arXiv subjects

Xiao Zhang

Publications and source records attributed to Xiao Zhang.

At least 523 records · Page 29Linked to original sources

arXiv2017

Electric Field Effect in Multilayer Cr2Ge2Te6: a Ferromagnetic Two-Dimensional Material

The emergence of two-dimensional (2D) materials has attracted a great deal of attention due to their fascinating physical properties and potential applications for future nanoelectronic devices. Since the first isolation of graphene, a Dirac material, a large family of new functional 2D materials have been discovered and characterized, including insulating 2D boron nitride, semiconducting 2D transition metal dichalcogenides and black phosphorus, and superconducting 2D bismuth strontium calcium copper oxide, molybdenum disulphide and niobium selenide, etc. Here, we report the identification of ferromagnetic thin flakes of Cr2Ge2Te6 (CGT) with thickness down to a few nanometers, which provides a very important piece to the van der Waals structures consisting of various 2D materials. We further demonstrate the giant modulation of the channel resistance of 2D CGT devices via electric field effect. Our results illustrate the gate voltage tunability of 2D CGT and the potential of CGT, a ferromagnetic 2D material, as a new functional quantum material for applications in future nanoelectronics and spintronics.

cond-mat.mtrl-sci↗

arXiv2017

Exact solutions to the (1+1)-dimensional nonlinear Maxwell equations in the orthogonal curvilinear coordinates

Characterizing electromagnetic wave propagation in nonlinear and inhomogeneous media is of great interest from both theoretical and practical perspectives, even though it is extremely complicated. In fact, it is still an unresolved issue to find the exact solutions to the nonlinear waves in the orthogonal curvilinear coordinates. In this paper, we present an analytic method to handle the problem of electromagnetic waves propagation in arbitrarily nonlinear and particularly inhomogeneous media without dispersion. Through the exact solutions of the (1+1)-dimensional nonlinear Maxwell equations, we discuss some nonlinear phenomena, including cylindrical shock waves, free nonlinear oscillations, and nonlinear superposition of waves.

physics.class-ph↗

arXiv2017

Line nodes, Dirac points and Lifshitz transition in 2D nonsymmorphic photonic crystals

Topological phase transitions, which have fascinated generations of physicists, are always demarcated by gap closures. In this work, we propose very simple 2D photonic crystal lattices with gap closure points, i.e. band degeneracies protected by nonsymmorphic symmetry. Our photonic structures are relatively easy to fabricate, consisting of two inequivalent dielectric cylinders per unit cell. Along high symmetry directions, they exhibit line degeneracies protected by glide reflection symmetry, which we explicitly demonstrate for $pg,pmg,pgg$ and $p4g$ nonsymmorphic groups. In the presence of time reversal symmetry, they also exhibit point degeneracies (Dirac points) protected by a $Z_2$ topological number associated with crystalline symmetry. Strikingly, the robust protection of $pg$-symmetry allows a Lifshitz transition to a type II Dirac cone across a wide range of experimentally accessible parameters, thus providing a convenient route for realizing anomalous refraction. Further potential applications include a stoplight device based on electrically induced strain that dynamically switches the lattice symmetry from $pgg$ to the higher $p4g$ symmetry. This controls the coalescence of Dirac points and hence the group velocity within the crystal.

cond-mat.mes-hall↗

arXiv2017

A Universal Variance Reduction-Based Catalyst for Nonconvex Low-Rank Matrix Recovery

We propose a generic framework based on a new stochastic variance-reduced gradient descent algorithm for accelerating nonconvex low-rank matrix recovery. Starting from an appropriate initial estimator, our proposed algorithm performs projected gradient descent based on a novel semi-stochastic gradient specifically designed for low-rank matrix recovery. Based upon the mild restricted strong convexity and smoothness conditions, we derive a projected notion of the restricted Lipschitz continuous gradient property, and prove that our algorithm enjoys linear convergence rate to the unknown low-rank matrix with an improved computational complexity. Moreover, our algorithm can be employed to both noiseless and noisy observations, where the optimal sample complexity and the minimax optimal statistical rate can be attained respectively. We further illustrate the superiority of our generic framework through several specific examples, both theoretically and experimentally.

stat.ML↗

arXiv2017

Stochastic Variance-reduced Gradient Descent for Low-rank Matrix Recovery from Linear Measurements

