Spectroscopic analysis of terrestrial analogues for the interpretation of surface composition of rocky bodies in the solar system. Effects of mineral mixing and particle size. Applications to moon, mars and implications for mercury.

Among the Solar System’s bodies, Moon, Mercury and Mars are at present, or have been in the recent years, object of space missions aimed, among other topics, also at improving our knowledge about surface composition. Between the techniques to detect planet’s mineralogical composition, both from remote and close range platforms, visible and near-infrared reflectance (VNIR) spectroscopy is a powerful tool, because crystal field absorption bands are related to particular transitional metals in well-defined crystal structures, e.g., Fe2+ in M1 and M2 sites of olivine or pyroxene (Burns, 1993). Thanks to the improvements in the spectrometers onboard the recent missions, a more detailed interpretation of the planetary surfaces can now be delineated. However, quantitative interpretation of planetary surface mineralogy could not always be a simple task. In fact, several factors such as the mineral chemistry, the presence of different minerals that absorb in a narrow spectral range, the regolith with a variable particle size range, the space weathering, the atmosphere composition etc., act in unpredictable ways on the reflectance spectra on a planetary surface (Serventi et al., 2014). One method for the interpretation of reflectance spectra of unknown materials involves the study of a number of spectra acquired in the laboratory under different conditions, such as different mineral abundances or different particle sizes, in order to derive empirical trends. This is the methodology that has been followed in this PhD thesis: the single factors previously listed have been analyzed, creating, in the laboratory, a set of terrestrial analogues with well-defined composition and size. The aim of this work is to provide new tools and criteria to improve the knowledge of the composition of planetary surfaces. In particular, mixtures composed with different content and chemistry of plagioclase and mafic minerals have been spectroscopically analyzed at different particle sizes and with different mineral relative percentages. The reflectance spectra of each mixture have been analyzed both qualitatively (using the software ORIGIN®) and quantitatively applying the Modified Gaussian Model (MGM, Sunshine et al., 1990) algorithm. In particular, the spectral parameter variations of each absorption band have been evaluated versus the volumetric FeO% content in the PL phase and versus the PL modal abundance. This delineated calibration curves of composition vs. spectral parameters and allow implementation of spectral libraries. Furthermore, the trends derived from terrestrial analogues here analyzed and from analogues in the literature have been applied for the interpretation of hyperspectral images of both plagioclase-rich (Moon) and plagioclase-poor (Mars) bodies.

Time trials on second-order and variable-learning-rate algorithms

The performance of seven minimization algorithms are compared on five neural network problems. These include a variable-step-size algorithm, conjugate gradient, and several methods with explicit analytic or numerical approximations to the Hessian.

The exact sample complexity of PAC-learning problems with unit VC dimension

The Vapnik-Chervonenkis (VC) dimension is a combinatorial measure of a certain class of machine learning problems, which may be used to obtain upper and lower bounds on the number of training examples needed to learn to prescribed levels of accuracy. Most of the known bounds apply to the Probably Approximately Correct (PAC) framework, which is the framework within which we work in this paper. For a learning problem with some known VC dimension, much is known about the order of growth of the sample-size requirement of the problem, as a function of the PAC parameters. The exact value of sample-size requirement is however less well-known, and depends heavily on the particular learning algorithm being used. This is a major obstacle to the practical application of the VC dimension. Hence it is important to know exactly how the sample-size requirement depends on VC dimension, and with that in mind, we describe a general algorithm for learning problems having VC dimension 1. Its sample-size requirement is minimal (as a function of the PAC parameters), and turns out to be the same for all non-trivial learning problems having VC dimension 1. While the method used cannot be naively generalised to higher VC dimension, it suggests that optimal algorithm-dependent bounds may improve substantially on current upper bounds.

Probing three-way interactions in moderated multiple regression:Development and application of a slope difference test

Researchers often use 3-way interactions in moderated multiple regression analysis to test the joint effect of 3 independent variables on a dependent variable. However, further probing of significant interaction terms varies considerably and is sometimes error prone. The authors developed a significance test for slope differences in 3-way interactions and illustrate its importance for testing psychological hypotheses. Monte Carlo simulations revealed that sample size, magnitude of the slope difference, and data reliability affected test power. Application of the test to published data yielded detection of some slope differences that were undetected by alternative probing techniques and led to changes of results and conclusions. The authors conclude by discussing the test's applicability for psychological research. Copyright 2006 by the American Psychological Association.

