We present a novel concept of a magnetically tunable plasmonic crystal based on the excitation of Fano lattice surface modes in periodic arrays of magnetic and optically anisotropic nanoantennas. We show how coherent diffractive far-field coupling between elliptical nickel nanoantennas is governed by the two in-plane, orthogonal and spectrally detuned plasmonic responses of the individual building block, one directly induced by the incident radiation and the other induced by the application of an external magnetic field. The consequent excitation of magnetic field-induced Fano lattice surface modes leads to highly tunable and amplified magneto-optical effects as compared to a continuous film or metasurfaces made of disordered noninteracting magnetoplasmonic anisotropic nanoantennas. The concepts presented here can be exploited to design novel magnetoplasmonic sensors based on coupled localized plasmonic resonances, and nanoscale metamaterials for precise control and magnetically driven tunability of light polarization states.

Nanoscale devices in which the interaction with light can be configured using external control signals hold great interest for next-generation optoelectronic circuits. Materials exhibiting a structural or electronic phase transition offer a large modulation contrast with multi-level optical switching and memory functionalities. In addition, plasmonic nanoantennas can provide an efficient enhancement mechanism for both the optically induced excitation and the readout of materials strategically positioned in their local environment. Here, we demonstrate picosecond all-optical switching of the local phase transition in plasmonic antenna-vanadium dioxide (VO2) hybrids, exploiting strong resonant field enhancement and selective optical pumping in plasmonic hotspots. Polarization- and wavelength-dependent pump–probe spectroscopy of multifrequency crossed antenna arrays shows that nanoscale optical switching in plasmonic hotspots does not affect neighboring antennas placed within 100 nm of the excited antennas. The antenna-assisted pumping mechanism is confirmed by numerical model calculations of the resonant, antenna-mediated local heating on a picosecond time scale. The hybrid, nanoscale excitation mechanism results in 20 times reduced switching energies and 5 times faster recovery times than a VO2 film without antennas, enabling fully reversible switching at over two million cycles per second and at local switching energies in the picojoule range. The hybrid solution of antennas and VO2 provides a conceptual framework to merge the field localization and phase-transition response, enabling precise, nanoscale optical memory functionalities.

The overgrowth-affected gingiva of patients treated with cyclosporin A after kidney transplant was examined with ultrastructural and histochemical methods to evaluate the involvement of connective tissue. Gingival overgrowth has the same clinical signs as local edema. The ultrastructural study showed that the dimensional increase was largely due to increased production of amorphous ground substance by fibroblasts, possibly resulting from an increased release of histamine by mast cells. The histochemical data revealed that the affected tissues contained higher levels of glycosaminoglycans and that cyclosporin A induced comparably high levels of glycosaminoglycans in in vitro cultures of fibroblasts obtained from normal gingiva. The combination of ultrastructural and histochemical data, therefore, strongly suggests that the response of the connective tissue in gingival overgrowth cannot be ignored and may be the main cause of the observed pathological condition.

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SummaryAg and Ag@MgO core–shell nanoparticles (NPs) with a diameter of d = 3–10 nm were obtained by physical synthesis methods and deposited on Si with its native ultrathin oxide layer SiOx (Si/SiOx). Scanning electron microscopy and transmission electron microscopy (TEM) images of bare Ag NPs revealed the presence of small NP aggregates caused by diffusion on the surface and agglomeration. Atomic resolution TEM gave evidence of the presence of crystalline multidomains in the NPs, which were due to aggregation and multitwinning occurring during NP growth in the nanocluster source. Co-deposition of Ag NPs and Mg atoms in an oxygen atmosphere gave rise to formation of a MgO shell matrix surrounding the Ag NPs. The behaviour of the surface plasmon resonance (SPR) excitation in surface differential reflectivity (SDR) spectra with p-polarised light was investigated for bare Ag and Ag@MgO NPs. It was shown that the presence of MgO around the Ag NPs caused a red shift of the plasmon excitation, and served to preserve its existence after prolonged (five months) exposure to air, realizing the possibility of technological applications in plasmonic devices. The Ag NP and Ag@MgO NP film features in the SDR spectra could be reproduced by classical electrodynamics simulations by treating the NP-containing layer as an effective Maxwell Garnett medium. The simulations gave results in agreement with the experiments when accounting for the experimentally observed aggregation.

In this work, we experimentally demonstrate magnetic modulation of mid-infrared (mid-IR) plasmon resonances in microantenna and hole-array metamaterial platforms made of Ni81Fe19/Au multilayers. The responsible mechanism is the magnetorefractive effect linked to the giant magnetoresistance (GMR) present in this system. Ni81Fe19/Au multilayers experience a modification in the electrical resistivity upon the application of a small magnetic field. This directly translates into a change in the optical constants of the multilayer, making it possible to magnetically modulate the plasmon resonances. Because GMR acts on conduction electrons, the optical modulation occurs in the low energy, mid-IR range, even being possible to extend it to the THz range. Electrodynamical calculations confirm the experimental observations. This approach improves by up to 2 orders of magnitude previous attempts for mid-IR magnetic modulation, is potentially ultrafast due to the characteristic spintronics dynamics, and establishes a roadmap for spintronically controlled devices in the whole mid-IR to THz band.

Here we introduce an approximated differentiable renderer to refine a 6-DoF pose prediction using only 2D alignment information. To this end, a two-branched convolutional encoder network is employed to jointly estimate the object class and its 6-DoF pose in the scene. We then propose a new formulation of an approximated differentiable renderer to re-project the 3D object on the image according to its predicted pose; in this way the alignment error between the observed and the re-projected object silhouette can be measured. Since the renderer is differentiable, it is possible to back-propagate through it to correct the estimated pose at test time in an online learning fashion. Eventually we show how to leverage the classification branch to profitably re-project a representative model of the predicted class (i.e. a medoid) instead. Each object in the scene is processed independently and novel viewpoints in which both objects arrangement and mutual pose are preserved can be rendered.

In this work we present a simple end-to-end trainable machine learning system capable of realistically simulating driving experiences. This can be used for verification of self-driving system performance without relying on expensive and time-consuming road testing. In particular, we frame the simulation problem as a Markov Process, leveraging deep neural networks to model both state distribution and transition function. These are trainable directly from the existing raw observations without the need of any handcrafting in the form of plant or kinematic models. All that is needed is a dataset of historical traffic episodes. Our formulation allows the system to construct never seen scenes that unfold realistically reacting to the self-driving car's behaviour. We train our system directly from 1,000 hours of driving logs and measure both realism, reactivity of the simulation as the two key properties of the simulation. At the same time we apply the method to evaluate performance of a recently proposed state-of-the-art ML planning system [1] trained from human driving logs. We discover this planning system is prone to previously unreported causal confusion issues that are difficult to test by non-reactive simulation. To the best of our knowledge, this is the first work that directly merges highly realistic data-driven simulations with a closed loop evaluation for self-driving vehicles. We make the data, code, and pre-trained models publicly available to further stimulate simulation development.