D-Wave’s quantum computer is good at solving a specific type of problems known as Ising spin problems. However, in order to solve one of these spin problems, you must first solve another hard problem—embedding the spin problem on D-Wave’s quantum processor.
From the land of discrete mathematics, this embedding problem falls into a well studied branch of graph theory known as graph minors. Being that this problem is difficult in and of itself, D-Wave has developed a heuristic solution. This project’s main aim is to help improve this embedding process.
Various forms of usability testing can be used to optimize interface design and maximize human-computer interaction principles . A well-integrated, intuitive interface has the capacity to improve human efficiency, mitigate errors or lapses and improve situational awareness. Usability methodologies such as heuristic evaluations and cognitive walkthroughs can be performed at any point in the design process to identify areas for improvement. Additionally, in-field usability testing with users of the technology can further pinpoint inadequacies in software or hardware design.
In the near future the way that we encrypt and authenticate information online may not be safe. For this reason, we need to create new tools that will enable secure communication for many coming years. The proposed research is to create such tools from a certain algebraic object called isogenies. These are functions that take one elliptic curve to another. Breaking isogeny-based encryption is thought to be difficult, and so we will be able to create other cryptographic tools from them besides encryption.
The project is to develop a middleware system for improving drinking water management system. The middleware integrates multiple data sources in addition to the real-time network data, including information of weather from satellite/ radar and water quality of surface water from remote sensing and then analyze them. It's smart algorithms will predict and prioritizes events depending on the severity of the problem.
Deep neural networks are effective at image classification and other types of predictive tasks, achieving higher accuracy than conventional machine learning methods. However, unlike these other methods, the predictions are less interpretable. While accuracy may be enough for applications where errors are not costly, for real world applications, we want to also know when the predictions are more likely to be correct. Estimating the likelihood that a prediction is correct is called confidence, or uncertainty.
The aim of this research project is to develop innovative tools that will help financial institutions deliver highly personalized services to their customers. We intend to use the most recent advances in statistical learning methods and machine learning algorithms mostly in deep learning, vector embeddings and autoencoders, to leverage the power of time series models by extracting high-level features from both assets and customers’ transactional data.
Analytical applications in large organizations across even intermediate time ranges are often made complex, costly or even impractical due to temporal inconsistencies in the available data. The ever-changing nature of organizations causes categorical labels in data to change over time. This is particularly true for HR data, as the organization adjusts to changes in skillsets, market and operations. This project aims at establishing automated methods of defining consistent employee group labelling across time.
In Canada, approximately 40,000 out of hospital cardiac arrests (OHCAs) occur annually. Survival rates are under 15%, and the only treatment is immediate use of an Automated External Defibrillator (AED), coupled with CPR. This project will focus on finding solutions to identified problems associated with locating and using an AED. Some of these solutions will focus on in-emergency technology that can increase the accessibility of publicly available AEDs, along with the ability for bystanders to locate and use these life-saving devices.
The public transportation system is crucial in alleviating urban congestion. The widespread of smart card automated fare collection (AFC) system produces massive data recording passengers’ day-to-day transport dynamic, which provides unprecedented opportunities to researchers and practitioners to understand and improve transit services. This project aims to make full use of the transit operational data (mainly smart card data) to enhance transit services. The main body of the research project is spatiotemporal behavior patterns mining.
The explosion of data from personal phones, apps, and sensors have enabled powerful machine learning algorithms to help computers identify, categorize, and evaluate information without the help of humans. However, teaching computers how to identify, categorize, and evaluate information usually requires feeding the computers a lot of data pre-labelled by humans. The pre-labelling process is costly and time consuming. The goal of this project is to develop new algorithms to teach computers to identify, categorize, and evaluate information with less pre-labelled data.