arXiv subjects

Xiao Wang

Publications and source records attributed to Xiao Wang.

At least 649 records · Page 36Linked to original sources

arXiv2020

Magnetic structures, spin-flop transition and coupling of Eu and Mn magnetism in the Dirac semimetal EuMnBi$_2$

We report here a comprehensive study of the AFM structures of the Eu and Mn magnetic sublattices as well as the interplay between Eu and Mn magnetism in this compound by using both polarized and non-polarized single-crystal neutron diffraction. Magnetic susceptibility, specific heat capacity measurements and the temperature dependence of magnetic diffractions suggest that the AFM ordering temperature of the Eu and Mn moments is at 22 and 337 K, respectively. The magnetic moments of both Eu and Mn ions are oriented along the crystallographic $c$ axis, and the respective magnetic propagation vector is $\textbf{k}_{Eu} = (0,0,1)$ and $\textbf{k}_{Mn}=(0,0,0)$. With proper neutron absorption correction, the ordered moments are refined at 3 K as 7.7(1) $μ_B$ and 4.1(1) $μ_B$ for the Eu and Mn ions, respectively. In addition, a spin-flop (SF) phase transition of the Eu moments in an applied magnetic field along the $c$ axis was confirmed to take place at a critical field of B$_c$ $\sim$ 5.3 T. The evolution of the Eu magnetic moment direction as a function of the applied magnetic field in the SF phase was also determined. Clear kinks in both field and temperature dependence of the magnetic reflections ($\pm1$, 0, 1) of Mn were observed at the onset of the SF phase transition and the AFM order of the Eu moments, respectively. This unambiguously indicates the existence of a strong coupling between Eu and Mn magnetism. The interplay between two magnetic sublattices could bring new possibilities to tune Dirac fermions via changing magnetic structures by applied fields in this class of magnetic topological semimetals.

cond-mat.str-el↗

arXiv2020

Deep Reinforced Query Reformulation for Information Retrieval

Query reformulations have long been a key mechanism to alleviate the vocabulary-mismatch problem in information retrieval, for example by expanding the queries with related query terms or by generating paraphrases of the queries. In this work, we propose a deep reinforced query reformulation (DRQR) model to automatically generate new reformulations of the query. To encourage the model to generate queries which can achieve high performance when performing the retrieval task, we incorporate query performance prediction into our reward function. In addition, to evaluate the quality of the reformulated query in the context of information retrieval, we first train our DRQR model, then apply the retrieval ranking model on the obtained reformulated query. Experiments are conducted on the TREC 2020 Deep Learning track MSMARCO document ranking dataset. Our results show that our proposed model outperforms several query reformulation model baselines when performing retrieval task. In addition, improvements are also observed when combining with various retrieval models, such as query expansion and BERT.

cs.IR↗

arXiv2020

Recent developments in the PySCF program package

PYSCF is a Python-based general-purpose electronic structure platform that both supports first-principles simulations of molecules and solids, as well as accelerates the development of new methodology and complex computational workflows. The present paper explains the design and philosophy behind PYSCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PYSCF as a development environment. We then summarize the capabilities of PYSCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PYSCF across the domains of quantum chemistry, materials science, machine learning and quantum information science.

physics.chem-ph↗

arXiv2020

AM-GCN: Adaptive Multi-channel Graph Convolutional Networks

Graph Convolutional Networks (GCNs) have gained great popularity in tackling various analytics tasks on graph and network data. However, some recent studies raise concerns about whether GCNs can optimally integrate node features and topological structures in a complex graph with rich information. In this paper, we first present an experimental investigation. Surprisingly, our experimental results clearly show that the capability of the state-of-the-art GCNs in fusing node features and topological structures is distant from optimal or even satisfactory. The weakness may severely hinder the capability of GCNs in some classification tasks, since GCNs may not be able to adaptively learn some deep correlation information between topological structures and node features. Can we remedy the weakness and design a new type of GCNs that can retain the advantages of the state-of-the-art GCNs and, at the same time, enhance the capability of fusing topological structures and node features substantially? We tackle the challenge and propose an adaptive multi-channel graph convolutional networks for semi-supervised classification (AM-GCN). The central idea is that we extract the specific and common embeddings from node features, topological structures, and their combinations simultaneously, and use the attention mechanism to learn adaptive importance weights of the embeddings. Our extensive experiments on benchmark data sets clearly show that AM-GCN extracts the most correlated information from both node features and topological structures substantially, and improves the classification accuracy with a clear margin.

