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Hua Wang

Publications and source records attributed to Hua Wang.

At least 217 records · Page 12Linked to original sources

Weighted Morrey spaces related to certain nonnegative potentials and Riesz transforms

Let $\mathcal L=-Δ+V$ be a Schrödinger operator, where $Δ$ is the Laplacian on $\mathbb R^d$ and the nonnegative potential $V$ belongs to the reverse Hölder class $RH_q$ for $q\geq d$. The Riesz transform associated with the operator $\mathcal L=-Δ+V$ is denoted by $\mathcal R=\nabla{(-Δ+V)}^{-1/2}$ and the dual Riesz transform is denoted by $\mathcal R^{\ast}=(-Δ+V)^{-1/2}\nabla$. In this paper, we first introduce some kinds of weighted Morrey spaces related to certain nonnegative potentials belonging to the reverse Hölder class $RH_q$ for $q\geq d$. Then we will establish the boundedness properties of the operators $\mathcal R$ and its adjoint $\mathcal R^{\ast}$ on these new spaces. Furthermore, weighted strong-type estimate and weighted endpoint estimate for the corresponding commutators $[b,\mathcal R]$ and $[b,\mathcal R^{\ast}]$ are also obtained. The classes of weights, the classes of symbol functions as well as weighted Morrey spaces discussed in this paper are larger than $A_p$, $\mathrm{BMO}(\mathbb R^d)$ and $L^{p,κ}(w)$ corresponding to the classical Riesz transforms ($V\equiv0$).

math.CA↗

Two-weight, weak type norm inequalities for fractional integral operators and commutators on weighted Morrey and amalgam spaces

Let $0<γ<n$ and $I_γ$ be the fractional integral operator of order $γ$, $I_γf(x)=\int_{\mathbb R^n}|x-y|^{γ-n}f(y)\,dy$, and let $[b,I_γ]$ be the linear commutator generated by a symbol function $b$ and $I_γ$, $[b,I_γ]f(x)=b(x)\cdot I_γf(x)-I_γ(bf)(x)$. This paper is concerned with two-weight, weak type norm estimates for such operators on the weighted Morrey and amalgam spaces. Based on weak-type norm inequalities on weighted Lebesgue spaces and certain $A_p$-type conditions on pairs of weights, we can establish the weak-type norm inequalities for fractional integral operator $I_γ$ as well as the corresponding commutator in the framework of weighted Morrey and amalgam spaces. Furthermore, some estimates for the extreme case are also obtained on these weighted spaces.

math.CA↗

Some extremal ratios of the distance and subtree problems in binary trees

Among many topological indices of trees the sum of distances $σ(T)$ and the number of subtrees $F(T)$ have been a long standing pair of graph invariants that are well known for their negative correlation. That is, among various given classes of trees, the extremal structures maximizing one usually minimize the other, and vice versa. By introducing the "local" versions of these invariants, $σ_T(v)$ for the sum of distance from $v$ to all other vertices and $F_T(v)$ for the number of subtrees containing $v$, extremal problems can be raised and studied for vertices within a tree. This leads to the concept of "middle parts" of a tree with respect to different indices. A challenging problem is to find extremal values of the ratios between graph indices and corresponding local functions at middle parts or leaves. This problem also provides new opportunities to further verify the the correlation between different indices such as $σ(T)$ and $F(T)$. Such extremal ratios, along with the extremal structures, were studied and compared for the distance and subtree problems for general trees In this paper this study is extended to binary trees, a class of trees with numerous practical applications in which the extremal ratio problems appear to be even more complicated. After justifying some basic properties on the distance and subtree problems in trees and binary trees, characterizations are provided for the extremal structures achieving two extremal ratios in binary trees of given order. The generalization of this work to $k$-ary trees is also briefly discussed. The findings are compared with the previous established extremal structures in general trees. Lastly some potential future work is mentioned.

math.CO↗

Two-weight, weak type norm inequalities for a class of sublinear operators on weighted Morrey and amalgam spaces

