Determination of stacking ordering in disordered close-packed structures from pairwise correlation functions

2019 ◽  
Vol 75 (1) ◽  
pp. 71-81
Author(s):  
Pablo Serrano-Alfaro ◽  
Ernesto Estevez-Rams ◽  
Raimundo Lora-Serrano ◽  
Beatriz Aragon-Fernandez
Keyword(s):  
Correlation Functions ◽  
Practical Problem ◽  
Synthetic Data ◽  
Approximate Solutions ◽  
Initial Guess ◽  
Stacking Sequence ◽  
Pairwise Correlation ◽  
Annealing Procedure ◽  
Simulated Annealing Procedure

It is shown how to reconstruct the stacking sequence from the pairwise correlation functions between layers in close-packed structures. First, of theoretical interest, the analytical formulation and solution of the problem are presented when the exact pairwise correlation counts are known. In the second part, the practical problem is approached. A simulated annealing procedure is developed to solve the problem using as initial guess approximate solutions from previous treatments. The robustness of the procedure is tested with synthetic data, followed by an experimental example. The developed approach performs robustly over different synthetic and experimental data, comparing favorably with the reported methods.

FIDDLE. Simultaneous Indexing and Structure Solution from Powder Diffraction Data using a Genetic Algorithm and Correlation Functions

2019 ◽  
Author(s):  
Carmen Guguta ◽  
Jan M.M. Smits ◽  
Rene de Gelder
Keyword(s):  
Genetic Algorithm ◽  
Powder Diffraction ◽  
Diffraction Data ◽  
Correlation Functions ◽  
Cross Correlation ◽  
Simultaneous Optimization ◽  
Powder Diffraction Data ◽  
Structure Solution ◽  
Matching Technique

A method for the determination of crystal structures from powder diffraction data is presented that circumvents the difficulties associated with separate indexing. For the simultaneous optimization of the parameters that describe a crystal structure a genetic algorithm is used together with a pattern matching technique based on auto and cross correlation functions.<br>

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

scholarly journals Geometrical four-point functions in the two-dimensional critical Q-state Potts model: the interchiral conformal bootstrap

2020 ◽  
Vol 2020 (12) ◽  
Author(s):  
Yifei He ◽  
Jesper Lykke Jacobsen ◽  
Hubert Saleur
Keyword(s):  
Potts Model ◽  
Correlation Functions ◽  
Liouville Theory ◽  
Analytical Determination ◽  
Conformal Weight ◽  
Two Dimensional ◽  
Conformal Blocks ◽  
Conformal Bootstrap ◽  
Point Correlation

Abstract Based on the spectrum identified in our earlier work [1], we numerically solve the bootstrap to determine four-point correlation functions of the geometrical connectivities in the Q-state Potts model. Crucial in our approach is the existence of “interchiral conformal blocks”, which arise from the degeneracy of fields with conformal weight hr,1, with r ∈ ℕ*, and are related to the underlying presence of the “interchiral algebra” introduced in [2]. We also find evidence for the existence of “renormalized” recursions, replacing those that follow from the degeneracy of the field $$ {\Phi}_{12}^D $$ Φ 12 D in Liouville theory, and obtain the first few such recursions in closed form. This hints at the possibility of the full analytical determination of correlation functions in this model.

scholarly journals A Subspace Preconditioned LSQR Gauss-Newton Method with a Constrained Line Search Path Applied to 3D Biomedical Microwave Imaging

2015 ◽  
Vol 2015 ◽  
pp. 1-21
Author(s):  
Jürgen De Zaeytijd ◽  
Ann Franchois
Keyword(s):  
Complex Permittivity ◽  
Line Search ◽  
Synthetic Data ◽  
Iterative Solution ◽  
Initial Guess ◽  
Microwave Imaging ◽  
Single Frequency ◽  
Problem Solution ◽  
Source Position ◽  
Search Path

Three contributions that can improve the performance of a Newton-type iterative quantitative microwave imaging algorithm in a biomedical context are proposed. (i) To speed up the iterative forward problem solution, we extrapolate the initial guess of the field from a few field solutions corresponding to previous source positions for the same complex permittivity (i.e., “marching on in source position”) as well as from a Born-type approximation that is computed from a field solution corresponding to one previous complex permittivity profile for the same source position. (ii) The regularized Gauss-Newton update system can be ill-conditioned; hence we propose to employ a two-level preconditioned iterative solution method. We apply the subspace preconditioned LSQR algorithm from Jacobsen et al. (2003) and we employ a 3D cosine basis. (iii) We propose a new constrained line search path in the Gauss-Newton optimization, which incorporates in a smooth manner lower and upper bounds on the object permittivity, such that these bounds never can be violated along the search path. Single-frequency reconstructions from bipolarized synthetic data are shown for various three-dimensional numerical biological phantoms, including a realistic breast phantom from the University of Wisconsin-Madison (UWCEM) online repository.

scholarly journals Estimate of FDG Excretion by means of Compartmental Analysis and Ant Colony Optimization of Nuclear Medicine Data

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Sara Garbarino ◽  
Giacomo Caviglia ◽  
Massimo Brignone ◽  
Michela Massollo ◽  
Gianmario Sambuceti ◽  
...  
Keyword(s):  
Ant Colony Optimization ◽  
Fdg Pet ◽  
Final Section ◽  
Synthetic Data ◽  
Compartmental Analysis ◽  
Ant Colony ◽  
Computational Method ◽  
Positron Emission ◽  
Glycolytic Activity

[18F]fluoro-2-deoxy-D-glucose (FDG) is one of the most utilized tracers for positron emission tomography (PET) applications in oncology. FDG-PET relies on higher glycolytic activity in tumors compared to normal structures as the basis of image contrast. As a glucose analog, FDG is transported into malignant cells which typically exhibit an increased radioactivity. However, different from glucose, FDG is not reabsorbed by the renal system and is excreted to the bladder. The present paper describes a novel computational method for the quantitative assessment of this excretion process. The method is based on a compartmental analysis of FDG-PET data in which the excretion process is explicitly accounted for by the bladder compartment and on the application of an ant colony optimization (ACO) algorithm for the determination of the tracer coefficients describing the FDG transport effectiveness. The validation of this approach is performed by means of both synthetic data and real measurements acquired by a PET device for small animals (micro-PET). Possible oncological applications of the results are discussed in the final section.

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