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Linh Huynh

dblp:225/8963 · DBLP profile ↗
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3ranked-venue papers
3as first author
0since 2021 · last 2018
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 2 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%
Databases, data mining, and information retrieval
1 paper
Data mining · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
genomics
0.312018
Contribution of Structural Variation to Genome Structure: TAD Fusion Discovery and Ranking · RECOMB 2018
Bioinformatics and computational biology › genomics › structural variation
structural variation analysis
0.312018
Contribution of Structural Variation to Genome Structure: TAD Fusion Discovery and Ranking · RECOMB 2018
Bioinformatics and computational biology › genomics
disease genomics
0.312017
Ultra-Accurate Complex Disorder Prediction: Case Study of Neurodevelopmental Disorders · RECOMB 2017
Data mining
predictive modeling
0.312017
Ultra-Accurate Complex Disorder Prediction: Case Study of Neurodevelopmental Disorders · RECOMB 2017
YearPublicationVenuePosition
2018 Contribution of Structural Variation to Genome Structure: TAD Fusion Discovery and Ranking
Linh Huynh, Fereydoun Hormozdiari
RECOMB1
2017 Ultra-Accurate Complex Disorder Prediction: Case Study of Neurodevelopmental Disorders
Linh Huynh, Fereydoun Hormozdiari
RECOMB1
1994 Multiplication and Division Free Adaptive Arithmetic Coding Techniques for Bi-Level Images
abstract
Two new approximate methods for coding the binary alphabet with negligible loss of compression efficiency are proposed. An overview is provided of arithmetic coding and bi-level image modeling, the proposed methods are described, followed by their implementation. A theoretical discussion of the the compression performance is also included with an empirical evaluation of the proposed techniques. The focus throughout is on encoding; the decoding process is similar.>
Linh Huynh
Data Compression Conference1