Block Tridiagonal Reduction of Perturbed Normal and Rank Structured Matrices

It is well known that if a matrix $A\in\mathbb C^{n\times n}$ solves the matrix equation $f(A,A^H)=0$, where $f(x, y)$ is a linear bivariate polynomial, then $A$ is normal; $A$ and $A^H$ can be simultaneously reduced in a finite number of operations to tridiagonal form by a unitary congruence and, moreover, the spectrum of $A$ is located on a straight line in the complex plane. In this paper we present some generalizations of these properties for almost normal matrices which satisfy certain quadratic matrix equations arising in the study of structured eigenvalue problems for perturbed Hermitian and unitary matrices.Comment: 13 pages, 3 figure

A CMV--based eigensolver for companion matrices

In this paper we present a novel matrix method for polynomial rootfinding. By exploiting the properties of the QR eigenvalue algorithm applied to a suitable CMV-like form of a companion matrix we design a fast and computationally simple structured QR iteration.Comment: 14 pages, 4 figure

Compression of unitary rank--structured matrices to CMV-like shape with an application to polynomial rootfinding

This paper is concerned with the reduction of a unitary matrix U to CMV-like shape. A Lanczos--type algorithm is presented which carries out the reduction by computing the block tridiagonal form of the Hermitian part of U, i.e., of the matrix U+U^H. By elaborating on the Lanczos approach we also propose an alternative algorithm using elementary matrices which is numerically stable. If U is rank--structured then the same property holds for its Hermitian part and, therefore, the block tridiagonalization process can be performed using the rank--structured matrix technology with reduced complexity. Our interest in the CMV-like reduction is motivated by the unitary and almost unitary eigenvalue problem. In this respect, finally, we discuss the application of the CMV-like reduction for the design of fast companion eigensolvers based on the customary QR iteration

Deriving the respiratory sinus arrhythmia from the heartbeat time series using Empirical Mode Decomposition

Heart rate variability (HRV) is a well-known phenomenon whose characteristics are of great clinical relevance in pathophysiologic investigations. In particular, respiration is a powerful modulator of HRV contributing to the oscillations at highest frequency. Like almost all natural phenomena, HRV is the result of many nonlinearly interacting processes; therefore any linear analysis has the potential risk of underestimating, or even missing, a great amount of information content. Recently the technique of Empirical Mode Decomposition (EMD) has been proposed as a new tool for the analysis of nonlinear and nonstationary data. We applied EMD analysis to decompose the heartbeat intervals series, derived from one electrocardiographic (ECG) signal of 13 subjects, into their components in order to identify the modes associated with breathing. After each decomposition the mode showing the highest frequency and the corresponding respiratory signal were Hilbert transformed and the instantaneous phases extracted were then compared. The results obtained indicate a synchronization of order 1:1 between the two series proving the existence of phase and frequency coupling between the component associated with breathing and the respiratory signal itself in all subjects.Comment: 12 pages, 6 figures. Will be published on "Chaos, Solitons and Fractals

Significance of REM Sleep in Depression: Effects on Neurogenesis

No abstract availabl

Experimental Study of a Parallel Iterative Solver for Markov Chain Modeling

This paper presents the results of a preliminary experimental investigation of the performance of a stationary iterative method based on a block staircase splitting for solving singular systems of linear equations arising in Markov chain modelling. From the experiments presented, we can deduce that the method is well suited for solving block banded or more generally localized systems in a parallel computing environment. The parallel implementation has been benchmarked using several Markovian models

A QR based approach for the nonlinear eigenvalue problem

We describe a fast and numerically robust approach based on the structured QR eigenvalue algorithm for computing approximations of the eigenvalues of a holomorphic matrix-valued function inside the unit circle. Numerical experiments confirm the effectiveness of the proposed method

A systematic review of a polyvagal perspective on embodied contemplative practices as promoters of cardiorespiratory coupling and traumatic stress recovery for ptsd and ocd: Research methodologies and state of the art

Baseline respiratory sinus arrhythmia (RSA) has been proposed as a transdiagnostic biomarker of stress vulnerability across psychopathologies, and a reliable association between PTSD, OCD and lower resting RSA was found. Contemplative practices have been linked to the activation of the vagus as well as to an increased RSA that, according to the polyvagal theory, reflects the activation of the ventral vagal complex (VVC) and may promote PTSD and OCD recovery. PubMed and Scopus databases were selected to conduct a search following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) 2020 guidelines, and A MeaSurement Tool to Assess systematic Reviews-2 (AMSTAR-2) was used to appraise the methodological quality for this systematic review. Six articles met the inclusion criteria (one cross-sectional study, one study with pre-post measurements, two cohort studies and two RCT studies). Mindfulness-related interventions promoted parasympathetic activity, an increased vagal tone and improvements in PTSD and OCD symptoms. According to the polyvagal theory, mindfulness-related and compassion-related meditations would be conceptualized as neural exercises expanding the capacity of the ventral vagal complex to regulate the present state and to promote resilience. Clinical and methodological issues are discussed