High School Grades and University Performance: A Case Study

A critical issue facing a number of colleges and universities is how to allocate first year places to incoming students. The decision to admit students if often based on a number of factors, but a key statistic is a student's high school grades. This paper reports on a case study of the subsequent performance at the University of Winnipeg of high school students from 84 Manitoba High Schools. By tracking the University performance of a set of students admitted for the years 1997-2002, we are able to estimate the likelihood of success of subsequent students based on their characteristics as well as their high school grades. In doing so, we use a number of alternative estimators including a Least Squares Dummy Variable Model and a Hierarchical Linear Model. The methodology should be of interest to admissions o±cers at other universities as an input into estimating the subsequent performance of first year students.

Higher-order Representation and Reasoning for Automated Ontology Evolution

Abstract: The GALILEO system aims at realising automated ontology evolution. This is necessary to enable intelligent agents to manipulate their own knowledge autonomously and thus reason and communicate effectively in open, dynamic digital environments characterised by the heterogeneity of data and of representation languages. Our approach is based on patterns of diagnosis of faults detected across multiple ontologies. Such patterns allow to identify the type of repair required when conflicting ontologies yield erroneous inferences. We assume that each ontology is locally consistent, i.e. inconsistency arises only across ontologies when they are merged together. Local consistency avoids the derivation of uninteresting theorems, so the formula for diagnosis can essentially be seen as an open theorem over the ontologies. The system’s application domain is physics; we have adopted a modular formalisation of physics, structured by means of locales in Isabelle, to perform modular higher-order reasoning, and visualised by means of development graphs.

Contract Damages and Investment Dynamics

The present article provides an economic analysis to examine how contract damages affects both breach and investment decisions over time. Unlike the standard static model, this article studies a model in which, upon signing a contract, a seller invests over two periods, and a buyer may breach at the end of each period. The dynamic structure of the model allows us to investigate investment dynamics under alternative contract damages. First, under expectation damages, the seller has an incentive to invest only in the first period (front-loading of investment). Second, under reliance damages, a similar front-loading of investment occurs, and the degree of front-loading is excessive relative to the expectation damages. Third, under restitution damages, the seller has an incentive to invest only in the second period. We also examine efficiency properties of new hybrid measures of damages in which damages depend on the timing of breach.Contract Damages, Investment Dynamics

Composite Ordinal Forecasting in Horse Racing - An Optimization Approach

Using horse racing data in Hong Kong as an example, this paper looks into the properties of an optimization model for making composite ordinal forecasts based on minimization of the absolute error of the joint distribution of the errors of twelve forecasters of race outcomes. It was found that the optimization model is not only sound theoretically, but it is also robust, and can handle situations when data are sparse

Probabilistic Perspectives on Collecting Human Uncertainty in Predictive Data Mining

In many areas of data mining, data is collected from humans beings. In this contribution, we ask the question of how people actually respond to ordinal scales. The main problem observed is that users tend to be volatile in their choices, i.e. complex cognitions do not always lead to the same decisions, but to distributions of possible decision outputs. This human uncertainty may sometimes have quite an impact on common data mining approaches and thus, the question of effective modelling this so called human uncertainty emerges naturally. Our contribution introduces two different approaches for modelling the human uncertainty of user responses. In doing so, we develop techniques in order to measure this uncertainty at the level of user inputs as well as the level of user cognition. With support of comprehensive user experiments and large-scale simulations, we systematically compare both methodologies along with their implications for personalisation approaches. Our findings demonstrate that significant amounts of users do submit something completely different (action) than they really have in mind (cognition). Moreover, we demonstrate that statistically sound evidence with respect to algorithm assessment becomes quite hard to realise, especially when explicit rankings shall be built

Melchizedek Passages in the Bible. A Case Study for Inner-Biblical and Inter-Biblical Interpretation

Melchizedek is a mysterious figure to many people. Adopting discourse analysis and text-linguistic approaches, Chan attempts to tackle the Melchizedek texts in Genesis 14, Psalm 110, and Hebrews 5-7. This seminal study illustrates how the mysterious figure is understood and interpreted by later biblical writers, "... Using the “blessing” motif as a framework, Chan also argues that Numbers 22-24, 2 Samuel 7 and the Psalter: Books I-V (especially Psalms 1-2) provide a reading paradigm of interpreting Psalm 110. In addition, the structure of Hebrews provides a clue to how the author interprets the Old Testament texts

Segmentation of the evolving left ventricle by learning the dynamics

We propose a method for recursive segmentation of the left ventricle (LV) across a temporal sequence of magnetic resonance (MR) images. The approach involves a technique for learning the LV boundary dynamics together with a particle-based inference algorithm on a loopy graphical model capturing the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation of the boundary, and boundary estimation involves incorporating curve evolution into state estimation. By formulating the problem as one of state estimation, the segmentation at each particular time is based not only on the data observed at that instant, but also on predictions based on past and future boundary estimates. We assess and demonstrate the effectiveness of the proposed framework on a large data set of breath-hold cardiac MR image sequences

Learning the dynamics and time-recursive boundary detection of deformable objects

We propose a principled framework for recursively segmenting deformable objects across a sequence of frames. We demonstrate the usefulness of this method on left ventricular segmentation across a cardiac cycle. The approach involves a technique for learning the system dynamics together with methods of particle-based smoothing as well as non-parametric belief propagation on a loopy graphical model capturing the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation of the boundary, and the boundary estimation involves incorporating curve evolution into recursive state estimation. By formulating the problem as one of state estimation, the segmentation at each particular time is based not only on the data observed at that instant, but also on predictions based on past and future boundary estimates. Although the paper focuses on left ventricle segmentation, the method generalizes to temporally segmenting any deformable object