The solution representing a brane-anti-brane system in matrix models breaks the usual matrix spacetime symmetry. We show that the spacetime symmetry on the branes is not broken, rather appears as a combination of the matrix spacetime transformation and a gauge transformation. As a result, the tachyon field, itself an off-diagonal entry in longitudinal matrices, transforms nontrivially under rot...

Polymorphic materials which are subjected to an external stress while their temperature is repeatedly cycled about their phase transformation range experience, on average, Newtonian flow, which is driven by the biasing of internal transformation mismatch stresses by the external stress (1). This phenomenon, termed transformation superplasticity, is well documented for metals (as reviewed in Ref...

The minimization of roundoff noise subject to l2-norm dynamic-range scaling constraints in two-dimensional (2-D) state-space digital filters is considered by using joint error feedback and coordinate transformation optimization. An iterative approach for minimizing the roundoff noise under l2-norm dynamic-range scaling constraints is developed by jointly optimizing a scalar error-feedback matri...

We study the matrix representation of Poincaré normalization using the Carleman linearization technique for non-autonomous differential systems with quasi-periodic coefficients. We provide a rigorous proof of the validity of the matrix representation of the normalization and obtain a recursive algorithm for computing the normalizing transformation and the normal form of the differential systems...

In this paper we present some theory for a non-equivalence transformation of matrix eigenvalues for -matrix polynomials. Application of this transformation to eigenvalue embedding for model tuning on a realistic industry problem is illustrated. The new approach has several advantages such as exibility, e ciency, and structure-preservation. A numerical experiment on a pseudosimulation data set f...

Presented in this paper is a new sparse linear solver methodology motivated by multigrid principles and based around general local transformations that diagonalize a matrix while maintaining its sparsity. These transformations are approximate, but the error they introduce can be systematically reduced. The cost of each transformation is independent of matrix size but dependent on the desired ac...

This paper addresses the problem of state and parameter estimation for a class of second-order systems with single output. A new filtered transformation is proposed for the system via dynamic vector and matrix. In this method, the dynamics of the vector and matrix are derived by immersion and invariance technique such that the state estimation condition is guaranteed. Compared to the classical ...

Many emerging applications formulate nonpositive semidefinite similarity matrices, and hence cannot fit into the framework of kernel machines. A popular approach to this problem is to transform the spectrum of the similarity matrix so as to generate a positive semidefinite kernel matrix. In this paper, we present an analytical framework to explore four representative transformation methods: den...

This paper proposes a robust reduced-rank scheme for adaptive beamforming based on joint iterative optimization (JIO) of adaptive filters. The scheme provides an efficient way to deal with filters with large number of elements. It consists of a bank of full-rank adaptive filters that forms a transformation matrix and an adaptive reduced-rank filter that operates at the output of the bank of fil...