Generative models for autonomous, video-based DJ systems
RaveDJ is all about enabling people to create music with AI. Over the past few years our team has worked tirelessly to make RaveDJ the world’s first fully autonomous DJ system. A system that can seamlessly mix together a set of songs to produce the optimal mix to listen to as well as unique combinations or mashups of two songs into one. RaveDJ is currently producing around 10-50 TB per day of mashups and mixes for people all around the world. RaveDJ is hosted on our website www.rave.dj as well as our app Rave (get.rave.io/download) which has millions of users from around the world. SOSCIP’s platform will enable us to investigate ways of using bleeding edge machine learning techniques to navigate around existing inefficiencies in our existing system, as well as to take a leap beyond its current level of performance with new research advances. SOSCIP’s platform is exceptional for helping us achieve these goals as it can support training of deep neural networks on very large datasets of music videos. This is a requirement for analyzing and generating thousands of high-resolution videos at record speed. The platform that SOSCIP provides is perfect for running the experiments that are required to produce models that can perform these tasks efficiently as well as addressing new and novel problems in using AI for creative applications. The collaboration is between Rave, Prof. Graham Taylor and Prof. Stefan Kramer at University of Guelph.
Genetic variation and structure-based drug polypharmacology: multiscale structural pharmacogenomics
Over the past 25 years, drug discovery efforts have been aimed at identifying small organic molecules that exert a strong effect on a single protein that corresponds to the biological target of a disease. Although designed to fulfill a single biological function, small molecule drugs can incidentally interact with hundreds of the ~20,000 known human gene products. These off-target interactions may lead to side effects, new therapeutic effects, or otherwise affect drug response. Mapping a drug to all of its human protein interaction partners is known as polypharmacology. Thus, it is advantageous to understand the polypharmacology of each drug in development prior to clinical testing in order to better anticipate off-target effects that could potentially cause toxicity. However, to address drug polypharmacology experimentally is prohibitively expensive in time and monetary cost. Predicting a drug’s polypharmacology is Cyclica’s central mandate. Our drug discovery platform is based on proteome-wide screening, and centered around PROBEx, a cloud-based software solution that simulates a drug’s interaction with 100,000’s of proteins on the basis of their molecular structures. PROBE x maps out the possible off-target protein partners and consequently pharmacology for any pharmaceutical or nutraceutical compounds, to service academic research institutes, hospitals, and pharmaceutical companies developing new disease therapies. Pharmacogenomics is the study of how genes affect a person’s response to drugs by combining pharmacology (the science of drugs), and genomics (the study of genes and their functions) to develop effective, safe medications that are tailored to a person’s genetic makeup.
This field, commonly termed “personalized medicine” has recently emerged and gained significant attention from many companies, including IBM Watson Life Sciences. To our knowledge however, information pertaining to the structural proteome and its relation to drug binding, has not yet been systematically leveraged to improve pharmacogenetic analyses. This technology gap represents a unique opportunity for Cyclica to contribute to personalized medicine, by working with SOSCIP and the IBM cloud analytics platform. Cyclica can integrate its drug polypharmacology predictions with clinical and genomic data to build models that can explain and predict variation in drug response between different individuals. Through Industrial Partner: Cyclica Inc. this program, we will create new tools that match patients to the pharmaceuticals best suited for their distinct genetic features. Ultimately, the effort will improve outcomes and limit the loss of valuable time associated with trial-and-error medicine.
Getting ahead of the curve: a novel way to find people who are likely to be asymptomatic carriers of COVID-19 before they infect others
The spread of COVID-19 has been slowed by physical distancing, self-isolation, lockdowns, masks and travel restrictions. Doing more testing and contact tracing of known cases has helped contain the spread. However, these approaches are ‘behind the virus’ – by at least 5-10 days. We need a way of getting ahead of the virus, before it spreads further. We know that about half the infections are transmitted by people who do not have any symptoms, and this is sufficient to cause a growing epidemic. Therefore, we need to find, isolate and test at least some asymptomatic people. Like intelligence officers hunting hidden terrorists, we need to prevent attacks before they happen, instead of chasing the culprits after the fact. In other words, we need to identify carriers before outbreaks occur, not after. Our team proposes a new way to do this.
