arXiv subjects

Tao Li

Publications and source records attributed to Tao Li.

At least 433 records · Page 24Linked to original sources

arXiv2020

Entanglement Witnesses Based on Symmetric Informationally Complete Measurements

We study entanglement witness and present a construction of entanglement witnesses in terms of the symmetric informationally complete measurements (SIC-POVM). The capability of our witness is shown by some examples and it can be found this witness detects more entanglement than previous separability method given also by SIC-POVM. What's more, comparing with the dimension dependence of SIC-POVM, we also give the entanglement witnesses can be always constructed from general symmetric informationally complete measurements (GSIC-POVM).

quant-ph↗

arXiv2020

Some Theoretical Results Concerning Time-varying Nonparametric Regression with Local Stationary Regressors and Error

With regard to a three-step estimation procedure, proposed without theoretical discussion by Li and You in Journal of Applied Statistics and Management, for a nonparametric regression model with time-varying regression function, local stationary regressors and time-varying AR(p) (tvAR(p)) error process , we established all necessary asymptotic properties for each of estimator. We derive the convergence rate and asymptotic normality of the preliminary estimation of nonparametric regression function, establish the asymptotic distribution of time-varying coefficient functions in the error term, and present the asymptotic property of the refined estimation of nonparametric regression function. In addition, with regard to the ULASSO method for variable selection and constant coefficient detection for error term structure, we show that the ULASSO estimator can identify the true error term structure consistently. We conduct two simulation studies to illustrate the finite sample performance of the estimators and validate our theoretical discussion on the properties of the estimators.

math.ST↗

arXiv2020

Enhanced magnetocaloric effect and magnetic phase diagrams of single-crystal GdCrO$_3$

The crystalline structure, magnetism, and magnetocaloric effect of a GdCrO$_3$ single crystal grown with the laser-diode-heated floating-zone technique have been studied. The GdCrO$_3$ single crystal crystallizes into an orthorhombic structure with the space group $Pmnb$ at room temperature. Upon cooling, under a magnetic field of 0.1 T, it undergoes a magnetic phase transition at $T_{\textrm{N-Cr}} =$ 169.28(2) K with Cr$^{3+}$ ions forming a canted antiferromagnetic (AFM) structure, accompanied by a weak ferromagnetism. Subsequently, a spin reorientation takes place at $T_{\textrm{SR}} =$ 5.18(2) K due to Gd$^{3+}$-Cr$^{3+}$ magnetic couplings. Finally, the long-range AFM order of Gd$^{3+}$ ions establishes at $T_{\textrm{N-Gd}} =$ 2.10(2) K. Taking into account the temperature-(in)dependent components of Cr$^{3+}$ moments, we obtained an ideal model for describing the paramagnetic behavior of Gd$^{3+}$ ions within 30--140 K. We observed a magnetic reversal (positive $\rightarrow$ negative $\rightarrow$ positive) at 50 Oe with a minimum centering around 162 K. In the studied temperature range of 1.8-300 K, there exists a strong competition between magnetic susceptibilities of Gd$^{3+}$ and Cr$^{3+}$ ions, leading to puzzling magnetic phenomena. We have built the magnetic-field-dependent phase diagrams of $T_{\textrm{N-Gd}}$, $T_{\textrm{SR}}$, and $T_{\textrm{N-Cr}}$, shedding light on the nature of the intriguing magnetism. Moreover, we calculated the magnetic entropy change and obtained a maximum value at 6 K and $Δμ_0H$ = 14 T, i.e., -$ΔS_{\textrm{M}} \approx$ 57.5 J/kg K. Among all RCrO$_3$ (R = $4f^n$ rare earths, $n =$ 7-14) compounds, the single-crystal GdCrO$_3$ compound exhibits the highest magnetic entropy change, as well as an enhanced adiabatic temperature, creating a prominent magnetocaloric effect for potential application in magnetic refrigeration.

cond-mat.str-el↗

arXiv2020

Gauge-induced Floquet topological states in photonic waveguides

Tremendous efforts have been devoted to the search for exotic topological states, which usually exist at an interface between lattices with differing topological invariants according to the bulk-edge correspondence. Here, we show a new finding of topological states localized at the interface between two gauge-shifted Floquet photonic lattices, despite the same topological order across the entire structure. The quasienergy band structures reveal that these new interface modes belong to the Floquet π modes, which are further found to enable a robust one-way propagation thanks to the flexible control of the Floquet gauge. The intriguing propagations of these π-interface modes are experimentally verified in a silicon waveguides platform at near-infrared wavelengths, which show both broad working bandwidth and high tolerance to the structural fluctuations. Our approach provides a new route for light manipulations with robust behaviours in Floquet engineering and beyond.

