Numerical Study of a near-Zero-Index Acoustic Metamaterial

This Letter studies a two-dimensional, membrane-based acoustic metamaterial with a near-zero refractive index. It yields a frequency-dependent effective density that is near-zero at a narrow frequency band centered around its first resonant frequency. This effective density results in its near-zero refractive index. Numerical simulations are shown which demonstrate that the phase in this metamaterial undergoes small changes, and the metamaterial functions as an angular filter such that only a wave with a near-zero incident angle can transmit. Its ability to tailor acoustic phase pattern is also discussed in this Letter

Global and partitioned reconstructions of undirected complex networks

It is a significant challenge to predict the network topology from a small amount of dynamical observations. Different from the usual framework of the node-based reconstruction, two optimization approaches (i.e., the global and partitioned reconstructions) are proposed to infer the structure of undirected networks from dynamics. These approaches are applied to evolutionary games occurring on both homogeneous and heterogeneous networks via compressed sensing, which can more efficiently achieve higher reconstruction accuracy with relatively small amounts of data. Our approaches provide different perspectives on effectively reconstructing complex networks.Comment: 6 pages, 2 figures, 1 table; revised version; added numerical results of the PR in Table 1 and expanded Section 4; added 7 reference

A new model for artificial seismic wave synthesis

A new model is proposed based on wavelet theory and genetic algorithms (GAs) in order to improve precision of artificial seismic wave. This model was mainly divided into three parts. Firstly, Mallat method was used to decompose power spectral density function with wavelet base. Then the initial artificial seismic wave was synthesized based on wavelet theory. Thirdly, the iteration processes of artificial seismic wave synthesis were optimized by genetic algorithms. Two numerical examples were given. The first numerical example mainly focuses on the analysis for the initial artificial seismic wave synthesis based on wavelet theory. And the second example mainly focuses on the analysis for the iterative process of artificial seismic wave synthesis based on genetic algorithms. Compared with the conventional method of cosine superposition, this model has smaller error between the calculated acceleration response spectrum and the target response spectrum and can be applied in engineering

Benchmarking competitiveness of cargo airports

Master'sMASTER OF ENGINEERIN

TriPINet: Tripartite Progressive Integration Network for Image Manipulation Localization

Image manipulation localization aims at distinguishing forged regions from the whole test image. Although many outstanding prior arts have been proposed for this task, there are still two issues that need to be further studied: 1) how to fuse diverse types of features with forgery clues; 2) how to progressively integrate multistage features for better localization performance. In this paper, we propose a tripartite progressive integration network (TriPINet) for end-to-end image manipulation localization. First, we extract both visual perception information, e.g., RGB input images, and visual imperceptible features, e.g., frequency and noise traces for forensic feature learning. Second, we develop a guided cross-modality dual-attention (gCMDA) module to fuse different types of forged clues. Third, we design a set of progressive integration squeeze-and-excitation (PI-SE) modules to improve localization performance by appropriately incorporating multiscale features in the decoder. Extensive experiments are conducted to compare our method with state-of-the-art image forensics approaches. The proposed TriPINet obtains competitive results on several benchmark datasets

Hamiltonian function selection principle for generalized Hamiltonian modelling

AbstractHamiltonian function is different, the internal structure relation given by generalized Hamiltonian model is different, and thus Hamiltonian function is core issue for generalized Hamiltonian modeling. In this paper, three principles of selecting Hamiltonian function are proposed, interface principle, energy principle and stability principle. For the class of system with single input and single output, applied methods of three principles are given. At last, the Hamiltonian modelling for nonlinear hydro turbine is taken as case to introduce its application

MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation

The rapid spread of the new pandemic, i.e., COVID-19, has severely threatened global health. Deep-learning-based computer-aided screening, e.g., COVID-19 infected CT area segmentation, has attracted much attention. However, the publicly available COVID-19 training data are limited, easily causing overfitting for traditional deep learning methods that are usually data-hungry with millions of parameters. On the other hand, fast training/testing and low computational cost are also necessary for quick deployment and development of COVID-19 screening systems, but traditional deep learning methods are usually computationally intensive. To address the above problems, we propose MiniSeg, a lightweight deep learning model for efficient COVID-19 segmentation. Compared with traditional segmentation methods, MiniSeg has several significant strengths: i) it only has 83K parameters and is thus not easy to overfit; ii) it has high computational efficiency and is thus convenient for practical deployment; iii) it can be fast retrained by other users using their private COVID-19 data for further improving performance. In addition, we build a comprehensive COVID-19 segmentation benchmark for comparing MiniSeg to traditional methods

Photometric Objects Around Cosmic Webs (PAC) Delineated in a Spectroscopic Survey. IV. High Precision Constraints on the Evolution of Stellar-Halo Mass Relation at Redshift z<0.7z<0.7

Taking advantage of the Photometric objects Around Cosmic webs (PAC) method developed in Paper I, we measure the excess surface density $\bar{n}_2w_{{\rm{p}}}$ of photometric objects around spectroscopic objects down to stellar mass $10^{8.0}M_{\odot}$, $10^{9.2}M_{\odot}$ and $10^{9.8}M_{\odot}$ in the redshift ranges of $z_s<0.2$, $0.2<z_s<0.4$ and $0.5<z_s<0.7$ respectively, using the data from the DESI Legacy Imaging Surveys and the spectroscopic samples of Slogan Digital Sky Survey (i.e. Main, LOWZ and CMASS samples). We model the measured $\bar{n}_2w_{{\rm{p}}}$ in N-body simulation using abundance matching method and constrain the stellar-halo mass relations (SHMR) in the three redshift ranges to percent level. With the accurate modeling, we demonstrate that the stellar mass scatter for given halo mass is nearly a constant, and that the empirical form of Behroozi et al describes the SHMR better than the double power law form at low mass. Our SHMR accurately captures the downsizing of massive galaxies since $z_s=0.7$, while it also indicates that small galaxies are still growing faster than their host halos. The galaxy stellar mass functions (GSMF) from our modeling are in perfect agreement with the {\it model-independent} measurements in Paper III, though the current work extends the GSMF to a much smaller stellar mass. Based on the GSMF and SHMR, we derive the stellar mass completeness and halo occupation distributions for the LOWZ and CMASS samples, which are useful for correctly interpreting their cosmological measurements such as galaxy-galaxy lensing and redshift space distortion.Comment: 18 + 9 (appendix) pages, 12 + 7 (appendix) figures. Main results in Figure 6-9. Submitted to ApJ. arXiv admin note: text overlap with arXiv:2207.1242