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Computationally efficient, camera-based, real-time human position tracking on low-end, edge devices would enable numerous applications, including privacy-preserving video redaction and analysis. Unfortunately, running most deep neural network based models in real time requires expensive hardware, making widespread deployment difficult, particularly on edge devices. Shifting inference to the cloud increases the attack surface, generally requiring that users trust cloud servers, and increases demands on wireless networks in deployment venues. Our goal is to determine the extreme to which edge video redaction efficiency can be taken, with a particular interest in enabling, for the first time, low-cost, real-time deployments with inexpensive commodity hardware. We present an efficient solution to the human detection (and redaction) problem based on singular value decomposition (SVD) background removal and describe a novel time- and energy-efficient sensor-fusion algorithm that leverages human position information in real-world coordinates to enable real-time visual human detection and tracking at the edge. These ideas are evaluated using a prototype built from (resource-constrained) commodity hardware representative of commonly used low-cost IoT edge devices. The speed and accuracy of the system are evaluated via a deployment study, and it is compared with the most advanced relevant alternatives. The multi-modal system operates at a frame rate ranging from 20 FPS to 60 FPS, achieves awIoU_0.3score (see Section 5.4) ranging from 0.71 to 0.79, and successfully performs complete redaction of privacy-sensitive pixels with a success rate of 91%–99% in human head regions and 77%–91% in upper body regions, depending on the number of individuals present in the field of view. These results demonstrate that it is possible to achieve adequate efficiency to enable real-time redaction on inexpensive, commodity edge hardware. 

Inspired by BatchNorm, there has been an explosion of normalization layers in deep learning. Recent works have identified a multitude of beneficial properties in BatchNorm to explain its success. However, given the pursuit of alternative normalization layers, these properties need to be generalized so that any given layer's success/failure can be accurately predicted. In this work, we take a first step towards this goal by extending known properties of BatchNorm in randomly initialized deep neural networks (DNNs) to several recently proposed normalization layers. Our primary findings follow: (i) similar to BatchNorm, activations-based normalization layers can prevent exponential growth of activations in ResNets, but parametric techniques require explicit remedies; (ii) use of GroupNorm can ensure an informative forward propagation, with different samples being assigned dissimilar activations, but increasing group size results in increasingly indistinguishable activations for different samples, explaining slow convergence speed in models with LayerNorm; and (iii) small group sizes result in large gradient norm in earlier layers, hence explaining training instability issues in Instance Normalization and illustrating a speed-stability tradeoff in GroupNorm. Overall, our analysis reveals a unified set of mechanisms that underpin the success of normalization methods in deep learning, providing us with a compass to systematically explore the vast design space of DNN normalization layers. 

We propose an unsupervised learning method that exploits client heterogeneity to enable privacy preserving, SOTA performance unsupervised federated learning. 

The poor interfacial stability of Li metal leads to formation of unstable solid-electrolyte interphases (SEIs) and severely limits its practical applications. Protecting Li metal with an artificial SEI that has balanced stability, conductivity and mechanical strength is critical. Here we demonstrate a design strategy for stabilizing Li using Mo 6 S 8 /carbon artificial SEI films. These films are directly coated on Li foil and the Mo 6 S 8 particles provide ordered conduction channels for fast but regulated Li-ion flux, and provide hybrid anodes that have nearly four times higher exchange current densities. They also have seamless contact with Li metal and protect it from parasitic reactions, and hence significantly improve its stability. Consequently, Li metal batteries in which the hybrid anodes were paired with LiNi 0.8 Mn 0.1 Co 0.1 O 2 cathodes (3.0 mA h per cell) exhibited significantly improved cycling stability (63% vs. 25% retention) and a stabilized Li interphase compared with pristine Li anodes.