Role Tension in the Academy: A Philosophical Inquiry into Faculty Teaching and Research

This dissertation seeks to understand the conjunction of faculty roles as teachers and as researchers. This understanding is pursued through philosophical analysis. Discourse ethics, in particular, is used as a framework by which to best understand the roles played by faculty and if the roles of teacher and researcher are, in fact, commensurable. The purpose of the work is two-fold: 1) to develop a construct that may be used by future researchers to better understand the roles played by faculty, and 2) to suggest a best-construct that enables future researchers to propose how actual lived roles should be instantiated in the world. The dissertation reviews a series of university handbooks, professional association ethical guidelines, and philosophical arguments to establish how the roles of faculty are best understood. The investigation illuminates the tensions at the heart of faculty roles. This tension is not definitionally embedded in the roles of faculty as teacher and researcher. Rather, the tension emerges from the failure of institutions to fully actualize faculty roles as normatively grounded in human communicative interaction. As a result, the work suggests that in order to best resolve the cognitive dissonance that may be experienced as a result of role ambiguity, faculty should engage in a process of self-reflection and community dialectic in order to best determine how “faculty” can be actualized in a way that best benefits all stakeholders

The Application of the Revised Principle of Alternate Possibilities in a Causality Determined Universe

According to Henry J. Frankfurt, the claim that “ought implies can” is taken by many philosophers as so foundational as to almost be considered an “a priori” truth. In his paper “Alternate Possibilities and Moral Responsibility,” Frankfurt challenges this assumption. He proposes the “revised principle of alternate possibilities,” asserting that we intuitively absolve agents of moral responsibility only if they act solely because they could not do otherwise. Ten years later, John Martin Fischer challenges Frankfurt’s claim, asserting that this cannot be the case if an agent exists within a universe governed by actual sequence causation and therefore, moral accountability and determinism remain non-reconcilable. These seemingly incompatible claims may be reconcilable after thorough analysis of intentionality. Even in the face of existence within a nominologically inevitable determinism, a kind of “Error Theory Compatibalism” is feasible

Bayesian models and inferential methods for forecasting disease outbreak severity

Timely monitoring and prediction of the trajectory of seasonal influenza epidemics allows hospitals and medical centers to prepare for, and provide better service to, patients with influenza. The U.S. Outpatient Influenza-like Illness Surveillance Network, or ILINet, collects data on influenza-like illnesses from over 3,300 health care providers, and uses these data to produce indicators of current influenza epidemic severity. ILINet data provide an unbiased estimate of the severity of a season\u27s influenza epidemic, and are typically reported at a lag of about two weeks. Other sources of influenza severity, such as indices calculated from search engine query data from Google, Twitter, and Wikipeida, are provided in near-real time. However, these sources of data are less direct measurements of influenza severity than ILINet indicators, and are likely to suffer from bias. We begin by describing general methods for inference on state space models implemented in the NIMBLE R package, and demonstrate these inferential methods as applied to influenza outbreak forecasting. We then examine model specifications to estimate epidemic severity which incorporate data from both ILINet and other real-time, possibly biased sources. We fit these models using Google Flu Trends data, which uses the number of Google searches for influenza related keywords to calculate an estimate of epidemic severity. We explicitly model the possible bias of the Google Flu Trends data, which allows us to make epidemic severity predictions which take advantage of the recency of Google Flu Trends data and the accuracy of ILINet data, and we preform estimation using Bayesian methods. Models with and without explicit bias modeling are compared to models using only ILINet data, and it is found that including GFT data significantly improves forecasting accuracy of epidemic severity. We also propose hierarchical models which incorporate multiple seasons of influenza data, and evaluate the forecasting benefits that hierarchical modeling confers

Sequential Monte Carlo Methods in the nimble and nimbleSMC R Packages

nimble is an R package for constructing algorithms and conducting inference on hierarchical models. The nimble package provides a unique combination of flexible model specification and the ability to program model-generic algorithms. Specifically, the package allows users to code models in the BUGS language, and it allows users to write algorithms that can be applied to any appropriate model. In this paper, we introduce the nimbleSMC R package. nimbleSMC contains algorithms for state-space model analysis using sequential Monte Carlo (SMC) techniques that are built using nimble. We first provide an overview of state-space models and commonly-used SMC algorithms. We then describe how to build a state-space model in nimble and conduct inference using existing SMC algorithms within nimbleSMC. SMC algorithms within nimbleSMC currently include the bootstrap filter, auxiliary particle filter, ensemble Kalman filter, IF2 method of iterated filtering, and a particle Markov chain Monte Carlo (MCMC) sampler. These algorithms can be run in R or compiled into C++ for more efficient execution. Examples of applying SMC algorithms to linear autoregressive models and a stochastic volatility model are provided. Finally, we give an overview of how model-generic algorithms are coded within nimble by providing code for a simple SMC algorithm. This illustrates how users can easily extend nimble's SMC methods in high-level code

