Sharad Vikram

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Recent technologies in vision sensors are capable of capturing 3D finger positions and movements. We propose a novel way to control and interact with computers by moving fingers in the air. The positions of fingers are precisely captured by a computer vision device. By tracking the moving patterns of fingers, we can then recognize users' intended control(More)
Recent technologies in vision sensors are capable of capturing 3D finger positions and movements. We propose a novel way to control and interact with computers by moving fingers in the air. The positions of fingers are precisely captured by a computer vision device. By tracking the moving patterns of fingers, we can then recognize users' intended control(More)
We present a character-level recurrent neural network that generates relevant and coherent text given auxiliary information such as a sentiment or topic. 1 Using a simple input replication strategy, we preserve the signal of auxiliary input across wider sequence intervals than can feasibly be trained by back-propagation through time. Our main results center(More)
We present a character-level recurrent neural network that generates relevant and coherent text given auxiliary information such as a sentiment or topic. 1 Using a simple input replication strategy, we preserve the signal of auxiliary input across wider sequence intervals than can feasibly be trained by back-propagation through time. Our main results center(More)
The advent of cost-effective DNA sequencing has provided clinics with high-resolution information about patient's genetic variants, which has resulted in the need for efficient interpretation of this genomic data. Traditionally, variant interpretation has been dominated by many manual, time-consuming processes due to the disparate forms of relevant(More)
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