Validation of adjoint-generated environmental gradients for the acoustic monitoring of a shallow water area

In the framework of the recent Maritime Rapid Environmental Assessment sea trial MREA07/BP'07 [Le Gac&Hermand, 2007] that was conducted in the same area south of the island of Elba as the earlier Yellow Shark trial (YS94), this paper examines the original YS94 acoustic data and the recent MREA07 oceanographic data to demonstrate adjoint-based acoustic monitoring of environmental parameters in Mediterranean shallow waters. First, adjoint-generated environmental gradients are validated for the application in geoacoustic inversion where the bottom acoustic parameters of the YS94 layered seabed are determined from the long-range waterborne propagation of a multi-frequency signal. Then, for the application in ocean acoustic tomography, the temporal variability of the MREA07/BP'07 oceanographic data is analyzed in terms of empirical orthogonal functions and the adjoint-based inversion scheme is used to track the time-varying sound speed profile of the experimental transect

A numerical adjoint parabolic equation (PE) method for tomography and geoacoustic inversion in shallow water

Recently, an analytic adjoint-based method of optimal nonlocal boundary control has been proposed for inversion of a waveguide acoustic field using the wide-angle parabolic equation [Meyer and Hermand, J. Acoust. Soc. Am. 117, 2937–2948 (2005)]. In this paper a numerical extension of this approach is presented that allows the direct inversion for the geoacoustic parameters which are embedded in a spectral integral representation of the nonlocal boundary condition. The adjoint model is generated numerically and the inversion is carried out jointly across multiple frequencies. The paper further discusses the application of the numerical adjoint PE method for ocean acoustic tomography. To show the effectiveness of the implemented numerical adjoint, preliminary inversion results of water sound-speed profile and bottom acoustic properties will be shown for the YELLOW SHARK ’94 experimental conditions

Variability Study of High Current Junctionless Silicon Nanowire Transistors

Silicon nanowires have numerous potential applications, including transistors, memories, photovoltaics, biosensors and qubits [1]. Fabricating a nanowire with characteristics required for a specific application, however, poses some challenges. For example, a major challenge is that as the transistors dimensions are reduced, it is difficult to maintain a low off-current (Ioff) whilst simultaneously maintaining a high on-current (Ion). This can be the result of quantum mechanical tunnelling, short channel effects or statistical variability [2]. A variety of new architectures, including ultra-thin silicon-on-insulator (SOI), double gate, FinFETs, tri-gate, junctionless and gate all-around (GAA) nanowire transistors, have therefore been developed to improve the electrostatic control of the conducting channel. This is essential since a low Ioff implies low static power dissipation and it will therefore improve power management in the multi-billion transistor circuits employed globally in microprocessors, sensors and memories

Adjoint approach to the physical characterization of a shallow-water environment

In underwater acoustics a variety of different applications of adjoint models has been proposed in recent years. Adjoints have been derived for normal modes and for both the standard parabolic equation and Claerbout’s wide-angle approximation. This paper reviews the analytic nonlocal boundary control approach proposed in an earlier paper by the authors [Meyer & Hermand, ‘‘Optimal nonlocal boundary control of the wide-angle parabolic equation for inversion of a waveguide acoustic field,’’ J. Acoust. Soc. Am. 117, 2937–2948 (2005)] and presents a numerical extension that allows direct inversion of the geoacoustic parameters that are embedded in a discrete representation of the nonlocal boundary condition at the water-sediment interface. The effectiveness of this numerical adjoint approach for the physical characterization of a shallow-water environment is illustrated with applications for geoacoustic inversion and ocean acoustic tomography. In particular, it is shown how a joint inversion across multiple frequencies can enhance the performance of the optimization process, especially for the case of a sparse receiver array spanning part of the water column. In an additional example we combine the two applications and discuss the feasibility of geoacoustic inversion in the presence of an uncertain sound-speed profile

Generalized Heisenberg algebra coherent states for Power-law potentials

Coherent states for power-law potentials are constructed using generalized Heisenberg algabras. Klauder's minimal set of conditions required to obtain coherent states are satisfied. The statistical properties of these states are investigated through the evaluation of the Mandel's parameter. It is shown that these coherent states are useful for describing the states of real and ideal lasers.Comment: 13 pages, 2 figure

Physiopathologie des diarrhées du chamelon au Maroc. Signes cliniques et perturbations métaboliques