physics.optics↗

arXiv2020

UnQovering Stereotyping Biases via Underspecified Questions

While language embeddings have been shown to have stereotyping biases, how these biases affect downstream question answering (QA) models remains unexplored. We present UNQOVER, a general framework to probe and quantify biases through underspecified questions. We show that a naive use of model scores can lead to incorrect bias estimates due to two forms of reasoning errors: positional dependence and question independence. We design a formalism that isolates the aforementioned errors. As case studies, we use this metric to analyze four important classes of stereotypes: gender, nationality, ethnicity, and religion. We probe five transformer-based QA models trained on two QA datasets, along with their underlying language models. Our broad study reveals that (1) all these models, with and without fine-tuning, have notable stereotyping biases in these classes; (2) larger models often have higher bias; and (3) the effect of fine-tuning on bias varies strongly with the dataset and the model size.

cs.CL↗

arXiv2020

On Data Augmentation for Extreme Multi-label Classification

In this paper, we focus on data augmentation for the extreme multi-label classification (XMC) problem. One of the most challenging issues of XMC is the long tail label distribution where even strong models suffer from insufficient supervision. To mitigate such label bias, we propose a simple and effective augmentation framework and a new state-of-the-art classifier. Our augmentation framework takes advantage of the pre-trained GPT-2 model to generate label-invariant perturbations of the input texts to augment the existing training data. As a result, it present substantial improvements over baseline models. Our contributions are two-factored: (1) we introduce a new state-of-the-art classifier that uses label attention with RoBERTa and combine it with our augmentation framework for further improvement; (2) we present a broad study on how effective are different augmentation methods in the XMC task.

cs.CL↗

arXiv2020

Crystalline and magnetic structures, magnetization, heat capacity and anisotropic magnetostriction effect in a yttrium-chromium oxide

We have studied a nearly stoichiometric insulating Y$_{0.97(2)}$Cr$_{0.98(2)}$O$_{3.00(2)}$ single crystal by performing measurements of magnetization, heat capacity, and neutron diffraction. Albeit that the YCrO$_3$ compound behaviors like a soft ferromagnet with a coersive force of $\sim$ 0.05 T, there exist strong antiferromagnetic (AFM) interactions between Cr$^{3+}$ spins due to a strongly negative paramagnetic Curie-Weiss temperature, i.e., -433.2(6) K. The coexistence of ferromagnetism and antiferromagnetism may indicate a canted AFM structure. The AFM phase transition occurs at $T_\textrm{N} =$ 141.5(1) K, which increases to $T_\textrm{N}$(5T) = 144.5(1) K at 5 T. Within the accuracy of the present neuron-diffraction studies, we determine a G-type AFM structure with a propagation vector \textbf{k} = (1 1 0) and Cr$^{3+}$ spin directions along the crystallographic \emph{c} axis of the orthorhombic structure with space group \emph{Pnma} below $T_\textrm{N}$. At 12 K, the refined moment size is 2.45(6) $μ_\textrm{B}$, $\sim$ 82\% of the theoretical saturation value 3 $μ_\textrm{B}$. The Cr$^{3+}$ spin interactions are probably two-dimensional Ising like within the reciprocal (1 1 0) scattering plane. Below $T_\textrm{N}$, the lattice configuration (\emph{a}, \emph{b}, \emph{c}, and \emph{V}) deviates largely downward from the Gr$\ddot{\textrm{u}}$neisen law, displaying an anisotropic magnetostriction effect and a magnetoelastic effect. Especially, the sample contraction upon cooling is enhanced below the AFM transition temperature. There is evidence to suggest that the actual crystalline symmetry of YCrO$_3$ compound is probably lower than the currently assumed one. Additionally, we compared the $t_{2\textrm{g}}$ YCrO$_3$ and the $e_\textrm{g}$ La$_{7/8}$Sr$_{1/8}$MnO$_3$ single crystals for a further understanding of the reason for the possible symmetry lowering.

