Grants and Contributions

Preventing the spread of COVID 19 by promoting good hygiene practices in vulnerable communities in the Marshall Islands, through community awareness and hand sanitising stations.

Improving prospects for a serious negotiation attempt for Venezuela in 2021

Improving environmental sustainability in Nauru through piloting a waste management program in a local community and school.

Up until August 2021, the ARTF delivered support to non-security, recurrent operational expenditures of the Afghan government, and provided grant financing for economic and social development priorities through a number of National Priority Programs. Since August 2021, the ARTF has adapted to avoid the collapse of Afghanistan’s critical services infrastructure, ensuring the delivery of principled basic needs and livelihoods services through UN agencies and selected NGO partners. Canada’s contributions to the ARTF prioritize health, education and women and girls’ rights and empowerment.

The Company has been developing a light-weight ground-penetrating radar solution for use with RPAS (drones) for near-surface data collection in large areas and complex ground terrain/structures.

Deep learning models are increasingly being adopted and rapidly improved for property prediction in intelligent design, including predicting the functionalities of a novel circuit, foreseeing the working behaviors of a new material, and anticipating the reactions of a fresh compound. State-of-the-art deep techniques leverage two building blocks: deep graph neural networks and supervised signals from large amount of labelled data. Graphs are powerful and versatile data structures to model the relationships among objects, and powered by the graph structure, supervised learning leverages large amount of labelled data to construct efficient graph representation, which then enables the prediction model to make accurate decisions on the properties of a new design. Such state-of-the-art graph neural networks for property prediction, rely heavily on supervised learning with large labelled datasets, which is typically expensive and time-consuming.
This Project will examine the development of fundamental techniques for graph representation learning in the presence of limited labeled data, aiming at accurate property prediction of novel designs. The successful outcome of this Project will be a learning framework that enables the efficient learning of graph representation with limited labelled data. Consequently, with improved graph representation, the properties of a new design can be more accurately and effectively evaluated, resulting in meaningfully speeding up the search for desirable designs.

Development of inline liquid-solid separation process, measurements and a control system to enhance discharge water quality and rapid consolidation of resulting tailings.

This project will enhance the Stepscan tile hardware and software capabilities to create a dual-authentication footstep-based biometric reader testing platform, with integrations needed for popular user access control systems.

To develop a national framework for improving and coordinating market research to guide the stakeholders' strategy and decision making for the wood construction industry.

The organization will provide services for victims of human trafficking by connecting them to agencies and the services they require including physical health, mental health, personal identification, housing, employment, addiction counselling and education. The goals of the project are to assist victims exit sex trafficking, improve their quality of life and minimize the risk of them returning to their trafficker.