42,289 research outputs found
Vector Quantization Video Encoder Using Hierarchical Cache Memory Scheme
A system compresses image blocks via successive hierarchical stages and motion encoders which employ caches updated by stack replacement algorithms. Initially, a background detector compares the present image block with a corresponding previously encoded image block and if similar, the background detector terminates the encoding procedure by setting a flag bit. Otherwise, the image block is decomposed into smaller present image subblocks. The smaller present image subblocks are each compared with a corresponding previously encoded image subblock of comparable size within the present image block. When a present image subblock is similar to a corresponding previously encoded image subblock, then the procedure is terminated by setting a flag bit. Alternatively, the present image subblock is forwarded to a motion encoder where it is compared with displaced image subblocks, which are formed by displacing previously encoded image subblocks by motion vectors that are stored in a cache, to derive a first distortion vector. When the first distortion vector is below a first threshold TM, the procedure is terminated and the present image subblock is encoded by setting flag bit and a cache index corresponding to the first distortion vector. Alternatively, the present image subblock is passed to a block matching encoder where it is compared with other previously encoded image subblocks to derive a second distortion vector. When the second distortion vector is below a second threshold Tm, the procedure is terminated by setting a flag bit, by generating the second distortion vector, and by updating the cache.Georgia Tech Research Corporatio
Cinnamons: A Computation Model Underlying Control Network Programming
We give the easily recognizable name "cinnamon" and "cinnamon programming" to
a new computation model intended to form a theoretical foundation for Control
Network Programming (CNP). CNP has established itself as a programming paradigm
combining declarative and imperative features, built-in search engine, powerful
tools for search control that allow easy, intuitive, visual development of
heuristic, nondeterministic, and randomized solutions. We define rigorously the
syntax and semantics of the new model of computation, at the same time trying
to keep clear the intuition behind and to include enough examples. The
purposely simplified theoretical model is then compared to both WHILE-programs
(thus demonstrating its Turing-completeness), and the "real" CNP. Finally,
future research possibilities are mentioned that would eventually extend the
cinnamon programming into the directions of nondeterminism, randomness, and
fuzziness.Comment: 7th Intl Conf. on Computer Science, Engineering & Applications
(ICCSEA 2017) September 23~24, 2017, Copenhagen, Denmar
Reified Context Models
A classic tension exists between exact inference in a simple model and
approximate inference in a complex model. The latter offers expressivity and
thus accuracy, but the former provides coverage of the space, an important
property for confidence estimation and learning with indirect supervision. In
this work, we introduce a new approach, reified context models, to reconcile
this tension. Specifically, we let the amount of context (the arity of the
factors in a graphical model) be chosen "at run-time" by reifying it---that is,
letting this choice itself be a random variable inside the model. Empirically,
we show that our approach obtains expressivity and coverage on three natural
language tasks
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