scholarly journals Estimation of the optimum yield in Yun-Lin area of Taiwan using loss function analysis

2002 ◽  
Vol 263 (1-4) ◽  
pp. 177-187 ◽  
Author(s):  
Hwa-Sheng Gau ◽  
Chen-Wuing Liu
Keyword(s):  
Loss Function ◽  
Function Analysis ◽  
Optimum Yield

scholarly journals Characterising the Area Under the Curve Loss Function Landscape

2022 ◽  
Author(s):  
Maximilian Paul Niroomand ◽  
Conor T Cafolla ◽  
John William Roger Morgan ◽  
David J Wales
Keyword(s):  
Loss Function ◽  
Operating Characteristic ◽  
Theoretical Foundation ◽  
Function Analysis ◽  
Standard Procedure ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Solution Space ◽  
Network Training ◽  
Neural Network Classifiers

Abstract One of the most common metrics to evaluate neural network classifiers is the area under the receiver operating characteristic curve (AUC). However, optimisation of the AUC as the loss function during network training is not a standard procedure. Here we compare minimising the cross-entropy (CE) loss and optimising the AUC directly. In particular, we analyse the loss function landscape (LFL) of approximate AUC (appAUC) loss functions to discover the organisation of this solution space. We discuss various surrogates for AUC approximation and show their differences. We find that the characteristics of the appAUC landscape are significantly different from the CE landscape. The approximate AUC loss function improves testing AUC, and the appAUC landscape has substantially more minima, but these minima are less robust, with larger average Hessian eigenvalues. We provide a theoretical foundation to explain these results. To generalise our results, we lastly provide an overview of how the LFL can help to guide loss function analysis and selection.

scholarly journals Optimization of Production Process of Nano-Calcium Oxide from Pinctada maxima Shell by Using Taguchi Method

2019 ◽  
Vol 19 (2) ◽  
pp. 356
Author(s):  
Kendri Wahyuningsih ◽  
Jumeri Jumeri ◽  
Wagiman Wagiman
Keyword(s):  
Taguchi Method ◽  
Production Process ◽  
Heterogeneous Catalyst ◽  
Calcium Oxide ◽  
Loss Function ◽  
Function Analysis ◽  
Process Condition ◽  
Weight Ratio ◽  
Control Factor ◽  
Pinctada Maxima

West Nusa Tenggara is a center of sea oyster farming for Pinctada maxima in Indonesia. The calcium carbonate (CaCO3) compounds in the shell are one of the decomposed natural minerals to produce calcium oxide (CaO) compound which is able to be used as an alternative heterogeneous catalyst in nanometer scale. This research aims to discover the control factors which influence the production process of nanometer-scaled CaO heterogeneous catalyst and choose the best condition in its production process with a better-quality product. Calcined pearl shell (P. maxima) powder is milled by using Shaker mill. The experimental design is performed by using Taguchi method with an orthogonal matrix consisting of 4 control factor variables, i.e. milling time, ball-to-powder weight ratio, the diameter of milling beads and extent of milling the vial. The selection of best control factor variable combination is computed by using multiresponse loss function. ANOVA analysis shows that the product quality parameter of nano-calcium oxide is influenced by all experiment factors. Multi-response loss function analysis results an optimum factor and level combination under process condition happens during the duration of 3 h milling, the ball-to-powder weight ratio is 1:10, the diameter of milling beads is 5 mm and 55% extent of filling the vial.

ROC and Loss Function Analysis in Sequential Testing

2006 ◽  
Vol 11 (1) ◽  
pp. 5-17 ◽  
Author(s):  
Arno M. M. Muijtjens ◽  
Scheltus J. Van Luijk ◽  
Cees P. M. Van Der Vleuten
Keyword(s):  
Loss Function ◽  
Function Analysis ◽  
Sequential Testing

Loss Function Analysis with R

2012 ◽  
pp. 63-75
Author(s):  
Emilio L. Cano ◽  
Javier M. Moguerza ◽  
Andrés Redchuk
Keyword(s):  
Loss Function ◽  
Function Analysis

Valence electron spectroscopy of inhomogeneous media

1992 ◽  
Vol 50 (2) ◽  
pp. 1150-1151
Author(s):  
A. Howie ◽  
D.W. McComb
Keyword(s):  
Specific Gravity ◽  
Loss Function ◽  
Electron Spectroscopy ◽  
Valence Electron ◽  
Peak Height ◽  
Loss Functions ◽  
Loss Peak ◽  
High Energies ◽  
Valence Electron Density ◽  
Electron Microscopist

The bulk loss function Im(-l/ε (ω)), a well established tool for the interpretation of valence loss spectra, is being progressively adapted to the wide variety of inhomogeneous samples of interest to the electron microscopist. Proportionality between n, the local valence electron density, and ε-1 (Sellmeyer's equation) has sometimes been assumed but may not be valid even in homogeneous samples. Figs. 1 and 2 show the experimentally measured bulk loss functions for three pure silicates of different specific gravity ρ - quartz (ρ = 2.66), coesite (ρ = 2.93) and a zeolite (ρ = 1.79). Clearly, despite the substantial differences in density, the shift of the prominent loss peak is very small and far less than that predicted by scaling e for quartz with Sellmeyer's equation or even the somewhat smaller shift given by the Clausius-Mossotti (CM) relation which assumes proportionality between n (or ρ in this case) and (ε - 1)/(ε + 2). Both theories overestimate the rise in the peak height for coesite and underestimate the increase at high energies.

Sensory and Cognitive Components of Auditory Processing in Individuals With Tinnitus

2019 ◽  
Vol 28 (4) ◽  
pp. 834-842
Author(s):  
Harini Vasudevan ◽  
Hari Prakash Palaniswamy ◽  
Ramaswamy Balakrishnan
Keyword(s):  
Selective Attention ◽  
Auditory Processing ◽  
Function Analysis ◽  
Event Related Potential ◽  
Oscillatory Activity ◽  
Default Mode ◽  
Auditory Selective Attention ◽  
Cognitive Components ◽  
Mode Function ◽  
Related Potentials

Purpose The main purpose of the study is to explore the auditory selective attention abilities (using event-related potentials) and the neuronal oscillatory activity in the default mode network sites (using electroencephalogram [EEG]) in individuals with tinnitus. Method Auditory selective attention was measured using P300, and the resting state EEG was assessed using the default mode function analysis. Ten individuals with continuous and bothersome tinnitus along with 10 age- and gender-matched control participants underwent event-related potential testing and 5 min of EEG recording (at wakeful rest). Results Individuals with tinnitus were observed to have larger N1 and P3 amplitudes along with prolonged P3 latency. The default mode function analysis revealed no significant oscillatory differences between the groups. Conclusion The current study shows changes in both the early sensory and late cognitive components of auditory processing. The change in the P3 component is suggestive of selective auditory attention deficit, and the sensory component (N1) suggests an altered bottom-up processing in individuals with tinnitus.

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