Data-driven design of 12-fold quasicrystal nanomechanical resonators achieves Q_m of approximately 10^7 and force sensitivity of 26.4 aN per square root Hz.
A rapid review of clustering algorithms.ArXiv Preprint ArXiv:2401.07389, 2024
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Gradient-boosted Li-Lee model improves fit and forecast accuracy on weekly mortality data from 30 countries while testing population clustering by improvement rates and seasonal strength.
Context-KG uses LLMs to extract user preferences and context from natural language, driving ontology-guided layouts and insights for knowledge graph visualization that improve interpretability and task performance over traditional force-directed methods.
UN-CCDs extend Cluster Catch Digraphs by using nearest-neighbor-distance Monte Carlo tests instead of Ripley's K to determine covering radii, yielding competitive performance on moderate-dimensional data with complex clusters and uniform noise.
citing papers explorer
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Quasicrystal Architected Nanomechanical Resonators via Data-Driven Design
Data-driven design of 12-fold quasicrystal nanomechanical resonators achieves Q_m of approximately 10^7 and force sensitivity of 26.4 aN per square root Hz.
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Gradient boosted multi-population mortality modelling with high-frequency data
Gradient-boosted Li-Lee model improves fit and forecast accuracy on weekly mortality data from 30 countries while testing population clustering by improvement rates and seasonal strength.
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Context-KG: Context-Aware Knowledge Graph Visualization with User Preferences and Ontological Guidance
Context-KG uses LLMs to extract user preferences and context from natural language, driving ontology-guided layouts and insights for knowledge graph visualization that improve interpretability and task performance over traditional force-directed methods.
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Clustering with Uniformity- and Neighbor-Based Random Geometric Graphs
UN-CCDs extend Cluster Catch Digraphs by using nearest-neighbor-distance Monte Carlo tests instead of Ripley's K to determine covering radii, yielding competitive performance on moderate-dimensional data with complex clusters and uniform noise.