Let $\mathcal T_α~(0\leqα<n)$ be a class of sublinear operators satisfying certain size conditions introduced by Soria and Weiss, and let $[b,\mathcal T_α]~(0\leqα<n)$ be the commutators generated by $\mathrm{BMO}(\mathbb R^n)$ functions and $\mathcal T_α$. This paper is concerned with two-weight, weak type norm estimates for these sublinear operators and their commutators on the weighted Morrey and amalgam spaces. Some boundedness criterions for such operators are given, under the assumptions that weak-type norm inequalities on weighted Lebesgue spaces are satisfied. As applications of our main results, we can obtain the weak-type norm inequalities for several integral operators as well as the corresponding commutators in the framework of weighted Morrey and amalgam spaces.

math.CA↗

Estimates for fractional integral operators and linear commutators on certain weighted amalgam spaces

In this paper, we first introduce some new classes of weighted amalgam spaces. Then we give the weighted strong-type and weak-type estimates for fractional integral operators $I_γ$ on these new function spaces. Furthermore, the weighted strong-type estimate and endpoint estimate of linear commutators $[b,I_γ]$ generated by $b$ and $I_γ$ are established as well. In addition, we are going to study related problems about two-weight, weak type inequalities for $I_γ$ and $[b,I_γ]$ on the weighted amalgam spaces and give some results. Based on these results and pointwise domination, we can prove norm inequalities involving fractional maximal operator $M_γ$ and generalized fractional integrals $\mathcal L^{-γ/2}$ in the context of weighted amalgam spaces, where $0<γ<n$ and $\mathcal L$ is the infinitesimal generator of an analytic semigroup on $L^2(\mathbb R^n)$ with Gaussian kernel bounds.

math.CA↗

Deep Residual Text Detection Network for Scene Text

Scene text detection is a challenging problem in computer vision. In this paper, we propose a novel text detection network based on prevalent object detection frameworks. In order to obtain stronger semantic feature, we adopt ResNet as feature extraction layers and exploit multi-level feature by combining hierarchical convolutional networks. A vertical proposal mechanism is utilized to avoid proposal classification, while regression layer remains working to improve localization accuracy. Our approach evaluated on ICDAR2013 dataset achieves F-measure of 0.91, which outperforms previous state-of-the-art results in scene text detection.

cs.CV↗

Estimates for vector-valued intrinsic square functions and their commutators on certain weighted amalgam spaces

In this paper, we first introduce some new kinds of weighted amalgam spaces. Then we deal with the vector-valued intrinsic square functions, which are given by \begin{equation*} \mathcal S_γ(\vec{f})(x) = \Bigg(\sum_{j=1}^\infty \big|\mathcal S_γ(f_j)(x) \big|^2\Bigg)^{1/2}, \end{equation*} where $0<γ\leq1$ and \begin{equation*} \mathcal S_γ(f_j)(x) = \left(\iint_{Γ(x)} \Big[\sup_{φ\in{\mathcal C}_γ} \big|φ_t*f_j(y) \big|\Big]^2 \frac{dydt}{t^{n+1}}\right)^{1/2}, \quad j=1,2,\dots. \end{equation*} In his fundamental work, Wilson established strong-type and weak-type estimates for vector-valued intrinsic square functions on weighted Lebesgue spaces. The goal of this paper is to extend his results to these weighted amalgam spaces. Moreover, we define vector-valued analogues of commutators with $BMO(\mathbb R^n)$ functions, and obtain the mapping properties of vector-valued commutators on the weighted amalgam spaces as well. In the endpoint case, we also establish the weighted weak $L\log L$-type estimates for vector-valued commutators in the setting of weighted Lebesgue spaces.

math.CA↗

2D Unitary ESPRIT Based Super-Resolution Channel Estimation for Millimeter-Wave Massive MIMO with Hybrid Precoding

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) with hybrid precoding is a promising technique for the future 5G wireless communications. Due to a large number of antennas but a much smaller number of radio frequency (RF) chains, estimating the high-dimensional mmWave massive MIMO channel will bring the large pilot overhead. To overcome this challenge, this paper proposes a super-resolution channel estimation scheme based on two-dimensional (2D) u- nitary ESPRIT algorithm. By exploiting the angular sparsity of mmWave channels, the continuously distributed angle of arrivals/departures (AoAs/AoDs) can be jointly estimated with high accuracy. Specifically, by designing the uplink training signals at both base station (BS) and mobile station (MS), we first use low pilot overhead to estimate a low-dimensional effective channel, which has the same shift-invariance of array response as the high-dimensional mmWave MIMO channel to be estimated. From the low-dimensional effective channel, the super- resolution estimates of AoAs and AoDs can be jointly obtained by exploiting the 2D unitary ESPRIT channel estimation algorithm. Furthermore, the associated path gains can be acquired based on the least squares (LS) criterion. Finally, we can reconstruct the high-dimensional mmWave MIMO channel according to the obtained AoAs, AoDs, and path gains. Simulation results have confirmed that the proposed scheme is superior to conventional schemes with a much lower pilot overhead.

