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.
Development of a COVID-19 in pregnancy data respository and prognostication algorithm
Professors Dafna Sussman & Rasha Kashef of Ryerson University are teaming up with Mount Sinai Hospital to tackle the very difficult problem of achieving successful early intervention for pregnant women diagnosed with COVID-19.
The project aims to support medical professionals who are directly treating COVID-19 pregnancies. A comprehensive, anonymized data repository will be deployed in conjunction with dedicated prediction algorithms to score patients for their risk of severe deterioration. The repository together with the algorithm are expected to radically transform Canadian and, potentially, international healthcare providers’ ability to identify, manage and treat cases of COVID-19 in pregnant patients.
Development of cardiac specific machine learning infrastructure
Analytics for Life, Inc. (A4L) is an early stage medical device company that specializes in the development of technologies to analyze patient physiological signals in order to evaluate cardiac performance, status and risk. A4L’s core competencies include identifying and developing mathematical features from physiological signals and assembling these features into clinically informative formulae using machine learning techniques. A4L has used third party machine learning tools (open source and licensed products) for the formula generation aspect of the product development cycle.
Specifically, A4L has used these tools to demonstrate the feasibility of computing left ventricular ejection fraction, cardiac ischemic burden and other cardiac performance/status parameters for simple to collect, non-invasive physiological signals (surface voltage gradients, SPO2, Impedance etc.). As a result of this experience, A4L have learned the benefits and insufficiencies of these tools for A4L’s specific purposes. A4L plans to file with the U.S. Food and Drug Administration (FDA) an application for approval of a physiological signal collection device and will soon afterwards be seeking market clearance for products assessing cardiac health emanating from the machine learning process. A4L believes it can build a machine learning tool specifically tailored for cardiac evaluation based on experience with the tools used to date.
This A4L-specific machine learning paradigm will search only relevant mathematical spaces, cutting down on time and CPU power needed to iterate to solutions and will allow for an assessment of a much wider array of potential solutions. Furthermore, this A4L-specific machine learning paradigm will provide a controlled and validated system that can be audited and evaluated by regulatory bodies, something that is not possible with the current machine learning tool(s). A4L proposes a hybridization of paradigms within a set mathematical space. This will create efficiency in the search, and therefore more searches can be performed in the same period of time. This will lead to more solutions being available for evaluation, resulting in more accurate and efficiently produced end solutions. If successful, this new paradigm will allow for simple, non-invasive, rapid and relatively inexpensive cardiac diagnostic capabilities, bringing tertiary care diagnostics to primary care settings and disrupting the current infrastructure and capital cost-centric model of diagnostic delivery.
Development of severity index of exacerbation for COVID-19 symptoms from abnormal respiratory patterns
Struggling to breathe has been a challenge that existing monitoring devices have faced, for identifying early symptoms of respiratory diseases such as novel Coronavirus (COVID-19). Measuring respiratory rate alone would result in potentially missing if a patient was unable to inhale a full breath.
To overcome this challenge, a novel technology solution invented in Canada is used through an Intelligent Bed Sheet, with the ability to continuously monitor if a patient has normal, shallow, or irregular breathing.
This collaboration with York University will revolve around developing a Severity Index by identifying earlier when a patient is struggling to breathe. Using the most advanced electronic fabric technology, the SOSCIP Platform will support computing challenges by using large amounts of breathing data in a constantly changing environment, to precisely identify before a patient’s breathing becomes more severe.
Distributed and scalable search in enterprise databases
Google search, and other search engines such as Bing and Yahoo!, provide a convenient way to find Webpages that contain various keywords or are related to particular topics. For the purposes of searching, Webpages are essentially loosely structured paragraphs of text. However, much of the world’s high-quality enterprise data are structured into well defined tables containing sets of well-defined columns.
One consequence of structured database design is that information about a single entity may be scattered across many columns in many tables, and must be stitched together in a meaningful way when answering user queries. This turns out to be significantly more difficult than finding Webpages or text documents containing various keywords.
As Dr. Surajit Chadhuri (a Distinguished Scientist at Microsoft Research) recently argued in a keynote talk at the IEEE Data Engineering conference, search over structured databases has fallen behind search over unstructured data. In the proposed research, we will develop a powerful and intuitive search system, akin to Web keyword search, for structured enterprise data. Our system will empower nontechnical users to explore enterprise databases and turn big data into actionable insight, just as Google search has empowered society to explore the Web.
