Sentencing Disparities in Yakima County: The Washington Sentencing Reform Act Revisited

This study expands upon an earlier exploration of sentencing disparity in the Yakima County, Washington judicial system. The Sentencing Reform Act was adopted in 1981, becoming effective in 1984, to end inequitable sentences imposed on individuals who are convicted of similar offenses. This work adds to the original study by including an investigation of exceptional sentences and offense type crime. Independent variables are defendants\u27 ethnicity (Hispanic, Native American, and White), age, and gender. The period of investigation includes fiscal years 1986 through 1991. Data was provided to the researchers by the Washington Sentencing Guidelines Commission and was processed using a difference of means test (ANOVA program). The findings suggest that sentencing disparity, while not being widespread, does persist nearly a decade after the Sentencing Reform Act was adopted. Hispanic defendants who had no prior criminal history were apt to receive disproportionately more severe sentences for similar crimes than Native Americans or whites

Mass Spectrum and Bounds on the Couplings in Yukawa Models With Mirror-Fermions

The $\rm SU(2)_L\otimes SU(2)_R$ symmetric Yukawa model with mirror-fermions in the limit where the mirror-fermion is decoupled is studied both analytically and numerically. The bare scalar self-coupling $\lambda$ is fixed at zero and infinity. The phase structure is explored and the relevant phase transition is found to be consistent with a second order one. The fermionic mass spectrum close to that transition is discussed and a first non-perturbative estimate of the influence of fermions on the upper and lower bounds on the renormalized scalar self-coupling is given. Numerical results are confronted with perturbative predictions.Comment: 7 (Latex) page

A Probabilistic Embedding Clustering Method for Urban Structure Detection

Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by learning via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.Comment: 6 pages, 7 figures, ICSDM201