Plant-based beverages are inappropriate alternatives to bovine milk, due to lower protein content and not sufficient amount of essential amino acids. To produce nutritionally complete plant-based beverage with high overall acceptability, the technological interventions and fortification techniques need to be developed. Oat is one of the promising raw material for preparation of functional plant-based milk due to the presence of dietary fibres and good nutritional quality of oat proteins.

The emerging techniques of machine learning and artificial intelligence are making revolutionary changes in all kinds of the industrial world. As a high-tech business solution company, Quartic.ai uses these modern techniques to help industrial manufactory companies work more efficiently. One of the challenging problems is to make the computer automatically recognize the status and behavior of the machine from the data collected by different sensors, so that people can record the history of the machine and conduct further analysis.

For patients with end-stage disease, like cirrhosis, their only treatment option is life-saving organ transplantation. The wait for an organ can take many years over which the patient's condition continues to deteriorate as there is no other treatment. However, maintaining physical activity and a good nutritional intake is necessary to remain on the wait and have successful health outcomes post-transplantation.

Glass sponges build their skeletons out of silicon dioxide (i.e. glass). While these animals are found all over the world in very deep water, they only exist shallower than 50 m in a few places in the world. In very rare cases, new sponges grow on top of existing, dead sponges and form reefs in a similar manner to coral reefs. As with coral reefs, the structure formed by the reefs is ecologically important because it provides complex habitat and shelter for other animals.

Reinforcement learning (RL) is a type of machine learning that focuses on allowing a physical or virtual agent to complete sequential decision-making tasks, such as video games. It has had many successes, but can be slow in practice, requiring large amounts of data. This project aims to speed up such learning problems by leveraging information from an existing agent. This existing agent need not be perfect – the algorithm developed will leverage information from the existing agent whenever possible and learn to outperform it where it is suboptimal.

Model-free Reinforcement Learning (RL) has recently demonstrated its great potential in solving difficult intelligent tasks. However, developing a successful RL model requires an extensive model tuning and tremendous training samples. Theoretical analysis of these RL methods, more specifically policy optimization methods, only stay in a simple setting where the learning happens in the policy space. This project attempts to advance the analysis of the policy optimization methods to a more realistic setting in the parameter space.

Across Canada, many families struggle to find high quality, affordable child care. Most child care options for families are limited to daytime and weekday work hours; this creates even greater challenges for shiftworkers who rely on paid child care. When child care is not available, mothers usually bear the greatest burden as many women, whether parenting alone or with a partner, act as the default primary caregiver.

MatchWork enables non-profit employment support organizations to support marginalized people to find meaningful employment opportunities. This includes people with physical and mental challenges, veterans, new immigrants and refugees.

Occupational stress conveys risk of Posttraumatic Stress Disorder (PTSD).