Racial Conflicts In Schools

That racially motivated conflicts occur in schools is an indisputable fact that becomes evident upon review of both academic literature and popular media. Events such as the Jena 6 incident (Maxwell & Zehr, 2007), school wide racially motivated riots (latimes.com), and court rulings (theithican.org) are distressing examples that racial barriers are real and potentially dangerous for many students in this country. However, little is written about the nature of racial conflicts, including the actual process school leaders engage in when determining how or even whether to intervene in racial conflicts, and the affect those racial conflicts have on the school climate and relevant stakeholders (e.g. directly involved students, other students, and school staff). To address this concern the current study is designed to provide insight into the decision-making process of school counselors in the intervention of racial conflicts that occur between students. The findings of this study will be pertinent and beneficial to all educational professionals as well as students. The following review provides context for understanding racial conflicts in schools, and addresses such issues as prevalence rates, causes, consequences, theories, and interventions to address such conflicts. Finally, the review concludes with a description of limitations in the research and a description of a proposed study

Measuring Quark-Gluon-Plasma Thermalization Time with Dileptons

We calculate the medium dilepton yield from a quark-gluon plasma which has a time-dependent local momentum-space anisotropy. A phenomenological model for the hard momentum scale, p_hard(tau), and plasma anisotropy parameter, xi(tau), is constructed which interpolates between free streaming behavior at early times (tau > tau_iso). We show that high-energy dilepton production is sensitive to the assumed plasma isotropization time, tau_iso, and can therefore be used to experimentally determine the time of onset for hydrodynamic expansion of a quark-gluon plasma and the magnitude of expected early-time momentum-space anisotropies.Comment: 4 pages, 5 figures; v3: update to match published versio

Connected by 25: Improving the Life Chances of the Country's Most Vulnerable Youth

Identifies the four groups of youth who are at the highest risk of long-term unemployment, incarceration, and social disconnection. Discusses a number of policy directions for helping these youth make successful transitions into adulthood

Connected by 25: Improving the Life Chances of the Country's Most Vulnerable 14-24 Year Olds

Virtually all youth not connected by age 25 begin the process of disconnection much earlier, usually before age 19. In our society, almost all youth require support until they have connected successfully with the labor force, which generally does not occur until their mid-twenties. Most young adults experience detours on the road to economic independence, including periods of unemployment and periodic interruptions in their education. In this paper, we address several issues relevant to developing such a system of services. We begin by identifying those groups of youth at highest risk of long-term disconnection.This is critical for developing policies and programs and for deciding how to target such rograms. Research indicates that those youth who are unable to make a successful ransition differ in important ways from other out of school/unemployed youth

Constraining the onset of viscous hydrodynamics

We derive two general criteria that can be used to constrain the initial time of the onset of 2nd-order conformal viscous hydrodynamics in relativistic heavy-ion collisions. We show this explicitly for 0+1 dimensional viscous hydrodynamics and discuss how to extend the constraint to higher dimensions.Comment: 2 pages, 2 figures - To appear in the conference proceedings for Quark Matter 2009, March 30 - April 4, Knoxville, Tennessee. Selected Poster for the Flash Talk Session at QM09. v3: typos corrected, minor format changes and updated reference

Reducing the Effects of Detrimental Instances

Not all instances in a data set are equally beneficial for inducing a model of the data. Some instances (such as outliers or noise) can be detrimental. However, at least initially, the instances in a data set are generally considered equally in machine learning algorithms. Many current approaches for handling noisy and detrimental instances make a binary decision about whether an instance is detrimental or not. In this paper, we 1) extend this paradigm by weighting the instances on a continuous scale and 2) present a methodology for measuring how detrimental an instance may be for inducing a model of the data. We call our method of identifying and weighting detrimental instances reduced detrimental instance learning (RDIL). We examine RIDL on a set of 54 data sets and 5 learning algorithms and compare RIDL with other weighting and filtering approaches. RDIL is especially useful for learning algorithms where every instance can affect the classification boundary and the training instances are considered individually, such as multilayer perceptrons trained with backpropagation (MLPs). Our results also suggest that a more accurate estimate of which instances are detrimental can have a significant positive impact for handling them.Comment: 6 pages, 5 tables, 2 figures. arXiv admin note: substantial text overlap with arXiv:1403.189

Boost-Invariant (2+1)-dimensional Anisotropic Hydrodynamics

We present results of the application of the anisotropic hydrodynamics (aHydro) framework to (2+1)-dimensional boost invariant systems. The necessary aHydro dynamical equations are derived by taking moments of the Boltzmann equation using a momentum-space anisotropic one-particle distribution function. We present a derivation of the necessary equations and then proceed to numerical solutions of the resulting partial differential equations using both realistic smooth Glauber initial conditions and fluctuating Monte-Carlo Glauber initial conditions. For this purpose we have developed two numerical implementations: one which is based on straightforward integration of the resulting partial differential equations supplemented by a two-dimensional weighted Lax-Friedrichs smoothing in the case of fluctuating initial conditions; and another that is based on the application of the Kurganov-Tadmor central scheme. For our final results we compute the collective flow of the matter via the lab-frame energy-momentum tensor eccentricity as a function of the assumed shear viscosity to entropy ratio, proper time, and impact parameter.Comment: 45 pages, 12 figures; v2 published versio

Missing Value Imputation With Unsupervised Backpropagation

Many data mining and data analysis techniques operate on dense matrices or complete tables of data. Real-world data sets, however, often contain unknown values. Even many classification algorithms that are designed to operate with missing values still exhibit deteriorated accuracy. One approach to handling missing values is to fill in (impute) the missing values. In this paper, we present a technique for unsupervised learning called Unsupervised Backpropagation (UBP), which trains a multi-layer perceptron to fit to the manifold sampled by a set of observed point-vectors. We evaluate UBP with the task of imputing missing values in datasets, and show that UBP is able to predict missing values with significantly lower sum-squared error than other collaborative filtering and imputation techniques. We also demonstrate with 24 datasets and 9 supervised learning algorithms that classification accuracy is usually higher when randomly-withheld values are imputed using UBP, rather than with other methods