Adjunct Control and edge features

2014 ◽  
pp. 79-108 ◽  
Author(s):  
Jairo Nunes
Keyword(s):  
Adjunct Control ◽  
Edge Features

Probing the Chemistry and Spectroscopy of Radiation-Sensitive Polymers by Parallel-Detection EELS

1990 ◽  
Vol 48 (2) ◽  
pp. 34-35
Author(s):  
E. G. Rightor ◽  
G. P. Young
Keyword(s):  
Electron Beam ◽  
Bisphenol A ◽  
Energy Loss ◽  
Parallel Detection ◽  
Commercial Grade ◽  
Incident Electron Beam ◽  
Ptfe Sample ◽  
Spectroscopic Information ◽  
Edge Features ◽  
Electron Energy Loss

Investigation of neat polymers by TEM is often thwarted by their sensitivity to the incident electron beam, which also limits the usefulness of chemical and spectroscopic information available by electron energy loss spectroscopy (EELS) for these materials. However, parallel-detection EELS systems allow reduced radiation damage, due to their far greater efficiency, thereby promoting their use to obtain this information for polymers. This is evident in qualitative identification of beam sensitive components in polymer blends and detailed investigations of near-edge features of homopolymers.Spectra were obtained for a poly(bisphenol-A carbonate) (BPAC) blend containing poly(tetrafluoroethylene) (PTFE) using a parallel-EELS and a serial-EELS (Gatan 666, 607) for comparison. A series of homopolymers was also examined using parallel-EELS on a JEOL 2000FX TEM employing a LaB6 filament at 100 kV. Pure homopolymers were obtained from Scientific Polymer Products. The PTFE sample was commercial grade. Polymers were microtomed on a Reichert-Jung Ultracut E and placed on holey carbon grids.

scholarly journals Anomaly detection with convolutional Graph Neural Networks

2021 ◽  
Vol 2021 (8) ◽  
Author(s):  
Oliver Atkinson ◽  
Akanksha Bhardwaj ◽  
Christoph Englert ◽  
Vishal S. Ngairangbam ◽  
Michael Spannowsky
Keyword(s):  
Neural Networks ◽  
Anomaly Detection ◽  
Top Quarks ◽  
New Physics ◽  
Broad Applicability ◽  
Latent Space ◽  
Edge Features ◽  
Graph Neural Networks ◽  
Promising Avenue ◽  
Do So

Abstract We devise an autoencoder based strategy to facilitate anomaly detection for boosted jets, employing Graph Neural Networks (GNNs) to do so. To overcome known limitations of GNN autoencoders, we design a symmetric decoder capable of simultaneously reconstructing edge features and node features. Focusing on latent space based discriminators, we find that such setups provide a promising avenue to isolate new physics and competing SM signatures from sensitivity-limiting QCD jet contributions. We demonstrate the flexibility and broad applicability of this approach using examples of W bosons, top quarks, and exotic hadronically-decaying exotic scalar bosons.

Extended X-ray absorption fine structure and multiple-scattering simulation of nickel dithiolene complexes Ni[S2C2(CF3)2]2n(n= −2, −1, 0) and an olefin adduct Ni[S2C2(CF3)2]2(1-hexene)

2015 ◽  
Vol 22 (1) ◽  
pp. 124-129 ◽  
Author(s):  
Weiwei Gu ◽  
Hongxin Wang ◽  
Kun Wang
Keyword(s):  
Fine Structure ◽  
Multiple Scattering ◽  
Edge Structure ◽  
X Ray ◽  
Absorption Fine ◽  
Square Planar ◽  
Square Planar Complexes ◽  
Uv Visible ◽  
Edge Features ◽  
X Ray Absorption

A series of Ni dithiolene complexes Ni[S2C2(CF3)]2n(n= −2, −1, 0) (1,2,3) and a 1-hexene adduct Ni[S2C2(CF3)2]2(C6H12) (4) have been examined by NiK-edge X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine-structure (EXAFS) spectroscopies. Ni XANES for1–3reveals clear pre-edge features and approximately +0.7 eV shift in the NiK-edge position for `one-electron' oxidation. EXAFS simulation shows that the Ni—S bond distances for1,2and3(2.11–2.16 Å) are within the typical values for square planar complexes and decrease by ∼0.022 Å for each `one-electron' oxidation. The changes in NiK-edge energy positions and Ni—S distances are consistent with the `non-innocent' character of the dithiolene ligand. The Ni—C interactions at ∼3.0 Å are analyzed and the multiple-scattering parameters are also determined, leading to a better simulation for the overall EXAFS spectra. The 1-hexene adduct4presents no pre-edge feature, and its NiK-edge position shifts by −0.8 eV in comparison with its starting dithiolene complex3. Consistently, EXAFS also showed that the Ni—S distances in4elongate by ∼0.046 Å in comparison with3. The evidence confirms that the neutral complex is `reduced' upon addition of olefin, presumably by olefin donating the π-electron density to the LUMO of3as suggested by UV/visible spectroscopy in the literature.

