STAD: Stack Trace Based Automatic Software Misconfiguration Diagnosis via Value Dependency Graph

2019 ◽  
pp. 135-152
Author(s):  
Yuan Liu ◽  
Xi Wang ◽  
Lintao Xian ◽  
Zhongwen Guo
Keyword(s):  
Dependency Graph ◽  
Automatic Software

scholarly journals Retinal vascular fractal dimensions and their association with macrovascular cardiac disease.

2021 ◽  
Author(s):  
Sebastian Dinesen ◽  
Pia Søndergaard Jensen ◽  
Maria Bloksgaard ◽  
Søren Leer Blindbæk ◽  
Jo G.R. De Mey ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Cardiac Disease ◽  
Artery Bypass ◽  
Fractal Dimensions ◽  
Macrovascular Disease ◽  
Control Group ◽  
Vascular Structure ◽  
Non Invasive ◽  
Automatic Software ◽  
Multivariable Regression

Introduction As the only part of the human vasculature, retina is available for direct, non-invasive inspection. Retinal vascular fractal dimension (DF) is a method to measure the structure of the retinal vascular tree, with higher non-integer values between 1 and 2 representing a more complex and dense retinal vasculature. Retinal vascular structure has been associated with a variety of systemic diseases and this study examined the association of DF and macrovascular cardiac disease in a case-control design. Methods Retinal fundus photos were captured with Topcon TRC-50X in 38 persons that had coronary artery bypass grafting (CABG, cases) and 37 cardiovascular healthy controls. The semi-automatic software VAMPIRE was used to measure retinal DF. Results Patients with CABG had lower DF of the retinal main venular vessels compared to the control group (1.15 vs. 1.18, p=0.01). In a multivariable regression model adjusted for gender and age, eyes in the fourth quartile with higher DF were less likely to have CABG compared to patients in the first (OR, 7.20; 95% confidence interval, 1.63 to 31.86; p=0.009) and second quartile (OR, 8.25; 95% confidence interval, 1.70 to 40.01; p=0.009). Conclusions This study demonstrates that lower complexity of main venular vessels associates with higher risk of having CABG. The research supports the hypothesis that the retinal vascular structure can be used to assess non-ocular macrovascular disease.

scholarly journals Quantitative Breast Density in Contrast-Enhanced Mammography

2021 ◽  
Vol 10 (15) ◽  
pp. 3309
Author(s):  
Gisella Gennaro ◽  
Melissa L. Hill ◽  
Elisabetta Bezzon ◽  
Francesca Caumo
Keyword(s):  
Breast Density ◽  
Potential Role ◽  
Multiple Correlation Coefficient ◽  
Low Energy ◽  
Coefficient Of Determination ◽  
Contrast Enhanced ◽  
Increased Risk ◽  
Significant Difference ◽  
Automatic Software ◽  
The Relationship

Contrast-enhanced mammography (CEM) demonstrates a potential role in personalized screening models, in particular for women at increased risk and women with dense breasts. In this study, volumetric breast density (VBD) measured in CEM images was compared with VBD obtained from digital mammography (DM) or tomosynthesis (DBT) images. A total of 150 women who underwent CEM between March 2019 and December 2020, having at least a DM/DBT study performed before/after CEM, were included. Low-energy CEM (LE-CEM) and DM/DBT images were processed with automatic software to obtain the VBD. VBDs from the paired datasets were compared by Wilcoxon tests. A multivariate regression model was applied to analyze the relationship between VBD differences and multiple independent variables certainly or potentially affecting VBD. Median VBD was comparable for LE-CEM and DM/DBT (12.73% vs. 12.39%), not evidencing any statistically significant difference (p = 0.5855). VBD differences between LE-CEM and DM were associated with significant differences of glandular volume, breast thickness, compression force and pressure, contact area, and nipple-to-posterior-edge distance, i.e., variables reflecting differences in breast positioning (coefficient of determination 0.6023; multiple correlation coefficient 0.7761). Volumetric breast density was obtained from low-energy contrast-enhanced spectral mammography and was not significantly different from volumetric breast density measured from standard mammograms.

scholarly journals Evaluation of an AI-based, automatic coronary artery calcium scoring software

2019 ◽  
Vol 30 (3) ◽  
pp. 1671-1678 ◽  
Author(s):  
Mårten Sandstedt ◽  
Lilian Henriksson ◽  
Magnus Janzon ◽  
Gusten Nyberg ◽  
Jan Engvall ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Coronary Artery ◽  
Coronary Artery Calcium ◽  
Rank Test ◽  
Agatston Score ◽  
Risk Category ◽  
Automatic Method ◽  
Calcium Scoring ◽  
Excellent Correlation ◽  
Automatic Software

