Collaborative Data Distribution with BitTorrent for Computational Desktop Grids

2006 ◽  
Author(s):  
Gilles Fedak Baohua Wei
Keyword(s):  
Data Distribution ◽  
Desktop Grids

Towards efficient data distribution on computational desktop grids with BitTorrent

2007 ◽  
Vol 23 (8) ◽  
pp. 983-989 ◽  
Author(s):  
Baohua Wei ◽  
Gilles Fedak ◽  
Franck Cappello
Keyword(s):  
Data Distribution ◽  
Desktop Grids ◽  
Efficient Data

An Adaptive Data Distribution Through Tree Rules in Frequent Pattern Mining

2018 ◽  
pp. 300-305
Keyword(s):  
Information Sharing ◽  
Pattern Mining ◽  
Data Distribution ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
General Development ◽  
Secure Information ◽  
Evaluation Parameters ◽  
Secure Information Sharing

Information sharing among the associations is a general development in a couple of zones like business headway and exhibiting. As bit of the touchy principles that ought to be kept private may be uncovered and such disclosure of delicate examples may impacts the advantages of the association that have the data. Subsequently the standards which are delicate must be secured before sharing the data. In this paper to give secure information sharing delicate guidelines are bothered first which was found by incessant example tree. Here touchy arrangement of principles are bothered by substitution. This kind of substitution diminishes the hazard and increment the utility of the dataset when contrasted with different techniques. Examination is done on certifiable dataset. Results shows that proposed work is better as appear differently in relation to various past strategies on the introduce of evaluation parameters.

SAXON-FPN NAWC/ERIM P-3 SAR Data Distribution and Analysis

1992 ◽  
Author(s):  
Christopher C. Wackerman
Keyword(s):  
Data Distribution ◽  
Sar Data

SOME Bus for Optical Data Distribution

2002 ◽  
Author(s):  
Jeffrey Kulick
Keyword(s):  
Data Distribution ◽  
Optical Data

Investigating impact of the heterogeneity of trajectory data distribution on origin-destination estimation: a spatial statistics approach

2020 ◽  
Vol 14 (10) ◽  
pp. 1218-1227
Author(s):  
Wenming Rao ◽  
Jingxin Xia ◽  
Chen Wang ◽  
Zhenbo Lu ◽  
Qian Chen
Keyword(s):  
Spatial Statistics ◽  
Data Distribution ◽  
Trajectory Data

Neural networks trained with high-dimensional functions approximation data in high-dimensional space

2021 ◽  
pp. 1-12
Author(s):  
Jian Zheng ◽  
Jianfeng Wang ◽  
Yanping Chen ◽  
Shuping Chen ◽  
Jingjin Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Dimensional Space ◽  
Data Distribution ◽  
High Dimensional ◽  
Sufficient Information ◽  
Sufficient Data ◽  
High Dimensional Space ◽  
Positive Effects ◽  
The Neural Networks ◽  
Using Data

Neural networks can approximate data because of owning many compact non-linear layers. In high-dimensional space, due to the curse of dimensionality, data distribution becomes sparse, causing that it is difficulty to provide sufficient information. Hence, the task becomes even harder if neural networks approximate data in high-dimensional space. To address this issue, according to the Lipschitz condition, the two deviations, i.e., the deviation of the neural networks trained using high-dimensional functions, and the deviation of high-dimensional functions approximation data, are derived. This purpose of doing this is to improve the ability of approximation high-dimensional space using neural networks. Experimental results show that the neural networks trained using high-dimensional functions outperforms that of using data in the capability of approximation data in high-dimensional space. We find that the neural networks trained using high-dimensional functions more suitable for high-dimensional space than that of using data, so that there is no need to retain sufficient data for neural networks training. Our findings suggests that in high-dimensional space, by tuning hidden layers of neural networks, this is hard to have substantial positive effects on improving precision of approximation data.

Rethinking of Deep Models Parameters with Respect to Data Distribution

2021 ◽  
Author(s):  
Shitala Prasad ◽  
Dongyun Lin ◽  
Yiqun Li ◽  
Dong Sheng ◽  
Oo Zaw Min
Keyword(s):  
Data Distribution
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