Criminals in Love (2014)

Promotional materials produced for Criminals in Love, performed by the UMBC Theatre Department in May 2014. Includes playbill, program, and nine publicity photographs.May 1-May 4, 201

Progressive Band Processing for Hyperspectral Imaging

Hyperspectral imaging has emerged as an image processing technique in many applications. The reason that hyperspectral data is called hyperspectral is mainly because the massive amount of information provided by the hundreds of spectral bands that can be used for data analysis. However, due to very high band-to-band correlation much information may be also redundant. Consequently, how to effectively and best utilize such rich spectral information becomes very challenging. One general approach is data dimensionality reduction which can be performed by data compression techniques, such as data transforms, and data reduction techniques, such as band selection. This dissertation presents a new area in hyperspectral imaging, to be called progressive hyperspectral imaging, which has not been explored in the past. Specifically, it derives a new theory, called Progressive Band Processing (PBP) of hyperspectral data that can significantly reduce computing time and can also be realized in real-time. It is particularly suited for application areas such as hyperspectral data communications and transmission where data can be communicated and transmitted progressively through spectral or satellite channels with limited data storage. Most importantly, PBP allows users to screen preliminary results before deciding to continue with processing the complete data set. These advantages benefit users of hyperspectral data by reducing processing time and increasing the timeliness of crucial decisions made based on the data such as identifying key intelligence information when a required response time is short

Analysis of Routing and Wavelength Assignment in Large WDM Networks

In Wavelength Division Multiplexing (WDM) network, for a given connection request, a route has to be found, and a dedicated wavelength has to be assigned along that route. This problem of assigning route and wavelength to the connection request, using minimum network resources, is called Routing and Wavelength Assignment (RWA) Problem. This thesis focuses on the analysis of proposed RWA algorithms in large WDM networks. We use simulations and analysis of randomly generated large networks under dynamic traffic and static traffic, with and without protection of the connection request. The protection of the primary route between source and destination is considered by setting up a dedicated backup path in case of failures. The wavelength requirements are analyzed using different wavelength assignment heuristics under different routing techniques for a set of connection requests. We find that, the fixed alternate routing packs the connection requests into less number of wavelengths than the fixed routing and most-used performs slightly better than the first-fit heuristic

ELLIPSOIDAL TOLERANCE REGIONS AND SIMULTANEOUS TOLERANCE INTERVALS FOR SOME MULTIVARIATE NORMAL POPULATIONS

A tolerance region for a population is a region computed using a random sample, so that the region will include a specified proportion or more of the population, with a given confidence level. The theory of statistical tolerance regions has undergone vigorous development during the last several years. In particular, the computation of satisfactory tolerance regions for multivariate normal populations and multivariate regression models have been investigated, and approximations have been developed for computing the required tolerance factor. The available literature in the case of multivariate normal populations deal with the derivation of ellipsoidal tolerance regions only. The present research is motivated by several observations: for ellipsoidal tolerance regions, the available numerical methods to compute the tolerance factor are unsatisfactory, and the available approximations are not always accurate. Furthermore, the literature does not address the computation of simultaneous tolerance intervals. For computing the tolerance factor required to obtain an ellipsoidal tolerance region, an approximation coupled with Monte Carlo simulation is investigated. The methodology is developed when the unknown population covariance matrix does not have any specific structure, and when it has the intra-class covariance structure. Numerical results show that this approach results in an accurate tolerance factor. The methodology is extended to the case of a multivariate linear regression model for obtaining an ellipsoidal tolerance region under a fixed set of values of the covariates. The second part of the research deals with the derivation of simultaneous tolerance intervals and simultaneous prediction intervals. This is motivated by the fact that an ellipsoidal tolerance region cannot provide information on the distribution of the individual components of the response vector. The case of a general covariance matrix, and that of the intra-class covariance matrix are separately considered, and the derivation of both one-sided and two-sided tolerance intervals are addressed. In the case of the intra-class covariance structure, the derivation of a tolerance rectangle is also investigated. Numerical results are given to assess the accuracy of the proposed solutions, and illustrative examples and applications are provided

The Implementation of Maryland's Bridge Plan for Academic Validation Policy in Anne Arundel County Public Schools

