The Poisson boundary of hyperbolic groups without moment conditions

Chawla, Kunal; Forghani, Behrang; Frisch, Joshua; Tiozzo, Giulio

doi:10.48550/arxiv.2209.02114

Cited by 1 publication

(3 citation statements)

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“…This proposition is proved in [CFFT22] when the underlying space is Gromov hyperbolic, by means of pivoting technique. Using the pivotal time construction in [Cho22a] for metric spaces with contracting isometries, their proof applies to the setting as in Proposition 7.1.…”

Section: Continuity Of the Entropymentioning

confidence: 94%

“…We prove Theorem F by combining Gouëzel's pivotal time technique in [Gou22] (modulo small modification as in [Cho22a]) and Chawla-Frisch-Forghani-Tiozzo's sublinear entropy growth of displacement [CFFT22].…”

Section: Introductionmentioning

confidence: 99%

“…In Section 6, we establish Proposition 4.4 and 5.2 using Gouëzel's pivoting technique [Gou22], as sketched in [Cho22a]. In Section 7, we recall Chawla-Forghani-Frisch-Tiozzo's sublinear growth of entropy of displacement [CFFT22] and a more complicated version of pivotal time construction [Gou22,Section 5C]. By combining them, we establish Theorem F.…”

Section: Introductionmentioning

confidence: 99%

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Correlation Between COVID-19 Infection Rate and Atmospheric Environmental Factors in Seoul Metropolitan City, South Korea

Choi

Kim

2022

Preprint

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The purpose of this study is to statistically analyze whether the change in the COVID-19 virus infection rate is affected by atmospheric environmental factors in Seoul metropolitan city of South Korea. The research period of this study is from January 1, 2020 to March 31, 2021. The correlation relationship between the COVID-19 virus infection rate and atmospheric environmental factors such as PM10, PM2.5, temperature, and humidity was analyzed by multiple regression analysis. In the correlation between PM10 and PM2.5 and the COVID-19 virus infection rate, correlation coefficient between PM10 and infection rate was r=0.114(p=0.019), showing a positive correlation. The correlation coefficient between PM2.5 and infection rate was r=0.147(p=0.002), which indicates a higher positive correlation than PM10. In the correlation between thermal factors and infection rate, the correlation coefficients between temperature and infection rate and between relative humidity and infection rate were r= -0.527(p=0.000) and r= -0.91(p=0.060), respectively. Unlike PM10 and PM2.5, temperature and relative humidity showed a negative correlation with the infection rate. Except for relative humidity, PM10, PM2.5 and temperature had statistically significant correlations with the infection rate (p<0.05). Based on the results obtained from this study, it is recommended to avoid outdoor activities as much as possible in weather with high PM10 and PM2.5 and low temperature and humidity.

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Section: Continuity Of the Entropymentioning

confidence: 94%