Grants and Contributions

Advances exploratory research under the New Beginnings Initiative

Scientific discovery, by nature, is a lengthy and arduous journey largely due to the vastness of its exploratory scope. To illustrate, the design of a 100 amino-acid protein sequence entails the exploration of mind-boggling 20^100 possibilities. Historical approaches to scientific discovery in realms such as protein, molecular, and material design have relied heavily on the artful blend of intuition, experience, and a dash of serendipity. These empirical-driven methods, while valuable, are markedly time-intensive and laden with numerous unsuccessful attempts. The advent of artificial intelligence (AI) promised a new velocity in the discovery process. Yet, prior efforts have generally centered on small-scale models with constrained generalizability, resulting in suboptimal outcomes. The emergence of large language models (LLMs), with their remarkable reasoning and generalization prowess, holds the potential to catalyze a seismic shift in this field, thereby dramatically streamlining the scientific discovery journey. The project aims to accelerate scientific discovery with big data. By utilizing the currently popular large foundation models, it will be possible to unleash their generalization capabilities to help researchers significantly shrink the search space. By leveraging AI techniques in the process, it can make the search process more explainable and systematic.

Decarbonizing the built environment through extensive green renovation requires a carbon footprint of deconstruction/renovation activities, which includes the carbon footprint of materials and components and a holistic approach. High-precision live digital twin models (DTMs) could integrate data repositories and provide a platform for scenario analysis, but standards and maturity are lacking. This project defines a model-building and application strategy, analyzes the requirements for standardizing renovation DTs by creating a digital building product passport and urban-scale DTs through open MIBSIG integration, automating the processes for generating renovation alternatives and their evaluation, combined with life-cycle cost and life-cycle analyses, integrated into DTs, with methods for calculating deconstruction and recoverability scores for renovation deployed by DT standards.

In the realm of Autonomous Driving (AD), the application of Deep Learning (DL) and Deep Reinforcement Learning (DRL) has been pivotal. However, ensuring the robustness of these technologies against cyber threats remains a critical challenge. This project proposes the development of a high-fidelity virtual platform to assess the certified robustness of DL and DRL algorithms in AD, focusing on cybersecurity vulnerabilities. The proposed framework leverages the capabilities of the Carla open-source simulator to create a realistic virtual environment that mimics real-world cyberattack scenarios on AD systems. This project will have three phases: the first phase will involve identifying and training baseline DL/DRL algorithms for AD and selecting the state-of-the-art methods suitable for various AD scenarios. In the second phase, the focus will be on developing a virtual platform prototype capable of accurately simulating diverse cyberattack scenarios, thereby providing a testbed for evaluating the resilience of DL/DRL-based AVs. The final phase involves training certified defense algorithms designed explicitly for DL/DRLbased AV systems, emphasizing their optimization for AD requirements and testing against a spectrum of cyber threats.

Not a Project (Mandated or Core Funding)

Supporting office equipment purchase.

Acquisition of equipment: The project aims to improve productivity and increase the production capacity of a company specializing in the manufacturing of condiments.

Upgrading facilities: In the aftermath of Hurricane Fiona, the project aims to restore economic and community activity at a tourist attraction.

Acquisition of equipment: The project aims to improve productivity and increase the production capacity of a company specializing in the manufacturing of condiments.

Grant made under the Particpant Funding funding stream to enable participation in Crown consultation activities for the NEBC Connector Project.