Introduces quantile-based effectiveness persistence function as tail mean divided by quantile, shows equivalence to first L-moment of scaled tail, and develops nonparametric estimator with bootstrap equivalence test for biosimilar evaluation.
Superconductivity in Tetragonal LaPt_{2-x}Ge_{2+x}
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
abstract
We find that a tetragonal CaBe_2Ge_2-type structure can be stabilized in non-stoichiometric LaPt_{2-x}Ge_{2+x}. We further discovered that tetragonal LaPt_{2-x}Ge_{2+x} with x=0.15 and 0.2 respectively superconduct at Tc=1.85 K and 1.95 K, which is about four time higher than that in monoclinic LaPt_2Ge_2.
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Markov chain Phase-Type decoders in VAEs overcome the structural inability of Gaussian-Lipschitz models to produce heavy-tailed outputs, cutting tail KS distance by up to 6x and extreme quantile error by up to 10x on synthetic Pareto data.
A maximum likelihood estimator based on geometric records estimates the tail index of Pareto-type distributions with proven consistency, asymptotic normality, and advantages over Hill's estimator in sequential or low-measurement contexts.
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Quantile-Based Effectiveness Persistence Function: A Tail-Focused Metric with Theory, Estimation, and Application to Biosimilar Evaluation
Introduces quantile-based effectiveness persistence function as tail mean divided by quantile, shows equivalence to first L-moment of scaled tail, and develops nonparametric estimator with bootstrap equivalence test for biosimilar evaluation.
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Markov Chain Decoders Overcome the Heavy-Tail Limitations of Lipschitz Generative Models
Markov chain Phase-Type decoders in VAEs overcome the structural inability of Gaussian-Lipschitz models to produce heavy-tailed outputs, cutting tail KS distance by up to 6x and extreme quantile error by up to 10x on synthetic Pareto data.
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Estimating the tail index of Pareto-type distributions from geometric records
A maximum likelihood estimator based on geometric records estimates the tail index of Pareto-type distributions with proven consistency, asymptotic normality, and advantages over Hill's estimator in sequential or low-measurement contexts.