Juries and Social Media: Northwest Ohio\u27s Response to this Phenomenon

The 21st century is an era in which the dependence on technology is at an all-time high. The availability of information on social networking sites has recently sparked a debate in regards to jury impartiality. Over the last few years states such as Florida, Michigan, and New York have received scholastic attention for making changes to local jury instructions to ensure defendant’s rights of an impartial jury guaranteed by the Sixth Amendment. Federal national jury instructions addressing juror social media usage has also drawn attention. Little, however, scholastic attention has been directed towards Northwest Ohio. This project will look at the changes Northwest Ohio’s judicial system has made to ensure that jurors do not utilize social media sites while performing their civic duty. This study analyzes the perceived effectiveness of strategies currently in use in Northwest Ohio based on interviews with judges and attorneys in this region

Protecting the Protectors: Preserving and Enhancing the Rights of Legal Observers

Non

A Bayesian approach for statistical–physical bulk parameterization of rain microphysics. Part II: Idealized Markov chain Monte Carlo experiments

Observationally informed development of a new framework for bulk rain microphysics, the Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS; described in Part I of this study), is demonstrated. This scheme’s development is motivated by large uncertainties in cloud and weather simulations associated with approximations and assumptions in existing microphysics schemes. Here, a proof-of-concept study is presented using a Markov chain Monte Carlo sampling algorithm with BOSS to probabilistically estimate microphysical process rates and parameters directly from a set of synthetically generated rain observations. The framework utilized is an idealized steady-state one-dimensional column rainshaft model with specified column-top rain properties and a fixed thermodynamical profile. Different configurations of BOSS—flexibility being a key feature of this approach—are constrained via synthetic observations generated from a traditional three-moment bulk microphysics scheme. The ability to retrieve correct parameter values when the true parameter values are known is illustrated. For cases when there is no set of true parameter values, the accuracy of configurations of BOSS that have different levels of complexity is compared. It is found that addition of the sixth moment as a prognostic variable improves prediction of the third moment (proportional to bulk rain mass) and rain rate. In contrast, increasing process rate formulation complexity by adding more power terms has little benefit—a result that is explained using further-idealized experiments. BOSS rainshaft simulations are shown to well estimate the true process rates from constraint by bulk rain observations, with the additional benefit of rigorously quantified uncertainty of these estimates

Prediction of combustion state through a semi-supervised learning model and flame imaging

Accurate prediction of combustion state is crucial for an in-depth understanding of furnace performance and optimize operation conditions. Traditional data-driven approaches such as artificial neural networks and support vector machine incorporate distinct features which require prior knowledge for feature extraction and suffers poor generalization for unseen combustion states. Therefore, it is necessary to develop an advanced and accurate prediction model to resolve these limitations. This study presents a novel semi-supervised learning model integrating denoising autoencoder (DAE), generative adversarial network (GAN) and Gaussian process classifier (GPC). The DAE network is established to extract representative features of flame images and the network trained through the adversarial learning mechanism of the GAN. Structural similarity (SSIM) metric is introduced as a novel loss function to improve the feature learning ability of the DAE network. The extracted features are then fed into the GPC to predict the seen and unseen combustion states. The effectiveness of the proposed semi-supervised learning model, i.e., DAE-GAN-GPC was evaluated through 4.2 MW heavy oil-fired boiler furnace flame images captured under different combustion states. The averaged prediction accuracy of 99.83% was achieved for the seen combustion states. The new states (unseen) were predicted accurately through the proposed model by fine-tuning of GPC without retraining the DAE-GAN and averaged prediction accuracy of 98.36% was achieved for the unseen states. A comparative study was also carried out with other deep neural networks and classifiers. Results suggested that the proposed model provides better prediction accuracy and robustness capability compared to other traditional prediction models

Feature-Based Parameter Estimation of the Nonlinear Cloud and Rain Equation and Global Bayesian Optimization in Data Assimilation

We introduce numerical methods for Bayesian estimation applications. The first chapter demonstrates the use of feature-based parameter estimation methods in atmospheric science. The nonlinear cloud and rain equation represents emergent behavior of stratocumulus clouds through a simplified predator-prey model with rain acting as a predator of the clouds. We use a large eddy simulation as the ``ground truth'' and extract cycles of cloud growth and decay from the simulation. Our method treats the cycles as features and subsequently performs a Bayesian inversion to estimate the model parameters. In the second chapter, we discuss the uses of global Bayesian optimization in data assimilation. Global Bayesian optimization is a derivative-free optimization technique designed for optimizing computationally expensive functions. We show how it can be coupled to an ensemble Kalman filter to estimate model parameters, model states, and simultaneously tune localization and inflation parameters. To illustrate these ideas, we present numerical experiments with the classical Lorenz models

Protecting the Protectors: Preserving and Enhancing the Rights of Legal Observers

Non

Cruel and Unusual Performance: (Re)producing Capital Punishment on the U.S. Stage

Thesis (Ph.D.)--University of Washington, 2020This dissertation examines theatrical representations of state-sanctioned executions in the U.S. from the late eighteenth century to the early twentieth century alongside real-life executions and federal capital punishment policy. Through an in-depth engagement with stage performance, contemporaneously circulating scholarly and legal discourses regarding the death penalty, and Foucauldian concepts of punishment, governmentality, and liberalism, my research reveals how theatre artists reformulated their works, genres, and the art form to engage and enter into a dialogue with oppressive death penalty politics. A majority of the stagings of death, dying, and the death penalty throughout the late eighteenth, nineteenth, and early twentieth centuries with which this dissertation engages did not simply (re)produce the conditions under which capital punishment in the U.S thrived. Rather, they reveal the nuanced ways in which theatre artists sought to consistently reassess their works, the genres, and the art form for the betterment of society. Moreover, when read through the lens of death penalty politics and stagings, these productions offer up new ways of understanding how liberalism was practiced throughout these eras. By turning to the theatre’s engagement with capital punishment and death penalty politics, nearly an additional century of critical engagement with the topic is unlocked, as Supreme Court cases surrounding the death penalty did not begin until 1879. Through plays by William Dunlap, Dion Boucicault, George Aiken, John Wexley, Elliott Lester, and Sophie Treadwell, as well as their relevant production records, this dissertation traces the development of execution on the U.S. stage alongside major wars in the country and major political, cultural, and technological developments that aided and/or hindered capital punishment. Through each work, not only are themes are liberalism read through in-depth critical readings, but also concepts of civility, security, and danger, which have proven paramount to the maintenance of capital punishment in the U.S