La diarrhée du chamelon est une pathologie majeure au Maroc. Dans une étude de terrain, 58 animaux diarrhéiques et 20 animaux sains issus de 30 troupeaux du Sud du Maroc ont été examinés et des prélèvements de sang et de matières fécales réalisés. L'état général des chamelons a semblé, en moyenne, relativement peu atteint. Les perturbations métaboliques remarquables ont été : une augmentation de l'hématocrite chez les chamelons malades (39 vs 29 p. 100), une diminution de la natrémie (152 vs 155 mmol/I) et de la bicarbonatémie (22 vs 25 mmol/1), une augmentation de la créatinine (97 vs 86 pmol/1) et une hypoglycémie (6,5 vs 7,7 mmol/1). Elles traduisaient une déshydratation hypotonique avec acidose compensée. La colibacillose a été observée dans un tiers des cas de diarrhée et la salmonellose (Salmonella enteritidis) dans 8,5 p. 100 des cas. Aucun cas de cryptosporidiose n'a été observé. (Résumé d'auteur

Dynamics of multipartite quantum correlations under decoherence

Quantum discord is an optimal resource for the quantification of classical and non-classical correlations as compared to other related measures. Geometric measure of quantum discord is another measure of quantum correlations. Recently, the geometric quantum discord for multipartite states has been introduced by Jianwei Xu [arxiv:quant/ph.1205.0330]. Motivated from the recent study [Ann. Phys. 327 (2012) 851] for the bipartite systems, I have investigated global quantum discord (QD) and geometric quantum discord (GQD) under the influence of external environments for different multipartite states. Werner-GHZ type three-qubit and six-qubit states are considered in inertial and non-inertial settings. The dynamics of QD and GQD is investigated under amplitude damping, phase damping, depolarizing and flipping channels. It is seen that the quantum discord vanishes for p>0.75 in case of three-qubit GHZ states and for p>0.5 for six qubit GHZ states. This implies that multipartite states are more fragile to decoherence for higher values of N. Surprisingly, a rapid sudden death of discord occurs in case of phase flip channel. However, for bit flip channel, no sudden death happens for the six-qubit states. On the other hand, depolarizing channel heavily influences the QD and GQD as compared to the amplitude damping channel. It means that the depolarizing channel has the most destructive influence on the discords for multipartite states. From the perspective of accelerated observers, it is seen that effect of environment on QD and GQD is much stronger than that of the acceleration of non-inertial frames. The degradation of QD and GQD happens due to Unruh effect. Furthermore, QD exhibits more robustness than GQD when the multipartite systems are exposed to environment.Comment: 15 pages, 4 figures, 4 table

Distribution of antioxidant components in roots of different red beets (Beta vulgaris L.) cultivars

The beetroot is typically on the table in winter in form of pickles or juice, but for its nutritional values it would deserve more common consumption. Its curative effect in great part is due to the several vitamins, minerals, and compounds with antioxidant activity. But the division of biological active compounds is very different in the parts of the root. Based on our results, we could compare the differences between the morphology and some inner contents (soluble solid content, colour, betacyanin, betaxanthin, and polyphenol contents, antioxidant activity, and some flavonoids) of two beetroot cultivars. The results of the morphological investigations showed that the ‘Cylindre’ cultivar had more favourable crop parameters than the ‘Alto F1’ cultivar. In the ‘Cylindre’ cultivar the polyphenol content and the antioxidant capacity were significantly higher than in the ‘Alto F1’ cultivar. By determination of the betanin contents of the investigated beetroots, our results showed both betacyanin and betaxanthin contents were higher in the ‘Cylindre’ cultivar. The chlorogenic acid, gallic acid, the cumaric acid have been identified based on the peaks of HPLC in the studied beetroot cultivars

Deep Frank-Wolfe for neural network optimization

Learning a deep neural network requires solving a challenging optimization problem: it is a high-dimensional, non-convex and non-smooth minimization problem with a large number of terms. The current practice in neural network optimization is to rely on the stochastic gradient descent (SGD) algorithm or its adaptive variants. However, SGD requires a hand-designed schedule for the learning rate. In addition, its adaptive variants tend to produce solutions that generalize less well on unseen data than SGD with a hand-designed schedule. We present an optimization method that offers empirically the best of both worlds: our algorithm yields good generalization performance while requiring only one hyper-parameter. Our approach is based on a composite proximal framework, which exploits the compositional nature of deep neural networks and can leverage powerful convex optimization algorithms by design. Specifically, we employ the Frank-Wolfe (FW) algorithm for SVM, which computes an optimal step-size in closed-form at each time-step. We further show that the descent direction is given by a simple backward pass in the network, yielding the same computational cost per iteration as SGD. We present experiments on the CIFAR and SNLI data sets, where we demonstrate the significant superiority of our method over Adam, Adagrad, as well as the recently proposed BPGrad and AMSGrad. Furthermore, we compare our algorithm to SGD with a hand-designed learning rate schedule, and show that it provides similar generalization while converging faster