Hidden Ancestor Graphs: Models for Detagging Property Graphs

Consider a graph $G$ where each vertex is visibly labelled as a member of a distinct class, but also has a hidden binary state: wild or tame. Edges with end points in the same class are called agreement edges. Premise: an edge connecting vertices in different classes -- a conflict edge -- is allowed only when at least one end point is wild. Interpret wild status as readiness to form connections with any other vertex, regardless of class -- a form of class disaffiliation. The learning goal is to classify each vertex as wild or tame using its neighborhood data. In applications such as communications metadata, bio-informatics, retailing, or bibliography, adjacency in $G$ is typically created by paths of length two in a transactional bipartite graph $B$. Class labelling, imported from a reference data source, is typically assortative, so agreement edges predominate. Conflict edges represent observed behavior (from $B$) inconsistent with prior labelling of $V(G)$. Wild vertices are those whose label is uninformative. The hidden ancestor graph constitutes a natural model for generating agreement edges and conflict edges, depending on a latent tree structure. The model is able to manifest high clustering rates and heavy-tailed degree distributions typical of social and spatial networks. It can be fitted to graph data using a few measurable graph parameters, and supplies a natural statistical classifier for wild versus tame.Comment: 35 pages, 12 figure

Systematic Effects in Interferometric Observations of the CMB Polarization

The detection of the primordial $B$-mode spectrum of the polarized cosmic microwave background (CMB) signal may provide a probe of inflation. However, observation of such a faint signal requires excellent control of systematic errors. Interferometry proves to be a promising approach for overcoming such a challenge. In this paper we present a complete simulation pipeline of interferometric observations of CMB polarization, including systematic errors. We employ two different methods for obtaining the power spectra from mock data produced by simulated observations: the maximum likelihood method and the method of Gibbs sampling. We show that the results from both methods are consistent with each other, as well as, within a factor of 6, with analytical estimates. Several categories of systematic errors are considered: instrumental errors, consisting of antenna gain and antenna coupling errors, and beam errors, consisting of antenna pointing errors, beam cross-polarization and beam shape (and size) errors. In order to recover the tensor-to-scalar ratio, $r$, within a 10% tolerance level, which ensures the experiment is sensitive enough to detect the $B$-signal at $r=0.01$ in the multipole range $28 < \ell < 384$, we find that, for a QUBIC-like experiment, Gaussian-distributed systematic errors must be controlled with precisions of $|g_{rms}| = 0.1$ for antenna gain, $|\epsilon_{rms}| = 5 \times 10^{-4}$ for antenna coupling, $\delta_{rms} \approx 0.7^\circ$ for pointing, $\zeta_{rms} \approx 0.7^\circ$ for beam shape, and $\mu_{rms} = 5 \times 10^{-4}$ for beam cross-polarization.Comment: 15 pages, 6 figures, submitted to ApJ

Bayesian Inference of Polarized CMB Power Spectra from Interferometric Data

Detection of B-mode polarization of the cosmic microwave background (CMB) radiation is one of the frontiers of observational cosmology. Because they are an order of magnitude fainter than E-modes, it is quite a challenge to detect B-modes. Having more manageable systematics, interferometers prove to have a substantial advantage over imagers in detecting such faint signals. Here, we present a method for Bayesian inference of power spectra and signal reconstruction from interferometric data of the CMB polarization signal by using the technique of Gibbs sampling. We demonstrate the validity of the method in the flat-sky approximation for a simulation of an interferometric observation on a finite patch with incomplete uv-plane coverage, a finite beam size and a realistic noise model. With a computational complexity of O(n^{3/2}), n being the data size, Gibbs sampling provides an efficient method for analyzing upcoming cosmology observations.Comment: 8 pages, 8 figures, expanded discussion and edited to match ApJS approved version, acknowledgments update

Bayesian semi-blind component separation for foreground removal in interferometric 21-cm observations

