arXiv subjects

Xu Wang

Publications and source records attributed to Xu Wang.

At least 415 records · Page 23Linked to original sources

arXiv2020

On the mechanical beta relaxation in glass and its relation to the double-peak phenomenon in impulse excited vibration at high temperatures

A viscoelastic model is established to reveal the relation between alpha-beta relaxation of glass and the double-peak phenomenon in the experiments of impulse excited vibration. In the modelling, the normal mode analysis (NMA) of potential energy landscape (PEL) picture is employed to describe mechanical alpha and beta relaxations in a glassy material. The model indicates that a small beta relaxation can lead to an apparent double-peak phenomenon resulted from the free vibration of a glass beam when the frequency of beta relaxation peak is close to the natural frequency of specimen. The theoretical prediction is validated by the acoustic spectrum of a fluorosilicate glass beam excited by a mid-span impulse. Furthermore, the experimental results indicate a negative temperature-dependence of the frequency of beta relaxation in the fluorosilicate glass S-FSL5 which can be explained based on the physical picture of fragmented oxide-network patches in liquid-like regions.

cond-mat.dis-nn↗

arXiv2020

Price Competition with LTE-U and WiFi

LTE-U is an extension of the Long Term Evolution (LTE) standard for operation in unlicensed spectrum. LTE-U differs from WiFi, the predominant technology used in unlicensed spectrum in that it utilizes a duty cycle mode for accessing the spectrum and allows for a more seamless integration with LTE deployments in licensed spectrum. There have been a number of technical studies on the co-existence of LTE-U and WiFi in unlicensed spectrum In this paper, we instead investigate the impact of such a technology from an economic perspective. We consider a model in which an incumbent service provider (SP) deploys a duty cycle-based technology like LTE-U in an unlicensed band along with operating in a licensed band and competes with one or more entrants that only operate in the unlicensed band using a different technology like WiFi. We characterize the impact of a technology like LTE-U on the market outcome and show that the welfare impacts of this technology are subtle, depending in part on the amount of unlicensed spectrum and number of entrants. The difference in spectral efficiency between LTE and WiFi also plays a role in the competition among SPs. Finally, we investigate the impact of the duty cycle and the portion of unlicensed spectrum used by the technology.

cs.GT↗

arXiv2019

Inverse random source scattering for the Helmholtz equation with attenuation

In this paper, a new model is proposed for the inverse random source scattering problem of the Helmholtz equation with attenuation. The source is assumed to be driven by a fractional Gaussian field whose covariance is represented by a classical pseudo-differential operator. The work contains three contributions. First, the connection is established between fractional Gaussian fields and rough sources characterized by their principal symbols. Second, the direct source scattering problem is shown to be well-posed in the distribution sense. Third, we demonstrate that the micro-correlation strength of the random source can be uniquely determined by the passive measurements of the wave field in a set which is disjoint with the support of the strength function. The analysis relies on careful studies on the Green function and Fourier integrals for the Helmholtz equation.

math.AP↗

arXiv2019

Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations

In this paper, we propose one novel model for point cloud semantic segmentation, which exploits both the local and global structures within the point cloud based on the contextual point representations. Specifically, we enrich each point representation by performing one novel gated fusion on the point itself and its contextual points. Afterwards, based on the enriched representation, we propose one novel graph pointnet module, relying on the graph attention block to dynamically compose and update each point representation within the local point cloud structure. Finally, we resort to the spatial-wise and channel-wise attention strategies to exploit the point cloud global structure and thereby yield the resulting semantic label for each point. Extensive results on the public point cloud databases, namely the S3DIS and ScanNet datasets, demonstrate the effectiveness of our proposed model, outperforming the state-of-the-art approaches. Our code for this paper is available at https://github.com/fly519/ELGS.

cs.CV↗

arXiv2019

Bergman kernel and oscillation theory of plurisubharmonic functions

Based on Harnack's inequality and convex analysis we show that each plurisubharmonic function is locally BUO (bounded upper oscillation) with respect to polydiscs of finite type but not for arbitrary polydiscs. We also show that each function in the Lelong class is globally BUO with respect to all polydiscs. A dimension-free BUO estimate is obtained for the logarithm of the modulus of a complex polynomial. As an application we obtain an approximation formula for the Bergman kernel that preserves all directional Lelong numbers. For smooth plurisubharmonic functions we derive a new asymptotic identity for the Bergman kernel from Berndtsson's complex Brunn--Minkowski theory, which also yields a slightly better version of the sharp Ohsawa--Takegoshi extension theorem in some special cases.

math.CV↗

arXiv2019

Joint Segmentation and Landmark Localization of Fetal Femur in Ultrasound Volumes