Distributed Deep Learning and Graph Analytics Using IBM Spectrum Computing Solutions
Deep learning is a popular machine learning technique and has been applied to many real-world problems, ranging from computer vision to natural language processing. In most cases deep learning outperformed previous work. However, training a deep neural network is very time-consuming, especially on big data. A popular solution is to distribute and parallel the training process across multiple machines. Indeed, the race is on to parallelize deep learning! Industry and academic research teams around the world are trying to make deep neural networks train as fast as possible on farms of GPU capable servers. We are working with our IBM partners to help develop advanced scheduling and messaging techniques for distributed deep learning. In addition, we will develop two real-world applications of distributed deep learning to demonstrate the efficiency and effectiveness of distributed deep learning. In one application, we address the video surveillance problem of tracking a moving target over a network of video cameras with partial or no overlaps in their coverage. We will use a deep learning approach to identify multiple pedestrians in each video frame, and a particle filter to track moving pedestrians. In the second application, we address the problem of fraud/intrusion detection. We will use graph-based detection that considers relationships between objects or individuals. Graph-based approaches are powerful because they do not operate on objects or individuals in isolation, but also consider their network information. We will emphasize on graph-based fraud detection methods that have a number of applications and potentially large impacts.
Dynamic microscopy image processing and analysis for infectious diseases, diagnosis and treatments
In vaccine and therapy development for infectious diseases, advanced optical imaging is used to measure the interaction of virus and the host cell. Current microscopes are relatively slow that will only provide a “snapshot” of the biological interactions. In order to develop diagnostic methods and effective treatment, it is necessary to image these dynamic interactions continuously like a movie. Additionally, the stream of images will also need to be processed and analyzed rapidly using new computation methods. To address such a challenge, we plan to develop high-speed microscopic imaging instruments and related image processing and analysis technology. These technologies will enable us to build a customized microscope capable of high-speed quantitative imaging of virus-host interactions in live cells for infectious disease diagnosis and therapeutic treatment research. This project module will primarily focus on image processing and analysis algorithm development.
Efficient deep learning for real-time traffic event detection
Miovision is interested in designing the first affordable, low-power, energy efficient real time traffic event detection system that can be installed without the need to be powered by the grid nor the need to be connected directly to city installed infrastructure. Deep learning for traffic event detection can provide overwhelmingly superior accuracy and addresses most of the real-world scenarios that make competing detectors unsuitable for customer adoption. The challenge with deep learning is its complexity, which is currently infeasible for a self-powered real-world embedded detection system. Working with Dr. Alexander Wong and the Vision and Image Processing Lab at the University of Waterloo, the goal of this project is to develop technologies that can significantly reduce the complexity of deep learning for traffic event detection, while maintaining its accuracy and market fit, so that it can be deployed on a low-cost and low-powered hardware platform.
Electrochemical Fischer-Tropsch synthesis of renewable liquid fuels from CO2
Renewable electricity costs have been rapidly declining, enabling clean consumption of energy in many sectors. However, there is still demand for energy-dense liquid fuels, such as in heavy freight and air transportation. In this project, we will harness machine learning to develop technologies that enable the synthesis of liquid fuels from carbon dioxide and/or synthesis gas using renewable electricity. Industrially, liquid fuels can be synthesized from a mixture of carbon monoxide and hydrogen called synthesis gas (syngas). However, this process requires high temperatures and pressures, and is itself responsible for significant greenhouse gas emissions. We propose the use of electrocatalysis to produce these liquid fuels. To accomplish this, we will use computational modeling and machine learning methods to design electrocatalysts that efficiently convert CO2 or syngas into dense chemical fuels. These computational efforts will be validated through a parallel experimental approach that includes the fabrication of new catalyst formulations and the construction of prototype electrochemical flow cells. This project will enable the synthesis of clean, energy-dense liquid fuels that can replace the use of fossil-derived fuels in industry and transportation sectors. This project is a logical extension of the existing CO2 related projects previously underway with SOSCIP. This project will allow the research to achieve the next milestones in the overall goal to achieve beneficial conversion of CO2.
End-to-End Machine Learning Based Demand Forecasting
This project aims to implement an end-to-end (E2E) Artificial Intelligence (AI) / Machine Learning (ML) driven demand forecasting predictive model pipeline to increase process efficiencies, help improve forecast accuracy, forecast bias and forecast value added and unlock trapped capacity in the business ensuring improved service, optimum inventory, better production scheduling and better P&L forecasting. As participants in this consortium, Unilever, Larus and SOSCIP bring unique contributions that augment the benefits of the initiative from both scientific and business perspectives. More specifically, Unilever brings unique knowledge of business and operational landscape, curated and diverse data sets and analytics expertise in story building and turning data into insights, Larus brings unique expertise in AI/ML decision making and optimization systems and SOSCIP provides the required high performance computing platforms to run ML algorithms utilizing large data sets with optimal performance.
Establishing best practices for generating synthetic pediatric health data
Professor Aaron Smith of the University of Ottawa, working with the CHEO Research Institute, will develop software to generate synthetic electronic health records (EHR) that have similar statistical properties to real EHR while preserving patient privacy.
The ability to share patient data without compromising patient privacy will allow more researchers to search for medically and COVID relevant insights and broad-based sharing will allow researchers to check their ideas about fighting COVID with both greater speed and accuracy.
