scholarly journals Enhancing Linear Algebraic Computation of Logic Programs Using Sparse Representation

2020 ◽  
Vol 325 ◽  
pp. 192-205
Author(s):  
Tuan Nguyen Quoc ◽  
Katsumi Inoue ◽  
Chiaki Sakama
Keyword(s):  
Sparse Representation ◽  
Logic Programs ◽  
Algebraic Computation

scholarly journals Enhancing Linear Algebraic Computation of Logic Programs Using Sparse Representation

2021 ◽  
Author(s):  
Tuan Quoc Nguyen ◽  
Katsumi Inoue ◽  
Chiaki Sakama
Keyword(s):  
Large Scale ◽  
Matrix Representation ◽  
Sparse Matrix ◽  
Sparse Matrices ◽  
Knowledge Bases ◽  
Algebraic Characterization ◽  
Logic Programs ◽  
Algebraic Computation ◽  
Sparse Matrix Representation

AbstractAlgebraic characterization of logic programs has received increasing attention in recent years. Researchers attempt to exploit connections between linear algebraic computation and symbolic computation to perform logical inference in large-scale knowledge bases. In this paper, we analyze the complexity of the linear algebraic methods for logic programs and propose further improvement by using sparse matrices to embed logic programs in vector spaces. We show its great power of computation in reaching the fixed point of the immediate consequence operator. In particular, performance for computing the least models of definite programs is dramatically improved using the sparse matrix representation. We also apply the method to the computation of stable models of normal programs, in which the guesses are associated with initial matrices, and verify its effect when there are small numbers of negation. These results show good enhancement in terms of performance for computing consequences of programs and depict the potential power of tensorized logic programs.

Prediction method for outliers over data stream based on sparse representation

2010 ◽  
Vol 30 (11) ◽  
pp. 2956-2958
Author(s):  
Xue-song XU ◽  
Ling-juan LI ◽  
Li-wei GUO
Keyword(s):  
Sparse Representation ◽  
Data Stream ◽  
Prediction Method

Generalized Disjunctive Well-Founded Semantics for Logic Programs

1990 ◽  
Author(s):  
Chitta Baral ◽  
Jorge Lobo ◽  
Jack Minker
Keyword(s):  
Logic Programs

Ultrasonic Block Compressed Sensing Imaging Reconstruction Algorithm Based on Wavelet Sparse Representation

2020 ◽  
Vol 16 (3) ◽  
pp. 262-272
Author(s):  
Guangzhi Dai ◽  
Zhiyong He ◽  
Hongwei Sun
Keyword(s):  
Compressed Sensing ◽  
Sparse Representation ◽  
Linear Array ◽  
Imaging System ◽  
Ultrasonic Imaging ◽  
Large Data ◽  
Reconstruction Algorithm ◽  
Array Transducer ◽  
Total Data ◽  
Block Compressed Sensing

Background: This study is carried out targeting the problem of slow response time and performance degradation of imaging system caused by large data of medical ultrasonic imaging. In view of the advantages of CS, it is applied to medical ultrasonic imaging to solve the above problems. Objective: Under the condition of satisfying the speed of ultrasound imaging, the quality of imaging can be further improved to provide the basis for accurate medical diagnosis. Methods: According to CS theory and the characteristics of the array ultrasonic imaging system, block compressed sensing ultrasonic imaging algorithm is proposed based on wavelet sparse representation. Results: Three kinds of observation matrices have been designed on the basis of the proposed algorithm, which can be selected to reduce the number of the linear array channels and the complexity of the ultrasonic imaging system to some extent. Conclusion: The corresponding simulation program is designed, and the result shows that this algorithm can greatly reduce the total data amount required by imaging and the number of data channels required for linear array transducer to receive data. The imaging effect has been greatly improved compared with that of the spatial frequency domain sparse algorithm.

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