Research on theoretical method of computational imaging astrometry

2020 ◽  
Author(s):  
HAO LUO ◽  
ZHAOXIANG QI ◽  
YONG YU ◽  
SHILONG LIAO
Keyword(s):  
Theoretical Method ◽  
Computational Imaging

A crystallographic analysis of the CoSi/Si(111) interface using Transmission Electron Microscopy

1990 ◽  
Vol 48 (4) ◽  
pp. 574-575
Author(s):  
A.C. Daykin ◽  
C.J. Kiely ◽  
R.C. Pond ◽  
J.L. Batstone
Keyword(s):  
Electron Microscopy ◽  
Transmission Electron Microscopy ◽  
Defect Structure ◽  
Room Temperature ◽  
Theoretical Method ◽  
Crystallographic Analysis ◽  
Si Substrate ◽  
Transmission Electron ◽  
Theoretical Predictions

When CoSi2 is grown onto a Si(111) surface it can form in two distinct orientations. A-type CoSi2 has the same orientation as the Si substrate and B-type is rotated by 180° degrees about the [111] surface normal.One method of producing epitaxial CoSi2 is to deposit Co at room temperature and anneal to 650°C.If greater than 10Å of Co is deposited then both A and B-type CoSi2 form via a number of intermediate silicides .The literature suggests that the co-existence of A and B-type CoSi2 is in some way linked to these intermediate silicides analogous to the NiSi2/Si(111) system. The phase which forms prior to complete CoSi2 formation is CoSi. This paper is a crystallographic analysis of the CoSi2/Si(l11) bicrystal using a theoretical method developed by Pond. Transmission electron microscopy (TEM) has been used to verify the theoretical predictions and to characterise the defect structure at the interface.

Network reconstruction based on steady-state data

2008 ◽  
Vol 45 ◽  
pp. 161-176 ◽  
Author(s):  
Eduardo D. Sontag
Keyword(s):  
Steady State ◽  
Reverse Engineering ◽  
Theoretical Method ◽  
Network Reconstruction ◽  
Quasi Steady State ◽  
State Data

This paper discusses a theoretical method for the “reverse engineering” of networks based solely on steady-state (and quasi-steady-state) data.

A graph-theoretical method for decoding some group MLD-codes

2020 ◽  
Vol 27 (2) ◽  
pp. 17-42
Author(s):  
V. M. Deundyak ◽  
E. A. Lelyuk
Keyword(s):  
Theoretical Method

scholarly journals Commonalities across computational workflows for uncovering explanatory variants in undiagnosed cases

2021 ◽  
Author(s):  
Shilpa Nadimpalli Kobren ◽  
◽  
Dustin Baldridge ◽  
Matt Velinder ◽  
Joel B. Krier ◽  
...  
Keyword(s):  
Online Survey ◽  
Variant Calling ◽  
Theoretical Method ◽  
The United States ◽  
Genomic Sequencing ◽  
Biomedical Data ◽  
Sequencing Data ◽  
Multimodal Data ◽  
Undiagnosed Diseases ◽  
Undiagnosed Diseases Network

Abstract Purpose Genomic sequencing has become an increasingly powerful and relevant tool to be leveraged for the discovery of genetic aberrations underlying rare, Mendelian conditions. Although the computational tools incorporated into diagnostic workflows for this task are continually evolving and improving, we nevertheless sought to investigate commonalities across sequencing processing workflows to reveal consensus and standard practice tools and highlight exploratory analyses where technical and theoretical method improvements would be most impactful. Methods We collected details regarding the computational approaches used by a genetic testing laboratory and 11 clinical research sites in the United States participating in the Undiagnosed Diseases Network via meetings with bioinformaticians, online survey forms, and analyses of internal protocols. Results We found that tools for processing genomic sequencing data can be grouped into four distinct categories. Whereas well-established practices exist for initial variant calling and quality control steps, there is substantial divergence across sites in later stages for variant prioritization and multimodal data integration, demonstrating a diversity of approaches for solving the most mysterious undiagnosed cases. Conclusion The largest differences across diagnostic workflows suggest that advances in structural variant detection, noncoding variant interpretation, and integration of additional biomedical data may be especially promising for solving chronically undiagnosed cases.

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