4,085 research outputs found
Controlling Citrate Synthase Expression by CRISPR/Cas9 Genome Editing for n-Butanol Production in Escherichia coli
Optimizing Parameters of Information-Theoretic Correlation Measurement for Multi-Channel Time-Series Datasets in Gravitational Wave Detectors
Data analysis in modern science using extensive experimental and
observational facilities, such as a gravitational wave detector, is essential
in the search for novel scientific discoveries. Accordingly, various techniques
and mathematical principles have been designed and developed to date. A
recently proposed approximate correlation method based on the information
theory is widely adopted in science and engineering. Although the maximal
information coefficient (MIC) method remains in the phase of improving its
algorithm, it is particularly beneficial in identifying the correlations of
multiple noise sources in gravitational-wave detectors including non-linear
effects. This study investigates various prospects for determining MIC
parameters to improve the reliability of handling multi-channel time-series
data, reduce high computing costs, and propose a novel method of determining
optimized parameter sets for identifying noise correlations in gravitational
wave data.Comment: 11 pages, 8 figure
Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts
We apply a machine learning algorithm, the artificial neural network, to the
search for gravitational-wave signals associated with short gamma-ray bursts.
The multi-dimensional samples consisting of data corresponding to the
statistical and physical quantities from the coherent search pipeline are fed
into the artificial neural network to distinguish simulated gravitational-wave
signals from background noise artifacts. Our result shows that the data
classification efficiency at a fixed false alarm probability is improved by the
artificial neural network in comparison to the conventional detection
statistic. Therefore, this algorithm increases the distance at which a
gravitational-wave signal could be observed in coincidence with a gamma-ray
burst. In order to demonstrate the performance, we also evaluate a few seconds
of gravitational-wave data segment using the trained networks and obtain the
false alarm probability. We suggest that the artificial neural network can be a
complementary method to the conventional detection statistic for identifying
gravitational-wave signals related to the short gamma-ray bursts.Comment: 30 pages, 10 figure
Mixture Trees for Modeling and Fast Conditional Sampling with Applications in Vision and Graphics
©2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 20-25 June 2005, San Diego, CA.DOI: 10.1109/CVPR.2005.224We introduce mixture trees, a tree-based data-structure
for modeling joint probability densities using a greedy hierarchical
density estimation scheme. We show that the mixture
tree models data efficiently at multiple resolutions, and
present fast conditional sampling as one of many possible
applications. In particular, the development of this datastructure
was spurred by a multi-target tracking application,
where memory-based motion modeling calls for fast
conditional sampling from large empirical densities. However,
it is also suited to applications such as texture synthesis,
where conditional densities play a central role. Results
will be presented for both these applications
- …