Severity: Warning
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Filename: drivers/Session_files_driver.php
Line Number: 177
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File: /var/www/html/index.php
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Function: require_once
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Filename: Session/Session.php
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File: /var/www/html/index.php
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Function: require_once
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Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
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Function: _error_handler
File: /var/www/html/index.php
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Function: require_once
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Filename: controllers/Detail.php
Line Number: 249
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File: /var/www/html/application/controllers/Detail.php
Line: 249
Function: _error_handler
File: /var/www/html/index.php
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Function: require_once
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Message: Trying to access array offset on value of type null
Filename: controllers/Detail.php
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Backtrace:
File: /var/www/html/application/controllers/Detail.php
Line: 249
Function: _error_handler
File: /var/www/html/index.php
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Function: require_once
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Message: Trying to access array offset on value of type null
Filename: controllers/Detail.php
Line Number: 249
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File: /var/www/html/application/controllers/Detail.php
Line: 249
Function: _error_handler
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Filename: models/Detail_model.php
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Function: strpos
File: /var/www/html/application/controllers/Detail.php
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Function: insertAPISummary
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Function: require_once
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Filename: helpers/my_audit_helper.php
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Function: str_replace
File: /var/www/html/application/controllers/Detail.php
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Function: formatAIDetailSummary
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Function: require_once
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Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
Line: 256
Function: _error_handler
File: /var/www/html/index.php
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Message: Trying to access array offset on value of type null
Filename: controllers/Detail.php
Line Number: 256
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File: /var/www/html/application/controllers/Detail.php
Line: 256
Function: _error_handler
File: /var/www/html/index.php
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Function: require_once
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Message: Trying to access array offset on value of type null
Filename: controllers/Detail.php
Line Number: 256
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File: /var/www/html/application/controllers/Detail.php
Line: 256
Function: _error_handler
File: /var/www/html/index.php
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Function: require_once
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Message: Undefined array key "usage"
Filename: controllers/Detail.php
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Function: _error_handler
File: /var/www/html/index.php
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Message: Trying to access array offset on value of type null
Filename: controllers/Detail.php
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Line: 257
Function: _error_handler
File: /var/www/html/index.php
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Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
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Function: _error_handler
File: /var/www/html/index.php
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Function: require_once
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Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
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Function: _error_handler
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Filename: controllers/Detail.php
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Function: _error_handler
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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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Function: _error_handler
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Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
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Function: file_get_contents
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Function: simplexml_load_file_from_url
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Function: require_once
In inertial confinement fusion (ICF), x-ray radiography is a critical diagnostic for measuring implosion dynamics, which contain rich three-dimensional (3D) information. Traditional methods for reconstructing 3D volumes from 2D radiographs, such as filtered backprojection, require radiographs from at least two different angles or lines of sight (LOS). In ICF experiments, the space for diagnostics is limited, and cameras that can operate on fast timescales are expensive to implement, limiting the number of projections that can be acquired. To improve the imaging quality as a result of this limitation, convolutional neural networks (CNNs) have recently been shown to be capable of producing 3D models from visible light images or medical x-ray images rendered by volumetric computed tomography. We propose a CNN to reconstruct 3D ICF spherical shells from single radiographs. We also examine the sensitivity of the 3D reconstruction to different illumination models using preprocessing techniques such as pseudo-flatfielding. To resolve the issue of the lack of 3D supervision, we show that training the CNN utilizing synthetic radiographs produced by known simulation methods allows for reconstruction of experimental data as long as the experimental data are similar to the synthetic data. We also show that the CNN allows for 3D reconstruction of shells that possess low mode asymmetries. Further comparisons of the 3D reconstructions with direct multiple LOS measurements are justified.
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Source |
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http://dx.doi.org/10.1063/5.0043653 | DOI Listing |
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