29 resultados para Fisher distance criterion


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Let us consider a large set of candidate parameter fields, such as hydraulic conductivity maps, on which we can run an accurate forward flow and transport simulation. We address the issue of rapidly identifying a subset of candidates whose response best match a reference response curve. In order to keep the number of calls to the accurate flow simulator computationally tractable, a recent distance-based approach relying on fast proxy simulations is revisited, and turned into a non-stationary kriging method where the covariance kernel is obtained by combining a classical kernel with the proxy. Once the accurate simulator has been run for an initial subset of parameter fields and a kriging metamodel has been inferred, the predictive distributions of misfits for the remaining parameter fields can be used as a guide to select candidate parameter fields in a sequential way. The proposed algorithm, Proxy-based Kriging for Sequential Inversion (ProKSI), relies on a variant of the Expected Improvement, a popular criterion for kriging-based global optimization. A statistical benchmark of ProKSI’s performances illustrates the efficiency and the robustness of the approach when using different kinds of proxies.

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Background To assess the criterion and construct validity of the KIDSCREEN-10 well-being and health-related quality of life (HRQoL) score, a short version of the KIDSCREEN-52 and KIDSCREEN-27 instruments. Methods The child self-report and parent report versions of the KIDSCREEN-10 were tested in a sample of 22,830 European children and adolescents aged 8–18 and their parents (n = 16,237). Correlation with the KIDSCREEN-52 and associations with other generic HRQoL measures, physical and mental health, and socioeconomic status were examined. Score differences by age, gender, and country were investigated. Results Correlations between the 10-item KIDSCREEN score and KIDSCREEN-52 scales ranged from r = 0.24 to 0.72 (r = 0.27–0.72) for the self-report version (proxy-report version). Coefficients below r = 0.5 were observed for the KIDSCREEN-52 dimensions Financial Resources and Being Bullied only. Cronbach alpha was 0.82 (0.78), test–retest reliability was ICC = 0.70 (0.67) for the self- (proxy-)report version. Correlations between other children self-completed HRQoL questionnaires and KIDSCREEN-10 ranged from r = 0.43 to r = 0.63 for the KIDSCREEN children self-report and r = 0.22–0.40 for the KIDSCREEN parent proxy report. Known group differences in HRQoL between physically/mentally healthy and ill children were observed in the KIDSCREEN-10 self and proxy scores. Associations with self-reported psychosomatic complaints were r = −0.52 (−0.36) for the KIDSCREEN-10 self-report (proxy-report). Statistically significant differences in KIDSCREEN-10 self and proxy scores were found by socioeconomic status, age, and gender. Conclusions Our results indicate that the KIDSCREEN-10 provides a valid measure of a general HRQoL factor in children and adolescents, but the instrument does not represent well most of the single dimensions of the original KIDSCREEN-52. Test–retest reliability was slightly below a priori defined thresholds.

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Our aim in this study was to compare intermolar widths after alignment of crowded mandibular dental arches in nonextraction adolescent patients between conventional and self-ligating brackets.

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Monte Carlo (MC) based dose calculations can compute dose distributions with an accuracy surpassing that of conventional algorithms used in radiotherapy, especially in regions of tissue inhomogeneities and surface discontinuities. The Swiss Monte Carlo Plan (SMCP) is a GUI-based framework for photon MC treatment planning (MCTP) interfaced to the Eclipse treatment planning system (TPS). As for any dose calculation algorithm, also the MCTP needs to be commissioned and validated before using the algorithm for clinical cases. Aim of this study is the investigation of a 6 MV beam for clinical situations within the framework of the SMCP. In this respect, all parts i.e. open fields and all the clinically available beam modifiers have to be configured so that the calculated dose distributions match the corresponding measurements. Dose distributions for the 6 MV beam were simulated in a water phantom using a phase space source above the beam modifiers. The VMC++ code was used for the radiation transport through the beam modifiers (jaws, wedges, block and multileaf collimator (MLC)) as well as for the calculation of the dose distributions within the phantom. The voxel size of the dose distributions was 2mm in all directions. The statistical uncertainty of the calculated dose distributions was below 0.4%. Simulated depth dose curves and dose profiles in terms of [Gy/MU] for static and dynamic fields were compared with the corresponding measurements using dose difference and γ analysis. For the dose difference criterion of ±1% of D(max) and the distance to agreement criterion of ±1 mm, the γ analysis showed an excellent agreement between measurements and simulations for all static open and MLC fields. The tuning of the density and the thickness for all hard wedges lead to an agreement with the corresponding measurements within 1% or 1mm. Similar results have been achieved for the block. For the validation of the tuned hard wedges, a very good agreement between calculated and measured dose distributions was achieved using a 1%/1mm criteria for the γ analysis. The calculated dose distributions of the enhanced dynamic wedges (10°, 15°, 20°, 25°, 30°, 45° and 60°) met the criteria of 1%/1mm when compared with the measurements for all situations considered. For the IMRT fields all compared measured dose values agreed with the calculated dose values within a 2% dose difference or within 1 mm distance. The SMCP has been successfully validated for a static and dynamic 6 MV photon beam, thus resulting in accurate dose calculations suitable for applications in clinical cases.

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The task considered in this paper is performance evaluation of region segmentation algorithms in the ground-truth-based paradigm. Given a machine segmentation and a ground-truth segmentation, performance measures are needed. We propose to consider the image segmentation problem as one of data clustering and, as a consequence, to use measures for comparing clusterings developed in statistics and machine learning. By doing so, we obtain a variety of performance measures which have not been used before in image processing. In particular, some of these measures have the highly desired property of being a metric. Experimental results are reported on both synthetic and real data to validate the measures and compare them with others.