Ryerson University

About: Ryerson University is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Computer science & Population. The organization has 7671 authors who have published 20164 publications receiving 394976 citations. The organization is also known as: Ryerson Polytechnical Institute & Ryerson Institute of Technology.

Perceived barriers in accessing food among recent Latin American immigrants in Toronto

Abstract: In Canada, recent immigrant households experience more food insecurity than the general population, but limited information is available about the personal, cultural, and social factors that contribute to this vulnerability. This study focused on recent Latin American (LA) immigrants to explore their perceived barriers in acquiring safe, nutritious, and culturally-appropriate food. A cross-sectional mixed-method design was applied to collect information from a convenience sample of 70 adult Spanish/Portuguese speakers who had arrived in Toronto within the last five years. Face-to-face interviews were conducted with primary household caregivers to obtain responses about barriers to acquiring food for their households; data were analyzed using a thematic analysis technique. Four main categories of barriers were identified: limited financial resources; language difficulty; cultural food preferences; and poor knowledge of available community-based food resources and services. Inadequate income was the main impediment in accessing adequate food, and was related to affordability of food items, accessibility of food outlets and transportation cost, and limited time for grocery shopping due to work conditions. Language barriers affected participants’ ability to obtain well-paid employment and their awareness about and access to available community-based food resources. Cultural barriers were related to food preferences and limited access to culturally-appropriate foods and resources. The main barrier to food security among our sample of LA newcomers to Toronto is limited financial resources, highlighting the need for policies and strategies that could improve their financial power to purchase sufficient, nutritious, and culturally-acceptable food. Linguistic barriers and limited information among newcomers suggest the need to provide linguistically- and culturally-appropriate information related to community-based food programs and resources, as well as accessible subsidized English language programs, in the community and at workplaces. Participatory community-based food programs can augment, in a socially acceptable manner, food resources and reduce the social stigma attached to food charity. Finally, it is crucial to monitor and evaluate existing social and community-based services for their accessibility, cultural appropriateness and diversity, and effectiveness.

Sparseness Meets Deepness: 3D Human Pose Estimation from Monocular Video

Abstract: This paper addresses the challenge of 3D full-body human pose estimation from a monocular image sequence. Here, two cases are considered: (i) the image locations of the human joints are provided and (ii) the image locations of joints are unknown. In the former case, a novel approach is introduced that integrates a sparsity-driven 3D geometric prior and temporal smoothness. In the latter case, the former case is extended by treating the image locations of the joints as latent variables. A deep fully convolutional network is trained to predict the uncertainty maps of the 2D joint locations. The 3D pose estimates are realized via an Expectation-Maximization algorithm over the entire sequence, where it is shown that the 2D joint location uncertainties can be conveniently marginalized out during inference. Empirical evaluation on the Human3.6M dataset shows that the proposed approaches achieve greater 3D pose estimation accuracy over state-of-the-art baselines. Further, the proposed approach outperforms a publicly available 2D pose estimation baseline on the challenging PennAction dataset.

Further Understanding of Trust and Performance in Virtual Teams

Abstract: Trust has been deemed to be critical in ensuring the efficient operation of virtual teams and organizations. This study empirically verifies ability and integrity as being antecedents of trust formation in virtual teams. However, effective team performance was found to be independent of the formation of trust. Further analysis suggests that information symmetry and good communication distinguish high performance teams from low performance teams.