Maintaining the integrity and value of a high school diploma has been a fundamental reason for the establishment of state graduation requirements. The No Child Left Behind (NCLB) Act of 2001 heightened the need for standards-based accountability programs within states. Once annual measurable objectives have been set by the state, it is the responsibility of individual schools and districts to make sure students meet these standards. To ensure uniformity in student assessment, most states design and require standardized tests as the indicator of adequate student progress. A problem accompanying this high-stakes testing approach is the large number of students that are unable to pass the assessments. In response to this reality, some school districts and states have designed alternative pathways to graduation. In Maryland, one of these pathways is called the Bridge Plan for Academic Validation - a performance-based assessment for students to demonstrate their knowledge in English 10, Algebra 1 and Biology. The implementation of this program varies across school districts and even schools within the same district. Through interviews with 4 school district leaders, this study examined the origin and the implementation of the Bridge program in Anne Arundel County Public Schools (AACPS). A survey instrument administered to 334 teachers at 11 comprehensive high schools in the AACPS system measured perceptions about the impact of the Bridge program on both their teaching practices and student attitudes. Follow-up interviews were conducted with 36 school-based personnel to confirm and explore survey results in greater depth. Using a theoretical framework of public policy implementation, this study describes both the agreement and disagreement among the perceptions of district leaders and school-based employees. Major themes arising from this analysis include the appropriateness of Bridge as an alternative to the HSA, the support provided to schools for the implementation of Bridge, and the variability of implementation in schools across the district. The findings of this study should inform policymakers of the practical impacts of implementing the Bridge Program for Academic Validation. It is important to examine both the intended and unintended consequences of this policy as future alternatives to standardized testing are considered in Maryland

Skin tone, Socioeconomic Status, and Ambulatory Blood Pressure among African-Americans: The Racism, Coping, and Ambulatory Blood Pressure Study

The purpose of the present study was to examine the potential relation between skin tone and ambulatory blood pressure (ABP), and if this relation was moderated by socioeconomic status (SES) among participants enrolled in the Racism, Coping, and Ambulatory Blood Pressure Study. 332 participants aged to 65 years old (M = 40 years, SD = 9.40). The main analyses consisted of multiple regressions with skin tone as a predictor of ABP (24hr, daytime, nighttime and dipping blood pressure). The main analyses also consisted of interactions of skin tone and SES indicators (poverty level, education and a composite) as predictors of ABP. Additional exploratory analyses investigated a three-way interaction between skin tone, SES indicators and gender as predictors of ABP. Controlling for standard cardiovascular risk factors, results indicated no association between skin tone and ABP or an interaction effect between skin tone and the SES indicators. However, controlling for standard cardiovascular risk factors, results indicated complex associations between skin tone, the SES indicators and gender as predictors of ABP. It is concluded that the relation between skin tone, SES indicators and gender as predictors of ABP is complex and may support the notion that the social experience at various permutations of these predictors may distinctly differ and affect ABP

Intimate partner violence recidivism over an 8-year follow-up

The study examined the criminal behavior of men who sought treatment for intimate partner violence (IPV). Criminal offenses were coded for the 5 years prior to treatment initiation and the 8 years after treatment initiation into three categories: Domestic Abuse, General Violence, and Other Protection Order Involvement. Of 115 men, 85 (73.91%) had no incidents coded as Domestic Abuse during the 8-year follow-up period. Overall, there were significant reductions in Domestic Abuse and General Violence between the two time periods. It was observed that 12 men (10.4%) accounted for 60% of the incidents of Domestic Abuse recidivism. The main study hypothesis was that borderline and antisocial personality characteristics would be predictive of criminal recidivism after treatment initiation. Counter to hypotheses, these personality characteristics were not predictive of criminal recidivism in the majority of analyses. Consistent with the hypothesis, self-report ratings of antisocial personality characteristics, assessed at treatment program intake, were significantly related to General Violence during the 8-year follow-up. When incidents of General Violence and Domestic Abuse recidivism were combined, this variable was also significantly related to previous self-ratings of antisocial personality characteristics. Self-reports of borderline personality characteristics were not significantly related to any of the criminal recidivism variables. The findings suggest that the majority of men seeking treatment for IPV have no criminal recidivism after treatment initiation, but that a small subsample of men maintain or increase the frequency of criminal behavior after treatment initiation. Future studies will need to examine other individual-level characteristics that may distinguish these men so that treatment can better target the risk factors of IPV recidivism

Modelling and Estimation of Characteristics of the Rainfall Distribution

Rainfall varies in space and time in a highly irregular manner and is described naturally in terms of a stochastic process. A characteristic of rainfall statistics is that they depend strongly on the space-time scales over which rain data are averaged. A spectral model of precipitation has been developed based on a stochastic differential equation of fractional order, which allows concise description of the second moment statistics over any space-time averaging scale. The model is thus capable of providing a unified description of both radar and rain gauge data. We test the model with radar and gauge data collected contemporaneously at the NASA TRMM ground validation sites located near Melbourne, Florida. Understanding precipitation is an essential component of climate modeling. Part of the calibration process for the recently launched GPM satellite involves comparison with radar observations. Ensuring that the radars are well calibrated is an import part of this process. We have used the developed stochastic model to explore sampling error for gauge and radar derived estimates of rain rates. This allows us to detect the presence and estimate the magnitude of any retrieval errors for the radar or gauge. We also formulated a standard linear regression analysis approach to the intercomparison of radar and gauge rain rate estimates in terms of the appropriate observed and model-derived quantities