scholarly journals Improving the Alignment Quality of Consistency Based Aligners with an Evaluation Function Using Synonymous Protein Words

PLoS ONE ◽  
2011 ◽  
Vol 6 (12) ◽  
pp. e27872 ◽  
Author(s):  
Hsin-Nan Lin ◽  
Cédric Notredame ◽  
Jia-Ming Chang ◽  
Ting-Yi Sung ◽  
Wen-Lian Hsu
Keyword(s):  
Evaluation Function ◽  
Alignment Quality

scholarly journals The concept of biaxial surface potential and polymer aligning layers in electro-optical cells based on helical nanostructures of ferroelectric liquid crystal

2021 ◽  
Vol 2056 (1) ◽  
pp. 012044
Author(s):  
T P Tkachenko ◽  
A A Zhukov ◽  
E P Pozhidaev
Keyword(s):  
Liquid Crystal ◽  
Liquid Crystals ◽  
Surface Potential ◽  
Ferroelectric Liquid Crystal ◽  
Alignment Quality ◽  
Ferroelectric Liquid Crystals ◽  
Helical Pitch

Abstract The paper considers the possibility of controlling the alignment quality of helical nanostructures of ferroelectric liquid crystals (FLCs) within the concept of biaxial surface potential due to variation the FLCs helical pitch p0 and polymer aligning layers structures.

Research of the Evaluation Function Based on Improved Neighborhood Difference Operator

2013 ◽  
Vol 433-435 ◽  
pp. 358-361
Author(s):  
Cheng Yun Wang ◽  
Long Ye
Keyword(s):  
Digital Video ◽  
Difference Operator ◽  
Robot Vision ◽  
Evaluation Function ◽  
Good Sensitivity ◽  
Noise Resistance ◽  
Key Technology ◽  
Video Systems ◽  
Automatic Focusing

Automatic focusing is one of the key technology of robot vision and digital video-systems, while play an important role in determining the quality of image. The performance of focusing depends on whether the evaluation function has unbiasedness, unimodality and noise resistance. This paper proposes a new evaluation function algorithm by improving image clarity-evaluation function of the traditional neighborhood difference operator. Compared with the existing algorithm, the results of experiments demonstrated the new algorithm has a good sensitivity, timeliness, good anti-noise ability and stability during the automatic focusing process.

Research of the Evaluation Function Based on Improved Neighborhood Difference Operator

2013 ◽  
Vol 427-429 ◽  
pp. 1566-1569
Author(s):  
Cheng Yun Wang ◽  
Long Ye
Keyword(s):  
Digital Video ◽  
Difference Operator ◽  
Robot Vision ◽  
Evaluation Function ◽  
Good Sensitivity ◽  
Noise Resistance ◽  
Key Technology ◽  
Video Systems ◽  
Automatic Focusing

Automatic focusing is one of the key technology of robot vision and digital video-systems, while play an important role in determining the quality of image. The performance of focusing depends on whether the evaluation function has unbiasedness, unimodality and noise resistance. This paper proposes a new evaluation function algorithm by improving image clarity-evaluation function of the traditional neighborhood difference operator. Compared with the existing algorithm, the results of experiments demonstrated the new algorithm has a good sensitivity, timeliness, good anti-noise ability and stability during the automatic focusing process.

Ontology Alignment Quality

2011 ◽  
Vol 2 (3) ◽  
pp. 1-23 ◽  
Author(s):  
Jennifer Sampson ◽  
John Krogstie ◽  
Csaba Veres
Keyword(s):  
Semantic Web ◽  
Promising Result ◽  
Heterogeneous Data ◽  
Ontology Alignment ◽  
Alignment Quality ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Automated Tools ◽  
Tools And Techniques

Recently semantic web technologies, such as ontologies, have been proposed as key enablers for integrating heterogeneous data schemas in business and governmental systems. Algorithms designed to align different but related ontologies have become necessary as differing ontologies proliferate. The process of ontology alignment seeks to find corresponding entities in a second ontology with the same or the closest meaning for each entity in a single ontology. This research is motivated by the need to provide tools and techniques to support the task of validating ontology alignment statements, since it cannot be guaranteed that the results from automated tools are accurate. The authors present a framework for understanding ontology alignment quality and describe how AlViz, a tool for visual ontology alignment, may be used to improve the quality of alignment results. An experiment was undertaken to test the claim that AlViz supports the task of validating ontology alignments. A promising result found that the tool has potential for identifying missing alignments and for rejecting false alignments.

