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algorithm phase » algorithm based (Expand Search), algorithm where (Expand Search), algorithm pre (Expand Search)
within function » fibrin function (Expand Search), python function (Expand Search), protein function (Expand Search)
algorithm cell » algorithm cl (Expand Search), algorithm could (Expand Search), algorithms real (Expand Search)
phase function » phase functions (Expand Search), sphere function (Expand Search), rate function (Expand Search)
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Taming Negative Ion Resonances Using Nonlocal Exchange-Correlation Functionals
Published 2024Subjects: -
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Comparison results of phase trajectories and local enlarged images between two systems.
Published 2024Subjects: “…Cell Biology…”
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<b>Opti2Phase</b>: Python scripts for two-stage focal reducer
Published 2025“…<p dir="ltr"><b>Opti2Phase: Python Scripts for Two-Stage Focal Reducer Design</b></p><p dir="ltr">The folder <b>Opti2Phase</b> contains the Python scripts used to generate the results presented in the manuscript. …”
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TV-BayesOpt algorithm performance for tracking a gradual drift in the optimal stimulation phase for phase-locked stimulation, <i>ψ</i>*.
Published 2023“…For each estimated GPR the confidence bounds observed at the predicted optimal phase value are small and become larger for values further away from this value due to the algorithm’s acquisition function prioritizing exploitation of the parameter space during the optimization process.…”
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TV-BayesOpt algorithm performance for tracking a periodic drift in the optimal stimulation phase for phase-locked stimulation, <i>ψ</i>*.
Published 2023“…For each estimated GPR the confidence bounds observed at the predicted optimal phase value are small and become larger for values further away from this value due to the algorithm’s acquisition function prioritizing exploitation of the parameter space during the optimization process.…”
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TV-BayesOpt algorithm performance for tracking a superimposed (gradual and periodic) drift in the optimal stimulation phase for phase-locked stimulation, <i>ψ</i>*.
Published 2023“…For each estimated GPR the confidence bounds observed at the predicted optimal phase value are small and become larger for values further away from this value due to the algorithm’s acquisition function prioritizing exploitation of the parameter space during the optimization process.…”
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