The goal of this project is to develop a low-cost ventilation system which accurately detects and records both patient treatment data and environmental data is in great need. Further, such a system will allow to create a global health map. This research and resulting hardware and software will not only benefit the current research of COVID-19, but also assist in the long term identification and management of potential respiratory pandemics.
A notorious phenomenon limiting general-purpose computing today is memory wall. Memory – the hardware used to store the data- is located relatively far from the central processing unit (CPU), so applications spend a lot of time waiting on data to travel from memory to the CPU. New memory hardware, such as the one addressed in this project, aims to address this problem at a fundamental level by adding processing units to the memory itself. This way, the data can be processed right where it lives, instead of being shuffled to and from the CPU. This idea is called Processing In Memory (PIM).
Dialogue is Canada's leading telemedicine provider, founded to improve well-being by using technology to deliver excellent care. Dialogue is a pioneer of virtual health care dedicated exclusively to organizations that want to improve the health and well-being of their members and families.
Options are financial instruments that are used to manage risk, hedge investments, and speculate. The value of these options depends on the price of the underlying asset and a multitude of different variables. As a result, pricing models can become complex, requiring infeasibly expensive routines or simulations to be run to price a single option. One reason this procedure can be slow is that the model’s parameters need to be tuned to the market’s current conditions, reflected by an implied volatility surface (IVS), which gives the value of options with different parameters.
Remotely Piloted Aircraft System (RPAS) will be essential in developing and monitoring Canada’s territories. This is in part due to a lack of suitable human pilots due to skill shortages and difficult conditions making recruitment difficult; there are also “dull, dirty and dangerous” aspects of the missions that make a remote pilot operation safer. Miniaturization, machine learning and robotics are all fields which may contribute to overcoming these challenges in new and affordable ways.
Food Convergence and Integrity (FCI) Canada is being formed to help agri-food companies mitigate the disruptions of Covid-19, increase interactions and innovations among Canadian agrifood companies and enable new agrifood business streams. Through its member platform, it will enhance resilience and competitiveness of the Canadian agri-food sector, resulting also in increased food security for Canadians.
Automatically assessing a pilot performance during a flight training session is a capability that can enhance the flight instructor during his duty. From data gathered during a flight maneuver, we are looking for a way to automatically assess pilot performance to augment instructor performance and provide objectivity during flight training assessment.
This project tackles the issue of knowledge incompleteness and lack of domain coverage in resume and job posting matching caused by the exploitation of domain-general resources. A variety of co-operative semantic/ontological resources will be used to filter out irrelevant resumes. A two-way (candidate to job and job to candidate) semantic-based automatic suitability ranking is proposed. The suitability is determined by the semantic distance of resumes and job postings, evaluated by their word embeddings.
Under the current pandemic of Covid-19, sharing health record data has tremendous benefits to control the spread of the infection and save lives globally. In medical research and discovery, Electronic medical records (EMRs) play the essential role for medical discovery in two categories, namely 1) cross-sectional study and 2) longitudinal study. Cross-sectional study compares different population groups at a single point in time while in longitudinal study, researchers conduct several observations of the same subjects over a period of time.
In this postdoc, we plan to focus on computer vision tasks where existing deep learning methods require lots of labeled samples to work well. Acquiring labeled samples is time-consuming and often impractical. Thus, we investigate three different classes of methods to alleviate the label scarcity problem: active learning, weakly-supervised learning, and few-shot learning. In active learning, the goal is to label the most important samples to maximize the performance of the model while reducing labeling costs. In weakly supervised learning, the goal is to train models using weak labels.