Ontology Alignment Quality

2013 ◽  
pp. 99-122
Author(s):  
Jennifer Sampson ◽  
John Krogstie ◽  
Csaba Veres
Keyword(s):  
Semantic Web ◽  
Promising Result ◽  
Heterogeneous Data ◽  
Ontology Alignment ◽  
Alignment Quality ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Automated Tools ◽  
Tools And Techniques

Recently semantic web technologies, such as ontologies, have been proposed as key enablers for integrating heterogeneous data schemas in business and governmental systems. Algorithms designed to align different but related ontologies have become necessary as differing ontologies proliferate. The process of ontology alignment seeks to find corresponding entities in a second ontology with the same or the closest meaning for each entity in a single ontology. This research is motivated by the need to provide tools and techniques to support the task of validating ontology alignment statements, since it cannot be guaranteed that the results from automated tools are accurate. The authors present a framework for understanding ontology alignment quality and describe how AlViz, a tool for visual ontology alignment, may be used to improve the quality of alignment results. An experiment was undertaken to test the claim that AlViz supports the task of validating ontology alignments. A promising result found that the tool has potential for identifying missing alignments and for rejecting false alignments.

scholarly journals Alignathon: A competitive assessment of whole genome alignment methods.

2014 ◽  
Author(s):  
Dent Earl ◽  
Ngan K Nguyen ◽  
Glenn Hickey ◽  
Robert S. Harris ◽  
Stephen Fitzgerald ◽  
...  
Keyword(s):  
Genome Alignment ◽  
Whole Genome ◽  
Alignment Quality ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Simulation Based ◽  
Benchmark Datasets ◽  
Whole Genome Alignment

Background: Multiple sequence alignments (MSAs) are a prerequisite for a wide variety of evolutionary analyses. Published assessments and benchmark datasets for protein and, to a lesser extent, global nucleotide MSAs are available, but less effort has been made to establish benchmarks in the more general problem of whole genome alignment (WGA). Results: Using the same model as the successful Assemblathon competitions, we organized a competitive evaluation in which teams submitted their alignments, and assessments were performed collectively after all the submissions were received. Three datasets were used: two of simulated primate and mammalian phylogenies, and one of 20 real fly genomes. In total 35 submissions were assessed, submitted by ten teams using 12 different alignment pipelines. Conclusions: We found agreement between independent simulation-based and statistical assessments, indicating that there are substantial accuracy differences between contemporary alignment tools. We saw considerable difference in the alignment quality of differently annotated regions, and found few tools aligned the duplications analysed. We found many tools worked well at shorter evolutionary distances, but fewer performed competitively at longer distances. We provide all datasets, submissions and assessment programs for further study, and provide, as a resource for future benchmarking, a convenient repository of code and data for reproducing the simulation assessments.

An Effective Method for Combining Kernels with Class Separability

2012 ◽  
Vol 220-223 ◽  
pp. 2224-2227
Author(s):  
Ting Hua Wang ◽  
Qiong Zhang ◽  
Hai Hui Xie ◽  
Jun Ting Chen
Keyword(s):  
Image Segmentation ◽  
Convex Combination ◽  
Experimental Results ◽  
Evaluation Function ◽  
Data Set ◽  
Kernel Weights ◽  
Class Separability ◽  
Improve Accuracy ◽  
The Individual

We propose a simple but effective method to determine the kernel weights for convex combination of multiple kernels. The key property of the proposed method is that it adopts a class separability criterion as the evaluation function to measure the goodness of the individual kernel. Based on the principle of class separability, we assign a weight to each kernel that is proportional to the quality of the kernel. Experimental results on Image Segmentation data set show the proposed method can improve accuracy in comparison with that using a single kernel or uniformly-combined kernel.

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