A geometric indicator from the normal width of the stochastic separatrix in a random two-state ecosystem model scales linearly with noise intensity and yields an affine relation to the logarithm of mean transition time.
Early-warning signals for critical transitions
4 Pith papers cite this work. Polarity classification is still indexing.
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The no-barber principle prohibits selection rules in the inaccessible game that appeal to external adjudicators, favoring the symmetric monoidal category NCFinProb over the cartesian FinProb as its internal language due to the absence of canonical copying maps.
Empirical tests on country data support a candidacy-and-trigger model of hierarchical collapse, where structural variables identify high-risk cases a decade in advance and a classifier achieves 0.91 AUC while rejecting endogenous explanations.
KLR Hopfield networks store up to 16-20 times their neuron count before dynamical instability from crosstalk noise causes collapse, with sharp attractor boundaries observed via morphing and SNR analysis.
citing papers explorer
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Geometric early warning indicator from stochastic separatrix structure in a random two-state ecosystem model
A geometric indicator from the normal width of the stochastic separatrix in a random two-state ecosystem model scales linearly with noise intensity and yields an affine relation to the logarithm of mean transition time.
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The No Barber Principle: Towards Formalised Selection in the Inaccessible Game
The no-barber principle prohibits selection rules in the inaccessible game that appeal to external adjudicators, favoring the symmetric monoidal category NCFinProb over the cartesian FinProb as its internal language due to the absence of canonical copying maps.
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Candidacy and Trigger: A Two-Phase Empirical Model of Hierarchical Collapse
Empirical tests on country data support a candidacy-and-trigger model of hierarchical collapse, where structural variables identify high-risk cases a decade in advance and a classifier achieves 0.91 AUC while rejecting endogenous explanations.
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Geometric and dynamical analysis of attractor boundaries and storage limits in kernel Hopfield networks
KLR Hopfield networks store up to 16-20 times their neuron count before dynamical instability from crosstalk noise causes collapse, with sharp attractor boundaries observed via morphing and SNR analysis.