cs.LG↗

arXiv2020

Decorrelated Clustering with Data Selection Bias

Most of existing clustering algorithms are proposed without considering the selection bias in data. In many real applications, however, one cannot guarantee the data is unbiased. Selection bias might bring the unexpected correlation between features and ignoring those unexpected correlations will hurt the performance of clustering algorithms. Therefore, how to remove those unexpected correlations induced by selection bias is extremely important yet largely unexplored for clustering. In this paper, we propose a novel Decorrelation regularized K-Means algorithm (DCKM) for clustering with data selection bias. Specifically, the decorrelation regularizer aims to learn the global sample weights which are capable of balancing the sample distribution, so as to remove unexpected correlations among features. Meanwhile, the learned weights are combined with k-means, which makes the reweighted k-means cluster on the inherent data distribution without unexpected correlation influence. Moreover, we derive the updating rules to effectively infer the parameters in DCKM. Extensive experiments results on real world datasets well demonstrate that our DCKM algorithm achieves significant performance gains, indicating the necessity of removing unexpected feature correlations induced by selection bias when clustering.

cs.LG↗

arXiv2020

Magnetic asymmetry induced anomalous spin-orbit torque in IrMn

We demonstrate an anomalous spin-orbit torque induced by the broken magnetic symmetry in the antiferromagnet IrMn. We study the magnetic structure of three phases of IrMn thin films using neutron diffraction technique. The magnetic mirror symmetry M' is broken laterally in both L10-IrMn and L12-IrMn3 but not γ-IrMn3. We observe an out-of-plane damping-like spin-orbit torque in both L10-IrMn/permalloy and L12-IrMn3/permalloy bilayers but not in γ-IrMn3/permalloy. This is consistent with both the symmetry analysis on the effects of a broken M' on spin-orbit torque and the theoretical predictions of the spin Hall effect and the Rashba-Edelstein effect. In addition, the measured spin-orbit torque efficiencies are 0.61+-0.01, 1.01+-0.03 and 0.80+-0.01 for the L10, L12 and γ phases, respectively. Our work highlights the critical roles of the magnetic asymmetry in spin-orbit torque generation.

cond-mat.mtrl-sci↗

arXiv2020

Measuring Compositional Generalization: A Comprehensive Method on Realistic Data

State-of-the-art machine learning methods exhibit limited compositional generalization. At the same time, there is a lack of realistic benchmarks that comprehensively measure this ability, which makes it challenging to find and evaluate improvements. We introduce a novel method to systematically construct such benchmarks by maximizing compound divergence while guaranteeing a small atom divergence between train and test sets, and we quantitatively compare this method to other approaches for creating compositional generalization benchmarks. We present a large and realistic natural language question answering dataset that is constructed according to this method, and we use it to analyze the compositional generalization ability of three machine learning architectures. We find that they fail to generalize compositionally and that there is a surprisingly strong negative correlation between compound divergence and accuracy. We also demonstrate how our method can be used to create new compositionality benchmarks on top of the existing SCAN dataset, which confirms these findings.

cs.LG↗

arXiv2020

Efficient Evaluation of Exact Exchange for Periodic Systems via Concentric Atomic Density Fitting

The evaluation of exact (Hartree--Fock, HF) exchange operator is a crucial ingredient for the accurate description of electronic structure in periodic systems through ab initio and hybrid density functional approaches. An efficient formulation of periodic HF exchange in LCAO representation presented here is based on the concentric atomic density fitting (CADF) approximation, a domain-free local density fitting approach in which the product of two atomic orbitals (AOs) is approximated using a linear combination of fitting basis functions centered at the same nuclei as the AOs in that product. Significant reduction in the computational cost of exact exchange is demonstrated relative to the conventional approach due to avoiding the need to evaluate four-center two-electron integrals, with sub-millihartree/atom errors in absolute Hartree-Fock energies and good cancellation of fitting errors in relative energies. Novel aspects of the evaluation of the Coulomb contribution to the Fock operator, such as the use of real two-center multipole expansions and spheropole-compensated unit cell densities are also described.

