The great variations of videographic skills in videography, camera designs, compression and processing protocols, communication and bandwidth environments, and displays leads to an enormous variety of video impairments. Current noreference (NR) video quality models are unable to handle this diversity of distortions. This is true in part because available video quality assessment databases contain very limited content, fixed resolutions, were captured using a small number of camera devices by a few videographers and have been subjected to a modest number of distortions. As such, these databases fail to adequately represent real world videos, which contain very different kinds of content obtained under highly diverse imaging conditions and are subject to authentic, complex and often commingled distortions that are difficult or impossible to simulate. As a result, NR video quality predictors tested on real-world video data often perform poorly. Towards advancing NR video quality prediction, we have constructed a largescale video quality assessment database containing 585 videos of unique content, captured by a large number of users, with wide ranges of levels of complex, authentic distortions. We collected a large number of subjective video quality scores via crowdsourcing. A total of 4776 unique participants took part in the study, yielding more than 205000 opinion scores, resulting in an average of 240 recorded human opinions per video. We demonstrate the value of the new resource, which we call the LIVE Video Quality Challenge Database (LIVE-VQC for short), by conducting a comparison of leading NR video quality predictors on it. This study is the largest video quality assessment study ever conducted along several key dimensions: number of unique contents, capture devices, distortion types and combinations of distortions, study participants, and recorded subjective scores. The database is available for download on this link: http://live.ece.utexas.edu/research/LIVEVQC/index.html.

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Excessive weight is connected with an increased risk of certain life-threatening diseases. However, some evidence shows that among patients with chronic diseases such as heart failure (HF) chronic kidney disease (CKD) and COPD, increased weight is paradoxically associated with a decreased risk of mortality. This counterintuitive phenomenon is referred to as the obesity paradox. The obesity paradox has been mostly observed among certain cohorts of patients with HF, but not specific to patients in the Intensive Care Unit (ICU) setting. This paper studies the relationship between obesity and mortality of ICU patients with and without HF and presents evidence supporting the existence of this paradox. The results provide helpful insights for developing more patient-centric care in ICUs. Additionally, we use both the MIMIC-II and (recently available) MIMIC-III databases, for which few comparative studies exist to date. We demonstrate an aspect of consistency between the databases, providing a significant step towards validating the use of the newly announced MIMIC-III in broader studies.