We study the problem of estimating low-rank matrices from linear measurements (a.k.a., matrix sensing) through nonconvex optimization. We propose an efficient stochastic variance reduced gradient descent algorithm to solve a nonconvex optimization problem of matrix sensing. Our algorithm is applicable to both noisy and noiseless settings. In the case with noisy observations, we prove that our algorithm converges to the unknown low-rank matrix at a linear rate up to the minimax optimal statistical error. And in the noiseless setting, our algorithm is guaranteed to linearly converge to the unknown low-rank matrix and achieves exact recovery with optimal sample complexity. Most notably, the overall computational complexity of our proposed algorithm, which is defined as the iteration complexity times per iteration time complexity, is lower than the state-of-the-art algorithms based on gradient descent. Experiments on synthetic data corroborate the superiority of the proposed algorithm over the state-of-the-art algorithms.

stat.ML↗

arXiv2016

Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition

A deep learning approach has been widely applied in sequence modeling problems. In terms of automatic speech recognition (ASR), its performance has significantly been improved by increasing large speech corpus and deeper neural network. Especially, recurrent neural network and deep convolutional neural network have been applied in ASR successfully. Given the arising problem of training speed, we build a novel deep recurrent convolutional network for acoustic modeling and then apply deep residual learning to it. Our experiments show that it has not only faster convergence speed but better recognition accuracy over traditional deep convolutional recurrent network. In the experiments, we compare the convergence speed of our novel deep recurrent convolutional networks and traditional deep convolutional recurrent networks. With faster convergence speed, our novel deep recurrent convolutional networks can reach the comparable performance. We further show that applying deep residual learning can boost the convergence speed of our novel deep recurret convolutional networks. Finally, we evaluate all our experimental networks by phoneme error rate (PER) with our proposed bidirectional statistical n-gram language model. Our evaluation results show that our newly proposed deep recurrent convolutional network applied with deep residual learning can reach the best PER of 17.33\% with the fastest convergence speed on TIMIT database. The outstanding performance of our novel deep recurrent convolutional neural network with deep residual learning indicates that it can be potentially adopted in other sequential problems.

cs.CL↗

arXiv2016

Composing Music with Grammar Argumented Neural Networks and Note-Level Encoding

Creating aesthetically pleasing pieces of art, including music, has been a long-term goal for artificial intelligence research. Despite recent successes of long-short term memory (LSTM) recurrent neural networks (RNNs) in sequential learning, LSTM neural networks have not, by themselves, been able to generate natural-sounding music conforming to music theory. To transcend this inadequacy, we put forward a novel method for music composition that combines the LSTM with Grammars motivated by music theory. The main tenets of music theory are encoded as grammar argumented (GA) filters on the training data, such that the machine can be trained to generate music inheriting the naturalness of human-composed pieces from the original dataset while adhering to the rules of music theory. Unlike previous approaches, pitches and durations are encoded as one semantic entity, which we refer to as note-level encoding. This allows easy implementation of music theory grammars, as well as closer emulation of the thinking pattern of a musician. Although the GA rules are applied to the training data and never directly to the LSTM music generation, our machine still composes music that possess high incidences of diatonic scale notes, small pitch intervals and chords, in deference to music theory.

cs.LG↗

arXiv2016

Range Loss for Deep Face Recognition with Long-tail

Convolutional neural networks have achieved great improvement on face recognition in recent years because of its extraordinary ability in learning discriminative features of people with different identities. To train such a well-designed deep network, tremendous amounts of data is indispensable. Long tail distribution specifically refers to the fact that a small number of generic entities appear frequently while other objects far less existing. Considering the existence of long tail distribution of the real world data, large but uniform distributed data are usually hard to retrieve. Empirical experiences and analysis show that classes with more samples will pose greater impact on the feature learning process and inversely cripple the whole models feature extracting ability on tail part data. Contrary to most of the existing works that alleviate this problem by simply cutting the tailed data for uniform distributions across the classes, this paper proposes a new loss function called range loss to effectively utilize the whole long tailed data in training process. More specifically, range loss is designed to reduce overall intra-personal variations while enlarging inter-personal differences within one mini-batch simultaneously when facing even extremely unbalanced data. The optimization objective of range loss is the $k$ greatest range's harmonic mean values in one class and the shortest inter-class distance within one batch. Extensive experiments on two famous and challenging face recognition benchmarks (Labeled Faces in the Wild (LFW) and YouTube Faces (YTF) not only demonstrate the effectiveness of the proposed approach in overcoming the long tail effect but also show the good generalization ability of the proposed approach.

cs.CV↗

arXiv2016

Physical properties of KMgBi single crystals

KMgBi single crystals are grown by using the Bi flux successfully. KMgBi shows semiconducting behavior with a metal-semiconductor transition at high temperature region and a resistivity plateau at low temperature region, suggesting KMgBi could be a topological insulator with a very small band gap. Moreover, KMgBi exhibits multiband feature with strong temperature dependence of carrier concentrations and mobilities.