physics.chem-ph↗

arXiv2020

Analysis of multiscale methods for stochastic dynamical systems driven by $α$-stable processes

In this paper, we first analyze the strong and weak convergence of projective integration methods for multiscale stochastic dynamical systems driven by $α$-stable processes, which are used to estimate the effect that the fast components have on slow ones. Then we obtain the $p$th moment error bounds between the solution of slow component produced by projective integration method and the solution of effective system with $p \in \left(1, α\right)$. Finally, we corroborate our analytical results through a specific numerical example.

math.PR↗

arXiv2020

Observation of E8 Particles in an Ising Chain Antiferromagnet

Near the transverse-field induced quantum critical point of the Ising chain, an exotic dynamic spectrum consisting of exactly eight particles was predicted, which is uniquely described by an emergent quantum integrable field theory with the symmetry of the $E_8$ Lie algebra, but rarely explored experimentally. Here we use high-resolution terahertz spectroscopy to resolve quantum spin dynamics of the quasi-one-dimensional Ising antiferromagnet BaCo$_2$V$_2$O$_8$ in an applied transverse field. By comparing to an analytical calculation of the dynamical spin correlations, we identify $E_8$ particles as well as their two-particle excitations.

cond-mat.str-el↗

arXiv2020

Phase transition dynamics and gravitational wave spectra of strong first-order phase transition in supercooled universe

Phase transition dynamics may play important roles in the evolution history of the early universe, such as its possible roles in electroweak baryogenesis and dark matter.We systematically discuss and clarify the important details of the phase transition dynamics during a strong first-order phase transition (SFOPT). We classify the SFOPT into four types: slight supercooling, mild supercooling, strong supercooling, and ultra supercooling. Using different characteristic temperatures, length scales and bubble wall velocities, the corresponding gravitational wave (GW) spectra are investigated in details. We emphasize the essential importance of using the correct characteristic temperature and length scale when the phase transition dynamics and GW spectra are calculated. Especially, for strong supercooling and ultra supercooling cases, there are obvious differences of the phase transition strength and GW spectra between the results calculated at the nucleation temperature and those derived at the percolation temperature. For ultra supercooling case, we propose a criterion to quantify whether the phase transition can terminate. Besides the model-independent discussions, we also study three representative models as concrete examples to clearly show the subtle points therein.

hep-ph↗

arXiv2020

Revisiting the Effect of f-Functions in Predicting the Right Reaction Mechanism for Hypervalent Iodine Reagents

To understand the effect of f-functions in predicting the right reaction mechanism for hypervalent iodine reagents, we adopt the Ahlrichs basis set family def2-SVP and def2-TZVP to revisit the potential energy surfaces of IBX-mediated oxidation and Togni I's isomerisation. Our results further prove that f-functions (in either Pople, Dunning, or Ahlrichs basis set series) are indispensable to predict the correct rate-determining step of hypervalent iodine reagents. The f-functions have a significant impact on the predicted reaction barriers for processes involving the I-X (X = O, OH, CF$_3$, etc.) bond cleavage and formation, e.g. in the reductive elimination step or the hypervalent twist step. We furthermore explore two hypervalent twist modes that account for the different influences of f-functions for IBX and Togni I. Our findings may be helpful for theoretical chemists to appropriately study the reaction mechanism of hypervalent iodine reagents.

physics.chem-ph↗

arXiv2020

\emph{cm}SalGAN: RGB-D Salient Object Detection with Cross-View Generative Adversarial Networks

Image salient object detection (SOD) is an active research topic in computer vision and multimedia area. Fusing complementary information of RGB and depth has been demonstrated to be effective for image salient object detection which is known as RGB-D salient object detection problem. The main challenge for RGB-D salient object detection is how to exploit the salient cues of both intra-modality (RGB, depth) and cross-modality simultaneously which is known as cross-modality detection problem. In this paper, we tackle this challenge by designing a novel cross-modality Saliency Generative Adversarial Network (\emph{cm}SalGAN). \emph{cm}SalGAN aims to learn an optimal view-invariant and consistent pixel-level representation for RGB and depth images via a novel adversarial learning framework, which thus incorporates both information of intra-view and correlation information of cross-view images simultaneously for RGB-D saliency detection problem. To further improve the detection results, the attention mechanism and edge detection module are also incorporated into \emph{cm}SalGAN. The entire \emph{cm}SalGAN can be trained in an end-to-end manner by using the standard deep neural network framework. Experimental results show that \emph{cm}SalGAN achieves the new state-of-the-art RGB-D saliency detection performance on several benchmark datasets.

