43,240 research outputs found
Monoidal computer III: A coalgebraic view of computability and complexity
Monoidal computer is a categorical model of intensional computation, where
many different programs correspond to the same input-output behavior. The
upshot of yet another model of computation is that a categorical formalism
should provide a much needed high level language for theory of computation,
flexible enough to allow abstracting away the low level implementation details
when they are irrelevant, or taking them into account when they are genuinely
needed. A salient feature of the approach through monoidal categories is the
formal graphical language of string diagrams, which supports visual reasoning
about programs and computations.
In the present paper, we provide a coalgebraic characterization of monoidal
computer. It turns out that the availability of interpreters and specializers,
that make a monoidal category into a monoidal computer, is equivalent with the
existence of a *universal state space*, that carries a weakly final state
machine for any pair of input and output types. Being able to program state
machines in monoidal computers allows us to represent Turing machines, to
capture their execution, count their steps, as well as, e.g., the memory cells
that they use. The coalgebraic view of monoidal computer thus provides a
convenient diagrammatic language for studying computability and complexity.Comment: 34 pages, 24 figures; in this version: added the Appendi
Semi-Supervised First-Person Activity Recognition in Body-Worn Video
Body-worn cameras are now commonly used for logging daily life, sports, and
law enforcement activities, creating a large volume of archived footage. This
paper studies the problem of classifying frames of footage according to the
activity of the camera-wearer with an emphasis on application to real-world
police body-worn video. Real-world datasets pose a different set of challenges
from existing egocentric vision datasets: the amount of footage of different
activities is unbalanced, the data contains personally identifiable
information, and in practice it is difficult to provide substantial training
footage for a supervised approach. We address these challenges by extracting
features based exclusively on motion information then segmenting the video
footage using a semi-supervised classification algorithm. On publicly available
datasets, our method achieves results comparable to, if not better than,
supervised and/or deep learning methods using a fraction of the training data.
It also shows promising results on real-world police body-worn video
Finding Near-Optimal Independent Sets at Scale
The independent set problem is NP-hard and particularly difficult to solve in
large sparse graphs. In this work, we develop an advanced evolutionary
algorithm, which incorporates kernelization techniques to compute large
independent sets in huge sparse networks. A recent exact algorithm has shown
that large networks can be solved exactly by employing a branch-and-reduce
technique that recursively kernelizes the graph and performs branching.
However, one major drawback of their algorithm is that, for huge graphs,
branching still can take exponential time. To avoid this problem, we
recursively choose vertices that are likely to be in a large independent set
(using an evolutionary approach), then further kernelize the graph. We show
that identifying and removing vertices likely to be in large independent sets
opens up the reduction space---which not only speeds up the computation of
large independent sets drastically, but also enables us to compute high-quality
independent sets on much larger instances than previously reported in the
literature.Comment: 17 pages, 1 figure, 8 tables. arXiv admin note: text overlap with
arXiv:1502.0168
A Coverage Criterion for Spaced Seeds and its Applications to Support Vector Machine String Kernels and k-Mer Distances
Spaced seeds have been recently shown to not only detect more alignments, but
also to give a more accurate measure of phylogenetic distances (Boden et al.,
2013, Horwege et al., 2014, Leimeister et al., 2014), and to provide a lower
misclassification rate when used with Support Vector Machines (SVMs) (On-odera
and Shibuya, 2013), We confirm by independent experiments these two results,
and propose in this article to use a coverage criterion (Benson and Mak, 2008,
Martin, 2013, Martin and No{\'e}, 2014), to measure the seed efficiency in both
cases in order to design better seed patterns. We show first how this coverage
criterion can be directly measured by a full automaton-based approach. We then
illustrate how this criterion performs when compared with two other criteria
frequently used, namely the single-hit and multiple-hit criteria, through
correlation coefficients with the correct classification/the true distance. At
the end, for alignment-free distances, we propose an extension by adopting the
coverage criterion, show how it performs, and indicate how it can be
efficiently computed.Comment: http://online.liebertpub.com/doi/abs/10.1089/cmb.2014.017
A Coverage Criterion for Spaced Seeds and its Applications to Support Vector Machine String Kernels and k-Mer Distances
Spaced seeds have been recently shown to not only detect more alignments, but
also to give a more accurate measure of phylogenetic distances (Boden et al.,
2013, Horwege et al., 2014, Leimeister et al., 2014), and to provide a lower
misclassification rate when used with Support Vector Machines (SVMs) (On-odera
and Shibuya, 2013), We confirm by independent experiments these two results,
and propose in this article to use a coverage criterion (Benson and Mak, 2008,
Martin, 2013, Martin and No{\'e}, 2014), to measure the seed efficiency in both
cases in order to design better seed patterns. We show first how this coverage
criterion can be directly measured by a full automaton-based approach. We then
illustrate how this criterion performs when compared with two other criteria
frequently used, namely the single-hit and multiple-hit criteria, through
correlation coefficients with the correct classification/the true distance. At
the end, for alignment-free distances, we propose an extension by adopting the
coverage criterion, show how it performs, and indicate how it can be
efficiently computed.Comment: http://online.liebertpub.com/doi/abs/10.1089/cmb.2014.017
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