It is shown that the twin index n calculated, according to Friedel, as a function of the indices (hkl) and [uvw] of the lattice plane and lattice direction defining the cell of the twin lattice applies only to twofold twins, i.e. twins where the twin element is of order 2. For manifold twins, where the twin operation is a three-, four- or sixfold (direct or inverse) rotation, it is shown that the generalized formula becomes n=NXi/xi, where N is the number of lattice planes of the (hkl) family passing within the cell of the twin lattice, Xi the two-dimensional coincidence index for a plane of the (hkl) family and xi the number of planes out of N of that family that are partially restored by the twin operation. The existence of twin lattice quasi-symmetry (TLQS) twins with zero-obliquity in manifold twins leads to the introduction of a new parameter as a general measure of the pseudo-symmetry of TLQS rotation twins: the twin misfit delta, defined as the distance between the first nodes along the two shortest directions in the plane of LT (quasi-)perpendicular to the twin axes that are quasi-restored by the twin operation. Taking the example of staurolite twins, several inconsistencies in the treatment of manifold twins are pointed out.
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http://dx.doi.org/10.1107/S0108767307012135 | DOI Listing |
bioRxiv
May 2024
Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305.
Learning disabilities affect a significant proportion of children worldwide, with far-reaching consequences for their academic, professional, and personal lives. Here we develop digital twins - biologically plausible personalized Deep Neural Networks (pDNNs) - to investigate the neurophysiological mechanisms underlying learning disabilities in children. Our pDNN reproduces behavioral and neural activity patterns observed in affected children, including lower performance accuracy, slower learning rates, neural hyper-excitability, and reduced neural differentiation of numerical problems.
View Article and Find Full Text PDFSci Rep
November 2022
Sandia National Laboratories, New Mexico, USA.
We propose a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches. Deep learning ROM (DL-ROM) using deep-convolutional autoencoders (DC-AE) has been shown to capture nonlinear solution manifolds but fails to perform adequately when linear subspace approaches such as proper orthogonal decomposition (POD) would be optimal. Besides, most DL-ROM models rely on convolutional layers, which might limit its application to only a structured mesh.
View Article and Find Full Text PDFJ Am Chem Soc
September 2020
Department of Chemistry and Biochemistry, University of Mississippi, Oxford, Mississippi 38677, United States.
Neuroimage
December 2018
Laboratory for Imagery, Vision and Artificial Intelligence, École de technologie supérieure, 1100 Notre-Dame W., Montreal, QC, H3C1K3, Canada.
This work presents an efficient framework, based on manifold approximation, for generating brain fingerprints from multi-modal data. The proposed framework represents images as bags of local features which are used to build a subject proximity graph. Compact fingerprints are obtained by projecting this graph in a low-dimensional manifold using spectral embedding.
View Article and Find Full Text PDFComput Intell Neurosci
May 2016
Yangtze University College of Technology and Engineering, Jingzhou 430023, China.
A novel semisupervised extreme learning machine (ELM) with clustering discrimination manifold regularization (CDMR) framework named CDMR-ELM is proposed for semisupervised classification. By using unsupervised fuzzy clustering method, CDMR framework integrates clustering discrimination of both labeled and unlabeled data with twinning constraints regularization. Aiming at further improving the classification accuracy and efficiency, a new multiobjective fruit fly optimization algorithm (MOFOA) is developed to optimize crucial parameters of CDME-ELM.
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