Cold-region Land-surface Simulations under Climate Change
This proposal is part of a larger modelling project, Canada 1 Water, for which separate RAC applications have been submitted. This request is in direct support of a Mitacs application and concerns a subset of simulations that will be performed by or will directly involve the Mitacs interns. It is anticipated that two 150-year climate projection runs using CRCM5 forcing data from the CORDEXarchive, two 50-year historical reanalysis simulations (using ERA5 and NRCan climate forcing) will be necessary, as well as 100 model-years for development, testing and spin-up, totalling 500 model-years.The model that will be used for this project is the Community Terrestrial Systems Model, which is the stand-alone version of CLM5, the land component of the Community Earth System Model v2. One model year of CTSM/CLM5 simulation for Canada at 4km resolution requires approx. 0.2 core-years and 114 GB of storage. This estimate is based on lower-resolution test runs; scaling is trivial since vertical columns do not communicate.
The anticipated 500 model-years would require 100 core-years of compute time and produce 57 TB of output; however, much of the output could also be stored on a separate tape archive or off-site. 50 years of ERA5 climate forcing data would require approximately 20 TB of continuous scratch or project storage; NRCan data and topographic data would require 10 TB, and each 150-year CRCM5 projection would require approximately 16 TB of storage, resulting in 64 TB of forcing data. However, only 50% of that would be required at any given time, so 100 TB of scratch storage would be sufficient. Note that these are estimates and resolutions and the number of simulations can be adjusted as resources permit. Furthermore, the intention is, to make all data publicly available for research.
Combination of optical imaging and deep learning for better seizure localization to improve epilepsy surgical and Deep Brain Stimulation Therapies
Epilepsy is a debilitating neurological condition defined by recurrent, unprovoked seizures characterized by hypersynchronous neuronal discharge. Although anti-epileptic drugs have shown effectiveness in treating epileptic seizures, approximately 30% of patients are medically refractory and not responsive to them.
This project will use computational strategies based on Neurescence’s proprietary hardware in combination with machine learning to extract a clinically relevant biomarker to support surgical planning, as well as to accurately determine seizure onset time to optimize DBS.
Computational high-throughput screening of catalyst materials for renewable fuel and feedstock generation
According to a World Energy Council Report, population growth and rising standards of living across the world will at least double global energy demand by 2050. Simultaneously, carbon dioxide emissions must be reduced significantly to prevent a catastrophic rise in global temperatures. Clean and abundant renewable energy sources are available; unfortunately, the intermittency of solar and wind power is a prevailing problem which is limiting the potential for widespread use. Our project seeks to address both of these issues through development of novel catalysts to electrochemically convert CO2 captured from power plants into fuels and other higher value chemical feedstocks using renewable electricity. This innovative strategy will (1) provide a long term storage solution by converting renewable electricity into a stable chemical fuel, (2) provide a means to intelligently recycle CO2 rather than storing it in deep underground aquifers, and (3) provide a cleaner and cheaper pathway for production of industrial chemical feedstocks and fuels. This could be a truly disruptive technology which would allow Canadian led manufacturing of high value chemicals and fuels in a low-cost and low-carbon fashion. Additionally, there are large benefits to Canada’s energy sector by facilitating the dispatchability of renewable power.
Computational support for big data analytics, information extraction and visualization
The Centre for Innovation in Visualization and Data Driven Design (CIVDDD), an Ontario ORF-RE project performs research for which SOSCIP resources are needed and they were awarded NSERC CRD funding with IBM Platform [Applications of IBM Platform Computing solutions for solving Data Analytics and 3D Scalable Video Cloud Transcoder Problems] beginning in July 2015. This project involves Big Data, Visualization and Transcoding and will train many HQP. We require access to equipment capable of running a multi-core cluster using IBM Symphony and Big Insights software with IBM Platform on data analytics, visualization and transcoding. Our objectives include:
IBM Platform:
- Test the applicability of Platform Symphony to Data Analytics problems to produce demonstrations of Symphony on application domains (we started by exploring streaming traffic analysis datasets) and identify improvements to Symphony to gain IBM advantage in the marketplace.
- Design and implement methods to greatly speed-up the search for high utility frequent itemsets in big data using Symphony in a parallel distributed environment.
- Design algorithms to determine which are suitable in such an environment.
- Identify commercialization venues in application domains.
