4,733 research outputs found
The regional distribution of spanish unemployment. A spatial analysis
This paper proposes a set of tools to analyse the regional distribution of unemployment. As we are interested in the characteristics of the whole distribution, we complement results from the traditional regression analysis with those from the estimation of its external shape before and after being conditioned to factors underlying regional unemployment. Besides, the paper explicitly addresses the spatial characteristics of the distribution, and the empirical model build to determine the explanatory factors includes spatial effects. We apply this framework to the study of the provincial distribution of unemployment rates in Spain in the mid-eighties and late nineties, when economic transformations could have caused different regional responses. Results point to increasing spatial dependence in the distribution of regional unemployment rates, and a change in the factors causing regional differentials over the last decade.
Learning how to be robust: Deep polynomial regression
Polynomial regression is a recurrent problem with a large number of
applications. In computer vision it often appears in motion analysis. Whatever
the application, standard methods for regression of polynomial models tend to
deliver biased results when the input data is heavily contaminated by outliers.
Moreover, the problem is even harder when outliers have strong structure.
Departing from problem-tailored heuristics for robust estimation of parametric
models, we explore deep convolutional neural networks. Our work aims to find a
generic approach for training deep regression models without the explicit need
of supervised annotation. We bypass the need for a tailored loss function on
the regression parameters by attaching to our model a differentiable hard-wired
decoder corresponding to the polynomial operation at hand. We demonstrate the
value of our findings by comparing with standard robust regression methods.
Furthermore, we demonstrate how to use such models for a real computer vision
problem, i.e., video stabilization. The qualitative and quantitative
experiments show that neural networks are able to learn robustness for general
polynomial regression, with results that well overpass scores of traditional
robust estimation methods.Comment: 18 pages, conferenc
Geometrical constraints on dark energy models
This contribution intends to give a pedagogical introduction to the topic of
dark energy (the mysterious agent supposed to drive the observed late time
acceleration of the Universe) and to various observational tests which require
only assumptions on the geometry of the Universe. Those tests are the
supernovae luminosity, the CMB shift, the direct Hubble data, and the baryon
acoustic oscillations test. An historical overview of Cosmology is followed by
some generalities on FRW spacetimes (the best large-scale description of the
Universe), and then the test themselves are discussed. A convenient section on
statistical inference is included as well.Comment: 28 pages, 15 pages, lecture notes prepared for the ``Advanced Summer
School in Physics 2007" organized by Cinvestav (Mexico DF
The influence of relationship networks on academic performance in higher education: a comparative study between students of a creative and a non-creative discipline
In recent years, the literature has highlighted the importance of relational aspects on student attainment in higher education. Much of this previous work agrees with the idea that students' connectedness has beneficial effects on their performance. However, this literature has generally overlooked the influence that the discipline of study may have on this relationship, especially when creative contexts are addressed. In this sense and with the aim of looking deeper into this topic, this paper attempts to analyze by means of social network analysis techniques the relationship between social ties and academic performance in two bachelor's degrees with divergent contents and competence profiles in terms of creativity. Our findings suggest that in non-creative disciplines, the closeness of the students to the core of relationships of their network may help them to perform better academically. 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What determines the temporal changes of species degree and strength in an oceanic island plant-disperser network?
Network models of frugivory and seed dispersal are usually static. To date, most studies on mutualistic networks assert that interaction properties such as species\u27 degree (k) and strength (s) are strongly influenced by species abundances. We evaluated how species\u27 degree and strength change as a function of temporal variation not only in species abundance, but also in species persistence (i.e., phenology length). In a two-year study, we collected community-wide data on seed dispersal by birds and examined the seasonal dynamics of the above-mentioned interaction properties. Our analyses revealed that species abundance is an important predictor for plant strength within a given sub-network. However, our analyses also reveal that species\u27 degree can often be best explained by the length of fruiting phenology (for plants degree) or by the number of fruiting species (for dispersers degree), which are factors that can be decoupled from the relative abundance of the species participating in the network. Moreover, our results suggest that generalist dispersers (when total study period is considered) act as temporal generalists, with degree constrained by the number of plant species displaying fruits in each span. Along with species identity, our findings underscore the need for a temporal perspective, given that seasonality is an inherent property of many mutualistic networks. © 2012 González-Castro et al
Relative importance of phenotypic trait matching and species\u27 abundances in determining plant - Avian seed dispersal interactions in a small insular community
Network theory has provided a general way to understand mutualistic plant-animal interactions at the community level. However, the mechanisms responsible for interaction patterns remain controversial. In this study we use a combination of statistical models and probability matrices to evaluate the relative importance of species morphological and nutritional (phenotypic) traits and species abundance in determining interactions between fleshyfruited plants and birds that disperse their seeds. The models included variables associated with species abundance, a suite of variables associated with phenotypic traits (fruit diameter, bird bill width, fruit nutrient compounds), and the species identity of the avian disperser. Results show that both phenotypic traits and species abundance are important determinants of pairwise interactions. However, when considered separately, fruit diameter and bill width were more important in determining seed dispersal interactions. The effect of fruit compounds was less substantial and only important when considered together with abundance-related variables and/or the factor \u27animal species\u27. © The Authors 2014
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