cond-mat.mtrl-sci↗

arXiv2016

A Unified Computational and Statistical Framework for Nonconvex Low-Rank Matrix Estimation

We propose a unified framework for estimating low-rank matrices through nonconvex optimization based on gradient descent algorithm. Our framework is quite general and can be applied to both noisy and noiseless observations. In the general case with noisy observations, we show that our algorithm is guaranteed to linearly converge to the unknown low-rank matrix up to minimax optimal statistical error, provided an appropriate initial estimator. While in the generic noiseless setting, our algorithm converges to the unknown low-rank matrix at a linear rate and enables exact recovery with optimal sample complexity. In addition, we develop a new initialization algorithm to provide a desired initial estimator, which outperforms existing initialization algorithms for nonconvex low-rank matrix estimation. We illustrate the superiority of our framework through three examples: matrix regression, matrix completion, and one-bit matrix completion. We also corroborate our theory through extensive experiments on synthetic data.

stat.ML↗

arXiv2016

Anomalous Hall effect of ferromagnetic Fe3Sn2 single crystal with geometrically frustrated kagome lattice

The anomalous Hall effect is investigated for ferromagnetic Fe3Sn2 single crystal with geometrically frustrated kagome bilayer of Fe. The scaling behavior between anomalous Hall resistivity rho_{xy}^{A} and longitudinal resistivity rho_{xx} is quadratic and further analysis implies that the AHE in Fe3Sn2 single crystal should be dominated by the intrinsic Karplus-Luttinger mechanism rather than extrinsic skew-scattering or side-jump mechanisms. Moreover, there is a sudden jump of anomalous Hall conductivity sigma_{xy}^{A} appearing at about 100 K where the spin-reorientation transition from the c axis to the ab plane is completed. This change of sigma_{xy}^{A} might be related to the evolution of Fermi surface induced by the spin-reorientation transition.

cond-mat.str-el↗

arXiv2016

Localized waves supported by the rotating waveguide array

We show that truncated rotating square waveguide arrays support new types of localized modes that exist even in the linear case, in complete contrast to localized excitations in nonrotating arrays requiring nonlinearity for their existence and forming above the energy flow threshold. These new modes appear either around array center, since rotation leads to the emergence of the effective attractive potential with a minimum at the rotation axis, or in the array corners, in which case localization occurs due to competition between centrifugal force (in terms of quasi-particle analogy) and total internal reflection at the interface of the truncated array. The degree of localization of the central and corner modes mediated by rotation increases with rotation frequency. Stable rotating soliton families bifurcating from linear modes are analyzed in both focusing and defocusing media.

physics.optics↗

arXiv2016

Expanding molecular bubble surrounding Tycho's supernova remnant (SN 1572) observed with IRAM 30 m telescope: evidence for a single-degenerate progenitor

Whether the progenitors of SNe Ia are single-degenerate or double-degenerate white dwarf (WD) systems is a highly debated topic. To address the origin of the Type Ia Tycho's supernova remnant (SNR), SN 1572, we have carried out a $^{12}$CO J=2-1 mapping and a 3-mm line survey toward the remnant using the IRAM 30 m telescope. We show that Tycho is surrounded by a clumpy molecular bubble at a local standard of rest velocity of $\sim 61$ km s$^{-1}$ which expands at a speed of $\sim 4.5$ km s$^{-1}$ and has a mass of $\sim 220$ M$_\odot$ (at the distance of 2.5 kpc). Enhanced $^{12}$CO J=2-1 line emission relative to $^{12}$CO J=1-0 emission and possible line broadenings (in velocity range $-64$ to $-60$ km s$^{-1}$) are found at the northeastern boundary of the SNR, where the shell is deformed and decelerated. These features, combined with the morphological correspondence between the expanding molecular bubble and Tycho, suggest that the SNR is associated with the bubble at the velocity range $-66$ to $-57$ km s$^{-1}$. The most plausible origin for the expanding bubble is the fast outflow (with velocity of hundreds km s$^{-1}$) driven from the vicinity of a WD as it accreted matter from a nondegenerate companion star. The SNR has been expanding in the low-density wind-blown bubble, and the shock wave has just reached the molecular cavity wall. The SNR has been expanding in the low-density wind-blown bubble, and the shock wave has just reached the molecular cavity wall. This is the first unambiguous detection of an expanding bubble driven by the progenitor of a Type-Ia SNR, which constitutes evidence for a single-degenerate progenitor for this SN Ia.

