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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Pith papers citing it
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cs.LG 2 cond-mat.mtrl-sci 1 cond-mat.soft 1 cond-mat.stat-mech 1 cs.CL 1 cs.CV 1 eess.SP 1 physics.ed-ph 1years
2026 9representative citing papers
AeroSense learns an end-to-end mapping from instantaneous aircraft-state situations to future regional traffic flow and reports higher accuracy than aggregation-based baselines on real data, especially in high-density conditions.
StructGP couples process-convolution Gaussian processes with differentiable DAG learning to forecast irregular clinical time series and recover inter-variable dependency graphs; LP-StructGP adds shared latent patient pathways for long-horizon forecasts.
FARM combines a masked autoencoder with a diffusion-based decoder to construct aerial radio maps in both condition-based and condition-free settings, trained on a new large-scale 3D radio dataset (ARM-Omni).
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.
Metallic-style point particles and hard polyhedra share the same polymorphs and multistep crystallization pathways because their interactions are effectively similar under a pairwise mapping.
Multimodal LLM reached 84% exact agreement with humans on binary computational thinking sub-practices in 100 essays but only 71% agreement on holistic quality scores.
citing papers explorer
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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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Unlocking air traffic flow prediction through microscopic aircraft-state modeling
AeroSense learns an end-to-end mapping from instantaneous aircraft-state situations to future regional traffic flow and reports higher accuracy than aggregation-based baselines on real data, especially in high-density conditions.
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Differentiable latent structure discovery for interpretable forecasting in clinical time series
StructGP couples process-convolution Gaussian processes with differentiable DAG learning to forecast irregular clinical time series and recover inter-variable dependency graphs; LP-StructGP adds shared latent patient pathways for long-horizon forecasts.
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FARM: Foundational Aerial Radio Map for Intelligent Low-Altitude Networking
FARM combines a masked autoencoder with a diffusion-based decoder to construct aerial radio maps in both condition-based and condition-free settings, trained on a new large-scale 3D radio dataset (ARM-Omni).
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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.
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Anatomy of a Complex Crystallization Pathway
Metallic-style point particles and hard polyhedra share the same polymorphs and multistep crystallization pathways because their interactions are effectively similar under a pairwise mapping.
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Using Large Language Models to Analyze Engagement in Computational Thinking via Computational Physics Essays
Multimodal LLM reached 84% exact agreement with humans on binary computational thinking sub-practices in 100 essays but only 71% agreement on holistic quality scores.
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