CMB component separation by parameter estimation

We propose a solution to the CMB component separation problem based on standard parameter estimation techniques. We assume a parametric spectral model for each signal component, and fit the corresponding parameters pixel by pixel in a two-stage process. First we fit for the full parameter set (e.g., component amplitudes and spectral indices) in low-resolution and high signal-to-noise ratio maps using MCMC, obtaining both best-fit values for each parameter, and the associated uncertainty. The goodness-of-fit is evaluated by a chi^2 statistic. Then we fix all non-linear parameters at their low-resolution best-fit values, and solve analytically for high-resolution component amplitude maps. This likelihood approach has many advantages: The fitted model may be chosen freely, and the method is therefore completely general; all assumptions are transparent; no restrictions on spatial variations of foreground properties are imposed; the results may be rigorously monitored by goodness-of-fit tests; and, most importantly, we obtain reliable error estimates on all estimated quantities. We apply the method to simulated Planck and six-year WMAP data based on realistic models, and show that separation at the muK level is indeed possible in these cases. We also outline how the foreground uncertainties may be rigorously propagated through to the CMB power spectrum and cosmological parameters using a Gibbs sampling technique.Comment: 20 pages, 10 figures, submitted to ApJ. For a high-resolution version, see http://www.astro.uio.no/~hke/docs/eriksen_et_al_fgfit.p

Metal-ligand complexation and clustering in mussel-inspired side-chain functionalized supramolecular hydrogels

Byssus threads of mussels have high resistance against abrasion in wave-swept habitats because of their outer cuticle, which is rich in amino acid dopa complexes with Fe3+ ions. This stems from the transient nature of metal–ligand complexes that creates extra relaxation mechanisms. Inspired by this concept, in this work, supramolecular hydrogels based on poly(acrylic acid) functionalized with nitrocatechol groups are synthesized. Polymer chains are physically crosslinked via nitrocatechol–Fe3+ complexes. The hydrogels have different polymer volume fractions as well as different nitrocatechol : Fe3+ molar ratios. The strength of the supramolecular crosslinks strongly depends on the pH of the medium. The dynamics of these hydrogels are studied by stress relaxation experiments followed by calculation of the relaxation time spectrum. Generally, samples have three relaxation modes, including dissociation of distinct metal–ligand complexes, reptation of sticky polymer chains, and disengagement of network segments from supramolecular aggregates and clusters. Such clusters hinder the terminal relaxation and potentially increase the stability of supramolecular hydrogels

Resolving the Radio Source Background: Deeper Understanding Through Confusion

We used the Karl G. Jansky Very Large Array (VLA) to image one primary beam area at 3 GHz with 8 arcsec FWHM resolution and 1.0 microJy/beam rms noise near the pointing center. The P(D) distribution from the central 10 arcmin of this confusion-limited image constrains the count of discrete sources in the 1 < S(microJy/beam) < 10 range. At this level the brightness-weighted differential count S^2 n(S) is converging rapidly, as predicted by evolutionary models in which the faintest radio sources are star-forming galaxies; and ~96$% of the background originating in galaxies has been resolved into discrete sources. About 63% of the radio background is produced by AGNs, and the remaining 37% comes from star-forming galaxies that obey the far-infrared (FIR) / radio correlation and account for most of the FIR background at lambda = 160 microns. Our new data confirm that radio sources powered by AGNs and star formation evolve at about the same rate, a result consistent with AGN feedback and the rough correlation of black hole and bulge stellar masses. The confusion at centimeter wavelengths is low enough that neither the planned SKA nor its pathfinder ASKAP EMU survey should be confusion limited, and the ultimate source detection limit imposed by "natural" confusion is < 0.01 microJy at 1.4 GHz. If discrete sources dominate the bright extragalactic background reported by ARCADE2 at 3.3 GHz, they cannot be located in or near galaxies and most are < 0.03 microJy at 1.4 GHz.Comment: 28 pages including 16 figures. ApJ accepted for publicatio

Nanorobotic investigation identifies novel visual, structural and functional correlates of autoimmune pathology in a blistering skin disease model

