A Novel Sparsity Adaptive Algorithm for Underwater Acoustic Signal Reconstruction.

In view of the fact that most of the traditional algorithms for reconstructing underwater acoustic signals from low-dimensional compressed data are based on known sparsity, a sparsity adaptive and variable step-size matching pursuit (SAVSMP) algorithm is proposed. Firstly, the algorithm uses Restricted Isometry Property (RIP) criterion to estimate the initial value of sparsity, and then employs curve fitting method to adjust the initial value of sparsity to avoid underestimation or overestimation, before finally realizing the close approach of the sparsity level with the adaptive step size. The algorithm selects the atoms by matching test, and uses the Least Squares Method to filter out the unsuitable atoms, so as to realize the precise reconstruction of underwater acoustic signal received by the sonar system. The experimental comparison reveals that the proposed algorithm overcomes the drawbacks of existing algorithms, in terms of high computation time and low reconstruction quality.

Active Chiral Plasmonics.

Active control over the handedness of a chiral metamaterial has the potential to serve as key element for highly integrated polarization engineering approaches, polarization sensitive imaging devices, and stereo display technologies. However, this is hard to achieve as it seemingly involves the reconfiguration of the metamolecule from a left-handed into a right-handed enantiomer and vice versa. This type of mechanical actuation is intricate and usually neither monolithically realizable nor viable for high-speed applications. Here, enabled by the phase change material Ge3Sb2Te6 (GST-326), we demonstrate a tunable and switchable mid-infrared plasmonic chiral metamaterial in a proof-of-concept experiment. A large tunability range of the circular dichroism response from λ = 4.15 to 4.90 µm is achieved, and we experimentally demonstrate that the combination of a passive bias-type chiral layer with the active chiral metamaterial allows for switchable chirality, that is, the reversal of the circular dichroism sign, in a fully planar, layered design without the need for geometrical reconfiguration. Because phase change materials can be electrically and optically switched, our designs may open up a path for highly integrated mid-IR polarization engineering devices that can be modulated on ultrafast time scales.

Interpreting chiral nanophotonic spectra: the plasmonic Born-Kuhn model.

One of the most intuitive ways to classically understand the generation of natural optical activity in chiral media is provided by the coupled oscillator model of Born and Kuhn consisting of two identical, vertically displaced, coupled oscillators. We experimentally realize and discuss its exact plasmonic analog in a system of corner-stacked gold nanorods. In particular, we analyze the arising circular dichroism and optical rotatory spectra in terms of hybridized electromagnetic modes and retardation. Specifically, we demonstrate how tuning the vertical distance between the nanorods can lead to a selective excitation of the occurring bonding and antibonding chiral plasmonic modes.

Plasmonic smart dust for probing local chemical reactions.

Locally probing chemical reactions or catalytic processes on surfaces under realistic reaction conditions has remained one of the main challenges in materials science and heterogeneous catalysis. Where conventional surface interrogation techniques usually require high-vacuum conditions or ensemble average measurements, plasmonic nanoparticles excel in extreme light focusing and can produce highly confined electromagnetic fields in subwavelength volumes without the need for complex near-field microscopes. Here, we demonstrate an all-optical probing technique based on plasmonic smart dust for monitoring local chemical reactions in real time. The silica shell-isolated gold nanoparticles that form the smart dust can work as strong light concentrators and optically report subtle environmental changes at their pinning sites on the probed surface during reaction processes. As a model system, we investigate the hydrogen dissociation and subsequent uptake trajectory in palladium with both "dust-on-film" and "film-on-dust" platforms. Using time-resolved single particle measurements, we demonstrate that our technique can in situ encode chemical reaction information as optical signals for a variety of surface morphologies. The presented technique offers a unique scheme for real-time, label-free, and high-resolution probing of local reaction kinetics in a plethora of important chemical reactions on surfaces, paving the way toward the development of inexpensive and high-output reaction sensors for real-world applications.

Metasurface-stabilized optical microcavities.

Cavities concentrate light and enhance its interaction with matter. Confining to microscopic volumes is necessary for many applications but space constraints in such cavities limit the design freedom. Here we demonstrate stable optical microcavities by counteracting the phase evolution of the cavity modes using an amorphous Silicon metasurface as cavity end mirror. Careful design allows us to limit the metasurface scattering losses at telecom wavelengths to less than 2% and using a distributed Bragg reflector as metasurface substrate ensures high reflectivity. Our demonstration experimentally achieves telecom-wavelength microcavities with quality factors of up to 4600, spectral resonance linewidths below 0.4 nm, and mode volumes below [Formula: see text]. The method introduces freedom to stabilize modes with arbitrary transverse intensity profiles and to design cavity-enhanced hologram modes. Our approach introduces the nanoscopic light control capabilities of dielectric metasurfaces to cavity electrodynamics and is industrially scalable using semiconductor manufacturing processes.

