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.
VII. Mathematical contributions to the theory of evolution.—III. Regression, heredity, and panmixia
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Neural ODEs reproduce 2RDM dynamics from data only when three-particle cumulant correlations are strong, mapping the validity regime of cumulant expansions.
A multi-plant ML framework uses process history to triple NOx prediction accuracy, forecasts overshoots nine minutes ahead, and projects 34-64% NOx cuts with 58,000 USD/year NH3 savings while preserving clinker quality.
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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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Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations
Neural ODEs reproduce 2RDM dynamics from data only when three-particle cumulant correlations are strong, mapping the validity regime of cumulant expansions.
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A Multi-Plant Machine Learning Framework for Emission Prediction, Forecasting, and Control in Cement Manufacturing
A multi-plant ML framework uses process history to triple NOx prediction accuracy, forecasts overshoots nine minutes ahead, and projects 34-64% NOx cuts with 58,000 USD/year NH3 savings while preserving clinker quality.