Greedy Localization and Color-Coding: Improved Matching and Packing Algorithms

2006 ◽  
pp. 84-95 ◽  
Author(s):  
Yang Liu ◽  
Songjian Lu ◽  
Jianer Chen ◽  
Sing-Hoi Sze
Keyword(s):  
Color Coding

Color-coding your menu

2009 ◽  
Author(s):  
Susan McQuillan
Keyword(s):  
Color Coding

Color coding and size enhancements of switch symbol critical features

1990 ◽  
Author(s):  
Kristen K. Barthslemy ◽  
Kim M. Mazur ◽  
John M. Reising
Keyword(s):  
Color Coding

An Improved Color Coding Algorithm Based on Hybrid Architecture

2010 ◽  
Vol 33 (6) ◽  
pp. 1024-1031
Author(s):  
Jian-Xin WANG ◽  
Zhi-Biao YANG ◽  
Yun-Long LIU ◽  
Jian-Er CHEN
Keyword(s):  
Hybrid Architecture ◽  
Color Coding

scholarly journals Faster Motif Counting via Succinct Color Coding and Adaptive Sampling

2021 ◽  
Vol 15 (6) ◽  
pp. 1-27
Author(s):  
Marco Bressan ◽  
Stefano Leucci ◽  
Alessandro Panconesi
Keyword(s):  
Adaptive Sampling ◽  
Relative Frequency ◽  
State Of The Art ◽  
Color Coding ◽  
Input Graph ◽  
Large Graphs ◽  
Running Time ◽  
Uniform Sampling ◽  
Current State ◽  
Connected Subgraphs

We address the problem of computing the distribution of induced connected subgraphs, aka graphlets or motifs , in large graphs. The current state-of-the-art algorithms estimate the motif counts via uniform sampling by leveraging the color coding technique by Alon, Yuster, and Zwick. In this work, we extend the applicability of this approach by introducing a set of algorithmic optimizations and techniques that reduce the running time and space usage of color coding and improve the accuracy of the counts. To this end, we first show how to optimize color coding to efficiently build a compact table of a representative subsample of all graphlets in the input graph. For 8-node motifs, we can build such a table in one hour for a graph with 65M nodes and 1.8B edges, which is times larger than the state of the art. We then introduce a novel adaptive sampling scheme that breaks the “additive error barrier” of uniform sampling, guaranteeing multiplicative approximations instead of just additive ones. This allows us to count not only the most frequent motifs, but also extremely rare ones. For instance, on one graph we accurately count nearly 10.000 distinct 8-node motifs whose relative frequency is so small that uniform sampling would literally take centuries to find them. Our results show that color coding is still the most promising approach to scalable motif counting.

Color Coding of Sutures in Laparoscopic Perforated Duodenal Ulcer: A New Concept

2002 ◽  
Vol 12 (3) ◽  
pp. 177-179 ◽  
Author(s):  
Simon Wemyss-Holden ◽  
Steven A. White ◽  
Gavin Robertson ◽  
David Lloyd
Keyword(s):  
Duodenal Ulcer ◽  
Perforated Duodenal Ulcer ◽  
Color Coding

scholarly journals Lateral feedback from monophasic horizontal cells to cones in carp retina. I. Experiments.

1989 ◽  
Vol 93 (4) ◽  
pp. 681-694 ◽  
Author(s):  
M Kamermans ◽  
B W van Dijk ◽  
H Spekreijse ◽  
R C Zweypfenning
Keyword(s):  
Horizontal Cell ◽  
Horizontal Cells ◽  
Color Coding ◽  
Cell Adaptation ◽  
Test Spot ◽  
Displacement Experiments ◽  
High Stimulus ◽  
Integration Area

The spatial and color coding of the monophasic horizontal cells were studied in light- and dark-adapted retinae. Slit displacement experiments revealed differences in integration area for the different cone inputs of the monophasic horizontal cells. The integration area measured with a 670-nm stimulus was larger than that measured with a 570-nm stimulus. Experiments in which the diameter of the test spot was varied, however, revealed at high stimulus intensities a larger summation area for 520-nm stimuli than for 670-nm stimuli. The reverse was found for low stimulus intensities. To investigate whether these differences were due to interaction between the various cone inputs to the monophasic horizontal cell, adaptation experiments were performed. It was found that the various cone inputs were not independent. Finally, some mechanisms for the spatial and color coding will be discussed.

Legibility in Industrial AR: Text Style, Color Coding, and Illuminance

2015 ◽  
Vol 35 (2) ◽  
pp. 52-61 ◽  
Author(s):  
Michele Gattullo ◽  
Antonio E. Uva ◽  
Michele Fiorentino ◽  
Joseph L. Gabbard
Keyword(s):  
Color Coding
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