University of New Mexico

About: University of New Mexico is a education organization based out in Albuquerque, New Mexico, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 28870 authors who have published 64767 publications receiving 2578371 citations. The organization is also known as: UNM & Universitatis Novus Mexico.

Observation of Associated Near-Side and Away-Side Long-Range Correlations in √sNN=5.02 TeV Proton-Lead Collisions with the ATLAS Detector

Abstract: Two-particle correlations in relative azimuthal angle (Delta phi) and pseudorapidity (Delta eta) are measured in root S-NN = 5.02 TeV p + Pb collisions using the ATLAS detector at the LHC. The measurements are performed using approximately 1 mu b(-1) of data as a function of transverse momentum (p(T)) and the transverse energy (Sigma E-T(Pb)) summed over 3.1 < eta < 4.9 in the direction of the Pb beam. The correlation function, constructed from charged particles, exhibits a long-range (2 < vertical bar Delta eta vertical bar < 5) "near-side" (Delta phi similar to 0) correlation that grows rapidly with increasing Sigma E-T(Pb). A long-range "away-side" (Delta phi similar to pi) correlation, obtained by subtracting the expected contributions from recoiling dijets and other sources estimated using events with small Sigma E-T(Pb), is found to match the near-side correlation in magnitude, shape (in Delta eta and Delta phi) and Sigma E-T(Pb) dependence. The resultant Delta phi correlation is approximately symmetric about pi/2, and is consistent with a dominant cos2 Delta phi modulation for all Sigma E-T(Pb) ranges and particle p(T).

Deep learning for neuroimaging: A validation study

Abstract: Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. These methods include deep belief networks and their building block the restricted Boltzmann machine. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.

Fluctuating asymmetry and sexual selection

Abstract: Behavioral ecologists are being attracted to the study of within-individual morphological variability, manifested in random deviations from bilateral symmetry, as a means of ascertaining the stress susceptibility of developmental regulatory mechanisms. Several early successes Indicate that incorporating measures of symmetry into sexual-selection studies may help link individual sexual success to a basic component of viability — developmental stability.

Second Language Use, Socialization, and Learning in Internet Interest Communities and Online Gaming

Abstract: In recent years, there has been a great deal of research and pedagogical experimentation relating to the uses of technology in second (L2) and foreign language education. The majority of this research has usefully described and examined the efficacy of in-class and directly classroom-related uses of technology. This article broadens the scope of inquiry to include L2 and foreign language-related uses of technology that extend into the interstitial spaces between instructed L2 contexts and entirely out-of-school noninstitutional realms of freely chosen digital engagement. Two demographically and sociologically significant phenomena are examined in detail; the first focuses on participation in Internet interest communities such as fan fiction and virtual diaspora community spaces and the second describes a continuum of three-dimensional graphically rendered virtual environments and online games. A review of research in each of these areas reveals extended periods of language socialization into sophisticated communicative practices and demonstrates the salience of creative expression and language use as tools for identity development and management. In the final section of the article, we suggest a number of possibilities for synergistically uniting the analytic rigor of instructed L2 education with the immediacy and vibrancy of language use in digital vernacular contexts. [ABSTRACT FROM AUTHOR]