Raising the Stakes in Child-at-Risk Cases Eliciting and Maintaining Parents' Motivation

This paper, the third in a series concerned with family therapy in cases involving children at risk of abuse, describes an approach to maximising parents' interest and commitment to therapy. After overviewing the difficulties faced by therapists in working with parents who are notorious for their ‘resistance’, we provide guidelines for minimising the negative consequences of notification and for constructing therapeutic leverage in order to elicit and maintain parents' motivation throughout the course of therapy.     

Measuring Substance Abuse Treatment Need Among Adults in Florida: A Social Indicators Approach

Determining the size and demographiccharacteristics of substance abuse populationsis extremely important for implementing publicpolicies aimed at the control of substanceabuse. Such information not only assists in theallocation of limited treatment resources bythe state, but also in the monitoring ofsubstance abuse trends over time and in theevaluation of innovative policy initiatives. Inthis study, we develop three composite measuresof treatment need. We then use these measuresto estimate treatment need for alcohol abuseand for controlled substance abuse within eachof Florida's 67 counties. This study providesan important empirical component of communityplanning, quantifying and, to a limited degree,specifying the level of need for the substanceabuse treatment of community residents. Anadditional benefit is the development of a costeffective and unobtrusive methodology fordetermining empirically when levels of need arechanging so that treatment levels can beadjusted accordingly. With proper use,policymakers can readily employ the methodologydeveloped in this study in Florida andelsewhere to make better-informed decisions inthe allocation of finite substance abusetreatment resources.     

THE POLITICAL ECONOMY OF HEINRICH VON STACKELBERG

Although Heinrich von Stackelberg is famous for his contributions to the theory of imperfect competition, until recently scant attention has been paid to the life, other theoretical work and political thinking of this National Socialist economist. This paper examines his political economy as well as the events in his life which helped form it. Stackelberg devised a unique defense of fascist economic policy based on neoclassical economic analysis. At the same time, he may be credited with creating a novel theoretical framework for addressing a still unfinished line of inquiry into unstable markets and related informational issues.     

Women with Turner's Syndrome: helping them reach their full potential

The physical and psychological effects of Turner's Syndrome, a female sex chromosome abnormality, briefly are described. Principles for helping women master the special challenges posed by the disorder are discussed. The authors argue that women with TS must take control of defining their identities and potential in order to attain full status within society.     

Implementation in generic environments

In this paper, we study implementation in “economic environments”. It is shown that there is a dense subset of the set of preference profiles such that given an arbitrary social choice function, f, and ε>0, there exists another social choice function, f _ε, within ε of f uniformly and implementable in Nash equilibrium on the dense subset.     

A Comparison of Partially Adaptive and Reweighted Least Squares Estimation

The small sample performance of least median of squares, reweighted least squares, least squares, least absolute deviations, and three partially adaptive estimators are compared using Monte Carlo simulations. Two data problems are addressed in the paper: (1) data generated from non-normal error distributions and (2) contaminated data. Breakdown plots are used to investigate the sensitivity of partially adaptive estimators to data contamination relative to RLS. One partially adaptive estimator performs especially well when the errors are skewed, while another partially adaptive estimator and RLS perform particularly well when the errors are extremely leptokur-totic. In comparison with RLS, partially adaptive estimators are only moderately effective in resisting data contamination; however, they outperform least squares and least absolute deviation estimators.     

Membranes, mitochondria and amoebae: shape models

Most real-world shapes and images are characterized by high variability- they are not rigid, like crystals, for example—but they are strongly structured. Therefore, a fundamental task in the understanding and analysis of such image ensembles is the construction of models that incorporate both variability and structure in a mathematically precise way. The global shape models introduced in Grenander's general pattern theory are intended to do this. In this paper, we describe the representation of two-dimensional mitochondria and membranes in electron microscope photographs, and three-dimensional amoebae in optical sectioning microscopy. There are three kinds of variability to all of these patterns, which these representations accommodate. The first is the variability in shape and viewing orientation. For this, the typical structure is represented via linear, circular and spherical templates, with the variability accomodated via the application of transformations applied to the templates. The transformations form groups: scale, rotation and translation. They are locally applied throughout the continuum and of high dimension. The second is the textural variability; the inside and outside of these basic shapes are subject to random variation, as well as sensor noise. For this, statistical sensor models and Markov random field texture models are used to connect the constituent structures of the shapes to the measured data. The third variability type is associated with the fact that each scene is made up of a variable number of shapes; this number is not assumed to be known a priori. Each scene has a variable number of parameters encoding the transformations of the templates appropriate for that scene. For this, a single posterior distribution is defined over the countable union of spaces representing models of varying numbers of shapes. Bayesian inference is performed via computation of the conditional expectation of the parametrically defined shapes under the posterior. These conditional mean estimates are generated using jump-diffusion processes. Results for membranes, mitochondria and amoebae are shown.