arXiv subjects

Hu Zhang

Publications and source records attributed to Hu Zhang.

At least 55 records · Page 3Linked to original sources

arXiv2022

Spin field-effect transistors based on massless birefringent Dirac fermions in polar Dirac semimetals

The Datta-Das-type spin field-effect transistor, using a two-dimensional electron gas in a semiconductor heterostructure as a channel, plays a key role in spintronics. Here, we theoretically present a type of spin field-effect transistor based on massless birefringent Dirac fermions in polar Dirac semimetals. The manipulation of spin arises from the existence of the strong spin-orbit coupling, polar space groups, and Dirac cones in a single phase. The oscillatory channel conductance can be controlled by the sign of gate voltage in addition to its magnitude due to the gapless band structures of polar Dirac semimetals. Such spin field-effect transistor provides guidance for the further design of spintronic devices.

cond-mat.mes-hall↗

arXiv2022

Parity-breaking in single-element phases: Ferroelectric-like elemental polar metals

Polar metals based on binary and ternary compounds have been demonstrated in literature. Here, we propose a design principle for ferroelectric-like elemental polar metals and relate it to real materials. The design principle is that, to be an elemental polar metal, atoms should occupy at least two inequivalent Wyckoff positions in a crystal with a polar space group, where inversion symmetry is spontaneously broken. According to this rule, we propose the first class of potential ferroelectric-like elemental polar metals in a distorted α-La-like structure with a polar space group P63mc in which two inequivalent Wyckoff positions 2a (0, 0, z) and 2b (1/3, 2/3, z) are occupied by group-V elements (phosphorus, arsenic, antimony, and bismuth). Analyses based on first-principles calculations indicate that the dynamically stable polar phase results from a lone pair driven polar distortion of the nonploar phase in P63/mmc symmetry where two inequivalent Wyckoff positions 2a (0, 0, 0) and 2c (1/3, 2/3, 1/4) are occupied. This ferroelectric-like transition involves a transition from a metallic state to a semimetallic state. These predicted polar phases are metastable with respect to their corresponding ground phases. Moreover, ionic bonding characters are found due to the inequivalence in Wyckoff positions between group-V atoms. Our work opens a route to single-element parity-breaking phases.

cond-mat.mtrl-sci↗

arXiv2022

Double version of the Rashba and Dresselhaus spin-orbit coupling

The Rashba and Dresselhaus types of spin-orbit coupling are two typical linear coupling forms. We establish the fundamental physics of a model which can be viewed as the double version of the Rashba and Dresselhaus spin-orbit coupling. This model describes the low energy physics of a class of massless Dirac fermions in spin-orbit systems. The physical properties of the massless Dirac fermions are determined by the mathematical relations of spin-orbit coefficients. For equal Rashba and Dresselhauss coupling constants, k-independent eigenspinors and a persistent spin helix combined with massless birefringent Dirac fermions emerge in this model. The spin-orbit coupled systems described by this model have potential technological applications from spintronics to quantum computation.

cond-mat.mes-hall↗

arXiv2021

Interlayer Quasi-Bonding Interactions in 2D Layered Materials: A Classification According to the Occupancy of Involved Energy Bands

Recent studies have revealed that the interlayer interaction in two-dimensional (2D) layered materials is not simply of van der Waals character but could coexist with quasi-bonding character. Here we classify the interlayer quasi-bonding interactions into two main categories (I: homo-occupancy interaction, II: hetero-occupancy interaction) according to the occupancy of the involved energy bands near the Fermi level. Then we investigate the quasi-bonding-interaction-induced band structure evolution of several representative 2D materials based on density functional theory calculations. Further calculations confirm that this classification is applicable to generic 2D layered materials and provides a unified understanding of the total strength of interlayer interaction, which is a synergetic effect of the van der Waals attraction and the quasi-bonding interaction. The latter is stabilizing in main category II and destabilizing in main category I. Thus, the total interlayer interaction strength is relatively stronger in category II and weaker in category I.

cond-mat.mtrl-sci↗

arXiv2021

Hole- and electron-injection driven phase transitions in transition metal dichalcogenides and beyond: A unified understanding

