University of Waterloo

About: University of Waterloo is a education organization based out in Waterloo, Ontario, Canada. It is known for research contribution in the topics: Population & Context (language use). The organization has 36093 authors who have published 93906 publications receiving 2948139 citations. The organization is also known as: UW & uwaterloo.

Holographic GB gravity in arbitrary dimensions

Abstract: We study the properties of the holographic CFT dual to Gauss-Bonnet gravity in general D(≥ 5) dimensions. We establish the AdS/CFT dictionary and in particular relate the couplings of the gravitational theory to the universal couplings arising in correlators of the stress tensor of the dual CFT. This allows us to examine constraints on the gravitational couplings by demanding consistency of the CFT. In particular, one can demand positive energy fluxes in scattering processes or the causal propagation of fluctuations. We also examine the holographic hydrodynamics, commenting on the shear viscosity as well as the relaxation time. The latter allows us to consider causality constraints arising from the second-order truncated theory of hydrodynamics.

Deep Parametric Continuous Convolutional Neural Networks

Abstract: Standard convolutional neural networks assume a grid structured input is available and exploit discrete convolutions as their fundamental building blocks. This limits their applicability to many real-world applications. In this paper we propose Parametric Continuous Convolution, a new learnable operator that operates over non-grid structured data. The key idea is to exploit parameterized kernel functions that span the full continuous vector space. This generalization allows us to learn over arbitrary data structures as long as their support relationship is computable. Our experiments show significant improvement over the state-of-the-art in point cloud segmentation of indoor and outdoor scenes, and lidar motion estimation of driving scenes.

Recent progress and perspectives on bi-functional oxygen electrocatalysts for advanced rechargeable metal–air batteries

Abstract: With continued dependence on carbon-based fuels and rising concerns of environmental issues, the development of rechargeable metal–air batteries has recently gained tremendous attention. However, due to the slow kinetics of electrochemical oxygen reactions, the charge and discharge processes of a rechargeable metal–air battery must be catalyzed by using bi-functional catalysts that are active towards both the oxygen reduction and oxygen evolution reactions. This review focuses on recent developments in bi-functional catalysts and their catalytic activity in relation to materials composition, morphology, and crystal structure obtained through various synthetic techniques. The discussion is divided into sections based on the main types of recent bi-functional catalysts such as transition metal- and carbon-based materials, and hybrids which consist of the two. The subsections are then divided based on the metal substituents, types of dopant, degree of doping, and defect densities, discussing the effects of composition. In parallel, morphological effects on the catalytic activity, such as unique nanostructured design, surface area enhancements, and porosity, are also discussed. Currently, bi-functional oxygen electrocatalyst research is heading in the direction of reducing the loading of precious metals, and developing cost-competitive non-precious metal- and carbon-based catalysts to enable commercialization of rechargeable metal–air batteries for various applications including electric-drive vehicles and smart-grid energy storage. To understand the origin of bi-functional catalytic activity, future catalyst research should be conducted in combination with in situ characterizations, and computational studies, which will allow exploitation of active sites to maximize the efficacy of bi-functional catalysts.