Matters of Argument: Materiality, Listening, and Practices of Openness in First-Year Writing Classes

This dissertation argues for the value of increased focus on practices of listening in rhetorical education, especially in first-year writing courses. Building on research in listening rhetorics, new materialism, and contemplative pedagogy, the author presents a pedagogical and rhetorical vision for more open argument. Open arguments function with open-heartedness, an open-ethos, openness to listening to Others and the material world, openness to a multiplicity of viewpoints, open-endedness, and openness to productive conflict. The author argues that students can learn to write these more open arguments through a combination of listening to the material world around them, listening to their own bodies, and listening to their interlocutors. These listening practices are explored through a pedagogical self-study that shows how listening to the material world can help writers move beyond the constraints of the thesis-support model into open-ended complexity; explore new materially based metaphors to write less combative deliberative arguments; and use greater awareness of one’s embodied reactions and positionality to listen to and dialogue with others across difference. Advisor: Robert Brook

A random wave model for the Aharonov-Bohm effect

We study an ensemble of random waves subject to the Aharonov-Bohm effect. The introduction of a point with a magnetic flux of arbitrary strength into a random wave ensemble gives a family of wavefunctions whose distribution of vortices (complex zeros) are responsible for the topological phase associated with the Aharonov-Bohm effect. Analytical expressions are found for the vortex number and topological charge densities as functions of distance from the flux point. Comparison is made with the distribution of vortices in the isotropic random wave model. The results indicate that as the flux approaches half-integer values, a vortex with the same sign as the fractional part of the flux is attracted to the flux point, merging with it at half-integer flux. Other features of the Aharonov-Bohm vortex distribution are also explored.Comment: 16 pages, 5 figure

Air Force Space Requirements: Can Industry Meet the Challenge for Space Systems?

A major issue in achieving the optimum employment of aerospace forces in space is the capability and capacity of the industrial base to produce the space systems necessary. The increasing number of space vehicles required by the Air Force arrl other U.S. Government, civilian, and foreign programs will have a profound impact on the industrial base. This paper presents the findings, conclusions, and recorrmendations resulting from an analysis of the space industrial base. The analysis, titled United States Air Force Production Base Analysis (PBA), is an ongoing assessment of the health and surge/mobilization capabilities of the defense industrial base. This paper focuses on the space industrial base; the space industries capability and capacity to produce space systems the Air Force needs through the year 1990

Abundance of conserved CRISPR-Cas9 target sites within the highly polymorphic genomes of Anopheles and Aedes mosquitoes.

A number of recent papers report that standing genetic variation in natural populations includes ubiquitous polymorphisms within target sites for Cas9-based gene drive (CGD) and that these "drive resistant alleles" (DRA) preclude the successful application of CGD for managing these populations. Here we report the results of a survey of 1280 genomes of the mosquitoes Anopheles gambiae, An. coluzzii, and Aedes aegypti in which we determine that ~90% of all protein-encoding CGD target genes in natural populations include at least one target site with no DRAs at a frequency of ≥1.0%. We conclude that the abundance of conserved target sites in mosquito genomes and the inherent flexibility in CGD design obviates the concern that DRAs present in the standing genetic variation of mosquito populations will be detrimental to the deployment of this technology for population modification strategies

Measurement of the psychosis continuum: Modelling the frequency and distress of subclinical psychotic experiences

Objective: Dimensional models of psychosis symptom frequency at clinical levels are representative of symptom dimensionality that is inclusive of distress. However, factor models of psychotic-like experiences, or subclinical symptomatology, in the general population have only ever been estimated using information on the frequency of occurrence. To ascertain whether dimensional representations of psychosis at subclinical levels are reflective of clinical manifestations of psychosis, factor models must utilise data that permits the measurement of both frequency and distress of psychosis experiences. Method: Psychotic-like experiences were assessed in a nonclinical sample (N = 462) using the 20 positive items from the CAPE42, which is a self-report questionnaire of psychotic experiences. For each item of the CAPE the frequency and distress ratings were recoded to form composite scores. Seven factor analytic models were specified and tested using confirmatory factor analysis. Results: The five-factor model of Wigman et al. (hallucinations, paranoia, grandiosity, delusions and paranormal beliefs factors) represented the best fitting model for both frequency and composite data. Conclusions: The findings constitute further evidence for a continuum of psychosis within the general population. Future analyses, aimed at delineating the dimensionality of psychosis, must advance towards the inclusion of distress as a central and necessary adjunct to measurement

Joint Multiple Testing Procedures for Graphical Model Selection with Applications to Biological Networks

Gaussian graphical models have become popular tools for identifying relationships between genes when analyzing microarray expression data. In the classical undirected Gaussian graphical model setting, conditional independence relationships can be inferred from partial correlations obtained from the concentration matrix (= inverse covariance matrix) when the sample size n exceeds the number of parameters p which need to estimated. In situations where n \u3c p, another approach to graphical model estimation may rely on calculating unconditional (zero-order) and first-order partial correlations. In these settings, the goal is to identify a lower-order conditional independence graph, sometimes referred to as a ‘0-1 graphs’. For either choice of graph, model selection may involve a multiple testing problem, in which edges in a graph are drawn only after rejecting hypotheses involving (saturated or lower-order) partial correlation parameters. Most multiple testing procedures applied in previously proposed graphical model selection algorithms rely on standard, marginal testing methods which do not take into account the joint distribution of the test statistics derived from (partial) correlations. We propose and implement a multiple testing framework useful when testing for edge inclusion during graphical model selection. Two features of our methodology include (i) a computationally efficient and asymptotically valid test statistics joint null distribution derived from influence curves for correlation-based parameters, and (ii) the application of empirical Bayes joint multiple testing procedures which can effectively control a variety of popular Type I error rates by incorpo- rating joint null distributions such as those described here (Dudoit and van der Laan, 2008). Using a dataset from Arabidopsis thaliana, we observe that the use of more sophisticated, modular approaches to multiple testing allows one to identify greater numbers of edges when approximating an undirected graphical model using a 0-1 graph. Our framework may also be extended to edge testing algorithms for other types of graphical models (e.g., for classical undirected, bidirected, and directed acyclic graphs)

Resampling-Based Empirical Bayes Multiple Testing Procedures for Controlling Generalized Tail Probability and Expected Value Error Rates:

This article proposes resampling-based empirical Bayes multiple testing procedures for controlling a broad class of Type I error rates, defined as generalized tail probability (gTP) error rates, gTP(q,g) = Pr(g(Vn,Sn) \u3e q), and generalized expected value (gEV) error rates, gEV(g) = [g(Vn,Sn)], for arbitrary functions g(Vn,Sn) of the numbers of false positives Vn and true positives Sn. Of particular interest are error rates based on the proportion g(Vn,Sn) = Vn/(Vn + Sn) of Type I errors among the rejected hypotheses, such as the false discovery rate (FDR), FDR = [Vn/(Vn + Sn)]. The proposed procedures offer several advantages over existing methods. They provide Type I error control for general data generating distributions, with arbitrary dependence structures among variables. Gains in power are achieved by deriving rejection regions based on guessed sets of true null hypotheses and null test statistics randomly sampled from joint distributions that account for the dependence structure of the data. The Type I error and power properties of an FDR-controlling version of the resampling-based empirical Bayes approach are investigated and compared to those of widely-used FDR-controlling linear step-up procedures in a simulation study. The Type I error and power trade-off achieved by the empirical Bayes procedures under a variety of testing scenarios allows this approach to be competitive with or outperform the Storey and Tibshirani [2003] linear step-up procedure, as an alternative to the classical Benjamini and Hochberg [1995] procedure