Abstract Objectives To evaluate an artificial intelligence (AI)–based, automatic coronary artery calcium (CAC) scoring software, using a semi-automatic software as a reference. Methods This observational study included 315 consecutive, non-contrast-enhanced calcium scoring computed tomography (CSCT) scans. A semi-automatic and an automatic software obtained the Agatston score (AS), the volume score (VS), the mass score (MS), and the number of calcified coronary lesions. Semi-automatic and automatic analysis time were registered, including a manual double-check of the automatic results. Statistical analyses were Spearman’s rank correlation coefficient (⍴), intra-class correlation (ICC), Bland Altman plots, weighted kappa analysis (κ), and Wilcoxon signed-rank test. Results The correlation and agreement for the AS, VS, and MS were ⍴ = 0.935, 0.932, 0.934 (p < 0.001), and ICC = 0.996, 0.996, 0.991, respectively (p < 0.001). The correlation and agreement for the number of calcified lesions were ⍴ = 0.903 and ICC = 0.977 (p < 0.001), respectively. The Bland Altman mean difference and 1.96 SD upper and lower limits of agreements for the AS, VS, and MS were − 8.2 (− 115.1 to 98.2), − 7.4 (− 93.9 to 79.1), and − 3.8 (− 33.6 to 25.9), respectively. Agreement in risk category assignment was 89.5% and κ = 0.919 (p < 0.001). The median time for the semi-automatic and automatic method was 59 s (IQR 35–100) and 36 s (IQR 29–49), respectively (p < 0.001). Conclusions There was an excellent correlation and agreement between the automatic software and the semi-automatic software for three CAC scores and the number of calcified lesions. Risk category classification was accurate but showing an overestimation bias tendency. Also, the automatic method was less time-demanding. Key Points • Coronary artery calcium (CAC) scoring is an excellent candidate for artificial intelligence (AI) development in a clinical setting. • An AI-based, automatic software obtained CAC scores with excellent correlation and agreement compared with a conventional method but was less time-consuming.

Automatic Software Diversity

2015 ◽  
Vol 13 (2) ◽  
pp. 30-37 ◽  
Author(s):  
Per Larsen ◽  
Stefan Brunthaler ◽  
Michael Franz
Keyword(s):  
Software Diversity ◽  
Automatic Software

scholarly journals UPR: deadlock-free dynamic network reconfiguration by exploiting channel dependency graph compatibility

2021 ◽  
Author(s):  
Juan-José Crespo ◽  
José L. Sánchez ◽  
Francisco J. Alfaro-Cortés ◽  
José Flich ◽  
José Duato
Keyword(s):  
Dynamic Network ◽  
Dependency Graph ◽  
Network Reconfiguration ◽  
Free Dynamic

Dependency Graph-based High-level Synthesis for Maximum Instruction Parallelism

2021 ◽  
Vol 14 (4) ◽  
pp. 1-15
Author(s):  
Zhenghua Gu ◽  
Wenqing Wan ◽  
Jundong Xie ◽  
Chang Wu
Keyword(s):  
Performance Optimization ◽  
Directed Acyclic Graph ◽  
Scheduling Algorithm ◽  
Dependency Graph ◽  
High Level Synthesis ◽  
Limiting Factor ◽  
Circuit Performance ◽  
State Transition Graph ◽  
High Level ◽  
Basic Blocks

Performance optimization is an important goal for High-level Synthesis (HLS). Existing HLS scheduling algorithms are all based on Control and Data Flow Graph (CDFG) and will schedule basic blocks in sequential order. Our study shows that the sequential scheduling order of basic blocks is a big limiting factor for achievable circuit performance. In this article, we propose a Dependency Graph (DG) with two important properties for scheduling. First, DG is a directed acyclic graph. Thus, no loop breaking heuristic is needed for scheduling. Second, DG can be used to identify the exact instruction parallelism. Our experiment shows that DG can lead to 76% instruction parallelism increase over CDFG. Based on DG, we propose a bottom-up scheduling algorithm to achieve much higher instruction parallelism than existing algorithms. Hierarchical state transition graph with guard conditions is proposed for efficient implementation of such high parallelism scheduling. Our experimental results show that our DG-based HLS algorithm can outperform the CDFG-based LegUp and the state-of-the-art industrial tool Vivado HLS by 2.88× and 1.29× on circuit latency, respectively.

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