cs.IT↗

Antiferromagnetic anisotropy determination by spin Hall magnetoresistance

An electric method for measuring magnetic anisotropy in antiferromagnetic insulators (AFIs) is proposed. When a metallic film with strong spin-orbit interactions, e.g., platinum (Pt), is deposited on an AFI, its resistance should be affected by the direction of the AFI N eel vector due to the spin Hall magnetoresistance (SMR). Accordingly, the direction of the AFI N eel vector, which is affected by both the external magnetic field and the magnetic anisotropy, is reflected in resistance of Pt. The magnetic field angle dependence of the resistance of Pt on AFI is calculated by consider- ing the SMR, which indicates that the antiferromagnetic anisotropy can be obtained experimentally by monitoring the Pt resistance in strong magnetic fields. Calculations are performed for realistic systems such as Pt/Cr2O3, Pt/NiO, and Pt/CoO.

cond-mat.mtrl-sci↗

R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection

In this paper, we propose a novel method called Rotational Region CNN (R2CNN) for detecting arbitrary-oriented texts in natural scene images. The framework is based on Faster R-CNN [1] architecture. First, we use the Region Proposal Network (RPN) to generate axis-aligned bounding boxes that enclose the texts with different orientations. Second, for each axis-aligned text box proposed by RPN, we extract its pooled features with different pooled sizes and the concatenated features are used to simultaneously predict the text/non-text score, axis-aligned box and inclined minimum area box. At last, we use an inclined non-maximum suppression to get the detection results. Our approach achieves competitive results on text detection benchmarks: ICDAR 2015 and ICDAR 2013.

cs.CV↗

Scattering below ground state of 3D focusing cubic fractional Schordinger equation with radial data

The aim of this note is to adapt the strategy in [4][See,B.Dodson, J.Murphy, a new proof of scattering below the ground state for the 3D radial focusing cubic NLS, arXiv:1611.04195 ] to prove the scattering of radial solutions below sharp threshold for certain focusing fractional NLS with cubic nonlinearity. The main ingredient is to apply the fractional virial identity proved in [11][See,T.Boulenger, D.Himmelsbach,E.Lenzmann, Blow up for fractional NLS,J.Func.Anal,271(2016),2569-2603] to exclude the concentration of mass near the origin.

math.AP↗

Auto-painter: Cartoon Image Generation from Sketch by Using Conditional Generative Adversarial Networks

Recently, realistic image generation using deep neural networks has become a hot topic in machine learning and computer vision. Images can be generated at the pixel level by learning from a large collection of images. Learning to generate colorful cartoon images from black-and-white sketches is not only an interesting research problem, but also a potential application in digital entertainment. In this paper, we investigate the sketch-to-image synthesis problem by using conditional generative adversarial networks (cGAN). We propose the auto-painter model which can automatically generate compatible colors for a sketch. The new model is not only capable of painting hand-draw sketch with proper colors, but also allowing users to indicate preferred colors. Experimental results on two sketch datasets show that the auto-painter performs better that existing image-to-image methods.

cs.CV↗

Some estimates for $θ$-type Calderón-Zygmund operators and linear commutators on certain weighted amalgam spaces

In this paper, we first introduce some new kinds of weighted amalgam spaces. Then we discuss the strong type and weak type estimates for a class of Calderón--Zygmund type operators $T_θ$ in these new weighted spaces. Furthermore, the strong type estimate and endpoint estimate of linear commutators $[b,T_θ]$ formed by $b$ and $T_θ$ are established. Also we study related problems about two-weight, weak type inequalities for $T_θ$ and $[b,T_θ]$ in the weighted amalgam spaces and give some results.

