Catastrophe-dispersion branching processes in varying environments have survival governed solely by the log-mean process, with a universal ordering of offspring means across four dispersal mechanisms and explicit extinction thresholds for Poisson growth with binomial survival.
The Annals of Mathematical Statistics 41(1), 164– 171 (1970)
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Sharp convergence rates and concentration bounds are established for empirical measures of point processes under a newly introduced Wasserstein distance on counting measures.
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Catastrophe-dispersion models in random and varying environments across generations
Catastrophe-dispersion branching processes in varying environments have survival governed solely by the log-mean process, with a universal ordering of offspring means across four dispersal mechanisms and explicit extinction thresholds for Poisson growth with binomial survival.
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Wasserstein convergence rates for empirical measures of point processes
Sharp convergence rates and concentration bounds are established for empirical measures of point processes under a newly introduced Wasserstein distance on counting measures.
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Learning Temporal Patterns in Financial Time Series: A Comparative Study of Quantum LSTM and Quantum Reservoir Computing
Quantum LSTM and quantum reservoir computing match classical baselines in univariate financial forecasting and modestly outperform them in multivariate cases with correlated inputs when using suitable lag structures and amplitude encoding.