Canonical Wiener-Hopf and spectral factorization of large-degree matrix polynomials

PAMM ◽  
2014 ◽  
Vol 14 (1) ◽  
pp. 817-818
Author(s):  
Albrecht Böttcher ◽  
Martin Halwass
Keyword(s):  
Large Degree ◽  
Spectral Factorization ◽  
Matrix Polynomials ◽  
Degree Matrix

Spectral factorization of proper matrix polynomials

1977 ◽  
Vol 42 (6) ◽  
pp. 569-579 ◽  
Author(s):  
K. F. BLACK ◽  
E. D. DENMAN
Keyword(s):  
Spectral Factorization ◽  
Matrix Polynomials

Characterization of homoepitaxial diamond films by Electron microscopy

1991 ◽  
Vol 49 ◽  
pp. 880-881
Author(s):  
D.P. Malta ◽  
S.A. Willard ◽  
R.A. Rudder ◽  
G.C. Hudson ◽  
J.B. Posthill ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Surface Defects ◽  
Substrate Surface ◽  
Diamond Films ◽  
Chemical Vapor ◽  
Polycrystalline Diamond ◽  
Large Degree ◽  
Rf Plasma ◽  
Semiconducting Diamond

Semiconducting diamond films have the potential for use as a material in which to build active electronic devices capable of operating at high temperatures or in high radiation environments. A major goal of current device-related diamond research is to achieve a high quality epitaxial film on an inexpensive, readily available, non-native substrate. One step in the process of achieving this goal is understanding the nucleation and growth processes of diamond films on diamond substrates. Electron microscopy has already proven invaluable for assessing polycrystalline diamond films grown on nonnative surfaces.The quality of the grown diamond film depends on several factors, one of which is the quality of the diamond substrate. Substrates commercially available today have often been found to have scratched surfaces resulting from the polishing process (Fig. 1a). Electron beam-induced current (EBIC) imaging shows that electrically active sub-surface defects can be present to a large degree (Fig. 1c). Growth of homoepitaxial diamond films by rf plasma-enhanced chemical vapor deposition (PECVD) has been found to planarize the scratched substrate surface (Fig. 1b).

Using Convolutional Neural Networks to measure the contribution of visual features to the representation of object animacy in the brain

2019 ◽  
Author(s):  
Sushrut Thorat
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Temporal Cortex ◽  
Large Degree ◽  
Visual Features ◽  
Visual Feature ◽  
Visual Stream ◽  
Feature Information ◽  
Ventral Visual Stream ◽  
Animal Images

A mediolateral gradation in neural responses for images spanning animals to artificial objects is observed in the ventral temporal cortex (VTC). Which information streams drive this organisation is an ongoing debate. Recently, in Proklova et al. (2016), the visual shape and category (“animacy”) dimensions in a set of stimuli were dissociated using a behavioural measure of visual feature information. fMRI responses revealed a neural cluster (extra-visual animacy cluster - xVAC) which encoded category information unexplained by visual feature information, suggesting extra-visual contributions to the organisation in the ventral visual stream. We reassess these findings using Convolutional Neural Networks (CNNs) as models for the ventral visual stream. The visual features developed in the CNN layers can categorise the shape-matched stimuli from Proklova et al. (2016) in contrast to the behavioural measures used in the study. The category organisations in xVAC and VTC are explained to a large degree by the CNN visual feature differences, casting doubt over the suggestion that visual feature differences cannot account for the animacy organisation. To inform the debate further, we designed a set of stimuli with animal images to dissociate the animacy organisation driven by the CNN visual features from the degree of familiarity and agency (thoughtfulness and feelings). Preliminary results from a new fMRI experiment designed to understand the contribution of these non-visual features are presented.

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