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We present a system which can recognize the contents of your meal from a single image, and then predict its nutritional contents, such as calories. The simplest version assumes that the user is eating at a restaurant for which we know the menu. In this case, we can collect images offline to train a multi-label classifier. At run time, we apply the(More)
This paper presents a set of full-resolution lossy image compression methods based on neural networks. Each of the architectures we describe can provide variable compression rates during deployment without requiring retraining of the network: each network need only be trained once. All of our architectures consist of a recurrent neural network (RNN)-based(More)
We propose a method for lossy image compression based on recurrent, convolutional neural networks that outper-forms BPG (4:2:0), WebP, JPEG2000, and JPEG as measured by MS-SSIM. We introduce three improvements over previous research that lead to this state-of-the-art result. First, we show that training with a pixel-wise loss weighted by SSIM increases(More)
The nursing literature is replete with articles and books that describe nursing conceptual frameworks and models and encourage their use in clinical, education, and research activities. Although much information exists on the content of nursing models, less has been written about how a model is to be chosen and the process that may facilitate the choice of(More)
Picosecond laser ultrasonics is a well established technique for measurement and diagnosis of micro-and nano-scale structures. One of the major drawbacks preventing widespread acceptance of this technique is that the data acquisitions speeds are slow making imaging applications impractical. We are engaged in a research program to accelerate the data capture(More)
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