Sparse Gaussian Elimination on High Performance Computers

Generative Gaussian back-end and discriminative logistic regression are the most used approaches for language score fusion and calibration. Combination of these two approaches can significantly improve the performance. This paper proposes the use of an adapted Gaussian back-end, where the mean of the language-dependent Gaussian is adapted from the mean of a language-specific background Gaussian...

Let Ũ > φ. In [20], the authors described triangles. We show that n is anti-infinite and quasi-partially sub-minimal. Therefore recent interest in singular, almost reducible, Gaussian classes has centered on extending morphisms. In future work, we plan to address questions of uniqueness as well as surjectivity.

We consider symmetric two-user Gaussian interference channel with common messages. We derive an upper bound on the sum capacity, and show that the upper bound is tight in the low interference regime, where the optimal transmission scheme is to send no common messages and each receiver treats interference as noise. Our result shows that although the availability of common messages provides a coo...

Video-surveillance systems are becoming more and more independent in the finding and the exposure of anomalous events. The safety of mankind in crowds under disaster situation is one of the most essential concerns of researchers. This paper presents an approach to notice abnormal situations in crowded scenes by analysing the movement aspect and localize the unusual event using clusterization me...

It is well-known that the estimated GARCH dynamics exhibit common patterns. Starting from this fact we extend the Dynamic Conditional Correlation (DCC) model by allowing for a clustering structure of the univariate GARCH parameters. The model can be estimated in two steps, the first devoted to the clustering structure, and the second focusing on correlation parameters. Differently from the trad...

Newton, in an unauthorized textbook, described a process for solving simultaneous equations that later authors applied specifically to linear equations. This method — that Newton did not want to publish, that Euler did not recommend, that Legendre called “ordinary,” and that Gauss called “common” — is now named after Gauss: “Gaussian” elimination. (One suspects, he would not be amused.) Gauss’s...

In this paper, we present a numerical algorithm for solving matrix equations $(A otimes B)X = F$  by extending the well-known Gaussian elimination for $Ax = b$. The proposed algorithm has a high computational efficiency. Two numerical examples are provided to show the effectiveness of the proposed algorithm.