cond-mat.str-el↗

arXiv2020

Attention-SLAM: A Visual Monocular SLAM Learning from Human Gaze

This paper proposes a novel simultaneous localization and mapping (SLAM) approach, namely Attention-SLAM, which simulates human navigation mode by combining a visual saliency model (SalNavNet) with traditional monocular visual SLAM. Most SLAM methods treat all the features extracted from the images as equal importance during the optimization process. However, the salient feature points in scenes have more significant influence during the human navigation process. Therefore, we first propose a visual saliency model called SalVavNet in which we introduce a correlation module and propose an adaptive Exponential Moving Average (EMA) module. These modules mitigate the center bias to enable the saliency maps generated by SalNavNet to pay more attention to the same salient object. Moreover, the saliency maps simulate the human behavior for the refinement of SLAM results. The feature points extracted from the salient regions have greater importance in optimization process. We add semantic saliency information to the Euroc dataset to generate an open-source saliency SLAM dataset. Comprehensive test results prove that Attention-SLAM outperforms benchmarks such as Direct Sparse Odometry (DSO), ORB-SLAM, and Salient DSO in terms of efficiency, accuracy, and robustness in most test cases.

cs.CV↗

arXiv2020

Variational study of the ground state and spin dynamics of the spin-$\frac{1}{2}$ Kagome antiferromagnetic Heisenberg model and its implication on Hebertsmithite ZnCu$_{3}$(OH)$_{6}$Cl$_{2}$

We find that the best RVB state of the spin-$\frac{1}{2}$ Kagome antiferromagnetic Heisenberg model(spin-$\frac{1}{2}$ KAFH) is described by a $Z_{2}$ gapped mean field ansatz, which hosts a mean field spinon dispersion very different from that of the widely studied $U(1)$ Dirac spin liquid state. However, we find that the physical spin fluctuation spectrum calculated from the Gutzwiller projected RPA(GRPA) theory above such an RVB state is actually gapless and is almost identical to that above the $U(1)$ Dirac spin liquid state. We find that such a peculiar behavior can be attributed to the unique flat band physics on the Kagome lattice, which makes the mapping between the mean field ansatz and the RVB state non-injective. We find that the spin fluctuation spectrum of the spin-$\frac{1}{2}$ KAFH is not at all featureless, but is characterized by a prominent spectral peak at about $0.25J$ around the $\mathbf{M}$ point, which is immersed in a broad continuum extending to $2.7J$. Based on these results, we argue that the spectral peak below 2 meV in the inelastic neutron scattering(INS) spectrum of Hebertsmithite ZnCu$_{3}$(OH)$_{6}$Cl$_{2}$, which has been attributed to the contribution of Cu$^{2+}$ impurity spins occupying the Zn$^{2+}$ site, should rather be understood as the intrinsic contribution from the Kagome layer. We propose to verify such a picture by measuring the Knight shift on the Cu site, rather than the O site, which is almost blind to the spin fluctuation at the $\mathbf{M}$ point as a result of the strong antiferromagnetic correlation between nearest neighboring spins on the Kagome lattice.

cond-mat.str-el↗

arXiv2020

Nonlinear optical effects in a nucleus

Intense laser technologies generate light with unprecedented and growing intensities. The possibility emerges that a nucleus responds nonlinearly to an intense light field, pointing to a yet little explored research area of nuclear nonlinear optics. We consider two-photon and three-photon absorption (with subsequent disintegration) processes of the deuteron, the simplest and the most fundamental nontrivial nucleus, as prototypes of nuclear nonlinear optical effects. Quantitative calculations are performed on these processes and novel observable effects are predicted.

physics.atom-ph↗

arXiv2020

Why the anti-nodal quasiparticle dispersion is so flat in the superconducting cuprates?

