Cloaking by coating: How effectively does a thin, stiff coating hide a soft substrate?

From human tissue to fruits, many soft materials are coated by a thin layer of a stiffer material. While the primary role of such a coating is often to protect the softer material, the thin, stiff coating also has an important effect on the mechanical behaviour of the composite material, making it appear significantly stiffer than the underlying material. We study this cloaking effect of a coating for the particular case of indentation tests, which measure the `firmness' of the composite solid: we use a combination of theory and experiment to characterize the firmness quantitatively. We find that the indenter size plays a key role in determining the effectiveness of cloaking: small indenters feel a mixture of the material properties of the coating and of the substrate, while large indenters sense largely the unadulterated substrate

Optical, physical and chemical characteristics of Australian continental aerosols: results from a field experiment

Mineral dust is one of the major components of the world's aerosol mix, having a number of impacts within the Earth system. However, the climate forcing impact of mineral dust is currently poorly constrained, with even its sign uncertain. As Australian deserts are more reddish than those in the Northern Hemisphere, it is important to better understand the physical, chemical and optical properties of this important aerosol. We have investigated the properties of Australian desert dust at a site in SW Queensland, which is strongly influenced by both dust and biomass burning aerosol. <br><br> Three years of ground-based monitoring of spectral optical thickness has provided a statistical picture of gross aerosol properties. The aerosol optical depth data showed a clear though moderate seasonal cycle with an annual mean of 0.06 ± 0.03. The Angstrom coefficient showed a stronger cycle, indicating the influence of the winter-spring burning season in Australia's north. AERONET size distributions showed a generally bimodal character, with the coarse mode assumed to be mineral dust, and the fine mode a mixture of fine dust, biomass burning and marine biogenic material. <br><br> In November 2006 we undertook a field campaign which collected 4 sets of size-resolved aerosol samples for laboratory analysis – ion beam analysis and ion chromatography. Ion beam analysis was used to determine the elemental composition of all filter samples, although elemental ratios were considered the most reliable output. Scatter plots showed that Fe, Al and Ti were well correlated with Si, and Co reasonably well correlated with Si, with the Fe/Al ratio somewhat higher than values reported from Northern Hemisphere sites (as expected). Scatter plots for Ca, Mn and K against Si showed clear evidence of a second population, which in some cases could be identified with a particular sample day or size fraction. These data may be used to attempt to build a signature of soil in this region of the Australian interior. <br><br> Ion chromatography was used to quantify water soluble ions for 2 of our sample sets, complementing the picture provided by ion beam analysis. The strong similarities between the MSA and SO<sub>4</sub><sup>2−</sup> size distributions argue strongly for a marine origin of much of the SO<sub>4</sub><sup>2−</sup>. The similarity of the Na<sup>+</sup>, Cl<sup>−</sup> and Mg<sup>2+</sup> size distributions also argue for a marine contribution. Further, we believe that both NO<sub>3</sub><sup>−</sup> and NH<sub>4</sub><sup>+</sup> are the result of surface reactions with appropriate gases

A new look inside Planetary Nebula LoTr 5: A long-period binary with hints of a possible third component

LoTr 5 is a planetary nebula with an unusual long-period binary central star. As far as we know, the pair consists of a rapidly rotating G-type star and a hot star, which is responsible for the ionization of the nebula. The rotation period of the G-type star is 5.95 days and the orbital period of the binary is now known to be $\sim$2700 days, one of the longest in central star of planetary nebulae. The spectrum of the G central star shows a complex H$\alpha$ double-peaked profile which varies with very short time scales, also reported in other central stars of planetary nebulae and whose origin is still unknown. We present new radial velocity observations of the central star which allow us to confirm the orbital period for the long-period binary and discuss the possibility of a third component in the system at $\sim$129 days to the G star. This is complemented with the analysis of archival light curves from SuperWASP, ASAS and OMC. From the spectral fitting of the G-type star, we obtain a effective temperature of $T_{\rm eff}$ = 5410$\pm$250 K and surface gravity of $\log g$ = 2.7$\pm$0.5, consistent with both giant and subgiant stars. We also present a detailed analysis of the H$\alpha$ double-peaked profile and conclude that it does not present correlation with the rotation period and that the presence of an accretion disk via Roche lobe overflow is unlikely.Comment: 12 pages, 12 figures, accepted for publication in MNRA

Retrodiction as a tool for micromaser field measurements

We use retrodictive quantum theory to describe cavity field measurements by successive atomic detections in the micromaser. We calculate the state of the micromaser cavity field prior to detection of sequences of atoms in either the excited or ground state, for atoms that are initially prepared in the excited state. This provides the POM elements, which describe such sequences of measurements.Comment: 20 pages, 4(8) figure

Kepler-91b: a planet at the end of its life. Planet and giant host star properties via light-curve variations

