GIVE-N-TAKE---a balanced code placement framework

1994 ◽  
Author(s):  
Reinhard von Hanxleden ◽  
Ken Kennedy
Keyword(s):  
Balanced Code ◽  
Code Placement

A balanced code placement framework

2000 ◽  
Vol 22 (5) ◽  
pp. 816-860 ◽  
Author(s):  
Reinhard von Hanxleden ◽  
Ken Kennedy
Keyword(s):  
Balanced Code ◽  
Code Placement

GIVE-N-TAKE—a balanced code placement framework

1994 ◽  
Vol 29 (6) ◽  
pp. 107-120 ◽  
Author(s):  
Reinhard von Hanxleden ◽  
Ken Kennedy
Keyword(s):  
Balanced Code ◽  
Code Placement

scholarly journals Unsupervised Deep Video Hashing with Balanced Rotation

2017 ◽  
Author(s):  
Gengshen Wu ◽  
Li Liu ◽  
Yuchen Guo ◽  
Guiguang Ding ◽  
Jungong Han ◽  
...  
Keyword(s):  
State Of The Art ◽  
Dimensional Space ◽  
Image Hashing ◽  
Neighborhood Structure ◽  
Function Learning ◽  
Video Hashing ◽  
Real World Datasets ◽  
Low Dimensional ◽  
Balanced Code ◽  
Hash Codes

Recently, hashing video contents for fast retrieval has received increasing attention due to the enormous growth of online videos. As the extension of image hashing techniques, traditional video hashing methods mainly focus on seeking the appropriate video features but pay little attention to how the video-specific features can be leveraged to achieve optimal binarization. In this paper, an end-to-end hashing framework, namely Unsupervised Deep Video Hashing (UDVH), is proposed, where feature extraction, balanced code learning and hash function learning are integrated and optimized in a self-taught manner. Particularly, distinguished from previous work, our framework enjoys two novelties: 1) an unsupervised hashing method that integrates the feature clustering and feature binarization, enabling the neighborhood structure to be preserved in the binary space; 2) a smart rotation applied to the video-specific features that are widely spread in the low-dimensional space such that the variance of dimensions can be balanced, thus generating more effective hash codes. Extensive experiments have been performed on two real-world datasets and the results demonstrate its superiority, compared to the state-of-the-art video hashing methods. To bootstrap further developments, the source code will be made publically available.

Comparison between MUSCL and MOOD techniques in a finite volume well-balanced code to solve SWE. The Tohoku-Oki, 2011 example

2018 ◽  
Vol 216 (2) ◽  
pp. 958-983 ◽  
Author(s):  
C Reis ◽  
J Figueiredo ◽  
S Clain ◽  
R Omira ◽  
M A Baptista ◽  
...  
Keyword(s):  
Finite Volume ◽  
Balanced Code
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