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. However, in creative contexts, the relationship between social ties and academic performance shows the form of an inverted U-shaped curve. These findings may have relevant implications for both educational research and university policies and strategies.Tomas Miquel, JV.; Expósito Langa, M.; Nicolau Juliá, D. (2016). The influence of relationship networks on academic performance in higher education: a comparative study between students of a creative and a non-creative discipline. Higher Education. 71(3):307-322. doi:10.1007/s10734-015-9904-8S307322713Allen, W. R., & Haniff, N. Z. (1991). Race, gender, and academic performance in US higher education. In W. R. Allen, E. G. Epps, & N. Z. Haniff (Eds.), College in Black and White: African American students in predominantly White and in historically Black public universities (pp. 95–110). Albany: State University of New York Press.Amabile, T. M. (1996). Creativity in context. Boulder, CO: Westview Press.Baldwin, T. T., Bedell, M. D., & Johnson, J. L. (1997). The social fabric of a team-based M.B.A. program: Network effects on student satisfaction and performance. Academy of Management Journal, 40, 1369–1397.Beck, R. J., Fitzgerald, W. J., & Pauksztat, B. (2003). Individual behaviors and social structure in the development of communication networks of self-organizing online discussion groups. In B. Wason, S. Ludvigson, & U. Hoppe (Eds.), Designing for change in networked learning environments (pp. 313–322). Dordrecht: Kluwer.Biancani, S., & McFarland, D. A. (2013). Social networks research in higher education. In M. B. Paulsen (Ed.), Higher education: Handbook of theory and research (pp. 151–215). The Netherlands: Springer.Borgatti, S. P., & Everett, M. G. (1999). Models of core/periphery structures. Social Networks, 21(4), 375–395.Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for Windows: Software for social network analysis. Natick, MA: Analytic Technologies.Cattani, G., & Ferriani, S. (2008). A core/periphery perspective on individual creative performance: Social networks and cinematic achievements in the Hollywood film industry. Organization Science, 19(6), 824–844.Chen, B., Wang, F., & Song, J. (2012). Are they connected? Exploring academic and social networks among MPA students at a Chinese University. Journal of Public Affairs Education, 18(1), 137–156.Cho, H., Gay, G., Davidson, B., & Ingraffea, A. (2007). Social networks, communication styles, and learning performance in a CSCL community. Computers and Education, 49(2), 309–329.Costantino, T., Kellam, N., Cramond, B., & Crowder, I. (2010). An interdisciplinary design studio: How can art and engineering collaborate to increase students’ creativity? Art Education, 63(2), 49–53.Dawson, S. (2008). A study of the relationship between student social networks and sense of community. Educational Technology and Society, 11(3), 224–238.Dayioğlu, M., & Türüt-Aşik, S. (2007). Gender differences in academic performance in a large public university in Turkey. Higher Education, 53(2), 255–277.Erickson, B. H., Nosanchuk, T. A., & Lee, E. (1981). Network sampling in practice: Some second steps. Social Networks, 3, 127–136.Freeman, L. C. (1979). Centrality in social networks. Conceptual clarification. Social Networks, 1, 215–239.French, J. R. P., & Snyder, R. (1959). Leadership and interpersonal power. In D. Cartwright (Ed.), Studies of social power. Ann Arbor, MI: Institute for Social Research.Gašević, D., Zouaq, A., & Janzen, R. (2013). Choose your classmates, your GPA is at stake!: The association of cross-class social ties and academic performance. American Behavioral Scientist, 57(10), 1460–1479.Guimerà, R., Uzzi, B., Spiro, J., & Amaral, L. A. N. (2005). Team assembly mechanisms determine collaboration network structure and team performance. Science, 308(5722), 697–702.Haythornthwaite, C. (2002). Strong, weak, and latent ties and the impact of new media. Information Society, 18, 385–401.Hommes, J., Rienties, B., Grave, W. D., Bos, G., Schuwirth, L., & Scherpbier, A. (2012). Visualising the invisible: A network approach to reveal the informal social side of student learning. Advances in Health Education, 17(5), 743–757.Hoskins, S. L., Newstead, S. E., & Dennis, I. (1997). Degree performance as a function of age, gender, prior qualifications and discipline studied. Assessment and Evaluation in Higher Education, 22(3), 317–328.Huang, L. (2009). Social capital and student achievement in Norwegian