ImmunoBiochem is developing novel anti-cancer therapeutics to address unmet need in intractable solid tumors. Because solid tumors are highly heterogeneous and evasive, recognizing cancerous cells, while avoiding damage to normal tissue, is a challenge. As a result, many targeted therapies quickly come up against resistance, resulting in patient relapses. ImmunoBiochem is solving the issue of tumor versus normal recognition by exploiting cancer targets in the tumor environment â a collection of features that are uniquely present in tumors and absent in the environment of normal cells.
In the real estate sector, a large volume of data is produced by businesses, commercial users and building visitors in a great variety of forms. For instance, three extensive sources of data come from unstructured text (e.g. documents, contracts), numerical data containing resources consumption and sensor/image-type data describing user behavior. A challenging problem for the sector is how to process the generated data into a useful asset that can provide insights to help business decisions, optimize user navigation and automate building-related processes.
AgnostiQ Labs is looking to develop immediately applicable encryption/obfuscation techniques for quantum computing. At present, encryption protocols developed in academia are unsuitable for real-world applications because they largely depend on quantum hardware that do not yet exist.
Goal Management Training® (GMT) is a Baycrest cognitive intervention that has been studied extensively, applied clinically, and manualized into kits for clinicians that have been commercially available since 2012. GMT targets executive functions, a collection of higher-level abilities involved in planning, organization, strategy, and inhibition. Executive impairment can be seen in normal aging as well as in numerous neurological conditions, including dementia and traumatic brain injury (TBI). GMT is the gold-standard treatment for executive impairment worldwide.
Recent rapid increase in the number of Chinese international students with English as a second language (ESL) attending Canadian schools has led to the exploration of in-service ESL teachers teaching strategies towards these students. Working with View-Wide International Education Group and using multiple case studies, this study will articulate the nature and challenges of ESL teaching and identify various ESL instruction means.
Deep neural network (DNN) is a class of machine learning algorithms which is inspired by biological neural networks. DNNs are themselves general function approximations, which is the reason they can be applied to almost any machine learning problem. Their applications can be found in visual object recognition in computer vision, translating texts in unsupervised learning, etc. DNNs are prone to overfitting because DNNs usually have many more parameters than the available training data. However, they usually have a low error on the test data.
The ultimate objective of this research project is to use a form of artificial intelligence to be able to classify and identify images of microscopic particles. Machine Learning is the term applied to this type of process, in which an algorithm is created by the computer software itself (i.e. mostly hidden from human intervention) to complete the task.
Transportation of oil and gas through pipeline networks remain a crucial infrastructure for sustainable economic growth in Canada. Pipeline wear and damage will remain a major concern as it can lead to catastrophic failures causing environmental and economic damage if undetected. For easier detection of damage on a large network of pipelines, an array of wireless radio frequency identification tags was developed for steel pipes. However, the material used for the tags were not suitable for pipes made with polymer composites as the stiffness of the copper could damage it.
In this research we will identify current types of customer, taking into account people who prefer to use a variety of platforms and different preferences in terms of how actively they manage their money. . We will carry out focus groups and interpret the results of a survey in terms of their implications for a set of factors that differentiate between banking customers. Using the factor scores obtained in a survey we will segment into meaningful groups (personas).