Complexity Estimation for Feature Tracking Data

Objective: The aim of this study was to quantify left ventricular torsion by newly applied cardiovascular magnetic resonance feature tracking (CMR-FT), and to evaluate the clinical value of the ventricular torsion as a sensitive indicator of cardiac function by comparison of preoperative and postoperative torsion.Methods: A total of 54 volunteers and 36 patients with previous myocardial infarction (MI) and LV ejection fraction (EF) between 30%-50% were screened preoperatively or postoperatively by MRI. The patients’ short axis views of the whole heart were acquired, and all patients had a scar area >75% in at least one of the anterior or inferior segments. Their apical and basal rotation values were analyzed by feature tracking, and the correlation analysis was performed for the improvement of LV torsion and ejection fraction after CABG. The intra- and inter-observer reliabilities of torsion measured by CMR-FT were assessed.Results: In normal hearts, the apex rotated counterclockwise in the systolic period with the peak rotation as 10.2 ± 4.8°, and the base rotated clockwise as the peak value was 7.0 ± 3.3°. There was a timing hiatus between the apex and base untwisting, during which period the heart recoils and its suction sets the stage for the following rapid filling period. The postoperative torsion and rotation significantly improved compared with preoperative ones. However, the traditional indicator of cardiac function, ejection fraction, didn’t show significant improvement.Conclusion: Left ventricular torsion derived from CMR-FT, which does not require specialized CMR sequences, was sensitive to patients with low ejection fraction whose cardiac function significantly improved after CABG. The rapid acquisition of this measurement has potential for the assessment of cardiac function in clinical practice.

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A computerized system for the application of fracture tracking data to aircraft management for the C-5A military airlift transport

10.2514/6.1982-760 ◽

1982 ◽

Author(s):

D. FINKLE ◽

J. WILSON

Keyword(s):

Tracking Data ◽

Computerized System ◽

Military Airlift

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The Opposition of Surprisal and Semantic Similarity in the Prediction of Language Processing: Evidence from Eye-tracking Data

10.31234/osf.io/zypk9 ◽

2020 ◽

Author(s):

Kun Sun

Keyword(s):

Eye Tracking ◽

Semantic Similarity ◽

Cognitive Processing ◽

Language Processing ◽

Language Comprehension ◽

Word Processing ◽

Reading Time ◽

Computational Models ◽

Tracking Data ◽

Dynamic Approach

Expectations or predictions about upcoming content play an important role during language comprehension and processing. One important aspect of recent studies of language comprehension and processing concerns the estimation of the upcoming words in a sentence or discourse. Many studies have used eye-tracking data to explore computational and cognitive models for contextual word predictions and word processing. Eye-tracking data has previously been widely explored with a view to investigating the factors that influence word prediction. However, these studies are problematic on several levels, including the stimuli, corpora, statistical tools they applied. Although various computational models have been proposed for simulating contextual word predictions, past studies usually preferred to use a single computational model. The disadvantage of this is that it often cannot give an adequate account of cognitive processing in language comprehension. To avoid these problems, this study draws upon a massive natural and coherent discourse as stimuli in collecting the data on reading time. This study trains two state-of-art computational models (surprisal and semantic (dis)similarity from word vectors by linear discriminative learning (LDL)), measuring knowledge of both the syntagmatic and paradigmatic structure of language. We develop a `dynamic approach' to compute semantic (dis)similarity. It is the first time that these two computational models have been merged. Models are evaluated using advanced statistical methods. Meanwhile, in order to test the efficiency of our approach, one recently developed cosine method of computing semantic (dis)similarity based on word vectors data adopted is used to compare with our `dynamic' approach. The two computational and fixed-effect statistical models can be used to cross-verify the findings, thus ensuring that the result is reliable. All results support that surprisal and semantic similarity are opposed in the prediction of the reading time of words although both can make good predictions. Additionally, our `dynamic' approach performs better than the popular cosine method. The findings of this study are therefore of significance with regard to acquiring a better understanding how humans process words in a real-world context and how they make predictions in language cognition and processing.

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Embedded Gold Markers for Improved TEM/STEM Tomography Reconstruction

ISTFA 2008: Conference Proceedings from the 34th International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2008p0172 ◽

2008 ◽

Author(s):

Jian-Shing Luo ◽

Chia-Chi Huang ◽

Jeremy D. Russell

Keyword(s):

3D Reconstruction ◽

Feature Tracking ◽

Semiconductor Device ◽

Electron Tomography ◽

Tracking Process ◽

Carbon Coated ◽

Novel Method ◽

Tomography Reconstruction ◽

Tilt Series ◽

20 Nm

Abstract Electron tomography includes four main steps: tomography data acquisition, image processing, 3D reconstruction, and visualization. After acquisition, tilt-series alignments are performed. Two methods are used to align the tilt-series: cross-correlation and feature tracking. Normally, about 10-20 nm of fiducial markers, such as gold beads, are deposited onto one side of 100 mesh carbon-coated grids during the feature-tracking process. This paper presents a novel method for preparing electron tomography samples with gold beads inside to improve the feature tracking process and quality of 3D reconstruction. Results show that the novel electron tomography sample preparation method improves image alignment, which is essential for successful tomography in many contemporary semiconductor device structures.

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