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Title: An objective approach to the quantification of strain in three-dimensions with consideration of error assessment
The quantification of strain in three-dimensions is a powerful tool for structural investigations, allowing for the direct consideration of the localization and delocalization of deformation in space, and potentially, in time. Furthermore, characterization of the distribution of strain in three-dimensions may yield information concerning large-scale kinematics that may not be obtained through the traditional use of asymmetric fabrics. In this contribution, we present a streamlined methodology for the calculation of three-dimensional strain using objective approaches that allow for consideration of error assessment. This approach begins with the collection of suitable samples for strain analysis following either the Rf/ϕ or normalized Fry techniques. Samples are cut along three mutually perpendicular orientations using a set of jigs designed for use in a large oil saw. Cut faces are polished and scanned in high resolution. Scanned images are processed following a standard convention. The boundaries of objects are outlined as “Regions Of Interest” in the open-source program ImageJ and saved. A script reads the saved files of object outlines and statistically fits an ellipse to each digitized object. The parameters of fitted objects are then extracted and saved. Two-dimensional strain analyses are completed following the normalized Fry method or the Rf/ϕ technique following more » a bootstrap statistical approach. For the normalized Fry method, an objective fitting routine modified from Mulchrone (2013) is used to determine the parameters of the central void. For the Rf/ϕ method, an inverse straining routine is applied and tests the resulting object orientations to a random uniform distribution following a Kolmogorov–Smirnov test in order to obtain the sectional strain ratio and orientation. Bootstrap sampling of Fry coordinates or objects results in a distribution of possible sectional strains that can be sampled for fitting of strain ellipsoids following the technique of Robin (2002). As such, the parameters of three-dimensional strain including Lode parameter and octahedral shear strain can be contoured based on confidence intervals for each sample processed. The application of the objective approach is presented in a corresponding poster. « less
Authors:
;
Award ID(s):
1650183
Publication Date:
NSF-PAR ID:
10055516
Journal Name:
Abstracts with programs - Geological Society of America
Volume:
50
Issue:
2
ISSN:
0016-7592
Sponsoring Org:
National Science Foundation
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    Objective

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    Methods

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  2. Obeid, I. (Ed.)
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It is well known that state of the art algorithms in machine learning require vast amounts of data. Fields such as speech recognition [3], image recognition [4] and text processing [5] are able to deliver impressive performance with complex deep learning models because they have developed large corpora to support training of extremely high-dimensional models (e.g., billions of parameters). Other fields that do notmore »have access to such data resources must rely on techniques in which existing models can be adapted to new datasets [6]. A preliminary version of this breast corpus release was tested in a pilot study using a baseline machine learning system, ResNet18 [7], that leverages several open-source Python tools. The pilot corpus was divided into three sets: train, development, and evaluation. Portions of these slides were manually annotated [1] using the nine labels in Table 1 [8] to identify five to ten examples of pathological features on each slide. 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The breast corpus subset should be released by November 2021. By December 2021 we should also release the unannotated FCCC data. We are currently annotating urinary tract data as well. We expect to release about 5,600 processed TUH slides in this subset. We have an additional 53,000 unprocessed TUH slides digitized. Corpora of this size will stimulate the development of a new generation of deep learning technology. In clinical settings where resources are limited, an assistive diagnoses model could support pathologists’ workload and even help prioritize suspected cancerous cases. ACKNOWLEDGMENTS This material is supported by the National Science Foundation under grants nos. CNS-1726188 and 1925494. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. REFERENCES [1] N. Shawki et al., “The Temple University Digital Pathology Corpus,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York City, New York, USA: Springer, 2020, pp. 67 104. https://www.springer.com/gp/book/9783030368432. [2] J. Picone, T. Farkas, I. Obeid, and Y. Persidsky, “MRI: High Performance Digital Pathology Using Big Data and Machine Learning.” Major Research Instrumentation (MRI), Division of Computer and Network Systems, Award No. 1726188, January 1, 2018 – December 31, 2021. https://www. isip.piconepress.com/projects/nsf_dpath/. [3] A. Gulati et al., “Conformer: Convolution-augmented Transformer for Speech Recognition,” in Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH), 2020, pp. 5036-5040. https://doi.org/10.21437/interspeech.2020-3015. [4] C.-J. Wu et al., “Machine Learning at Facebook: Understanding Inference at the Edge,” in Proceedings of the IEEE International Symposium on High Performance Computer Architecture (HPCA), 2019, pp. 331–344. https://ieeexplore.ieee.org/document/8675201. [5] I. Caswell and B. Liang, “Recent Advances in Google Translate,” Google AI Blog: The latest from Google Research, 2020. [Online]. Available: https://ai.googleblog.com/2020/06/recent-advances-in-google-translate.html. [Accessed: 01-Aug-2021]. [6] V. Khalkhali, N. Shawki, V. Shah, M. Golmohammadi, I. Obeid, and J. Picone, “Low Latency Real-Time Seizure Detection Using Transfer Deep Learning,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2021, pp. 1 7. https://www.isip. piconepress.com/publications/conference_proceedings/2021/ieee_spmb/eeg_transfer_learning/. [7] J. Picone, T. Farkas, I. Obeid, and Y. 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