The phase transitions among polymorphic two-dimensional (2D) transition metal dichalcogenides (TMDs) have attracted increasing attention for their potential in enabling distinct functionalities in the same material for making integrated devices. Electron-injection to TMDs has been proved to be a feasible way to drive structural phase transition from the semiconducting H-phase to the semimetal dT-phase. In this contribution, based on density-functional theory (DFT) calculations, firstly we demonstrate that hole-injection drives the transition of the H-phase more efficiently to the metallic T-phase than to the semimetallic dT-phase for group VI-B TMDs (MoS2, WS2, and MoSe2, etc.). The origin can be attributed to the smaller work function of the T-phase than that of the dT-phase. Our work function analysis can distinguish the T and dT phases quantitatively while it is challenging for the commonly used crystal field splitting analysis. In addition, our analysis provides a unified understanding for both hole- and electron-injection induced phase transitions for 2D materials beyond TMDs, such as the newly synthesized MoSi2N4 family. Moreover, the hole-driven T-phase transition mechanism can explain the recent experiment of WS2 phase transition by hole-doping with yttrium (Y) atoms. Using 1/3 Y-doped WS2 and MoSe2 as examples, we show that the Mo and W valency increases to 5+. These above findings open up an avenue to obtain the metallic T-phase, which expands the possible stable phases of 2D materials.

cond-mat.mtrl-sci↗

arXiv2021

EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural Network

Recently, it has been demonstrated that the performance of a deep convolutional neural network can be effectively improved by embedding an attention module into it. In this work, a novel lightweight and effective attention method named Pyramid Squeeze Attention (PSA) module is proposed. By replacing the 3x3 convolution with the PSA module in the bottleneck blocks of the ResNet, a novel representational block named Efficient Pyramid Squeeze Attention (EPSA) is obtained. The EPSA block can be easily added as a plug-and-play component into a well-established backbone network, and significant improvements on model performance can be achieved. Hence, a simple and efficient backbone architecture named EPSANet is developed in this work by stacking these ResNet-style EPSA blocks. Correspondingly, a stronger multi-scale representation ability can be offered by the proposed EPSANet for various computer vision tasks including but not limited to, image classification, object detection, instance segmentation, etc. Without bells and whistles, the performance of the proposed EPSANet outperforms most of the state-of-the-art channel attention methods. As compared to the SENet-50, the Top-1 accuracy is improved by 1.93% on ImageNet dataset, a larger margin of +2.7 box AP for object detection and an improvement of +1.7 mask AP for instance segmentation by using the Mask-RCNN on MS-COCO dataset are obtained. Our source code is available at:https://github.com/murufeng/EPSANet.

cs.CV↗

arXiv2021

Power-product matrix: nonsingularity, sparsity and determinant

We prove the nonsingularity of a class of integer matrices V(n,d), namely power-product matrix, for positive integers n and d. Some technical proofs are mainly based on linear algebra and enumerative combinatorics, particularly the generating function method and involution principle. We will show that the matrix V(n,d) is nonsingular for all positive integers n and d, and often with sparse structure. Special attention is given to the computation of the determinant V(2,d) with positive integer d.

math.CO↗

arXiv2020

Dynamic fingerprint of fractionalized excitations in single-crystalline Cu$_3$Zn(OH)$_6$FBr

Quantum spin liquid (QSL) represents a new class of condensed matter states characterized by the long-range many-body entanglement of topological orders. The most prominent feature of the elusive QSL state is the existence of fractionalized spin excitations. Subject to the strong quantum fluctuations, the spin-1/2 antiferromagnetic system on a kagome lattice is the promising candidate for hosting a QSL ground state, but the structurally ideal realization is rare. Here, we report Raman scattering on the single crystalline Cu$_3$Zn(OH)$_6$FBr, and confirm that the ideal kagome structure remains down to low temperatures without any lattice distortion by the angle-resolved polarized Raman responses and second-harmonic-generation measurements. Furthermore, at low temperatures the Raman scattering reveals a continuum of the spin excitations in Cu$_3$Zn(OH)$_6$FBr, in contrast to the sharp magnon peak in the ordered kagome antiferromagnet EuCu$_3$(OH)$_6$Cl$_3$. Such magnetic Raman continuum, in particular, the substantial low-energy one-pair spinon excitation serves as strong evidence for fractionalized spin excitations in Cu$_3$Zn(OH)$_6$FBr.

cond-mat.str-el↗

arXiv2020

Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior

Deep neural networks are known to be susceptible to adversarial noise, which are tiny and imperceptible perturbations. Most of previous work on adversarial attack mainly focus on image models, while the vulnerability of video models is less explored. In this paper, we aim to attack video models by utilizing intrinsic movement pattern and regional relative motion among video frames. We propose an effective motion-excited sampler to obtain motion-aware noise prior, which we term as sparked prior. Our sparked prior underlines frame correlations and utilizes video dynamics via relative motion. By using the sparked prior in gradient estimation, we can successfully attack a variety of video classification models with fewer number of queries. Extensive experimental results on four benchmark datasets validate the efficacy of our proposed method.

cs.CV↗

arXiv2020

Evolutionary Reinforcement Learning via Cooperative Coevolutionary Negatively Correlated Search

