Asset allocation – the decision of how to divide a portfolio among the major asset classes such as cash, stocks and bonds – is a key determinant of portfolio performance. Because financial markets go through periods of strong and weak economies, the performance of an asset class varies with shifting economic conditions. These regime shifts pose a challenge to the asset allocation decision because they impact the portfolio’s return and risk.
G protein coupled receptors (GCPRs) are proteins found at the surface of cells are responsible for activating numerous intracellular signaling pathways and thus are involved in regulating about every physiological response. Activation of GPCRs occurs by compounds as varied as photons, lipids, ions, small hormonal or neurotransmitter compounds or larger peptidic and protein molecules. As such, GPCRs are currently the target of up to 34% of marketed drugs.
The goal of this research is to improve breast cancer local control and reduce treatment side effects by demonstrating that a new cold plasma technology, which generates locally specific reactive oxygen species, has an additive positive treatment impact when combined with conventional radiotherapy. This project has the potential to enable a Canadian technology, to become an adjuvant for radiotherapy.
Prostate cancer is the disease of the old, effecting 1 in 7 Canadians with a death burden of 1 in 28. The management of the disease becomes extremely difficult with the development of the nonresponsive and aggressive form of the cancer. Thus, the clinicians are rendered helpless and hence there is an urgent need for the development of new targeted therapies. Response to chemotherapy drugs varies for every individual, leading to adverse effects in many. Hence every individual responds to chemotherapy drugs differentially, matching the right dose for individual patient is a big challenge.
The issue has attracted researchers from multiple disciplines, including Danielle Benesch, who is examining how perceptions of free will could impact our response to the overdose crisis. Danielle, a Mitacs intern from the Universität Osnabrück in Germany, has studied free will and decision-making for years. She travelled to Canada this summer to work on a project, supervised by Professor Eric Racine of the Université de Montréal, to research the relationship between perceptions of free will and addiction.
The procurement process of an organization is key to understand company costs. Organizations gather large amounts of data coming from different sources (e.g. income statement, balance sheet, general ledger lines). This information is heterogeneous in nature as it is a mix of unstructured and structured data. Moreover, it needs to be cleaned and consolidated in a taxonomy to enable category management. The objective is to group like-to-like items and/or services into categories from Supply Market Analysis point of view and consider category management for the holistic spend.
Given a set of financial instruments with inherent characteristics at different time intervals, we are interested in finding an optimal trading rule in a high-frequency trading context. A trading rule is defined as a combination of indicators as well as an entry threshold (and potentially other trading parameters). The objective function we are trying to maximize is the profits of the strategy based on the trading rule. One impact of the non-linearity of such problems is that the gradient of the objective function is hard to estimate using a black-box approach.
The ability to impede/reduce complication of the damaged heart presents a major challenge in the treatment of cardiovascular diseases. Complications include heart failure, which has a high mortality even with current treatments. The use of a new drug to stimulate protection of the heart during an ongoing myocardial infarct and long term changes leading to heart failure would be very relevant to the clinical setting, to help patients suffering from diverse heart problems.
This project aims at evaluating whether recent results in deep learning models, trained to exploit weak labels (Hwang, 2016) can serve to extract meaningful lesion localizations from image-level labels, either from individual scans or given a (longitudinal) sequence thereof. To this end, we will scale up existing models that have been shown to work on 2D images to a 3D context, studying labeling performance as the dataset size grows.