The overall goal of this project is to develop models and an intervention prototype to predict which individuals are most likely to be exposed to COVID-19 and are therefore most at risk of onward transmission. We will apply statistical modeling and Artificial Intelligence (AI) methods to demographic, occupational, social networking and geolocation data. In simulations, we will test different ‘smart isolation and testing strategies’ that could be used by public health officials to determine which would most effectively reduce viral transmission. Applying AI-informed strategies could enable partial relaxation of confinement rules for lower-risk segments, and allow greater reopening of economic and social life without risking a second wave and further lockdowns.
Harnessing the diversity of phage displace libraries to capitalize on single domain antibodies with high target affinity and improved protease stability
AbCelex Technologies is developing a line of novel antibody-based products delivered as feed additives to poultry for the prevention of foodborne illnesses caused by Campylobacter and Salmonella. AbCelex’s single domain antibody (sdAb) platform technology is based on camelid antibodies, which have significantly higher thermal and protease stability, accessibility to target due to their small size (1/10 of conventional antibody) while providing affinities that exceed that of conventional antibodies. The current platform utilizes bioinformatics approaches that take into account genetic and protein diversity of the pathogens and in silico antibody engineering predictions to inform optimal design of sdAbs with competitive affinity, effectivity and cost as feed additives. In this project, we aim to combine the benefits of the natural scaffold of the camelid antibody and the expertise we have developed on understanding the interactions of these sdAbs with their target to develop computational libraries that we can then validate in vitro, ex vivo and ultimately in vivo.
For this purpose, we propose to collaborate with Dr. Mauricio Terebiznik, with whom we have been developing a detailed database of the target-antibody interaction mechanisms. He is an expert in cellular biology mechanisms altered by the pathogens such as Salmonella. The major barrier to achieve this aim is access to high performance computing and the SOSCIP platform is ideal to overcome this barrier. Furthermore, the postdoctoral funding will allow the candidate to build on the backbone of camelid antibodies rationally selected peptides that would provide target specificity and cross-reactivity across different strains of the same bacterial pathogen through this proposed collaboration.
High fidelity simulations and low-order aero-acoustic modeling of engine test cells
The testing and certification of gas turbines demand the well-controlled environment provided by an engine test cell. The resonant acoustic coupling between the flow generated noise from the gas turbine exhaust and the engine test cell impacts the quality and reliability of the engine testing and certification. Predictive modeling of flow generated noise using high-fidelity numerical simulations is central to an a priori acoustic assessment and for the development of noise-mitigating designs. As part of this effort, the Multi-Physics Interaction Lab and University of Waterloo will numerically study, with the help of high fidelity, large-eddy simulations of the SOSCIP high-performance computers, the acoustic noise generation in partially confined jets undergoing a re-acceleration through the test cell ejector system. As a direct outcome, the researchers will develop a low order Aero-acoustic model that will be used by our industrial partner to predict resonant acoustic models to within +/- 20% the frequency and amplitude of the coupling phenomena. This OCE and NSERC-funding project will permit the training and mentoring of four HQP for careers in science and technology within Canada.
High performance computing for assessing and mitigating the effect of extreme wind on building and cities
As the second largest country in the world, Canada’s diverse geography and climate increases our cities exposure to different types of natural hazards, such as snow storms, hurricanes, tornadoes and floods. The insurance industry estimates that insured catastrophic losses in North America average $80B per year. In Toronto (2005), for example. a single tornado event resulted in $500M loss. This is further compounded by changes in climate, population growth and aging infrastructure.