Data analysis methods in optometry Part 7: multiple linear regression

Multiple regression analysis is a complex statistical method with many potential uses. It has also become one of the most abused of all statistical procedures since anyone with a data base and suitable software can carry it out. An investigator should always have a clear hypothesis in mind before carrying out such a procedure and knowledge of the limitations of each aspect of the analysis. In addition, multiple regression is probably best used in an exploratory context, identifying variables that might profitably be examined by more detailed studies. Where there are many variables potentially influencing Y, they are likely to be intercorrelated and to account for relatively small amounts of the variance. Any analysis in which R squared is less than 50% should be suspect as probably not indicating the presence of significant variables. A further problem relates to sample size. It is often stated that the number of subjects or patients must be at least 5-10 times the number of variables included in the study.5 This advice should be taken only as a rough guide but it does indicate that the variables included should be selected with great care as inclusion of an obviously unimportant variable may have a significant impact on the sample size required.

Use of imaging technology to better understand soft contact lens fit dynamics

The principal theme of this thesis is the identification of additional factors affecting, and consequently to better allow, the prediction of soft contact lens fit. Various models have been put forward in an attempt to predict the parameters that influence soft contact lens fit dynamics; however, the factors that influence variation in soft lens fit are still not fully understood. The investigations in this body of work involved the use of a variety of different imaging techniques to both quantify the anterior ocular topography and assess lens fit. The use of Anterior-Segment Optical Coherence Tomography (AS-OCT) allowed for a more complete characterisation of the cornea and corneoscleral profile (CSP) than either conventional keratometry or videokeratoscopy alone, and for the collection of normative data relating to the CSP for a substantial sample size. The scleral face was identified as being rotationally asymmetric, the mean corneoscleral junction (CSJ) angle being sharpest nasally and becoming progressively flatter at the temporal, inferior and superior limbal junctions. Additionally, 77% of all CSJ angles were within ±50 of 1800, demonstrating an almost tangential extension of the cornea to form the paralimbal sclera. Use of AS-OCT allowed for a more robust determination of corneal diameter than that of white-to-white (WTW) measurement, which is highly variable and dependent on changes in peripheral corneal transparency. Significant differences in ocular topography were found between different ethnicities and sexes, most notably for corneal diameter and corneal sagittal height variables. Lens tightness was found to be significantly correlated with the difference between horizontal CSJ angles (r =+0.40, P =0.0086). Modelling of the CSP data gained allowed for prediction of up to 24% of the variance in contact lens fit; however, it was likely that stronger associations and an increase in the modelled prediction of variance in fit may have occurred had an objective method of lens fit assessment have been made. A subsequent investigation to determine the validity and repeatability of objective contact lens fit assessment using digital video capture showed no significant benefit over subjective evaluation. The technique, however, was employed in the ensuing investigation to show significant changes in lens fit between 8 hours (the longest duration of wear previously examined) and 16 hours, demonstrating that wearing time is an additional factor driving lens fit dynamics. The modelling of data from enhanced videokeratoscopy composite maps alone allowed for up to 77% of the variance in soft contact lens fit, and up to almost 90% to be predicted when used in conjunction with OCT. The investigations provided further insight into the ocular topography and factors affecting soft contact lens fit.

Application of the Heterogeneous System Prediction Method to Pattern Recognition Problem

* This work was financially supported by RFBR-04-01-00858.

Application of the Multivariate Prediction Method to Time Series

* This work was financially supported by RFBR-04-01-00858.

