Correctional Education: A Pathway to Reducing Recidivism in Wisconsin?

The United States has the highest number of incarcerated individuals per capita of any country in the world. Every single year, a large number of these individuals are released from prison and re-enter their communities. However, within three years,many of them will have recidivated and returned to prison. In Wisconsin, roughly 40% of all individuals released from prison will recidivate within three years. The financial burden of keeping such a high number of people incarcerated is monumental and rising. Identifying solutions to the problem of recidivism is critical to reducing the overall costs of incarceration for society. Correctional education programs have been shown to be one avenue in reducing the recidivism rates of individuals who participate in them while they are incarcerated. To date, Wisconsin has had very limited study of its correctional education programs and their effectiveness in reducing recidivism rates. In this descriptive piece of work, I will be examining the correctional education programs offered within the Wisconsin correctional system. Additionally, I will provide a basic analysis of the effectiveness that involvement in Wisconsin’s correctional education programs has on reducing recidivism rates

Irrigation Laws of the West

Thesis for Degree of LL.B

Deep Learning Hyperparameter Optimization for Breast Mass Detection in Mammograms

Accurate breast cancer diagnosis through mammography has the potential to save millions of lives around the world. Deep learning (DL) methods have shown to be very effective for mass detection in mammograms. Additional improvements of current DL models will further improve the effectiveness of these methods. A critical issue in this context is how to pick the right hyperparameters for DL models. In this paper, we present GA-E2E, a new approach for tuning the hyperparameters of DL models for brest cancer detection using Genetic Algorithms (GAs). Our findings reveal that differences in parameter values can considerably alter the area under the curve (AUC), which is used to determine a classifier's performance