Coherent perfect absorption in photonic structures

The ability to drive a system with an external input is a fundamental aspect of light-matter interaction. The coherent perfect absorption (CPA) phenomenon extends to the general multibeam interference phenomenology the well known critical coupling concepts. This interferometric control of absorption can be employed to reach full delivery of optical energy to nanoscale systems such as plasmonic nanoparticles, and multi-port interference can be used to enhance the absorption of a nanoscale device when it is embedded in a strongly scattering system, with potential applications to nanoscale sensing. Here we review the two-port CPA in reference to photonic structures which can resonantly couple to the external fields. A revised two-port theory of CPA is illustrated, which relies on the Scattering Matrix formalism and is valid for all linear two-port systems with reciprocity. Through a semiclassical approach, treating two-port critical coupling conditions in a non-perturbative regime, it is demonstrated that the strong coupling regime and the critical coupling condition can indeed coexist; in this situation, termed strong critical coupling, all the incoming energy is converted into polaritons. Experimental results are presented, which clearly display the elliptical trace of absorption as function of input unbalance in a thin metallo-dielectric metamaterial, and verify polaritonic CPA in an intersubband-polariton photonic-crystal membrane resonator. Concluding remarks discuss the future perspectives of CPA with photonic structures.Comment: arXiv admin note: substantial text overlap with arXiv:1605.0890

Stochastic Training of Neural Networks via Successive Convex Approximations

This paper proposes a new family of algorithms for training neural networks (NNs). These are based on recent developments in the field of non-convex optimization, going under the general name of successive convex approximation (SCA) techniques. The basic idea is to iteratively replace the original (non-convex, highly dimensional) learning problem with a sequence of (strongly convex) approximations, which are both accurate and simple to optimize. Differently from similar ideas (e.g., quasi-Newton algorithms), the approximations can be constructed using only first-order information of the neural network function, in a stochastic fashion, while exploiting the overall structure of the learning problem for a faster convergence. We discuss several use cases, based on different choices for the loss function (e.g., squared loss and cross-entropy loss), and for the regularization of the NN's weights. We experiment on several medium-sized benchmark problems, and on a large-scale dataset involving simulated physical data. The results show how the algorithm outperforms state-of-the-art techniques, providing faster convergence to a better minimum. Additionally, we show how the algorithm can be easily parallelized over multiple computational units without hindering its performance. In particular, each computational unit can optimize a tailored surrogate function defined on a randomly assigned subset of the input variables, whose dimension can be selected depending entirely on the available computational power.Comment: Preprint submitted to IEEE Transactions on Neural Networks and Learning System

Fire Hose instability driven by alpha particle temperature anisotropy

We investigate properties of a solar wind-like plasma including a secondary alpha particle population exhibiting a parallel temperature anisotropy with respect to the background magnetic field, using linear and quasi-linear predictions and by means of one-dimensional hybrid simulations. We show that anisotropic alpha particles can drive a parallel fire hose instability analogous to that generated by protons, but that, remarkably, the instability can be triggered also when the parallel plasma beta of alpha particles is below unity. The wave activity generated by the alpha anisotropy affects the evolution of the more abundant protons, leading to their anisotropic heating. When both ion species have sufficient parallel anisotropies both of them can drive the instability, and we observe generation of two distinct peaks in the spectra of the fluctuations, with longer wavelengths associated to alphas and shorter ones to protons. If a non-zero relative drift is present, the unstable modes propagate preferentially in the direction of the drift associated with the unstable species. The generated waves scatter particles and reduce their temperature anisotropy to marginally stable state, and, moreover, they significantly reduce the relative drift between the two ion populations. The coexistence of modes excited by both species leads to saturation of the plasma in distinct regions of the beta/anisotropy parameter space for protons and alpha particles, in good agreement with in situ solar wind observations. Our results confirm that fire hose instabilities are likely at work in the solar wind and limit the anisotropy of different ion species in the plasma.Comment: 10 pages, 9 figures, Accepted for publication in The Astrophysical Journa

Output Regulation for Systems on Matrix Lie-group

This paper deals with the problem of output regulation for systems defined on matrix Lie-Groups. Reference trajectories to be tracked are supposed to be generated by an exosystem, defined on the same Lie-Group of the controlled system, and only partial relative error measurements are supposed to be available. These measurements are assumed to be invariant and associated to a group action on a homogeneous space of the state space. In the spirit of the internal model principle the proposed control structure embeds a copy of the exosystem kinematic. This control problem is motivated by many real applications fields in aerospace, robotics, projective geometry, to name a few, in which systems are defined on matrix Lie-groups and references in the associated homogenous spaces

