Large enhancement of the effective second-order nonlinearity in graphene metasurfaces

Using a powerful homogenization technique, one- and two-dimensional graphene metasurfaces are homogenized both at the fundamental frequency (FF) and second harmonic (SH). In both cases, there is excellent agreement between the predictions of the homogenization method and those based on rigorous numerical solutions of Maxwell equations. The homogenization technique is then employed to demonstrate that, owing to a double-resonant plasmon excitation mechanism that leads to strong, simultaneous field enhancement at the FF and SH, the effective second-order susceptibility of graphene metasurfaces can be enhanced by more than three orders of magnitude as compared to the intrinsic second-order susceptibility of a graphene sheet placed on the same substrate. In addition, we explore the implications of our results on the development of new active nanodevices that incorporate nanopatterned graphene structures.Comment: 11 pages, 12 figure

Numerical and Monte Carlo Bethe ansatz method: 1D Heisenberg model

In this paper we present two new numerical methods for studying thermodynamic quantities of integrable models. As an example of the effectiveness of these two approaches, results from numerical solutions of all sets of Bethe ansatz equations, for small Heisenberg chains, and Monte Carlo simulations in quasi-momentum space, for a relatively larger chains, are presented. Our results agree with those obtained by thermodynamics Bethe ansatz (TBA) and Quantum Transfer Matrix (QTM).Comment: 8 pages, 6 figure

A pQCD-based description of heavy and light flavor jet quenching

We present a successful description of the medium modification of light and heavy flavor jets within a perturbative QCD (pQCD) based approach. Only the couplings involving hard partons are assumed to be weak. The effect of the medium on a hard parton, per unit time, is encoded in terms of three non-perturbative, related transport coefficients which describe the transverse momentum squared gained, the elastic energy loss and diffusion in elastic energy transfer. A fit of the centrality dependence of the suppression and the azimuthal anisotropy of leading hadrons tends to favor somewhat larger transport coefficients for heavy quarks. Imposing additional constraints based on leading order (LO) Hard Thermal Loop (HTL) effective theory, leads to a worsening of the fit.Comment: v2, 4 pages, 3 figure

X-Ray Diffuse Scattering Study on Ionic-Pair Displacement Correlations in Relaxor Lead Magnesium Niobate

Ionic-pair equal-time displacement correlations in relaxor lead magnesium niobate, $Pb(Mg_{1/3}Nb_{2/3})O_{3}$, have been investigated at room temperature in terms of an x-ray diffuse scattering technique. Functions of the distinct correlations have been determined quantitatively. The results show the significantly strong rhombohedral-polar correlations regarding Pb-O, Mg/Nb-O, and O-O' pairs. Their spatial distribution forms an ellipse or a sphere with the radii of 30-80$\AA$. This observation of local structure in the system proves precursory presence of the polar microregions in the paraelectric state which leads to the dielectric dispersion.Comment: 11 pages, 3 figure

Generation and control of Greenberger-Horne-Zeilinger entanglement in superconducting circuits

Going beyond the entanglement of microscopic objects (such as photons, spins, and ions), here we propose an efficient approach to produce and control the quantum entanglement of three macroscopic coupled superconducting qubits. By conditionally rotating, one by one, selected Josephson charge qubits, we show that their Greenberger-Horne-Zeilinger (GHZ) entangled states can be deterministically generated. The existence of GHZ correlations between these qubits could be experimentally demonstrated by effective single-qubit operations followed by high-fidelity single-shot readouts. The possibility of using the prepared GHZ correlations to test the macroscopic conflict between the noncommutativity of quantum mechanics and the commutativity of classical physics is also discussed.Comment: 4 Pages with 1 figure. to appear in Physical Review Letter

Decompression-metamorphism of Dabie Complex and rapid tectonic-uplift from deep level of the orogenic belt

The Dabie Complex can be divided into two metamorphic facies belts, granulite facies and amphibolite facies. Growth zoning in the inner segments of garnets is well preserved in the granulite belt. By contrast, garnets in the amphibolite belt have no composition variations in the inner segments, but show growth zoning in the outer segments. This may imply different incipient metamorphic history for the two metamorphic belts. However, both reaction textures and composition trends that reflect the decompression process are commonly in both of the two belts. Pressure decreased about 0.70 and 0.85 GPa for the granulite and the amphibolite belts, respectively, estimated from mineral thermobarometers. The metamorphic P-T paths are characteristic of collision and subduction, implying that the Dabie Complex underwent rapid subsidence and rapid tectonic uplift. Uplift of the ultrahigh pressure eclogites in the region could also be related to the process.published_or_final_versio

Developing a Generic Predictive Computational Model using Semantic data Pre-Processing with Machine Learning Techniques and its application for Stock Market Prediction Purposes

In this paper, we present a Generic Predictive Computational Model (GPCM) and apply it by building a Use Case for the FTSE 100 index forecasting. This involves the mining of heterogeneous data based on semantic methods (ontology), graph-based methods (knowledge graphs, graph databases) and advanced Machine Learning methods. The main focus of our research is data pre-processing aimed at a more efficient selection of input features. The GPCM model pipeline’s cycles involve the propagation of the (initially raw) data to the Graph Database structured by an ontology and regular updates of the features’ weights in the Graph Database by the feedback loop from the Machine Learning Engine. The Graph Database queries output the most valuable features that, in turn, serve as the input for the Machine Learning-based prediction. The end-product of this process is fed back to the Graph Database to update the weights. We report on practical experiments evaluating the effectiveness of the GPCM application in forecasting the FTSE 100 index. The underlying dataset contains multiple parameters related to predicting time-series data, where Long Short-Term Memory (LSTM) is known to be one of the most efficient machine learning methods. The most challenging task here has been to overcome the known restrictions of LSTM, which is capable of analysing one input parameter only. We solved this problem by combining several parallel LSTMs, a Concatenation unit, which merges the LSTMs’ outputs (into a time-series matrix), and a Linear Regression Unit, which produces the final resul