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The David W. Fentress Family Letters, 1856-1969
No full textTranscript of a letter by an unidentified author to David Fentress regarding sharing federal newspapers and the banning of federal newspapers in some areas. The author passes on the news of the war including the destruction of the Federal merchantmen by the Confederate fleet. He passes along world news: Russia preparing to go to War with Europe and how that could negatively affect the Confederacy. There is also speculation on the future of the war
The David W. Fentress Family Letters, 1856-1969
No full textTranscript of a letter by an unidentified author to David Fentress regarding sharing federal newspapers and the banning of federal newspapers in some areas. The author passes on the news of the war including the destruction of the Federal merchantmen by the Confederate fleet. He passes along world news: Russia preparing to go to War with Europe and how that could negatively affect the Confederacy. There is also speculation on the future of the war
Junior Recital, Meredith Haynie, soprano
Full text linkThis recital will fulfill in part the requirements for the Bachelor of Music degree in Music Education. Meredith Haynie studies voice and receives vocal coaching from James R. Smith-Parham
David Hayward, Lisa Neville, Ben Bodna and Meredith Canter, 1995
No full textDr. David Hayward with members of "The People Together" project, which grew out of the Victorian Community Summit held in 1993. Left - Right: David Hayward, Lecturer, Sociology; Lisa Nevile; Ben Bodna and Meredith Canter. Swinburne Staff News 2 March 1995
Portrait of author David Foster at the National Library of Australia, Canberra, 8 June 2011 /
No full textTitle from acquisitions documentation.; Part of the collection: Portraits of author David Foster at the National Library of Australia, Canberra, 8 June 2011.; Acquired in digital format; access copy available online.; Mode of access: Online.; Photographed by a staff member of the National Library of Australia
David, Erik, and Meredith Use Reasoning to Resolve Conjectures
No full textMeredith rebuilds the previous day’s models for comparing 2/3 and 3/4. The longer model is 24 cm long; a train of a blue rod, a black rod, and a brown rod, representing 1. In this model, a dark green rod represents 1/4, a brown rod represents 1/3, a red rod represents 1/12, and a white rod represents 1/24. Meredith shows that the difference between three greens and two browns is one red (1/12) or two whites (2/24). The other model is 12 cm long; it consists of an orange rod plus a red rod. In this model, a light green rod represents 1/4, a purple rod represents 1/3, and a white rod represents 1/12. Meredith shows that in this model, the difference (1/12) is represented by one white rod.
Researcher Maher challenges Meredith, David, and other students to predict what a larger model would look like without building it. David and Meredith conjecture that there could be a larger model in which the white represents 1/48.
Students are asked to work on a model to verify David’s conjecture. Erik builds another model of length 36 cm (3 blue rods plus 9 white rods), which can be used to show thirds and fourths, but which does not model David’s conjecture.
After realizing that they have not built a model for David’s conjecture, students help David build a model in which the white rod represents 1/48. When asked, David says that he is not surprised at this result; that’s what he thought it would be
Analysing Music with Point-Set Compression Algorithms
Full text linkSeveral point-set pattern-discovery and compression algorithms designed for analysing music are reviewed and evaluated. Each algorithm takes as input a point-set representation of a score in which each note is represented as a point in pitch-time space. Each algorithm computes the maximal translatable patterns (MTPs) in this input and the translational equivalence classes (TECs) of these MTPs, where each TEC contains all the occurrences of a given MTP. Each TEC is encoded as a ⟨pattern,vector set⟩ pair, in which the vector set gives all the vectors by which the pattern can be translated in pitch-time space to give other patterns in the input dataset. Encoding TECs in this way leads, in general, to compression, since each occurrence of a pattern within a TEC (apart from one) is encoded by a single vector, that has the same information content as one point. The algorithms reviewed here adopt different strategies aimed at selecting a set of MTP TECs that collectively cover (or almost cover) the input dataset in a way that maximizes compression. The algorithms are evaluated on two musicological tasks: classifying folk song melodies into tune families and discovering repeated themes and sections in pieces of classical music. On the first task, the best-performing algorithms achieved success rates of around 84%. In the second task, the best algorithms achieved mean F1 scores of around 0.49, with scores for individual pieces rising as high as 0.71
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