Ownership of demand deposits

Bank deposits

Ownership of demand deposits

Bank deposits

Ownership of demand deposits

Bank deposits ; Banks and banking

Space Warps II. New Gravitational Lens Candidates from the CFHTLS Discovered through Citizen Science

We report the discovery of 29 promising (and 59 total) new lens candidates from the CFHT Legacy Survey (CFHTLS) based on about 11 million classifications performed by citizen scientists as part of the first Space Warps lens search. The goal of the blind lens search was to identify lens candidates missed by robots (the RingFinder on galaxy scales and ArcFinder on group/cluster scales) which had been previously used to mine the CFHTLS for lenses. We compare some properties of the samples detected by these algorithms to the Space Warps sample and find them to be broadly similar. The image separation distribution calculated from the Space Warps sample shows that previous constraints on the average density profile of lens galaxies are robust. SpaceWarps recovers about 65% of known lenses, while the new candidates show a richer variety compared to those found by the two robots. This detection rate could be increased to 80% by only using classifications performed by expert volunteers (albeit at the cost of a lower purity), indicating that the training and performance calibration of the citizen scientists is very important for the success of Space Warps. In this work we present the SIMCT pipeline, used for generating in situ a sample of realistic simulated lensed images. This training sample, along with the false positives identified during the search, has a legacy value for testing future lens finding algorithms. We make the pipeline and the training set publicly available.Comment: 23 pages, 12 figures, MNRAS accepted, minor to moderate changes in this versio

Space Warps: I. Crowd-sourcing the Discovery of Gravitational Lenses

We describe Space Warps, a novel gravitational lens discovery service that yields samples of high purity and completeness through crowd-sourced visual inspection. Carefully produced colour composite images are displayed to volunteers via a web- based classification interface, which records their estimates of the positions of candidate lensed features. Images of simulated lenses, as well as real images which lack lenses, are inserted into the image stream at random intervals; this training set is used to give the volunteers instantaneous feedback on their performance, as well as to calibrate a model of the system that provides dynamical updates to the probability that a classified image contains a lens. Low probability systems are retired from the site periodically, concentrating the sample towards a set of lens candidates. Having divided 160 square degrees of Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) imaging into some 430,000 overlapping 82 by 82 arcsecond tiles and displaying them on the site, we were joined by around 37,000 volunteers who contributed 11 million image classifications over the course of 8 months. This Stage 1 search reduced the sample to 3381 images containing candidates; these were then refined in Stage 2 to yield a sample that we expect to be over 90% complete and 30% pure, based on our analysis of the volunteers performance on training images. We comment on the scalability of the SpaceWarps system to the wide field survey era, based on our projection that searches of 10$^5$ images could be performed by a crowd of 10$^5$ volunteers in 6 days.Comment: 21 pages, 13 figures, MNRAS accepted, minor to moderate changes in this versio

The Grizzly, April 6, 1984

Loyalty Fund Phon-a-thon, Successful • Parsons Re-elected VP of Historical Society • Nagy\u27s Stellar Lecture • McQuaid Presents Views on Candidates • Letters to the Editor: Fraternities Help to Combat Apathy; Bubble World ; UC Social Life Needs Help; Pledging Not Destructive; Pledging Enhances Development; To Ban Pledging not a Positive Aspect; In Memory of Zeta Chi; Pledging to Expand Socially • Patrol Pattern Changed, Woman is Assaulted • The Limerick Syndrome: What Would UC do? • Mardi Gras Ball Declared Success • Changing Picture of Health in \u2780\u27s • Ride for Your Life! • Two Views on the Bussing Situation • Women\u27s Tennis Splits in Opening Matches • Softball Undefeated at 6-0 • Tennis Team Nets Two Victories • Denning Captures Fencing Title • Strizke, Rosenberg Qualify for Gymnastics Championship • Men\u27s Track Kicks off \u2784 Season • Men\u27s Lacrosse Thumps Bloomsburg • Hot Hitting Leads Bears Batsmen to Victories • Baseball Team Raises Record to 6-3 • Women\u27s Track Promisinghttps://digitalcommons.ursinus.edu/grizzlynews/1116/thumbnail.jp

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy.

MotivationMultiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.ResultsWe performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementationDatasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary informationSupplementary data are available at Bioinformatics online

SpaceWarps- II. New gravitational lens candidates from the CFHTLS discovered through citizen science

We report the discovery of 29 promising (and 59 total) new lens candidates from the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) based on about 11 million classifications performed by citizen scientists as part of the first SpaceWarps lens search. The goal of the blind lens search was to identify lens candidates missed by robots (the ringfinder on galaxy scales and arcfinder on group/cluster scales) which had been previously used to mine the CFHTLS for lenses. We compare some properties of the samples detected by these algorithms to the SpaceWarps sample and find them to be broadly similar. The image separation distribution calculated from the SpaceWarps sample shows that previous constraints on the average density profile of lens galaxies are robust. SpaceWarps recovers about 65 per cent of known lenses, while the new candidates show a richer variety compared to those found by the two robots. This detection rate could be increased to 80 per cent by only using classifications performed by expert volunteers (albeit at the cost of a lower purity), indicating that the training and performance calibration of the citizen scientists is very important for the success of SpaceWarps. In this work we present the SIMCT pipeline, used for generating in situ a sample of realistic simulated lensed images. This training sample, along with the false positives identified during the search, has a legacy value for testing future lens-finding algorithms. We make the pipeline and the training set publicly availabl

SpaceWarps - I. Crowdsourcing the discovery of gravitational lenses

We describe SpaceWarps, a novel gravitational lens discovery service that yields samples of high purity and completeness through crowdsourced visual inspection. Carefully produced colour composite images are displayed to volunteers via a web-based classification interface, which records their estimates of the positions of candidate lensed features. Images of simulated lenses, as well as real images which lack lenses, are inserted into the image stream at random intervals; this training set is used to give the volunteers instantaneous feedback on their performance, as well as to calibrate a model of the system that provides dynamical updates to the probability that a classified image contains a lens. Low-probability systems are retired from the site periodically, concentrating the sample towards a set of lens candidates. Having divided 160 deg2 of Canada-France-Hawaii Telescope Legacy Survey imaging into some 430000 overlapping 82 by 82arcsec tiles and displaying them on the site, we were joined by around 37000 volunteers who contributed 11 million image classifications over the course of eight months. This stage 1 search reduced the sample to 3381 images containing candidates; these were then refined in stage 2 to yield a sample that we expect to be over 90 per cent complete and 30 per cent pure, based on our analysis of the volunteers performance on training images. We comment on the scalability of the SpaceWarps system to the wide field survey era, based on our projection that searches of 105 images could be performed by a crowd of 105 volunteers in 6