Compressibility of Mixed-State Signals

We present a formula that determines the optimal number of qubits per message that allows asymptotically faithful compression of the quantum information carried by an ensemble of mixed states. The set of mixed states determines a decomposition of the Hilbert space into the redundant part and the irreducible part. After removing the redundancy, the optimal compression rate is shown to be given by the von Neumann entropy of the reduced ensemble.Comment: 7 pages, no figur

Mixed Signals Among Panel Cointegration Tests

Time series cointegration tests, even in the presence of large sample sizes, often yield conflicting conclusions (?mixed signals?) as measured by, inter alia, a low correlation of empirical p-values [see Gregory et al., 2004, Journal of Applied Econometrics]. Using their methodology, we present evidence suggesting that the problem of mixed signals persists for popular panel cointegration tests. As expected, there is weaker correlation between residual and system-based tests than between tests of the same group. --Panel cointegration tests,Monte Carlo comparison

Innovations orthogonalization: a solution to the major pitfalls of EEG/MEG "leakage correction"

The problem of interest here is the study of brain functional and effective connectivity based on non-invasive EEG-MEG inverse solution time series. These signals generally have low spatial resolution, such that an estimated signal at any one site is an instantaneous linear mixture of the true, actual, unobserved signals across all cortical sites. False connectivity can result from analysis of these low-resolution signals. Recent efforts toward "unmixing" have been developed, under the name of "leakage correction". One recent noteworthy approach is that by Colclough et al (2015 NeuroImage, 117:439-448), which forces the inverse solution signals to have zero cross-correlation at lag zero. One goal is to show that Colclough's method produces false human connectomes under very broad conditions. The second major goal is to develop a new solution, that appropriately "unmixes" the inverse solution signals, based on innovations orthogonalization. The new method first fits a multivariate autoregression to the inverse solution signals, giving the mixed innovations. Second, the mixed innovations are orthogonalized. Third, the mixed and orthogonalized innovations allow the estimation of the "unmixing" matrix, which is then finally used to "unmix" the inverse solution signals. It is shown that under very broad conditions, the new method produces proper human connectomes, even when the signals are not generated by an autoregressive model.Comment: preprint, technical report, under license "Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)", https://creativecommons.org/licenses/by-nc-nd/4.0

Blind separation of underdetermined mixtures with additive white and pink noises

This paper presents an approach for underdetermined blind source separation in the case of additive Gaussian white noise and pink noise. Likewise, the proposed approach is applicable in the case of separating I + 3 sources from I mixtures with additive two kinds of noises. This situation is more challenging and suitable to practical real world problems. Moreover, unlike to some conventional approaches, the sparsity conditions are not imposed. Firstly, the mixing matrix is estimated based on an algorithm that combines short time Fourier transform and rough-fuzzy clustering. Then, the mixed signals are normalized and the source signals are recovered using modified Gradient descent Local Hierarchical Alternating Least Squares Algorithm exploiting the mixing matrix obtained from the previous step as an input and initialized by multiplicative algorithm for matrix factorization based on alpha divergence. The experiments and simulation results show that the proposed approach can separate I + 3 source signals from I mixed signals, and it has superior evaluation performance compared to some conventional approaches

Mixed Signals Among Tests for Panel Cointegration

In this paper, we study the effect that different serial correlation adjustment methods can have on panel cointegration testing. As an example, we consider the very popular tests developed by Pedroni (1999, 2004). Results based on both simulated and real data suggest that different adjustment methods can lead to significant variations in test outcome, and thus also in the conclusions.Panel Data; Cointegration Testing; Parametric and Semiparametric Methods

New Signals of Quark-Gluon-Hadron Mixed Phase Formation

Here we present several remarkable irregularities at chemical freeze-out which are found using an advanced version of the hadron resonance gas model. The most prominent of them are the sharp peak of the trace anomaly existing at chemical freeze-out at the center of mass energy 4.9 GeV and two sets of highly correlated quasi-plateaus in the collision energy dependence of the entropy per baryon, total pion number per baryon, and thermal pion number per baryon which we found at the center of mass energies 3.8-4.9 GeV and 7.6-10 GeV. The low energy set of quasi-plateaus was predicted a long time ago. On the basis of the generalized shock-adiabat model we demonstrate that the low energy correlated quasi-plateaus give evidence for the anomalous thermodynamic properties inside the quark-gluon-hadron mixed phase. It is also shown that the trace anomaly sharp peak at chemical freeze-out corresponds to the trace anomaly peak at the boundary between the mixed phase and quark gluon plasma. We argue that the high energy correlated quasi-plateaus may correspond to a second phase transition and discuss its possible origin and location. Besides we suggest two new observables which may serve as clear signals of these phase transformations.Comment: 14 pages, 4 figures, new signals of QGP formation are suggeste

Single channel speech-music separation using matching pursuit and spectral masks

A single-channel speech music separation algorithm based on matching pursuit (MP) with multiple dictionaries and spectral masks is proposed in this work. A training data for speech and music signals is used to build two sets of magnitude spectral vectors of each source signal. These vectors’ sets are called dictionaries, and the vectors are called atoms. Matching pursuit is used to sparsely decompose the magnitude spectrum of the observed mixed signal as a nonnegative weighted linear combination of the best atoms in the two dictionaries that match the mixed signal structure. The weighted sum of the resulting decomposition terms that include atoms from the speech dictionary is used as an initial estimate of the speech signal contribution in the mixed signal, and the weighted sum of the remaining terms for the music signal contribution. The initial estimate of each source is used to build a spectral mask that is used to reconstruct the source signals. Experimental results show that integrating MP with spectral mask gives good separation results