Motion Matters: Neural Motion Transfer for Better Camera Physiological Sensing

Machine learning models for camera-based physiological measurement can have weak generalization due to a lack of representative training data. Body motion is one of the most significant sources of noise when attempting to recover the subtle cardiac pulse from a video. We explore motion transfer as a form of data augmentation to introduce motion variation while preserving physiological changes. We adapt a neural video synthesis approach to augment videos for the task of remote photoplethysmography (PPG) and study the effects of motion augmentation with respect to 1) the magnitude and 2) the type of motion. After training on motion-augmented versions of publicly available datasets, the presented inter-dataset results on five benchmark datasets show improvements of up to 75% over existing state-of-the-art results. Our findings illustrate the utility of motion transfer as a data augmentation technique for improving the generalization of models for camera-based physiological sensing. We release our code and pre-trained models for using motion transfer as a data augmentation technique on our project page: https://motion-matters.github.io/

Extracting Success from IBM's 20-Qubit Machines Using Error-Aware Compilation

NISQ (Noisy, Intermediate-Scale Quantum) computing requires error mitigation to achieve meaningful computation. Our compilation tool development focuses on the fact that the error rates of individual qubits are not equal, with a goal of maximizing the success probability of real-world subroutines such as an adder circuit. We begin by establishing a metric for choosing among possible paths and circuit alternatives for executing gates between variables placed far apart within the processor, and test our approach on two IBM 20-qubit systems named Tokyo and Poughkeepsie. We find that a single-number metric describing the fidelity of individual gates is a useful but imperfect guide. Our compiler uses this subsystem and maps complete circuits onto the machine using a beam search-based heuristic that will scale as processor and program sizes grow. To evaluate the whole compilation process, we compiled and executed adder circuits, then calculated the KL-divergence (a measure of the distance between two probability distributions). For a circuit within the capabilities of the hardware, our compilation increases estimated success probability and reduces KL-divergence relative to an error-oblivious placement.