Improving Data Distribution in Branching Point Based Multicast Protocols

Author(s):  
Mozafar Bag-Mohammadi ◽  
Siavash Samadian-Barzoki ◽  
Nasser Yazdani

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.


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

2002 ◽  
Author(s):  
Jeffrey Kulick

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

2021 ◽  
pp. 1-12
Author(s):  
Jian Zheng ◽  
Jianfeng Wang ◽  
Yanping Chen ◽  
Shuping Chen ◽  
Jingjin Chen ◽  
...  

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.


2021 ◽  
Vol 11 (9) ◽  
pp. 4048
Author(s):  
Javier A. Linares-Pastén ◽  
Lilja Björk Jonsdottir ◽  
Gudmundur O. Hreggvidsson ◽  
Olafur H. Fridjonsson ◽  
Hildegard Watzlawick ◽  
...  

The structures of glycoside hydrolase family 17 (GH17) catalytic modules from modular proteins in the ndvB loci in Pseudomonas aeruginosa (Glt1), P. putida (Glt3) and Bradyrhizobium diazoefficiens (previously B. japonicum) (Glt20) were modeled to shed light on reported differences between these homologous transglycosylases concerning substrate size, preferred cleavage site (from reducing end (Glt20: DP2 product) or non-reducing end (Glt1, Glt3: DP4 products)), branching (Glt20) and linkage formed (1,3-linkage in Glt1, Glt3 and 1,6-linkage in Glt20). Hybrid models were built and stability of the resulting TIM-barrel structures was supported by molecular dynamics simulations. Catalytic amino acids were identified by superimposition of GH17 structures, and function was verified by mutagenesis using Glt20 as template (i.e., E120 and E209). Ligand docking revealed six putative subsites (−4, −3, −2, −1, +1 and +2), and the conserved interacting residues suggest substrate binding in the same orientation in all three transglycosylases, despite release of the donor oligosaccharide product from either the reducing (Glt20) or non-reducing end (Glt1, Gl3). Subsites +1 and +2 are most conserved and the difference in release is likely due to changes in loop structures, leading to loss of hydrogen bonds in Glt20. Substrate docking in Glt20 indicate that presence of covalently bound donor in glycone subsites −4 to −1 creates space to accommodate acceptor oligosaccharide in alternative subsites in the catalytic cleft, promoting a branching point and formation of a 1,6-linkage. The minimum donor size of DP5, can be explained assuming preferred binding of DP4 substrates in subsite −4 to −1, preventing catalysis.


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