Feeling, Knowledge, Self-Preservation: Audre Lorde’s Oppositional Agency and Some Implications for Ethics

Throughout her work, Audre Lorde maintains that her self-preservation in the face of oppression depends on acting from the recognition and valorization of her feelings as a deep source of knowledge. This claim, taken as a portrayal of agency, poses challenges to standard positions in ethics, epistemology, and moral psychology. This article examines the oppositional agency articulated by Lorde’s thought, locating feeling, poetry, and the power she calls “the erotic” within her avowed project of self-preservation. It then explores the implications of taking seriously Lorde’s account, particularly for theorists examining ethics and epistemology under nonideal social conditions. For situations of sexual intimacy, for example, Lorde’s account unsettles prevailing assumptions about the role of consent in responsibility between sexual partners. I argue that obligations to solicit consent and respect refusal are not sufficient to acknowledge the value of agency in intimate encounters when agency is oppositional in the way Lorde describes

A Project Based Approach to Statistics and Data Science

In an increasingly data-driven world, facility with statistics is more important than ever for our students. At institutions without a statistician, it often falls to the mathematics faculty to teach statistics courses. This paper presents a model that a mathematician asked to teach statistics can follow. This model entails connecting with faculty from numerous departments on campus to develop a list of topics, building a repository of real-world datasets from these faculty, and creating projects where students interface with these datasets to write lab reports aimed at consumers of statistics in other disciplines. The end result is students who are well prepared for interdisciplinary research, who are accustomed to coping with the idiosyncrasies of real data, and who have sharpened their technical writing and speaking skills

Using simulation to verify life history relations indicated by time series analysis

Time series analysis of population abundance is not based on assumptions about the dynamics of populations, but sometimes the results can be interpreted biologically. At other times the results are difficult to interpret. To better understand the results of a time series analysis of a walleye fish population, as it related to the walleye's life history, I compared a time series analysis of walleye field data with a time series analysis of simulated data from a population dynamics model. In the simulations, the nature of the time lags could be identified by changing model parameters. The simulations indicated that a partial autocorrelation coefficient (PAC) at lag 1 would result from density dependence, that a PAC at lag 5 would result from the time required for maturation, and that a negative sign at lag 5 would result from high larval survival. The simulation results help in the interpretation of the PACs obtained in the time series analysis of field data. Copyright © 1999 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/35235/1/348_ftp.pd

Curriculum Guidelines for Undergraduate Programs in Data Science

The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in Data Science. The group consisted of 25 undergraduate faculty from a variety of institutions in the U.S., primarily from the disciplines of mathematics, statistics and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in Data Science

Wavelet Neural Networks: A Practical Guide

Wavelet networks (WNs) are a new class of networks which have been used with great success in a wide range of application. However a general accepted framework for applying WNs is missing from the literature. In this study, we present a complete statistical model identification framework in order to apply WNs in various applications. The following subjects were thorough examined: the structure of a WN, training methods, initialization algorithms, variable significance and variable selection algorithms, model selection methods and finally methods to construct confidence and prediction intervals. In addition the complexity of each algorithm is discussed. Our proposed framework was tested in two simulated cases, in one chaotic time series described by the Mackey-Glass equation and in three real datasets described by daily temperatures in Berlin, daily wind speeds in New York and breast cancer classification. Our results have shown that the proposed algorithms produce stable and robust results indicating that our proposed framework can be applied in various applications

Improved tools for efficient mapping of fission yeast genes: identification of microtubule nucleation modifier mod22-1 as an allele of chromatin- remodelling factor gene swr1

Fission yeast genes identified in genetic screens are usually cloned by transformation of mutants with plasmid libraries. However, for some genes this can be difficult, and positional cloning approaches are required. The mutation swi5-39 reduces recombination frequency in homozygous crosses and has been used as a tool in mapping gene position (Schmidt, 1993). However, strain construction in swi5-39-based mapping is significantly more laborious than is desirable. Here we describe a set of strains designed to make swi5-based mapping more efficient and more powerful. The first improvement is the use of a swi5Δ strain marked with kanamycin (G418) resistance, which greatly facilitates identification of swi5 mutants. The second improvement, which follows directly from the first, is the introduction of a large number of auxotrophic markers into mapping strains, increasing the likelihood of finding close linkage between a marker and the mutation of interest. We combine these new mapping strains with a rec12Δ-based approach for initial mapping of a mutation to an individual chromosome. Together, the two methods allow an approximate determination of map position in only a small number of crosses. We used these to determine that mod22-1, a modifier of microtubule nucleation phenotypes, encodes a truncation allele of Swr1, a chromatin-remodelling factor involved in nucleosomal deposition of H2A.Z histone variant Pht1. Expression microarray analysis of mod22-1, swr1Δ and pht1Δ cells suggests that the modifier phenotype of mod22-1 mutants may be due to small changes in expression of one or more genes involved in tubulin function. Copyright © 2009 John Wiley & Sons, Ltd

Curriculum Guidelines for Undergraduate Programs in Data Science

The Park City Math Institute 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in data science. The group consisted of 25 undergraduate faculty from a variety of institutions in the United States, primarily from the disciplines of mathematics, statistics, and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in data science