Results 221 to 230 of about 85,010 (268)
Simulation-Based Training for Nursing Students to Improve Patient Safety: Systematic Review. [PDF]
JMIR NursGonzález-Sanz A +7 more
europepmc +1 more source
Common Risk Factors for Knee Injuries in Runners: A Systematic Review. [PDF]
Orthop J Sports MedSenthil K +7 more
europepmc +1 more source
How to optimise the use of innovative invasive devices: considerations for feasibility studies. [PDF]
Pilot Feasibility StudHotton E +5 more
europepmc +1 more source
Design of an Agile Training System Based on Wireless Mesh Network
IEEE Access, 2022 Given the problems of using traditional training methods and insufficient funds in college sports agility training, the agility training system based on Wireless MESH Network is developed. The lower computer realizes the automatic networking between nodes based on the ESP-MESH network, and describes the networking process, intra-group communication and
Tan Baohua, Enpu Wang
exaly +3 more sources
NeuralAnnot: Neural Annotator for 3D Human Mesh Training Sets
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022 Most 3D human mesh regressors are fully supervised with 3D pseudo-GT human model parameters and weakly supervised with GT 2D/3D joint coordinates as the 3D pseudo-GTs bring great performance gain. The 3D pseudo-GTs are obtained by annotators, systems that iteratively fit 3D human model parameters to GT 2D/3D joint coordinates of training sets in the ...
Gyeongsik Moon, Hongsuk Choi
exaly +3 more sources
Optimal Training Sets for Bayesian Prediction of MeSH(R) Assignment [PDF]
Journal of the American Medical Informatics Association: JAMIA, 2008 The aim of this study was to improve naïve Bayes prediction of Medical Subject Headings (MeSH) assignment to documents using optimal training sets found by an active learning inspired method.The authors selected 20 MeSH terms whose occurrences cover a range of frequencies.
Sunghwan Sohn +2 more
exaly +3 more sources
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Bayesian Training in Photonic Neural Meshes
2022 IEEE Workshop on Complexity in Engineering (COMPENG), 2022Neural networks based on reconfigurable photonic integrated chips (RPICs) can offer zero-latency processing, marginal power consumption and operational flexibility. On the other hand, they are subject to, performance affecting, operational/fabrication deviations in their building blocks. Here, we present a Bayesian learning framework that when combined
Mesaritakis, Charis +3 more
openaire +2 more sources