University of Maryland, Baltimore County

About: University of Maryland, Baltimore County is a education organization based out in Baltimore, Maryland, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 8749 authors who have published 20843 publications receiving 795706 citations. The organization is also known as: UMBC.

Motivating Batterers to Change in the Treatment Context

Abstract: Many treatment programs for domestic abuse perpetrators rely on consistent, direct, and often intense confrontation of defenses. These interventions may unwittingly increase rather than decrease resistance and defensiveness and may reinforce the belief that relationships are based on coercive influence. Available research suggests that confrontational, hostile, and critical therapist behaviors limit treatment effectiveness and can harm vulnerable clients. Conversely, a supportive and collaborative working alliance between therapist and client enhances treatment effects. Supportive strategies are available to increase motivation to change in resistant clients. These techniques rely on a comprehensive model of the change process and match therapist interventions to the client's readiness for change.

A Modeling and Observational Framework for Diagnosing Local Land–Atmosphere Coupling on Diurnal Time Scales

Abstract: Land-atmosphere interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface temperature and moisture states. The degree of coupling between the land surface and PBL in numerical weather prediction and climate models remains largely unexplored and undiagnosed due to the complex interactions and feedbacks present across a range of scales. Further, uncoupled systems or experiments (e.g., the Project for Intercomparison of Land Parameterization Schemes, PILPS) may lead to inaccurate water and energy cycle process understanding by neglecting feedback processes such as PBL-top entrainment. In this study, a framework for diagnosing local land-atmosphere coupling is presented using a coupled mesoscale model with a suite of PBL and land surface model (LSM) options along with observations during field experiments in the U. S. Southern Great Plains. Specifically, the Weather Research and Forecasting (WRF) model has been coupled to the Land Information System (LIS), which provides a flexible and high-resolution representation and initialization of land surface physics and states. Within this framework, the coupling established by each pairing of the available PBL schemes in WRF with the LSMs in LIS is evaluated in terms of the diurnal temperature and humidity evolution in the mixed layer. The co-evolution of these variables and the convective PBL is sensitive to and, in fact, integrative of the dominant processes that govern the PBL budget, which are synthesized through the use of mixing diagrams. Results show how the sensitivity of land-atmosphere interactions to the specific choice of PBL scheme and LSM varies across surface moisture regimes and can be quantified and evaluated against observations. As such, this methodology provides a potential pathway to study factors controlling local land-atmosphere coupling (LoCo) using the LIS-WRF system, which will serve as a testbed for future experiments to evaluate coupling diagnostics within the community.

Diversity in Independent Component and Vector Analyses: Identifiability, algorithms, and applications in medical imaging

Abstract: Starting with a simple generative model and the assumption of statistical independence of the underlying components, independent component analysis (ICA) decomposes a given set of observations by making use of the diversity in the data, typically in terms of statistical properties of the signal. Most of the ICA algorithms introduced to date have considered one of the two types of diversity: non-Gaussianity?i.e., higher-order statistics (HOS)?or, sample dependence. A recent generalization of ICA, independent vector analysis (IVA), generalizes ICA to multiple data sets and adds the use of one more diversity, dependence across multiple data sets for achieving an independent decomposition, jointly across multiple data sets. Finally, both ICA and IVA, when implemented in the complex domain, enjoy the addition of yet another type of diversity, noncircularity of the sources?underlying components. Mutual information rate provides a unifying framework such that all these statistical properties?types of diversity?can be jointly taken into account for achieving the independent decomposition. Most of the ICA methods developed to date can be cast as special cases under this umbrella, as well as the more recently developed IVA methods. In addition, this formulation allows us to make use of maximum likelihood theory to study large sample properties of the estimator, derive the Cram?r?Rao lower bound (CRLB) and determine the conditions for the identifiability of the ICA and IVA models. In this overview article, we first present ICA, and then its generalization to multiple data sets, IVA, both using mutual information rate, present conditions for the identifiability of the given linear mixing model and derive the performance bounds. We address how various methods fall under this umbrella and give examples of performance for a few sample algorithms compared with the performance bound. We then discuss the importance of approaching the performance bound depending on the goal, and use medical image analysis as the motivating example.

Social support, organizational characteristics, psychological well-being, and group appraisal in three self-help group populations

Abstract: This study examined the relationship of three social support and three organizational variables to two well-being and two group appraisal variables among 144 members of Compassionate Friends, Multiple Sclerosis, and Overeaters Anonymous self-help groups. An anonymous questionnaire was the major research instrument. Receiving social support was not significantly related to depression or anxiety but was positively related to perceived group benefits and group satisfaction. Providing social support and friendship were each positively related to one well-being and one group appraisal variable. Bidirectional supporters (i.e., individuals high on both receiving and providing support) reported more favorable well-being and group appraisal than Receivers, Providers, and Low Supporters. At the group level of analysis (n = 15 groups), groups with higher levels of role differentiation, greater order and organization, and in which leaders were perceived as more capable contained members who reported more positive well-being and group appraisal. The implications for future research and professional consultation to self-help groups are discussed.

The XMM deep survey in the CDF-S - I. First results on heavily obscured AGN

Abstract: We present the first results of the spectroscopy of distant, o bscured AGN as obtained with the ultra‐deep (�3.3 Ms) XMM‐Newton survey in the Chandra Deep Field South (CDF‐S). One of the primary goals of the project is to characterize the X‐ray spectral properties of obscured and heavily obscured Compton‐thick AGN over the range of redhifts and luminosities that are relevant in terms of their contribution to the X‐ray background. The ultra‐deep exposure, coupled with the XMM detector’s spectral throughput, allowed us to accumulate good quality X‐ray spectra for a large number of X‐ray sources and, in particular, for heavily obscured AGN at cosmological redshifts. Specifically we present the X ‐ray spectral properties of two high‐redshift ‐ z= 1.53 and z=3.70 ‐