Ultralow-threshold, continuous-wave upconverting lasing from subwavelength plasmons.

Miniaturized lasers are an emerging platform for generating coherent light for quantum photonics, in vivo cellular imaging, solid-state lighting and fast three-dimensional sensing in smartphones1-3. Continuous-wave lasing at room temperature is critical for integration with opto-electronic devices and optimal modulation of optical interactions4,5. Plasmonic nanocavities integrated with gain can generate coherent light at subwavelength scales6-9, beyond the diffraction limit that constrains mode volumes in dielectric cavities such as semiconducting nanowires10,11. However, insufficient gain with respect to losses and thermal instabilities in nanocavities has limited all nanoscale lasers to pulsed pump sources and/or low-temperature operation6-9,12-15. Here, we show continuous-wave upconverting lasing at room temperature with record-low thresholds and high photostability from subwavelength plasmons. We achieve selective, single-mode lasing from Yb3+/Er3+-co-doped upconverting nanoparticles conformally coated on Ag nanopillar arrays that support a single, sharp lattice plasmon cavity mode and greater than wavelength λ/20 field confinement in the vertical dimension. The intense electromagnetic near-fields localized in the vicinity of the nanopillars result in a threshold of 70 W cm-2, orders of magnitude lower than other small lasers. Our plasmon-nanoarray upconverting lasers provide directional, ultra-stable output at visible frequencies under near-infrared pumping, even after six hours of constant operation, which offers prospects in previously unrealizable applications of coherent nanoscale light

Gradient and Passive Circuit Structure in a Class of Non-linear Dynamics on a Graph

We consider a class of non-linear dynamics on a graph that contains and generalizes various models from network systems and control and study convergence to uniform agreement states using gradient methods. In particular, under the assumption of detailed balance, we provide a method to formulate the governing ODE system in gradient descent form of sum-separable energy functions, which thus represent a class of Lyapunov functions; this class coincides with Csisz\'{a}r's information divergences. Our approach bases on a transformation of the original problem to a mass-preserving transport problem and it reflects a little-noticed general structure result for passive network synthesis obtained by B.D.O. Anderson and P.J. Moylan in 1975. The proposed gradient formulation extends known gradient results in dynamical systems obtained recently by M. Erbar and J. Maas in the context of porous medium equations. Furthermore, we exhibit a novel relationship between inhomogeneous Markov chains and passive non-linear circuits through gradient systems, and show that passivity of resistor elements is equivalent to strict convexity of sum-separable stored energy. Eventually, we discuss our results at the intersection of Markov chains and network systems under sinusoidal coupling

Semi-physical neural modeling for linear signal restoration

International audienceThis paper deals with the design methodology of an Inverse Neural Network (INN) model. The basic idea is to carry out a semi-physical model gathering two types of information: the a priori knowledge of the deterministic rules which govern the studied system and the observation of the actual conduct of this system obtained from experimental data. This hybrid model is elaborated by being inspired by the mechanisms of a neuromimetic network whose structure is constrained by the discrete reverse-time state-space equations. In order to validate the approach, some tests are performed on two dynamic models. The first suggested model is a dynamic system characterized by an unspecified r-order Ordinary Differential Equation (ODE). The second one concerns in particular the mass balance equation for a dispersion phenomenon governed by a Partial Differential Equation (PDE) discretized on a basic mesh. The performances are numerically analyzed in terms of generalization, regularization and training effort