Topological Schr\"odinger cats: Non-local quantum superpositions of topological defects

Topological defects (such as monopoles, vortex lines, or domain walls) mark locations where disparate choices of a broken symmetry vacuum elsewhere in the system lead to irreconcilable differences. They are energetically costly (the energy density in their core reaches that of the prior symmetric vacuum) but topologically stable (the whole manifold would have to be rearranged to get rid of the defect). We show how, in a paradigmatic model of a quantum phase transition, a topological defect can be put in a non-local superposition, so that - in a region large compared to the size of its core - the order parameter of the system is "undecided" by being in a quantum superposition of conflicting choices of the broken symmetry. We demonstrate how to exhibit such a "Schr\"odinger kink" by devising a version of a double-slit experiment suitable for topological defects. Coherence detectable in such experiments will be suppressed as a consequence of interaction with the environment. We analyze environment-induced decoherence and discuss its role in symmetry breaking.Comment: 7 pages, 4 figure

A Bayesian analysis of the effect of estimating annual average daily traffic for heavy-duty trucks using training and validation data-sets

The precise estimation of annual average daily traffic (AADT) is of significant importance worldwide for transportation agencies. This paper uses three modeling frameworks to predict the AADT for heavy-duty trucks. In total, 12 models are developed based on regression and Bayesian analysis using a training data-set. A separate validation data-set is used to compare the results from the 12 models, spanning the years 2005 through 2007 and taken from 67 continuous data recorders. Parameters of significance include roadway functional class, population density, and spatial location; five regional areas – northeast, northwest, central, southeast, and southwest – of the state of Ohio in the USA; and average daily truck traffic. The results show that a full Bayesian negative binomial model with a coefficient offset is the most efficient model framework for all four seasons of the year. This model is able to account for between 87% and 92% of the variability within the data-set

EB Ford revisited: assessing the long-term stability of wing-spot patterns and population genetic structure of the meadow brown butterfly on the Isles of Scilly

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.Data files of wing spot sizes and AFLP genotypes available from the Dryad Digital Repository: http://dx.doi.org/10.5061/dryad.j7v42.Understanding selection in the wild remains a major aim of evolutionary ecology and work by Ford and colleagues on the meadow brown butterfly Maniola jurtina did much to ignite this agenda. A great deal of their work was conducted during the 1950s on the Isles of Scilly. They documented island-specific wing-spot patterns that remained consistent over about a decade, but patterns on some islands changed after environmental perturbation. It was suggested that these wing-spot patterns reflected island-specific selection and that there was little migration between islands. However, genetic studies to test the underlying assumption of restricted migration are lacking and it is also unknown whether the originally described wing-spot patterns have persisted over time. We therefore collected female butterflies from five of Ford's original study locations, including three large islands (St Mary's, St Martin's and Tresco) and two small islands (Tean and St Helen's). Wing-spot patterns had not changed appreciably over time on three of the islands (two large and one small), but were significantly different on the other two. Furthermore, analysis of 176 amplified fragment length polymorphisms revealed significant genome-wide differentiation among the five islands. Our findings are consistent with Ford's conclusions that despite the close proximity of these islands, there is restricted gene flow among them.Heredity advance online publication, 2 November 2016; doi:10.1038/hdy.2016.94.We thank the Genetics Society for a fieldwork grant (to DJH) that funded the collection trip and DJH thanks Mike Johnson for sparking interest in this area. SWB is supported by the Australian Research Council and a Ramsay Fellowship, NW by a Royal Society Wolfson Fellowship and NERC and DJH by the Leverhulme Trust

'Designer atoms' for quantum metrology

Entanglement is recognized as a key resource for quantum computation and quantum cryptography. For quantum metrology, the use of entangled states has been discussed and demonstrated as a means of improving the signal-to-noise ratio. In addition, entangled states have been used in experiments for efficient quantum state detection and for the measurement of scattering lengths. In quantum information processing, manipulation of individual quantum bits allows for the tailored design of specific states that are insensitive to the detrimental influences of an environment. Such 'decoherence-free subspaces' protect quantum information and yield significantly enhanced coherence times. Here we use a decoherence-free subspace with specifically designed entangled states to demonstrate precision spectroscopy of a pair of trapped Ca+ ions; we obtain the electric quadrupole moment, which is of use for frequency standard applications. We find that entangled states are not only useful for enhancing the signal-to-noise ratio in frequency measurements - a suitably designed pair of atoms also allows clock measurements in the presence of strong technical noise. Our technique makes explicit use of non-locality as an entanglement property and provides an approach for 'designed' quantum metrology

Intra-amniotic delivery of CFTR-expressing adenovirus does not reverse cystic fibrosis phenotype in inbred CFTR-knockout mice

