Off-diagonal Entries Research Articles

Diffusion processes involving multiple conserved charges: A study from kinetic theory and implications to the fluid-dynamical modeling of heavy ion collisions

The bulk nuclear matter produced in heavy ion collisions carries a multitude of conserved quantum numbers: electric charge, baryon number, and strangeness. Therefore, the diffusion processes associated to these conserved charges cannot occur independently and must be described in terms of a set of coupled diffusion equations. This physics is implemented by replacing the traditional diffusion coefficients for each conserved charge by a diffusion coefficient matrix, which quantifies the coupling between the conserved quantum numbers. The diagonal coefficients of this matrix are the usual charge diffusion coefficients, while the off-diagonal entries describe the diffusive coupling of the charge currents. In this paper, we show how to calculate this diffusion coefficient matrix from kinetic theory and provide results for a hadron resonance gas and a gas of partons. We further find that the off-diagonal entries can reach similar magnitudes compared to the diagonal entries. In order to provide some insight on the influence that the coupling between the net charge diffusion currents can have on heavy ion observables, we present first results for the diffusive evolution of a hadronic system in a simple (1+1)D-fluid dynamics approach, and study different configurations of the diffusion matrix.

Statistics of off-diagonal entries of Wigner K-matrix for chaotic wave systems with absorption

Using the random matrix theory approach we derive explicit distributions of the real and imaginary parts for off-diagonal entries of the Wigner reaction matrix for wave chaotic scattering in systems with and without time-reversal invariance, in the presence of an arbitrary uniform absorption. Whereas for time-reversal invariant system () the scattering channels are assumed to be random and orthogonal on average, for broken time-reversal () we consider the case of nontrivially correlated channel vectors.

A note on Jordan algebras, three generations and exceptional periodicity

It is shown that the algebra [Formula: see text] based on the complexified Exceptional Jordan, and the complex Clifford algebra in 4D, is rich enough to describe all the spinorial degrees of freedom of three generations of fermions in 4D, and include additional fermionic dark matter candidates. Furthermore, the model described in this paper can account also for the Standard Model gauge symmetries. We extend these results to the Magic Star algebras of Exceptional Periodicity developed by Marrani–Rios–Truini and based on the Vinberg cubic [Formula: see text] algebras which are generalizations of exceptional Jordan algebras. It is found that there is a one-to-one correspondence among the real spinorial degrees of freedom of four generations of fermions in 4D with the off-diagonal entries of the spinorial elements of the [Formula: see text] [Formula: see text] of Vinberg matrices at level [Formula: see text]. These results can be generalized to higher levels [Formula: see text] leading to a higher number of generations beyond 4. Three [Formula: see text] of [Formula: see text] algebras and their conjugates [Formula: see text] were essential in the Magic Star construction of Exceptional Periodicity that extends the [Formula: see text] algebra to [Formula: see text] with [Formula: see text] integer.

Reciprocal matrices: properties and approximation by a transitive matrix

Reciprocal matrices and, in particular, transitive matrices, appear in several applied areas. Among other applications, they have an important role in decision theory in the context of the analytical hierarchical process, introduced by Saaty. In this paper, we study the possible ranks of a reciprocal matrix and give a procedure to construct a reciprocal matrix with the rank and the off-diagonal entries of an arbitrary row (column) prescribed. We apply some techniques from graph theory to the study of transitive matrices, namely to determine the maximum number of equal entries, and distinct from $$\pm 1$$, in a transitive matrix. We then focus on the n-by-n reciprocal matrix, denoted by C(n, x), with all entries above the main diagonal equal to $$x>0.$$ We show that there is a Toeplitz transitive matrix and a transitive matrix preserving the maximum possible number of entries of C(n, x), whose distances to C(n, x), measured in the Frobenius norm, are smaller than the one of the transitive matrix proposed by Saaty, constructed from the right Perron eigenvector of C(n, x). We illustrate our results with some numerical examples.

A trace bound for integer-diagonal positive semidefinite matrices

AbstractWe prove that an n-by-n complex positive semidefinite matrix of rank r whose graph is connected, whose diagonal entries are integers, and whose non-zero off-diagonal entries have modulus at least one, has trace at least n + r − 1.

Spectral enclosures for a class of block operator matrices

We prove new spectral enclosures for the non-real spectrum of a class of 2×2 block operator matrices with self-adjoint operators A and D on the diagonal and operators B and −B⁎ as off-diagonal entries. One of our main results resembles Gershgorin's circle theorem. The enclosures are applied to J-frame operators.

