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Threshold Parameter (threshold + parameter)

Distribution by Scientific Domains
Medical Sciences40%

Selected Abstracts

Models for the estimation of a ,no effect concentration'

ENVIRONMETRICS, Issue 1 2002
Ana M. Pires
Abstract The use of a no effect concentration (NEC), instead of the commonly used no observed effect concentration (NOEC), has been advocated recently. In this article models and methods for the estimation of an NEC are proposed and it is shown that the NEC overcomes many of the objections to the NOEC. The NEC is included as a threshold parameter in a non-linear model. Numerical methods are then used for point estimation and several techniques are proposed for interval estimation (based on bootstrap, profile likelihood and asymptotic normality). The adequacy of these methods is empirically confirmed by the results of a simulation study. The profile likelihood based interval has emerged as the best method. Finally the methodology is illustrated with data obtained from a 21 day Daphnia magna reproduction test with a reference substance, 3,4-dichloroaniline (3,4-DCA), and with a real effluent. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Modeling Age Differences in Infant Category Learning

INFANCY, Issue 2 2004
Thomas R. Shultz
We used an encoder version of cascade correlation to simulate Younger and Cohen's (1983, 1986) finding that 10-month-olds recover attention on the basis of correlations among stimulus features, but 4- and 7-month-olds recover attention on the basis of stimulus features. We captured these effects by varying the score threshold parameter in cascade correlation, which controls how deeply training patterns are learned. When networks learned deeply, they showed more error to uncorrelated than to correlated test patterns, indicating that they abstracted correlations during familiarization. When prevented from learning deeply, networks decreased error during familiarization and showed as much error to correlated as to uncorrelated tests but less than to test items with novel features, indicating that they learned features but not correlations among features. Our explanation is that older infants learn more from the same exposure than do younger infants. Unlike previous explanations that postulate unspecified qualitative shifts in processing with age, our explanation focuses on quantitatively deeper learning with increasing age. Finally, we provide some new empirical evidence to support this explanation. [source]

A practical determination strategy of optimal threshold parameter for matrix compression in wavelet BEM

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Issue 2 2003
Kazuhiro Koro
Abstract A practical strategy is developed to determine the optimal threshold parameter for wavelet-based boundary element (BE) analysis. The optimal parameter is determined so that the amount of storage (and computational work) is minimized without reducing the accuracy of the BE solution. In the present study, the Beylkin-type truncation scheme is used in the matrix assembly. To avoid unnecessary integration concerning the truncated entries of a coefficient matrix, a priori estimation of the matrix entries is introduced and thus the truncated entries are determined twice: before and after matrix assembly. The optimal threshold parameter is set based on the equilibrium of the truncation and discretization errors. These errors are estimated in the residual sense. For Laplace problems the discretization error is, in particular, indicated with the potential's contribution ,c, to the residual norm ,R, used in error estimation for mesh adaptation. Since the normalized residual norm ,c,/,u, (u: the potential components of BE solution) cannot be computed without main BE analysis, the discretization error is estimated by the approximate expression constructed through subsidiary BE calculation with smaller degree of freedom (DOF). The matrix compression using the proposed optimal threshold parameter enables us to generate a sparse matrix with O(N1+,) (0,,<1) non-zero entries. Although the quasi-optimal memory requirements and complexity are not attained, the compression rate of a few per cent can be achieved for N,1000. Copyright © 2003 John Wiley & Sons, Ltd. [source]

Simple anatomical measurements do not correlate significantly to individual peripheral nerve stimulation thresholds as measured in MRI gradient coils

JOURNAL OF MAGNETIC RESONANCE IMAGING, Issue 6 2003
Blaine A. Chronik PhD
Abstract Purpose To examine peripheral nerve stimulation (PNS) thresholds for normal human subjects in magnetic resonance imaging (MRI) gradient coils, and determine if observed thresholds could be predicted based on gross physiologic measurements. Materials and Methods PNS thresholds for 21 healthy normal subjects were measured using a whole-body gradient coil. Subjects were exposed to a trapezoidal echo-planar imaging (EPI) gradient waveform and the total change in gradient strength (,G) required to cause PNS as a function of the duration of the gradient switching time (,) were measured. Correlation coefficients and corresponding P values were calculated for the PNS threshold measurements against simple physiologic measurements taken of the subjects, including weight, height, girth, and average body fat percentage, in order to determine if there were any easily observable dependencies. Results No convincing correlations between threshold parameters and gross physiologic measurements were observed. Conclusion These results suggest it is unlikely that a simple physiologic measurement of subject anatomy can be used to guide the operation of MRI scanners in a subject-specific manner in order to increase gradient system performance while avoiding PNS. J. Magn. Reson. Imaging 2003;17:716,721. © 2003 Wiley-Liss, Inc. [source]