8,725 research outputs found
Gaussian process single-index models as emulators for computer experiments
A single-index model (SIM) provides for parsimonious multi-dimensional
nonlinear regression by combining parametric (linear) projection with
univariate nonparametric (non-linear) regression models. We show that a
particular Gaussian process (GP) formulation is simple to work with and ideal
as an emulator for some types of computer experiment as it can outperform the
canonical separable GP regression model commonly used in this setting. Our
contribution focuses on drastically simplifying, re-interpreting, and then
generalizing a recently proposed fully Bayesian GP-SIM combination, and then
illustrating its favorable performance on synthetic data and a real-data
computer experiment. Two R packages, both released on CRAN, have been augmented
to facilitate inference under our proposed model(s).Comment: 23 pages, 9 figures, 1 tabl
Bayesian Quantile Regression for Single-Index Models
Using an asymmetric Laplace distribution, which provides a mechanism for
Bayesian inference of quantile regression models, we develop a fully Bayesian
approach to fitting single-index models in conditional quantile regression. In
this work, we use a Gaussian process prior for the unknown nonparametric link
function and a Laplace distribution on the index vector, with the latter
motivated by the recent popularity of the Bayesian lasso idea. We design a
Markov chain Monte Carlo algorithm for posterior inference. Careful
consideration of the singularity of the kernel matrix, and tractability of some
of the full conditional distributions leads to a partially collapsed approach
where the nonparametric link function is integrated out in some of the sampling
steps. Our simulations demonstrate the superior performance of the Bayesian
method versus the frequentist approach. The method is further illustrated by an
application to the hurricane data.Comment: 26 pages, 8 figures, 10 table
The evaluation of non-native speaking English language trainee teachers’ practice: unfolding university supervisors’ and host teachers’ perspectives on judging performance
Androgen receptor phosphorylation status at serine 578 predicts poor outcome in prostate cancer patients
Purpose: Prostate cancer growth is dependent upon androgen receptor (AR) activation, regulated via phosphorylation. Protein kinase C (PKC) is one kinase that can mediate AR phosphorylation. This study aimed to establish if AR phosphorylation by PKC is of prognostic significance.
Methods: Immunohistochemistry for AR, AR phosphorylated at Ser-81 (pARS81), AR phosphorylated at Ser-578 (pARS578), PKC and phosphorylated PKC (pPKC) was performed on 90 hormone-naĂŻve prostate cancer specimens. Protein expression was quantified using the weighted histoscore method and examined with regard to clinico-pathological factors and outcome measures; time to biochemical relapse, survival from biochemical relapse and disease-specific survival.
Results: Nuclear PKC expression strongly correlated with nuclear pARS578 (c.c. 0.469, p=0.001) and cytoplasmic pARS578 (c.c. 0.426 p=0.002). High cytoplasmic and nuclear pARS578 were associated with disease-specific survival (p<0.001 and p=0.036 respectively). High nuclear PKC was associated with lower disease-specific survival when combined with high pARS578 in the cytoplasm (p=0.001) and nucleus (p=0.038). Combined high total pARS81 and total pARS578 was associated with decreased disease-specific survival (p=0.005)
Conclusions: pARS578 expression is associated with poor outcome and is a potential independent prognostic marker in hormone-naĂŻve prostate cancer. Furthermore, PKC driven AR phosphorylation may promote prostate cancer progression and provide a novel therapeutic target
Spatiotemporal Mapping of Photocurrent in a Monolayer Semiconductor Using a Diamond Quantum Sensor
The detection of photocurrents is central to understanding and harnessing the
interaction of light with matter. Although widely used, transport-based
detection averages over spatial distributions and can suffer from low
photocarrier collection efficiency. Here, we introduce a contact-free method to
spatially resolve local photocurrent densities using a proximal quantum
magnetometer. We interface monolayer MoS2 with a near-surface ensemble of
nitrogen-vacancy centers in diamond and map the generated photothermal current
distribution through its magnetic field profile. By synchronizing the
photoexcitation with dynamical decoupling of the sensor spin, we extend the
sensor's quantum coherence and achieve sensitivities to alternating current
densities as small as 20 nA per micron. Our spatiotemporal measurements reveal
that the photocurrent circulates as vortices, manifesting the Nernst effect,
and rises with a timescale indicative of the system's thermal properties. Our
method establishes an unprecedented probe for optoelectronic phenomena, ideally
suited to the emerging class of two-dimensional materials, and stimulates
applications towards large-area photodetectors and stick-on sources of magnetic
fields for quantum control.Comment: 19 pages, 4 figure
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