Markov process representation of semigroups whose generators include negative rates

Generators of Markov processes on a countable state space can be represented as finite or infinite matrices. One key property is that the off-diagonal entries corresponding to jump rates of the Markov process are non-negative. Here we present stochastic characterizations of the semigroup generated by a generator with possibly negative rates. This is done by considering a larger state space with one or more particles and antiparticles, with antiparticles being particles carrying a negative sign.

Feature Graph Learning for 3D Point Cloud Denoising

Identifying an appropriate underlying graph kernel that reflects pairwise similarities is critical in many recent graph spectral signal restoration schemes, including image denoising, dequantization, and contrast enhancement. Existing graph learning algorithms compute the most likely entries of a properly defined graph Laplacian matrix $\mathbf{L}$, but require a large number of signal observations $\mathbf{z}$'s for a stable estimate. In this work, we assume instead the availability of a relevant feature vector $\mathbf{f}_i$ per node $i$, from which we compute an optimal feature graph via optimization of a feature metric. Specifically, we alternately optimize the diagonal and off-diagonal entries of a Mahalanobis distance matrix $\mathbf{M}$ by minimizing the graph Laplacian regularizer (GLR) $\mathbf{z}^{\top} \mathbf{L} \mathbf{z}$, where edge weight is $w_{i,j} = \exp\{-(\mathbf{f}_i - \mathbf{f}_j)^{\top} \mathbf{M} (\mathbf{f}_i - \mathbf{f}_j) \}$, given a single observation $\mathbf{z}$. We optimize diagonal entries via proximal gradient (PG), where we constrain $\mathbf{M}$ to be positive definite (PD) via linear inequalities derived from the Gershgorin circle theorem. To optimize off-diagonal entries, we design a block descent algorithm that iteratively optimizes one row and column of $\mathbf{M}$. To keep $\mathbf{M}$ PD, we constrain the Schur complement of sub-matrix $\mathbf{M}_{2,2}$ of $\mathbf{M}$ to be PD when optimizing via PG. Our algorithm mitigates full eigen-decomposition of $\mathbf{M}$, thus ensuring fast computation speed even when feature vector $\mathbf{f}_i$ has high dimension. To validate its usefulness, we apply our feature graph learning algorithm to the problem of 3D point cloud denoising, resulting in state-of-the-art performance compared to competing schemes in extensive experiments.

Gershgorin and Rayleigh Bounds on the Eigenvalues of the Finite-Element Global Matrices via Optimal Similarity Transformations

The large finite element global stiffness matrix is an algebraic, discreet, even-order, differential operator of zero row sums. Direct application of the, practically convenient, readily applied, Gershgorin’s eigenvalue bounding theorem to this matrix inherently fails to foresee its positive definiteness, predictably, and routinely failing to produce a nontrivial lower bound on the least eigenvalue of this, theoretically assured to be positive definite, matrix. Considered here are practical methods for producing an optimal similarity transformation for the finite-elements global stiffness matrix, following which non trivial, realistic, lower bounds on the least eigenvalue can be located, then further improved. The technique is restricted here to the common case of a global stiffness matrix having only non-positive off-diagonal entries. For such a matrix application of the Gershgorin bounding method may be carried out by a mere matrix vector multiplication.

Stabilizing the Metzler matrices with applications to dynamical systems

Real matrices with non-negative off-diagonal entries play a crucial role for modelling the positive linear dynamical systems. In the literature, these matrices are referred to as Metzler matrices or negated Z-matrices. Finding the closest stable Metzler matrix to an unstable one (and vice versa) is an important issue with many applications. The stability considered here is in the sense of Hurwitz, and the distance between matrices is measured in $$l_\infty ,\,l_1$$, and in the max norm. We provide either explicit solutions or efficient algorithms for obtaining the closest (un)stable matrix. The procedure for finding the closest stable Metzler matrix is based on the recently introduced selective greedy spectral method for optimizing the Perron eigenvalue. Originally intended for non-negative matrices, here is generalized to Metzler matrices. The efficiency of the new algorithms is demonstrated in examples and numerical experiments for the dimension of up to 2000. Applications to dynamical systems, linear switching systems, and sign-matrices are considered.

Nonnegative realizability with Jordan structure

A general method is given for merging blocks in the Jordan canonical form of a nonnegative matrix. As a consequence, results, more general than any prior ones, are given for the universal realizability of spectra, that is, spectra which are realizable by a nonnegative matrix for each possible Jordan canonical form allowed by the spectrum. In particular, we generalize a classical result due to Minc, regarding positive diagonalizable matrices. For example, any spectrum that is diagonalizably realizable by a nonnegative matrix with mostly positive off-diagonal entries is universally realizable.

