Analytical study of tunneling times in flat histogram Monte Carlo

We present a model for the dynamics in energy space of multicanonical simulation methods that lends itself to a rather complete analytic characterization. The dynamics is completely determined by the density of states. In the \pm J 2D spin glass the transitions between the ground state level and the first excited one control the long time dynamics. We are able to calculate the distribution of tunneling times and relate it to the equilibration time of a starting probability distribution. In this model, and possibly in any model in which entering and exiting regions with low density of states are the slowest processes in the simulations, tunneling time can be much larger (by a factor of O(N)) than the equilibration time of the probability distribution. We find that these features also hold for the energy projection of single spin flip dynamics.Comment: 7 pages, 4 figures, published in Europhysics Letters (2005

NoSOCS in SDSS. VI. The Environmental Dependence of AGN in Clusters and Field in the Local Universe

We investigated the variation in the fraction of optical active galactic nuclei (AGN) hosts with stellar mass, as well as their local and global environments. Our sample is composed of cluster members and field galaxies at $z \le 0.1$ and we consider only strong AGN. We find a strong variation in the AGN fraction ($F_{AGN}$) with stellar mass. The field population comprises a higher AGN fraction compared to the global cluster population, especially for objects with log $M_* > 10.6$. Hence, we restricted our analysis to more massive objects. We detected a smooth variation in the $F_{AGN}$ with local stellar mass density for cluster objects, reaching a plateau in the field environment. As a function of clustercentric distance we verify that $F_{AGN}$ is roughly constant for R $>$ R$_{200}$, but show a steep decline inwards. We have also verified the dependence of the AGN population on cluster velocity dispersion, finding a constant behavior for low mass systems ($\sigma_P \lesssim 650-700$ km s$^{-1}$). However, there is a strong decline in $F_{AGN}$ for higher mass clusters ($>$ 700 km s$^{-1}$). When comparing the $F_{AGN}$ in clusters with or without substructure we only find different results for objects at large radii (R $>$ R$_{200}$), in the sense that clusters with substructure present some excess in the AGN fraction. Finally, we have found that the phase-space distribution of AGN cluster members is significantly different than other populations. Due to the environmental dependence of $F_{AGN}$ and their phase-space distribution we interpret AGN to be the result of galaxy interactions, favored in environments where the relative velocities are low, typical of the field, low mass groups or cluster outskirts.Comment: 11 pages, 10 figures, Accepted to MNRA

Transport through quantum rings

The transport of fermions through nanocircuits plays a major role in mesoscopic physics. Exploring the analogy with classical wave scattering, basic notions of nanoscale transport can be explained in a simple way, even at the level of undergraduate Solid State Physics courses, and more so if these explanations are supported by numerical simulations of these nanocircuits. This paper presents a simple tight-binding method for the study of the conductance of quantum nanorings connected to one-dimensional leads. We show how to address the effects of applied magnetic and electric fields and illustrate concepts such as Aharonov-Bohm conductance oscillations, resonant tunneling and destructive interference.Comment: 8 pages, 4 figure

Striving against invalidity in qualitative research: Discussing a reflective framework

The aim of this paper is to discuss a reflective validation framework related with the study of teaching approaches, teaching styles or teaching orientations of university academics. In the recent years, and particularly since the eighties, there have been a growing number of investigations linking teaching conceptions with teaching practices. The majority of investigations dealing with university teachers’ conceptions and practices draw their conclusions based on indirect observation, since data gathering involves mainly semi-structured interviews or the application of questionnaires and inventories. Therefore ‘only-half-the-story’ has been reported. The presented validation framework has a five-part three-stage structure and was built upon earlier work (Selvaruby, O’Sullivan, & Watts, 2007). In this model validity is conceptualized as an ‘iterative-interactive-process’, therefore integrating a set of specific strategies envisaging the maximization of scientific quality. The application of the model is illustrated by using it for the discussion of a longitudinal study involving the investigation of the relationship between questioning practices and Trigwell and co-workers’ concept of preferential teaching approaches (Trigwell, Prosser & Taylor, 1994). Field work of this naturalistic-interpretative research was conducted during two academic years (2009/2010 and 2010/2011) and implied close collaboration with a group of four university teachers lecturing biology to undergraduates.This work was financed by Fundação para a Ciência e a Tecnologia (SFRH/BD/44611/2008) and by Fundos FEDER através do Programa Operacional Fatores de Competitividade – COMPETE e por Fundos Nacionais através da FCT (PTDC/CPE-CED/117516/2010)

Robust Identification of Target Genes and Outliers in Triple-negative Breast Cancer Data

Correct classification of breast cancer sub-types is of high importance as it directly affects the therapeutic options. We focus on triple-negative breast cancer (TNBC) which has the worst prognosis among breast cancer types. Using cutting edge methods from the field of robust statistics, we analyze Breast Invasive Carcinoma (BRCA) transcriptomic data publicly available from The Cancer Genome Atlas (TCGA) data portal. Our analysis identifies statistical outliers that may correspond to misdiagnosed patients. Furthermore, it is illustrated that classical statistical methods may fail in the presence of these outliers, prompting the need for robust statistics. Using robust sparse logistic regression we obtain 36 relevant genes, of which ca. 60\% have been previously reported as biologically relevant to TNBC, reinforcing the validity of the method. The remaining 14 genes identified are new potential biomarkers for TNBC. Out of these, JAM3, SFT2D2 and PAPSS1 were previously associated to breast tumors or other types of cancer. The relevance of these genes is confirmed by the new DetectDeviatingCells (DDC) outlier detection technique. A comparison of gene networks on the selected genes showed significant differences between TNBC and non-TNBC data. The individual role of FOXA1 in TNBC and non-TNBC, and the strong FOXA1-AGR2 connection in TNBC stand out. Not only will our results contribute to the breast cancer/TNBC understanding and ultimately its management, they also show that robust regression and outlier detection constitute key strategies to cope with high-dimensional clinical data such as omics data