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September 21 2023 (Thursday) 4:30-5:30pm
Can machines sense without cameras or sensors? Computer vision allows machines to “see,” but their perception capabilities based on cameras are fundamentally limited to a specific field of view and good lighting conditions – they cannot see through any occlusions or in the dark. In this talk, I will introduce Wireless AI Perception that opens a new sense for machine perception to decipher the physical world, even in absolute darkness and through walls and obstacles. To achieve this, Wireless AI leverages ambient wireless signals for sensing and turns any Wi-Fi devices from a pure communication medium into a ubiquitous all-in-one sensing platform. We will first introduce the concepts, principles, and grand challenges of Wi-Fi sensing, and then share our unique solution of Wireless AI, which has been commercialized and deployed as real-world products, such as motion sensing, sleep monitoring, fall detection, indoor tracking, just to name a few. We foresee that Wi-Fi Sensing will enter billions of devices and millions of homes, and today is just the beginning of this revolution.
September 19, 2023 (Tuesday) 4:30-5:30pm
Driven by the remarkable success of artificial intelligence (AI) and edge computing, the deployment of well-trained private AI models on third-party edge devices for mission-critical applications has become increasingly prevalent. Safeguarding these private models on untrusted devices, while simultaneously speeding up model serving (i.e., inference) through accelerators like GPUs, has escalated in urgency.
We introduce SOTER, a new AI serving system that, for the first time, achieves both high security and high performance. Harnessing the associativity property of AI operators, SOTER presents an innovative approach—transforming computationally expensive AI operators into parameter-morphed equivalents for secure execution on untrusted but fast GPUs, and losslessly restoring inference results within trusted execution environments (TEEs) in CPUs. Experimental results on six prevalent AI models in the three most popular categories show that, even with stronger model protection, SOTER achieves comparable performance with baselines while retaining the same high accuracy as insecure AI model inference.
September 14, 2023 (Thursday) 4:30-5:30pm
On-demand ride services or ride-sourcing services, offered by transportation network companies like Uber, Lyft and Didi, have been experiencing fast development and steadily reshaping the way people travel in the past decade. Various mathematical models and optimization algorithms, including reinforcement learning approaches, have been developed in the literature to help ride-sourcing platforms design better operational strategies to achieve higher operational efficiency. However, due to cost and reliability issues (implementing an immature algorithm for real operations may result in system turbulence), it is commonly infeasible to validate these models and train/test these optimization algorithms within real-world ride sourcing platforms. Acting as a useful test bed, a simulation platform for ride-sourcing systems will thus be very important for both researchers and industrial practitioners to conduct algorithm training/testing or model validation through trails and errors. While previous studies have established a variety of simulators for their own tasks, it lacks a fair and public platform for comparing the models/algorithms proposed by different researchers. In addition, the existing simulators still face many challenges, ranging from their closeness to real environments of ride-sourcing systems, to the completeness of different tasks they can implement. To address the challenges, we propose a novel multi-functional and open-sourced simulation platform for ride-sourcing systems, which can simulate the behaviors and movements of various agents (including drivers and passengers) on a real transportation network. It provides a few accessible portals for users to train and test various optimization algorithms, especially reinforcement learning algorithms, for a variety of tasks, including on-demand matching, idle vehicle repositioning, and dynamic pricing. Evaluated by experiments based on real-world datasets, the simulator is demonstrated to be an efficient and effective test bed for various tasks related to on-demand ride service operations.
September 12, 2023 (Tuesday) 4:30-5:30pm
The rapid development of communication technologies enables real-time information exchange between vehicles, thus being virtually connected. However, the full connected vehicle (CV) deployment will take a long time and may never be achievable, due to privacy, security, and willingness. Knowledge of the CV penetration rate is thus crucial for realizing numerous beneficial applications during the prolonged transition period. Although several novel models have been proposed for CV penetration rate estimation, they are solely point estimators. Direct use of these point estimators without considering their variability can lead to biased models or suboptimal solutions. To bridge this research gap, this study proposes a series of analytical models to accurately estimate the variability of CV penetration rate. Comprehensive VISSIM simulations, real-world applications, and a simple CV-based adaptive signal control scheme demonstrate the readiness of the models for use in real-world situations and the potential of the models to improve system optimizations.