Phase-Edge Features and the Syntax of Polarity Particles

2013 ◽  
Vol 44 (3) ◽  
pp. 345-389 ◽  
Author(s):  
J.-Marc Authier
Keyword(s):  
Intermediate Layer ◽  
Feature Sharing ◽  
Embedded Clauses ◽  
Edge Features

In this article, I argue that the phase edge in the C field shares features via Agree with an intermediate layer (FinP) and with a lower projection (ΣP), allowing it to determine the type of clause and its polarity. I adopt a feature-sharing relation of Agree that connects all of the polarity features present on heads (be they Σ, Fin, and, in some cases, VFoc) to a polarity feature in Force, the relevant phase-edge position for clausal typing. This explains, among other things, why embedded clauses containing a polarity feature can only satisfy the selectional properties of a particular class of (matrix) verbs.

DCNN-based Ship Classification using Enhanced Edge Information and Inception Module

2021 ◽  
Author(s):  
Bo Wang ◽  
Xiaoting Yu ◽  
Chengeng Huang ◽  
Qinghong Sheng ◽  
Yuanyuan Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Classification Performance ◽  
Image Features ◽  
Deep Convolutional Neural Networks ◽  
Edge Information ◽  
Average Accuracy ◽  
Ship Classification ◽  
Edge Features ◽  
High Level ◽  
Better Than

The excellent feature extraction ability of deep convolutional neural networks (DCNNs) has been demonstrated in many image processing tasks, by which image classification can achieve high accuracy with only raw input images. However, the specific image features that influence the classification results are not readily determinable and what lies behind the predictions is unclear. This study proposes a method combining the Sobel and Canny operators and an Inception module for ship classification. The Sobel and Canny operators obtain enhanced edge features from the input images. A convolutional layer is replaced with the Inception module, which can automatically select the proper convolution kernel for ship objects in different image regions. The principle is that the high-level features abstracted by the DCNN, and the features obtained by multi-convolution concatenation of the Inception module must ultimately derive from the edge information of the preprocessing input images. This indicates that the classification results are based on the input edge features, which indirectly interpret the classification results to some extent. Experimental results show that the combination of the edge features and the Inception module improves DCNN ship classification performance. The original model with the raw dataset has an average accuracy of 88.72%, while when using enhanced edge features as input, it achieves the best performance of 90.54% among all models. The model that replaces the fifth convolutional layer with the Inception module has the best performance of 89.50%. It performs close to VGG-16 on the raw dataset and is significantly better than other deep neural networks. The results validate the functionality and feasibility of the idea posited.

X-ray absorption near edge structures in cobalt oxides

1996 ◽  
Vol 11 (9) ◽  
pp. 2242-2256 ◽  
Author(s):  
T. Jiang ◽  
D. E. Ellis
Keyword(s):  
Wave Functions ◽  
Site Symmetry ◽  
Local Geometry ◽  
Final State ◽  
Cluster Technique ◽  
Edge Structure ◽  
X Ray ◽  
Local Electronic Structure ◽  
Edge Features ◽  
X Ray Absorption

Theoretical studies have been made of K-edge x-ray absorption near edge structure (XANES) of Co in CoO, Co(OH)2, CoTiO3, Co3O4, and CoAl2O4. Correlations of experimental near edge features with site symmetry, local geometry, local electronic structure, i.e., atomic configuration, charge transfer, and backscattering from neighboring atomic potentials are interpreted. The self-consistent Discrete Variational Xa Method (DV-Xα) within an embedded cluster technique has been used to generate the crystal potential. A multiple scattering (MS) approach is then used to solve for the final state wave function. The ground state DV wave functions are analyzed in terms of the projected density of states, whereas the final state MS continuum wave functions are analyzed through the concept of photoelectron trapping time.

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