Electrodynamics of tilted Dirac and Weyl materials: A unique platform for unusual surface plasmon polaritons

The electrodynamics of Weyl semimetals (WSMs) is an extension of Maxwell's theory where in addition to field strength tensor $F_{\mu\nu}$, an axion field enters the theory which is parameterized by a four-vector $b^\mu=(b_0,\bf b)$. In the tilted Weyl matter (TWM) an additional set of parameters ${\bf\zeta}=(\zeta_x,\zeta_y,\zeta_z)$ enter the theory that can be encoded into the metric of the spacetime felt by electrons in TWM. This allows an extension of Maxwell's electrodynamics that describes electric and magnetic fields in TWMs and tilted Dirac material (TDM) when $b^\mu=0$. The tilt parameter $\bf\zeta$ appearing as off-diagonal metric entries mixing time and space components mingles $\bf E$ and $\bf B$ fields whereby modifies the inhomogeneous Maxwell's equations. Surface plasmon polariton (SPP) in these systems describes the propagation of electromagnetic waves at the {\it interface of two different spacetime geometries}. In the case of TDM, we find a characteristic dependence of SPP spectrum on the tilt parameter $\zeta$ which can be used map $\zeta$ from SPP measurements. In the case of TWM, depending on whether the interface with vacuum supports a Fermi arc or not, and whether the propagation direction is along the Fermi arc or transverse to it, we find many unusual spectral features for SPP modes. Our detailed study of the dependence of SPP spectra on the arrangements of three vectors $(\bf b, q,\zeta)$, the first two of which are at our control, can be utilized to map the tilt characteristics and Fermi arc characteristics from SPP measurements.

Decoherence and Coherence Preservation in the Solutions of the GKSL Equation in the Theory of Open Quantum Systems

The properties of solutions of the Gorini–Kossakowski-Sudarshan–Lindblad (GKSL) equation for the density operator (matrix) of a system that has nondegenerate energy spectrum and weakly interacts with a reservoir are considered. Conditions for the existence of solutions for which the density matrix has off-diagonal entries (“coherences”) not tending to zero at large times are derived.

The inverse eigenvalue problem of a graph: Multiplicities and minors

The inverse eigenvalue problem of a given graph G is to determine all possible spectra of real symmetric matrices whose off-diagonal entries are governed by the adjacencies in G. Barrett et al. introduced the Strong Spectral Property (SSP) and the Strong Multiplicity Property (SMP) in [Generalizations of the Strong Arnold Property and the minimum number of distinct eigenvalues of a graph. Electron. J. Combin., 2017]. In that paper it was shown that if a graph has a matrix with the SSP (or the SMP) then a supergraph has a matrix with the same spectrum (or ordered multiplicity list) augmented with simple eigenvalues if necessary, that is, subgraph monotonicity. In this paper we extend this to a form of minor monotonicity, with restrictions on where the new eigenvalues appear. These ideas are applied to solve the inverse eigenvalue problem for all graphs of order five, and to characterize forbidden minors of graphs having at most one multiple eigenvalue.

Neural networks with block diagonal inner product layers: a look at neural network architecture through the lens of random matrices

Two difficulties continue to burden deep learning researchers and users: (1) neural networks are cumbersome tools, and (2) the activity of the fully connected (FC) layers remains mysterious. We make contributions to these two issues by considering a modified version of the FC layer we call a block diagonal inner product (BDIP) layer. These modified layers have weight matrices that are block diagonal, turning a single FC layer into a set of densely connected neuron groups; they can be achieved by either initializing a purely block diagonal weight matrix or by iteratively pruning off-diagonal block entries. This idea is a natural extension of group, or depthwise separable, convolutional layers. This method condenses network storage and speeds up the run time without significant adverse effect on the testing accuracy, addressing the first problem. Looking at the distribution of the weights through training when varying the number of blocks in a layer gives insight into the second problem. We observe that, even after thousands of training iterations, inner product layers have singular value distributions that resemble that of truly random matrices with iid entries and that each block in a BDIP layer behaves like a smaller copy. For network architectures differing only by the number of blocks in one inner product layer, the ratio of the variance of the weights remains approximately constant for thousands of iterations, that is, the relationship in structure is preserved in the parameter distribution.