Formation of chiral fields in a symmetric environment.

Chiral fields, i. e., electromagnetic fields with nonvanishing optical chirality, can occur next to symmetric nanostructures without geometrical chirality illuminated with linearly polarized light at normal incidence. A simple dipole model is utilized to explain this behavior theoretically. Illuminated with circularly polarized light, the chiral near-fields are still dominated by the distributions found for the linear polarization but show additional features due to the optical chirality of the incident light. Rotating the angle of linear polarization introduces more subtle changes to the distribution of optical chirality. Using our findings, we propose a novel scheme to obtain chiroptical far-field response using linearly polarized light, which could be utilized for applications such as optical enantiomer sensing.

Live-Birth Prediction of Natural-Cycle In Vitro Fertilization Using 57,558 Linked Cycle Records: A Machine Learning Perspective.

Background: Natural-cycle in vitro fertilization (NC-IVF) is an in vitro fertilization (IVF) cycle without gonadotropins or any other stimulation of follicular growth. Previous studies on live-birth prediction of NC-IVF were very few; the sample size was very limited. This study aims to construct a machine learning model to predict live-birth occurrence of NC-IVF using 57,558 linked cycle records and help clinicians develop treatment strategies. Design and Methods: The dataset contained 57,558 anonymized register patient records undergoing NC-IVF cycles from 2005 to 2016 filtered from 7bsp;60,732 records in the Human Fertilisation and Embryology Authority (HFEA) data. We selected matching records and features through data filtering and feature selection methods. Two groups of twelve machine learning models were trained and tested. Eight metrics, e.g., F1 score, Matthews correlation coefficient (MCC), the area under the receiver operating characteristic curve (AUC), etc., were computed to evaluate the performance of each model. Results: Two groups of twelve models were trained and tested. The artificial neural network (ANN) model performed the best in the machine learning group (F1 score, 70.87%; MCC, 50.37%; and AUC score, 0.7939). The LogitBoost model obtained the best scores in the ensemble learning group (F1 score, 70.57%; MCC, 50.75%; and AUC score, 0.7907). After the comparison between the two models, the LogitBoost model was recognized as an optimal one. Conclusion: In this study, NC-IVF-related datasets were extracted from the HFEA data, and a machine learning-based prediction model was successfully constructed through this largest NC-IVF dataset currently. This model is universal and stable, which can help clinicians predict the live-birth success rate of NC-IVF in advance before developing IVF treatment strategies and then choose the best benefit treatment strategy according to the patients' wishes. As "use less stimulation and back to natural condition" becomes more and more popular, this model is more meaningful in the decision-making assistance system for IVF.

The Application of Artificial Intelligence in Predicting Embryo Transfer Outcome of Recurrent Implantation Failure.

Background: Recurrent implantation failure (RIF) refers to that infertile patients have undergone multiple in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) cycles and transferred multiple embryos without embryo implantation or clinical pregnancy. Due to the lack of clear evidence-based medical guidelines for the number of embryos to be transferred in RIF patients, how to obtain the highest single cycle pregnancy success rate with as few embryos transferred as possible while avoiding multiple pregnancy as much as possible, that is, how to balance the pregnancy success rate and multiple pregnancy rate, is a great challenge for clinicians and RIF patients. We urgently need an effective and reliable assisted decision-making method to help clinicians find this balance, and an artificial intelligence (AI) system will provide an efficient solution. Design and Methods: In this research, we filtered out the RIF data set (n = 45,921) from the Human Fertilisation and Embryology Authority (HFEA) database from 2005 to 2016. The data set was divided into two groups according to the number of embryos transferred, Group A and B. Group A included 34,175 cycles with two embryos transferred, while Group B included 11,746 cycles with only one embryo transferred, each containing 44 features and a prediction label (pregnancy). Four machine learning algorithms (RF, GBDT, AdaBoost, and MLP) were used to train Group A and Group B data set respectively and 10-folder cross validation method was used to validate the models. Results: The results revealed that the AdaBoost model of Group A obtained the best performance, while the GBDT model in Group B was proved to be the best model. Both models had been proved to provide accurate prediction of transfer outcome. Conclusion: Our research provided a new approach for targeted and personalized treatment of RIF patients to help them achieve efficient and reliable pregnancy. And an AI-assisted decision-making system will be designed to help clinicians and RIF patients develop personalized transfer strategies, which not only guarantees efficient and reliable pregnancy, but also avoids the risk of multiple pregnancy as much as possible.

