Cavity squeezing by a quantum conductor

Hybrid architectures integrating mesoscopic electronic conductors with resonant microwave cavities have a great potential for investigating unexplored regimes of electron-photon coupling. In this context, producing nonclassical squeezed light is a key step towards quantum communication with scalable solid-state devices. Here we show that parametric driving of the electronic conductor induces a squeezed steady state in the cavity. We find that squeezing properties of the cavity are essentially determined by the electronic noise correlators of the quantum conductor. In the case of a tunnel junction, we predict that squeezing is optimized by applying a time-periodic series of quantized $\delta-$peaks in the bias voltage. For an asymmetric quantum dot, we show that a sharp Leviton pulse is able to achieve perfect cavity squeezing.Comment: 13 pages, 4 figures, includes Supplementary inf

Estimators of the multiple correlation coefficient: local robustness and confidence intervals.

Many robust regression estimators are defined by minimizing a measure of spread of the residuals. An accompanying R-2-measure, or multiple correlation coefficient, is then easily obtained. In this paper, local robustness properties of these robust R-2-coefficients axe investigated. It is also shown how confidence intervals for the population multiple correlation coefficient can be constructed in the case of multivariate normality.Cautionary note; High breakdown-point; Influence function; Intervals; Model; Multiple correlation coefficient; R-2-measure; Regression analysis; Residuals; Robustness; Squares regression;

High resolution, high capacity, spatial specificity in perceptual learning.

Research of perceptual learning has received significant interest due to findings that training on perceptual tasks can yield learning effects that are specific to the stimulus features of that task. However, recent studies have demonstrated that while training a single stimulus at a single location can yield a high-degree of stimulus specificity, training multiple features, or at multiple locations can reveal a broad transfer of learning to untrained features or stimulus locations. We devised a high resolution, high capacity, perceptual learning procedure with the goal of testing whether spatial specificity can be found in cases where observers are highly trained to discriminate stimuli in many different locations in the visual field. We found a surprising degree of location specific learning, where performance was significantly better when target stimuli were presented at 1 of the 24 trained locations compared to when they were placed in 1 of the 12 untrained locations. This result is particularly impressive given that untrained locations were within a couple degrees of visual angle of those that were trained. Given the large number of trained locations, the fact that the trained and untrained locations were interspersed, and the high-degree of spatial precision of the learning, we suggest that these results are difficult to account for using attention or decision strategies and instead suggest that learning may have taken place for each location separately in retinotopically organized visual cortex

Adsorption behavior of conjugated {C}3-oligomers on Si(100) and HOPG surfaces

A pi-conjugated {C}3h-oligomer involving three dithienylethylene branches bridged at the meta positions of a central benzenic core has been synthesized and deposited either on the Si(100) surface or on the HOPG surface. On the silicon surface, scanning tunneling microscopy allows the observation of isolated molecules. Conversely, by substituting the thiophene rings of the oligomers with alkyl chains, a spontaneous ordered film is observed on the HOPG surface. As the interaction of the oligomers is different with both surfaces, the utility of the Si(100) surface to characterize individual oligomers prior to their use into a 2D layer is discussed

Thin film solid-state reactions forming carbides as contact materials for carbon-containing semiconductors

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Hodge metrics and positivity of direct images

Building on Fujita-Griffiths method of computing metrics on Hodge bundles, we show that the direct image of an adjoint semi-ample line bundle by a projective submersion has a continuous metric with Griffiths semi-positive curvature. This shows that for every holomorphic semi-ample vector bundle $E$ on a complex manifold, and every positive integer $k$, the vector bundle $S^kE\otimes\det E$ has a continuous metric with Griffiths semi-positive curvature. If $E$ is ample on a projective manifold, the metric can be made smooth and Griffiths positive.Comment: revised and expanded version of "A positivity property of ample vector bundles

The breakdown behavior of the maximum likelihood estimator in the logistic regression model.

In this note we discuss the breakdown behavior of the maximum likelihood (ML) estimator in the logistic regression model. We formally prove that the ML-estimator never explodes to infinity, but rather breaks down to zero when adding severe outliers to a data set. An example confirms this behavior. (C) 2002 Published by Elsevier Science B.V.breakdown point; logistic regression; maximum likelihood; robust estimation; generalized linear-models; robustness; existence; fits;

Robust estimation for ordinal regression.

Ordinal regression is used for modelling an ordinal response variable as a function of some explanatory variables. The classical technique for estimating the unknown parameters of this model is Maximum Likelihood (ML). The lack of robustness of this estimator is formally shown by deriving its breakdown point and its influence function. To robustify the procedure, a weighting step is added to the Maximum Likelihood estimator, yielding an estimator with bounded influence function. We also show that the loss in efficiency due to the weighting step remains limited. A diagnostic plot based on the Weighted Maximum Likelihood estimator allows to detect outliers of different types in a single plot.Breakdown point; Diagnostic plot; Influence function; Ordinal regression; Weighted maximum likelihood; Robust distances;

The breakdown behavior of the maximum likelihood estimator in the logistic regression model.

Abstract: In this note we discuss the breakdown behavior of the Maximum Likelihood (ML) estimator in the logistic regression model. We formally prove that the ML-estimator never explodes to infinity, but rather breaks down to zero when adding severe outliers to a data set. Numerical experiments confirm this behavior. As a more robust alternative, a Weighted Maximum Likelihood (WML) estimator will be considered.Model; Data;