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In computer science, a binary tree is a k-ary k = 2 tree data structure in which each node has at most two children, which are referred to as the and the . A recursive definition using just set theory notions is that a (non-empty) binary tree is a tuple (L, S, R), where L and R are binary trees or the empty set and S is a singleton set containing the root. Some authors allow the binary tree to be the empty set as well. From a graph theory perspective, binary (and K-ary) trees as defined here are arborescences. A binary tree may thus be also called a bifurcating arborescence—a term which appears in some very old programming books, before the modern computer science terminology prevailed. It is also possible to interpret a binary tree as an undirected, rather than a directed graph, in which case a binary tree is an ordered, rooted tree. Some authors use rooted binary tree instead of binary tree to emphasize the fact that the tree is rooted, but as

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Hausdorff dimension of some groups acting on the binary tree

Olivier Siegenthaler

De Gruyter, Walter de Gruyter GmbH & Co. KG2008

We investigate Farley’s CAT(0) cubical model for Thompson’s group F (we adopt the classical language of F, using binary trees and piecewise linear maps, avoiding the one of diagram groups and pictures). Main results include: in general, Thompson’s group elements are parabolic; we find simple, exact formulas for the CAT(0) translation lengths, in particular the elements of F are ballistic and uniformly bounded away from zero; there exist flats of any dimension and we construct explicitly many of them; we reveal large regions in the Tits Boundary, for example the positive part of a non-separable Hilbert sphere , but also more complicated objects. En route, we solve several open problems proposed in Farley’s papers.

EPFL2012

Chord Representations for Probabilistic Models

Samy Bengio, Jean-François Paiement

Chord progressions are the building blocks from which tonal music is constructed. Inferring chord progressions is thus an essential step towards modeling long term dependencies in music. In this paper, three different representations for chords are designed. In a first representation, Euclidean distances roughly correspond to psychoacoustic dissimilarities between chords. Estimated probabilities of chord substitutions are then derived from these distances and are used to introduce smoothing in graphical models observing another chord representation. Finally, a third representation where we model directly each chord components leads to a probabilistic model considering the interaction between melodies and chord progressions. Parameters in the graphical models are learnt with the EM algorithm and the classical Junction Tree algorithm is used for inference. Various model architectures are compared in terms of conditional out-of-sample likelihood. Both perceptual and statistical evidence show that binary trees related to meter are well suited to capture chord dependencies.

IDIAP2005

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