To maintain the prosperity of our communities, it is imperative that a comprehensive framework be developed to assess and mitigate the impacts of extreme climate on cities. The current project aims to develop a multi-scale climate responsive design framework that accounts for the complex interaction between buildings and wind (including hurricane and tornado). This computational framework, at neighborhood scale, models urban micro-climate necessary to assess the impact of changing city topology on the pedestrian level wind, air quality and to generate boundary conditions for small-scale simulations. At building scale, it develops a full numerical aeroealstic model (e.g. building model that flex) immersed in turbulent city flows, for the first time. This frame work when integrated with artificial intelligence based optimization procedures, allow optimizing tall building aerodynamics (shape) and dynamics (structural systems) appropriate for current era of booming tall building construction.
As a result, Ontario will save materials and energy in one of the most resource intensive sector, while enhancing the safety of Ontarians during extreme climate. For successful implementation of the framework, a high performance computing environment and experimental validations are necessary, which will be enabled by two unique research facilities in Ontario, Blue Gene Q and WindEEE Dome, respectively.
High-fidelity aerodynamic analysis of unmanned multirotor vehicles
The objective of the proposed research is to gain a better understanding of the complex aerodynamics of small multirotor vehicles, such as quadcopters. This will enable Aeryon Labs, a Canadian company and world leader in small unmanned systems, to improve the development cycles of their products and respond faster to customer needs. Multirotor vehicles are popular platforms for many remote sensing applications because of the relative ease to control them at hover. During fast flight, however, control becomes challenging, because of highly nonlinear aerodynamics. These nonlinearities are due to the small-scale aerodynamics typical for these vehicles, and the interaction of the flow fields of several rotors that operate in close proximity. The proposed research builds on existing research on multirotor-vehicle aerodynamics. In order to expand our understanding of the complex aerodynamics of multirotor vehicles, we propose to model a quadcopter using Computational Fluid Dynamics (CFD). The CFD results will be compared with the lower-fidelity predictions and experimental results. The investigation of the predictive method will benefit from experiments performed in the large low-speed wind tunnel at Ryerson University, flight tests and an existing collaboration with Aeryon Labs. As part of the proposed research project one postdoctoral fellow, one doctoral student and two MASc students will receive training in the area of applied aerodynamics. The results will benefit Aeryon Labs with superior design tools that will improve their product line. Small aerial systems represent a rapidly expanding market segment worldwide, in which Canadian companies, such as Aeryon, play an important role.
High-throughput transcriptomic and eQTL analyses of silicon-induced resistance against Fusarium head blight on wheat
Wheat is an essential part of the agriculture and agri-food industry in Ontario with an average annual production of 2.5 Million tons. High yield and quality of wheat is seriously threatened by biotic and abiotic factors, among which Fusarium head blight (FHB, caused by the fungus Fusarium graminearum) has historically been most damaging. The mycotoxin produced by this fungus is harmful for human health and livestock feed and productivity. Conventional chemical fungicides are commonly used as an important mean to control FHB; however, they also pose a serious risk to the health of humankind and the environment. One of the most promising non-hazardous, eco-friendly methods to control different plant pathogens is the use of silicon, which induces resistance.
Backed by SOSCIP high-performance computing resources, and using novel bioinformatic approaches, this study aims at conducting high-throughput genome-wide transcriptomic analysis of silicon-induced resistance against FHB, and possibly other important diseases, in wheat. Such study will lead us to:
1. understanding the complex underlying induced defense mechanisms
2. identifying gene modules and regulatory elements that control such mechanisms, and finally and more importantly
3. providing valuable building blocks and framework for future breeding programs which will be focused on the development of novel disease-resistant wheat cultivars in Ontario.
HPC cloud analytics / machine learning support for Watson Pepper clinical study
Skin cancer is the most common type of cancer. 80,000 cases of cancer are diagnosed in Canada every year. 5000 of these cases are melanoma, the deadliest form of cancer (Canadian Skin Cancer Foundation). Current prevention efforts to reduce skin cancer focus on educating individuals on preventative actions that they can take to reduce the risk of this cancer. However, research has shown that both communication failure and information overload are significant problems affecting the quality of patient centered care. Social robotics and artificial intelligence have been used effectively to communicate and positively influence behavior, thus this research proposes to develop and test these combined technologies as an intervention for skin cancer prevention education.
