Design and first performance results of novel robotic optical-relay positioners for the MOSAIC instrument on the ELT.
Introduction to Graph Neural Networks for Machine Learning Engineers
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abstract
Graph neural networks are deep neural networks designed for graphs with attributes attached to nodes or edges. The number of research papers in the literature concerning these models is growing rapidly due to their impressive performance on a broad range of tasks. This survey introduces graph neural networks through the encoder-decoder framework and provides examples of decoders for a range of graph analytic tasks. It uses theory and numerous experiments on homogeneous graphs to illustrate the behavior of graph neural networks under different training sizes and degrees of graph complexity, with an emphasis on oversmoothing and oversquashing.
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UNVERDICTED 2representative citing papers
Thermal qualification tests on 6.2-mm-pitch fiber positioners confirm stable repeatability, backlash, and linearity across -20°C to 30°C with no degradation.
citing papers explorer
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MOSAIC at ELT: Design and First Performance Results of Novel Robotic Optical-Relay Positioners
Design and first performance results of novel robotic optical-relay positioners for the MOSAIC instrument on the ELT.
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Thermal Characterization of a 6-Positioner, 6.2-mm-Pitch Module for Stage-5 Telescopes
Thermal qualification tests on 6.2-mm-pitch fiber positioners confirm stable repeatability, backlash, and linearity across -20°C to 30°C with no degradation.