Reconciliation through Description: Using Metadata to Realize the Vision of the National Research Centre for Truth and Reconciliation

PostprintThis articlewill discuss the history and context surrounding the document collection and statement gathering mandates of the Truth and Reconciliation Commission of Canada and the challenges the newly established National Research Centre for Truth and Reconciliation will face in applying the Commission’s metadata set in the realization of its vision. By working respectfully with Indigenous people through the implementation of Indigenous knowledge best practices and the application of contrasting traditional/nontraditional, archival/user-generated, and institutional/Indigenous descriptive elements, the Centre will attempt to create a “living archive” and facilitate Indigenous participation, collaboration, and ultimately, the process of reconciliation.https://www-tandfonline-com.uwinnipeg.idm.oclc.org/doi/full/10.1080/01639374.2015.100871

Unlabeled sample compression schemes and corner peelings for ample and maximum classes

We examine connections between combinatorial notions that arise in machine learning and topological notions in cubical/simplicial geometry. These connections enable to export results from geometry to machine learning. Our first main result is based on a geometric construction by Tracy Hall (2004) of a partial shelling of the cross-polytope which can not be extended. We use it to derive a maximum class of VC dimension 3 that has no corners. This refutes several previous works in machine learning from the past 11 years. In particular, it implies that all previous constructions of optimal unlabeled sample compression schemes for maximum classes are erroneous. On the positive side we present a new construction of an unlabeled sample compression scheme for maximum classes. We leave as open whether our unlabeled sample compression scheme extends to ample (a.k.a. lopsided or extremal) classes, which represent a natural and far-reaching generalization of maximum classes. Towards resolving this question, we provide a geometric characterization in terms of unique sink orientations of the 1-skeletons of associated cubical complexes

Inferring causality from noisy time series data

Convergent Cross-Mapping (CCM) has shown high potential to perform causal inference in the absence of models. We assess the strengths and weaknesses of the method by varying coupling strength and noise levels in coupled logistic maps. We find that CCM fails to infer accurate coupling strength and even causality direction in synchronized time-series and in the presence of intermediate coupling. We find that the presence of noise deterministically reduces the level of cross-mapping fidelity, while the convergence rate exhibits higher levels of robustness. Finally, we propose that controlled noise injections in intermediate-to-strongly coupled systems could enable more accurate causal inferences. Given the inherent noisy nature of real-world systems, our findings enable a more accurate evaluation of CCM applicability and advance suggestions on how to overcome its weaknesses.Comment: 9 pages, 11 figures, submitted to COMPLEXIS 201

On the Normality of Numbers to Different Bases

We prove independence of normality to different bases We show that the set of real numbers that are normal to some base is Sigma^0_4 complete in the Borel hierarchy of subsets of real numbers. This was an open problem, initiated by Alexander Kechris, and conjectured by Ditzen 20 years ago

The Cowl - v.16 - n.8 - Dec 09, 1953

The Cowl - student newspaper of Providence College. Volume 16, Number 8 - Dec 09, 1953, 1951. 8 pages