resin throughout non-crimp fabric within a mould cavity under low (0.1 MPa) injection pressure. The contribution of the primary processing parameters to the mechanical properties of a carbon composite component (bulk-head lug section), and the interactions between parameters, was determined. The initial mould gap (di) was identified as having the most significant effect on all measured mechanical properties, but complex interactions between di, n (number of fabric layers), and vc (mould closure rate) were observed. The process capability was low due to the manual processing, but was improved through process optimization, and delivered properties comparable to high-pressure resin transfer moulding

Vacuum state of the quantum string without anomalies in any number of dimensions

We show that the anomalies of the Virasoro algebra are due to the asymmetric behavior of raising and lowering operators with respect to the ground state of the string. With the adoption of a symmetric vacuum we obtain a non-anomalous theory in any number of dimensions. In particular for D=4.Comment: 14 pages, LaTex, no figure

Using network analysis for the prediction of treatment dropout in patients with mood and anxiety disorders: a methodological proof-of-concept study

There are large health, societal, and economic costs associated with attrition from psychological services. The recently emerged, innovative statistical tool of complex network analysis was used in the present proof-of-concept study to improve the prediction of attrition. Fifty-eight patients undergoing psychological treatment for mood or anxiety disorders were assessed using Ecological Momentary Assessments four times a day for two weeks before treatment (3,248 measurements). Multilevel vector autoregressive models were employed to compute dynamic symptom networks. Intake variables and network parameters (centrality measures) were used as predictors for dropout using machine-learning algorithms. Networks for patients differed significantly between completers and dropouts. Among intake variables, initial impairment and sex predicted dropout explaining 6% of the variance. The network analysis identified four additional predictors: Expected force of being excited, outstrength of experiencing social support, betweenness of feeling nervous, and instrength of being active. The final model with the two intake and four network variables explained 32% of variance in dropout and identified 47 out of 58 patients correctly. The findings indicate that patients’ dynamic network structures may improve the prediction of dropout. When implemented in routine care, such prediction models could identify patients at risk for attrition and inform personalized treatment recommendations.This work was supported by the German Research Foundation National Institute (DFG, Grant nos. LU 660/8-1 and LU 660/10-1 to W. Lutz). The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript. The corresponding author had access to all data in the study and had final responsibility for the decision to submit for publication. Dr. Hofmann receives financial support from the Alexander von Humboldt Foundation (as part of the Humboldt Prize), NIH/NCCIH (R01AT007257), NIH/NIMH (R01MH099021, U01MH108168), and the James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition - Special Initiative. (LU 660/8-1 - German Research Foundation National Institute (DFG); LU 660/10-1 - German Research Foundation National Institute (DFG); Alexander von Humboldt Foundation; R01AT007257 - NIH/NCCIH; R01MH099021 - NIH/NIMH; U01MH108168 - NIH/NIMH; James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition - Special Initiative)Accepted manuscrip

Mollusks of the Cheat River Watershed of West Virginia and Pennsylvania, with Comments on Present Distributions

Author Institution: Chesapeake Biological Laboratory, Solomons, Maryland and Mt. St. Mary's College, Emmitsburg, Marylan