Jet Identification with Zest

We present a new observable zest and demonstrate its potential to differentiate between jets originated by gluons, top quark and vector bosons. Zest has salient properties such as boost invariance, stability against global color flow of partons and inclusion or exclusion of a few soft particles to the jet. For a gluon jet, zest distribution is also insensitive to the jet mass. We show that when zest is used in conjunction with other observables, it can yield high gluon rejection while retaining high signal sample.Comment: 3 pages, 5 figures, XXII DAE-BRNS Symposium Proceeding

Improving Visual Field Examination of the Macula Using Structural Information

Purpose: To investigate a novel approach for structure-function modeling in glaucoma to improve visual field testing in the macula. Methods: We acquired data from the macular region in 20 healthy eyes and 31 with central glaucomatous damage. Optical coherence tomography (OCT) scans were used to estimate the local macular ganglion cell density. Perimetry was performed with a fundus-tracking device using a 10-2 grid. OCT scans were matched to the retinal image from the fundus perimeter to accurately map the tested locations onto the structural damage. Binary responses from the subjects to all presented stimuli were used to calculate the structure-function model used to generate prior distributions for a ZEST (Zippy Estimation by Sequential Testing) Bayesian strategy. We used simulations based on structural and functional data acquired from an independent dataset of 20 glaucoma patients to compare the performance of this new strategy, structural macular ZEST (MacS-ZEST), with a standard ZEST. Results: Compared to the standard ZEST, MacS-ZEST reduced the number of presentations by 13% in reliable simulated subjects and 14% with higher rates (≥20%) of false positive or false negative errors. Reduction in mean absolute error was not present for reliable subjects but was gradually more important with unreliable responses (≥10% at 30% error rate). Conclusions: Binary responses can be modeled to incorporate detailed structural information from macular OCT into visual field testing, improving overall speed and accuracy in poor responders. Translational Relevance: Structural information can improve speed and reliability for macular testing in glaucoma practice

COSMOS morphological classification with ZEST (the Zurich Estimator of Structural Types) and the evolution since z=1 of the Luminosity Function of early-, disk-, and irregular galaxies

(ABRIDGED) Motivated by the desire to reliably and automatically classify structure of thousands of COSMOS galaxies, we present ZEST, the Zurich Estimator of Structural Types. To classify galaxy structure, ZEST uses: (i) Five non-parametric diagnostics: asymmetry, concentration, Gini coefficient, 2nd-order moment of the brightest 20% of galaxy pixels, and ellipticity; and (ii) The exponent n of single--Sersic fits to the 2D surface brightness distributions. To fully exploit the wealth of information while reducing the redundancy present in these diagnostics, ZEST performs a principal component (PC) Analysis. We use a sample of ~56,000 I<24 COSMOS galaxies to show that the first three PCs fully describe the key aspects of the galaxy structure, i.e., to calibrate a three-dimensional classification grid of axis PC_1, PC_2, and PC_3. We demonstrate the robustness of the ZEST grid on the z=0 sample of Frei et al. (1996). The ZEST classification breaks most of the degeneracy between different galaxy populations that affects morphological classifications based on only some of the diagnostics included in ZEST. As a first application, we present the evolution since z~1 of the Luminosity Functions of COSMOS galaxies of early, disk and irregular galaxies and, for disk galaxies, of different bulge-to-disk ratios. Overall, we find that the LF up to a redshift z=1 is consistent with a pure-luminosity evolution (of about 0.95 magnitudes at z \~0.7). We highlight however two trends, that are in general agreement with a down-sizing scenario for galaxy formation: (1.) A deficit of a factor of about two at z~0.7 of MB>-20.5 structurally--classified early--type galaxies; and (2.) An excess of a factor of about three, at a similar redshift, of irregular galaxies.Comment: Accepted for publication in the ApJ COSMOS special issue. A version with high resolution figures is available at http://www.exp-astro.phys.ethz.ch/scarlata/papers/ApJS_ZEST.pd

Taking off at Linfield

For Tom Wiggins ’09, a zest for life combines a full-load of classes and a busy career

Sharing His Zest for Britain

Even after taking seven January Term classes to England, Ken Ericksen hasn’t lost his zest for all things British nor his joy in sharing that passion with Linfield students

Improving the Accuracy and Speed of Visual Field Testing in Glaucoma With Structural Information and Deep Learning

Purpose: To assess the performance of a perimetric strategy using structure–function predictions from a deep learning (DL) model. Methods: Visual field test–retest data from 146 eyes (75 patients) with glaucoma with (median [5th–95th percentile]) 10 [7, 10] tests per eye were used. Structure–function predictions were generated with a previously described DL model using cicumpapillary optical coherence tomography (OCT) scans. Structurally informed prior distributions were built grouping the observed measured sensitivities for each predicted value and recalculated for each subject with a leave-one-out approach. A zippy estimation by sequential testing (ZEST) strategy was used for the simulations (1000 per eye). Groundtruth sensitivities for each eye were the medians of the test–retest values. Two variations of ZEST were compared in terms of speed (average total number of presentations [NP] per eye) and accuracy (average mean absolute error [MAE] per eye), using either a combination of normal and abnormal thresholds (ZEST) or the calculated structural distributions (S-ZEST) as prior information. Two additional versions of these strategies employing spatial correlations were tested. Results: S-ZEST was significantly faster, with a mean average NP of 213.87 (SD = 28.18), than ZEST, with a mean average NP of 255.65 (SD = 50.27) (P < 0.001). The average MAE was smaller for S-ZEST (1.98; SD = 2.37) than ZEST (2.43; SD = 2.69) (P < 0.001). Spatial correlations further improved both strategies (P < 0.001), but the differences between ZEST and S-ZEST remained significant (P < 0.001). Conclusions: DL structure–function predictions can significantly improve perimetric tests. Translational Relevance: DL structure–function predictions from clinically available OCT scans can improve perimetry in glaucoma patients

Zest and work

Zest is a positive trait reflecting a person's approach to life with anticipation, energy, and excitement. In the present study, 9803 currently employed adult respondents to an Internet site completed measures of dispositional zest, orientation to work as a calling, and satisfaction with work and life in general. Across all occupations, zest predicted the stance that work was a calling ( r  = .39), as well as work satisfaction ( r  = .46) and general life satisfaction ( r  = .53). Zest deserves further attention from organizational scholars, especially how it can be encouraged in the workplace. Copyright © 2009 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/61871/1/584_ftp.pd