A quantum coin toss

In a recent paper \cite{Landsman1}, it has been claimed that the outcomes of a quantum coin toss which is idealized as an infinite binary sequence is {\it 1-random}. We also defend the correctness of this claim and assert that the outcomes of quantum measurements can be considered as an infinite {\it 1-random} or {\it n-random} sequence. In this brief note we present our comments on this claim. We have mostly positive but also some negative comments on the arguments of the paper \cite{Landsman1}. Furthermore, we speculate a logical-axiomatic study of nature which we believe can intrinsically provide quantum mechanical probabilities based on {\it 1(n)-randomness}.

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Decisions by Coin Toss and the Limits of Rationality

PsycEXTRA Dataset ◽

10.1037/e722852011-077 ◽

2007 ◽

Cited By ~ 1

Author(s):

Gideon Keren ◽

Karl H. Teigen

Keyword(s):

Coin Toss

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The effect of mask use on maternal oxygen saturation in term pregnancies during the COVID-19 process

Journal of Perinatal Medicine ◽

10.1515/jpm-2020-0422 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Erzat Toprak ◽

Ayça Nazlı Bulut

Keyword(s):

Oxygen Saturation ◽

Pregnant Women ◽

Stress Test ◽

Statistical Significance ◽

Categorical Variables ◽

Fisher Exact Test ◽

Coin Toss ◽

Arterial Blood ◽

Surgical Masks ◽

Before And After

AbstractObjectivesOur aim was to evaluate the effect of standard surgical and N-95 respiratory mask use on maternal oxygen saturation, vital signs and result on non-stress tests in term pregnancies.MethodsIt is a prospective observational study. The study included healthy, not in labor, singleton pregnant women of 370/7–410/7 weeks who were applied to our hospital for routine obstetric control examination between March 1, 2020, and August 31, 2020. Patients were randomised by coin toss method. Oxygen saturation, systolic, and diastolic arterial blood pressure, pulse, respiratory rate, and temperature of pregnant women using surgical masks and respiratory masks were measured before and after the non-stress test. The tolerance of the masks was also evaluated. Student’s t-test was used for variables showing parametric distribution and the Mann Whitney U-test was used for non-parametric tests. The categorical variables between the groups was analyzed by using the Chi square test or Fisher Exact test. The statistical significance level was taken as p<0.05 in all tests.ResultsA total of 297 pregnant women using masks were included in the study. The effect of mask type on oxygen saturation before and after the non-stress test was found to be significant (97.1±1.8 corresponds to 95.3±2.6 for the surgical mask, p=0.0001; 97.8±1.7 corresponds to 93.7±2.0 for the respiratory mask, p=0.0001). Mask tolerance of patients using respiratory masks was significantly higher than those using surgical masks (mean 8, 1–10, p=0.0001).ConclusionsSurgical mask and respiratory mask usage decreased significantly in oxygen saturation in term pregnancies.

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Clear Heads and Bayesian Tales: Predictive Value and the Coin Toss?

Archives of Pediatrics and Adolescent Medicine ◽

10.1001/archpedi.1989.02150210016008 ◽

1989 ◽

Vol 143 (9) ◽

pp. 1000

Keyword(s):

Predictive Value ◽

Coin Toss

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Coin Toss Project

App Development Recipes for iOS and watchOS ◽

10.1007/978-1-4842-1820-4_15 ◽

2016 ◽

pp. 445-460

Author(s):

Molly K. Maskrey

Keyword(s):

Coin Toss

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Null Models for Formal Contexts

Information ◽

10.3390/info11030135 ◽

2020 ◽

Vol 11 (3) ◽

pp. 135

Author(s):

Maximilian Felde ◽

Tom Hanika ◽

Gerd Stumme

Keyword(s):

Formal Concept Analysis ◽

Null Model ◽

Null Models ◽

Formal Concept ◽

Model Generation ◽

Coin Toss ◽

Novel Approach ◽

Random Models ◽

Real World Datasets ◽

Stochastic Properties

Null model generation for formal contexts is an important task in the realm of formal concept analysis. These random models are in particular useful for, but not limited to, comparing the performance of algorithms. Nonetheless, a thorough investigation of how to generate null models for formal contexts is absent. Thus we suggest a novel approach using Dirichlet distributions. We recollect and analyze the classical coin-toss model, recapitulate some of its shortcomings and examine its stochastic properties. Building upon this we propose a model which is capable of generating random formal contexts as well as null models for a given input context. Through an experimental evaluation we show that our approach is a significant improvement with respect to the variety of contexts generated. Furthermore, we demonstrate the applicability of our null models with respect to real world datasets.

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