math.CA↗

On different "middle parts" of a tree

We determine the maximum distance between any two of the center, centroid, and subtree core among trees with a given order. Corresponding results are obtained for trees with given maximum degree and also for trees with given diameter. The problem of the maximum distance between the centroid and the subtree core among trees with given order and diameter becomes difficult. It can be solved in terms of the problem of minimizing the number of root-containing subtrees in a rooted tree of given order and height. While the latter problem remains unsolved, we provide a partial characterization of the extremal structure.

math.CO↗

Friendship-based Cooperative Jamming for Secure Communication in Poisson Networks

Wireless networks with the consideration of social relationships among network nodes are highly appealing for lots of important data communication services. Ensuring the security of such networks is of great importance to facilitate their applications in supporting future social-based services with strong security guarantee. This paper explores the physical layer security-based secure communication in a finite Poisson network with social friendships among nodes, for which a social friendship-based cooperative jamming scheme is proposed. The jamming scheme consists of a Local Friendship Circle (LFC) and a Long-range Friendship Annulus (LFA), where all legitimate nodes in the LFC serve as jammers, but the legitimate nodes in the LFA are selected as jammers through three location-based policies. To understand both the security and reliability performance of the proposed jamming scheme, we first model the sum interference at any location in the network by deriving its Laplace transform under two typical path loss scenarios. With the help of the interference Laplace transform results, we then derive the exact expression for the transmission outage probability (TOP) and determine both the upper and lower bounds on the secrecy outage probability (SOP), such that the overall outage performances of the proposed jamming scheme can be depicted. Finally, we present extensive numerical results to validate the theoretical analysis of TOP and SOP and also to illustrate the impacts of the friendship-based cooperative jamming on the network performances.

cs.IT↗

Maximum atom-bond connectivity index with given graph parameters

The atom-bond connectivity (ABC) index is a degree-based topological index. It was introduced due to its applications in modeling the properties of certain molecular structures and has been since extensively studied. In this note, we examine the influence on the extremal values of the ABC index by various graph parameters. More specifically, we consider the maximum ABC index of connected graphs of given order, with fixed independence number, number of pendent vertices, chromatic number and edge-connectivity respectively. We provide characterizations of extremal structures as well as some conjectures. Numerical analysis of the extremal values are also presented.

math.CO↗

Supervised Anomaly Detection in Uncertain Pseudoperiodic Data Streams

Uncertain data streams have been widely generated in many Web applications. The uncertainty in data streams makes anomaly detection from sensor data streams far more challenging. In this paper, we present a novel framework that supports anomaly detection in uncertain data streams. The proposed framework adopts an efficient uncertainty pre-processing procedure to identify and eliminate uncertainties in data streams. Based on the corrected data streams, we develop effective period pattern recognition and feature extraction techniques to improve the computational efficiency. We use classification methods for anomaly detection in the corrected data stream. We also empirically show that the proposed approach shows a high accuracy of anomaly detection on a number of real datasets.

cs.AI↗

Two-Dimensional Multiferroics: Ferroelasticity, Ferroelectricity, Domain Wall, and Potential Mechano-Opto-Electronic Applications

Low-dimensional multiferroic materials hold great promises in miniaturized device applications such as nanoscale transducers, actuators, sensors, photovoltaics, and nonvolatile memories. Here, using first-principles theory we predict that two-dimensional (2D) monolayer Group IV monochalcogenides including GeS, GeSe, SnS, and SnSe are a class of 2D semiconducting multiferroics with strongly coupled giant in-plane spontaneous ferroelectric polarization and spontaneous ferroelastic lattice strain that are thermodynamically stable at room temperature and beyond, and can be effectively modulated by elastic strain engineering. Their optical absorption spectra exhibit strong in-plane anisotropy with visible-spectrum excitonic gaps and sizable exciton binding energies, rendering the unique characteristics of low-dimensional semiconductors. More importantly, the predicted low domain wall energy and small migration barrier together with the coupled multiferroic order and anisotropic electronic structures suggest their great potentials for tunable multiferroic functional devices by manipulating external electrical, mechanical, and optical field to control the internal responses, and enable the development of four device concepts including 2D ferroelectric memory, 2D ferroelastic memory, and 2D ferroelastoelectric nonvolatile photonic memory as well as 2D ferroelectric excitonic photovoltaics.

cond-mat.mtrl-sci↗