A Deep Moving-camera Background Model

In video analysis, background models have many applications such as background/foreground separation, change detection, anomaly detection, tracking, and more. However, while learning such a model in a video captured by a static camera is a fairly-solved task, in the case of a Moving-camera Background Model (MCBM), the success has been far more modest due to algorithmic and scalability challenges that arise due to the camera motion. Thus, existing MCBMs are limited in their scope and their supported camera-motion types. These hurdles also impeded the employment, in this unsupervised task, of end-to-end solutions based on deep learning (DL). Moreover, existing MCBMs usually model the background either on the domain of a typically-large panoramic image or in an online fashion. Unfortunately, the former creates several problems, including poor scalability, while the latter prevents the recognition and leveraging of cases where the camera revisits previously-seen parts of the scene. This paper proposes a new method, called DeepMCBM, that eliminates all the aforementioned issues and achieves state-of-the-art results. Concretely, first we identify the difficulties associated with joint alignment of video frames in general and in a DL setting in particular. Next, we propose a new strategy for joint alignment that lets us use a spatial transformer net with neither a regularization nor any form of specialized (and non-differentiable) initialization. Coupled with an autoencoder conditioned on unwarped robust central moments (obtained from the joint alignment), this yields an end-to-end regularization-free MCBM that supports a broad range of camera motions and scales gracefully. We demonstrate DeepMCBM's utility on a variety of videos, including ones beyond the scope of other methods. Our code is available at https://github.com/BGU-CS-VIL/DeepMCBM .Comment: 26 paged, 5 figures. To be published in ECCV 202

Diagnosed prevalence of Alzheimer’s disease and related dementias in Medicare Advantage plans

IntroductionOne- third of Medicare beneficiaries are enrolled in Medicare Advantage (MA). Yet, little is known about MA beneficiaries diagnosed with Alzheimer’s disease (AD) and AD- related dementias (AD/ADRD).MethodsWe calculated the prevalence of AD/ADRD diagnoses in 2014 and 2016 in three MA plans. We determined the demographic characteristics of beneficiaries diagnosed with AD/ADRD, and whether they disenrolled from the MA plan for any reason within 364 days from the index date.ResultsIn 2014 and 2016, the overall prevalence of AD/ADRD diagnoses was 5.6% and 6.5%, respectively. In 2016, AD/ADRD beneficiaries were on average 82.4 (SD = 7.4) years of age, 61.8% female, and had multiple comorbidities. By 364 days post- index date, 32% of beneficiaries with AD/ADRD had disenrolled from their plan. The demographic characteristics of 2014 beneficiaries with diagnosed AD/ADRD were similar to their 2016 counterparts.DiscussionThe prevalence of AD/ADRD diagnosis in MA is lower than rates reported in Medicare fee- for- service.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156169/2/dad212048.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156169/1/dad212048_am.pd

Seroprevalence of 13 common pathogens in a rapidly growing U.S. minority population: Mexican Americans from San Antonio, TX

<p>Abstract</p> <p>Background</p> <p>Infection risks vary among individuals and between populations. Here we present information on the seroprevalence of 13 common infectious agents in a San Antonio-based sample of Mexican Americans. Mexican Americans represent the largest and most rapidly growing minority population in the U.S., and they are also considered a health disparities population.</p> <p>Methods</p> <p>We analyzed 1227 individuals for antibody titer to <it>Chlamydophila pneumoniae, Helicobacter pylori, Toxoplasma gondii</it>, cytomegalovirus, Epstein-Barr virus, herpes simplex virus-1, herpes simplex virus-2 (HSV-2), human herpesvirus-6 (HHV-6), varicella zoster virus (VZV), adenovirus-36, hepatitis A virus, and influenza A and B. Seroprevalence was examined as a function of sex, age, household income, and education.</p> <p>Results</p> <p>Seroprevalence estimates ranged from 9% for <it>T. gondii</it> to 92% for VZV, and were similar in both sexes except for HSV-2, which was more prevalent in women. Many pathogens exhibited a significant seroprevalence change over the examined age range (15-94 years), with 7 pathogens increasing and HHV-6 decreasing with age. Socioeconomic status significantly correlated with serostatus for some pathogens.</p> <p>Conclusions</p> <p>Our findings demonstrate substantial seroprevalence rates of these common infections in this sample of Mexican Americans from San Antonio, Texas that suffers from high rates of chronic diseases including obesity and type-2 diabetes.</p

Comparison of Money Primes

Comparison of Manipulations to Activate the Concept of Mone

Show Me the Money: A Systematic Exploration of Manipulations, Moderators, and Mechanisms of Priming Effects.

A major challenge for accumulating knowledge in psychology is the variation in methods and participant populations across studies in a single domain. We offer a systematic approach to addressing this challenge and implement it in the domain of money priming. In three preregistered experiments ( N = 4,649), participants were exposed to one of a number of money manipulations before completing self-report measures of money activation (Study 1); engaging in a behavioral-persistence task (Study 3); completing self-report measures of subjective wealth, self-sufficiency, and communion-agency (Studies 1-3); and completing demographic questions (Studies 1-3). Four of the five manipulations we tested activated the concept of money, but, contrary to what we expected based on the preponderance of the published literature, no manipulation consistently affected any dependent measure. Moderation by sociodemographic characteristics was sparse and inconsistent across studies. We discuss implications for theories of money priming and explain how our approach can complement recent efforts to build a reproducible, cumulative psychological science