The Taiwan ECDFS Near-Infrared Survey: Ultra-deep J and Ks Imaging in the Extended Chandra Deep Field-South

We present ultra-deep J and Ks imaging observations covering a 30' * 30' area of the Extended Chandra Deep Field-South (ECDFS) carried out by our Taiwan ECDFS Near-Infrared Survey (TENIS). The median 5-sigma limiting magnitudes for all detected objects in the ECDFS reach 24.5 and 23.9 mag (AB) for J and Ks, respectively. In the inner 400 arcmin^2 region where the sensitivity is more uniform, objects as faint as 25.6 and 25.0 mag are detected at 5-sigma. So this is by far the deepest J and Ks datasets available for the ECDFS. To combine the TENIS with the Spitzer IRAC data for obtaining better spectral energy distributions of high-redshift objects, we developed a novel deconvolution technique (IRACLEAN) to accurately estimate the IRAC fluxes. IRACLEAN can minimize the effect of blending in the IRAC images caused by the large point-spread functions and reduce the confusion noise. We applied IRACLEAN to the images from the Spitzer IRAC/MUSYC Public Legacy in the ECDFS survey (SIMPLE) and generated a J+Ks selected multi-wavelength catalog including the photometry of both the TENIS near-infrared and the SIMPLE IRAC data. We publicly release the data products derived from this work, including the J and Ks images and the J+Ks selected multiwavelength catalog.Comment: 25 pages, 25 figures, ApJS in pres

Multiple Imputation For Interval Censored Data With Auxiliary Variables

We propose a nonparametric multiple imputation scheme, NPMLE imputation, for the analysis of interval censored survival data. Features of the method are that it converts interval-censored data problems to complete data or right censored data problems to which many standard approaches can be used, and the measures of uncertainty are easily obtained. In addition to the event time of primary interest, there are frequently other auxiliary variables that are associated with the event time. For the goal of estimating the marginal survival distribution, these auxiliary variables may provide some additional information about the event time for the interval censored observations. We extend the imputation methods to incorporate information from auxiliary variables with potentially complex structures. To conduct the imputation, we use a working failure-time proportional hazards model to define an imputing risk set for each censored observations. The imputation schemes consist of using the data in the imputing risk set to create an exact event time for each interval censored observation. In simulation studies we show that the use of multiple imputation methods can improve the efficiency of estimators and reduce the effect of missing visits when compared to simpler approaches. We apply the approach to cytomegalovirus shedding data from an AIDS clinical trial, in which CD4 count is the auxiliary variable

Survival Analysis Using Auxiliary Variables Via Nonparametric Multiple Imputation

We develop an approach, based on multiple imputation, that estimates the marginal survival distribution in survival analysis using auxiliary variable to recover information for censored observations. To conduct the imputation, we use two working survival model to define the nearest neighbor imputing risk set. One model is for the event times and the other for the censoring times. Based on the imputing risk set, two nonparametric multiple imputation methods are considered: risk set imputation, and Kaplan-Meier estimator. For both methods a future event or censoring time is imputed for each censored observation. With a categorical auxiliary variable, we show that with a large number of imputes the estimates from the Kaplan-Meier imputation method correspond to the weighted Kaplan-Meier estimator. We also show that the Kaplan-Meier imputation method is robust to misspecification of either one of the two working models. In a simulation study with the time independent and time dependent auxiliary variables, we compare the multiple imputation approaches with an inverse probability of censoring weighted method. We show that all approaches can reduce bias due to dependent censoring and improve the efficiency. We apply the approaches to AIDS clinical trial data comparing ZDV and placebo, in which CD4 count is the time-dependent auxiliary variable

Survival estimation and testing via multiple imputation

Multiple imputation is a technique for handling data sets with missing values. The method fills in each missing value several times, creating many augmented data sets. Each augmented data set is analyzed separately and the results combined to give a final result consisting of an estimate and a measure of uncertainty. In this paper we consider nonparametric multiple-imputation methods to handle missing event times for censored observations in the context of nonparametric survival estimation and testing. Two nonparametric imputation schemes are considered. In risk set imputation the censored time is replaced by a random draw of the observed times amongst those at risk after the censoring time. In Kaplan–Meier (KM) imputation the imputed time is a draw from the estimated distribution of event times amongst those at risk after the censoring time. We show that with a large number of imputes the estimates from both methods reproduce the KM estimator. In a simulation study we show that the inclusion of a bootstrap stage in the multiple imputation algorithm gives coverage rates of confidence intervals that are comparable to that from Greenwood’s formula. Connections to the redistribute to the right algorithm are discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/91899/1/Taylor Stat Prob Let 2002.pd

How may I persuade you to trust AI? Promote Customized Explainable AI through Information Vividness

Artificial intelligence (AI) has been criticized for its black- box nature that confuses how outputs are derived. Some have proposed that explainable artificial intelligence (XAI) can address the issue and enhance users’ trust in AI. Drawing on the lens of persuasion theory, we develop a research model that depicts how explanation with vividness and user characteristics independently and jointly shape trust in AI. To test the model and associated hypotheses, we conduct an online experiment. The results suggest that individual characteristics not only directly affect trust but also moderate the relationship between explanation vividness and trust

