Plugin procedure in segmentation and application to hyperspectral image segmentation

In this article we give our contribution to the problem of segmentation with plug-in procedures. We give general sufficient conditions under which plug in procedure are efficient. We also give an algorithm that satisfy these conditions. We give an application of the used algorithm to hyperspectral images segmentation. Hyperspectral images are images that have both spatial and spectral coherence with thousands of spectral bands on each pixel. In the proposed procedure we combine a reduction dimension technique and a spatial regularisation technique. This regularisation is based on the mixlet modelisation of Kolaczyck and Al

Two loop detection mechanisms: a comparison

In order to compare two loop detection mechanisms we describe two calculi for theorem proving in intuitionistic propositional logic. We call them both MJ Hist, and distinguish between them by description as `Swiss' or `Scottish'. These calculi combine in different ways the ideas on focused proof search of Herbelin and Dyckhoff & Pinto with the work of Heuerding emphet al on loop detection. The Scottish calculus detects loops earlier than the Swiss calculus but at the expense of modest extra storage in the history. A comparison of the two approaches is then given, both on a theoretic and on an implementational level

The Weierstrass subgroup of a curve has maximal rank

We show that the Weierstrass points of the generic curve of genus $g$ over an algebraically closed field of characteristic 0 generate a group of maximal rank in the Jacobian

Gaussian process model based predictive control

Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coefficients to be optimized. This paper illustrates possible application of Gaussian process models within model-based predictive control. The extra information provided within Gaussian process model is used in predictive control, where optimization of control signal takes the variance information into account. The predictive control principle is demonstrated on control of pH process benchmark

Adaptive, cautious, predictive control with Gaussian process priors

Nonparametric Gaussian Process models, a Bayesian statistics approach, are used to implement a nonlinear adaptive control law. Predictions, including propagation of the state uncertainty are made over a k-step horizon. The expected value of a quadratic cost function is minimised, over this prediction horizon, without ignoring the variance of the model predictions. The general method and its main features are illustrated on a simulation example

Gaussian Process priors with uncertain inputs? Application to multiple-step ahead time series forecasting

We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. k-step ahead forecasting of a discrete-time non-linear dynamic system can be performed by doing repeated one-step ahead predictions. For a state-space model of the form y t = f(Yt-1 ,..., Yt-L ), the prediction of y at time t + k is based on the point estimates of the previous outputs. In this paper, we show how, using an analytical Gaussian approximation, we can formally incorporate the uncertainty about intermediate regressor values, thus updating the uncertainty on the current prediction

Origin of Second Harmonic Generation from individual Silicon Nanowires

We investigate Second Harmonic Generation from individual silicon nanowires and study the influence of resonant optical modes on the far-field nonlinear emission. We find that the polarization of the Second Harmonic has a size-dependent behavior and explain this phenomenon by a combination of different surface and bulk nonlinear susceptibility contributions. We show that the Second Harmonic Generation has an entirely different origin, depending on whether the incident illumination is polarized parallel or perpendicularly to the nanowire axis. The results open perspectives for further geometry-based studies on the origin of Second Harmonic Generation in nanostructures of high-index centrosymmetric semiconductors.Comment: 7 Pages, 4 Figures + 3 Pages, 6 Figures in Appendi

Model-independent Limits from Spin-dependent WIMP Dark Matter Experiments

Spin-dependent WIMP searches have traditionally presented results within an odd group approximation and by suppressing one of the spin-dependent interaction cross sections. We here elaborate on a model-independent analysis in which spin-dependent interactions with both protons and neutrons are simultaneously considered. Within this approach, equivalent current limits on the WIMP-nucleon interaction at WIMP mass of 50 GeV/c$^{2}$ are either $\sigma_{p}\leq0.7$ pb, $\sigma_{n}\leq0.2$ pb or $|a_{p}|\leq0.4$, $|a_{n}|\leq0.7$ depending on the choice of cross section or coupling strength representation. These limits become less restrictive for either larger or smaller masses; they are less restrictive than those from the traditional odd group approximation regardless of WIMP mass. Combination of experimental results are seen to produce significantly more restrictive limits than those obtained from any single experiment. Experiments traditionally considered spin-independent are moreover found to severely limit the spin-dependent phase space. The extension of this analysis to the case of positive signal experiments is explored.Comment: 12 pages, 12 figures, submitted to Phys. Rev.