scholarly journals Multilinear algebra for minimum storage regenerating codes: a generalization of the product-matrix construction

2021 ◽  
Author(s):  
Iwan Duursma ◽  
Hsin-Po Wang
Keyword(s):  
Multilinear Algebra ◽  
Product Matrix ◽  
Regenerating Codes ◽  
Matrix Construction

scholarly journals Scalar MSCR Codes via the Product Matrix Construction

2020 ◽  
Vol 66 (2) ◽  
pp. 995-1006 ◽  
Author(s):  
Yaqian Zhang ◽  
Zhifang Zhang
Keyword(s):  
Product Matrix ◽  
Matrix Construction

Parallel Fock Matrix Construction Algorithm for Molecular Orbital Calculation Specific Computer

2006 ◽  
Vol 5 (1) ◽  
pp. 179-188
Author(s):  
Hiroaki UMEDA ◽  
Yuichi INADOMI ◽  
Hiroaki HONDA ◽  
Umpei NAGASHIMA
Keyword(s):  
Molecular Orbital ◽  
Molecular Orbital Calculation ◽  
Construction Algorithm ◽  
Matrix Construction

The theme trends and knowledge structure of H19 gene in cancer research: a scientometric analysis (Preprint)

2019 ◽  
Author(s):  
Zhigang Cui ◽  
Zhihua Yin ◽  
Lei Cui
Keyword(s):  
Cancer Research ◽  
Social Network ◽  
Citation Analysis ◽  
High Frequency ◽  
Cancer Metastasis ◽  
Intellectual Structure ◽  
Scientometric Analysis ◽  
Research Areas ◽  
Pivotal Study ◽  
Matrix Construction

BACKGROUND Background:H19 gene is maternally expressed imprinted oncofetal gene. This study aimed to explore distribution pattern and intellectual structure of H19 in cancer. OBJECTIVE Published scientific 826 papers related to H19 from Jan 1st, 2000 to March 22st, 2019 were obtained from the Web of Science core collection. METHODS We performed extraction of keywords and co-word matrix construction using BICOMB software. Then gCLUTO software, ucinet, excel software, Citespace, Vosviewer were successfully used for double -cluster analysis, social network analysis, Strategic coordinate analysis, co-citation analysis, and journal analysis. RESULTS We analyzed the distributions of included article of H19, identified 34 high-frequency keywords and classified them into 6 categories. Through co-word analysis and co-citation analysis for these categories, we identified the hotspot areas and intellectual basis about H19 in cancer research. Then the prospects of hotspots and their associations were accesssed by strategic coordinate diagrams and social network diagrams. CONCLUSIONS 6 research categories of 34 high-frequency keywords could represent the theme trends on H19 to some extent. Mir-675, cancer metastasis and risk, Wnt/β-catenin signaling pathway, SNP, and ceRNA network were core and mature research areas in this field. There is a lack of promising areas of H19 research. Matouk(2006) article play a key role in H19 research, and Murphy SK(2006)and Luo M(2013) articles serve knowledge transmission as pivotal study.

Tree-Structured Parallel Regeneration Based on Regenerating Codes for Multiple Data Losses in Distributed Storage Systems

2014 ◽  
Vol 918 ◽  
pp. 295-300
Author(s):  
Peng Fei You ◽  
Yu Xing Peng ◽  
Zhen Huang ◽  
Chang Jian Wang
Keyword(s):  
Storage Systems ◽  
Distributed Storage ◽  
Data Loss ◽  
Data Reliability ◽  
Data Redundancy ◽  
Regeneration Time ◽  
Multiple Data ◽  
Distributed Storage Systems ◽  
Regenerating Codes ◽  
Reliability And Availability

In distributed storage systems, erasure codes represent an attractive data redundancy solution which can provide the same reliability as replication requiring much less storage space. Multiple data losses happens usually and the lost data should be regenerated to maintain data redundancy in distributed storage systems. Regeneration for multiple data losses is expected to be finished as soon as possible, because the regeneration time can influence the data reliability and availability of distributed storage systems. However, multiple data losses is usually regenerated by regenerating single data loss one by one, which brings high entire regeneration time and severely reduces the data reliability and availability of distributed storage systems. In this paper, we propose a tree-structured parallel regeneration scheme based on regenerating codes (TPRORC) for multiple data losses in distributed storage systems. In our scheme, multiple regeneration trees based on regenerating code are constructed. Firstly, these trees are created independently, each of which dose not share any edges from the others and is responsible for one data loss; secondly, every regeneration tree based on regenerating codes owns the least network traffic and bandwidth optimized-paths for regenerating its data loss. Thus it can perform parallel regeneration for multiple data losses by using multiple optimized topology trees, in which network bandwidth is utilized efficiently and entire regeneration is overlapped. Our simulation results show that the tree-structured parallel regeneration scheme reduces the regeneration time significantly, compared to other regular regeneration schemes.

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