Subtree power analysis finds optimal species for comparative genomics

Sequence comparison across multiple organisms aids in the detection of regions under selection. However, resource limitations require a prioritization of genomes to be sequenced. This prioritization should be grounded in two considerations: the lineal scope encompassing the biological phenomena of interest, and the optimal species within that scope for detecting functional elements. We introduce a statistical framework for optimal species subset selection, based on maximizing power to detect conserved sites. In a study of vertebrate species, we show that the optimal species subset is not in general the most evolutionarily diverged subset. Our results suggest that marsupials are prime sequencing candidates.Comment: 16 pages, 3 figures, 3 table

Comment on "Support Vector Machines with Applications"

Comment on "Support Vector Machines with Applications" [math.ST/0612817]Comment: Published at http://dx.doi.org/10.1214/088342306000000475 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Searching for Prosociality in Qualitative Data: Comparing Manual, Closed-Vocabulary, and Open-Vocabulary Methods

Although most people present themselves as possessing prosocial traits, people differ in the extent to which they actually act prosocially in everyday life. Qualitative data that were not ostensibly collected to measure prosociality might contain information about prosocial dispositions that is not distorted by self–presentation concerns. This paper seeks to characterise charitable donors from qualitative data. We compared a manual approach of extracting predictors from participants’ self–described personal strivings to two automated approaches: A summation of words predefined as prosocial and a support vector machine classifier. Although variables extracted by the support vector machine predicted donation behaviour well in the training sample ( N = 984), virtually, no variables from any method significantly predicted donations in a holdout sample ( N = 496). Raters’ attempts to predict donations to charity based on reading participants’ personal strivings were also unsuccessful. However, raters’ predictions were associated with past charitable involvement. In sum, predictors derived from personal strivings did not robustly explain variation in charitable behaviour, but personal strivings may nevertheless contain some information about trait prosociality. The sparseness of personal strivings data, rather than the irrelevance of open–ended text or individual differences in goal pursuit, likely explains their limited value in predicting prosocial behaviour. © 2020 European Association of Personality Psycholog

Typical tropospheric aerosol backscatter profiles for Southern Ireland: The Cork Raman lidar

A Raman lidar instrument (UCLID) was established at the University College Cork as part of the European lidar network EARLINET. Raman backscatter coefficients, extinction coefficients and lidar ratios were measured within the period 28/08/2010 and 24/04/2011. Typical atmospheric scenarios over Southern Ireland in terms of the aerosol load in the planetary boundary layer are outlined. The lidar ratios found are typical for marine atmospheric condition (lidar ratio ca. 20–25 sr). The height of the planetary boundary layer is below 1000 m and therefore low in comparison to heights found at other lidar sites in Europe. On the 21st of April a large aerosol load was detected, which was assigned to a Saharan dust event based on HYSPLIT trajectories and DREAM forecasts along with the lidar ratio (70 sr) for the period concerned. The dust was found at two heights, pure dust at 2.5 km and dust mixing with pollution from 0.7 to 1.8 km with a lidar ratio of 40–50 sr

Mid-infrared optical sensing using sub-wavelength gratings

Optical sensing has shown great potential for both quantitative and qualitative analysis of compounds. In particular sensors which are capable of detecting changes in refractive index at a surface as well as in bulk material have received much attention. Much of the recent research has focused on developing technologies that enable such sensors to be deployed in an integrated photonic device. In this work we demonstrate experimentally, using a sub-wavelength grating the detection of ethanol in aqueous solution by interrogating its large absorption band at 9.54 μm. Theoretical investigation of the operating principle of our grating sensor shows that in general, as the total field interacting with the analyte is increased, the corresponding absorption is also increased. We also theoretically demonstrate how sub-wavelength gratings can detect changes in the real part of the refractive index, similar to conventional refractive index (RI) sensors

ISCAN: a System for Integrated Phonetic Analyses Across Speech Corpora

Speech corpora of many languages, styles, and formats exist in the world, representing significant potential for the phonetic sciences. However in practice there are significant practical and methodological barriers to conducting the “same study” across corpora, including necessary technical skills and non-comparability of results using non-standardized measures. We introduce an open-source software system for Integrated Speech Corpus ANalysis (ISCAN), which enables automated acoustic phonetic analysis across spoken corpora of diverse formats and sizes. A web-browser-based GUI and Python package allow for different user backgrounds. The system is a major update of core functionality for fully- automated speech corpus analysis (importing, enriching, querying) from a previous version, to meet new goals: different user configurations, working with restricted datasets, and interacting with data (visualization and correction). The system’s flexibility for different projects is shown in two use cases: large-scale automatic segmental analysis of spontaneous speech across English dialects, and smallerscale semi-automatic prosodic analysis

Tropospheric aerosol detection over Southern Ireland using a backscatter Raman lidar

Lidar is an optical remote sensing instrument that can measure atmospheric parameters. A Raman lidar instrument (UCLID) was established at University College Cork to contribute to the European lidar network, EARLINET. System performance tests were carried out to ensure strict data quality assurance for submission to the EARLINET database. Procedures include: overlap correction, telecover test, Rayleigh test and zero bin test. Raman backscatter coefficients, extinction coefficients and lidar ratio were measured from April 2010 to May 2011 and February 2012 to June 2012. Statistical analysis of the profiles over these periods provided new information about the typical atmospheric scenarios over Southern Ireland in terms of aerosol load in the lower troposphere, the planetary boundary layer (PBL) height, aerosol optical density (AOD) at 532 nm and lidar ratio values. The arithmetic average of the PBL height was found to be 608 ± 138 m with a median of 615 m, while average AOD at 532 nm for clean marine air masses was 0.119 ± 0.023 and for polluted air masses was 0.170 ± 0.036. The lidar ratio showed a seasonal dependence with lower values found in winter and autumn (20 ± 5 sr) and higher during spring and winter (30 ± 12 sr). Detection of volcanic particles from the eruption of the volcano Eyjafjallajökull in Iceland was measured between 21 April and 7 May 2010. The backscatter coefficient of the ash layer varied between 2.5 Mm-1sr-1 and 3.5 Mm-1sr-1, and estimation of the AOD at 532 nm was found to be between 0.090 and 0.215. Several aerosol loads due to Saharan dust particles were detected in Spring 2011 and 2012. Lidar ratio of the dust layers were determine to be between 45 and 77 sr and AOD at 532 nm during the dust events range between 0.84 to 0.494

Variational inference for large-scale models of discrete choice

Discrete choice models are commonly used by applied statisticians in numerous fields, such as marketing, economics, finance, and operations research. When agents in discrete choice models are assumed to have differing preferences, exact inference is often intractable. Markov chain Monte Carlo techniques make approximate inference possible, but the computational cost is prohibitive on the large data sets now becoming routinely available. Variational methods provide a deterministic alternative for approximation of the posterior distribution. We derive variational procedures for empirical Bayes and fully Bayesian inference in the mixed multinomial logit model of discrete choice. The algorithms require only that we solve a sequence of unconstrained optimization problems, which are shown to be convex. Extensive simulations demonstrate that variational methods achieve accuracy competitive with Markov chain Monte Carlo, at a small fraction of the computational cost. Thus, variational methods permit inferences on data sets that otherwise could not be analyzed without bias-inducing modifications to the underlying model.Comment: 29 pages, 2 tables, 2 figure