The emergence of the coherent quasiparticle peak and the development of the peak-dip-hump structure in the anti-nodal region below $T_{c}$ is the most prominent non-BCS signature of the underdoped high-$T_{c}$ cuprates, in which no coherent quasiparticle can be defined in the anti-nodal region above $T_{c}$. The peak-dip-hump structure has been commonly interpreted as the result of the coupling of the electron to some Bosonic mode. However, such an electron-Boson coupling picture does not answer the question of \textit{why the quasiparticle dispersion is so flat in the anti-nodal region}, a behavior totally unexpected for Bogoliubov quasiparticle in a d-wave BCS superconductor. Here we show that the sharp quasiparticle peak in the anti-nodal region should be understood as a new pole in the electron Green's function generated by the strong coupling of the electron to diffusive spin fluctuation around the antiferromagnetic wave vector $\mathbf{Q}=(π,π)$, rather than a nearly free Bogoliubov quasiparticle in a d-wave BCS superconductor. More specifically, we find that the normal self-energy of the electron from the scattering with the diffusive spin fluctuation manifests itself mainly as a level repulsion effect and is responsible for the reduction of both the quasiparticle dispersion and the quasiparticle dissipation rate in the anti-nodal region. We argue that the peak-dip separation in the anti-nodal spectrum should not be interpreted as the energy of the pairing glue.

cond-mat.supr-con↗

arXiv2020

A continuous family of fully-frustrated Heisenberg model on the Kagome lattice

We find that the antiferromagnetic Heisenberg model on the Kagome lattice with nearest neighboring exchange coupling(NN-KAFH) belongs to a continuous family of fully-frustrated Heisenberg model on the Kagome lattice, which has no preferred classical ordering pattern. The model within this family consists of the first, second and the third neighboring exchange coupling $J_{1}$, $J_{2}$, and $J_{3}$, with $J_{2}=J_{3}$. We find that when $-J_{1}\leq J_{2}=J_{3}\leq 0.2J_{1}$, the lowest band of $J(\mathbf{q})$, namely, the Fourier transform of the exchange coupling, is totally non-dispersive. Exact diagonalization calculation indicates that the ground state of the spin-$\frac{1}{2}$ NN-KAFH is locally stable under the perturbation of $J_{2}$ and $J_{3}$ when and only when $J_{2}=J_{3}$. Interestingly, we find that the same flat band physics is also playing an important role in the RVB description of the spin liquid state on the Kagome lattice. In particular, we show that the extensively studied $U(1)$ Dirac spin liquid state on the Kagome lattice can actually be generated from a continuous family of gauge inequivalent RVB mean field ansatz, which host very different mean field spinon dispersion.

cond-mat.str-el↗

arXiv2020

Augmenting Neural Networks with First-order Logic

Today, the dominant paradigm for training neural networks involves minimizing task loss on a large dataset. Using world knowledge to inform a model, and yet retain the ability to perform end-to-end training remains an open question. In this paper, we present a novel framework for introducing declarative knowledge to neural network architectures in order to guide training and prediction. Our framework systematically compiles logical statements into computation graphs that augment a neural network without extra learnable parameters or manual redesign. We evaluate our modeling strategy on three tasks: machine comprehension, natural language inference, and text chunking. Our experiments show that knowledge-augmented networks can strongly improve over baselines, especially in low-data regimes.

cs.LG↗

arXiv2020

Dynamics of planar vector fields near a non-smooth equilibrium

In this paper we contribute to qualitative and geometric analysis of planar piecewise smooth vector fields, which consist of two smooth vector fields separated by the straight line $y=0$ and sharing the origin as a non-degenerate equilibrium. In the sense of $Σ$-equivalence, we provide a sufficient condition for linearization and give phase portraits and normal forms for these linearizable vector fields. This condition is hard to be weakened because there exist vector fields which are not linearizable when this condition is not satisfied. Regarding perturbations, a necessary and sufficient condition for local $Σ$-structural stability is established when the origin is still an equilibrium of both smooth vector fields under perturbations. In the opposition to this case, we prove that for any piecewise smooth vector field studied in this paper there is a limit cycle bifurcating from the origin, and there are some piecewise smooth vector fields such that for any positive integer $m$ there is a perturbation having exactly $m$ limit cycles bifurcating from the origin. Here $m$ maybe infinity.

