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In this paper, we design a tunable phase-modulated metasurface composed of periodically distributed piezoelectric patches with resonant-type shunt circuits. The electroelastic metasurface can control the wavefront of the lowest antisymmetric mode Lamb wave (A₀mode) in a small footprint due to its subwavelength features. The fully coupled electromechanical model is established to study the transmission characteristics of the metasurface unit and validated through numerical and experimental studies. Based on the analysis of the metasurface unit, we first explore the performance of electroelastic metasurface with single-resonant shunts and then extend its capability with multi-resonant shunts. By only tuning the electric loads in the shunt circuits, we utilize the proposed metasurface to accomplish wave deflection and wave focusing ofA₀mode Lamb waves at different angles and focal points, respectively. Numerical simulations show that the metasurface with single-resonant shunts can deflect the wavefront of 5 kHz and 6 kHz flexural waves by desired angles with less than$2\mathrm{\%}$deviation. In addition, it can be tuned to achieve nearly three times displacement amplification at the designed focal point for a wide range of angles from$-{75}^{\circ }$to 75^∘. Furthermore, with multi-resonant shunts, the piezoelectric-based metasurface can accomplish anomalous wave control over flexural waves atmore »multiple frequencies (i.e. simultaneously at 5 kHz and 10 kHz), developing new potentials toward a broad range of engineering applications such as demultiplexing various frequency components or guiding and focusing them at different positions.

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We propose and investigate an additive regression model for symmetric positive-definite matrix-valued responses and multiple scalar predictors. The model exploits the Abelian group structure inherited from either of the log-Cholesky and log-Euclidean frameworks for symmetric positive-definite matrices and naturally extends to general Abelian Lie groups. The proposed additive model is shown to connect to an additive model on a tangent space. This connection not only entails an efficient algorithm to estimate the component functions, but also allows one to generalize the proposed additive model to general Riemannian manifolds. Optimal asymptotic convergence rates and normality of the estimated component functions are established, and numerical studies show that the proposed model enjoys good numerical performance, and is not subject to the curse of dimensionality when there are multiple predictors. The practical merits of the proposed model are demonstrated through an analysis of brain diffusion tensor imaging data.

The ingenious approach of Chatterjee (2021) to estimate a measure of dependence first proposed by Dette et al. (2013) based on simple rank statistics has quickly caught attention. This measure of dependence has the appealing property of being between 0 and 1, and being 0 or 1 if and only if the corresponding pair of random variables is independent or one is a measurable function of the other almost surely. However, more recent studies (Cao & Bickel 2020; Shi et al. 2022b) showed that independence tests based on Chatterjee’s rank correlation are unfortunately rate inefficient against various local alternatives and they call for variants. We answer this call by proposing an improvement to Chatterjee’s rank correlation that still consistently estimates the same dependence measure, but provably achieves near-parametric efficiency in testing against Gaussian rotation alternatives. This is possible by incorporating many right nearest neighbours in constructing the correlation coefficients. We thus overcome the ‘ only one disadvantage’ of Chatterjee’s rank correlation (Chatterjee, 2021, § 7).

Abstract Galaxy clusters identified via the Sunyaev-Zel’dovich effect (SZ) are a key ingredient in multi-wavelength cluster cosmology. We present and compare three methods of cluster identification: the standard Matched Filter (MF) method in SZ cluster finding, a Convolutional Neural Networks (CNN), and a ‘combined’ identifier. We apply the methods to simulated millimeter maps for several observing frequencies for a survey similar to SPT-3G, the third-generation camera for the South Pole Telescope. The MF requires image pre-processing to remove point sources and a model for the noise, while the CNN requires very little pre-processing of images. Additionally, the CNN requires tuning of hyperparameters in the model and takes cutout images of the sky as input, identifying the cutout as cluster-containing or not. We compare differences in purity and completeness. The MF signal-to-noise ratio depends on both mass and redshift. Our CNN, trained for a given mass threshold, captures a different set of clusters than the MF, some with SNR below the MF detection threshold. However, the CNN tends to mis-classify cutouts whose clusters are located near the edge of the cutout, which can be mitigated with staggered cutouts. We leverage the complementarity of the two methods, combining the scores from eachmore »method for identification. The purity and completeness are both 0.61 for MF, and 0.59 and 0.61 for CNN. The combined method yields 0.60 and 0.77, a significant increase for completeness with a modest decrease in purity. We advocate for combined methods that increase the confidence of many low signal-to-noise clusters.« less

Simultaneous human activities, such as the Super Bowl game, would cause certain impacts on frequency fluctuations in power systems. With the help of FNET/GridEye measurements, this paper aims to give comprehensive analyses on the frequency fluctuations during Super Bowl LIV held on Feb. 2, 2020, so as to better understand several phenomena caused by simultaneous activities which will help system operations and controls. First, recent developments of the FNET/GridEye are briefly introduced. Second, the frequency fluctuations of the Eastern Interconnection (EI), western electricity coordinating council (WECC), and electric reliability council of Texas (ERCOT) power systems during Super Bowl LIV are analyzed. Third, frequency fluctuations of Super Bowl Sunday and ordinary Sundays in 2020 are compared. Finally, the differences of frequency fluctuations among different years during the Super Bowl and their change trends are also given. Furthermore, several possible explanations, including the simultaneity of electricity consumption at the beginning of commercial breaks and the halftime show, the increasing usage of the Internet, and the increasing size of TV screens, are illustrated in detail in this paper.