Dynamic Time Warping with a shared warping path across parameters aligns binary stellar tracks for accurate interpolation while preserving physical relationships such as the Stefan-Boltzmann law.
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4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4verdicts
UNVERDICTED 4representative citing papers
A multi-branch β-VAE on tropical Pacific SST, OHC, and OLR fields yields a latent space that reconstructs data well and aligns with physical ENSO and longer-term coupled variability modes.
PREFAB applies preference learning grounded in the peak-end rule to let users annotate only key affective change segments while interpolating the rest, reducing workload and improving confidence in a 25-participant study.
Nine care-trajectory clusters derived from Dynamic Time Warping and hierarchical clustering independently predict mortality in cancer patients and show an inverse link to baseline anxiety in high-utilization groups.
citing papers explorer
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Irregularly Sampled Time Series Interpolation for Binary Evolution Simulations Using Dynamic Time Warping
Dynamic Time Warping with a shared warping path across parameters aligns binary stellar tracks for accurate interpolation while preserving physical relationships such as the Stefan-Boltzmann law.
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What's in the latent space? Exploring coupled tropical Pacific variability within a Multi-branch $\beta$-Variational Autoencoder
A multi-branch β-VAE on tropical Pacific SST, OHC, and OLR fields yields a latent space that reconstructs data well and aligns with physical ENSO and longer-term coupled variability modes.
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PREFAB: PREFerence-based Affective Modeling for Low-Budget Self-Annotation
PREFAB applies preference learning grounded in the peak-end rule to let users annotate only key affective change segments while interpolating the rest, reducing workload and improving confidence in a 25-participant study.
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Care Trajectories Are Linked to Mental Health and Mortality in Cancer Patients
Nine care-trajectory clusters derived from Dynamic Time Warping and hierarchical clustering independently predict mortality in cancer patients and show an inverse link to baseline anxiety in high-utilization groups.