Properties of Disks and Bulges of Spiral and Lenticular Galaxies in the Sloan Digital Sky Survey

A bulge-disk decomposition is made for 737 spiral and lenticular galaxies drawn from a SDSS galaxy sample for which morphological types are estimated. We carry out the bulge-disk decomposition using the growth curve fitting method. It is found that bulge properties, effective radius, effective surface brightness, and also absolute magnitude, change systematically with the morphological sequence; from early to late types, the size becomes somewhat larger, and surface brightness and luminosity fainter. In contrast disks are nearly universal, their properties remaining similar among disk galaxies irrespective of detailed morphologies from S0 to Sc. While these tendencies were often discussed in previous studies, the present study confirms them based on a large homogeneous magnitude-limited field galaxy sample with morphological types estimated. The systematic change of bulge-to-total luminosity ratio, $B/T$, along the morphological sequence is therefore not caused by disks but mostly by bulges. It is also shown that elliptical galaxies and bulges of spiral galaxies are unlikely to be in a single sequence. We infer the stellar mass density (in units of the critical mass density) to be $\Omega=$0.0021 for spheroids, i.e., elliptical galaxies plus bulges of spiral galaxies, and $\Omega=$0.00081 for disks.Comment: 30 pages, 9 figure

CardioCam: Leveraging Camera on Mobile Devices to Verify Users While Their Heart is Pumping

With the increasing prevalence of mobile and IoT devices (e.g., smartphones, tablets, smart-home appliances), massive private and sensitive information are stored on these devices. To prevent unauthorized access on these devices, existing user verification solutions either rely on the complexity of user-defined secrets (e.g., password) or resort to specialized biometric sensors (e.g., fingerprint reader), but the users may still suffer from various attacks, such as password theft, shoulder surfing, smudge, and forged biometrics attacks. In this paper, we propose, CardioCam, a low-cost, general, hard-to-forge user verification system leveraging the unique cardiac biometrics extracted from the readily available built-in cameras in mobile and IoT devices. We demonstrate that the unique cardiac features can be extracted from the cardiac motion patterns in fingertips, by pressing on the built-in camera. To mitigate the impacts of various ambient lighting conditions and human movements under practical scenarios, CardioCam develops a gradient-based technique to optimize the camera configuration, and dynamically selects the most sensitive pixels in a camera frame to extract reliable cardiac motion patterns. Furthermore, the morphological characteristic analysis is deployed to derive user-specific cardiac features, and a feature transformation scheme grounded on Principle Component Analysis (PCA) is developed to enhance the robustness of cardiac biometrics for effective user verification. With the prototyped system, extensive experiments involving 25 subjects are conducted to demonstrate that CardioCam can achieve effective and reliable user verification with over 99% average true positive rate (TPR) while maintaining the false positive rate (FPR) as low as 4%

Retrieving Bulge and Disk Parameters and Asymptotic Magnitudes from the Growth Curves of Galaxies

We show that the growth curves of galaxies can be used to determine their bulge and disk parameters and bulge-to-total luminosity ratios, in addition to their conventional asymptotic magnitudes, provided that the point spread function is accurately known and signal-to-noise ratio is modest (S/N$\gtrsim30$). The growth curve is a fundamental quantity that most future large galaxy imaging surveys will measure. Bulge and disk parameters retrieved from the growth curve will enable us to perform statistical studies of luminosity structure for a large number of galaxies.Comment: 28 pages including 13 PS figures; accepted for publication in PAS

Streaming visualisation of quantitative mass spectrometry data based on a novel raw signal decomposition method

As data rates rise, there is a danger that informatics for high-throughput LC-MS becomes more opaque and inaccessible to practitioners. It is therefore critical that efficient visualisation tools are available to facilitate quality control, verification, validation, interpretation, and sharing of raw MS data and the results of MS analyses. Currently, MS data is stored as contiguous spectra. Recall of individual spectra is quick but panoramas, zooming and panning across whole datasets necessitates processing/memory overheads impractical for interactive use. Moreover, visualisation is challenging if significant quantification data is missing due to data-dependent acquisition of MS/MS spectra. In order to tackle these issues, we leverage our seaMass technique for novel signal decomposition. LC-MS data is modelled as a 2D surface through selection of a sparse set of weighted B-spline basis functions from an over-complete dictionary. By ordering and spatially partitioning the weights with an R-tree data model, efficient streaming visualisations are achieved. In this paper, we describe the core MS1 visualisation engine and overlay of MS/MS annotations. This enables the mass spectrometrist to quickly inspect whole runs for ionisation/chromatographic issues, MS/MS precursors for coverage problems, or putative biomarkers for interferences, for example. The open-source software is available from http://seamass.net/viz/