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.

SEPARABILITY: A NEW APPROACH FROM THE CONIC STRUCTURE OF POSITIVE OPERATORS

2011 ◽  
Vol 09 (supp01) ◽  
pp. 415-422
Author(s):  
D. SALGADO ◽  
J. L. SÁNCHEZ-GÓMEZ ◽  
M. FERRERO
Keyword(s):  
Pure State ◽  
Convex Combination ◽  
Quantum States ◽  
Necessary Condition ◽  
New Approach ◽  
Product Matrix ◽  
Separability Problem ◽  
Multipartite System ◽  
Arbitrary Dimensions ◽  
Bipartite Case

We exploit the cone structure of unnormalized quantum states to reformulate the separability problem. Firstly a convex combination of every quantum state ρ in terms of a state Cρ with the same rank and another one Eρ with lower rank is perfomed, with weights 1 − λρ and λρ, respectively. Secondly a scalar [Formula: see text] is computed. Then ρ is separable if, and only if, [Formula: see text]. The computation of [Formula: see text] has been undergone under the simplest choice for Cρ as a product matrix and Eρ being a pure state, valid for any bipartite and multipartite system in arbitrary dimensions. A necessary condition is also formulated when Eρ is not pure in the bipartite case.

scholarly journals A Residual-Based Approach to Validate Q-Matrix Specifications

2017 ◽  
Vol 41 (4) ◽  
pp. 277-293 ◽  
Author(s):  
Jinsong Chen
Keyword(s):  
Real Life ◽  
Attribute Level ◽  
Simulation Studies ◽  
Cognitive Diagnosis Models ◽  
Modeling Process ◽  
Response Formats ◽  
Item Level ◽  
Q Matrix ◽  
Matrix Construction ◽  
Test Level

Q-matrix validation is of increasing concern due to the significance and subjective tendency of Q-matrix construction in the modeling process. This research proposes a residual-based approach to empirically validate Q-matrix specification based on a combination of fit measures. The approach separates Q-matrix validation into four logical steps, including the test-level evaluation, possible distinction between attribute-level and item-level misspecifications, identification of the hit item, and fit information to aid in item adjustment. Through simulation studies and real-life examples, it is shown that the misspecified items can be detected as the hit item and adjusted sequentially when the misspecification occurs at the item level or at random. Adjustment can be based on the maximum reduction of the test-level measures. When adjustment of individual items tends to be useless, attribute-level misspecification is of concern. The approach can accommodate a variety of cognitive diagnosis models (CDMs) and be extended to cover other response formats.

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