Thermal lattice fluctuations in Re6Se8Cl2 suppress solid-state high-harmonic yield via weakened per-configuration responses and ensemble phase dispersion, producing an abrupt yield increase below 50 K where vibrations freeze out.
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Two systems with fundamentally different particle interactions crystallize into the same complex structure through the same pathways because their effective interactions match.
StructGP discovers sparse DAG structures and latent pathways in Gaussian processes to improve calibrated forecasting on irregular clinical time series.
FoL++ achieves state-of-the-art visual place recognition performance with 40% faster inference by modeling reliable regions, using spatial alignment losses, and adaptively fusing global and local matches.
CAMO is an ensemble technique that dynamically improves minority class predictions in imbalanced language model evaluations and achieves the highest macro F1 scores on two domain-specific benchmarks.
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Probing lattice fluctuations using solid-state high-harmonic spectroscopy
Thermal lattice fluctuations in Re6Se8Cl2 suppress solid-state high-harmonic yield via weakened per-configuration responses and ensemble phase dispersion, producing an abrupt yield increase below 50 K where vibrations freeze out.
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Anatomy of a Complex Crystallization Pathway
Two systems with fundamentally different particle interactions crystallize into the same complex structure through the same pathways because their effective interactions match.
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Differentiable latent structure discovery for interpretable forecasting in clinical time series
StructGP discovers sparse DAG structures and latent pathways in Gaussian processes to improve calibrated forecasting on irregular clinical time series.
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Region Matters: Efficient and Reliable Region-Aware Visual Place Recognition
FoL++ achieves state-of-the-art visual place recognition performance with 40% faster inference by modeling reliable regions, using spatial alignment losses, and adaptively fusing global and local matches.
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CAMO: A Class-Aware Minority-Optimized Ensemble for Robust Language Model Evaluation on Imbalanced Data
CAMO is an ensemble technique that dynamically improves minority class predictions in imbalanced language model evaluations and achieves the highest macro F1 scores on two domain-specific benchmarks.