scholarly journals Approximate zero modes for the Pauli operator on a region

2016 ◽  
Vol 6 (2) ◽  
pp. 373-413 ◽  
Author(s):  
Daniel Elton
2006 ◽  
Vol 233 (1) ◽  
pp. 135-172 ◽  
Author(s):  
Grigori Rozenblum ◽  
Nikolai Shirokov

1995 ◽  
Vol 06 (01) ◽  
pp. 123-134 ◽  
Author(s):  
H. DILGER

We present an improvement of global Metropolis updating steps, the instanton hits, used in a hybrid Monte Carlo simulation of the two-flavor Schwinger model with staggered fermions. These hits are designed to change the topological sector of the gauge field. In order to match these hits to an unquenched simulation with pseudofermions, the approximate zero mode structure of the lattice Dirac operator has to be considered explicitly.


2019 ◽  
Vol 35 (3) ◽  
pp. 317-325 ◽  
Author(s):  
Dorota Reis

Abstract. Interoception is defined as an iterative process that refers to receiving, accessing, appraising, and responding to body sensations. Recently, following an extensive process of development, Mehling and colleagues (2012) proposed a new instrument, the Multidimensional Assessment of Interoceptive Awareness (MAIA), which captures these different aspects of interoception with eight subscales. The aim of this study was to reexamine the dimensionality of the MAIA by applying maximum likelihood confirmatory factor analysis (ML-CFA), exploratory structural equation modeling (ESEM), and Bayesian structural equation modeling (BSEM). ML-CFA, ESEM, and BSEM were examined in a sample of 320 German adults. ML-CFA showed a poor fit to the data. ESEM yielded a better fit and contained numerous significant cross-loadings, of which one was substantial (≥ .30). The BSEM model with approximate zero informative priors yielded an excellent fit and confirmed the substantial cross-loading found in ESEM. The study demonstrates that ESEM and BSEM are flexible techniques that can be used to improve our understanding of multidimensional constructs. In addition, BSEM can be seen as less exploratory than ESEM and it might also be used to overcome potential limitations of ESEM with regard to more complex models relative to the sample size.


2020 ◽  
Vol 102 (15) ◽  
Author(s):  
Victor Chua ◽  
Katharina Laubscher ◽  
Jelena Klinovaja ◽  
Daniel Loss
Keyword(s):  

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Christian P. Chen ◽  
Marcin Szyniszewski ◽  
Henning Schomerus
Keyword(s):  

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
V. Vadimov ◽  
T. Hyart ◽  
J. L. Lado ◽  
M. Möttönen ◽  
T. Ala-Nissila

2021 ◽  
pp. 168440
Author(s):  
Tohru Kawarabayashi ◽  
Yuya Inoue ◽  
Ryo Itagaki ◽  
Yasuhiro Hatsugai ◽  
Hideo Aoki

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