University of Windsor

About: University of Windsor is a education organization based out in Windsor, Ontario, Canada. It is known for research contribution in the topics: Population & Argumentation theory. The organization has 10654 authors who have published 22307 publications receiving 435906 citations. The organization is also known as: UWindsor & Assumption University of Windsor.

Causes of latitudinal gradients in species richness: a test with fishes of the tropical eastern pacific

Abstract: Recent advances in analytical methods and exploration of regional diversity patterns in greater depth than simple whole-fauna patterns may change our understanding of the determinants of latitudinal gradients in species richness Using a comprehensive database on the geographical distributions of the large fauna of endemic shorefishes from the Tropical Eastern Pacific (TEP), we delineated latitudinal diversity gradients (LDGs) of species with different range sizes and assessed how the mid-domain effect, energy supply, environmental variability, and habitat availability predicted the various LDGs We used statistical methods that account for spatial autocorrelation within each variable and consider collinearity among them We found marked variation in LDGs among species with different range sizes: species with large ranges contributed the most to the convex form of the entire-fauna LDG while species with small ranges generated most deviations from that form The mid-domain effect was the strongest determinant for the entire-fauna LDG, largely due to the strength of its effect on widespread species Habitat variables were the best predictors for LDGs of small-range species but had no significant effects on the entire-fauna pattern We found that any relationship between energy supply and LDGs likely is spurious and arises from statistical effects of (1) the marked spatial autocorrelation of the residuals in those relationships and (2) strong covariation between energy supply and the mid-domain effect, two factors that have never been analyzed together in marine studies (and seldom in terrestrial studies) There was no indication that environmental stability was an important LDG determinant at any level within the fauna We found that latitudinal (Rapoport) trends in range size in this fauna arise as corollaries of the mid-domain effect The disparate contributions of species with different range sizes to diversity patterns demonstrate the failure of traditional, whole-fauna LDGs to adequately represent all faunal components and their determinants, particularly those of small-range (and more threatened) species We argue that, just as analyses of whole-fauna patterns within a region obscure variation in determinants among faunal components, analyses of patterns at transoceanic (multiregional) scales are likely to conceal important regional variation in determinants of diversity gradients

Students as Consumers of Knowledge: Are They Buying What We’re Selling?

Abstract: Academic entitlement, an attitude marked by students’ beliefs that they are owed something in the educational experience apart from what they might earn from their effort, has received attention recently in the literature. In previous work, academic entitlement has been shown to be related to parenting styles and personality constructs. The current study departs from previous research by taking a phenomenological approach to understanding academic entitlement. Focus groups were conducted with a total of 52 first-year students. Responses were coded into six facets of academic entitlement: product value of education, social promotion, role of professors, teaching assistants, administrators, and shoppers or scholars.

Robust Student's-t Mixture Model With Spatial Constraints and Its Application in Medical Image Segmentation

Abstract: Finite mixture model based on the Student's-t distribution, which is heavily tailed and more robust than Gaussian, has recently received great attention for image segmentation. A new finite Student's-t mixture model (SMM) is proposed in this paper. Existing models do not explicitly incorporate the spatial relationships between pixels. First, our model exploits Dirichlet distribution and Dirichlet law to incorporate the local spatial constrains in an image. Secondly, we directly deal with the Student's-t distribution in order to estimate the model parameters, whereas, the Student's-t distributions in previous models are represented as an infinite mixture of scaled Gaussians that lead to an increase in complexity. Finally, instead of using expectation maximization (EM) algorithm, the proposed method adopts the gradient method to minimize the higher bound on the data negative log-likelihood and to optimize the parameters. The proposed model is successfully compared to the state-of-the-art finite mixture models. Numerical experiments are presented where the proposed model is tested on various simulated and real medical images.