Masculinities Representations Inventory (MRI, English Version): A measure of gender (re)presentation

This article introduces the Masculinities Representations Inventory (MRI), English version, as a multidimensional measure of gender (re)presentation. It provides structural, convergent, and divergent validity, as well as reliability evidence, in support of its use among English speakers in South Africa. Principal components analysis with a male student sample (n = 319) confirms the measure’s construct multidimensionality. Three factors inform a 29-item total- and subscale measure, including dominant Representations of Othering (Anti-Effeminacy and Homo-Negativity), Responsibility (Dependability and Success), and Control (Dominance and Toughness). Evidence of convergent validity is seen in predicted patterns of correlation between MRI scale scores and those of the Male Role Norms Inventory as well as Gender Role Conflict Scales. Evidence of divergent validity is apparent in nonsignificant correlations, in all but one case (Masculinity), with the Personal Attributes Questionnaire scale scores

Positive Semidefinite Metric Learning Using Boosting-like Algorithms

The success of many machine learning and pattern recognition methods relies heavily upon the identification of an appropriate distance metric on the input data. It is often beneficial to learn such a metric from the input training data, instead of using a default one such as the Euclidean distance. In this work, we propose a boosting-based technique, termed BoostMetric, for learning a quadratic Mahalanobis distance metric. Learning a valid Mahalanobis distance metric requires enforcing the constraint that the matrix parameter to the metric remains positive definite. Semidefinite programming is often used to enforce this constraint, but does not scale well and easy to implement. BoostMetric is instead based on the observation that any positive semidefinite matrix can be decomposed into a linear combination of trace-one rank-one matrices. BoostMetric thus uses rank-one positive semidefinite matrices as weak learners within an efficient and scalable boosting-based learning process. The resulting methods are easy to implement, efficient, and can accommodate various types of constraints. We extend traditional boosting algorithms in that its weak learner is a positive semidefinite matrix with trace and rank being one rather than a classifier or regressor. Experiments on various datasets demonstrate that the proposed algorithms compare favorably to those state-of-the-art methods in terms of classification accuracy and running time.Comment: 30 pages, appearing in Journal of Machine Learning Researc

A Detail Based Method for Linear Full Reference Image Quality Prediction

In this paper, a novel Full Reference method is proposed for image quality assessment, using the combination of two separate metrics to measure the perceptually distinct impact of detail losses and of spurious details. To this purpose, the gradient of the impaired image is locally decomposed as a predicted version of the original gradient, plus a gradient residual. It is assumed that the detail attenuation identifies the detail loss, whereas the gradient residuals describe the spurious details. It turns out that the perceptual impact of detail losses is roughly linear with the loss of the positional Fisher information, while the perceptual impact of the spurious details is roughly proportional to a logarithmic measure of the signal to residual ratio. The affine combination of these two metrics forms a new index strongly correlated with the empirical Differential Mean Opinion Score (DMOS) for a significant class of image impairments, as verified for three independent popular databases. The method allowed alignment and merging of DMOS data coming from these different databases to a common DMOS scale by affine transformations. Unexpectedly, the DMOS scale setting is possible by the analysis of a single image affected by additive noise.Comment: 15 pages, 9 figures. Copyright notice: The paper has been accepted for publication on the IEEE Trans. on Image Processing on 19/09/2017 and the copyright has been transferred to the IEE

Prospect relativity: how choice options influence decision under risk.

In many theories of decision under risk (e.g., expected utility theory, rank-dependent utility theory, and prospect theory), the utility of a prospect is independent of other options in the choice set. The experiments presented here show a large effect of the available options, suggesting instead that prospects are valued relative to one another. The judged certainty equivalent for a prospect is strongly influenced by the options available. Similarly, the selection of a preferred prospect is strongly influenced by the prospects available. Alternative theories of decision under risk (e.g., the stochastic difference model, multialternative decision field theory, and range frequency theory), where prospects are valued relative to one another, can provide an account of these context effects

