Degree Landscapes in Scale-Free Networks

We generalize the degree-organizational view of real-world networks with broad degree-distributions in a landscape analogue with mountains (high-degree nodes) and valleys (low-degree nodes). For example, correlated degrees between adjacent nodes corresponds to smooth landscapes (social networks), hierarchical networks to one-mountain landscapes (the Internet), and degree-disassortative networks without hierarchical features to rough landscapes with several mountains. We also generate ridge landscapes to model networks organized under constraints imposed by the space the networks are embedded in, associated to spatial or, in molecular networks, to functional localization. To quantify the topology, we here measure the widths of the mountains and the separation between different mountains.Comment: 4 pages, 5 figure

Mid-infrared Variability from the Spitzer Deep Wide-field Survey

We use the multi-epoch, mid-infrared Spitzer Deep Wide-Field Survey to investigate the variability of objects in 8.1 deg^2 of the NOAO Deep Wide Field Survey Boötes field. We perform a Difference Image Analysis of the four available epochs between 2004 and 2008, focusing on the deeper 3.6 and 4.5 μm bands. Out of 474, 179 analyzed sources, 1.1% meet our standard variability selection criteria that the two light curves are strongly correlated (r > 0.8) and that their joint variance (σ_(12)) exceeds that for all sources with the same magnitude by 2σ. We then examine the mid-IR colors of the variable sources and match them with X-ray sources from the XBoötes survey, radio catalogs, 24 μm selected active galactic nucleus (AGN) candidates, and spectroscopically identified AGNs from the AGN and Galaxy Evolution Survey (AGES). Based on their mid-IR colors, most of the variable sources are AGNs (76%), with smaller contributions from stars (11%), galaxies (6%), and unclassified objects, although most of the stellar, galaxy, and unclassified sources are false positives. For our standard selection criteria, 11%-12% of the mid-IR counterparts to X-ray sources, 24 μm AGN candidates, and spectroscopically identified AGNs show variability. The exact fractions depend on both the search depth and the selection criteria. For example, 12% of the 1131 known z>1 AGNs in the field and 14%-17% of the known AGNs with well-measured fluxes in all four Infrared Array Camera bands meet our standard selection criteria. The mid-IR AGN variability can be well described by a single power-law structure function with an index of γ ≈ 0.5 at both 3.6 and 4.5 μm, and an amplitude of S _0 ≃ 0.1 mag on rest-frame timescales of 2 yr. The variability amplitude is higher for shorter rest-frame wavelengths and lower luminosities

BICEP3: a 95GHz refracting telescope for degree-scale CMB polarization

Bicep3 is a 550 mm-aperture refracting telescope for polarimetry of radiation in the cosmic microwave background at 95 GHz. It adopts the methodology of Bicep1, Bicep2 and the Keck Array experiments | it possesses sufficient resolution to search for signatures of the inflation-induced cosmic gravitational-wave background while utilizing a compact design for ease of construction and to facilitate the characterization and mitigation of systematics. However, Bicep3 represents a significant breakthrough in per-receiver sensitivity, with a focal plane area 5x larger than a Bicep2/Keck Array receiver and faster optics (f=1:6 vs. f=2:4). Large-aperture infrared-reflective metal-mesh filters and infrared-absorptive cold alumina filters and lenses were developed and implemented for its optics. The camera consists of 1280 dual-polarization pixels; each is a pair of orthogonal antenna arrays coupled to transition-edge sensor bolometers and read out by multiplexed SQUIDs. Upon deployment at the South Pole during the 2014-15 season, Bicep3 will have survey speed comparable to Keck Array 150 GHz (2013), and will signifcantly enhance spectral separation of primordial B-mode power from that of possible galactic dust contamination in the Bicep2 observation patch

Degree-scale Cosmic Microwave Background Polarization Measurements from Three Years of BICEP1 Data

BICEP1 is a millimeter-wavelength telescope designed specifically to measure the inflationary B-mode polarization of the cosmic microwave background at degree angular scales. We present results from an analysis of the data acquired during three seasons of observations at the South Pole (2006-2008). This work extends the two-year result published in Chiang et al., with additional data from the third season and relaxed detector-selection criteria. This analysis also introduces a more comprehensive estimation of band power window functions, improved likelihood estimation methods, and a new technique for deprojecting monopole temperature-to-polarization leakage that reduces this class of systematic uncertainty to a negligible level. We present maps of temperature, E- and B-mode polarization, and their associated angular power spectra. The improvement in the map noise level and polarization spectra error bars are consistent with the 52% increase in integration time relative to Chiang et al. We confirm both self-consistency of the polarization data and consistency with the two-year results. We measure the angular power spectra at 21 ≤ ℓ ≤ 335 and find that the EE spectrum is consistent with Lambda cold dark matter cosmology, with the first acoustic peak of the EE spectrum now detected at 15σ. The BB spectrum remains consistent with zero. From B-modes only, we constrain the tensor-to-scalar ratio to r = 0.03^(+0.27)_(-0.23), or r < 0.70 at 95% confidence level