math.DS↗

arXiv2020

Vertical CVD Growth of Highly Uniform Transition Metal Dichalcogenides

Two-dimensional (2D) transition metal dichalcogenides (TMDCs) have attracted great attention due to their physical and chemical properties that make them promising in electronics and optoelectronics. Because of the difficulties in controlling concentrations of solid precursors and spatially non-uniform growth dynamics, it is challenging to grow wafer-scale 2D TMDCs with good uniformity and reproducibility so far, which significantly hinders their practical use. Here we report a vertical chemical vapor deposition (VCVD) design to grow monolayer TMDCs with a uniform density and high quality over the whole wafer, and with excellent reproducibility. The use of such VCVD design can easily control the three key growth parameters of precursor concentration, gas flow and temperature, which cannot be done in currently widely-used horizontal CVD system. Statistical results show that VCVD-grown monolayer TMDCs including MoS2 and WS2 are of high uniformity and quality on substrates over centimeter size. We also fabricated multiple van der Waals heterostructures by the one-step transfer of VCVD-grown TMDC samples, owning to its good uniformity. This work opens a way to grow 2D materials with high uniformity and reproducibility on the wafer scale, which can be used for the scalable fabrication of 2D materials and their heterostructures.

physics.app-ph↗

arXiv2020

Real-time Universal Style Transfer on High-resolution Images via Zero-channel Pruning

Extracting effective deep features to represent content and style information is the key to universal style transfer. Most existing algorithms use VGG19 as the feature extractor, which incurs a high computational cost and impedes real-time style transfer on high-resolution images. In this work, we propose a lightweight alternative architecture - ArtNet, which is based on GoogLeNet, and later pruned by a novel channel pruning method named Zero-channel Pruning specially designed for style transfer approaches. Besides, we propose a theoretically sound sandwich swap transform (S2) module to transfer deep features, which can create a pleasing holistic appearance and good local textures with an improved content preservation ability. By using ArtNet and S2, our method is 2.3 to 107.4 times faster than state-of-the-art approaches. The comprehensive experiments demonstrate that ArtNet can achieve universal, real-time, and high-quality style transfer on high-resolution images simultaneously, (68.03 FPS on 512 times 512 images).

cs.CV↗

arXiv2020

Ferroelastic-switching-driven colossal shear strain and piezoelectricity in a hybrid ferroelectric

Materials that can produce large controllable strains are widely used in shape memory devices, actuators and sensors. Great efforts have been made to improve the strain outputs of various material systems. Among them, ferroelastic transitions underpin giant reversible strains in electrically-driven ferro/piezoelectrics and thermally- or magneticallydriven shape memory alloys. However, large-strain ferroelastic switching in conventional ferroelectrics is very challenging while magnetic and thermal controls are not desirable for applications. Here, we demonstrate an unprecedentedly large shear strain up to 21.5 % in a hybrid ferroelectric, C6H5N(CH3)3CdCl3. The strain response is about two orders of magnitude higher than those of top-performing conventional ferroelectric polymers and oxides. It is achieved via inorganic bond switching and facilitated by the structural confinement of the large organic moieties, which prevents the undesired 180-degree polarization switching. Furthermore, Br substitution can effectively soften the bonds and result in giant shear piezoelectric coefficient (d35 ~ 4800 pm/V) in Br-rich end of the solid solution, C6H5N(CH3)3CdBr3xCl3(1-x). The superior electromechanical properties of the compounds promise their potential in lightweight and high energy density devices, and the strategy described here should inspire the development of next-generation piezoelectrics and electroactive materials based on hybrid ferroelectrics.

cond-mat.mtrl-sci↗

arXiv2020

VecQ: Minimal Loss DNN Model Compression With Vectorized Weight Quantization

Quantization has been proven to be an effective method for reducing the computing and/or storage cost of DNNs. However, the trade-off between the quantization bitwidth and final accuracy is complex and non-convex, which makes it difficult to be optimized directly. Minimizing direct quantization loss (DQL) of the coefficient data is an effective local optimization method, but previous works often neglect the accurate control of the DQL, resulting in a higher loss of the final DNN model accuracy. In this paper, we propose a novel metric called Vector Loss. Based on this new metric, we develop a new quantization solution called VecQ, which can guarantee minimal direct quantization loss and better model accuracy. In addition, in order to speed up the proposed quantization process during model training, we accelerate the quantization process with a parameterized probability estimation method and template-based derivation calculation. We evaluate our proposed algorithm on MNIST, CIFAR, ImageNet, IMDB movie review and THUCNews text data sets with numerical DNN models. The results demonstrate that our proposed quantization solution is more accurate and effective than the state-of-the-art approaches yet with more flexible bitwidth support. Moreover, the evaluation of our quantized models on Saliency Object Detection (SOD) tasks maintains comparable feature extraction quality with up to 16$\times$ weight size reduction.

cs.CV↗