An algebraic and Bayesian technology for object recognition

This paper presents a new robust, low computational cost technology for recognizing free-form objects in three-dimensional (3D) range data, or, in two dimensional (2D) curve data in the image plane. Objects are represented by implicit polynomials (i.e. 3D algebraic surfaces or 2D algebraic curves) of degree greater than two, and are recognized by computing and matching vectors of their algebraic invariants (which are functions of their coefficients that are invariant to translations, rotations and general linear transformations). Such polynomials of the fourth degree can represent objects considerably more complicated than quadrics and super-quadrics, and can realize object recognition at significantly lower computational cost. Unfortunately, the coefficients of high degree implicit polynomials are highly sensitive to small changes in the data to which the polynomials are fit, thus often making recognition based on these polynomial coefficients or their invariants unreliable. We take two approaches to the problem: one involves restricting the polynomials to those which represent bounded curves and surfaces, and the other approach is to use Bayesian recognizers. The Bayesian recognizers are amazingly stable and reliable, even when the polynomials have unbounded zero sets and very large coefficient variability. The Bayesian recognizers are a unique interplay of algebraic functions and statistical methods. In this paper, we present these recognizers and show that they work effectively, even when data are missing along a large portion of an object boundary due, for example, to partial occlusion.     

Nonstop Flying Is Safer Than Driving

The relative safety of driving and flying is important in many situations that involve selecting a mode of transportation. The traditional view, that flying via scheduled airlines is safer than driving, has recently been challenged by Evans et al. ⁽¹⁾ They concluded that for a low-risk driver it is safer to drive on rural interstate highways (the safest roads) than to fly if the trip length is less than 602 miles. We reestimated the fatality probabilities for flying by taking into account that the risk of flying is dependent on the number of nonstop segments flown, but, for all practical purposes, is independent of the length of the trip. Our calculations indicate that, for average or high-risk drivers, it is always safer to fly than to drive. Furthermore, even for a low-risk driver, nonstop flying is safer than driving on rural interstate highways for a trip distance of more than 303 miles; the corresponding breakeven distances for flights that involve two and three segments are 606 and 909 miles, respectively.     

The Impact of Religious Homogeneity on the Rate of Divorce in the United States

This study extends the understanding of the relationship between religious homogeneity, that is, the extent to which formal religious groups are concentrated at the county level, and the rate of persons currently divorced in those counties. Linking the research question to Durkheimian precepts of religion as an integrative force in social life, the essential question is, “At the county level, does the rate of currently divorced vary inversely with more concentrated affiliation with formal religious organizations?” We investigate this relationship using data from the 1990 U.S. Census and from the Glenmary Research Center that encompasses 621 counties in the U.S., that is, a 20 percent random sample of counties from each state. As hypothesized, the divorced rate is inversely related to religious homogeneity, even after controlling for a series of factors that have been shown to be correlated with divorce in other studies.     

Bargaining and Strategic Demand Commitment

On occasion, in multilateral negotiations, interested parties make unilateral demands. Certain agreements need unanimity. However, a lesser degree of consensus may be feasible. In this paper, an alternating demand bargaining game among n players is proposed, which envisages varying consensus requirements and commitment, both crucial in generating a unique and efficient outcome of the bargaining process.     

Semi parametric estimation of employment duration models

Semi parametric methods provide estimates of finite parameter vectors without requiring that the complete data generation process be assumed in a finite-dimensional family. By avoiding bias from incorrect specification, such estimators gain robustness, although usually at the cost of decreased precision. The most familiar semi parametric method in econometrics is ordi¬nary least squares, which estimates the parameters of a linear regression model without requiring that the distribution of the disturbances be in a finite-parameter family. The recent literature in econometric theory has extended semi parametric methods to a variety of non-linear models, including models appropriate for analysis of censored duration data. Horowitz and Newman make perhaps the first empirical application of these methods, to data on employment duration. Their analysis provides insights into the practical problems of implementing these methods, and limited information on performance. Their data set, containing 226 male controls from the Denver income maintenance experiment in 1971-74, does not show any significant covariates (except race), even when a fully parametric model is assumed. Consequently, the authors are unable to reject the fully parametric model in a test against the alternative semi parametric estimators. This provides some negative, but tenuous, evidence that in practical applications the reduction in bias using semi parametric estimators is insufficient to offset loss in precision. Larger samples, and data sets with strongly significant covariates, will need to be interval, and if the observation period is long enough will eventually be more loyal on average for those starting employment spells near the end of the observation period.     

Marital Violence: Dimensions of the Problem and Modes of Intervention*

This paper reviews data on the incidence of marital violence and recommends methods of intervention on a family and social level. Myths which may block awareness of this widespread problem are briefly described. Particular attention is given to the inadequacy of the catharsis hypothesis in explaining and treating marital violence. Interventions for couple counseling aimed at enhancing the couple's problem-solving abilities and eliminating abusive behavior are described and illustrated with case examples. Several suggestions are made for social interventions including police training, legal reform, and the formation of shelter homes for the victims of abuse.     

SMALL SAMPLE VARIANCE ESTIMATORS FOR U-STATISTICS

New unbiased estimators are presented for the dominant and lower-order terms of the variance expansion for U-statistics. In small samples these provide important corrections to the usual estimate of asymptotic standard error which is based on the leading term in the expansion. The new estimators for the first term cannot be recommended. The ordinary jackknife estimator is found to be more effective than the direct estimates of the separate terms.     

Child therapy outcome in a community mental health center

Studies by the Davis Child Therapy Effectiveness Project reveal that brief therapy is the norm. A retrospective outcome study in one mental health center shows a large proportion of adjustment reactions. Outcome ratings from therapists, parents and patients show fairly high levels of outcome and significant improvement in rated impairment. The children who were more impaired at intake were found to have the most visits and numbers of visits were unrelated to success in treatment. Examples are provided illustrating the use of parent responses to modify center practices.