96 research outputs found
Grocery Food Taxes and U.S. County Obesity and Diabetes Rates
BACKGROUND: Grocery food taxes represent a stable tax revenue stream for state and municipal government during times of adverse economic shocks such as that observed under the coronavirus disease 2019 (COVID-19) pandemic. Previous research, however, suggests a possible mechanism through which grocery taxes may adversely affect health. Our objectives are to document the spatial and temporal variation in grocery taxes and to empirically examine the statistical relationship between county-level grocery taxes and obesity and diabetes.
METHODS: We collect and assemble a novel national dataset of annual county and state-level grocery taxes from 2009 through 2016. We link this data to three-year, county-level estimates based on data from the Centers for Disease Control and Prevention on rates of obesity and diabetes and provide a nation-wide spatial characterization of grocery taxes and these two health outcomes. Using a county-level fixed effects estimator, we estimate the effect of grocery taxes on obesity and diabetes rates, also controlling for a subset of potential confounders that vary over time.
RESULTS: We find a 1 percentage point increase in grocery taxes is associated with 0.588 and 0.215 percentage point increases in the county-level obesity and diabetes rates.
CONCLUSION: Counties with grocery taxes have increased prevalence of obesity and diabetes. We estimate the economic burden of increased obesity and diabetes rates resulting from grocery taxes to be $5.9 billion. Based on this estimate, the benefit-cost ratio of removing grocery taxes across the United States only considering the effects on obesity and diabetes rates is 1.90
SNN2ANN: A Fast and Memory-Efficient Training Framework for Spiking Neural Networks
Spiking neural networks are efficient computation models for low-power
environments. Spike-based BP algorithms and ANN-to-SNN (ANN2SNN) conversions
are successful techniques for SNN training. Nevertheless, the spike-base BP
training is slow and requires large memory costs. Though ANN2NN provides a
low-cost way to train SNNs, it requires many inference steps to mimic the
well-trained ANN for good performance. In this paper, we propose a SNN-to-ANN
(SNN2ANN) framework to train the SNN in a fast and memory-efficient way. The
SNN2ANN consists of 2 components: a) a weight sharing architecture between ANN
and SNN and b) spiking mapping units. Firstly, the architecture trains the
weight-sharing parameters on the ANN branch, resulting in fast training and low
memory costs for SNN. Secondly, the spiking mapping units ensure that the
activation values of the ANN are the spiking features. As a result, the
classification error of the SNN can be optimized by training the ANN branch.
Besides, we design an adaptive threshold adjustment (ATA) algorithm to address
the noisy spike problem. Experiment results show that our SNN2ANN-based models
perform well on the benchmark datasets (CIFAR10, CIFAR100, and Tiny-ImageNet).
Moreover, the SNN2ANN can achieve comparable accuracy under 0.625x time steps,
0.377x training time, 0.27x GPU memory costs, and 0.33x spike activities of the
Spike-based BP model
Fourier-Flow model generating Feynman paths
As an alternative but unified and more fundamental description for quantum
physics, Feynman path integrals generalize the classical action principle to a
probabilistic perspective, under which the physical observables' estimation
translates into a weighted sum over all possible paths. The underlying
difficulty is to tackle the whole path manifold from finite samples that can
effectively represent the Feynman propagator dictated probability distribution.
Modern generative models in machine learning can handle learning and
representing probability distribution with high computational efficiency. In
this study, we propose a Fourier-flow generative model to simulate the Feynman
propagator and generate paths for quantum systems. As demonstration, we
validate the path generator on the harmonic and anharmonic oscillators. The
latter is a double-well system without analytic solutions. To preserve the
periodic condition for the system, the Fourier transformation is introduced
into the flow model to approach a Matsubara representation. With this novel
development, the ground-state wave function and low-lying energy levels are
estimated accurately. Our method offers a new avenue to investigate quantum
systems with machine learning assisted Feynman Path integral solving
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