Detection of elliptical shapes via cross-entropy clustering

The problem of finding elliptical shapes in an image will be considered. We discuss the solution which uses cross-entropy clustering. The proposed method allows the search for ellipses with predefined sizes and position in the space. Moreover, it works well for search of ellipsoids in higher dimensions

Model selection in High-Dimensions: A Quadratic-risk based approach

In this article we propose a general class of risk measures which can be used for data based evaluation of parametric models. The loss function is defined as generalized quadratic distance between the true density and the proposed model. These distances are characterized by a simple quadratic form structure that is adaptable through the choice of a nonnegative definite kernel and a bandwidth parameter. Using asymptotic results for the quadratic distances we build a quick-to-compute approximation for the risk function. Its derivation is analogous to the Akaike Information Criterion (AIC), but unlike AIC, the quadratic risk is a global comparison tool. The method does not require resampling, a great advantage when point estimators are expensive to compute. The method is illustrated using the problem of selecting the number of components in a mixture model, where it is shown that, by using an appropriate kernel, the method is computationally straightforward in arbitrarily high data dimensions. In this same context it is shown that the method has some clear advantages over AIC and BIC.Comment: Updated with reviewer suggestion

Thermonuclear burn-up in deuterated methane CD4CD_4

The thermonuclear burn-up of highly compressed deuterated methane CD$_4$ is considered in the spherical geometry. The minimal required values of the burn-up parameter $x = \rho_0 \cdot r_f$ are determined for various temperatures $T$ and densities $\rho_0$. It is shown that thermonuclear burn-up in $CD_4$ becomes possible in practice if its initial density $\rho_0$ exceeds $\approx 5 \cdot 10^3$ $g \cdot cm^{-3}$. Burn-up in CD$_2$T$_2$ methane requires significantly ($\approx$ 100 times) lower compressions. The developed approach can be used in order to compute the critical burn-up parameters in an arbitrary deuterium containing fuel

Filling the Void: Bolstering Attachment Security in Committed Relationships

Attachment security has many salutary effects in adulthood, yet little is known about the specific interpersonal processes that increase attachment security over time. Using data from 134 romantically committed couples in a longitudinal study, we examined trust (whether a partner is perceived as available and dependable) and perceived goal validation (whether a partner is perceived as encouraging one’s personal goal pursuits). In concurrent analyses, trust toward a partner was uniquely associated with lower attachment anxiety, whereas perceiving one’s goal pursuits validated by a partner was uniquely associated with lower attachment avoidance. In longitudinal analyses, however, the inverse occurred: Trust toward a partner uniquely predicting reduced attachment avoidance over time and perceived goal validation uniquely predicting reduced attachment anxiety over time. These findings highlight distinct temporal paths for bolstering the security of attachment anxious versus attachment avoidant individuals

Preheat Effects on Microballoon Laser-Fusion Implosions

Nonequilibrium hydroburn simulations of early laser-driven compression experiments indicate that low energy photons from the vicinity of the ablation surface are preheating the microballoon-pushers, thereby severely limiting the compressions achieved (similar degradation may result from 1 to 4 percent energy deposition by superthermal electrons). This implies an 8- to 27-fold increase in the energy requirements for breakeven, unless radiative preheat can be drastically reduced by, say, the use of composite ablator-pushers. (auth

A novel approach to the clustering of microarray data via nonparametric density estimation

<p>Abstract</p> <p>Background</p> <p>Cluster analysis is a crucial tool in several biological and medical studies dealing with microarray data. Such studies pose challenging statistical problems due to dimensionality issues, since the number of variables can be much higher than the number of observations.</p> <p>Results</p> <p>Here, we present a general framework to deal with the clustering of microarray data, based on a three-step procedure: (i) gene filtering; (ii) dimensionality reduction; (iii) clustering of observations in the reduced space. Via a nonparametric model-based clustering approach we obtain promising results both in simulated and real data.</p> <p>Conclusions</p> <p>The proposed algorithm is a simple and effective tool for the clustering of microarray data, in an unsupervised setting.</p

Comparison of scores for bimodality of gene expression distributions and genome-wide evaluation of the prognostic relevance of high-scoring genes

<p>Abstract</p> <p>Background</p> <p>A major goal of the analysis of high-dimensional RNA expression data from tumor tissue is to identify prognostic signatures for discriminating patient subgroups. For this purpose genome-wide identification of bimodally expressed genes from gene array data is relevant because distinguishability of high and low expression groups is easier compared to genes with unimodal expression distributions.</p> <p>Recently, several methods for the identification of genes with bimodal distributions have been introduced. A straightforward approach is to cluster the expression values and score the distance between the two distributions. Other scores directly measure properties of the distribution. The kurtosis, e.g., measures divergence from a normal distribution. An alternative is the outlier-sum statistic that identifies genes with extremely high or low expression values in a subset of the samples.</p> <p>Results</p> <p>We compare and discuss scores for bimodality for expression data. For the genome-wide identification of bimodal genes we apply all scores to expression data from 194 patients with node-negative breast cancer. Further, we present the first comprehensive genome-wide evaluation of the prognostic relevance of bimodal genes. We first rank genes according to bimodality scores and define two patient subgroups based on expression values. Then we assess the prognostic significance of the top ranking bimodal genes by comparing the survival functions of the two patient subgroups. We also evaluate the global association between the bimodal shape of expression distributions and survival times with an enrichment type analysis.</p> <p>Various cluster-based methods lead to a significant overrepresentation of prognostic genes. A striking result is obtained with the outlier-sum statistic (<it>p </it>< 10^-12). Many genes with heavy tails generate subgroups of patients with different prognosis.</p> <p>Conclusions</p> <p>Genes with high bimodality scores are promising candidates for defining prognostic patient subgroups from expression data. We discuss advantages and disadvantages of the different scores for prognostic purposes. The outlier-sum statistic may be particularly valuable for the identification of genes to be included in prognostic signatures. Among the genes identified as bimodal in the breast cancer data set several have not yet previously been recognized to be prognostic and bimodally expressed in breast cancer.</p

Insight, duration of untreated psychosis and attachment in first-episode psychosis:A prospective study of psychiatric recovery over 12-month follow-up

BACKGROUND: Increasing evidence shows attachment security influences symptom expression and adaptation in people diagnosed with schizophrenia and other psychoses. AIMS: To describe the distribution of secure and insecure attachment in a cohort of individuals with first-episode psychosis, and to explore the relationship between attachment security and recovery from positive and negative symptoms in the first 12 months. METHOD: The study was a prospective 12-month cohort study. The role of attachment, duration of untreated psychosis (DUP), baseline symptoms and insight in predicting and mediating recovery from symptoms was investigated using multiple regression analysis and path analysis. RESULTS: Of the 79 participants, 54 completed the Adult Attachment Interview (AAI): 37 (68.5%) were classified as insecure, of which 26 (48.1%) were insecure/dismissing and 11 (20.4%) insecure preoccupied. Both DUP and insight predicted recovery from positive symptoms at 12 months. Attachment security, DUP and insight predicted recovery from negative symptoms at 12 months. CONCLUSIONS: Attachment is an important construct contributing to understanding and development of interventions promoting recovery following first-episode psychosis