The final result of this project will be a software package that can generate synthetic electronic health records using minimal computational resources for moderately sized datasets.
Fast and accurate biophotonic simulations for personalized photodynamic cancer therapy treatment planning
There are many medical uses of light, both for diagnostic purposes (medical imaging) and for treatment. We will build a very fast and accurate simulator of where light inserted with fiber optic probes travels within a person’s body; by using this simulator we can enable a promising new cancer therapy, among other medical applications. The key use of our light simulator in this project will be for photodynamic therapy (PDT), a promising new cancer therapy. It uses non-toxic light activated drugs (called a photosensitizer) which is harmless until it is activated by light of a certain wavelength; where the activated photosensitizer destroys cells. Hence if we can localize the light to a cancerous tumour, we can destroy it with minimal damage to surrounding healthy tissue and with fewer side effects and less cost than would be achieved with ionizing radiation therapy or surgery. PDT is used today to destroy “superficial” cancers, such as those on the skin where it is easy to localize the light to the tumour.
PDT can also be used to destroy tumours within the body by inserting fiber optic probes through needles into the body. The key challenge is that the light reflects, refracts and is absorbed in complex ways when it leaves the fiber, making it hard for a physician to determine where to place the fibers and whether the tumour will be completely destroyed. We aim to fill this gap by developing a fast and accurate simulator that can determine the light density throughout a person’s tissue that will result from a given fiber optic probe placement and light input intensity. To do so we will compute the path taken by hundreds of millions of simulated photons — a task so computationally intense that we will use an unconventional IBM computing platform that uses special programmable hardware to offload key calculations from the conventional processor. We believe that by doing so we can speed up the computation and reduce the power required by a factor of 60. This large speed up will allow us not just to simulate one probe placement but many possible light probe placements — we seek to return the best probe placement and the expected treatment results to the physician in less than an hour. Overall this project will pave the way for a much-needed new, less invasive, more effective, and lower-cost cancer treatment. It will also use a new style of computation where special hardware does much of the computation, instead of a general purpose CPU, showing how such an approach can be used to make future computers faster and less power-hungry.
Forecasting Covid-19 Epidemic in Canada with Spatial-Temporal Models That Exploit Population Behaviour on Twitter
The Covid-19 pandemic is creating unprecedented damage to the public health and world economy. Being able to accurately forecast the spread of Covid-19 is critical for the federal and provincial governments of Canada to devise policies and measures maximally protecting the lives of Canadians and rapidly reviving the Canadian economy. Not only important at present, developing advanced epidemic projection algorithms and techniques also helps prevent future epidemics of other infectious diseases. In this project, we set out to develop advanced forecasting algorithms for the spread of Covid-19 across Canada. Specifically, we will exploit the behaviour information of the population revealed on social media as well as the correlation of the Covid-19 spread across different regions of Canada. Our development will integrate modern AI techniques in data analytics, machine learning and natural language processing with the conventional mathematical models for infectious diseases. The developed algorithms will be hosted on a web platform, which will provide accurate daily predictions of Covid-19 spread and release them to the public. These predictions will assist the governments to strategically adjust policies for the protection of Canadian lives and the revival of the Canadian economy. Individuals, families, schools and businesses will all benefit from this new source of information in their planning processes.
Full Monte: fast hardware for Monte Carlo biophotonic simluations
There are many medical applications of light, such as bioluminescent imaging (BLI) and photodynamic therapy for cancer treatment (PDT). These applications require rapid and accurate simulation of how light will scatter and be absorbed in complex human tissue in order to image tissue (e.g. BLI), or to determine where a light-sensitive drug will be activated and destroy cells (e.g. PDT). This project will use agile computing (FPGA hardware) to simulate light propagation very accurately with the Monte Carlo method but much more quickly and power-efficiently than in a conventional computer. The first aspect of the project is to complete our prototype agile implementation of this simulator and scale up its performance and the problem sizes it can handle. The second aspect is to complete a workflow using this simulator along with meshing, visualization and light source placement optimization to make a clinically useful PDT treatment planning system.
Generalized heterogeneous radio signal processing
A new generation of radio telescopes is opening new windows on the Universe, allowing astronomers to observe the cosmos in unprecedented ways. Powered by the ongoing revolution in computing, these new telescopes operate at the cutting edge of digital technologies. New algorithms are being developed at a spectacular pace, and we are forging a new partnership with the Markham branch of Advanced Micro Devices (AMD) to add to these, developing new tools and opening new possibilities in radio astronomy, software defined radio, and similar telecommunications technologies. High-cadence mitigation of Radio Frequency Interference (RFI), advanced Digital Beamforming techniques, and Dynamic Spectral reshaping will all be developed and ported to an open software framework, allowing it to be used on a wide variety of computational and signal processing hardware.
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.