The epidemic threshold for the SIRS process on configuration-model power-law graphs with exponent τ>2 is zero, with exponential survival sustained by hierarchical stars of order 2.
Title resolution pending
5 Pith papers cite this work. Polarity classification is still indexing.
citation-role summary
citation-polarity summary
years
2026 5roles
background 2polarities
background 2representative citing papers
A single scalar rank-stickiness parameter nonparametrically point-identifies the entire treatment-effect distribution by selecting the Bregman-Sinkhorn copula that maximizes average rank correlation subject to a relative-entropy constraint.
The quiet-Sun temperature ratio R≈2.4 equals the KL-divergence difference between a κ=2.5 distribution and its EUV and radio Maxwellian projections, satisfying ΔD_KL = (3/2)[R0 − ln R0 − 1] = (3/2) d_IS(T_eff, T_core).
The deep SPAR model shows concurrent floods and droughts becoming more likely in the Upper Danube by 2100 under high emissions, with changes in the dependence between catchments contributing substantially to the increase.
citing papers explorer
-
Waning Immunity Fails to Restore a Positive Epidemic Threshold on Power-Law Networks
The epidemic threshold for the SIRS process on configuration-model power-law graphs with exponent τ>2 is zero, with exponential survival sustained by hierarchical stars of order 2.
-
Nonparametric Point Identification of Treatment Effect Distributions via Rank Stickiness
A single scalar rank-stickiness parameter nonparametrically point-identifies the entire treatment-effect distribution by selecting the Bregman-Sinkhorn copula that maximizes average rank correlation subject to a relative-entropy constraint.
-
Diagnostic Disagreement as an Information-Projection Divergence: An Information-Theoretic Reading of the Quiet-Sun Temperature Ratio
The quiet-Sun temperature ratio R≈2.4 equals the KL-divergence difference between a κ=2.5 distribution and its EUV and radio Maxwellian projections, satisfying ΔD_KL = (3/2)[R0 − ln R0 − 1] = (3/2) d_IS(T_eff, T_core).
-
Exploring climate change effects on concurrent floods and concurrent droughts via statistical deep learning
The deep SPAR model shows concurrent floods and droughts becoming more likely in the Upper Danube by 2100 under high emissions, with changes in the dependence between catchments contributing substantially to the increase.
- Finite-sample Borel--Cantelli inequalities under mixing conditions