176 research outputs found
Adaptive-Rate Compressive Sensing Using Side Information
We provide two novel adaptive-rate compressive sensing (CS) strategies for
sparse, time-varying signals using side information. Our first method utilizes
extra cross-validation measurements, and the second one exploits extra
low-resolution measurements. Unlike the majority of current CS techniques, we
do not assume that we know an upper bound on the number of significant
coefficients that comprise the images in the video sequence. Instead, we use
the side information to predict the number of significant coefficients in the
signal at the next time instant. For each image in the video sequence, our
techniques specify a fixed number of spatially-multiplexed CS measurements to
acquire, and adjust this quantity from image to image. Our strategies are
developed in the specific context of background subtraction for surveillance
video, and we experimentally validate the proposed methods on real video
sequences
Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces
While recent advances in deep reinforcement learning have allowed autonomous
learning agents to succeed at a variety of complex tasks, existing algorithms
generally require a lot of training data. One way to increase the speed at
which agents are able to learn to perform tasks is by leveraging the input of
human trainers. Although such input can take many forms, real-time,
scalar-valued feedback is especially useful in situations where it proves
difficult or impossible for humans to provide expert demonstrations. Previous
approaches have shown the usefulness of human input provided in this fashion
(e.g., the TAMER framework), but they have thus far not considered
high-dimensional state spaces or employed the use of deep learning. In this
paper, we do both: we propose Deep TAMER, an extension of the TAMER framework
that leverages the representational power of deep neural networks in order to
learn complex tasks in just a short amount of time with a human trainer. We
demonstrate Deep TAMER's success by using it and just 15 minutes of
human-provided feedback to train an agent that performs better than humans on
the Atari game of Bowling - a task that has proven difficult for even
state-of-the-art reinforcement learning methods.Comment: 9 pages, 6 figure
Alien Registration- Warnell, Ella W. (Livermore Falls, Androscoggin County)
https://digitalmaine.com/alien_docs/27182/thumbnail.jp
History of Long Cave
The story of Long Cave, later to become Grand Avenue Cave, is thoroughly intertwined in the rich history of saltpeter production and the show cave industry of Central Kentucky. The cave’s history parallels the early history of Mammoth Cave that is five miles away, the history of nearby Short Cave, and the development of Diamond Cave and Proctor Cave as show caves by the Proctor families. Today the cave is an important bat hibernaculum protected by the National Park Service. The cave is gated and locked, and entry is by research approval only
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