Cofinality

Abstract: We prove that the statement “For every pair A, B , stationary subsets of ω 2 , composed of points of cofinality ω , there exists an ordinal α such that both A ∩ α and B ∩ α are stationary subsets of α is equiconsistent with the existence of weakly compact cardinal. (This completes results of Baumgartner and Harrington and Shelah.) We also prove, assuming the existence of infinitely many supercompact cardinals, the statement “Every stationary subset of ω ω+1 has a stationary initial segment.”

Abstract: We prove that ifis a row-finite k-graph with no sources, then the associated C � -algebra is simple if and only ifis cofinal and satisfies Kumjian and Pask's aperiodicity condition, known as Condition (A). We prove that the aperiodicity condition is equivalent to a suitably modified version of Robertson and Steger's original nonperiodicity condition (H3) which in particular involves only finite paths. We also characterise both cofinality and aperiodicity ofin terms of ideals in C � (�).

Abstract: A dependent theory is a (first order complete theory) T which does not have the independence property. A major result here is: if we expand a model of T by the traces on it of sets definable in a bigger model then we preserve its being dependent. Another one justifies the cofinality restriction in the theorem (from a previous work) saying that pairwise perpendicular indiscernible sequences, can have arbitrary dual-cofinalities in some models containing them. We introduce “strongly dependent” and look at definable groups; and also at dividing, forking and relatives.

Abstract: We study the problem of ordinal embedding: given a set of ordinal constraints of the form distance(i, j) < distance(k, l) for some quadruples (i, j, k, l) of indices, the goal is to construct a point configuration x1,..., xn in Rp that preserves these constraints as well as possible. Our first contribution is to suggest a simple new algorithm for this problem, Soft Ordinal Embedding. The key feature of the algorithm is that it recovers not only the ordinal constraints, but even the density structure of the underlying data set. As our second contribution we prove that in the large sample limit it is enough to know "local ordinal information" in order to perfectly reconstruct a given point configuration. This leads to our Local Ordinal Embedding algorithm, which can also be used for graph drawing.