cs.CV↗

arXiv2020

The second Yang-Baxter homology for the Homflypt polynomial

In this article, we adjust the Yang-Baxter operators constructed by Jones for the HOMFLYPT polynomal. Then we compute the second homology for this family of Yang-Baxter operators. It has the potential to yield 2-cocycle invariant for links.

math.GT↗

arXiv2020

Convergence to Second-Order Stationarity for Non-negative Matrix Factorization: Provably and Concurrently

Non-negative matrix factorization (NMF) is a fundamental non-convex optimization problem with numerous applications in Machine Learning (music analysis, document clustering, speech-source separation etc). Despite having received extensive study, it is poorly understood whether or not there exist natural algorithms that can provably converge to a local minimum. Part of the reason is because the objective is heavily symmetric and its gradient is not Lipschitz. In this paper we define a multiplicative weight update type dynamics (modification of the seminal Lee-Seung algorithm) that runs concurrently and provably avoids saddle points (first order stationary points that are not second order). Our techniques combine tools from dynamical systems such as stability and exploit the geometry of the NMF objective by reducing the standard NMF formulation over the non-negative orthant to a new formulation over (a scaled) simplex. An important advantage of our method is the use of concurrent updates, which permits implementations in parallel computing environments.

cs.LG↗

arXiv2020

Numerical analysis and applications of Fokker-Planck equations for stochastic dynamical systems with multiplicative $α$-stable noises

The Fokker-Planck equations (FPEs) for stochastic systems driven by additive symmetric $α$-stable noises may not adequately describe the time evolution for the probability densities of solution paths in some practical applications, such as hydrodynamical systems, porous media, and composite materials. As a continuation of previous works on additive case, the FPEs for stochastic dynamical systems with multiplicative symmetric $α$-stable noises are derived by the adjoint operator method, which satisfy the nonlocal partial differential equations. A finite difference method for solving the nonlocal Fokker-Planck equation (FPE) is constructed, which is shown to satisfy the discrete maximum principle and to be convergent. Moreover, an example is given to illustrate this method. For asymmetric case, general finite difference schemes are proposed, and some analyses of the corresponding numerical schemes are given. Furthermore, the corresponding result is successfully applied to the nonlinear filtering problem.

math.DS↗

arXiv2020

FMT:Fusing Multi-task Convolutional Neural Network for Person Search

Person search is to detect all persons and identify the query persons from detected persons in the image without proposals and bounding boxes, which is different from person re-identification. In this paper, we propose a fusing multi-task convolutional neural network(FMT-CNN) to tackle the correlation and heterogeneity of detection and re-identification with a single convolutional neural network. We focus on how the interplay of person detection and person re-identification affects the overall performance. We employ person labels in region proposal network to produce features for person re-identification and person detection network, which can improve the accuracy of detection and re-identification simultaneously. We also use a multiple loss to train our re-identification network. Experiment results on CUHK-SYSU Person Search dataset show that the performance of our proposed method is superior to state-of-the-art approaches in both mAP and top-1.

cs.CV↗

arXiv2020

AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks

Designing effective defense against adversarial attacks is a crucial topic as deep neural networks have been proliferated rapidly in many security-critical domains such as malware detection and self-driving cars. Conventional defense methods, although shown to be promising, are largely limited by their single-source single-cost nature: The robustness promotion tends to plateau when the defenses are made increasingly stronger while the cost tends to amplify. In this paper, we study principles of designing multi-source and multi-cost schemes where defense performance is boosted from multiple defending components. Based on this motivation, we propose a multi-source and multi-cost defense scheme, Adversarially Trained Model Switching (AdvMS), that inherits advantages from two leading schemes: adversarial training and random model switching. We show that the multi-source nature of AdvMS mitigates the performance plateauing issue and the multi-cost nature enables improving robustness at a flexible and adjustable combination of costs over different factors which can better suit specific restrictions and needs in practice.

cs.LG↗