Overview of Constrained PARAFAC Models

In this paper, we present an overview of constrained PARAFAC models where the constraints model linear dependencies among columns of the factor matrices of the tensor decomposition, or alternatively, the pattern of interactions between different modes of the tensor which are captured by the equivalent core tensor. Some tensor prerequisites with a particular emphasis on mode combination using Kronecker products of canonical vectors that makes easier matricization operations, are first introduced. This Kronecker product based approach is also formulated in terms of the index notation, which provides an original and concise formalism for both matricizing tensors and writing tensor models. Then, after a brief reminder of PARAFAC and Tucker models, two families of constrained tensor models, the co-called PARALIND/CONFAC and PARATUCK models, are described in a unified framework, for $N^{th}$ order tensors. New tensor models, called nested Tucker models and block PARALIND/CONFAC models, are also introduced. A link between PARATUCK models and constrained PARAFAC models is then established. Finally, new uniqueness properties of PARATUCK models are deduced from sufficient conditions for essential uniqueness of their associated constrained PARAFAC models

A method for evaluating models that use galaxy rotation curves to derive the density profiles

There are some approaches, either based on General Relativity (GR) or modified gravity, that use galaxy rotation curves to derive the matter density of the corresponding galaxy, and this procedure would either indicate a partial or a complete elimination of dark matter in galaxies. Here we review these approaches, clarify the difficulties on this inverted procedure, present a method for evaluating them, and use it to test two specific approaches that are based on GR: the Cooperstock-Tieu (CT) and the Balasin-Grumiller (BG) approaches. Using this new method, we find that neither of the tested approaches can satisfactorily fit the observational data without dark matter. The CT approach results can be significantly improved if some dark matter is considered, while for the BG approach no usual dark matter halo can improve its results.Comment: 11 pages, 2 figures, 4 tables. v2: diverse text improvements, no changes in the conclusions. Version accepted in MNRA

CONSUMER PERCEPTION ON ALTERNATIVE POULTRY

Food Consumption/Nutrition/Food Safety, Livestock Production/Industries,

Physical properties of the Schur complement of local covariance matrices

General properties of global covariance matrices representing bipartite Gaussian states can be decomposed into properties of local covariance matrices and their Schur complements. We demonstrate that given a bipartite Gaussian state $\rho_{12}$ described by a $4\times 4$ covariance matrix \textbf{V}, the Schur complement of a local covariance submatrix $\textbf{V}_1$ of it can be interpreted as a new covariance matrix representing a Gaussian operator of party 1 conditioned to local parity measurements on party 2. The connection with a partial parity measurement over a bipartite quantum state and the determination of the reduced Wigner function is given and an operational process of parity measurement is developed. Generalization of this procedure to a $n$-partite Gaussian state is given and it is demonstrated that the $n-1$ system state conditioned to a partial parity projection is given by a covariance matrix such as its $2 \times 2$ block elements are Schur complements of special local matrices.Comment: 10 pages. Replaced with final published versio

Energy efficiency of mmWave massive MIMO precoding with low-resolution DACs

With the congestion of the sub-6 GHz spectrum, the interest in massive multiple-input multiple-output (MIMO) systems operating on millimeter wave spectrum grows. In order to reduce the power consumption of such massive MIMO systems, hybrid analog/digital transceivers and application of low-resolution digital-to-analog/analog-to-digital converters have been recently proposed. In this work, we investigate the energy efficiency of quantized hybrid transmitters equipped with a fully/partially-connected phase-shifting network composed of active/passive phase-shifters and compare it to that of quantized digital precoders. We introduce a quantized single-user MIMO system model based on an additive quantization noise approximation considering realistic power consumption and loss models to evaluate the spectral and energy efficiencies of the transmit precoding methods. Simulation results show that partially-connected hybrid precoders can be more energy-efficient compared to digital precoders, while fully-connected hybrid precoders exhibit poor energy efficiency in general. Also, the topology of phase-shifting components offers an energy-spectral efficiency trade-off: active phase-shifters provide higher data rates, while passive phase-shifters maintain better energy efficiency.Comment: Published in IEEE Journal of Selected Topics in Signal Processin

Studying DNA Double-Strand Break Repair: An Ever-Growing Toolbox

To ward off against the catastrophic consequences of persistent DNA double-strand breaks (DSBs), eukaryotic cells have developed a set of complex signaling networks that detect these DNA lesions, orchestrate cell cycle checkpoints and ultimately lead to their repair. Collectively, these signaling networks comprise the DNA damage response (DDR). The current knowledge of the molecular determinants and mechanistic details of the DDR owes greatly to the continuous development of ground-breaking experimental tools that couple the controlled induction of DSBs at distinct genomic positions with assays and reporters to investigate DNA repair pathways, their impact on other DNA-templated processes and the specific contribution of the chromatin environment. In this review, we present these tools, discuss their pros and cons and illustrate their contribution to our current understanding of the DDR.European Research Council (ERC-2014-CoG 647344

Neural Network Detection of Fatigue Crack Growth in Riveted Joints Using Acoustic Emission

The purpose of this research was to demonstrate the capability of neural networks to discriminate between individual acoustic emission (AE) signals originating from crack growth and rivet rubbing (fretting) in aluminum lap joints. AE waveforms were recorded during tensile fatigue cycling of six notched and riveted 7075-T6 specimens using a broadband piezoelectric transducer and a computer interfaced oscilloscope. The source of 1,311 signals was identified based on triggering logic, amplitude relationships, and time of arrival data collected from the broad-band transducer and three additional 300 Hz resonant transducers bonded to the specimens. The power spectrum of each waveform was calculated and normalized to correct for variable specimen geometry and wave propagation effects. In order to determine the variation between individual signals of the same class, the normalized spectra were clustered onto a two-dimensional feature space using a Kohonen self organizing map (SOM). Then 132 crack growth and 137 rivet rubbing spectra were used to train a back-propagation neural network to provide automatic pattern classification. Although there was some overlap between the clusters mapped in the Kohonen feature space, the trained back-propagation neural network was able to classify the remaining 463 crack growth signals with a 94% accuracy and the 367 rivet rubbing signals with a 99% accuracy