Carleton University

About: Carleton University is a education organization based out in Ottawa, Ontario, Canada. It is known for research contribution in the topics: Population & Context (language use). The organization has 15852 authors who have published 39650 publications receiving 1106610 citations.

Optimal attack and reinforcement of a network

Abstract: In a nonnegative edge-weighted network, the weight of an edge represents the effort required by an attacker to destroy the edge, and the attacker derives a benefit for each new component created by destroying edges. The attacker may want to minimize over subsets of edges the difference between (or the ratio of) the effort incurred and the benefit received. This idea leads to the definition of the “strength” of the network, a measure of the resistance of the network to such attacks. Efficient algorithms for the optimal attack problem, the problem of computing the strength, and the problem of finding a minimum cost “reinforcement” to achieve a desired strength are given. These problems are also solved for a different model, in which the attacker wants to separate vertices from a fixed central vertex.

The social determinants of early child development: An overview

Abstract: Aim: This paper offers an overview of the current state of knowledge of the critical social determinants of child development and the complex ways in which these can influence health trajectories. Methods: We conducted an overview of the research conducted by medical and social scientists in the attempt to uncover the conditions under which children reach optimal health and developmental. Results: The first years of life represent a critical period during which trajectories of health vulnerability are determined by the complex interplay between biological, genetic, and environmental conditions. Conclusions: There are fundamental principles of optimal child development that apply to all human beings, regardless of language and culture.

Partially Coupled Stochastic Gradient Identification Methods for Non-Uniformly Sampled Systems

Abstract: This technical note addresses identification problems of non-uniformly sampled systems. For the input-output representation of non-uniform discrete-time systems, a partially coupled stochastic gradient (C-SG) algorithm is proposed to estimate the model parameters with high computational efficiency compared with the standard stochastic gradient (SG) algorithm. The analysis indicates that the partially C-SG algorithm can give more accurate parameter estimates than the SG algorithm. The parameter estimates obtained using the partially C-SG algorithm converge to their true values as the data length approaches infinity.

What can be known and how? Narrated subjects and the Listening Guide

Abstract: This article grapples with the question of 'what can be known?' about research subjects and how we can come to know them. Set against a backdrop of theoretical tensions over the concept of subjectivity in feminist theory, our article makes a three-fold argument. First, we argue that theoretical impasses between critical and constructed subjects can be addressed through the evolving concept of a narrated subject. Second, we suggest that this concept needs to be further interrogated by asking what can be known about narrated subjects both inside and outside of narrative. Third, we argue that greater attention must be given to how narrated subjects can be operationalized within research methodology, and we suggest that an emerging interpretive approach, the Listening Guide, provides a multi-layered way of tapping into methodological, theoretical, epistemological, and ontological dimensions of the narrated subject.