A survey of first grade children's likes and dislikes in a basal reader

Thesis (Ed.M.)--Boston Universit

Small-Diameter Subchondral Drilling Improves DNA and Proteoglycan Content of the Cartilaginous Repair Tissue in a Large Animal Model of a Full-Thickness Chondral Defect

This study quantified changes in the DNA content and extracellular matrix composition of both the cartilaginous repair tissue and the adjacent cartilage in a large animal model of a chondral defect treated by subchondral drilling. Content of DNA, proteoglycans, and Type II and Type I collagen, as well as their different ratios were assessed at 6 months in vivo after treatment of full-thickness cartilage defects in the femoral trochlea of adult sheep with six subchondral drill holes, each of either 1.0 mm or 1.8 mm in diameter by biochemical analyses of the repair tissue and the adjacent cartilage and compared with the original cartilage. Only subchondral drilling which were 1.0 mm in diameter significantly increased both DNA and proteoglycan content of the repair tissue compared to the original cartilage. DNA content correlated with the proteoglycan and Type II collagen content within the repair tissue. Significantly higher amounts of Type I collagen within the repair tissue and significantly increased DNA, proteoglycan, and Type I collagen content in the adjacent cartilage were identified. These translational data support the use of small-diameter bone-cutting devices for marrow stimulation. Signs of early degeneration were present within the cartilaginous repair tissue and the adjacent cartilage

Neural Subgoal Generation using Backpropagation

Building a world model takes exponential computational costs with the number of obstacles. In real world applications are usually many obstacles, possibly changing their positions over time. In order to cope with a changing environment, a solution has to be adaptive. In order to plan complex trajectories, a system that plans hierarchically shows many advantages. In this article we report on results with a neural (therefore inherently adaptive) subgoal generation system. We show that meaningful subgoals can be produced for two joint manipulators in an environment with obstacles. Unlike many other approaches our approach works (once trained) fast and remains adaptive. 1 Motivation and Introduction Trajectory generation for manipulators is a difficult problem, up to now not satisfyingly solved. Of course there are several classical algorithms that are able to construct collision free paths (e.g. [4]) using only very limited computational resources. Normally these algorithms are based on ..

Hierarchical Planning Using Neural Subgoal Generation

Building a world model takes exponential computational costs in the dimension of the configuration space. Furthermore complexity increases with the number of obstacles, which in real world applications usually is high. Conventional algorithms can not even cope with slowly changing environments. In order to plan complex trajectories, a system that plans hierarchically shows many advantages. In this article we report on results with a neural (therefore inherently adaptive) subgoal generation system. We show that meaningful subgoals can be produced for manipulators in an environment with obstacles. Opposite to many other approaches our approach works (once trained) fast but remains adaptive. 1 Motivation and Introduction Trajectory generation for manipulators is a difficult problem, up to now not satisfyingly solved. Of course there are several classical algorithms that are able to construct collision free paths (e.g. [10], [6]) using only very limited computational resources, therefore ..

A Topologically Distributed Encoding to Facilitate Learning

We describe how to solve linear-non-separable problems using simple feed-forward perceptrons without hidden layers. A biologically motivated topologically distributed encoding for input data is used. We point out advantages of neural networks compared to classic mathematical algorithms without loosing performance. The Iris-dataset from Fisher [1] is analyzed as a practical example. Keywords: Classification, Iris dataset, perceptrons, topologically distributed encoding Topologically Distributed Encoding 3 1 Introduction In this paper we examine the performance of neural classification networks dealing with real world problems. We show that neural networks can provide results comparable to mathematical methods (c.f. [2]). But in contrast to mathematical methods neural networks do not require applicability preconditions, which in most cases are not fulfilled. To clarify this we will give two examples of well known analytic methods. In our opinion the Bayesian decision theory has two s..