Grants and Contributions

To enhance health services access for French and English linguistic minority communities in Canada and improve the accessibility, quality, sustainability and accountability of Canada’s health care system.

The objective of this project is to develop general machine learning techniques for graph generation, with the end application of smart design including new material discovery, advanced circuit design, and novel drug invention, amongst many others. Research will focus on deep generative models and reinforcement learning for the generation of graphs with optimized properties. The representation power of graph will be leveraged to sufficiently encode the key compositional behaviours and their interplays of the target domain, and treat developing a novel design as a new graph structure generation process with various composition constraints. The decomposition in the former can be attained through deep generative models with disentangled latent variables, and the composition search space in the latter can be effectively explored by deep enforcement learning.

The objective of the SDG Funding Program is to support sustainable development projects that:.• Support the development and utilization of effective approaches to address sustainable development and the social, economic and environmental issues and challenges;.• Develop, exchange and apply knowledge, tools and resources that address sustainable development needs of individuals, families and communities;.• Foster partnerships and networks to address sustainable development and the existing and emerging social, economic and environmental issues;.• Recognize and support the ability of organizations to identify and address sustainable development priorities; .• Recognize and promote engagement initiatives at the international, national, subnational, and/or local level that mobilize assets and develop capacities and resources for action; and/or.• Foster Indigenous traditional knowledge on sustainable development..

Digital technologies have ushered in the current digital culture. There is widespread use of smartphones/tablets and the internet to interact with digitized artifacts, enabling more collective and networked forms of culture. Cisco forecasts global IP traffic to surpass 4.8 ZB (of which wireless and mobile traffic will account for > 70%) and the number of devices connected to IP networks to exceed 3 times the global population by 2022. The global IT infrastructure must expand to support this growth and demand in data traffic. Solutions for ultra-high capacity optical interconnections—from long-haul (undersea) to short-reach (data center interconnections)—and optical-wireless (RF) communications are required.
The main objective of this project is to explore integrated approaches for optical frequency comb generation, including quantum dot lasers and silicon photonic technologies, for applications in flexible high-capacity optical transport from data center interconnections to long haul transmission to fiber-wireless networks.

This development project will address key issues encountered with conventional electronic beamforming networks (BFN) used in space based Radio-Frequency (RF) phased array antennas. Those antennas are gaining in popularity thanks to the recent plans for low cost, large, Low Earth Orbit (LEO) constellation projects. Large bandwidth performances are thus expected to be much improved over the wide scan angles needed for operations in LEO. This feasibility study will evaluate a concept for a large scale true time delay optical beamforming for RF phased arrays.

The objective of this project is to develop general machine learning techniques for graph generation, with the end application of smart design including new material discovery, advanced circuit design, and novel drug invention, amongst many others. Research will focus on deep generative models and reinforcement learning for the generation of graphs with optimized properties. The representation power of graph will be leveraged to sufficiently encode the key compositional behaviours and their interplays of the target domain, and treat developing a novel design as a new graph structure generation process with various composition constraints. The decomposition in the former can be attained through deep generative models with disentangled latent variables, and the composition search space in the latter can be effectively explored by deep enforcement learning.

Digital Citizen Contribution Program

The Book Clubs for Inmates (BCFI) is a national organization with 36 book clubs in penitentiaries, healing lodges and transitional housing across Canada that are run by approximately 130 volunteers. BCFI organizes and administers volunteer-led book clubs which help inmates develop empathy, listening skills, and self-awareness. The skills acquired by book club participants improve their chances of a successful reintegration into society.

BCFI’s target population is incarcerated men and women, their children, and paroled individuals.

The Church Council on Justice and Corrections (CCJC) is a coalition of eight national church bodies and faith groups with a mandate to promote education on restorative justice and to sponsor initiatives to build safer communities, through research, projects, workshops and presentations.

CCJC has a broad target population which includes anyone (Canadian and International individuals and organizations) interested in healthy, safe and socially responsible communities.

To improve rail infrastructure at road/rail grade crossings