Worst-case complexity of cyclic coordinate descent: $$O(n^2)$$ gap with randomized version

This paper concerns the worst-case complexity of cyclic coordinate descent (C-CD) for minimizing a convex quadratic function, which is equivalent to Gauss–Seidel method, Kaczmarz method and projection onto convex sets (POCS) in this simple setting. We observe that the known provable complexity of C-CD can be $$\mathcal {O}(n^2)$$ times slower than randomized coordinate descent (R-CD), but no example was proven to exhibit such a large gap. In this paper we show that the gap indeed exists. We prove that there exists an example for which C-CD takes at least $$\mathcal {O}(n^4 \kappa _{\text {CD}} \log \frac{1}{\epsilon })$$ operations, where $$\kappa _{\text {CD}}$$ is related to Demmel’s condition number and it determines the convergence rate of R-CD. It implies that in the worst case C-CD can indeed be $$\mathcal {O}(n^2)$$ times slower than R-CD, which has complexity $$\mathcal {O}( n^2 \kappa _{\text {CD}} \log \frac{1}{\epsilon })$$ . Note that for this example, the gap exists for any fixed update order, not just a particular order. An immediate consequence is that for Gauss–Seidel method, Kaczmarz method and POCS, there is also an $$\mathcal {O}(n^2) $$ gap between the cyclic versions and randomized versions (for solving linear systems). One difficulty with the analysis is that the spectral radius of a non-symmetric iteration matrix does not necessarily constitute a lower bound for the convergence rate. Finally, we design some numerical experiments to show that the size of the off-diagonal entries is an important indicator of the practical performance of C-CD.

Lα → 3lβ in minimal R-symmetric supersymmetric Standard Model

Lepton flavor violation decays are channels which may lead to fundamental discoveries in the forthcoming years and this makes it an exciting research field for beyond the Standard Model (SM) searches. In this work, we present an analysis of the lepton flavor violation decays [Formula: see text] in Minimal R-symmetric Supersymmetric Standard Model. The prediction for [Formula: see text] depends on the off-diagonal entries of the slepton mass matrix. The contributions to Wilson coefficients can be classified into Higgs penguins, photon penguins, Z penguins and box diagrams. It shows the contribution from Z penguins dominates the predictions for [Formula: see text], and the contributions from Higgs penguins and box diagrams play different roles in different decay channels. The theoretical predictions for [Formula: see text] can reach the future experimental limits, and the channels are very promising to be observed in near future experiment.

Largest entries of sample correlation matrices from equi-correlated normal populations

The paper studies the limiting distribution of the largest off-diagonal entry of the sample correlation matrices of high-dimensional Gaussian populations with equi-correlation structure. Assume the entries of the population distribution have a common correlation coefficient $\rho >0$ and both the population dimension $p$ and the sample size $n$ tend to infinity with $\log p=o(n^{\frac{1}{3}})$. As $0<\rho <1$, we prove that the largest off-diagonal entry of the sample correlation matrix converges to a Gaussian distribution, and the same is true for the sample covariance matrix as $0<\rho <1/2$. This differs substantially from a well-known result for the independent case where $\rho =0$, in which the above limiting distribution is an extreme-value distribution. We then study the phase transition between these two limiting distributions and identify the regime of $\rho $ where the transition occurs. If $\rho $ is less than, larger than or is equal to the threshold, the corresponding limiting distribution is the extreme-value distribution, the Gaussian distribution and a convolution of the two distributions, respectively. The proofs rely on a subtle use of the Chen–Stein Poisson approximation method, conditioning, a coupling to create independence and a special property of sample correlation matrices. An application is given for a statistical testing problem.

$M$-operators on partially ordered Banach spaces

‎For a matrix $A \in \mathbb{R}^{n \times n}$ whose off-diagonal entries are nonpositive‎, ‎there are several well-known properties that are equivalent to $A$ being an invertible $M$-matrix‎. ‎One of them is the positive stability of $A$‎. ‎A generalization of this characterization to partially ordered Banach spaces is considered in this article‎. ‎Relationships with certain other equivalent conditions are derived‎. ‎An important result on singular irreducible $M$-matrices is generalized using the concept of $M$-operators and irreducibility‎. ‎Certain other invertibility conditions of $M$-operators are also investigated‎.

LFV decays of Z boson in Minimal R-symmetric Supersymmetric Standard Model * * Supported by the National Natural Science Foundation of China (NNSFC) (11747064, 11805140), the Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (2017113), the Foundation of Department of Education of Liaoning province (2016TSPY10) and the Youth Foundation of the University of Science and Technology Liaoning

A future Z-factory will offer the possibility of studying rare Z decays , such as those leading to lepton flavor violation (LFV) final states. In this work, by considering the constraints from radiative two-body decays , we investigate the LFV decays in the framework of the Minimal R-symmetric Supersymmetric Standard Model with two benchmark points from the existing literature. The flavor-violating off-diagonal entries , and are constrained by the current experimental bounds of . Considering recent experimental constraints, we also investigate Br( ) as a function of . The numerical results show that the theoretical predictions of Br( ) in the MRSSM are several orders of magnitude below the current experimental bounds. The LFV decays and may be promising observation targets in future experiments.