Conductivity Imaging in Plates Using Current Injection Tomography

The task of reconstructing an unknown distribution of electrical conductivity is widely recognized as a central theoretical problem in eddy-current nondestructive evaluation [1]. Rather than using an eddy-current method, we address this problem using DC injection of current into conductive materials. Experimental methods of the magnetic imaging of injected currents using high-resolution SQUID magnetometers have been described elsewhere [2]. In this paper we describe a tomographic method for using magnetically-imaged, injected currents to reconstruct distributions of electrical conductivity. Much of what we describe should also be applicable to data obtained using uniform colinear eddy currents induced by means of planar sheet inducers [4, 5]

Efficient coupling of photons to a single molecule and the observation of its resonance fluorescence

Single dye molecules at cryogenic temperatures display many spectroscopic phenomena known from free atoms and are thus promising candidates for fundamental quantum optical studies. However, the existing techniques for the detection of single molecules have either sacrificed the information on the coherence of the excited state or have been inefficient. Here we show that these problems can be addressed by focusing the excitation light near to the absorption cross section of a molecule. Our detection scheme allows us to explore resonance fluorescence over 9 orders of magnitude of excitation intensity and to separate its coherent and incoherent parts. In the strong excitation regime, we demonstrate the first observation of the Mollow triplet from a single solid-state emitter. Under weak excitation we report the detection of a single molecule with an incident power as faint as 150 attoWatt, paving the way for studying nonlinear effects with only a few photons.Comment: 6 figure

Motor contagion: the contribution of trajectory and end-points

Increased involuntary arm movement deviation when observing an incongruent human arm movement has been interpreted as a strong indicator of motor contagion. Here, we examined the contribution of trajectory and end-point information on motor contagion by altering congruence between the stimulus and arm movement. Participants performed cyclical horizontal arm movements whilst simultaneously observing a stimulus representing human arm movement. The stimuli comprised congruent horizontal movements or vertical movements featuring incongruent trajectory and end-points. A novel, third, stimulus comprised curvilinear movements featuring congruent end-points, but an incongruent trajectory. In Experiment 1, our dependent variables indicated increased motor contagion when observing the vertical compared to horizontal movement stimulus. There was even greater motor contagion in the curvilinear stimulus condition indicating an additive effect of an incongruent trajectory comprising congruent end-points. In Experiment 2, this additive effect was also present when facing perpendicular to the display, and thus with end-points represented as a product of the movement rather than an external spatial reference. Together, these findings support the theory of event coding (Hommel et al., Behav Brain Sci 24:849–878, 2001), and the prediction that increased motor contagion takes place when observed and executed actions share common features (i.e., movement end-points)

Women’s pelvic floor muscle strength and urinary and anal incontinence after childbirth: a cross-sectional study

Abstract OBJECTIVE To analyse pelvic floor muscle strength (PFMS) and urinary and anal incontinence (UI and AI) in the postpartum period. METHOD Cross-sectional study carried out with women in their first seven months after child birth. Data were collected through interviews, perineometry (Peritron™), and the International Consultation on Incontinence Questionnaire-Short Form (ICIQ-SF). RESULTS 128 women participated in the study. The PFMS mean was 33.1 (SD=16.0) cmH2O and the prevalence of UI and AI was 7.8% and 5.5%, respectively. In the multiple analyses, the variables associated with PFMS were type of birth and cohabitation with a partner. Newborn’s weight, previous pregnancy, UI during pregnancy, and sexual activity showed an association with UI after child birth. Only AI prior to pregnancy was associated with AI after childbirth. CONCLUSION Vaginal birth predisposes to the reduction of PFMS, and caesarean section had a protective effect to its reduction. The occurrence of UI during pregnancy is a predictor of UI after childbirth, and women with previous pregnancies and newborns with higher weights are more likely to have UI after childbirth.AI prior to pregnancy is the only risk factor for its occurrence after childbirth. Associations between PFMS and cohabitation with a partner, and between UI and sexual activity do not make possible to conclude that these variables are directly associated

ER and HER2 expression are positively correlated in HER2 non-overexpressing breast cancer

PMCID: PMC3446380This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Towards Quantum Repeaters with Solid-State Qubits: Spin-Photon Entanglement Generation using Self-Assembled Quantum Dots

In this chapter we review the use of spins in optically-active InAs quantum dots as the key physical building block for constructing a quantum repeater, with a particular focus on recent results demonstrating entanglement between a quantum memory (electron spin qubit) and a flying qubit (polarization- or frequency-encoded photonic qubit). This is a first step towards demonstrating entanglement between distant quantum memories (realized with quantum dots), which in turn is a milestone in the roadmap for building a functional quantum repeater. We also place this experimental work in context by providing an overview of quantum repeaters, their potential uses, and the challenges in implementing them.Comment: 51 pages. Expanded version of a chapter to appear in "Engineering the Atom-Photon Interaction" (Springer-Verlag, 2015; eds. A. Predojevic and M. W. Mitchell