The research team and collaborating research partner IBM will integrate IBM Watson cognitive computing applications with Softbank Robotics advanced robotics platform, the Pepper robot. The Watson Pepper prototype will be used as a controlled variable in a randomized controlled clinical trial (N = 200) to assess the efficacy of socially assistive robotics intervention for behavioural change in skin cancer prevention knowledge and practices among medical patients, the first clinically tested implementation of a Watson Pepper robot for healthcare communication. The research proposes commercialization and business implementation of the integrated IBM Watson robot in an expanded scale and scope of healthcare communication applications. To support the achievement of this innovative technology milestone, SOSCIP will provide the critical cloud data analytics and memory capacity to support the analysis and modeling of the large multivariate data sets associated with this project.
Improved numerical combustion models for understanding and predicting nvPM/Soot formation and emissions in aviation gas turbine engines
Aviation gas turbine engines that burn hydrocarbon based fuels emit nanometer-sized carbonaceous non-volatile (not readily vaporized) particulate matter (nvPM) in addition to the usual gaseous emissions, such as green-house gases (GHG, largely CO2, actually a combustion product), nitric oxide (NOx) and carbon monoxide (CO). Also known as soot, smoke, or black carbon, these very small size nvPM has been shown to impact global warming and climate change by altering the radiation balance in the atmosphere through induced cloud cover and deposition of PM on arctic ice.
For these reasons, the manufacturers of gas turbine engines are today facing more and more stringent governmental and/or environmental regulations pertaining to PM emissions and there is a pressing need for reduced emission strategies. Unfortunately, the physical processes governing how nvPM and its precursors are formed in the high pressure flames and combustion systems of gas turbines is currently a matter of intense debate and a complete fundamental understanding of soot formation and emission processes is not firmly established.
The proposed two-year research project will consider the development of new and improved mathematical theory and computational models for understanding and predicting nvPM formation and emissions in aviation gas turbine engines. Through collaboration with the industrial partner, Pratt & Whitney Canada Corp. (P&WC), this new knowledge and understanding will be subsequently transferred to an industrial setting where it will be put to use in the design of next generation gas turbine engines having reduced PM emissions.
Improvement of Precipitation Gauge Collection in Remote Locations
To properly understand the global water cycle, improve analysis of climate variability, verify climate models and assist in local decision-making of surface or air transport, it is necessary to have better field tools for measurement of snow. Previous work has developed models of the Geonor precipitation guage and has included k-epsilon based numerical models of the flow around shielded gauges. To improve these results, it is essential to pursue advanced turbulence models as well as to develop benchmark experimental results. Large-Eddy Simulation, or LES, has become the method of choice for computationally-intensive simulations resolved to the necessary scales. Traditional Reynolds-averaged methods (RANS), although useful, require significant assumptions that compromise the fidelity of the flow physics obtained.
Direct Numerical Simulation (DNS) which resolves all the scales remains a prohibitive method due to its computational requirements. LES bridges between RANS and DNS, where the energetic large scales are resolved and computed directly whereas the smaller more universal scales are modeled. By modeling the subgrid scales within the inertial subrange, it is possible to extract high-fidelity flow information that can be used to improve local conditions. However, LES is computationally intensive and micro-climate modeling is beyond the capability of most desktop computers. As well, until recently LES models were either lab-developed codes or commercial codes. Lab-developed codes are difficult to transfer to industry partners as they are not necessarily client-friendly. While there exists excellent commercial codes, using these on multi-processor machines is prohibitor expensive. To model this flow, OpenFoam, an open-source turbulence code, will be used. The use of LES, instead of RANS modeling, would allow improved physical modeling of snow precipitate and ensure better comparison to real flow.