A tale of two niches: methods, concepts, and evolution

Being snapshots in time, species ranges may fall short of representing all of the geographic or environmental space that they are able to occupy. This has important implications for niche studies yet most comparative studies overlook the transient nature of species distributions and assume that they are at equilibrium. We review the methods most widely used for niche comparisons today and suggest a modified framework to describe and compare niches based on snapshot species range data. First, we introduce a new environmental space-based Niche Equivalence Statistic to test niche similarity between two species, which explicitly incorporates the spatial distribution of environments and their availability into statistical tests. We also introduce a new Background Statistic to measure the ability of this Niche Equivalence Statistic to detect differences based on the available environmental-space. These metrics enable fair comparisons between different geographies when the ranges of species are out of equilibrium. Based on distinct parameterizations of the new Equivalence and Background statistics, we then propose a Niche Divergence Test and a Niche Overlap Test, which allow assessment of whether differences between species emerge from true niche divergences. These methods are implemented in a new R package, ‘humboldt’ and applied to simulated species with pre-defined niches. The new methods improve accuracy of niche similarity and associated tests – consistently outperforming other tests. We show that the quantification of niche similarity should be performed only in environmental space, which is less sensitive than geographic space to the spatial abundance of key environmental variables. Further, our methods characterize the relationships between non-analogous and analogous climates in the species’ distributions, something not available previously. These improvements allow assessment of whether the different environmental spaces occupied by two taxa emerge from true niche evolution, as opposed to differences in life history and biological interactors, or differences in the variety and configuration of environments accessible to them

A Bootstrap Method for Identifying and Evaluating a Structural Vector Autoregression

Graph-theoretic methods of causal search based in the ideas of Pearl (2000), Spirtes, Glymour, and Scheines (2000), and others have been applied by a number of researchers to economic data, particularly by Swanson and Granger (1997) to the problem of finding a data-based contemporaneous causal order for the structural autoregression (SVAR), rather than, as is typically done, assuming a weakly justified Choleski order. Demiralp and Hoover (2003) provided Monte Carlo evidence that such methods were effective, provided that signal strengths were sufficiently high. Unfortunately, in applications to actual data, such Monte Carlo simulations are of limited value, since the causal structure of the true data-generating process is necessarily unknown. In this paper, we present a bootstrap procedure that can be applied to actual data (i.e., without knowledge of the true causal structure). We show with an applied example and a simulation study that the procedure is an effective tool for assessing our confidence in causal orders identified by graph-theoretic search procedures.vector autoregression (VAR), structural vector autoregression (SVAR),causality, causal order, Choleski order, causal search algorithms, graph-theoretic methods

Choices under Risk in Rural Peru

This paper estimates the risk preferences of cotton farmers in Southern Peru, using the results from a multiple-price-list lottery game. Assuming that preferences conform to two of the leading models of decision under risk--Expected Utility Theory (EUT) and Cumulative Prospect Theory (CPT)--we find strong evidence of moderate risk aversion. Once we include individual characteristics in the estimation of risk parameters, we observe that farmers use subjective nonlinear probability weighting, a behavior consistent with CPT. Interestingly, when we allow for preference heterogeneity via the estimation of mixture models--where the proportion of subjects who behave according to EUT or to CPT is endogenously determined--we find that the majority of farmers' choices are best explained by CPT. We further hypothesize that the multiple switching behavior observed in our sample can be explained by nonlinear probability weighting made in a context of large random calculation mistakes; the evidence found on this regard is mixed. Finally, we find that attaining higher education is the single most important individual characteristic correlated with risk preferences, a result that suggests a connection between cognitive abilities and behavior towards risk.

Convergence of iterative methods based on Neumann series for composite materials: theory and practice

Iterative Fast Fourier Transform methods are useful for calculating the fields in composite materials and their macroscopic response. By iterating back and forth until convergence, the differential constraints are satisfied in Fourier space, and the constitutive law in real space. The methods correspond to series expansions of appropriate operators and to series expansions for the effective tensor as a function of the component moduli. It is shown that the singularity structure of this function can shed much light on the convergence properties of the iterative Fast Fourier Transform methods. We look at a model example of a square array of conducting square inclusions for which there is an exact formula for the effective conductivity (Obnosov). Theoretically some of the methods converge when the inclusions have zero or even negative conductivity. However, the numerics do not always confirm this extended range of convergence and show that accuracy is lost after relatively few iterations. There is little point in iterating beyond this. Accuracy improves when the grid size is reduced, showing that the discrepancy is linked to the discretization. Finally, it is shown that none of the three iterative schemes investigated over-performs the others for all possible microstructures and all contrasts.Comment: 41 pages, 14 figures, 1 tabl