[Review of] Yanick St. Jean and Joe R. Feagin. Double Burden: Black Women and Everyday Racism

The women interviewed in Double Burden share personal accounts of what it is like to be black and female in the contemporary United States. Drawing on over two hundred interviews with middle-class, well-educated black women, Yannick St. Jean and Joe R. Feagin present a collective memory of the misrepresentation of black women in our history, as well as individual experiences and triumphs. Through excerpts of personal narratives on topics including career, work, physical appearance, media representation, relationships with white women, and motherhood, the women recount experiences dealing with everyday racism, the denigrating social messages about their beauty, self-worth, sexuality, intelligence, and drive

Bayesian Entropy Estimation for Countable Discrete Distributions

We consider the problem of estimating Shannon's entropy $H$ from discrete data, in cases where the number of possible symbols is unknown or even countably infinite. The Pitman-Yor process, a generalization of Dirichlet process, provides a tractable prior distribution over the space of countably infinite discrete distributions, and has found major applications in Bayesian non-parametric statistics and machine learning. Here we show that it also provides a natural family of priors for Bayesian entropy estimation, due to the fact that moments of the induced posterior distribution over $H$ can be computed analytically. We derive formulas for the posterior mean (Bayes' least squares estimate) and variance under Dirichlet and Pitman-Yor process priors. Moreover, we show that a fixed Dirichlet or Pitman-Yor process prior implies a narrow prior distribution over $H$, meaning the prior strongly determines the entropy estimate in the under-sampled regime. We derive a family of continuous mixing measures such that the resulting mixture of Pitman-Yor processes produces an approximately flat prior over $H$. We show that the resulting Pitman-Yor Mixture (PYM) entropy estimator is consistent for a large class of distributions. We explore the theoretical properties of the resulting estimator, and show that it performs well both in simulation and in application to real data.Comment: 38 pages LaTeX. Revised and resubmitted to JML

The equivalence of information-theoretic and likelihood-based methods for neural dimensionality reduction

Stimulus dimensionality-reduction methods in neuroscience seek to identify a low-dimensional space of stimulus features that affect a neuron's probability of spiking. One popular method, known as maximally informative dimensions (MID), uses an information-theoretic quantity known as "single-spike information" to identify this space. Here we examine MID from a model-based perspective. We show that MID is a maximum-likelihood estimator for the parameters of a linear-nonlinear-Poisson (LNP) model, and that the empirical single-spike information corresponds to the normalized log-likelihood under a Poisson model. This equivalence implies that MID does not necessarily find maximally informative stimulus dimensions when spiking is not well described as Poisson. We provide several examples to illustrate this shortcoming, and derive a lower bound on the information lost when spiking is Bernoulli in discrete time bins. To overcome this limitation, we introduce model-based dimensionality reduction methods for neurons with non-Poisson firing statistics, and show that they can be framed equivalently in likelihood-based or information-theoretic terms. Finally, we show how to overcome practical limitations on the number of stimulus dimensions that MID can estimate by constraining the form of the non-parametric nonlinearity in an LNP model. We illustrate these methods with simulations and data from primate visual cortex

Longwood is Long Gone

This is the first of many journal entries I plan to submit giving a perspective into the COVID-19 crisis from a student that remained on campus.https://digitalcommons.longwood.edu/covid19/1051/thumbnail.jp

Clashing Policies or Confusing Precedents: The Gross Negligence Exception to Consequential Damages Disclaimers

Consequential damages can easily amount to millions of dollars. Commercial parties often disclaim consequential damages in their contracts. This Article posits that such disclaimers between commercial parties under the Uniform Commercial Code (UCC) should not be found unenforceable based on gross negligence. Article 2 of the UCC promotes the policy of freedom of contract. Consistent with that policy, section 2-719 of the UCC provides that contractual consequential damages disclaimers should be enforceable absent a finding of unconscionability. This Article analyzes the interplay among UCC section 2-719, “public policy” exceptions to enforcing limitations of liability, and the law of gross negligence. This Article concludes that but for those rare circumstances in which a commercial buyer may invoke unconscionability, courts should uphold consequential damages disclaimers absent a clear showing of willful misconduct. This standard provides a more discernible “bright-line” that comports with the general treatment of economic losses under the UCC

The Globe

The Globe Theater--common word to many, even those who have just heard of Shakespeare--is gone forever. Where? Perhaps, for firewood, for the building of other houses, and removed again for another home. If wood could only talk, what fascinating stories we would have. The building is gone--and we wish that there was some sort of historical record. Therefore, reconstruction is the answer; the interesting phase in this exercise is that the truth could never be known. Where the historical records end, the imagination begins

The Organized Church: An Analysis

This paper is a survey and analysis of a survey which was conducted on the campus of Ouachita Baptist University. It was given to fifty college-age males chosen at random. The statements in the survey were taken from The Measurement of Attitude by Thurstone and Chave, University of Chicago Press