A search for disk-galaxy lenses in the Sloan Digital Sky Survey

We present the first automated spectroscopic search for disk-galaxy lenses, using the Sloan Digital Sky Survey database. We follow up eight gravitational lens candidates, selected among a sample of ~40000 candidate massive disk galaxies, using a combination of ground-based imaging and long-slit spectroscopy. We confirm two gravitational lens systems: one probable disk galaxy, and one probable S0 galaxy. The remaining systems are four promising disk-galaxy lens candidates, as well as two probable gravitational lenses whose lens galaxy might be an S0 galaxy. The redshifts of the lenses are z_lens ~ 0.1. The redshift range of the background sources is z_source ~ 0.3 - 0.7. The systems presented here are (confirmed or candidate) galaxy-galaxy lensing systems, that is, systems where the multiple images are faint and extended, allowing an accurate determination of the lens galaxy mass and light distributions without contamination from the background galaxy. Moreover, the low redshift of the (confirmed or candidates) lens galaxies is favorable for measuring rotation points to complement the lensing study. We estimate the rest-frame total mass-to-light ratio within the Einstein radius for the two confirmed lenses: we find M_tot/L_I = 5.4 +- 1.5 within 3.9 +- 0.9 kpc for SDSS J081230.30+543650.9, and M_tot/L_I = 1.5 +- 0.9 within 1.4 +- 0.8 kpc for SDSS J145543.55+530441.2 (all in solar units). Hubble Space Telescope or Adaptive Optics imaging is needed to further study the systems.Comment: ApJ, accepte

Skyline queries over incomplete multidimensional database

In recent years, there has been much focus on skyline queries that incorporate and provide more flexible query operators that return data items which are dominating other data items in all attributes (dimensions).Several techniques for skyline have been proposed in the literature.Most of the existing skyline techniques aimed to find the skyline query results by supposing that the values of dimensions are always present for every data item.In this paper we aim to evaluate the skyline preference queries in which some dimension values are missing.We proposed an approach for answering preference queries in a database by utilizing the concept of skyline technique.The skyline set selected for a given query operation is then optimized so that the missing values are replaced with some approximate values that provide a skyline answer with complete data.This will significantly reduce the number of comparisons between data items.Beside that, the number of retrieved skyline data items is reduced and this guides the users to select the most appropriate data items from the several alternative complete skyline data items

Using a multifrontal sparse solver in a high performance, finite element code

We consider the performance of the finite element method on a vector supercomputer. The computationally intensive parts of the finite element method are typically the individual element forms and the solution of the global stiffness matrix both of which are vectorized in high performance codes. To further increase throughput, new algorithms are needed. We compare a multifrontal sparse solver to a traditional skyline solver in a finite element code on a vector supercomputer. The multifrontal solver uses the Multiple-Minimum Degree reordering heuristic to reduce the number of operations required to factor a sparse matrix and full matrix computational kernels (e.g., BLAS3) to enhance vector performance. The net result in an order-of-magnitude reduction in run time for a finite element application on one processor of a Cray X-MP

SkyLens: Visual analysis of skyline on multi-dimensional data

Skyline queries have wide-ranging applications in fields that involve multi-criteria decision making, including tourism, retail industry, and human resources. By automatically removing incompetent candidates, skyline queries allow users to focus on a subset of superior data items (i.e., the skyline), thus reducing the decision-making overhead. However, users are still required to interpret and compare these superior items manually before making a successful choice. This task is challenging because of two issues. First, people usually have fuzzy, unstable, and inconsistent preferences when presented with multiple candidates. Second, skyline queries do not reveal the reasons for the superiority of certain skyline points in a multi-dimensional space. To address these issues, we propose SkyLens, a visual analytic system aiming at revealing the superiority of skyline points from different perspectives and at different scales to aid users in their decision making. Two scenarios demonstrate the usefulness of SkyLens on two datasets with a dozen of attributes. A qualitative study is also conducted to show that users can efficiently accomplish skyline understanding and comparison tasks with SkyLens.Comment: 10 pages. Accepted for publication at IEEE VIS 2017 (in proceedings of VAST