August 28, 2023 (Monday) 2-3pm
Thermodynamics is a science concerning the state of a system, whether it is stable, metastable, or unstable. Its derivatives to natural variables give fundamental physico-chemical properties of the system. It is historically divided into four categories: equilibrium thermodynamics by Gibbs, statistical thermodynamics by Gibbs and Landau, irreversible thermodynamics by Onsager and Prigogine, and quantum mechanics. The development of density function theory (DFT) enabled the quantitative prediction of properties of the ground state of a system from quantum mechanics. Their integration into predictive theories will be discussed in this presentation along with future perspectives. It will be shown that the zentropy theory combines the bottom-up DFT predictions with the revised top-down statistical thermodynamics, while the theory of cross phenomena keeps the entropy production due to irreversible processes in the combine law of thermodynamics to revise the Onsager flux equations. The zentropy theory is capable of quantitatively predicting free energy landscape, singularity and emergent divergences of properties at critical point free of parameters, while the theory of cross phenomena can predict the coefficients of internal processes between non-conjugate variables.
August 3, 2023 (Thursday) 4:30-5:30pm
Mouse embryonic stem cells (ESCs) derived from the epiblast contribute to the somatic lineages and the germline upon reintroduction to the blastocyst but are excluded from the extraembryonic tissues in the placenta that are derived from the trophectoderm (TE) and the primitive endoderm (PrE). By inhibiting signal pathways implicated in the earliest embryo development, we established cultures of mouse expanded potential stem cells (EPSCs) from individual 4-cell and 8-cell blastomeres, by direct conversion of embryonic stem cells (ESCs) and through reprogramming somatic cells. Bona fide trophoblast stem cell (TSC) lines, extra-embryonic endoderm stem (XEN) cells, and ESCs could be directly derived from EPSCs in vitro. The knowledge of mouse EPSCs has enabled the establishment of EPSCs of human, pig, bovine and additional mammalian species. EPSCs of these species share similar molecular features and developmental potentials. They are genetically and epigenetically stable, can be maintained in homogenous long-term cultures and permit efficient precision and complex genome editing. EPSCs thus provide new tools for studying normal development and open up new avenues for translational research in biotechnology, agriculture, and regenerative medicine. For example, we find that early syncytiotrophoblasts produced from human TSCs are highly susceptible to coronavirus infection. This finding has enabled the development of a new stem cell-based antiviral drug discovery technology. I will discuss our thoughts on collaborations with engineering colleagues.
Global responses to the COVID-19 pandemic have largely been suboptimal due to significant underdevelopment of infrastructure, human capital and analytics in pandemic prevention, preparedness, and response (PPR). In particular, epidemic nowcasting has been universally challenging because it requires distilling informative or actionable insights from diverse range of real-world data which are often biased. Misinterpretation, misrepresentation or otherwise misuse of these nowcasts will fuel infodemics, as we have learned to our detriment during the COVID-19 pandemic. We will discuss some lessons learned from COVID-19 and how we can strengthen pandemic PPR in the Age of Information.
The Longyou Caves represent an important historical site in China that has undergone periodic water level changes over several centuries. The ground water flow through the intact rock and fractures is an important factor in the geotechnical assessment of the site. The Environmental Geomechanics Laboratory at McGill University has focused on the development of innovative theoretical approaches and experimental facilities for wide range of rocks including Indiana Limestone, the Cobourg Limestone, the Vermont Granite and the Lac du Bonnet Granite, using both steady state and transient techniques. In this Teck Talk, Professor Selvadurai will present a range of experimental techniques, their theoretical interpretations that can be used to estimate of fluid transport processes through intact rocks that can be described by Darcy’s law. The theoretical and experimental techniques are used to determine the intact permeability of Longyou claystone recovered from the site.
Emerging infectious diseases, such as COVID-19 and pandemic influenza, have a significant impact on the healthcare system and the society. Rapid diagnostic tests are essential for guiding patient management and infection control measures, which lead to improvement in patient outcome and prevent outbreaks in the community and in hospitals. In recent years, fully automated testing has greatly reduced the complexity of diagnostic testing and shortened the turn-around time. Despite their potential benefits, several challenges need to be addressed. In this talk, Professor To will present the advances in rapid diagnostic testing, and will discuss about the hurdles in implementing these novel technologies in real-life settings.