- Exploration of a Scalable Video Cloud Transcoder for Wireless Multicasts
Computer aided diagnosis of COVID-19 symptoms using medical sensors
The challenge that currently has arisen because of COVID is that in-person appointments with doctors are not possible and everyone has to use Telemedicine. Though Telemedicine is a great convenience, the problem with current Telemedicine systems is that they cannot be used to virtually examine patients and identify if COVID-19 symptoms are present. The main reason for this is because our current Telemedicine systems lack integration with medical devices that allow capturing physiological signals over the web. We are working towards integrating these medical devices into Telemedicine platforms so that the diagnostic utility of Telemedicine can be improved, and these platforms can be used to virtually assess patients over the web.
Our software platform integrates digital medical devices into Telemedicine video conference platforms that allows doctors to assess COVID-19 symptoms from captured physiological signals and provides large scale machine learning aided COVID screening at home. Imagine Telemedicine appointments where in addition to just consulting with your doctor via video tele-conference you can have them hear your heart/lung sounds, take your temperature, blood pressure, weight, blood oxygenation all in real-time and over the internet. We are building the software infrastructure that will allow Telemedicine platforms to seamlessly integrate the plethora of digital medical devices on the market, which enable this functionality, natively into their software ecosystems. Additionally, our platform also offers an intelligent layer of machine learning software that aids doctors in consolidating patient data, clinical decision making, computer aided diagnosis and carrying out appropriate follow-ups and referrals.
Computer vision powered digital twin for tracking manual manufacturing processes
Over 70% of tasks in manufacturing are still manual; therefore, over 75% of the variation in manufacturing comes from human beings. Human errors were the primary driver behind $22.1 billion in vehicle recalls in 2016. Currently, when plant operators want to gain an understanding of their manual processes, they send out their highly paid industrial engineers to run time studies. These studies produce highly biased and inaccurate data that provides minimal value to manufacturing teams. This project aims to develop a computer vision powered digital twin prototype that is ready to test on the client’s site, which helps manufacturing plant operators gain unprecedented visibility into their manual production operations, allowing them to optimize their worker efficiency while maximizing productivity. This will be done by automated data generation using computer vision, conversion of raw data into useable information, visualization of information using standard Business Intelligence methodologies and lastly, prediction of future plant performance based on historical information, as well as information about other market drivers.
Continuous vital sign monitoring using intelligent bed sheet
Studio 1 labs developed wireless intelligent bed sheet patient monitoring system that continuously captures client vital signs. In collaboration with Dr. Laura Nicholson and York University’s Faculty of Health, Studio 1 labs will work with SOSCIP infrastructure to match vital signs with gold standards approved medical decides for the highest level of accuracy through clinical validation and scientific evidence. With millions of data points collected from each device to output clinical grade quality information continuously, AI solutions allow modeling to predict health emergencies and diseases. This contributes to efficient health monitoring solutions that are simple and effective for use by older adults and healthcare providers.
COVID-19 AI based screening and monitoring of COVID-19 respiration patterns using acoustic sensors
This project addresses the pressing need for remote monitoring of long-term care homes to ensure potential cases of COVID-19 are identified early, isolated and treated.
Ryerson University’s Xiao-Ping Zhang, in concert with Altum View Systems Inc., will develop AI-based algorithms and systems to screen and monitor acoustic respiration patterns for COVID-19 in real-time, using customer mobile devices (e.g., mobile phones, wireless headphones, smart wrist-watches, etc.), low-cost electronic stethoscopes, and professional respiration monitor diagnostic devices. The COVID-19 acoustic respiration pattern screening and monitoring system will be incorporated into and complement Altum View Systems Inc.’s current camera-based health monitoring system for home care and long-term care facilities.
COVID-19: Agent-based framework for modelling pandemics in urban environment
The development of COVID-19 pandemic raises important questions on optimal policy design for managing and controlling the number of people affected. In order to answer these questions, one needs to better understand determinants of pandemic dynamics. Indeed, the development of epidemics depends on various factors including the intensity and frequency of social contacts and the amount of care and protection applied during those contacts. In particular, one area where the disease can be transmitted is the urban space of a large city such as Toronto.
The goal of the project is to create an agent-based framework for building virtual models of an urban area. This framework will be used as a virtual laboratory for testing various scenarios and their implications for the development of pandemics. In order for conclusions to be reliable, the models (known in the literature as synthetic population models or digital twins) have to be up to scale, with the number of agents comparable with the population of the city. This, in turn, requires implementations ready to be run in a large-scale distributed computing environment in the cloud as the algorithms behind the engine need high-performance computing power.