Copyright © 2014 Seiffert-Sinha et al. There remain major gaps in our knowledge regarding the detailed mechanisms by which autoantibodies mediate damage at the tissue level. We have undertaken novel strategies at the interface of engineering and clinical medicine to integrate nanoscale visual and structural data using nanorobotic atomic force microscopy with cell functional analyses to reveal previously unattainable details of autoimmune processes in real-time. Pemphigus vulgaris is a life-threatening autoimmune blistering skin condition in which there is disruption of desmosomal cell-cell adhesion structures that are associated with the presence of antibodies directed against specific epithelial proteins including Desmoglein (Dsg) 3. We demonstrate that pathogenic (blister-forming) anti-Dsg3 antibodies, distinct from non-pathogenic (non-blister forming) anti-Dsg3 antibodies, alter the structural and functional properties of keratinocytes in two sequential steps - an initial loss of cell adhesion and a later induction of apoptosis-related signaling pathways, but not full apoptotic cell death. We propose a ''2-Hit'' model for autoimmune disruption associated with skin-specific pathogenic autoantibodies. These data provide unprecedented details of autoimmune processes at the tissue level and offer a novel conceptual framework for understanding the action of selfreactive antibodies.published_or_final_versio

Influence of supramolecular forces on the linear viscoelasticity of gluten

Stress relaxation behavior of hydrated gluten networks was investigated by means of rheometry combined with μ-computed tomography (μ-CT) imaging. Stress relaxation behavior was followed over a wide temperature range (0–70 °C). Modulation of intermolecular bonds was achieved with urea or ascorbic acid in an effort to elucidate the presiding intermolecular interactions over gluten network relaxation. Master curves of viscoelasticity were constructed, and relaxation spectra were computed revealing three relaxation regimes for all samples. Relaxation commences with a well-defined short-time regime where Rouse-like modes dominate, followed by a power law region displaying continuous relaxation concluding in a terminal zone. In the latter zone, poroelastic relaxation due to water migration in the nanoporous structure of the network also contributes to the stress relief in the material. Hydrogen bonding between adjacent protein chains was identified as the determinant force that influences the relaxation of the networks. Changes in intermolecular interactions also resulted in changes in microstructure of the material that was also linked to the relaxation behavior of the networks

First Season QUIET Observations: Measurements of CMB Polarization Power Spectra at 43 GHz in the Multipole Range 25 <= ell <= 475

The Q/U Imaging ExperimenT (QUIET) employs coherent receivers at 43GHz and 95GHz, operating on the Chajnantor plateau in the Atacama Desert in Chile, to measure the anisotropy in the polarization of the CMB. QUIET primarily targets the B modes from primordial gravitational waves. The combination of these frequencies gives sensitivity to foreground contributions from diffuse Galactic synchrotron radiation. Between 2008 October and 2010 December, >10,000hours of data were collected, first with the 19-element 43GHz array (3458hours) and then with the 90-element 95GHz array. Each array observes the same four fields, selected for low foregrounds, together covering ~1000deg^2. This paper reports initial results from the 43GHz receiver which has an array sensitivity to CMB fluctuations of 69uK sqrt(s). The data were extensively studied with a large suite of null tests before the power spectra, determined with two independent pipelines, were examined. Analysis choices, including data selection, were modified until the null tests passed. Cross correlating maps with different telescope pointings is used to eliminate a bias. This paper reports the EE, BB and EB power spectra in the multipole range ell=25-475. With the exception of the lowest multipole bin for one of the fields, where a polarized foreground, consistent with Galactic synchrotron radiation, is detected with 3sigma significance, the E-mode spectrum is consistent with the LCDM model, confirming the only previous detection of the first acoustic peak. The B-mode spectrum is consistent with zero, leading to a measurement of the tensor-to-scalar ratio of r=0.35+1.06-0.87. The combination of a new time-stream double-demodulation technique, Mizuguchi-Dragone optics, natural sky rotation, and frequent boresight rotation leads to the lowest level of systematic contamination in the B-mode power so far reported, below the level of r=0.1Comment: 19 pages, 14 figures, higher quality figures are available at http://quiet.uchicago.edu/results/index.html; Fixed a typo and corrected statistical error values used as a reference in Figure 14, showing our systematic uncertainties (unchanged) vs. multipole; Revision to ApJ accepted version, this paper should be cited as "QUIET Collaboration et al. (2011)

Software defect prediction: do different classifiers find the same defects?

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.During the last 10 years, hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in NASA, open source and commercial datasets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty of each classifier is compared. Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. Our results confirm that a unique subset of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Given our results, we conclude that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.Peer reviewedFinal Published versio