A Machine Learning Trainable Model to Assess the Accuracy of Probabilistic Record Linkage

Record linkage (RL) is the process of identifying and linking data that relates to the same physical entity across multiple heterogeneous data sources. Deterministic linkage methods rely on the presence of common uniquely identifying attributes across all sources while probabilistic approaches use non-unique attributes and calculates similarity indexes for pair wise comparisons. A key component of record linkage is accuracy assessment — the process of manually verifying and validating matched pairs to further refine linkage parameters and increase its overall effectiveness. This process however is time-consuming and impractical when applied to large administrative data sources where millions of records must be linked. Additionally, it is potentially biased as the gold standard used is often the reviewer’s intuition. In this paper, we present an approach for assessing and refining the accuracy of probabilistic linkage based on different supervised machine learning methods (decision trees, naïve Bayes, logistic regression, random forest, linear support vector machines and gradient boosted trees). We used data sets extracted from huge Brazilian socioeconomic and public health care data sources. These models were evaluated using receiver operating characteristic plots, sensitivity, specificity and positive predictive values collected from a 10-fold cross-validation method. Results show that logistic regression outperforms other classifiers and enables the creation of a generalized, very accurate model to validate linkage results

Photonic quantum technologies

The first quantum technology, which harnesses uniquely quantum mechanical effects for its core operation, has arrived in the form of commercially available quantum key distribution systems that achieve enhanced security by encoding information in photons such that information gained by an eavesdropper can be detected. Anticipated future quantum technologies include large-scale secure networks, enhanced measurement and lithography, and quantum information processors, promising exponentially greater computation power for particular tasks. Photonics is destined for a central role in such technologies owing to the need for high-speed transmission and the outstanding low-noise properties of photons. These technologies may use single photons or quantum states of bright laser beams, or both, and will undoubtably apply and drive state-of-the-art developments in photonics

Incorporating Prediction in Models for Two-Dimensional Smooth Pursuit

A predictive component can contribute to the command signal for smooth pursuit. This is readily demonstrated by the fact that low frequency sinusoidal target motion can be tracked with zero time delay or even with a small lead. The objective of this study was to characterize the predictive contributions to pursuit tracking more precisely by developing analytical models for predictive smooth pursuit. Subjects tracked a small target moving in two dimensions. In the simplest case, the periodic target motion was composed of the sums of two sinusoidal motions (SS), along both the horizontal and the vertical axes. Motions following the same or similar paths, but having a richer spectral composition, were produced by having the target follow the same path but at a constant speed (CS), and by combining the horizontal SS velocity with the vertical CS velocity and vice versa. Several different quantitative models were evaluated. The predictive contribution to the eye tracking command signal could be modeled as a low-pass filtered target acceleration signal with a time delay. This predictive signal, when combined with retinal image velocity at the same time delay, as in classical models for the initiation of pursuit, gave a good fit to the data. The weighting of the predictive acceleration component was different in different experimental conditions, being largest when target motion was simplest, following the SS velocity profiles

Quantum Computing

Quantum mechanics---the theory describing the fundamental workings of nature---is famously counterintuitive: it predicts that a particle can be in two places at the same time, and that two remote particles can be inextricably and instantaneously linked. These predictions have been the topic of intense metaphysical debate ever since the theory's inception early last century. However, supreme predictive power combined with direct experimental observation of some of these unusual phenomena leave little doubt as to its fundamental correctness. In fact, without quantum mechanics we could not explain the workings of a laser, nor indeed how a fridge magnet operates. Over the last several decades quantum information science has emerged to seek answers to the question: can we gain some advantage by storing, transmitting and processing information encoded in systems that exhibit these unique quantum properties? Today it is understood that the answer is yes. Many research groups around the world are working towards one of the most ambitious goals humankind has ever embarked upon: a quantum computer that promises to exponentially improve computational power for particular tasks. A number of physical systems, spanning much of modern physics, are being developed for this task---ranging from single particles of light to superconducting circuits---and it is not yet clear which, if any, will ultimately prove successful. Here we describe the latest developments for each of the leading approaches and explain what the major challenges are for the future.Comment: 26 pages, 7 figures, 291 references. Early draft of Nature 464, 45-53 (4 March 2010). Published version is more up-to-date and has several corrections, but is half the length with far fewer reference