Two-dimensional Hybrid Simulations of Kinetic Plasma Turbulence: Current and Vorticity vs Proton Temperature

Proton temperature anisotropies between the directions parallel and perpendicular to the mean magnetic field are usually observed in the solar wind plasma. Here, we employ a high-resolution hybrid particle-in-cell simulation in order to investigate the relation between spatial properties of the proton temperature and the peaks in the current density and in the flow vorticity. Our results indicate that, although regions where the proton temperature is enhanced and temperature anisotropies are larger correspond approximately to regions where many thin current sheets form, no firm quantitative evidence supports the idea of a direct causality between the two phenomena. On the other hand, quite a clear correlation between the behavior of the proton temperature and the out-of-plane vorticity is obtained.Comment: 4 pages, 2 figures, Proceedings of the Fourteenth International Solar Wind Conferenc

Solar wind turbulence from MHD to sub-ion scales: high-resolution hybrid simulations

We present results from a high-resolution and large-scale hybrid (fluid electrons and particle-in-cell protons) two-dimensional numerical simulation of decaying turbulence. Two distinct spectral regions (separated by a smooth break at proton scales) develop with clear power-law scaling, each one occupying about a decade in wave numbers. The simulation results exhibit simultaneously several properties of the observed solar wind fluctuations: spectral indices of the magnetic, kinetic, and residual energy spectra in the magneto-hydrodynamic (MHD) inertial range along with a flattening of the electric field spectrum, an increase in magnetic compressibility, and a strong coupling of the cascade with the density and the parallel component of the magnetic fluctuations at sub-proton scales. Our findings support the interpretation that in the solar wind large-scale MHD fluctuations naturally evolve beyond proton scales into a turbulent regime that is governed by the generalized Ohm's law.Comment: 5 pages, 5 figures; introduction and conclusions changed, references updated, accepted for publication in ApJ

The Earth transiting the Sun as seen from Jupiter's moons: detection of an inverse Rossiter-McLaughlin effect produced by the Opposition Surge of the icy Europa

We report on a multi-wavelength observational campaign which followed the Earth's transit on the Sun as seen from Jupiter on 5 Jan the 2014. Simultaneous observations of Jupiter's moons Europa and Ganymede obtained with HARPS from La Silla, Chile, and HARPS-N from La Palma, Canary Islands, were performed to measure the Rossiter-McLaughlin effect due to the Earth's passage using the same technique successfully adopted for the 2012 Venus Transit (Molaro et al 2013). The expected modulation in radial velocities was of about 20 cm/s but an anomalous drift as large as 38 m/s, i.e. more than two orders of magnitude higher and opposite in sign, was detected instead. The consistent behaviour of the two spectrographs rules out instrumental origin of the radial velocity drift and BiSON observations rule out the possible dependence on the Sun's magnetic activity. We suggest that this anomaly is produced by the Opposition Surge on Europa's icy surface, which amplifies the intensity of the solar radiation from a portion of the solar surface centered around the crossing Earth which can then be observed as a a sort of inverse Rossiter-McLaughling effect. in fact, a simplified model of this effect can explain in detail most features of the observed radial velocity anomalies, namely the extensions before and after the transit, the small differences between the two observatories and the presence of a secondary peak closer to Earth passage. This phenomenon, observed here for the first time, should be observed every time similar Earth alignments occur with rocky bodies without atmospheres. We predict it should be observed again during the next conjunction of Earth and Jupiter in 2026.Comment: 9 pages, 7 figure

Image fusion techniqes for remote sensing applications

Image fusion refers to the acquisition, processing and synergistic combination of information provided by various sensors or by the same sensor in many measuring contexts. The aim of this survey paper is to describe three typical applications of data fusion in remote sensing. The first study case considers the problem of the Synthetic Aperture Radar (SAR) Interferometry, where a pair of antennas are used to obtain an elevation map of the observed scene; the second one refers to the fusion of multisensor and multitemporal (Landsat Thematic Mapper and SAR) images of the same site acquired at different times, by using neural networks; the third one presents a processor to fuse multifrequency, multipolarization and mutiresolution SAR images, based on wavelet transform and multiscale Kalman filter. Each study case presents also results achieved by the proposed techniques applied to real data