Structure analysis of single- and multi-frequency subspace migrations in inverse scattering problems

In this literature, we carefully investigate the structure of single- and multi-frequency imaging functions, that are usually employed in inverse scattering problems. Based on patterns of the singular vectors of the Multi-Static Response (MSR) matrix, we establish a relationship between imaging functions and the Bessel function. This relationship indicates certain properties of imaging functions and the reason behind enhancement in the imaging performance by multiple frequencies. Several numerical simulations with a large amount of noisy data are performed in order to support our investigation.Comment: 11 pages, 10 figure

Over-Fit: Noisy-Label Detection based on the Overfitted Model Property

Due to the increasing need to handle the noisy label problem in a massive dataset, learning with noisy labels has received much attention in recent years. As a promising approach, there have been recent studies to select clean training data by finding small-loss instances before a deep neural network overfits the noisy-label data. However, it is challenging to prevent overfitting. In this paper, we propose a novel noisy-label detection algorithm by employing the property of overfitting on individual data points. To this end, we present two novel criteria that statistically measure how much each training sample abnormally affects the model and clean validation data. Using the criteria, our iterative algorithm removes noisy-label samples and retrains the model alternately until no further performance improvement is made. In experiments on multiple benchmark datasets, we demonstrate the validity of our algorithm and show that our algorithm outperforms the state-of-the-art methods when the exact noise rates are not given. Furthermore, we show that our method can not only be expanded to a real-world video dataset but also can be viewed as a regularization method to solve problems caused by overfitting.Comment: 10 pages, 7 figure

Permeability Enhancement in Porous‐Sintered Reaction‐Bonded Silicon Nitrides

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87172/1/j.1744-7402.2010.02511.x.pd

The uncalibrated pulse contour cardiac output during off-pump coronary bypass surgery: performance in patients with a low cardiac output status and a reduced left ventricular function

BACKGROUND: We compared the continuous cardiac index measured by the FloTrac/Vigileo™ system (FCI) to that measured by a pulmonary artery catheter (CCI) with emphasis on the accuracy of the FCI in patients with a decreased left ventricular ejection fraction (LVEF) and a low cardiac output status during off-pump coronary bypass surgery (OPCAB). We also assessed the influence of several factors affecting the pulse contour, such as the mean arterial pressure (MAP), the systemic vascular resistance index (SVRI) and the use of norepinephrine. METHODS: Fifty patients who were undergoing OPCAB (30 patients with a LVEF ≥ 40%, 20 patients with a LVEF < 40%) were enrolled. The FCI and CCI were measured and we performed a Bland-Altman analysis. Subgroup analyses were done according to the LVEF (< 40%), the CCI (≤ 2.4 L/min/m), the MAP (60-80 mmHg), the SVRI (1,600-2,600 dyne/s/cm(5)/m(2)) and the use of norepinephrine. RESULTS: The FCI was reliable at all the time points of measurement with an overall bias and limit of agreement of -0.07 and 0.67 L/min/m(2), respectively, resulting in a percentage error of 26.9%. The percentage errors in the patients with a decreased LVEF and in a low cardiac output status were 28.2% and 22.3%, respectively. However, the percentage error in the 91 data pairs outside the normal range of the SVRI was 40.2%. CONCLUSIONS: The cardiac output measured by the FloTrac/Vigileo™ system was reliable even in patients with a decreased LVEF and in a low cardiac output status during OPCAB. Acceptable agreement was also noted during the period of heart displacement and grafting of the obtuse marginalis branch.ope