Integrated platform for distributed analytics of biometric data
Avertus is a company focused on providing tools for physicians to provide better patient care and treatment and researchers who are seeking new and improved treatments. Its first platform is a high frequency wireless brain activity monitor with a first application for seizure pattern detection. This will help physicians assess types and causes of seizures as well as response to drugs and other treatments and longer term help deliver future treatment technologies. With support from the SOSCIP and University of Toronto researchers, we will also implement the first commercially available high performance distributed computing platform for easy to use biomarker discovery tools and both batch and real time big data analytic capability for clinicians and researchers.
Intelligent emergency response using the internet of things (IoT)
Smart mobile devices and wireless Internet access are allowing emergency responders to access and share valuable emergency-related information coming from data repositories, citizens, surveillance cameras, and many other sources. However, because there is no accurate and energy-efficient indoor positioning solution, emergency responders cannot translate that wealth of information into a much-needed situational awareness. This project addresses this unique challenge by using data analytics and the Internet of Things (IoT). More specifically, this project devises an accurate, scalable, energy efficient, robust, and resilient indoor positioning solution using Bluetooth Low Energy (BLE) beacons.
These battery-operated beacons will be deployed in buildings as part of emergency preparedness strategy. When an emergency happens, the communication between these beacons and the smartphones and tablets of emergency responders will give emergency responders the necessary situational awareness at the right time and the right location. This positioning solution is based on a thorough analysis of signal propagation and coverage measurements using machine-learning tools. This solution will then be used to prototype a Location-Based Service (LBS). This software application gives emergency responders necessary and sufficient situational awareness while responding to building emergencies. This LBS is designed and developed based on a comprehensive analysis of buildings’ information, emergency response plans, and emergency records.
This combined repository of information is processed using text mining to identify points of interest at any building and during any emergency and give a statistical model for the workflow of emergency responders between these points. Deploying BLE beacons at these points will help expedite the preparation and execution of emergency response plans, and hence enhance the situational-awareness, efficiency, and effectiveness of emergency responders. This is the first project to combine data analytics and IoT in the public safety domain. Its findings will have a ripple effect on the design and development of software solutions for emergency responders.
Joint optimization of route design and schedules for fixed route transit systems
The current method of optimizing routes and schedules for fixed route transit systems is sequential. Typically, route planning (involving determining route path, stops and service pattern) occurs initially, followed by schedule optimization (examining factors such as vehicle availability, operating requirements, safety restrictions, union contracts and employee pay).
This sequential optimization process produces a sub-optimal overall solution, inefficiently allocating agency resources, or allocating them in a way that may not be providing transit users with the best route and service. As a result, a method that could handle both simultaneously would be unique in the industry and extremely valuable to all fixed route transit agencies and service providers. Handling the numerous variables and constraints in route and schedule planning requires a method capable of intelligently searching for solutions.
The proposed method to tackle these dual requirements is simulation-based optimization using constraint programming—which is an optimization technique where knowledge of the problem is used to reduce the solution search space based on constraints. Evaluation of the feasible solutions is proposed to be accomplished using a simulation of the transit service that would more accurately represent service performance and passenger experience, where the structure of constraint programming methods lends themselves well to parallelization—ideal for the multi-core setup of the SOSCIP platforms.
Large scale simulation of nanostructured optical surfaces
Plasmonic metasurfaces are engineered human-fabricated surfaces on which there is nanometer scale structure, usually containing metallic features. These nanoscale features can resonantly interact with light through the excitation of electric charge density oscillations, or surface plasmons. As plasmonic metasurfaces have essentially infinite design potential — including designer resonant behaviour and strong nanoscale light field enhancements — there is currently a great deal of interest in using them for a wide range of applications, from flat optical devices to biosensing to enhanced nonlinear optical signals3 to colour production4. A major focus of this project will be to perform large scale computational electrodynamics simulations on the Blue Gene Q to understand and design plasmonic metasurfaces. The work will be connected to projects underway with several Canadian industrial partners, as well as a plan to engage with several others, and will involve multiple trainees at various stages in their education – from undergraduate to postdoctoral fellow.