This article is available open access through the publisher’s website at the link below. Copyright © 2008 The American Society of Gene Therapy.Due to its early onset and severe prognosis, cystic fibrosis (CF) has been suggested as a candidate disease for in utero gene therapy. In 1997, a study was published claiming that to how transient prenatal expression of CF transmembrane conductance regulator (CFTR) from an in utero –injected adenovirus vector could achieve permanent reversal of the CF intestinal pathology in adult CF knockout mice, despite the loss of CFTR transgene expression by birth. This would imply that the underlying cause of CF is a prenatal defect for which lifelong cure can be achieved by transient prenatal expression of CFTR. Despite criticism at the time of publication, no independent verification of this contentious finding has been published so far. This is vital for the development of future therapeutic strategies as it may determine whether CF gene therapy should be performed prenatally or postnatally. We therefore reinvestigated this finding with an identical adenoviral vector and a knockout CF mouse line (CftrtmlCam) with a completely inbred genetic background to eliminate any effects due to genetic variation. After delivery of the CFTR-expressing adenovirus to the fetal mouse, both vector DNA and transgenic CFTR expression were detected in treated animals postpartum but statistically no significant difference in survival was observed between the Cftr–/– mice treated with the CFTR-adenovirus and those treated with the control vector.Sport Aiding Medical Research for Kids, the Cystic Fibrosis Trust, and the Katharine Dormandy Trust

Competition-based model of pheromone component ratio detection in the moth

For some moth species, especially those closely interrelated and sympatric, recognizing a specific pheromone component concentration ratio is essential for males to successfully locate conspecific females. We propose and determine the properties of a minimalist competition-based feed-forward neuronal model capable of detecting a certain ratio of pheromone components independently of overall concentration. This model represents an elementary recognition unit for the ratio of binary mixtures which we propose is entirely contained in the macroglomerular complex (MGC) of the male moth. A set of such units, along with projection neurons (PNs), can provide the input to higher brain centres. We found that (1) accuracy is mainly achieved by maintaining a certain ratio of connection strengths between olfactory receptor neurons (ORN) and local neurons (LN), much less by properties of the interconnections between the competing LNs proper. An exception to this rule is that it is beneficial if connections between generalist LNs (i.e. excited by either pheromone component) and specialist LNs (i.e. excited by one component only) have the same strength as the reciprocal specialist to generalist connections. (2) successful ratio recognition is achieved using latency-to-first-spike in the LN populations which, in contrast to expectations with a population rate code, leads to a broadening of responses for higher overall concentrations consistent with experimental observations. (3) when longer durations of the competition between LNs were observed it did not lead to higher recognition accuracy

Acceleration of generalized hypergeometric functions through precise remainder asymptotics

We express the asymptotics of the remainders of the partial sums {s_n} of the generalized hypergeometric function q+1_F_q through an inverse power series z^n n^l \sum_k c_k/n^k, where the exponent l and the asymptotic coefficients {c_k} may be recursively computed to any desired order from the hypergeometric parameters and argument. From this we derive a new series acceleration technique that can be applied to any such function, even with complex parameters and at the branch point z=1. For moderate parameters (up to approximately ten) a C implementation at fixed precision is very effective at computing these functions; for larger parameters an implementation in higher than machine precision would be needed. Even for larger parameters, however, our C implementation is able to correctly determine whether or not it has converged; and when it converges, its estimate of its error is accurate.Comment: 36 pages, 6 figures, LaTeX2e. Fixed sign error in Eq. (2.28), added several references, added comparison to other methods, and added discussion of recursion stabilit

The meaning and measurement of implementation climate

<p>Abstract</p> <p>Background</p> <p>Climate has a long history in organizational studies, but few theoretical models integrate the complex effects of climate during innovation implementation. In 1996, a theoretical model was proposed that organizations could develop a positive climate for implementation by making use of various policies and practices that promote organizational members' means, motives, and opportunities for innovation use. The model proposes that implementation climate--or the extent to which organizational members perceive that innovation use is expected, supported, and rewarded--is positively associated with implementation effectiveness. The implementation climate construct holds significant promise for advancing scientific knowledge about the organizational determinants of innovation implementation. However, the construct has not received sufficient scholarly attention, despite numerous citations in the scientific literature. In this article, we clarify the meaning of implementation climate, discuss several measurement issues, and propose guidelines for empirical study.</p> <p>Discussion</p> <p>Implementation climate differs from constructs such as organizational climate, culture, or context in two important respects: first, it has a strategic focus (implementation), and second, it is innovation-specific. Measuring implementation climate is challenging because the construct operates at the organizational level, but requires the collection of multi-dimensional perceptual data from many expected innovation users within an organization. In order to avoid problems with construct validity, assessments of within-group agreement of implementation climate measures must be carefully considered. Implementation climate implies a high degree of within-group agreement in climate perceptions. However, researchers might find it useful to distinguish implementation climate level (the average of implementation climate perceptions) from implementation climate strength (the variability of implementation climate perceptions). It is important to recognize that the implementation climate construct applies most readily to innovations that require collective, coordinated behavior change by many organizational members both for successful implementation and for realization of anticipated benefits. For innovations that do not possess these attributes, individual-level theories of behavior change could be more useful in explaining implementation effectiveness.</p> <p>Summary</p> <p>This construct has considerable value in implementation science, however, further debate and development is necessary to refine and distinguish the construct for empirical use.</p