EnCoDe enables design-time prediction of block-level energy consumption in Python code via static features and ML models trained on a dataset from 18,000 programs, achieving R²=0.75 and 80.6% hotspot classification accuracy.
Choice modelling in the age of machine learning - Discussion paper , volume =
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Sunlight produces polarization-entangled photons through SPDC, achieving concurrence 0.905, fidelity 0.939, and Bell violation S=2.54 exceeding the classical limit.
Large vision-language models applied to multi-scale remote sensing imagery can generate recommendations on built environment design, constructability, land use, and risks for smart city decision-making.
citing papers explorer
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EnCoDe: Energy Estimation of Source Code At Design-Time
EnCoDe enables design-time prediction of block-level energy consumption in Python code via static features and ML models trained on a dataset from 18,000 programs, achieving R²=0.75 and 80.6% hotspot classification accuracy.
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Generating quantum entanglement from sunlight
Sunlight produces polarization-entangled photons through SPDC, achieving concurrence 0.905, fidelity 0.939, and Bell violation S=2.54 exceeding the classical limit.
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Built Environment Reasoning from Remote Sensing Imagery Using Large Vision--Language Models
Large vision-language models applied to multi-scale remote sensing imagery can generate recommendations on built environment design, constructability, land use, and risks for smart city decision-making.