We present in this paper a new Bayesian semi-blind approach for foreground removal in observations of the 21-cm signal with interferometers. The technique, which we call HIEMICA (HI Expectation-Maximization Independent Component Analysis), is an extension of the Independent Component Analysis (ICA) technique developed for two-dimensional (2D) CMB maps to three-dimensional (3D) 21-cm cosmological signals measured by interferometers. This technique provides a fully Bayesian inference of power spectra and maps and separates the foregrounds from signal based on the diversity of their power spectra. Only relying on the statistical independence of the components, this approach can jointly estimate the 3D power spectrum of the 21-cm signal and, the 2D angular power spectrum and the frequency dependence of each foreground component, without any prior assumptions about foregrounds. This approach has been tested extensively by applying it to mock data from interferometric 21-cm intensity mapping observations under idealized assumptions of instrumental effects. We also discuss the impact when the noise properties are not known completely. As a first step toward solving the 21 cm power spectrum analysis problem we compare the semi-blind HIEMICA technique with the commonly used Principal Component Analysis (PCA). Under the same idealized circumstances the proposed technique provides significantly improved recovery of the power spectrum. This technique can be applied straightforwardly to all 21-cm interferometric observations, including epoch of reionization measurements, and can be extended to single-dish observations as well.Comment: 18 pages, 7 figures, added some discussions about the impact of noise misspecificatio

The Cut & Enhance method : selecting clusters of galaxies from the SDSS commissioning data

We describe an automated method, the Cut & Enhance method (CE) for detecting clusters of galaxies in multi-color optical imaging surveys. This method uses simple color cuts, combined with a density enhancement algorithm, to up-weight pairs of galaxies that are close in both angular separation and color. The method is semi-parametric since it uses minimal assumptions about cluster properties in order to minimize possible biases. No assumptions are made about the shape of clusters, their radial profile or their luminosity function. The method is successful in finding systems ranging from poor to rich clusters of galaxies, of both regular and irregular shape. We determine the selection function of the CE method via extensive Monte Carlo simulations which use both the real, observed background of galaxies and a randomized background of galaxies. We use position shuffled and color shuffled data to perform the false positive test. We have also visually checked all the clusters detected by the CE method. We apply the CE method to the 350 deg^2 of the SDSS (Sloan Digital Sky Survey) commissioning data and construct a SDSS CE galaxy cluster catalog with an estimated redshift and richness for each cluster. The CE method is compared with other cluster selection methods used on SDSS data such as the Matched Filter (Postman et al. 1996, Kim et al. 2001), maxBCG technique (Annis et al. 2001) and Voronoi Tessellation (Kim et al. 2001). The CE method can be adopted for cluster selection in any multi-color imaging surveys.Comment: 62 pages, 32 figures, Accepted for publication in the Astronomical Journal, "the CE galaxy cluster catalog can be downloaded from, http://astrophysics.phys.cmu.edu/~tomo/ce/

Vacuum Strength of Two Candidate Glasses for a Space Observatory

The strengths of two candidate glass types for use in a space observatory were measured. Samples of ultra-low expansion glass (ULE) and borosilicate (Pyrex) were tested in air and in vacuum at room temperature (20 degrees C) and in vacuum after being heated to 200 degrees C. Both glasses tested in vacuum showed a significant increase in strength over those tested in air. However, there was no statistical difference between the strength of samples tested in vacuum at room temperature and those tested in vacuum after heating to 200 degrees C

The influence of resuscitation preferences on obstetrical management of periviable deliveries

Objective Determine the relative influence of patient's resuscitation preferences on periviable delivery management. Methods Surveyed 295 obstetrician-gynecologists about managing periviable preterm premature rupture of membranes. Across 10 vignettes, we systematically varied gestational age; occupation; method of conception; and resuscitation preference. Physicians rated their likelihood (0-10) of proceeding with induction, steroids, and cesarean. Data were analyzed via conjoint analysis. Results 205 physician responses were included. Median ratings for management decisions were: induction 1.89; steroids 5.00; cesarean for labor 3.89; cesarean for distress 4.11. Gestational age had the greatest influence on physician ratings across all decisions (importance values ranging from 72.6-86.6), followed by patient's resuscitation preference (range= 9.3-21.4). Conclusion Gestational age is weighted more heavily than patients’ resuscitation preferences in obstetricians’ decision-making for periviable delivery management. Misalignment of antenatal management with parental resuscitation preferences may adversely affect periviable outcomes. Interventions are needed to facilitate more patient-centered decision-making in periviable care

Vacuum Strength of Two Candidate Glasses for a Space Observatory

The strengths of two candidate glass types for use in a space observatory were measured. Samples of ultra-low expansion glass (ULE) and borosilicate (Pyrex) were tested in air and in vacuum at room temperature (20 C) and in vacuum after being heated to 200 C. Both glasses tested in vacuum showed an increase in strength over those tested in air. However, there was no statistical difference between the strength of samples tested in vacuum at room temperature and those tested in vacuum after heating to 200 C