Optical Properties of Crystals with Spatial Dispersion: Josephson Plasma Resonance in Layered Superconductors

We derive the transmission coefficient, $T(\omega)$, for grazing incidence of crystals with spatial dispersion accounting for the excitation of multiple modes with different wave vectors ${\bf k}$ for a given frequency $\omega$. The generalization of the Fresnel formulas contains the refraction indices of these modes as determined by the dielectric function $\epsilon(\omega,{\bf k})$. Near frequencies $\omega_e$, where the group velocity vanishes, $T(\omega)$ depends also on an additional parameter determined by the crystal microstructure. The transmission $T$ is significantly suppressed, if one of the excited modes is decaying into the crystal. We derive these features microscopically for the Josephson plasma resonance in layered superconductors.Comment: 4 pages, 2 figures, epl.cls style file, minor change

The effect of targeted dropsonde observations during the 1999 winter storm reconnaissance program

In this paper, the effects of targeted dropsonde observations on operational global numerical weather analyses and forecasts made at the National Centers for Environmental Prediction (NCEP) are evaluated. The data were collected during the 1999 Winter Storm Reconnaissance field program at locations that were found optimal by the ensemble transform technique for reducing specific forecast errors over the continental United States and Alaska. Two parallel analysis-forecast cycles are compared; one assimilates all operationally available data including those from the targeted dropsondes, whereas the other is identical except that it excludes all dropsonde data collected during the program. It was found that large analysis errors appear in areas of intense baroclinic energy conversion over the northeast Pacific and are strongly associated with errors in the first-guess field. The "signal," defined by the difference between analysis-forecast cycles with and without the dropsonde data, propagates at an average speed of 30 degrees per day along the storm track to the east. Hovmoller diagrams and eddy statistics suggest that downstream development plays a significant role in spreading out the effect of the dropsondes in space and time. On average, the largest rms surface pressure errors are reduced by 10%-20% associated with the eastward-propagating leading edge of the signal. The dropsonde data seem to be more effective in reducing forecast errors when zonal how prevails over the eastern Pacific. Results from combined verification statistics (based on surface pressure, tropospheric winds, and precipitation amount) indicate that the dropsonde data improved the forecasts in 18 of the 25 targeted cases, while the impact was negative (neutral) in only 5 (2) cases

Statics and Dynamics of an Inhomogeneously-Nonlinear Lattice

We introduce an inhomogeneously-nonlinear Schr{\"o}dinger lattice, featuring a defocusing segment, a focusing segment and a transitional interface between the two. We illustrate that such inhomogeneous settings present vastly different dynamical behavior than the one expected in their homogeneous counterparts in the vicinity of the interface. We analyze the relevant stationary states, as well as their stability by means of perturbation theory and linear stability analysis. We find good agreement with the numerical findings in the vicinity of the anti-continuum limit. For larger values of the coupling, we follow the relevant branches numerically and show that they terminate at values of the coupling strength which are larger for more extended solutions. The dynamical development of relevant instabilities is also monitored in the case of unstable solutions.Comment: 14 pages, 4 figure

Nonlinear Lattices Generated from Harmonic Lattices with Geometric Constraints

Geometrical constraints imposed on higher dimensional harmonic lattices generally lead to nonlinear dynamical lattice models. Helical lattices obtained by such a procedure are shown to be described by sine- plus linear-lattice equations. The interplay between sinusoidal and quadratic potential terms in such models is shown to yield localized nonlinear modes identified as intrinsic resonant modes

Quantum machine learning: a classical perspective

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets are motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed-up classical machine learning algorithms. Here we review the literature in quantum machine learning and discuss perspectives for a mixed readership of classical machine learning and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in machine learning are identified as promising directions for the field. Practical questions, like how to upload classical data into quantum form, will also be addressed.Comment: v3 33 pages; typos corrected and references adde

Fast Ensemble Smoothing

Smoothing is essential to many oceanographic, meteorological and hydrological applications. The interval smoothing problem updates all desired states within a time interval using all available observations. The fixed-lag smoothing problem updates only a fixed number of states prior to the observation at current time. The fixed-lag smoothing problem is, in general, thought to be computationally faster than a fixed-interval smoother, and can be an appropriate approximation for long interval-smoothing problems. In this paper, we use an ensemble-based approach to fixed-interval and fixed-lag smoothing, and synthesize two algorithms. The first algorithm produces a linear time solution to the interval smoothing problem with a fixed factor, and the second one produces a fixed-lag solution that is independent of the lag length. Identical-twin experiments conducted with the Lorenz-95 model show that for lag lengths approximately equal to the error doubling time, or for long intervals the proposed methods can provide significant computational savings. These results suggest that ensemble methods yield both fixed-interval and fixed-lag smoothing solutions that cost little additional effort over filtering and model propagation, in the sense that in practical ensemble application the additional increment is a small fraction of either filtering or model propagation costs. We also show that fixed-interval smoothing can perform as fast as fixed-lag smoothing and may be advantageous when memory is not an issue

Nuclear receptors in vascular biology

Nuclear receptors sense a wide range of steroids and hormones (estrogens, progesterone, androgens, glucocorticoid, and mineralocorticoid), vitamins (A and D), lipid metabolites, carbohydrates, and xenobiotics. In response to these diverse but critically important mediators, nuclear receptors regulate the homeostatic control of lipids, carbohydrate, cholesterol, and xenobiotic drug metabolism, inflammation, cell differentiation and development, including vascular development. The nuclear receptor family is one of the most important groups of signaling molecules in the body and as such represent some of the most important established and emerging clinical and therapeutic targets. This review will highlight some of the recent trends in nuclear receptor biology related to vascular biology