Improved Criteria for Estimating Calibration Factors for Highway Safety Manual (HSM) Applications

The Highway Safety Manual (HSM) estimates roadway safety performance based on predictive models that were calibrated using national data. Calibration factors are then used to adjust these predictive models to local conditions for local applications. The HSM recommends that local calibration factors be estimated using 30 to 50 randomly selected sites that experienced at least a total of 100 crashes per year. It also recommends that the factors be updated every two to three years, preferably on an annual basis. However, these recommendations are primarily based on expert opinions rather than data-driven research findings. Furthermore, most agencies do not have data for many of the input variables recommended in the HSM. This dissertation is aimed at determining the best way to meet three major data needs affecting the estimation of calibration factors: (1) the required minimum sample sizes for different roadway facilities, (2) the required frequency for calibration factor updates, and (3) the influential variables affecting calibration factors. In this dissertation, statewide segment and intersection data were first collected for most of the HSM recommended calibration variables using a Google Maps application. In addition, eight years (2005-2012) of traffic and crash data were retrieved from existing databases from the Florida Department of Transportation. With these data, the effect of sample size criterion on calibration factor estimates was first studied using a sensitivity analysis. The results showed that the minimum sample sizes not only vary across different roadway facilities, but they are also significantly higher than those recommended in the HSM. In addition, results from paired sample t-tests showed that calibration factors in Florida need to be updated annually. To identify influential variables affecting the calibration factors for roadway segments, the variables were prioritized by combining the results from three different methods: negative binomial regression, random forests, and boosted regression trees. Only a few variables were found to explain most of the variation in the crash data. Traffic volume was consistently found to be the most influential. In addition, roadside object density, major and minor commercial driveway densities, and minor residential driveway density were also identified as influential variables.

Holocene Mg/Ca, alkenones, and light stable isotope measurements on the outer North Iceland shelf, sediment core MD99-2269

We present new Holocene century to millennial-scale proxies for the well-dated piston core MD99-2269 from Húnaflóadjúp on the North Iceland Shelf. The core is located in 365 mwd and lies close to the fluctuating boundary between Atlantic and Arctic/Polar waters. The proxies are: alkenone-based SST°C, and Mg/Ca SST°C estimates and stable d13C and d18O values on planktonic and benthic foraminifera. The data were converted to 60 yr equi-spaced time-series. Significant trends in the data were extracted using Singular Spectrum Analysis and these accounted for between 50% and 70% of the variance. A comparison between these data with previously published climate proxies from MD99-2269 was carried out on a data set which consisted of 14-variable data set covering the interval 400-9200 cal yr BP at 100 yr time steps. This analysis indicated that the 1st two PC axes accounted for 57% of the variability with high loadings clustering primarily into "nutrient" and "temperature" proxies. Clustering on the 100 yr time-series indicated major changes in environment at ~6350 and ~3450 cal yr BP, which define early, mid- and late Holocene climatic intervals. We argue that a pervasive freshwater cap during the early Holocene resulted in warm SST°s, a stratified water column, and a depleted nutrient supply. The loss of the freshwater layer in the mid-Holocene resulted in high carbonate production, and the late Holocene/neoglacial interval was marked by significantly more variable sea surface conditions.

Distributed Feature Selection in Large n and Large p Regression Problems

Fitting statistical models is computationally challenging when the sample size or the dimension of the dataset is huge. An attractive approach for down-scaling the problem size is to first partition the dataset into subsets and then fit using distributed algorithms. The dataset can be partitioned either horizontally (in the sample space) or vertically (in the feature space), and the challenge arise in defining an algorithm with low communication, theoretical guarantees and excellent practical performance in general settings. For sample space partitioning, I propose a MEdian Selection Subset AGgregation Estimator ({\em message}) algorithm for solving these issues. The algorithm applies feature selection in parallel for each subset using regularized regression or Bayesian variable selection method, calculates the `median' feature inclusion index, estimates coefficients for the selected features in parallel for each subset, and then averages these estimates. The algorithm is simple, involves very minimal communication, scales efficiently in sample size, and has theoretical guarantees. I provide extensive experiments to show excellent performance in feature selection, estimation, prediction, and computation time relative to usual competitors.

While sample space partitioning is useful in handling datasets with large sample size, feature space partitioning is more effective when the data dimension is high. Existing methods for partitioning features, however, are either vulnerable to high correlations or inefficient in reducing the model dimension. In the thesis, I propose a new embarrassingly parallel framework named {\em DECO} for distributed variable selection and parameter estimation. In {\em DECO}, variables are first partitioned and allocated to m distributed workers. The decorrelated subset data within each worker are then fitted via any algorithm designed for high-dimensional problems. We show that by incorporating the decorrelation step, DECO can achieve consistent variable selection and parameter estimation on each subset with (almost) no assumptions. In addition, the convergence rate is nearly minimax optimal for both sparse and weakly sparse models and does NOT depend on the partition number m. Extensive numerical experiments are provided to illustrate the performance of the new framework.