The framework will allow us to evaluate different COVID-19 mitigation policy designs. This includes possible decisions such as decreasing proneness to wearing masks, closing down some non-essential, high-contact, social network nodes (for example, hairdressers), limiting the number of people having simultaneous social gatherings or reducing the number of people on streets altogether via promoting actions such as #stayathome.
Creating a Predictive Financial Model to Optimize Digital Marketing Budget of SMEs Post COVID-19
In recent years, digital marketing has surpassed offline marketing and made marketing technology (Martech) the number one spending priority of businesses that didn’t exist ten years ago (Johnsen, 2017). Moreover, in the coming months, businesses are going to become more reliant than ever on their digital strategy. Without wanting to sound too alarmist, in many cases it will be the deciding factor in whether they make it through the tough times ahead. One thing is clear: marketers in the post-COVID-19 era will have to rethink what technologies they really need, which ones can help them save money, and which ones can help them transform their businesses that have been altered by this crisis. Data science can help companies acquire more customers, it can tell when, where and how to pitch target audience to maximize yields and minimize waste (it is estimated that on average 26% of marketing budgets are wasted on ineffective channels and strategies (Blake, Nosko, Tadelis, 2015)).
Using the right data sources, we can build simple (and more complex) models to predict impact on the customers’ behaviors and PPC (pay-per-click) conversion rates respectively, if you run a campaign at a certain point in time. These predictive analytics can estimate when the desired action will happen and what can impact it, and what would be the financial value.
The objective of this research is to introduce a predictive financial model to help Genius Camp and other SMEs in the Education industry, which are disrupted by COVID-19 situation, to allocate their digital marketing budget more efficiently. The data-backed solutions from this model will equip them with tools to make more informed decisions about going virtual and their resulting digital marketing initiatives. (i.e. how to structure their social media campaigns to achieve the highest ROI)
FinTech Deep Learning in Financial Modeling
The last four decades saw the development of the financial derivatives valuation technology. Only a few practical models, however, can be solved in closed form, and as most utilize numerical methods such as finite-difference partial differential equation solvers, discrete-time trees, or Monte-Carlo simulators, these traditional methods are quite slow. Running book valuation and risk processes on hundreds of CPU cores requires an overnight process to comply with regulatory and accounting standards. This creates a huge cost(~$10 million/year) and a commensurate environmental and carbon impact. Similar issues are now facing the Insurance industry due to the accounting standard IFRS 17. The key goal of the partnership is to develop deep learning, and more generally machine and reinforcement learning, models to accelerate these processes, and to provide efficient simulation engines for asset prices, volatility surfaces, derivative valuation, and hedging.
Deep Learning with Big Data for Innovation Acceleration
Presently, XLScout hosts data of over 130 million patents and 200+ million research publications occupying approximately 8TB of storage. Searching such a massive database using basic, mostly keyword-based search is very cumbersome and time-consuming. Moreover, it requires domain-specific knowledge about patents. With this project, XLScout aims to alleviate the pain of searching this massive system by employing machine learning (ML) and natural language processing(NL)techniques.Text autocompletion and recommendation will provide a better and smarter way for the end-users to search thismassive database. The auto-completion will be based on the corpus of patent documents and research publication to provide suggestions ofrelevant content. The MLmodel will be trained on that massive corpus taking into consideration language semantics. Techniques such as BERT and GPT 2/3 will be considered together with various pre-processing techniques. The size of the document database, document diversity, together with the subjectivity of desired results will make it challenging to evaluate such a system. We will employ both human-centric and automated evaluation approaches.Document categorization will also be based on the semantics, and it will group documents into labeled categories. Unsupervised techniques will be examined in their ability to do this categorization. However, different companies, XLScout clients, have different preferences in respect to this categorization. Therefore, an approach will be developed for the end-users to express their preferences by providing sample categories. Then, the model will learn from those preferences and carry out categorization. The challenge in this categorization is to enable unsupervised categorizationwhile supporting semi-supervision and customization. This categorization will be client-company specific and the modelwill have to learn from a limited number of example classes identified by the end-user.
