Task-Specific Experience and Task-Specific Talent: Decomposing the Productivity of High School Teachers

We use administrative panel data to decompose worker performance into components relating to general talent, task-specific talent, general experience, and task-specific experience. We consider the context of high school teachers, in which tasks consist of teaching particular subjects in particular tracks. Using the timing of changes in the subjects and levels to which teachers are assigned to provide identifying variation, we show that much of the productivity gains to teacher experience estimated in the literature are actually subject-specific. By contrast, very little of the variation in the permanent component of productivity among teachers is subject-specific or level-specific. Counterfactual simulations suggest that maximizing the value of task-specific experience could produce nearly costless efficiency gains on the order of .02 test score standard deviations

Learning Membership Functions in a Function-Based Object Recognition System

Functionality-based recognition systems recognize objects at the category level by reasoning about how well the objects support the expected function. Such systems naturally associate a ``measure of goodness'' or ``membership value'' with a recognized object. This measure of goodness is the result of combining individual measures, or membership values, from potentially many primitive evaluations of different properties of the object's shape. A membership function is used to compute the membership value when evaluating a primitive of a particular physical property of an object. In previous versions of a recognition system known as Gruff, the membership function for each of the primitive evaluations was hand-crafted by the system designer. In this paper, we provide a learning component for the Gruff system, called Omlet, that automatically learns membership functions given a set of example objects labeled with their desired category measure. The learning algorithm is generally applicable to any problem in which low-level membership values are combined through an and-or tree structure to give a final overall membership value.Comment: See http://www.jair.org/ for any accompanying file

Predictions for the fracture toughness of cancellous bone of fracture neck of femur patients

Current protocol in determining if a patient is osteoporotic and their fracture risk is based on dual energy X-ray absorptiometry (DXA). DXA gives an indication of their bone mineral density (BMD) which is the product of both the porosity and density of the mineralized bone tissue; this is usually taken at the hip. The DXA results are assessed using the fracture risk assessment tool as recommended by the World Health Organization. While this provides valuable data on a person’s fracture risk advancements in medical imagining technology enables development of more robust and accurate risk assessment tools. In order to develop such tools in vitro analysis of bone is required to assess the morphological properties of bone osteoporotic bone tissue and how these pertain to the fracture toughness (Kcmax) of the tissue.Support was provided by the EPSRC (EP/K020196: Point-ofCare High Accuracy Fracture Risk Prediction), the UK Department of Transport under the BOSCOS (Bone Scanning for Occupant Safety) project, and approved by Gloucester and Cheltenham NHS Trust hospitals under ethical consent (BOSCOS – Mr. Curwen CI REC ref 01/179G)

The money market

Money market

A structural, spectroscopic and theoretical study of the triphenylphosphine chalcogenide complexes of tungsten carbonyl, [W(XPPh3)(CO)5], X=O, S, Se

The series [W(XPPh3)(CO)5], X=O, S, Se has been structurally determined by X-ray crystallography and fully characterised spectroscopically to provide data for comparing the bonding of the Ph3PX ligands to the metal. The P-X-W angles are 134.3°, 113.2° and 109.2°, respectively, for X=O, S, Se. The bonding has been analysed using EHMO calculations which suggest that lower P-X-W angles depend on the relative importance of σ-bonding, which in turn depends on the chalcogen in the order X=Se > S > O. The effect is enhanced by lower energies of the metal σ and π orbital energies