Search

Let T 1 and T 2 be two rooted trees with an equal number of leaves. The leaves are labeled, and the labeling of the leaves in T 2 is a permutation of those in T 1 . Nodes are associated with weight, such that the weight of a node u, denoted by W(u), is more than the weight of its parent. A node x ∈ T 1 and a node y ∈ T 2 are induced, iff their subtrees have at least one common leaf label. A heaviest induced ancestor query HIA (u 1 , u 2) with input nodes u 1 ∈ T 1 and u 2 ∈ T 2 asks to output the pair (u 1 ∗ , u 2 ∗) of induced nodes with the highest combined weight W (u 1 ∗) + W (u 2 ∗) , such that u 1 ∗ is an ancestor of u 1 and u 2 ∗ is an ancestor of u 2 . This is a useful primitive in several text processing applications. Gagie et al. (Proceedings of the 25th Canadian Conference on Computational Geometry, CCCG 2013, Waterloo, Ontario, Canada, 2013) introduced this problem and proposed three data structures with the following space-time trade-offs: (i) O (n log 2 n) space and O (log n log log n) query time, (ii) O (n log n) space and O (log 2 n) query time, and (iii) O(n) space and O (log 3 + ϵ n) query time. Here n is the number of nodes in both trees combined and ϵ > 0 is an arbitrarily small constant. We present two new data structures with better space-time trade-offs: (i) O (n log n) space and O (log n log log n) query time, and (ii) O(n) space and O (log 2 n / log log n) query time. Additionally, we present new applications of these results. [ABSTRACT FROM AUTHOR]

We study the use of ranked set sampling (RSS) with binary outcomes in cluster‐randomized designs (CRDs), where a generalized linear mixed model (GLMM) is used to model the hierarchical data structure involved. Under the GLMM‐based framework, we propose three different approaches to estimate the treatment effect, including the nonparametric (NP), maximum likelihood (ML) and pseudo likelihood (PL) estimators. We investigate their asymptotic properties and examine their finite‐sample performance via simulation. Based on these three RSS estimators, we further develop procedures for testing the existence of the treatment effect. We examine the power and size of our proposed RSS tests and compare them with existing tests based on simple random sampling (SRS). All the proposed RSS estimation and test methods are illustrated with two data examples, one for rare events and the other for non‐extreme events. Throughout our investigations, we also consider the possible effect of imperfect ranking. Among the proposed methods, we provide recommendations on whether to use RSS rather than SRS with binary outcomes in CRDs and, if yes, when to use which RSS method. The Canadian Journal of Statistics 48: 342–365; 2020 © 2019 Statistical Society of Canada [ABSTRACT FROM AUTHOR]

The Ontario Brain Institute (OBI) has begun to catalyze scientific discovery in the field of neuroscience through its large-scale informatics platform, known as Brain-CODE. The platform supports the capture, storage, federation, sharing, and analysis of different data types across several brain disorders. Underlying the platform is a robust and scalable data governance structure which allows for the flexibility to advance scientific understanding, while protecting the privacy of research participants. Recognizing the value of an open science approach to enabling discovery, the governance structure was designed not only to support collaborative research programs, but also to support open science by making all data open and accessible in the future. OBI's rigorous approach to data sharing maintains the accessibility of research data for big discoveries without compromising privacy and security. Taking a Privacy by Design approach to both data sharing and development of the platform has allowed OBI to establish some best practices related to large-scale data sharing within Canada. The aim of this report is to highlight these best practices and develop a key open resource which may be referenced during the development of similar open science initiatives. [ABSTRACT FROM AUTHOR]

Background: It is widely acknowledged that health policy and management decisions rarely reflect research evidence. Therefore, it is important to determine how to improve evidence-informed decision-making. The primary aim of this systematic review was to evaluate the effectiveness of research implementation strategies for promoting evidence-informed policy and management decisions in healthcare. The secondary aim of the review was to describe factors perceived to be associated with effective strategies and the inter-relationship between these factors.Methods: An electronic search was developed to identify studies published between January 01, 2000, and February 02, 2016. This was supplemented by checking the reference list of included articles, systematic reviews, and hand-searching publication lists from prominent authors. Two reviewers independently screened studies for inclusion, assessed methodological quality, and extracted data.Results: After duplicate removal, the search strategy identified 3830 titles. Following title and abstract screening, 96 full-text articles were reviewed, of which 19 studies (21 articles) met all inclusion criteria. Three studies were included in the narrative synthesis, finding policy briefs including expert opinion might affect intended actions, and intentions persisting to actions for public health policy in developing nations. Workshops, ongoing technical assistance, and distribution of instructional digital materials may improve knowledge and skills around evidence-informed decision-making in US public health departments. Tailored, targeted messages were more effective in increasing public health policies and programs in Canadian public health departments compared to messages and a knowledge broker. Sixteen studies (18 articles) were included in the thematic synthesis, leading to a conceptualisation of inter-relating factors perceived to be associated with effective research implementation strategies. A unidirectional, hierarchal flow was described from (1) establishing an imperative for practice change, (2) building trust between implementation stakeholders and (3) developing a shared vision, to (4) actioning change mechanisms. This was underpinned by the (5) employment of effective communication strategies and (6) provision of resources to support change.Conclusions: Evidence is developing to support the use of research implementation strategies for promoting evidence-informed policy and management decisions in healthcare. The design of future implementation strategies should be based on the inter-relating factors perceived to be associated with effective strategies.Trial Registration: This systematic review was registered with Prospero (record number: 42016032947). [ABSTRACT FROM AUTHOR]

In a parametric model, parameters are often partitioned into parameters of interest and nuisance parameters. However, as the data structure becomes more complex, inference based on the full likelihood may be computationally intractable or sensitive to potential model misspecification. Alternative likelihood-based methods proposed in these settings include pseudo-likelihood and composite likelihood. We propose a simple adjustment to these likelihood functions to reduce the impact of nuisance parameters. The advantages of the modification are illustrated through examples and reinforced through simulations. The adjustment is still novel even if attention is restricted to the profile likelihood. The Canadian Journal of Statistics 42: 544-562; 2014 © 2014 Statistical Society of Canada [ABSTRACT FROM AUTHOR]

In this study, we examine the degree of construct comparability and possible sources of incomparability of the English and French versions of the Programme for International Student Assessment (PISA) 2003 problem-solving measure administered in Canada. Several approaches were used to examine construct comparability at the test- (examination of test data structure, reliability comparisons and test characteristic curves) and item-levels (differential item functioning, item parameter correlations, and linguistic comparisons). Results from the test-level analyses indicate that the two language versions of PISA are highly similar as shown by similarity of internal consistency coefficients, test data structure (same number of factors and item factor loadings) and test characteristic curves for the two language versions of the tests. However, results of item-level analyses reveal several differences between the two language versions as shown by large proportions of items displaying differential item functioning, differences in item parameter correlations (discrimination parameters) and number of items found to contain linguistic differences. [ABSTRACT FROM PUBLISHER]

The article presents guest editorial, published in the May 2000 issue of the periodical "IEEE Transactions on Software Engineering," vol. 26, no. 5. The Fifth International Conference on Software Reuse that was held in Victoria B.C., Canada, included papers, mini-workshops, panels, and tutorials dedicated to various aspects of the software reuse enterprise, software architecture, and reuse over the Internet. Out of the 31 papers that were presented, a select few were nominated by the program committee as representing special, innovative contributions. In addition to that the article presents information on the two papers related to major technological approaches to software reuse. The first paper, considers the problem of selecting the best realizations and algorithms for a general purpose data structure. The second paper, is concerned with the problem of adapting class libraries for specific requirements.