2,469 research outputs found
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
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
Taking advantage of the Photometric objects Around Cosmic webs (PAC) method
developed in Paper I, we measure the excess surface density
of photometric objects around spectroscopic objects
down to stellar mass , and
in the redshift ranges of , and
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 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 , 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
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