Metamaterial Broadband Angular Selectivity

We demonstrate how broadband angular selectivity can be achieved with stacks of one-dimensionally periodic photonic crystals, each consisting of alternating isotropic layers and effective anisotropic layers, where each effective anisotropic layer is constructed from a multilayered metamaterial. We show that by simply changing the structure of the metamaterials, the selective angle can be tuned to a broad range of angles; and, by increasing the number of stacks, the angular transmission window can be made as narrow as desired. As a proof of principle, we realize the idea experimentally in the microwave regime. The angular selectivity and tunability we report here can have various applications such as in directional control of electromagnetic emitters and detectors.Comment: 5 pages, 5 figure

Experimental observation of Weyl points

In 1929, Hermann Weyl derived the massless solutions from the Dirac equation - the relativistic wave equation for electrons. Neutrinos were thought, for decades, to be Weyl fermions until the discovery of the neutrino mass. Moreover, it has been suggested that low energy excitations in condensed matter can be the solutions to the Weyl Hamiltonian. Recently, photons have also been proposed to emerge as Weyl particles inside photonic crystals. In all cases, two linear dispersion bands in the three-dimensional (3D) momentum space intersect at a single degenerate point - the Weyl point. Remarkably, these Weyl points are monopoles of Berry flux with topological charges defined by the Chern numbers. These topological invariants enable materials containing Weyl points to exhibit a wide variety of novel phenomena including surface Fermi arcs, chiral anomaly, negative magnetoresistance, nonlocal transport, quantum anomalous Hall effect, unconventional superconductivity[15] and others [16, 17]. Nevertheless, Weyl points are yet to be experimentally observed in nature. In this work, we report on precisely such an observation in an inversion-breaking 3D double-gyroid photonic crystal without breaking time-reversal symmetry.Comment: 4 pages, 3 figure

Weak Signal Detection Based on Adaptive Cascaded Bistable Stochastic Resonance System

AbstractStochastic resonance system is an effective method to extract weak signal, however, system output is directly influenced by system parameters. Aiming to this, a method about weak periodic signal extraction was developed based on adaptive stochastic resonance. Firstly cascaded stochastic resonance system was established in order to achieve better low-pass filtering effect. And then, variance of zero point distance was chosen as measurement index of cascade system. It's able to overcome the shortage that traditional adaptive stochastic resonance system needs to know the signal frequency beforehand. Also, it could obtain optimum system parameters adaptively. Basing on these parameters, input signal will be handled, and optimum output could be obtained. Furthermore, different periodic signal have been recognized, and finally the validity of the method is verified through simulation experiments

Negative Group Velocity in the Absence of Absorption Resonance

Scientific community has well recognized that a Lorentzian medium exhibits anomalous dispersion behavior in its resonance absorption region. To satisfy the Krammers-Kronig relation, such an anomalous region has to be accompanied with significant loss, and thus, experimental observations of negative group velocity in this region generally require a gain-assisted approach. In this letter, we demonstrate that the negative group velocity can also be observed in the absence of absorption resonance. We show that the k-surface of a passive uniaxial Lorentzian medium undergoes a distortion near the plasma frequency. This process yields an anomalous dispersion bandwidth that is far away from the absorption resonance region, and enables the observation of negative group velocity at the plasma frequency band. Introducing anomalous dispersion in a well-controlled manner would greatly benefit the research of ultrafast photonics and find potential applications in optical delay lines, optical data storage and devices for quantum information processing

DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release

Machine learning models are known to memorize private data to reduce their training loss, which can be inadvertently exploited by privacy attacks such as model inversion and membership inference. To protect against these attacks, differential privacy (DP) has become the de facto standard for privacy-preserving machine learning, particularly those popular training algorithms using stochastic gradient descent, such as DPSGD. Nonetheless, DPSGD still suffers from severe utility loss due to its slow convergence. This is partially caused by the random sampling, which brings bias and variance to the gradient, and partially by the Gaussian noise, which leads to fluctuation of gradient updates. Our key idea to address these issues is to apply selective updates to the model training, while discarding those useless or even harmful updates. Motivated by this, this paper proposes DPSUR, a Differentially Private training framework based on Selective Updates and Release, where the gradient from each iteration is evaluated based on a validation test, and only those updates leading to convergence are applied to the model. As such, DPSUR ensures the training in the right direction and thus can achieve faster convergence than DPSGD. The main challenges lie in two aspects -- privacy concerns arising from gradient evaluation, and gradient selection strategy for model update. To address the challenges, DPSUR introduces a clipping strategy for update randomization and a threshold mechanism for gradient selection. Experiments conducted on MNIST, FMNIST, CIFAR-10, and IMDB datasets show that DPSUR significantly outperforms previous works in terms of convergence speed and model utility.Comment: This paper has been accepted by VLDB 202

1-Allyl-3-amino-1H-pyrazole-4-carboxylic acid

The title compound, C7H9N3O2, was prepared by alkaline hydrolysis of ethyl 1-allyl-3-amino-1H-pyrazole-4-carboxyl­ate. The crystal structure is stabilized by three types of inter­molecular hydrogen bond (N—H⋯O, N—H⋯N and O—H⋯N)