Volumetric ultrasound has great potentials in promoting prenatal examinations. Automated solutions are highly desired to efficiently and effectively analyze the massive volumes. Segmentation and landmark localization are two key techniques in making the quantitative evaluation of prenatal ultrasound volumes available in clinic. However, both tasks are non-trivial when considering the poor image quality, boundary ambiguity and anatomical variations in volumetric ultrasound. In this paper, we propose an effective framework for simultaneous segmentation and landmark localization in prenatal ultrasound volumes. The proposed framework has two branches where informative cues of segmentation and landmark localization can be propagated bidirectionally to benefit both tasks. As landmark localization tends to suffer from false positives, we propose a distance based loss to suppress the noise and thus enhance the localization map and in turn the segmentation. Finally, we further leverage an adversarial module to emphasize the correspondence between segmentation and landmark localization. Extensively validated on a volumetric ultrasound dataset of fetal femur, our proposed framework proves to be a promising solution to facilitate the interpretation of prenatal ultrasound volumes.

eess.IV↗

arXiv2019

Convex expansion for finite distributive lattices with applications

The concept of cutting is first explicitly introduced. By the concept, a convex expansion for finite distributive lattices is considered. Thus, a more general method for drawing the Hasse diagram is given, and the rank generating function of a finite distributive lattice is obtained. In addition, we have several enumerative properties on finite distributive lattices and verify the generalized Euler formula for polyhedrons.

math.CO↗

arXiv2019

Algebraic fiber spaces and curvature of higher direct images

In this article we are interested in the differential geometric properties of certain higher direct images of exterior powers of the sheaf of relative differentials twisted with a line bundle. We obtain explicit curvature formulas, especially in case where the said line bundle satisfies a natural curvature assumption. Several applications are obtained, including a proof of a result by Viehweg-Zuo in the context of a canonically polarized family of maximal variation.

math.DG↗

arXiv2019

An inverse random source problem for the time fractional diffusion equation driven by a fractional Brownian motion

This paper is concerned with the mathematical analysis of the inverse random source problem for the time fractional diffusion equation, where the source is assumed to be driven by a fractional Brownian motion. Given the random source, the direct problem is to study the stochastic time fractional diffusion equation. The inverse problem is to determine the statistical properties of the source from the expectation and variance of the final time data. For the direct problem, we show that it is well-posed and has a unique mild solution under a certain condition. For the inverse problem, the uniqueness is proved and the instability is characterized. The major ingredients of the analysis are based on the properties of the Mittag--Leffler function and the stochastic integrals associated with the fractional Brownian motion.

math.AP↗

arXiv2019

A Survey of Automatic Generation of Source Code Comments: Algorithms and Techniques

As an integral part of source code files, code comments help improve program readability and comprehension. However, developers sometimes do not comment on their program code adequately due to the incurred extra efforts, lack of relevant knowledge, unawareness of the importance of code commenting or some other factors. As a result, code comments can be inadequate, absent or even mismatched with source code, which affects the understanding, reusing and the maintenance of software. To solve these problems of code comments, researchers have been concerned with generating code comments automatically. In this work, we aim at conducting a survey of automatic code commenting researches. First, we generally analyze the challenges and research framework of automatic generation of program comments. Second, we present the classification of representative algorithms, the design principles, strengths and weaknesses of each category of algorithms. Meanwhile, we also provide an overview of the quality assessment of the generated comments. Finally, we summarize some future directions for advancing the techniques of automatic generation of code comments and the quality assessment of comments.

cs.SE↗

arXiv2019

Optimal Strong Convergence Rate of a Backward Euler Type Scheme for the Cox--Ingersoll--Ross Model Driven by Fractional Brownian Motion

In this paper, we investigate the optimal strong convergence rate of numerical approximations for the Cox--Ingersoll--Ross model driven by fractional Brownian motion with Hurst parameter $H\in(1/2,1)$. To deal with the difficulties caused by the unbounded diffusion coefficient, we study an auxiliary equation based on Lamperti transformation. By means of Malliavin calculus, we prove that the backward Euler scheme applied to this auxiliary equation ensures the positivity of the numerical solution, and is of strong order one. Furthermore, a numerical approximation for the original model is obtained and converges with the same order.

math.NA↗

arXiv2019

High Sensitivity Snapshot Spectrometer Based on Deep Network Unmixing

In this paper, we present a convolution neural network based method to recover the light intensity distribution from the overlapped dispersive spectra instead of adding an extra light path to capture it directly for the first time. Then, we construct a single-path sub-Hadamard snapshot spectrometer based on our previous dual-path snapshot spectrometer. In the proposed single-path spectrometer, we use the reconstructed light intensity as the original light intensity and recover high signal-to-noise ratio spectra successfully. Compared with dual-path snapshot spectrometer, the network based single-path spectrometer has a more compact structure and maintains snapshot and high sensitivity. Abundant simulated and experimental results have demonstrated that the proposed method can obtain a better reconstructed signal-to-noise ratio spectrum than the dual-path sub-Hadamard spectrometer because of its higher light throughput.

