Historical Revelation for Present-Day Liberation: What the Most Famous Prison Uprising in US History Can Teach Us About Social Change

The Attica Prison Uprising of 1971, in which prisoners held control of the facility for four days, ended with an assault on the prisoners gathered in D yard. When the tear gas cleared, 29 prisoners and 10 of the guards they had taken hostage were dead, and 128 more men were wounded—all at the hand of the state. The retaking was historically significant not only for the magnitude of the bloodshed but for the cover-up that followed. Investigations were stymied; what investigators ultimately did discover was redacted or sealed. In this paper I examine the Attica Uprising through a lens of prison abolition and explore it as a tool of critical pedagogy. Depending heavily on Heather Ann Thompson’s recent revelations about the Attica Uprising, I retell the history of the uprising guided by Paulo Freire’s pedagogy for liberation. I conclude that such a critical retelling can teach us several lessons about solidarity and social movements in a divided society

Introduction to Tapestries Volume 7: Breaking the Shackles of Silence: Knowledge Production as Activism and Resistance

Introduction to volume 7 of Macalester College\u27s journal Tapestries: Interwoven voices of local and global identities

Reducing dose for digital cranial radiography : The increased source to the image-receptor distance approach

This investigation proposes that an increased source to the image-receptor distance (SID) technique can be used to optimize occipital frontal and lateral cranial radiographs acquired with direct digital radiography. Although cranial radiography is not performed on a routine basis, it should nonetheless be optimized to keep the dose to the patient as low as reasonably achievable, particularly because it can form part of the facial bone and sinus series. Dose measurements were acquired at various SIDs, and image quality was assessed using visual grading analysis. Statistically significant reductions in the effective dose between 19.2% and 23.9% were obtained when the SID was increased from the standard 100 to 150 cm (P ≤.05), and visual grading analysis scores indicate that image quality remained diagnostically acceptable for both projections. This investigation concludes that increasing the SID effectively optimizes occipital frontal and lateral skull radiographs. Radiology departments must be advised of the benefits of this technique with the goal of introducing an updated reference SID of 150 cm into clinical practice.Peer reviewe

CPPN2GAN: Combining Compositional Pattern Producing Networks and GANs for Large-Scale Pattern Generation

Generative Adversarial Networks (GANs) are proving to be a powerful indirect genotype-to-phenotype mapping for evolutionary search, but they have limitations. In particular, GAN output does not scale to arbitrary dimensions, and there is no obvious way of combining multiple GAN outputs into a cohesive whole, which would be useful in many areas, such as the generation of video game levels. Game levels often consist of several segments, sometimes repeated directly or with variation, organized into an engaging pattern. Such patterns can be produced with Compositional Pattern Producing Networks (CPPNs). Specifically, a CPPN can define latent vector GAN inputs as a function of geometry, which provides a way to organize level segments output by a GAN into a complete level. This new CPPN2GAN approach is validated in both Super Mario Bros. and The Legend of Zelda. Specifically, divergent search via MAP-Elites demonstrates that CPPN2GAN can better cover the space of possible levels. The layouts of the resulting levels are also more cohesive and aesthetically consistent.Comment: GECCO 2020. arXiv admin note: text overlap with arXiv:2004.0015

Interactive Evolution and Exploration within Latent Level-Design Space of Generative Adversarial Networks

Generative Adversarial Networks (GANs) are an emerging form of indirect encoding. The GAN is trained to induce a latent space on training data, and a real-valued evolutionary algorithm can search that latent space. Such Latent Variable Evolution (LVE) has recently been applied to game levels. However, it is hard for objective scores to capture level features that are appealing to players. Therefore, this paper introduces a tool for interactive LVE of tile-based levels for games. The tool also allows for direct exploration of the latent dimensions, and allows users to play discovered levels. The tool works for a variety of GAN models trained for both Super Mario Bros. and The Legend of Zelda, and is easily generalizable to other games. A user study shows that both the evolution and latent space exploration features are appreciated, with a slight preference for direct exploration, but combining these features allows users to discover even better levels. User feedback also indicates how this system could eventually grow into a commercial design tool, with the addition of a few enhancements.Comment: GECCO 202

evidence from silent sites

data and scripts from companion paper Bontrager et al. http://dx.doi.org/10.1101/03591

Evolving Mario levels in the latent space of a deep convolutional generative adversarial network

© 2018 Copyright held by the owner/author(s). Generative Adversarial Networks (GANs) are a machine learning approach capable of generating novel example outputs across a space of provided training examples. Procedural Content Generation (PCG) of levels for video games could benefit from such models, especially for games where there is a pre-existing corpus of levels to emulate. This paper trains a GAN to generate levels for Super Mario Bros using a level from the Video Game Level Corpus. The approach successfully generates a variety of levels similar to one in the original corpus, but is further improved by application of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Specifically, various fitness functions are used to discover levels within the latent space of the GAN that maximize desired properties. Simple static properties are optimized, such as a given distribution of tile types. Additionally, the champion A* agent from the 2009 Mario AI competition is used to assess whether a level is playable, and how many jumping actions are required to beat it. These fitness functions allow for the discovery of levels that exist within the space of examples designed by experts, and also guide the search towards levels that fulfill one or more specified objectives

Data from: Statistical evidence for common ancestry: application to primates

Since Darwin, biologists have come to recognize that the theory of descent from common ancestry is very well supported by diverse lines of evidence. However, while the qualitative evidence is overwhelming, we also need formal methods for quantifying the evidential support for common ancestry (CA) over the alternative hypothesis of separate ancestry (SA). In this paper we explore a diversity of statistical methods, using data from the primates. We focus on two alternatives to CA, species SA (the separate origin of each named species) and family SA (the separate origin of each family). We implemented statistical tests based on morphological, molecular, and biogeographic data and developed two new methods: one that tests for phylogenetic autocorrelation while correcting for variation due to confounding ecological traits and a method for examining whether fossil taxa have fewer derived differences than living taxa. We overwhelmingly rejected both species and family SA, with infinitesimal p-values. We compare these results with those from two companion papers, which also found tremendously strong support for the CA of all primates, and discuss future directions and general philosophical issues that pertain to statistical testing of historical hypotheses such as CA