Design and Development of Autonomous Disinfecting Embedded Systems for COVID-19
One of the major challenges during the COVID-19 pandemic is frequent disinfecting. This is very critical for places like hospitals and long-term care. In most places, human operators perform the cleaning but it may cause them to be infected with the virus because of the shortages of personal protective equipment (PPE) and many of the unknowns of COVID-19. The aim of thisCOVID-19 project is to improve Cyberworks Robotics’ navigation technology on existing (a) floor disinfection machines (e.g. wet floor scrubbers) of various types used in hospitals, (b) high-intensity UV disinfection machines, and (c) chemical mist disinfection machines. This would allow hospitals to disinfect the hospital surfaces on a more frequent basis than is possible with human cleaners (due to both the cost and availability of human operators) and also simultaneously to increase the quality of cleaning by ensuring that some surfaces are not missed due to human error and neglect
Design of OLED materials for manufacturing and improved product quality
Organic light emitting diodes (OLEDs) present a unique opportunity to produce thinner and more efficient lighting and displays. This will change the way we interact with light. The main barrier to mass adoption of OLEDs is the manufacturing process, due to the need for high throughput while maintaining nanoscale precision. High throughput operation requires materials that can undergo elevated temperature without decomposing. Our objective is to use computational chemistry to model innovative materials that can withstand these elevated temperatures while still providing high performing OLEDs. We will simulate targeted compounds using SOSCIP’s computer cluster examining properties relevant to OLED manufacturing processes. Promising materials will be synthesized and their properties experimentally measured then compared to the simulation results. The most promising materials will then be integrated into OLEDs and characterized by OTI Lumionics in their pilot scale manufacturing line located in Toronto, ON.
Detailed computational fluid dynamics modeling of UV-AOPs photoreactors for micropollutants oxidation in water and wastewater
Micropollutants such as bisphenol-A and N-nitrosodimethylamine pose a significant threat to aquatic life, animals, and humans beings due to their persistent and potentially carcinogenic nature. While most conventional water treatment methods cannot remove these contaminants, ultraviolet-driven (UV) advanced oxidation processes (AOPs) are effective in degrading micropollutants. As UV-AOPs require electrical energy to enable the treatment, energy costs present a barrier to the widespread adoption of this technology. In this project, we focus on the optimization of UV-AOPs-based reactors to enhance their degradation performance while reducing their energy consumption. In this respect, we will develop a detailed numerical model that integrates hydraulics, optics and chemistry to investigate UV-AOP photoreactors in a comprehensive manner.
The resulting information will then be utilized to design the next-generation of UV-AOP photoreactors commercialized by Trojan Technologies. The design space will be explored by high-performance computer simulations of full-scale photoreactors rather than simplified or scaled-down models. This will be accomplished by leveraging opensource software, artificial-intelligence optimization techniques and the second-to-none parallel-computing capabilities offered by Blue Gene/Q. Once the optimization of UVAOPs-based reactors is complete, the advanced modeling results generated using Blue Gene/Q will be utilized in the development of a simplified model for sizing purposes. This will be accomplished through combined use of metamodeling techniques and cloud computing. In brief, the concept is to simplify the detailed model developed earlier so that it can be simulated using hand-held mobile devices, which will allow the company’s sales personnel to market the optimized reactors. Consequently, it will allow the company to increase its competitiveness on global scale as well as to increase the rate of adoption of advanced water treatment technologies by water utilities and end-users.
Detecting and Responding to Hostile Information Activities: unsupervised methods for measuring the quality of graph embeddings
The rise in online organized disinformation campaigns presents a significant challenge to Canadian national security. State and non-state hostile actors manipulate users on social media platforms to advance their interests. Patagona Technologies is a Toronto-based software development company started by two Ryerson alumni. The project with Ryerson University is a larger initiative with the Canadian Department of National Defense to address the challenges posed by online hostile actors by analyzing the structure and content of social networks.
Developing Efficient Machine Learning Models for Price Bidding
Curate Mobile operates a demand site platform (DSP), which is an advertising platform responsible forbidding in real time ad placements from various publishers. This process is a blind auction, happeningover 50,000 times a second, and during this bidding process we have less then 100ms to determinewhich of our clients should bid for this ad placement, how much it might be worth to them, and whatprice we believe we can win this auction for. During this project, we will add machine learning modelsto our DSP to provide fast decisions in real time to maximize the return on ad spend of our clients’campaigns. The main goal for this project is to add a proof-of-concept machine learning model to CurateMobile’sDSP, with a pipeline that will continually update the models with new data as it is ingested. Wewill also design a validation module to monitor and validate the performance of the developed models.