scholarly journals Conformal bootstrap analysis for the Yang–Lee edge singularity

2018 ◽  
Vol 2018 (5) ◽  
Author(s):  
S Hikami
Keyword(s):  
Bootstrap Analysis ◽  
Edge Singularity ◽  
Conformal Bootstrap

scholarly journals Dimensional reduction by conformal bootstrap

2019 ◽  
Vol 2019 (8) ◽  
Author(s):  
Shinobu Hikami
Keyword(s):  
Ising Model ◽  
Random Field ◽  
Dimensional Reduction ◽  
Bootstrap Method ◽  
Polymer Model ◽  
Random Field Ising Model ◽  
Branched Polymer ◽  
Five Dimensions ◽  
Edge Singularity ◽  
Conformal Bootstrap

Abstract Dimensional reductions in the branched polymer model and the random field Ising model (RFIM) are discussed by a conformal bootstrap method. Small minors are applied for the evaluations of the scale dimensions of these two models and the results are compared to the $D'=D-2$D Yang–Lee edge singularity and to the pure $D'=D-2$D Ising model, respectively. For the former case, the dimensional reduction is shown to be valid for $3 \le D \le 8$ and, for the latter case, the deviation from the dimensional reduction can be seen below five dimensions.

scholarly journals 3d N=4 Bootstrap and Mirror Symmetry

2021 ◽  
Vol 10 (4) ◽  
Author(s):  
Chi-Ming Chang ◽  
Martin Fluder ◽  
Ying-Hsuan Lin ◽  
Shu-Heng Shao ◽  
Yifan Wang
Keyword(s):  
Mirror Symmetry ◽  
Flavor Symmetry ◽  
Bootstrap Analysis ◽  
Conformal Bootstrap ◽  
Perturbative Methods ◽  
Topological Quantum ◽  
Exact Determination ◽  
The Moment ◽  
Mirror Theory

We investigate the non-BPS realm of 3d {N} = 4N=4 superconformal field theory by uniting the non-perturbative methods of the conformal bootstrap and supersymmetric localization, and utilizing special features of 3d {N} = 4N=4 theories such as mirror symmetry and a protected sector described by topological quantum mechanics (TQM). Supersymmetric localization allows for the exact determination of the conformal and flavor central charges, and the latter can be fed into the mini-bootstrap of the TQM to solve for a subset of the OPE data. We examine the implications of the Z_2Z2 mirror action for the SCFT single- and mixed-branch crossing equations for the moment map operators, and apply numerical bootstrap to obtain universal constraints on OPE data for given flavor symmetry groups. A key ingredient in applying the bootstrap analysis is the determination of the mixed-branch superconformal blocks. Among other results, we show that the simplest known self-mirror theory with SU(2) \times SU(2)SU(2)×SU(2) flavor symmetry saturates our bootstrap bounds, which allows us to extract the non-BPS data and examine the self-mirror Z_2Z2 symmetry thereof.

Bootstrap Analysis for Asian REIT's Portfolios

2015 ◽  
Author(s):  
Enareta Kurtbegu ◽  
Juliana Caicedo-Llano
Keyword(s):  
Bootstrap Analysis

Skill among Social Traders - A Bootstrap Analysis

2018 ◽  
Author(s):  
Alexander Deneke ◽  
Sebastian Seidens
Keyword(s):  
Bootstrap Analysis

Bootstrap Analysis

2017 ◽  
Author(s):  
Russell Cheng
Keyword(s):  
Goodness Of Fit ◽  
Null Distribution ◽  
Real Data ◽  
Confidence Regions ◽  
Confidence Bands ◽  
Cumulative Distribution ◽  
Attractive Alternative ◽  
Bootstrap Analysis ◽  
Parametric Bootstrapping ◽  
Anderson Darling

Parametric bootstrapping (BS) provides an attractive alternative, both theoretically and numerically, to asymptotic theory for estimating sampling distributions. This chapter summarizes its use not only for calculating confidence intervals for estimated parameters and functions of parameters, but also to obtain log-likelihood-based confidence regions from which confidence bands for cumulative distribution and regression functions can be obtained. All such BS calculations are very easy to implement. Details are also given for calculating critical values of EDF statistics used in goodness-of-fit (GoF) tests, such as the Anderson-Darling A2 statistic whose null distribution is otherwise difficult to obtain, as it varies with different null hypotheses. A simple proof is given showing that the parametric BS is probabilistically exact for location-scale models. A formal regression lack-of-fit test employing parametric BS is given that can be used even when the regression data has no replications. Two real data examples are given.

How can organizations foster job crafting behaviors and thriving at work?

2020 ◽  
pp. 1-18
Author(s):  
Sari Mansour ◽  
Diane-Gabrielle Tremblay
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Job Demands ◽  
Social Resources ◽  
Job Crafting ◽  
Equation Modeling ◽  
Bootstrap Analysis ◽  
Mediating Role ◽  
Thriving At Work

Abstract This study investigates whether the perceived opportunity to craft (POC) is related to job crafting (JC) strategies and whether these strategies are related to thriving at work, in terms of both vitality and learning. It aims to verify the mediating role of JC between POC and thriving. Data were collected from 424 accounting professionals in Canada. The structural equation modeling based on bootstrap analysis was used to test mediation. The results indicate that POC is positively related to increasing structural and social resources and challenging job demands and negatively to decreasing hindering job demands. They reveal that increasing structural and social resources enhances learning and mediates the relation between POC and vitality and learning, as do challenging job demands, whereas decreasing hindering job demands does not. This study is one of the first to confirm that POC influences vitality and learning via JC behaviors as mediators.

scholarly journals A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI

2021 ◽  
Vol 11 (8) ◽  
pp. 3636
Author(s):  
Faria Zarin Subah ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
Takeshi Koshiba
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
State Of The Art ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Bootstrap Analysis ◽  
Proposed Model ◽  
Art Methods ◽  
The Mean

Autism spectrum disorder (ASD) is a complex and degenerative neuro-developmental disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) to detect ASD with a very limited dataset which provides high accuracy but results in poor generalization. To overcome this limitation and to enhance the performance of the automated autism diagnosis model, in this paper, we propose an ASD detection model using functional connectivity features of resting-state fMRI data. Our proposed model utilizes two commonly used brain atlases, Craddock 200 (CC200) and Automated Anatomical Labelling (AAL), and two rarely used atlases Bootstrap Analysis of Stable Clusters (BASC) and Power. A deep neural network (DNN) classifier is used to perform the classification task. Simulation results indicate that the proposed model outperforms state-of-the-art methods in terms of accuracy. The mean accuracy of the proposed model was 88%, whereas the mean accuracy of the state-of-the-art methods ranged from 67% to 85%. The sensitivity, F1-score, and area under receiver operating characteristic curve (AUC) score of the proposed model were 90%, 87%, and 96%, respectively. Comparative analysis on various scoring strategies show the superiority of BASC atlas over other aforementioned atlases in classifying ASD and control.

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