Joint Probabilities Reproducing Three EPR Experiments On Two Qubits

An eight parameter family of the most general nonnegative quadruple probabilities is constructed for EPR-Bohm-Aharonov experiments when only 3 pairs of analyser settings are used. It is a simultaneous representation of 3 Bohr-incompatible experimental configurations valid for arbitrary quantum states.Comment: Typo corrected in abstrac

Neural Network-Based Equations for Predicting PGA and PGV in Texas, Oklahoma, and Kansas

Parts of Texas, Oklahoma, and Kansas have experienced increased rates of seismicity in recent years, providing new datasets of earthquake recordings to develop ground motion prediction models for this particular region of the Central and Eastern North America (CENA). This paper outlines a framework for using Artificial Neural Networks (ANNs) to develop attenuation models from the ground motion recordings in this region. While attenuation models exist for the CENA, concerns over the increased rate of seismicity in this region necessitate investigation of ground motions prediction models particular to these states. To do so, an ANN-based framework is proposed to predict peak ground acceleration (PGA) and peak ground velocity (PGV) given magnitude, earthquake source-to-site distance, and shear wave velocity. In this framework, approximately 4,500 ground motions with magnitude greater than 3.0 recorded in these three states (Texas, Oklahoma, and Kansas) since 2005 are considered. Results from this study suggest that existing ground motion prediction models developed for CENA do not accurately predict the ground motion intensity measures for earthquakes in this region, especially for those with low source-to-site distances or on very soft soil conditions. The proposed ANN models provide much more accurate prediction of the ground motion intensity measures at all distances and magnitudes. The proposed ANN models are also converted to relatively simple mathematical equations so that engineers can easily use them to predict the ground motion intensity measures for future events. Finally, through a sensitivity analysis, the contributions of the predictive parameters to the prediction of the considered intensity measures are investigated.Comment: 5th Geotechnical Earthquake Engineering and Soil Dynamics Conference, Austin, TX, USA, June 10-13. (2018

Expressive vocabulary predicts non-verbal executive function: a 2-year longitudinal study of deaf and hearing children

Numerous studies suggest an association between language and executive function (EF), but evidence of a developmental relationship remains inconclusive. Data were collected from 75 deaf/hard-of-hearing (DHH) children and 82 hearing age-matched controls. Children were 6-11 years old at first time of testing, and completed a battery of nonverbal EF tasks and a test of expressive vocabulary. These tasks were completed again two years later. Both groups improved their scores on all tasks over this period. DHH children performed significantly less well than hearing peers on some EF tasks and the vocabulary test at both time points. Cross-lagged panel models showed that vocabulary at Time 1 predicted change in EF scores for both DHH and hearing children but not the reverse

A Comparison of Surface Acoustic Wave Modeling Methods

Surface Acoustic Wave (SAW) technology is low cost, rugged, lightweight, extremely low power and can be used to develop passive wireless sensors. For these reasons, NASA is investigating the use of SAW technology for Integrated Vehicle Health Monitoring (IVHM) of aerospace structures. To facilitate rapid prototyping of passive SAW sensors for aerospace applications, SAW models have been developed. This paper reports on the comparison of three methods of modeling SAWs. The three models are the Impulse Response Method a first order model, and two second order matrix methods; the conventional matrix approach, and a modified matrix approach that is extended to include internal finger reflections. The second order models are based upon matrices that were originally developed for analyzing microwave circuits using transmission line theory. Results from the models are presented with measured data from devices

Frequency Domain Modeling of SAW Devices

New SAW sensors for integrated vehicle health monitoring of aerospace vehicles are being investigated. SAW technology is low cost, rugged, lightweight, and extremely low power. However, the lack of design tools for MEMS devices in general, and for Surface Acoustic Wave (SAW) devices specifically, has led to the development of tools that will enable integrated design, modeling, simulation, analysis and automatic layout generation of SAW devices. A frequency domain model has been created. The model is mainly first order, but it includes second order effects from triple transit echoes. This paper presents the model and results from the model for a SAW delay line device

Outdoor learning in urban schools: Effects on 4–5 year old children's noise and physiological stress

Natural outdoor environments reduce physiological stress. But in an urban school context, does outdoor learning still have beneficial effects even where nature exposure is more limited? The current, pre-registered study used wearable devices including heart rate monitors and actigraphs to examine physiological stress in 4–5 year old children across 8 matched indoor and outdoor sessions (N = 76 children, N = 601 sessions in total). Results revealed that children's resting heart rates while seated and listening to a teacher were significantly lower when outside compared to indoors (p < 0.001, d = 0.512). Children also moved more while seated during indoor sessions (p < 0.001, d = 0.546). Despite activities and resources being matched across conditions, outdoor learning sessions were significantly quieter than indoor ones, both when children were seated, listening to a teacher (p = 0.004, d = −0.455) and when actively engaged in play and learning activities (p < 0.001, d = 1.064). There was a significant positive correlation between noise levels and resting heart rate in the indoor condition (r(97) = 0.364, p < 0.001) but not in the outdoor condition. These findings suggest that learning outdoors, even in urban settings, associates with lower physiological stress in children and that this effect may partly be due to reduced noise. The fact that noise associates with resting heart rate indoors but not outdoors may indicate that being outside buffers children against the stressful effects of excess noise

Studies on optimizing potential energy functions for maximal intrinsic hyperpolarizability

We use numerical optimization to study the properties of (1) the class of one-dimensional potential energy functions and (2) systems of point charges in two-dimensions that yield the largest hyperpolarizabilities, which we find to be within 30% of the fundamental limit. We investigate the character of the potential energy functions and resulting wavefunctions and find that a broad range of potentials yield the same intrinsic hyperpolarizability ceiling of 0.709.Comment: 9 pages, 9 figure

Localization of adaptive variants in human genomes using averaged one-dependence estimation.

Statistical methods for identifying adaptive mutations from population genetic data face several obstacles: assessing the significance of genomic outliers, integrating correlated measures of selection into one analytic framework, and distinguishing adaptive variants from hitchhiking neutral variants. Here, we introduce SWIF(r), a probabilistic method that detects selective sweeps by learning the distributions of multiple selection statistics under different evolutionary scenarios and calculating the posterior probability of a sweep at each genomic site. SWIF(r) is trained using simulations from a user-specified demographic model and explicitly models the joint distributions of selection statistics, thereby increasing its power to both identify regions undergoing sweeps and localize adaptive mutations. Using array and exome data from 45 ‡Khomani San hunter-gatherers of southern Africa, we identify an enrichment of adaptive signals in genes associated with metabolism and obesity. SWIF(r) provides a transparent probabilistic framework for localizing beneficial mutations that is extensible to a variety of evolutionary scenarios