Command filter based adaptive control of asymmetric output-constrained switched stochastic nonlinear systems.

In this paper, adaptive tracking control problem is investigated for a class of switched stochastic nonlinear systems with an asymmetric output constraint. By introducing a nonlinear mapping (NM), the asymmetric output-constrained switched stochastic system is first transformed into a new system without any constraint, which achieves the equivalent control objective. The command filter technique is employed to handle the "explosion of complexity" in traditional backstepping design, and neural networks (NNs) are directly utilized to cope with the completely unknown nonlinear functions and stochastic disturbances existing in systems. At last, on the basis of stochastic Lyapunov function method, an adaptive neural controller is developed for the considered system. It is shown that the designed adaptive controller can guarantee that all the signals remain semi-globally uniformly ultimately bounded (SGUUB), while the output constraint is satisfied and the desired signal can be tracked with a small domain of the origin. Simulation results are offered to illustrate the feasibility of the newly designed control scheme.

Polariton nanophotonics using phase-change materials.

Polaritons formed by the coupling of light and material excitations enable light-matter interactions at the nanoscale beyond what is currently possible with conventional optics. However, novel techniques are required to control the propagation of polaritons at the nanoscale and to implement the first practical devices. Here we report the experimental realization of polariton refractive and meta-optics in the mid-infrared by exploiting the properties of low-loss phonon polaritons in isotopically pure hexagonal boron nitride interacting with the surrounding dielectric environment comprising the low-loss phase change material Ge3Sb2Te6. We demonstrate rewritable waveguides, refractive optical elements such as lenses, prisms, and metalenses, which allow for polariton wavefront engineering and sub-wavelength focusing. This method will enable the realization of programmable miniaturized integrated optoelectronic devices and on-demand biosensors based on high quality phonon resonators.

Beam switching and bifocal zoom lensing using active plasmonic metasurfaces.

Compact nanophotonic elements exhibiting adaptable properties are essential components for the miniaturization of powerful optical technologies such as adaptive optics and spatial light modulators. While the larger counterparts typically rely on mechanical actuation, this can be undesirable in some cases on a microscopic scale due to inherent space restrictions. Here, we present a novel design concept for highly integrated active optical components that employs a combination of resonant plasmonic metasurfaces and the phase-change material Ge3Sb2Te6. In particular, we demonstrate beam switching and bifocal lensing, thus, paving the way for a plethora of active optical elements employing plasmonic metasurfaces, which follow the same design principles.

A Switchable Mid-Infrared Plasmonic Perfect Absorber with Multispectral Thermal Imaging Capability.

A switchable perfect absorber with multispectral thermal imaging capability is presented. Aluminum nanoantenna arrays above a germanium antimony telluride (GST) spacer layer and aluminum mirror provide efficient wavelength-tunable absorption in the mid-infrared. Utilizing the amorphous-to-crystalline phase transition in GST, this device offers switchable absorption with strong reflectance contrast at resonance and large phase-change-induced spectral shifts.

Quantitative angle-resolved small-spot reflectance measurements on plasmonic perfect absorbers: impedance matching and disorder effects.

Plasmonic devices with absorbance close to unity have emerged as essential building blocks for a multitude of technological applications ranging from trace gas detection to infrared imaging. A crucial requirement for such elements is the angle independence of the absorptive performance. In this work, we develop theoretically and verify experimentally a quantitative model for the angular behavior of plasmonic perfect absorber structures based on an optical impedance matching picture. To achieve this, we utilize a simple and elegant k-space measurement technique to record quantitative angle-resolved reflectance measurements on various perfect absorber structures. Particularly, this method allows quantitative reflectance measurements on samples where only small areas have been nanostructured, for example, by electron-beam lithography. Combining these results with extensive numerical modeling, we find that matching of both the real and imaginary parts of the optical impedance is crucial to obtain perfect absorption over a large angular range. Furthermore, we successfully apply our model to the angular dispersion of perfect absorber geometries with disordered plasmonic elements as a favorable alternative to current array-based designs.

Large-area 3D chiral plasmonic structures.

We manufacture large-area plasmonic structures featuring 3-dimensional chirality by colloidal nanohole lithography. By varying the polar rotating speed of the samples during gold evaporation, we can fabricate spiral-type ramp nanostructures. The optical properties show chiroptical resonances in the 100 to 400 THz frequency region (750 to 3000 nm), with circular dichroism values of up to 13%. Our method offers a simple low-cost manufacturing method of cm(2)-sized chiral plasmonic templates for chiroptical applications such as stereochemical enantiomer sensors.