Evolutionary algorithms (EAs) have been successfully applied to optimize the policies for Reinforcement Learning (RL) tasks due to their exploration ability. The recently proposed Negatively Correlated Search (NCS) provides a distinct parallel exploration search behavior and is expected to facilitate RL more effectively. Considering that the commonly adopted neural policies usually involves millions of parameters to be optimized, the direct application of NCS to RL may face a great challenge of the large-scale search space. To address this issue, this paper presents an NCS-friendly Cooperative Coevolution (CC) framework to scale-up NCS while largely preserving its parallel exploration search behavior. The issue of traditional CC that can deteriorate NCS is also discussed. Empirical studies on 10 popular Atari games show that the proposed method can significantly outperform three state-of-the-art deep RL methods with 50% less computational time by effectively exploring a 1.7 million-dimensional search space.

cs.NE↗

arXiv2020

Query-efficient Meta Attack to Deep Neural Networks

Black-box attack methods aim to infer suitable attack patterns to targeted DNN models by only using output feedback of the models and the corresponding input queries. However, due to lack of prior and inefficiency in leveraging the query and feedback information, existing methods are mostly query-intensive for obtaining effective attack patterns. In this work, we propose a meta attack approach that is capable of attacking a targeted model with much fewer queries. Its high queryefficiency stems from effective utilization of meta learning approaches in learning generalizable prior abstraction from the previously observed attack patterns and exploiting such prior to help infer attack patterns from only a few queries and outputs. Extensive experiments on MNIST, CIFAR10 and tiny-Imagenet demonstrate that our meta-attack method can remarkably reduce the number of model queries without sacrificing the attack performance. Besides, the obtained meta attacker is not restricted to a particular model but can be used easily with a fast adaptive ability to attack a variety of models.The code of our work is available at https://github.com/dydjw9/MetaAttack_ICLR2020/.

cs.CV↗

arXiv2019

Effective Hamiltonian for superconducting Ni oxides Nd$_{1-x}$Sr$_x$NiO$_2$

We derive the effective single-band Hamiltonian in the flat NiO$_2$ planes for nickelate compounds Nd$_{1-x}$Sr$_x$NiO$_2$. We first implement the first-principles calculation to study electronic structures of nickelates using the Heyd-Scuseria-Ernzerhof hybrid density functional and derive a three-band Hubbard model for Ni-O $pdσ$ bands of Ni$^+$ $3d_{x^2-y^2}$ and O$^{2-}$ $2p_{x/y}$ orbitals in the NiO$_2$ planes. To obtain the effective one-band $t$-$t'$-$J$ model Hamiltonian, we perform the exact diagonalization of the three-band Hubbard model for the Ni$_5$O$_{16}$ cluster and map the low-energy spectra onto the effective one-band models. We find that the undoped NiO$_2$ plane is a Hubbard Mott insulator, and the doped holes primarily locate on Ni sites. The physics of the NiO$_2$ plane is a doped Mott insulator, described by the one-band $t$-$t'$-$J$ model with $t=265$~meV, $t'=-21$~meV and $J=28.6$~meV. We also discuss the electronic structure for the "self-doping" effect and heavy fermion behavior of electron pockets of Nd$^{3+}$ $5d$ character in Nd$_{1-x}$Sr$_x$NiO$_2$.

cond-mat.supr-con↗

arXiv2019

Multi-Likelihood Methods for Developing Stock Relationship Networks Using Financial Big Data

Development of stock networks is an important approach to explore the relationship between different stocks in the era of big-data. Although a number of methods have been designed to construct the stock correlation networks, it is still a challenge to balance the selection of prominent correlations and connectivity of networks. To address this issue, we propose a new approach to select essential edges in stock networks and also maintain the connectivity of established networks. This approach uses different threshold values for choosing the edges connecting to a particular stock, rather than employing a single threshold value in the existing asset-value method. The innovation of our algorithm includes the multiple distributions in a maximum likelihood estimator for selecting the threshold value rather than the single distribution estimator in the existing methods. Using the Chinese Shanghai security market data of 151 stocks, we develop a stock relationship network and analyze the topological properties of the developed network. Our results suggest that the proposed method is able to develop networks that maintain appropriate connectivities in the type of assets threshold methods.

q-fin.ST↗

arXiv2019

Influences of spin-orbit coupling on Fermi surfaces and Dirac cones in ferroelectric-like polar metals

Based on first-principles calculations and k .p effective models, we report physical properties of ferroelectric-like hexagonal polar metals with P63mc symmetry, which are distinct from those of conventional metals with spatial inversion symmetry. Spin textures exist on the Fermi surface of polar metals (e.g. ternary LiGaGe and elemental polar metal of Bi) and spin-momentum locking exist on accidental Dirac cones on the sixfold rotational axis, due to the spin-orbit coupling and lack of inversion symmetry in polar space group. This effect has potential applications in spin-orbitronics. Dirac points are also predicted in LiGaGe.

