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To compare hospitals that did and did not participate in clinical trials evaluating potential inpatient COVID-19 therapeutics.

We conducted a cross-sectional study of hospitals participating in trials that were registered on clinicaltrials.gov between April and August 2020. Using the 2019 RAND Hospital Dataset and 2019 American Community Survey, we used logistic regression modeling to compare hospital-level traits including demographic features between trial and non-trial hospitals.

We included 488 hospitals that were participating in 298 interventional trials and 4232 non-participating hospitals. After controlling for demographic and other hospital traits, we found that teaching status (OR 2.11, 95% CI 1.52–2.95), higher patient acuity (OR 7.48, 4.39, 13.1), and location in the Northeast (OR 1.83, 95% CI 1.18, 2.85) and in wealthier counties (OR: 1.32, 95% CI 1.16–1.51) were associated with increased odds of trial participation, while being in counties with more White residents was associated with reduced odds (OR 0.98, 95% CI 0.98–0.99).

Hospitals participating and not participating in COVID-19 inpatient treatment clinical trials differed in many ways, resulting in important implications for the generalizability of trial data.

 

A common problem practitioners face is to select rare events in a large dataset. Unfortunately, standard techniques ranging from pre-trained models to active learning do not leverage proximity structure present in many datasets and can lead to worse-than-random results. To address this, we propose EZMODE, an algorithm for iterative selection of rare events in large, unlabeled datasets. EZMODE leverages active learning to iteratively train classifiers, but chooses the easiest positive examples to label in contrast to standard uncertainty techniques. EZMODE also leverages proximity structure (e.g., temporal sampling) to find difficult positive examples. We show that EZMODE can outperform baselines by up to 130× on a novel, real-world, 9,000 GB video dataset. 

Systems for ML inference are widely deployed today, but they typically optimize ML inference workloads using techniques designed for conventional data serving workloads and miss critical opportunities to leverage the statistical nature of ML. In this paper, we present WILLUMP, an optimizer for ML inference that introduces two statistically-motivated optimizations targeting ML applications whose performance bottleneck is feature computation. First, WILLUMP automatically cascades feature computation for classification queries: WILLUMP classifies most data inputs using only high-value, low-cost features selected through empirical observations of ML model performance, improving query performance by up to 5× without statistically significant accuracy loss. Second, WILLUMP accurately approximates ML top-K queries, discarding low-scoring inputs with an automatically constructed approximate model and then ranking the remainder with a more powerful model, improving query performance by up to 10× with minimal accuracy loss. WILLUMP automatically tunes these optimizations’ parameters to maximize query performance while meeting an accuracy target. Moreover, WILLUMP complements these statistical optimizations with compiler optimizations to automatically generate fast inference code for ML applications. We show that WILLUMP improves the end-to-end performance of real-world ML inference pipelines curated from major data science competitions by up to 16× without statistically significant loss of accuracy. 

Microchannel surfaces are common to microfluidics, biofluidics, thermal management, and energy applications. Due to processing limitations for the majority of metallic materials, the majority of hyperfine microchannels used in microfluidics and thermo‐fluids are fabricated on non‐metallic substrates, for example, silicon and polydimethylsiloxane. Here, a technique to fabricate ultrasmall microchannels on arbitrary metallic materials is developed using photolithography in combination with electrochemical deposition. The technique is used to prepare copper microchannels and to investigate the pool boiling heat transfer performance with a focus on the three‐phase contact line dynamics. The hydrodynamics of nucleating bubbles during boiling are observed in situ using in‐liquid endoscopy. The results show that the variation of critical heat flux enhancement has a linear relationship with the contact line increase ratio. The scalable microchannel surfaces exhibit superior heat transfer performance with a maximum heat transfer coefficient) enhancement of 930% with ultra‐low wall superheat of 5 °C. This work not only develops a scalable manufacturing method to develop ultra‐small microchannels on metallic materials, it outlines design guidelines for structure optimization of pool boiling heat transfer for temperature sensitive applications, such as electronics thermal management.