Survival Analysis Using Auxiliary Variables via Nonparametric Multiple Imputation

We develop an approach, based on multiple imputation, that estimates the marginal survival distribution in survival analysis using auxiliary variables to recover information for censored observations. To conduct the imputation, we use two working survival models to de fine a nearest neighbour imputing risk set. One model is for the event times and the other for the censoring times. Based on the imputing risk set, two non-parametric multiple imputation methods are considered: risk set imputation, and Kaplan–Meier imputation. For both methods a future event or censoring time is imputed for each censored observation. With a categorical auxiliary variable, we show that with a large number of imputes the estimates from the Kaplan–Meier imputation method correspond to the weighted Kaplan–Meier estimator. We also show that the Kaplan–Meier imputation method is robust to mis-speci cation of either one of the two working models. In a simulation study with time independent and time-dependent auxiliary variables, we compare the multiple imputation approaches with an inverse probability of censoring weighted method. We show that all approaches can reduce bias due to dependent censoring and improve the e ciency. We apply the approaches to AIDS clinical trial data comparing ZDV and placebo, in which CD4 count is the time-dependent auxiliary variable. Copyright 2005 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/91939/1/Hsu Stat in Med paper 2006.pd

Interferometric 12CO(J=2-1) image of the Nuclear Region of Seyfert 1 Galaxy NGC 1097

We have mapped the central region of the Seyfert 1 galaxy NGC 1097 in 12CO(J=2-1) with the Submillieter Array (SMA). The 12CO(J=2-1) map shows a central concentration and a surrounding ring, which coincide respectively with the Seyfert nucleus and a starburst ring. The line intensity peaks at the nucleus, whereas in a previously published 12CO(J=1-0) map the intensity peaks at the starburst ring. The molecular ring has an azimuthally averaged 12CO(J=2-1)/(J=1-0) intensity ratio (R21) of about unity, which is similar to those in nearby active star forming galaxies, suggesting that most of the molecular mass in the ring is involved in fueling the starburst. The molecular gas can last for only about 1.2\times10^8 years without further replenishment assuming a constant star formation rate and a perfect conversion of gas to stars. The velocity map shows that the central molecular gas is rotating with the molecular ring in the same direction, while its velocity gradient is much steeper than that of the ring. This velocity gradient of the central gas is similar to what is usually observed in some Seyfert 2 galaxies. To view the active nucleus directly in the optical, the central molecular gas structure can either be a low-inclined disk or torus but not too low to be less massive than the mass of the host galaxy itself, be a highly-inclined thin disk or clumpy and thick torus, or be an inner part of the galactic disk. The R21 value of ~1.9 of the central molecular gas component, which is significantly higher than the value found at the molecular gas ring, indicates that the activity of the Seyfert nucleus may have a significant influence on the conditions of the molecular gas in the central component.Comment: 22 pages, 4 figures, accepted by Ap

Profiles of SUMO and ubiquitin conjugation in an Alzheimer's disease model

► Global levels of ubiquitinated proteins increased in the hippocampus of Tg2576 mice. ► No global changes in either SUMO-1 or SUMO-2/3 conjugation in any brain regions analysed. ► SUMO conjugating and deconjugating enzymes, UBC9 and SENP-1, unaltered in Tg2576 mice. ► Total levels of AMPA and kainate receptors were also unaffected in Tg2576 mice. ► Posttranslational modification by ubiquitin may play a role in Alzheimer's disease

Enhanced Recycling Bin

Residing in Vancouver, one of the greenest cities in the world, 510 Innovations is currently developing an enhanced recycling bin system, the Green Bin. The proposed design will automatically sort and separate recyclable bottles/cans composed of glass, plastic and aluminum from everyday garbage. It will be designed for use by the typical consumer within office buildings, malls, and other high traffic locations. As the condition of the refuse is unknown, a series of sensors will be employed to determine its composition.&nbsp

Estimation for paired binomial data with application to radiation therapy

We compare and contrast several different methods for estimating the effect of treatment when responses are paired binomial observations. The ratio of binomial probabilities is the parameter of interest, while the binomial probabilities are nuisance parameters which may vary between pairs. The application is a meta-analysis of the treatment of rectal cancer, with observations in each study indicating the number of recurrences of the cancer in each of two groups, one with radiation therapy and one without. The ratio of the probabilities of recurrence in the radiation to non-radiation groups is of substantive interest, and is modelled as a logistic or complementary log-log function of an unknown linear combination of the covariates. The three methods we consider are maximum likelihood, a Bayesian approach and an approach based on estimating equations. For the MLE and Bayesian approach the potentially large number of nuisance parameters are estimated together with the parameters of interest, whereas for the estimating equation approach only the parameters of interest are estimated. A simulation study is performed to compare the methods and evaluate the impact of overdispersion. Copyright 2001 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/34858/1/890_ftp.pd