secondary schools. Learning and Individual Differences, 19(2), 320–325.Ibarra, H. (1993). Personal networks of women and minorities in management: A conceptual framework. Academy of Management Review, 18(1), 56–87.Ibarra, H. (1995). Race, opportunity, and diversity of social circles in managerial networks. Academy of Management Journal, 38(3), 673–703.Johnson, D. W., & Johnson, R. T. (1993). Structuring groups for cooperative learning. Mastering management education: Innovations in teaching effectiveness. Newbury Park, CA: Sage.Kassim, M. A., Hanafi, R. M., & Hancock, D. R. (2008). Test anxiety and its consequences on academic performance among university students. Advance in Psychology Research, 53, 75–95.Kornbrot, D. E. (1987). Degree performance as a function of discipline studied, parental occupation and gender. Higher Education, 16(5), 513–534.Kronegger, L., Ferligoj, A., & Doreian, P. (2011). On the dynamics of national scientific systems. Quality and Quantity, 45(5), 989–1015.Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge: Cambridge University Press.Leydesdorff, L., & Wagner, C. S. (2008). International collaboration in science and the formation of a core group. Journal of Informetrics, 2(4), 317–325.Martens, K., & Starke, P. (2008). Small country, big business? New Zealand as education exporter. Comparative Education, 44(1), 3–19.McKenzie, K., & Schweitzer, R. (2001). Who succeeds at University? Factors predicting academic performance in first year Australian university students. Higher Education, 20(1), 21–33.Meece, J. L., Anderman, E. M., & Anderman, L. H. (2006). Classroom goal structure, student motivation, and academic achievement. Annual Review of Psychology, 57, 487–503.Morrison, A., & Rabellotti, R. (2005). Knowledge and information networks: Evidence from an Italian wine local system. Working paper 174/2005, CESPRI, Università Bocconi, Milano.Pekrun, R., Elliot, A. J., & Maier, M. A. (2009). Achievement goals and achievement emotions: Testing a model of their joint relations with academic performance. Journal of Educational Psychology, 101, 115–135.Perry-Smith, J. E., & Shalley, C. E. (2003). The social side of creativity: A static and dynamic social network perspective. Academy of Management Review, 28(1), 89–106.Power, C., Robertson, F., & Baker, M. (1987). Success in higher education. Canberra: Australian Government Publishing Service.Raven, B. H. (1965). Social influence and power. In I. D. Steiner & M. Fishbein (Eds.), Current studies in social psychology. New York: Holt, Rinehart and Winston.Rizzuto, T. E., LeDoux, J., & Hatala, J. P. (2009). It’s not just what you know, it’s who you know: Testing a model of the relative importance of social networks to academic performance. Social Psychology of Education, 12, 175–189.Schilling, M. A. (2005). A ‘small-world’ network model of cognitive insight. Creativity Research Journal, 17(2–3), 131–154.Smith, J., & Naylor, R. (2001). Determinants of degree performance in UK universities: A statistical analysis of the 1993 student cohort. Oxford Bulletin of Economics and Statistics, 63(1), 29–60.Smith, R. A., & Peterson, B. L. (2007). “Psst… What do you think?”. The relationship between advice prestige, type of advice, and academic performance. Communication Education, 56(3), 278–291.Sparrowe, R. T., Liden, R. C., Wayne, S. J., & Kraimer, M. L. (2001). Social networks and the performance of individuals and groups. Academy of Management Journal, 44(2), 316–325.Stassen, M. L. A. (2003). Student outcomes: The impact of varying living–learning community models. Research in Higher Education, 44(5), 581–613.Thomas, S. L. (2000). Ties that bind: A social network approach to understanding student integration and persistence. Journal of Higher Education, 71(5), 591.Uzzi, B., & Spiro, J. (2005). Collaboration and creativity: The small-world problem. American Journal of Sociology, 111(2), 447–504.Wasserman, S., & Faust, K. (2007). Social network analysis: Methods and applications. Cambridge: Cambridge university Press.Yang, H. L., & Tang, J. H. (2003). Effects of social network on student performance: Web-based forum study in Taiwan. Journal of Asynchronous Learning Network, 7(3), 93–107.Yin, R. K. (1989). Case study research, design and methods. Beverly Hills: Sage.Yuan, Y. C., Gay, G., & Hembrooke, H. (2006). Focused activities and the development of social capital in a distributed learning “community”. The Information Society, 22(1), 25–39

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