The evolution of planetary systems is intimately linked to the evolution of their host star. Our understanding of the whole planetary evolution process is based on the large planet diversity observed so far. To date, only few tens of planets have been discovered orbiting stars ascending the Red Giant Branch. Although several theories have been proposed, the question of how planets die remains open due to the small number statistics. In this work we study the giant star Kepler-91 (KOI-2133) in order to determine the nature of a transiting companion. This system was detected by the Kepler Space Telescope. However, its planetary confirmation is needed. We confirm the planetary nature of the object transiting the star Kepler-91 by deriving a mass of $M_p=0.88^{+0.17}_{-0.33} ~M_{\rm Jup}$ and a planetary radius of $R_p=1.384^{+0.011}_{-0.054} ~R_{\rm Jup}$. Asteroseismic analysis produces a stellar radius of $R_{\star}=6.30\pm 0.16 ~R_{\odot}$ and a mass of $M_{\star}=1.31\pm 0.10 ~ M_{\odot}$. We find that its eccentric orbit ($e=0.066^{+0.013}_{-0.017}$) is just $1.32^{+0.07}_{-0.22} ~ R_{\star}$ away from the stellar atmosphere at the pericenter. Kepler-91b could be the previous stage of the planet engulfment, recently detected for BD+48 740. Our estimations show that Kepler-91b will be swallowed by its host star in less than 55 Myr. Among the confirmed planets around giant stars, this is the planetary-mass body closest to its host star. At pericenter passage, the star subtends an angle of $48^{\circ}$, covering around 10% of the sky as seen from the planet. The planetary atmosphere seems to be inflated probably due to the high stellar irradiation.Comment: 21 pages, 8 tables and 11 figure

Precursors of extreme increments

We investigate precursors and predictability of extreme increments in a time series. The events we are focusing on consist in large increments within successive time steps. We are especially interested in understanding how the quality of the predictions depends on the strategy to choose precursors, on the size of the event and on the correlation strength. We study the prediction of extreme increments analytically in an AR(1) process, and numerically in wind speed recordings and long-range correlated ARMA data. We evaluate the success of predictions via receiver operator characteristics (ROC-curves). Furthermore, we observe an increase of the quality of predictions with increasing event size and with decreasing correlation in all examples. Both effects can be understood by using the likelihood ratio as a summary index for smooth ROC-curves

Testing linear hypotheses in high-dimensional regressions

For a multivariate linear model, Wilk's likelihood ratio test (LRT) constitutes one of the cornerstone tools. However, the computation of its quantiles under the null or the alternative requires complex analytic approximations and more importantly, these distributional approximations are feasible only for moderate dimension of the dependent variable, say $p\le 20$. On the other hand, assuming that the data dimension $p$ as well as the number $q$ of regression variables are fixed while the sample size $n$ grows, several asymptotic approximations are proposed in the literature for Wilk's \bLa including the widely used chi-square approximation. In this paper, we consider necessary modifications to Wilk's test in a high-dimensional context, specifically assuming a high data dimension $p$ and a large sample size $n$. Based on recent random matrix theory, the correction we propose to Wilk's test is asymptotically Gaussian under the null and simulations demonstrate that the corrected LRT has very satisfactory size and power, surely in the large $p$ and large $n$ context, but also for moderately large data dimensions like $p=30$ or $p=50$. As a byproduct, we give a reason explaining why the standard chi-square approximation fails for high-dimensional data. We also introduce a new procedure for the classical multiple sample significance test in MANOVA which is valid for high-dimensional data.Comment: Accepted 02/2012 for publication in "Statistics". 20 pages, 2 pages and 2 table

Causal connectivity of evolved neural networks during behavior

To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality,' for characterizing causal interactions generated within intact neural mechanisms. This method, called 'causal connectivity analysis' is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics

Six Peaks Visible in the Redshift Distribution of 46,400 SDSS Quasars Agree with the Preferred Redshifts Predicted by the Decreasing Intrinsic Redshift Model

The redshift distribution of all 46,400 quasars in the Sloan Digital Sky Survey (SDSS) Quasar Catalog III, Third Data Release, is examined. Six Peaks that fall within the redshift window below z = 4, are visible. Their positions agree with the preferred redshift values predicted by the decreasing intrinsic redshift (DIR) model, even though this model was derived using completely independent evidence. A power spectrum analysis of the full dataset confirms the presence of a single, significant power peak at the expected redshift period. Power peaks with the predicted period are also obtained when the upper and lower halves of the redshift distribution are examined separately. The periodicity detected is in linear z, as opposed to log(1+z). Because the peaks in the SDSS quasar redshift distribution agree well with the preferred redshifts predicted by the intrinsic redshift relation, we conclude that this relation, and the peaks in the redshift distribution, likely both have the same origin, and this may be intrinsic redshifts, or a common selection effect. However, because of the way the intrinsic redshift relation was determined it seems unlikely that one selection effect could have been responsible for both.Comment: 12 pages, 12 figure, accepted for publication in the Astrophysical Journa

Frequency Tracking and Parameter Estimation for Robust Quantum State-Estimation

In this paper we consider the problem of tracking the state of a quantum system via a continuous measurement. If the system Hamiltonian is known precisely, this merely requires integrating the appropriate stochastic master equation. However, even a small error in the assumed Hamiltonian can render this approach useless. The natural answer to this problem is to include the parameters of the Hamiltonian as part of the estimation problem, and the full Bayesian solution to this task provides a state-estimate that is robust against uncertainties. However, this approach requires considerable computational overhead. Here we consider a single qubit in which the Hamiltonian contains a single unknown parameter. We show that classical frequency estimation techniques greatly reduce the computational overhead associated with Bayesian estimation and provide accurate estimates for the qubit frequencyComment: 6 figures, 13 page