Medical image registration based on random line sampling

One of the key aspects in 3D-image registration is the computation of the joint intensity histogram. We propose a new approach to compute this histogram using uniformly distributed random lines to sample stochastically the overlapping volume between two 3D-images. The intensity values are captured from the lines at evenly spaced positions, taking an initial random offset different for each line. This method provides us with an accurate, robust and fast mutual information-based registration. The interpolation effects are drastically reduced, due to the stochastic nature of the line generation, and the alignment process is also accelerated. The results obtained show a better performance of the introduced method than the classic computation of the joint histogram

Using niche-based models to improve the sampling of rare species

Because data on rare species usually are sparse, it is important to have efficient ways to sample additional data. Traditional sampling approaches are of limited value for rare species because a very large proportion of randomly chosen sampling sites are unlikely to shelter the species. For these species, spatial predictions from niche-based distribution models can be used to stratify the sampling and increase sampling efficiency. New data sampled are then used to improve the initial model. Applying this approach repeatedly is an adaptive process that may allow increasing the number of new occurrences found. We illustrate the approach with a case study of a rare and endangered plant species in Switzerland and a simulation experiment. Our field survey confirmed that the method helps in the discovery of new populations of the target species in remote areas where the predicted habitat suitability is high. In our simulations the model-based approach provided a significant improvement (by a factor of 1.8 to 4 times, depending on the measure) over simple random sampling. In terms of cost this approach may save up to 70% of the time spent in the field.

A new heuristic method for solving spatially constrained forest planning problems based on mitigation of infeasibilities radiating outward from a forced choice

[Abstract]

Forest owners' acceptance of incentive based policy instruments in forest biodiversity conservation : a choice experiment based approach

Abstract

The gender-based stereotype about food is on the table. Food choice also depends on co-eater's gender

Previous research has shown that different foods are stereotypically associated with gender and that eating in a role-congruent way fulfills an impression management function. On the other hand, other studies revealed that adapting one's food consumption to that of the co-eaters is a means to gain social approval as well. In the present study, we bridge these two distinct lines of research by studying what happens when the two norms (conforming to the gender-based stereotype and imitating the co-eater) conflict, that is with opposite-sex co-eaters. Results indicated that the tendency to match the co-eaters' supposed consumption generally appeared over and above one's gender-congruent choice. In addition, as expected, gender differences also emerged: while men were always willing to adapt to the co-eaters, women's intention to eat the feminine food was independent from the co-eaters' gender.

Child care by choice or by default? : examining the experiences of unregulated home-based child care for women in paid work and training

This thesis aims to uncover the dynamics, causes and outcomes of women's reliance on unregulated home-based child care in Ontario, Canada, and the implications ofthis form of care for women's equality. Drawing on a longitudinal qualitative study, I examine the diverse experience of 14 women using home-based child care and engaged in both paid work/training and care work for children under the age of six, and draw comparisons with users of other forms of child care. I argue that home-based child care involves high levels of instability for continuity of care and is chosen largely as a default position based on economic considerations. It represents a compromise between the demands of social reproduction and paid work/training that entangles mothers in relations of exploitation with care providers. Doing so leaves both mothers and care providers socially and economically vulnerable and relying on social networks to fill in the gaps.

Guidelines based on validity criteria for the development of multiple choice items

Resumen tomado de la publicaci??n

Traditional scores versus IRT estimates on forced-choice tests based on a dominance model

Resumen tomado de la publicaci??n

Medical image registration based on random line sampling

One of the key aspects in 3D-image registration is the computation of the joint intensity histogram. We propose a new approach to compute this histogram using uniformly distributed random lines to sample stochastically the overlapping volume between two 3D-images. The intensity values are captured from the lines at evenly spaced positions, taking an initial random offset different for each line. This method provides us with an accurate, robust and fast mutual information-based registration. The interpolation effects are drastically reduced, due to the stochastic nature of the line generation, and the alignment process is also accelerated. The results obtained show a better performance of the introduced method than the classic computation of the joint histogram

PDFOS: PDF estimation based over-sampling for imbalanced two-class problems

This contribution proposes a novel probability density function (PDF) estimation based over-sampling (PDFOS) approach for two-class imbalanced classification problems. The classical Parzen-window kernel function is adopted to estimate the PDF of the positive class. Then according to the estimated PDF, synthetic instances are generated as the additional training data. The essential concept is to re-balance the class distribution of the original imbalanced data set under the principle that synthetic data sample follows the same statistical properties. Based on the over-sampled training data, the radial basis function (RBF) classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier’s structure and the parameters of RBF kernels are determined using a particle swarm optimisation algorithm based on the criterion of minimising the leave-one-out misclassification rate. The effectiveness of the proposed PDFOS approach is demonstrated by the empirical study on several imbalanced data sets.