Music education

Prior to the 1980s the music curriculum consisted of class-singing, sol-fah deciphering and music appreciation. Typical resources were the piano, the Curwen modulator, numerous sets of song and sight-reading books (to suit single gender class groupings) and a record player. More enlightened teachers would have some percussion instruments or recorders in the classroom and, from the 1970s, the odd guitar. The Scottish Examination Board 'O' Grade examination at the end of year 4 was designed to be overtaken by pupils who had expertise on an instrument or voice to the equivalent of Associated Board Grade 5, tuition on which was given outwith the classroom while the teacher concentrated on historical study, rudiments and analysis. Such elitism fuelled growing disillusionment in pupils and many teachers who experienced a different world of music in their private lives (Witkin, 1974). Significant and effective change came in 1978 with the publication by the Scottish Education Department of the highly controversial Curriculum Paper 16, Music in Scottish Schools. This was the dividing line between past practices and future developments which radically changed the way in which music was taught and which clearly focused music teachers' and educators' energies and ideas. It encapsulated many of the ideas and innovations which had been forming in Britain through the work of Paynter and Aston (1970), Witkin (1974) and in the USA since the 1960s (Choksy et al., 1986) and placed them into a Scottish context. This provided the impetus for a root and branch overhaul of the curriculum which would reshape music in the classroom into an action-based experience, open to all children, regardless of their musical or academic ability. In the contexts of both primary and secondary schools, Curriculum Paper 16 recommended syllabus content and teaching and learning strategies, the review of assessment approaches and most significantly, staffing, resource and accommodation requirements to enable 'music for all' to be implemented. These recommendations gave teachers and headteachers the tools and impetus to make demands on local authorities to fund the developments appropriately

An Empirical Examination of Traditional Neighborhood Development

This study analyzes the impact of the new urbanism on single-family home prices. Specifically, we explore the price differential that homebuyers pay for houses in new urbanist developments relative to houses in conventional suburban developments. Using data on over 5,000 single-family home sales from 1994 to 1997 in three different neighborhoods, hedonic regression results reveal that consumers pay more for homes in new urbanist communities than those in conventional suburban developments. Further analyses indicate that the price premium is not attributable to differences in improvement age and other housing characteristics

Who Bears the Balloon Risk in Commercial MBS?

Much of the literature on the pricing of commercial mortgages underlying commercial mortgage-backed securities pools focuses on the effect of term default (default during the term of the loan), and ignores the possibility of balloon risk, the borrower\u27s inability to pay off the mortgage at maturity through refinancing or property sale. A contingent-claims mortgage pricing model that includes two default triggers—a cash flow trigger and an asset value trigger—may be used to assess the effect of balloon risk on the pricing of CMBS tranches. Simulations of cash flows for individual loans in a CMBS framework reveal how individual tranches are affected by balloon risk. Balloon risk is low at the whole-loan level, but under a number of scenarios total credit risk and balloon risk creep into investment-grade CMBS tranches and significantly impact their valuation

Term Default, Balloon Risk, and Credit Risk in Commercial Mortgages

Term default and balloon risk play an interactive role in the pricing of credit risk in commercial mortgages. Most commercial mortgage pricing studies assume a borrower\u27s default decision is based solely on the property value; the mortgage valuation model here also incorporates a property income trigger. The model considers both the risk of default during the term of the loan and the risk of loss at maturity (balloon risk). Monte Carlo simulation analyses reveal that pricing models based solely on property value overestimate the probability of term default and the resulting credit risk premium. Adding a property income default trigger without considering balloon risk, however, underestimates the overall credit risk premium. In essence, a double-trigger default model that incorporates balloon risk is critical for accurate assessment of the credit risk in commercial mortgages

Extension Risk in Commercial Mortgages

Historical data and Monte Carlo simulation is used to examine the likelihood of loan extension and potential losses associated with extension. It is found that extension probability is highly sensitive to property NOI growth, to NOI volatility, to the amortization schedule, and to the loan term. It is found that extension risk is largely unaffected by changing credit spreads, changing yield curve assumptions, and changing term default assumptions. It is found that changing the underwriting standards affects the probability of loan extension in a somewhat muted way. It is estimated that the loss during extension is approximately 2%-3% of the outstanding loan amount at maturity

Transcription Factor-DNA Binding Via Machine Learning Ensembles

We present ensemble methods in a machine learning (ML) framework combining predictions from five known motif/binding site exploration algorithms. For a given TF the ensemble starts with position weight matrices (PWM's) for the motif, collected from the component algorithms. Using dimension reduction, we identify significant PWM-based subspaces for analysis. Within each subspace a machine classifier is built for identifying the TF's gene (promoter) targets (Problem 1). These PWM-based subspaces form an ML-based sequence analysis tool. Problem 2 (finding binding motifs) is solved by agglomerating k-mer (string) feature PWM-based subspaces that stand out in identifying gene targets. We approach Problem 3 (binding sites) with a novel machine learning approach that uses promoter string features and ML importance scores in a classification algorithm locating binding sites across the genome. For target gene identification this method improves performance (measured by the F1 score) by about 10 percentage points over the (a) motif scanning method and (b) the coexpression-based association method. Top motif outperformed 5 component algorithms as well as two other common algorithms (BEST and DEME). For identifying individual binding sites on a benchmark cross species database (Tompa et al., 2005) we match the best performer without much human intervention. It also improved the performance on mammalian TFs. The ensemble can integrate orthogonal information from different weak learners (potentially using entirely different types of features) into a machine learner that can perform consistently better for more TFs. The TF gene target identification component (problem 1 above) is useful in constructing a transcriptional regulatory network from known TF-target associations. The ensemble is easily extendable to include more tools as well as future PWM-based information.Comment: 33 page