For datasets with both large sample sizes and high dimensionality, I propose a new "divided-and-conquer" framework {\em DEME} (DECO-message) by leveraging both the {\em DECO} and the {\em message} algorithm. The new framework first partitions the dataset in the sample space into row cubes using {\em message} and then partition the feature space of the cubes using {\em DECO}. This procedure is equivalent to partitioning the original data matrix into multiple small blocks, each with a feasible size that can be stored and fitted in a computer in parallel. The results are then synthezied via the {\em DECO} and {\em message} algorithm in a reverse order to produce the final output. The whole framework is extremely scalable.

The Effect of Authentic Literacy Experiences as Book Buddies with Hispanic Fourth Graders on Preservice Teachers’ Literacy Content Knowledge and Reading Maturity

From a sociocultural perspective, individuals learn best from contextualized experiences. In preservice teacher education, contextualized experiences include authentic literacy experiences, which include a real reader and writer and replicate real life communication. To be prepared to teach well, preservice teachers need to gain literacy content knowledge and possess reading maturity. The purpose of this study was to examine the effect of authentic literacy experiences as Book Buddies with Hispanic fourth graders on preservice teachers’ literacy content knowledge and reading maturity. The study was a pretest/posttest design conducted over 12 weeks. Preservice teacher participants, the focus of the study, were elementary education majors taking the third of four required reading courses in non-probabilistic convenience groups, 43 (n = 33 experimental, n = 10 comparison) Elementary Education majors. The Survey of Preservice Teachers’ Knowledge of Teaching and Technology (SPTKTT), specifically designed for preservice teachers majoring in elementary or early childhood education and the Reading Maturity Survey (RMS) were used in this study. Preservice teachers chose either the experimental or comparison group based on the opportunity to earn extra credit points (experimental = 30 points, comparison = 15). After exchanging introductory letters preservice teachers and Hispanic fourth graders each read four books. After reading each book preservice teachers wrote letters to their student asking higher order thinking questions. Preservice teachers received scanned copies of their student’s unedited letters via email which enabled them to see their student’s authentic answers and writing levels. A series of analyses of covariance were used to determine whether there were significant differences in the dependent variables between the experimental and comparison groups. This quasi-experimental study tested two hypotheses. Using the appropriate pretest scores as covariates for adjusting the posttest means of the subcategory Literacy Content Knowledge (LCK), of the SPTKTT and the RMS, the mean adjusted posttest scores from the experimental group and comparison group were compared. No significant differences were found on the LCK dependent variable using the .05 level of significance, which may be due to Type II error caused by the small sample size. Significant differences were found on RMS using the .05 level of significance.

(Table 1) Oxygen and carbon isotopes for planktonic foraminifers at DSDP Hole 64-480

During the cleaning of the HPC core surfaces from Hole 480 for photography, the material removed was conserved carefully in approximately 10 cm intervals (by K. Kelts); this material was made available to us in the hope that it would be possible to obtain oxygen isotope stratigraphy for the site. The samples were, of course, somewhat variable in size, but the majority were probably between 5 and 10 cm**3. Had this been a normal marine environment, such sample sizes would have contained abundant planktonic foraminifers together with a small number of benthics. However, this is clearly not the case, for many samples contained no foraminifers, whereas others contained more benthics than planktonics. Among the planktonic foraminifers the commonest species are Globigerina bulloides, Neogloboquadrina dutertrei, and N. pachyderma. A few samples contain a more normal fauna with Globigerinoides spp. and occasional Globorotalia spp. Sample 480-3-3, 20-30 cm contained Globigerina rubescens, isolated specimens of which were noted in a few other samples in Cores 3,4, and 5. This is a particularly solution-sensitive species; in the open Pacific it is only found widely distributed at horizons of exceptionally low carbonate dissolution, such as. the last glacial-to-interglacial transition.

