W. E. B. Du Bois on Brown v. Board of Education

The 1960s have been described as the civil rights decade in American history. Few scholar-activists have been identified as strongly with the legal, social, economic, and political changes culminating in the 1960s as has African American historian, sociologist, psychologist W. E. B. Du Bois. Inexplicably, in 2003, the 100-year anniversary of Du Bois\u27 classic, The Souls of Black Folk (1903), came and went with little fanfare within or outside of academia. However, in 2004, the 50-year anniversary of the initial U. S. Supreme Court decision in Brown v. Board of Education (1954) presents an opportunity for ethnic studies in general, and Black studies in particular, to acknowledge the intellectual and political contributions of Du Bois to the civil rights movement in the United States. In the post-Civil Rights Era, some authors have suggested that Du Bois opposed the initial Brown v. Board of Education (1954) ruling. In contrast, I observe in the present paper that Du Bois (1957) opposed the U. S. Supreme Court\u27s subsequent (1955) ruling that invoked the much-criticized term with all deliberate speed, rather than the initial (1954) ruling that rendered the separate but equal doctrine unconstitutional. Moreover, I contend that Du Bois\u27 own values and attitudes were fully consistent with his position on the (1954, 1955) decisions

Automatically Designing CNN Architectures for Medical Image Segmentation

Deep neural network architectures have traditionally been designed and explored with human expertise in a long-lasting trial-and-error process. This process requires huge amount of time, expertise, and resources. To address this tedious problem, we propose a novel algorithm to optimally find hyperparameters of a deep network architecture automatically. We specifically focus on designing neural architectures for medical image segmentation task. Our proposed method is based on a policy gradient reinforcement learning for which the reward function is assigned a segmentation evaluation utility (i.e., dice index). We show the efficacy of the proposed method with its low computational cost in comparison with the state-of-the-art medical image segmentation networks. We also present a new architecture design, a densely connected encoder-decoder CNN, as a strong baseline architecture to apply the proposed hyperparameter search algorithm. We apply the proposed algorithm to each layer of the baseline architectures. As an application, we train the proposed system on cine cardiac MR images from Automated Cardiac Diagnosis Challenge (ACDC) MICCAI 2017. Starting from a baseline segmentation architecture, the resulting network architecture obtains the state-of-the-art results in accuracy without performing any trial-and-error based architecture design approaches or close supervision of the hyperparameters changes.Comment: Accepted to Machine Learning in Medical Imaging (MLMI 2018

The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System

Natural evolution has produced a tremendous diversity of functional organisms. Many believe an essential component of this process was the evolution of evolvability, whereby evolution speeds up its ability to innovate by generating a more adaptive pool of offspring. One hypothesized mechanism for evolvability is developmental canalization, wherein certain dimensions of variation become more likely to be traversed and others are prevented from being explored (e.g. offspring tend to have similarly sized legs, and mutations affect the length of both legs, not each leg individually). While ubiquitous in nature, canalization almost never evolves in computational simulations of evolution. Not only does that deprive us of in silico models in which to study the evolution of evolvability, but it also raises the question of which conditions give rise to this form of evolvability. Answering this question would shed light on why such evolvability emerged naturally and could accelerate engineering efforts to harness evolution to solve important engineering challenges. In this paper we reveal a unique system in which canalization did emerge in computational evolution. We document that genomes entrench certain dimensions of variation that were frequently explored during their evolutionary history. The genetic representation of these organisms also evolved to be highly modular and hierarchical, and we show that these organizational properties correlate with increased fitness. Interestingly, the type of computational evolutionary experiment that produced this evolvability was very different from traditional digital evolution in that there was no objective, suggesting that open-ended, divergent evolutionary processes may be necessary for the evolution of evolvability.Comment: SI can be found at: http://www.evolvingai.org/files/SI_0.zi