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Marginal warming associated with a COVID-19 quarantine and the implications for disease transmissionFree, publicly-accessible full text available August 1, 2022
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Free, publicly-accessible full text available June 1, 2023
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Abstract Prediction of ice formation in clouds presents one of the grand challenges in the atmospheric sciences. Immersion freezing initiated by ice-nucleating particles (INPs) is the dominant pathway of primary ice crystal formation in mixed-phase clouds, where supercooled water droplets and ice crystals coexist, with important implications for the hydrological cycle and climate. However, derivation of INP number concentrations from an ambient aerosol population in cloud-resolving and climate models remains highly uncertain. We conducted an aerosol–ice formation closure pilot study using a field-observational approach to evaluate the predictive capability of immersion freezing INPs. The closure study relies on collocated measurementsmore »Free, publicly-accessible full text available October 1, 2022
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This paper proposes a computer vision framework aimed to segment hot steel sections and contribute to rolling precision. The steel section dimensions are calculated for the purposes of automating a high temperature rolling process. A structured forest algorithm along with the developed steel bar edge detection and regression algorithms extract the edges of the high temperature bars in optical videos captured by a GoPror camera. To quantify the impact of noises that affect the segmentation process and the final diameter measurements, a weighted variance is calculated, providing a level of trust in the measurements. The results show an accuracy whichmore »
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Bayesian neural networks are powerful inference methods by accounting for randomness in the data and the network model. Uncertainty quantification at the output of neural networks is critical, especially for applications such as autonomous driving and hazardous weather forecasting. However, approaches for theoretical analysis of Bayesian neural networks remain limited. This paper makes a step forward towards mathematical quantification of uncertainty in neural network models and proposes a cubature-rule-based computationally efficient uncertainty quantification approach that captures layerwise uncertainties of Bayesian neural networks. The proposed approach approximates the first two moments of the posterior distribution of the parameters by propagating cubaturemore »
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We present a database and analyze ground motions recorded during three events that occurred as part of the July 2019 Ridgecrest earthquake sequence: a moment magnitude (M) 6.5 foreshock on a left‐lateral cross fault in the Salt Wells Valley fault zone, an M 5.5 foreshock in the Paxton Ranch fault zone, and the M 7.1 mainshock, also occurring in the Paxton Ranch fault zone. We collected and uniformly processed 1483 three‐component recordings from an array of 824 sensors spanning 10 seismographic networks. We developed site metadata using available data and multiple models for the time‐averaged shear‐wave velocity in the uppermore »
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Abstract The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hardmore »Free, publicly-accessible full text available December 1, 2023