Large scale simulations of photonic quantum computers
Current quantum computers are in the “NISQ”, or Noisy-Intermediate-Scale-Quantum regime. The true potential of quantum computing will only be realized when noise levels are reduced or controlled, and large scale is achieved. Xanadu’s approach is to use photonic technology as the building blocks of their machines. This project addresses the question: How do we build a useful quantum computer based on imperfect photonics components? There are 2 specific aspects of this general question: A – In which conditions does a photonic quantum computer reach quantum advantage (demonstrating large speedups compared to today’s most powerful conventional computers)? B – What are the resources required to build a scalable fault-tolerant photonic quantum computer? This project will provide the team with a much more detailed understanding of the requirements and tradeoffs involved in the future, much larger-scale generations of quantum photonic hardware that must be built in order to fully realize the theoretical potential of quantum computing.By mapping out a pathway to demonstrating quantum advantage, and large-scale, fault-tolerant quantum computing, this project will guide Xanadu’s ongoing efforts to build more powerful quantum computers which can deliver commercial benefits to customers and ensure economic impact.
Machine Learning for Materials Discovery and Design
The objective of this project is to combine machine learning methodologies and electronic structure theory for the purpose of designing new materials through computational modelling. Achieving this goal will be important for the fields of Advanced Manufacturing and Energy (Materials). We will perform electronic structure calculations on a large database of existing materials (transition metal surfaces) and use results of these simulations as input to a machine learning model. The developed model will then be tested against new materials outside of the test set to confirm the model’s validity and transferability. This machine learning model will be used to identify new catalytic materials for use in water splitting and CO2 reforming devices. The project will combine high performance computing and machine learning to enable accelerated material discovery.
Mining microbiome community structure and biomarker identification through data intensive biology, machine learning, and high throughput technologies
Microbial ecosystems such as those associated with the mining industry are complex networks of interacting species and biochemical dependencies. Modeling responses to different or changing environmental conditions is a considerable computational and statistical challenge. Current approaches largely identify important features through differential abundance, ignoring the disparate influence some species or functions exert on the community, greatly simplifying biological complexity. Additionally, these approaches can ignore poorly annotated features (e.g., hypothetical genes, microbial dark matter, ORFans). Excluding these known unknowns and unknown unknowns reduces the resolution and sensitivity of these analyses. Metagenomic feature selection using machine learning has been most widely applied to the human microbiome, which currently has more extensive data than other systems. We will apply a superficially similar but much higher resolution approach to less studied, more dynamic industrial microbiomes, such as mining.
Mobilizing ‘Big energy data’ for building conservation to socialize sustainability
This project will establish the first customer-centric universal tool using mobile/cloud technology to deliver & share energy information electronically with energy customers to stimulate social change, encourage conservation & help manage/reduce GHG emissions.
A global desire to manage climate change is here. There needs to be a joint effort to help the energy user gain access to electronic information, so the data can be used to better manage conservation, sustainability and technology integration. Innovation in the energy sector continues to create data silos, as new technologies used don’t allow for the data to freely flow out of the systems to other solutions. Customers require easy access & a simplified understanding of electric information (not print or PDFs). For this to happen, the end customer requires data to be managed, simplified & shared, so information can be “open” to innovation and electronic data to be standardized.
The market needs Green practices and conservation to flourish in the private sector and public sector. Internationally and in Ontario, there are regulatory and political requirements to provide end-user access to electronic data, but without a simplified roadmap to interconnectivity how will it happen? The market needs linkages to the “Internet of Things” to learn, inform and conserve. In a few short years, there will be more than 25 billion devices generating data on every topic imaginable. The energy customer and building owner needs simplified data from multiple sources. Screaming has an opportunity to deliver this information cost-effectively to the utility, government & customer.