Equianalytic and equisingular families of curves on surfaces

We consider flat families of reduced curves on a smooth surface S such that each member C has the same number of singularities of fixed singularity types and the corresponding (locally closed) subscheme H of the Hilbert scheme of S. We are mainly concerned with analytic resp. topological singularity types and give a sufficient condition for the smoothness of H (at C). Our results for S=P^2 seem to be quite sharp for families of cuves of small degree d.Comment: LaTeX v 2.0

Non-Gaussian velocity distributions - The effect on virial mass estimates of galaxy groups

We present a study of 9 galaxy groups with evidence for non-Gaussianity in their velocity distributions out to 4R200. This sample is taken from 57 groups selected from the 2PIGG catalog of galaxy groups. Statistical analysis indicates that non-Gaussian groups have masses significantly higher than Gaussian groups. We also have found that all non-Gaussian systems seem to be composed of multiple velocity modes. Besides, our results indicate that multimodal groups should be considered as a set of individual units with their own properties. In particular, we have found that the mass distribution of such units are similar to that of Gaussian groups. Our results reinforce the idea of non-Gaussian systems as complex structures in the phase space, likely corresponding to secondary infall aggregations at a stage before virialization. The understanding of these objects is relevant for cosmological studies using groups and clusters through the mass function evolution.Comment: 5 pages, 4 figures and 2 tables. Accepted for publication in the MNRA

Bayesian solutions to the label switching problem

The label switching problem, the unidentifiability of the permutation of clusters or more generally latent variables, makes interpretation of results computed with MCMC sampling difficult. We introduce a fully Bayesian treatment of the permutations which performs better than alternatives. The method can be used to compute summaries of the posterior samples even for nonparametric Bayesian methods, for which no good solutions exist so far. Although being approximative in this case, the results are very promising. The summaries are intuitively appealing: A summarized cluster is defined as a set of points for which the likelihood of being in the same cluster is maximized

Red wine polyphenols prevent metabolic and cardiovascular alterations associated with obesity in Zucker fatty rats (Fa/Fa)

Peer reviewedPublisher PD

Analysis of ChIP-seq data via Bayesian finite mixture models with a non-parametric component

In large discrete data sets which requires classification into signal and noise components, the distribution of the signal is often very bumpy and does not follow a standard distribution. Therefore the signal distribution is further modelled as a mixture of component distributions. However, when the signal component is modelled as a mixture of distributions, we are faced with the challenges of justifying the number of components and the label switching problem (caused by multimodality of the likelihood function). To circumvent these challenges, we propose a non-parametric structure for the signal component. This new method is more efficient in terms of precise estimates and better classifications. We demonstrated the efficacy of the methodology using a ChIP-sequencing data set

An Adaptive Interacting Wang-Landau Algorithm for Automatic Density Exploration

While statisticians are well-accustomed to performing exploratory analysis in the modeling stage of an analysis, the notion of conducting preliminary general-purpose exploratory analysis in the Monte Carlo stage (or more generally, the model-fitting stage) of an analysis is an area which we feel deserves much further attention. Towards this aim, this paper proposes a general-purpose algorithm for automatic density exploration. The proposed exploration algorithm combines and expands upon components from various adaptive Markov chain Monte Carlo methods, with the Wang-Landau algorithm at its heart. Additionally, the algorithm is run on interacting parallel chains -- a feature which both decreases computational cost as well as stabilizes the algorithm, improving its ability to explore the density. Performance is studied in several applications. Through a Bayesian variable selection example, the authors demonstrate the convergence gains obtained with interacting chains. The ability of the algorithm's adaptive proposal to induce mode-jumping is illustrated through a trimodal density and a Bayesian mixture modeling application. Lastly, through a 2D Ising model, the authors demonstrate the ability of the algorithm to overcome the high correlations encountered in spatial models.Comment: 33 pages, 20 figures (the supplementary materials are included as appendices

Acute Pesticide Illnesses Associated with Off-Target Pesticide Drift from Agricultural Applications: 11 States, 1998–2006

Background: Pesticides are widely used in agriculture, and off-target pesticide drift exposes workers and the public to harmful chemicals

The human capital transition and the role of policy

Along with information and communication technology, infrastructure, and the innovation system, human capital is a key pillar of the knowledge economy with its scope for increasing returns. With this in mind, the purpose of this chapter is to investigate how industrialized economies managed to achieve the transition from low to high levels of human capital. The first phase of the human capital transition was the result of the interaction of supply and demand, triggered by technological change and boosted by the demands for (immaterial) services. The second phase of the human capital transition (i.e., mass education) resulted from enforced legislation and major public investment. The state’s aim to influence children’s beliefs appears to have been a key driver in public investment. Nevertheless, the roles governments played differed according to the developmental status and inherent socioeconomic and political characteristics of their countries. These features of the human capital transition highlight the importance of understanding governments’ incentives and roles in transitions

Gender gaps in education

This chapter reviews the growing body of research in economics which concentrates on the education gender gap and its evolution, over time and across countries. The survey first focuses on gender differentials in the historical period that roughly goes from 1850 to the 1940s and documents the deep determinants of the early phase of female education expansion, including preindustrial conditions, religion, and family and kinship patterns. Next, the survey describes the stylized facts of contemporaneous gender gaps in education, from the 1950s to the present day, accounting for several alternative measures of attainment and achievement and for geographic and temporal differentiations. The determinants of the gaps are then summarized, while keeping a strong emphasis on an historical perspective and disentangling factors related to the labor market, family formation, psychological elements, and societal cultural norms. A discussion follows of the implications of the education gender gap for multiple realms, from economic growth to family life, taking into account the potential for reverse causation. Special attention is devoted to the persistency of gender gaps in the STEM and economics fields