A self-consistent framework with generalized local order parameters is derived for the Kuramoto model with dyadic and triadic interactions on hypergraphs, showing bistability onset depends on eigenvector correlations between dyadic and triadic structures.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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CQP fuses magnitude and criticality into an importance metric for iterative SNN pruning, delivering 95.6% MNIST accuracy at 90% sparsity and 73% energy reduction at 70% sparsity.
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Self-consistent analysis of the Kuramoto model with higher-order interactions
A self-consistent framework with generalized local order parameters is derived for the Kuramoto model with dyadic and triadic interactions on hypergraphs, showing bistability onset depends on eigenvector correlations between dyadic and triadic structures.
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Criticality-Constrained Iterative Pruning for Energy-Efficient Spiking Neural Networks via Combined Importance Scoring
CQP fuses magnitude and criticality into an importance metric for iterative SNN pruning, delivering 95.6% MNIST accuracy at 90% sparsity and 73% energy reduction at 70% sparsity.