Gaussian entanglement for quantum key distribution from a single-mode squeezing source

We report the suitability of an Einstein-Podolsky-Rosen entanglement source for Gaussian continuous-variable quantum key distribution at 1550 nm. Our source is based on a single continuous-wave squeezed vacuum mode combined with a vacuum mode at a balanced beam splitter. Extending a recent security proof, we characterize the source by quantifying the extractable length of a composable secure key from a finite number of samples under the assumption of collective attacks. We show that distances in the order of 10 km are achievable with this source for a reasonable sample size despite the fact that the entanglement was generated including a vacuum mode. Our security analysis applies to all states having an asymmetry in the field quadrature variances, including those generated by superposition of two squeezed modes with different squeezing strengths.

The interpersonal and mental health implications of the coherence of coming out narratives for a sample of gay men in committed romantic relationships

The coming out process has been conceptualized as a developmental imperative for those who will eventually accept their same-sex attractions. It is widely accepted that homophobia, heterosexism, and homonegativity are cultural realities that may complicate this developmental process for gay men. The current study views coming out as an extra-developmental life task that is at best a stressful event, and at worst traumatic when coming out results in the rupture of salient relationships with parents, siblings, and/or close friends. To date, the minority stress model (Meyer, 1995; 2003) has been utilized as an organizing framework for how to empirically examine external stressors and mental health disparities for lesbians, gay men, and bisexual individuals in the United States. The current study builds on this literature by focusing on the influence of how gay men make sense of and represent the coming out process in a semi-structured interview, more specifically, by examining the legacy of the coming out process on indicators of wellness. In a two-part process, this study first employs the framework well articulated in the adult attachment literature of coherence of narratives to explore both variation and implications of the coming out experience for a sample of gay men (n = 60) in romantic relationships (n = 30). In particular, this study employed constructs identified in the adult attachment literature, namely Preoccupied and Dismissing current state of mind, to code a Coming Out Interview (COI). In the present study current state of mind refers to the degree of coherent discourse produced about coming out experiences as relayed during the COI. Multilevel analyses tested the extent to which these COI dimensions, as revealed through an analysis of coming out narratives in the COI, were associated with relationship quality, including self-reported satisfaction and observed emotional tone in a standard laboratory interaction task and self-reported symptoms of psychopathology. In addition, multilevel analyses also assessed the Acceptance by primary relationship figures at the time of disclosure, as well as the degree of Outness at the time of the study. Results revealed that participant’s narratives on the COI varied with regard to Preoccupied and Dismissing current state of mind, suggesting that the AAI coding system provides a viable organizing framework for extracting meaning from coming out narratives as related to attachment relevant constructs. Multilevel modeling revealed construct validity of the attachment dimensions assessed via the COI; attachment (i.e., Preoccupied and Dismissing current state of mind) as assessed via the Adult Attachment Interview (AAI) was significantly correlated with the corresponding COI variables. These finding suggest both methodological and conceptual convergence between these two measures. However, with one exception, COI Preoccupied and Dismissing current state of mind did not predict relationship outcomes or self-reported internalizing and externalizing symptoms. However, further analyses revealed that the degree to which one is out to others moderated the relationship between COI Preoccupied and internalizing. Specifically, for those who were less out to others, there was a significant and positive relationship between Preoccupied current state of mind towards coming out and internalizing symptoms. In addition, the degree of perceived acceptance of sexual orientation by salient relationship figures at the time of disclosure emerged as a predictor of mental health. In particular, Acceptance was significantly negatively related to internalizing symptoms. Overall, the results offer preliminary support that gay men’s narratives do reflect variation as assessed by attachment dimensions and highlights the role of Acceptance by salient relationship figures at the time of disclosure. Still, for the most part, current state of mind towards coming out in this study was not associated with relationship quality and self-reported indicators of mental health. This finding may be a function of low statistical power given the modest sample size. However, the relationship between Preoccupied current state of mind and mental health (i.e., internalizing) appears to depend on degree of Outness. In addition, the response of primary relationships figures to coming out may be a relevant factor in shaping mental health outcomes for gay men. Limitations and suggestions for future research and clinical intervention are offered.