The High Frequency Instrument of Planck: Requirements and Design

The Planck satellite is a project of the European Space Agency based on a wide international collaboration, including United States and Canadian laboratories. It is dedicated to the measurement of the anisotropy of the Cosmic Microwave Background (CMB) with unprecedented sensitivity and angular resolution. The detectors of its High frequency Instrument (HFI) are bolometers cooled down to 100 mK. Their sensitivity will be limited by the photon noise of the CMB itself at low frequencies, and of the instrument background at high frequencies. The requirements on the measurement chain are directly related to the strategy of observation used for the satellite. Due to the scanning on the sky, time features of the measurement chain are directly transformed into angular features in the sky maps. This impacts the bolometer design as well as other elements: For example, the cooling system must present outstanding temperature stability, and the amplification chain must show, down to very low frequencies, a flat noise spectrum

SPIDER: CMB Polarimetry from the Edge of Space

Spider is a balloon-borne instrument designed to map the polarization of the millimeter-wave sky at large angular scales. Spider targets the B-mode signature of primordial gravitational waves in the cosmic microwave background (CMB), with a focus on mapping a large sky area with high fidelity at multiple frequencies. Spider ’s first long-duration balloon (LDB) flight in January 2015 deployed a total of 2400 antenna-coupled transition-edge sensors (TESs) at 90 GHz and 150 GHz. In this work we review the design and in-flight performance of the Spider instrument, with a particular focus on the measured performance of the detectors and instrument in a space-like loading and radiation environment. Spider ’s second flight in December 2018 will incorporate payload upgrades and new receivers to map the sky at 285 GHz, providing valuable information for cleaning polarized dust emission from CMB maps

Use of High Sensitivity Bolometers for Astronomy: Planck High Frequency Instrument

The Planck satellite is dedicated to the measurement of the anisotropy of the Cosmic Microwave Background (CMB) with unprecedented sensitivity and angular resolution. It is a project of the European Space Agency based on a wide international collaboration, including United States and Canadian laboratories. The detectors of its High Frequency Instrument (HFI) are bolometers cooled down to 100 mK. Their sensitivity will be limited by the photon noise of the CMB itself at low frequencies, and of the instrument background at high frequencies. The requirements on the measurement chain are directly related to the strategy of observation used for the satellite. This impacts the bolometer design as well as other elements: The cooling system must present outstanding temperature stability, and the amplification chain must show a flat noise spectrum down to very low frequencies

An approach to predicting patient experience through machine learning and social network analysis.

OBJECTIVE:Improving the patient experience has become an essential component of any healthcare system\u27s performance metrics portfolio. In this study, we developed a machine learning model to predict a patient\u27s response to the Hospital Consumer Assessment of Healthcare Providers and Systems survey\u27s Doctor Communications domain questions while simultaneously identifying most impactful providers in a network. MATERIALS AND METHODS:This is an observational study of patients admitted to a single tertiary care hospital between 2016 and 2020. Using machine learning algorithms, electronic health record data were used to predict patient responses to Hospital Consumer Assessment of Healthcare Providers and Systems survey questions in the doctor domain, and patients who are at risk for responding negatively were identified. Model performance was assessed by area under receiver-operating characteristic curve. Social network analysis metrics were also used to identify providers most impactful to patient experience. RESULTS:Using a random forest algorithm, patients\u27 responses to the following 3 questions were predicted: During this hospital stay how often did doctors. 1) treat you with courtesy and respect? 2) explain things in a way that you could understand? 3) listen carefully to you? with areas under the receiver-operating characteristic curve of 0.876, 0.819, and 0.819, respectively. Social network analysis found that doctors with higher centrality appear to have an outsized influence on patient experience, as measured by rank in the random forest model in the doctor domain. CONCLUSIONS:A machine learning algorithm identified patients at risk of a negative experience. Furthermore, a doctor social network framework provides metrics for identifying those providers that are most influential on the patient experience