astro-ph.GA↗

arXiv2016

Random graph models for dynamic networks

We propose generalizations of a number of standard network models, including the classic random graph, the configuration model, and the stochastic block model, to the case of time-varying networks. We assume that the presence and absence of edges are governed by continuous-time Markov processes with rate parameters that can depend on properties of the nodes. In addition to computing equilibrium properties of these models, we demonstrate their use in data analysis and statistical inference, giving efficient algorithms for fitting them to observed network data. This allows us, for instance, to estimate the time constants of network evolution or infer community structure from temporal network data using cues embedded both in the probabilities over time that node pairs are connected by edges and in the characteristic dynamics of edge appearance and disappearance. We illustrate our methods with a selection of applications, both to computer-generated test networks and real-world examples.

cs.SI↗

arXiv2016

Negative differential resistance and characteristic nonlinear electromagnetic response of a Topological Insulator

Materials exhibiting negative differential resistance have important applications in technologies involving microwave generation, which range from motion sensing to radio astronomy. Despite their usefulness, there has been few physical mechanisms giving rise to materials with such properties, i.e. GaAs employed in the Gunn diode. In this work, we show that negative differential resistance also generically arise in Dirac ring systems, an example of which has been experimentally observed in the surface states of Topological Insulators. This novel realization of negative differential resistance is based on a completely different physical mechanism from that of the Gunn effect, relying on the characteristic non-monotonicity of the response curve that remains robust in the presence of nonzero temperature, chemical potential, mass gap and impurity scattering. As such, it opens up new possibilities for engineering applications, such as frequency upconversion devices which are highly sought for terahertz signal generation. Our results may be tested with thin films of Bi2Se3 Topological Insulators, and are expected to hold qualitatively even in the absence of a strictly linear Dirac dispersion, as will be the case in more generic samples of Bi2Se3 and other materials with topologically nontrivial Fermi sea regions.

cond-mat.mes-hall↗

arXiv2016

Superconductivity in topologically nontrivial material Au2Pb

The search for nontrivial superconductivity in novel quantum materials is currently a most attractive topic in condensed matter physics and material science. The experimental studies have progressed quickly over the past couple of years. In this article, we report systematic studies of superconductivity in Au2Pb single crystals. The bulk superconductivity (onset transition temperature, Tconset= 1.3 K) of Au2Pb is characterized by both transport and diamagnetic measurements, where the upper critical field Hc2 shows unusual quasi-linear temperature dependence. The superconducting gap is revealed by point contact measurement with gold tip. However, when using tungsten (W) tip, which is much harder, the superconducting gap probed is largely enhanced as demonstrated by the increases of both Tconset and upper critical field (Hc2). This can be interpreted as a result of increase in density of states under external anisotropic stress imposed by the tip, as revealed by first-principles calculations. Furthermore, novel phase winding of the pseudospin texture along k-space loops around the Fermi energy is uncovered from the calculations, indicating that the observed superconductivity in Au2Pb may have nontrivial topology.

cond-mat.supr-con↗

arXiv2016

Expectation on Observation of Supernova Remnants with the LHAASO Project

Supernova remnants (SNRs) are believed to be the most important acceleration sites for cosmic rays (CRs) below $\sim10^{15}$ eV in the Galaxy. High energy photons, either directly from the shocks of the SNRs or indirectly from the interaction between SNRs and the nearby clouds, are crucial probes for the CR acceleration. Big progresses on observations of SNRs have been achieved by space- and ground-based $γ$-ray facilities. However, whether $γ$-rays come from accelerated hadrons or not, as well as their connection with the CRs observed at Earth, remains in debate. Large High Altitude Air Shower Observatory (LHAASO), the next generation experiment, is designed to survey the northern part of the very high energy $γ$-ray sky from $\sim 0.3$ TeV to PeV with the sensitivity of $\lesssim1\%$ of the Crab nebula flux. In this paper, we indicate that LHAASO will be dedicated to enlarging the $γ$-ray SNR samples and improving the spectral and morphological measurements. These measurements, especially at energies above 30 TeV, will be important for us to finally understand the CR acceleration in SNRs.

astro-ph.HE↗

arXiv2016

Discovery of Lorentz-violating Weyl fermion semimetal state in LaAlGe materials

We report theoretical and experimental discovery of Lorentz-violating Weyl fermion semimetal type-II state in the LaAlGe class of materials. Previously type-II Weyl state was predicted in WTe2 materials which remains unrealized in surface experiments. We show theoretically and experimentally that LaAlGe class of materials are the robust platforms for the study of type-II Weyl physics.

cond-mat.mes-hall↗