eess.IV↗

arXiv2019

DeepSquare: Boosting the Learning Power of Deep Convolutional Neural Networks with Elementwise Square Operators

Modern neural network modules which can significantly enhance the learning power usually add too much computational complexity to the original neural networks. In this paper, we pursue very efficient neural network modules which can significantly boost the learning power of deep convolutional neural networks with negligible extra computational cost. We first present both theoretically and experimentally that elementwise square operator has a potential to enhance the learning power of neural networks. Then, we design four types of lightweight modules with elementwise square operators, named as Square-Pooling, Square-Softmin, Square-Excitation, and Square-Encoding. We add our four lightweight modules to Resnet18, Resnet50, and ShuffleNetV2 for better performance in the experiment on ImageNet 2012 dataset. The experimental results show that our modules can bring significant accuracy improvements to the base convolutional neural network models. The performance of our lightweight modules is even comparable to many complicated modules such as bilinear pooling, Squeeze-and-Excitation, and Gather-Excite. Our highly efficient modules are particularly suitable for mobile models. For example, when equipped with a single Square-Pooling module, the top-1 classification accuracy of ShuffleNetV2-0.5x on ImageNet 2012 is absolutely improved by 1.45% with no additional parameters and negligible inference time overhead.

cs.CV↗

arXiv2019

Some enumerative properties of a class of Fibonacci-like cubes

A filter lattice is a distributive lattice formed by all filters of a poset in the anti-inclusion order. We study the combinatorial properties of the Hasse diagrams of filter lattices of certain posets, so called Fibonacci-like cubes, in this paper. Several enumerative polynomials, e.g.\ rank generating function, cube polynomials and degree sequence polynomials are obtained. Some of these results relate to Fibonacci sequence and Padovan sequence.

math.CO↗

arXiv2019

Optimal cube factors of Fibonacci and matchable Lucas cubes

The optimal cube factor of a graph, a special kind of component factor, is first introduced. Furthermore, the optimal cube factors of Fibonacci and matchable Lucas cubes are studied; and some results on the Padovan sequence and binomial coefficients are obtained.

math.CO↗

arXiv2019

Extremely asymmetrical acoustic metasurface mirror at the exceptional point

Previous research has attempted to minimize the influence of loss in reflection- and transmission-type acoustic metasurfaces. This letter shows that, by treating the acoustic metasurface as a non-Hermitian system and by harnessing loss, unconventional wave behaviors that do not exist in lossless metasurfaces can be uncovered. Specifically, we theoretically and experimentally demonstrate a non-Hermitian acoustic metasurface mirror featuring extremely asymmetrical reflection at the exception point. As an example, the metasurface mirror is designed to have high-efficiency retro-reflection when the wave incidents from one side and complete absorption when the wave incidents from the other side. This work marries conventional gradient index metasurfaces with the exception point from non-Hermitian systems, and paves the way for identifying new mechanisms and functionalities for wave manipulation.

physics.app-ph↗

arXiv2019

Structure and enumeration results of matchable Lucas cubes

A lucasene is a hexagon chain that is similar to a fibonaccene, an $L$-fence is a poset the Hasse diagram of which is isomorphic to the directed inner dual graph of the corresponding lucasene. A new class of cubes, which named after matchable Lucas cubes according to the number of its vertices (or elements), are a series of directed or undirected Hasse diagrams of filter lattices of $L$-fences. The basic properties and several classes of polynomials, e.g.\ rank generating functions, cube polynomials and degree sequence polynomials, of matchable Lucas cubes are obtained. Some special conclusions on binomial coefficients and Lucas triangle are given.

math.CO↗

arXiv2019

Self-learning photonic signal processor with an optical neural network chip

Photonic signal processing is essential in the optical communication and optical computing. Numerous photonic signal processors have been proposed, but most of them exhibit limited reconfigurability and automaticity. A feature of fully automatic implementation and intelligent response is highly desirable for the multipurpose photonic signal processors. Here, we report and experimentally demonstrate a fully self-learning and reconfigurable photonic signal processor based on an optical neural network chip. The proposed photonic signal processor is capable of performing various functions including multichannel optical switching, optical multiple-input-multiple-output descrambler and tunable optical filter. All the functions are achieved by complete self-learning. Our demonstration suggests great potential for chip-scale fully programmable optical signal processing with artificial intelligence.

eess.SP↗