scholarly journals Metric Selection for GPU Kernel Classification

2019 ◽  
Vol 15 (4) ◽  
pp. 1-27 ◽  
Author(s):  
S.-Kazem Shekofteh ◽  
Hamid Noori ◽  
Mahmoud Naghibzadeh ◽  
Hadi Sadoghi Yazdi ◽  
Holger Fröning
Keyword(s):  
Selection For ◽  
Metric Selection ◽  
Kernel Classification

A Brief Review on Recent Trends in Image Restoration

2014 ◽  
pp. 77-92
Author(s):  
Saurav Prakash
Keyword(s):  
Image Restoration ◽  
Image Denoising ◽  
Performance Metrics ◽  
Research Community ◽  
Point Of View ◽  
Assessment And Evaluation ◽  
Know How ◽  
Recent Trends ◽  
Selection For ◽  
Metric Selection

This chapter gives the opportunity to get an idea of recent trends in image denoising and restoration. It relates to the present research scenario in the field of image restoration. As much as possible the newest break-through regarding the methods of denoising as well as the performance metrics of evaluation has been dealt. The assessments done by the researchers have been included first so as to know how much analysis they propose to be done with respect to the application point of view of the denoising methods. The concept behind the metric selection for the assessment and evaluation has been introduced along with the need for shifting the dependence of the research community towards the newly proposed metrics than the old ones. The new trends in image denoising have been referred duly so that the readers can directly refer to the main algorithms and techniques from the papers proposed by their authors.

A Brief Review on Recent Trends in Image Restoration

2015 ◽  
pp. 162-177
Author(s):  
Saurav Prakash
Keyword(s):  
Image Restoration ◽  
Image Denoising ◽  
Performance Metrics ◽  
Research Community ◽  
Point Of View ◽  
Assessment And Evaluation ◽  
Know How ◽  
Recent Trends ◽  
Selection For ◽  
Metric Selection

This chapter gives the opportunity to get an idea of recent trends in image denoising and restoration. It relates to the present research scenario in the field of image restoration. As much as possible the newest break-through regarding the methods of denoising as well as the performance metrics of evaluation has been dealt. The assessments done by the researchers have been included first so as to know how much analysis they propose to be done with respect to the application point of view of the denoising methods. The concept behind the metric selection for the assessment and evaluation has been introduced along with the need for shifting the dependence of the research community towards the newly proposed metrics than the old ones. The new trends in image denoising have been referred duly so that the readers can directly refer to the main algorithms and techniques from the papers proposed by their authors.

Concordance of multiple methods to define resiliency and vulnerability to sleep loss depends on Psychomotor Vigilance Test metric

SLEEP ◽  
2021 ◽  
Author(s):  
Erika M Yamazaki ◽  
Courtney E Casale ◽  
Tess E Brieva ◽  
Caroline A Antler ◽  
Namni Goel
Keyword(s):  
Group Performance ◽  
Sleep Loss ◽  
Vulnerable Groups ◽  
Sleep Restriction ◽  
Psychomotor Vigilance Test ◽  
Performance Change ◽  
Vigilance Test ◽  
Selection For ◽  
Psychomotor Vigilance ◽  
Metric Selection

Abstract Study Objectives Sleep restriction (SR) and total sleep deprivation (TSD) reveal well-established individual differences in Psychomotor Vigilance Test (PVT) performance. While prior studies have used different methods to categorize such resiliency/vulnerability, none have systematically investigated whether these methods categorize individuals similarly. Methods 41 adults participated in a 13-day laboratory study consisting of 2 baseline, 5 SR, 4 recovery, and one 36h TSD night. The PVT was administered every 2h during wakefulness. Three approaches (Raw Score [average SR performance], Change from Baseline [average SR minus average baseline performance], and Variance [intraindividual variance of SR performance]), and within each approach, six thresholds (±1 standard deviation and the best/worst performing 12.5%, 20%, 25%, 33%, and 50%) classified Resilient/Vulnerable groups. Kendall’s tau-b correlations examined the concordance of group categorizations of approaches within and between PVT lapses and 1/reaction time (RT). Bias-corrected and accelerated bootstrapped t-tests compared group performance. Results Correlations comparing the approaches ranged from moderate to perfect for lapses and zero to moderate for 1/RT. Defined by all approaches, the Resilient groups had significantly fewer lapses on nearly all study days. Defined by the Raw Score approach only, the Resilient groups had significantly faster 1/RT on all study days. Between-measures comparisons revealed significant correlations between the Raw Score approach for 1/RT and all approaches for lapses. Conclusion The three approaches defining vigilant attention resiliency/vulnerability to sleep loss resulted in groups comprised of similar individuals for PVT lapses but not for 1/RT. Thus, both method and metric selection for defining vigilant attention resiliency/vulnerability to sleep loss is critical.

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