Assessing the Performance of Models from the 2022 RSNA Cervical Spine Fracture Detection Competition at a Level I Trauma Center.

Purpose To evaluate the performance of the top models from the RSNA 2022 Cervical Spine Fracture Detection challenge on a clinical test dataset of both noncontrast and contrast-enhanced CT scans acquired at a level I trauma center. Materials and Methods Seven top-performing models in the RSNA 2022 Cervical Spine Fracture Detection challenge were retrospectively evaluated on a clinical test set of 1828 CT scans (from 1829 series: 130 positive for fracture, 1699 negative for fracture; 1308 noncontrast, 521 contrast enhanced) from 1779 patients (mean age, 55.8 years ± 22.

Laboratory earthquake forecasting: A machine learning competition.

Earthquake prediction, the long-sought holy grail of earthquake science, continues to confound Earth scientists. Could we make advances by crowdsourcing, drawing from the vast knowledge and creativity of the machine learning (ML) community? We used Google's ML competition platform, Kaggle, to engage the worldwide ML community with a competition to develop and improve data analysis approaches on a forecasting problem that uses laboratory earthquake data. The competitors were tasked with predicting the time remaining before the next earthquake of successive laboratory quake events, based on only a small portion of the laboratory seismic data.

Optimization of Transparent Organic Light-Emitting Diodes by Simulation-Based Design of Organic Capping Layers.

In this work, we use the transfer matrix method to optimize TPBi capping layers deposited on organic light emitting diodes with respect to light extraction and transmittance. The green transparent organic light emitting diodes comprise three organic semiconductors (CBP, Ir(ppy)₃ and TPBi) forming an efficient simplified phosphorescent organic light emitting diode stack. A transparent cathode of 2 nm Cs₂CO₃, 2 nm Al and 16 nm Au is deposited by thermal evaporation.