Nested Adaptation of MCMC Algorithms

© 2020 International Society for Bayesian Analysis Markov chain Monte Carlo (MCMC) methods are ubiquitous tools for simulation-based inference in many fields but designing and identifying good MCMC samplers is still an open question. This paper introduces a novel MCMC algorithm, namely, Nested Adaptation MCMC. For sampling variables or blocks of variables, we use two levels of adaptation where the inner adaptation optimizes the MCMC performance within each sampler, while the outer adaptation explores the space of valid kernels to find the optimal samplers. We provide a theoretical foundation for our approach. To show the generality and usefulness of the approach, we describe a framework using only standard MCMC samplers as candidate samplers and some adaptation schemes for both inner and outer iterations. In several benchmark problems, we show that our proposed approach substantially outperforms other approaches, including an automatic blocking algorithm, in terms of MCMC efficiency and computational time

Functional mammalian spliceosomal complex E contains SMN complex proteins in addition to U1 and U2 snRNPs

Copyright @ 2011 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.Spliceosomes remove introns from primary gene transcripts. They assemble de novo on each intron through a series of steps that involve the incorporation of five snRNP particles and multiple non-snRNP proteins. In mammals, all the intermediate complexes have been characterized on one transcript (MINX), with the exception of the very first, complex E. We have purified this complex by two independent procedures using antibodies to either U1-A or PRPF40A proteins, which are known to associate at an early stage of assembly. We demonstrate that the purified complexes are functional in splicing using commitment assays. These complexes contain components expected to be in the E complex and a number of previously unrecognized factors, including survival of motor neurons (SMN) and proteins of the SMN-associated complex. Depletion of the SMN complex proteins from nuclear extracts inhibits formation of the E complex and causes non-productive complexes to accumulate. This suggests that the SMN complex stabilizes the association of U1 and U2 snRNPs with pre-mRNA. In addition, the antibody to PRPF40A precipitated U2 snRNPs from nuclear extracts, indicating that PRPF40A associates with U2 snRNPs

Development of the preterm gut microbiome in twins at risk of necrotising enterocolitis and sepsis

The preterm gut microbiome is a complex dynamic community influenced by genetic and environmental factors and is implicated in the pathogenesis of necrotising enterocolitis (NEC) and sepsis. We aimed to explore the longitudinal development of the gut microbiome in preterm twins to determine how shared environmental and genetic factors may influence temporal changes and compared this to the expressed breast milk (EBM) microbiome. Stool samples (n = 173) from 27 infants (12 twin pairs and 1 triplet set) and EBM (n = 18) from 4 mothers were collected longitudinally. All samples underwent PCR-DGGE (denaturing gradient gel electrophoresis) analysis and a selected subset underwent 454 pyrosequencing. Stool and EBM shared a core microbiome dominated by Enterobacteriaceae, Enterococcaceae, and Staphylococcaceae. The gut microbiome showed greater similarity between siblings compared to unrelated individuals. Pyrosequencing revealed a reduction in diversity and increasing dominance of Escherichia sp. preceding NEC that was not observed in the healthy twin. Antibiotic treatment had a substantial effect on the gut microbiome, reducing Escherichia sp. and increasing other Enterobacteriaceae. This study demonstrates related preterm twins share similar gut microbiome development, even within the complex environment of neonatal intensive care. This is likely a result of shared genetic and immunomodulatory factors as well as exposure to the same maternal microbiome during birth, skin contact and exposure to EBM. Environmental factors including antibiotic exposure and feeding are additional significant determinants of community structure, regardless of host genetics

User-friendly automatic transcription of low-resource languages: Plugging ESPnet into Elpis

This paper reports on progress integrating the speech recognition toolkit ESPnet into Elpis,a web front-end originally designed to provide access to the Kaldi automatic speech recognition toolkit. The goal of this work is to makeend-to-end speech recognition models avail-able to language workers via a user-friendlygraphical interface. Encouraging results are reported on (i) development of an ESPnet recipe for use in Elpis, with preliminary resultson data sets previously used for training acoustic models with the Persephone toolkit alongwith a new data set that had not previously been used in speech recognition, and (ii) in-corporating ESPnet into Elpis along with UIe nhancements and a CUDA-supported Docker file