cond-mat.mtrl-sci↗

arXiv2019

Sentence Compression via DC Programming Approach

Sentence compression is an important problem in natural language processing. In this paper, we firstly establish a new sentence compression model based on the probability model and the parse tree model. Our sentence compression model is equivalent to an integer linear program (ILP) which can both guarantee the syntax correctness of the compression and save the main meaning. We propose using a DC (Difference of convex) programming approach (DCA) for finding local optimal solution of our model. Combing DCA with a parallel-branch-and-bound framework, we can find global optimal solution. Numerical results demonstrate the good quality of our sentence compression model and the excellent performance of our proposed solution algorithm.

cs.CL↗

arXiv2016

Learning from Non-Stationary Stream Data in Multiobjective Evolutionary Algorithm

Evolutionary algorithms (EAs) have been well acknowledged as a promising paradigm for solving optimisation problems with multiple conflicting objectives in the sense that they are able to locate a set of diverse approximations of Pareto optimal solutions in a single run. EAs drive the search for approximated solutions through maintaining a diverse population of solutions and by recombining promising solutions selected from the population. Combining machine learning techniques has shown great potentials since the intrinsic structure of the Pareto optimal solutions of an multiobjective optimisation problem can be learned and used to guide for effective recombination. However, existing multiobjective EAs (MOEAs) based on structure learning spend too much computational resources on learning. To address this problem, we propose to use an online learning scheme. Based on the fact that offsprings along evolution are streamy, dependent and non-stationary (which implies that the intrinsic structure, if any, is temporal and scale-variant), an online agglomerative clustering algorithm is applied to adaptively discover the intrinsic structure of the Pareto optimal solution set; and to guide effective offspring recombination. Experimental results have shown significant improvement over five state-of-the-art MOEAs on a set of well-known benchmark problems with complicated Pareto sets and complex Pareto fronts.

cs.NE↗

arXiv2015

Revealing the Anisotropic Thermal Conductivity of Black Phosphorus using the Time-Resolved Magneto-Optical Kerr Effect

Black phosphorus (BP) has emerged as a direct-bandgap semiconducting material with great application potentials in electronics, photonics, and energy conversion. Experimental characterization of the anisotropic thermal properties of BP, however, is extremely challenging due to the lack of reliable and accurate measurement techniques to characterize anisotropic samples that are micrometers in size. Here, we report measurement results of the anisotropic thermal conductivity of bulk BP along three primary crystalline orientations, using the novel time-resolved magneto-optical Kerr effect (TR-MOKE) with enhanced measurement sensitivities. Two-dimensional beam-offset TR-MOKE signals from BP flakes yield the thermal conductivity along the zigzag crystalline direction to be 84 ~ 101 W/(m*K), nearly three times as large as that along the armchair direction (26 ~ 36 W/(m*K)). The through-plane thermal conductivity of BP ranges from 4.3 to 5.5 W/(m*K). The first-principles calculation was performed for the first time to predict the phonon transport in BP both along the in-plane zigzag and armchair directions and along the through-plane direction. This work successfully unveiled the fundamental mechanisms of anisotropic thermal transport along the three crystalline directions in bulk BP, as demonstrated by the excellent agreement between our first-principles-based theoretical predictions and experimental characterizations on the anisotropic thermal conductivities of bulk BP.

cond-mat.mtrl-sci↗

arXiv2015

Numerical Predictions of Effective Thermal Conductivities for Three-dimensional Four-directional Braided Composites Using the Lattice Boltzmann Method

In this paper, a multiple-relaxation-time lattice Boltzmann model with an off-diagonal collision matrix was adopted to predict the effective thermal conductivities of the anisotropic heterogeneous materials whose components are also anisotropic. The half lattice division scheme was adopted to deal with the internal boundaries to guarantee the heat flux continuity at the interfaces. Accuracy of the model was confirmed by comparisons with benchmark results and existing simulation data. The present method was then adopted to numerically predict the transverse and longitudinal effective thermal conductivities of three-dimensional (3D) four-directional braided composites. Some corresponding experiments based on the Hot Disk method were conducted to measure their transverse and longitudinal effective thermal conductivities. The predicted data fit the experiment data well. Influences of fiber volume fractions and interior braiding angles on the effective thermal conductivities of 3D four-directional braided composites were then studied. The results show that a larger fiber volume fraction leads to a larger effective thermal conductivity along the transverse and longitudinal directions; a larger interior braiding angle brings a larger transverse thermal conductivity but a smaller one along the longitudinal direction. It is also shown that for anisotropic materials the periodic boundary condition is different from the adiabatic boundary condition and for periodic microstructure unit cell the periodic boundary condition should be used. Key words: effective thermal conductivities, anisotropic, multi-relaxation-time, lattice Boltzmann method, three-dimensional four-directional braided composites

physics.comp-ph↗