arXiv subjects

Xing Wang

Publications and source records attributed to Xing Wang.

At least 181 records · Page 10Linked to original sources

arXiv2021

Quantum spin Hall effect in two-dimensional transition-metal chalcogenides

Based on first-principles calculations, we have found a family of 2D transition-metal (TM) chalcogenides MX5 (M = Zr, Hf and X = S, Se and Te) can host quantum spin Hall (QSH) effect. The molecular dynamics simulation indicate that they are all thermal-dynamically stable at room temperature, the largest band gap is 0.19 eV. We have investigated MX5's electronic properties and found their properties are very similar. The single-layer ZrX5 are all gapless semimetals without consideration of spin-orbit coupling (SOC). The consideration of SOC will result in insulating phases with band gaps of 0.05 eV (direct), 0.18 eV (direct) and 0.13 eV (indirect) for ZrS5, ZrSe5 to ZrTe5, respectively. The evolution of Wannier charge centers and edge states confirm they are all QSH insulators. The mechanisms for QSH effect in ZrX5 originate from the special nonsymmorphic space group features. In addition, the QSH state of ZrS5 survives at a large range of strain as long as the interchain coupling is not strong enough to reverse the band ordering. The single-layer ZrS5 will occur a topological insulator (TI)-to-semimetal (metal) or metal-to-semimetal transition under certain strain. Monolayer MX5 expand the TI materials based on TM chalcogenides and may open up a new way to fabricate novel low power spintronic devices at room temperature.

cond-mat.mtrl-sci↗

arXiv2021

WIMPs at High Energy Muon Colliders

The Weakly Interacting Massive Particle (WIMP) paradigm is one of the most compelling scenarios for particle dark matter (DM). We show in this paper that a high energy muon collider can make decisive statements about the WIMP DM, and this should serve as one of its main physics driver cases. We demonstrate this by employing the DM as the lightest member of an electroweak (EW) multiplet, which is a simple, yet one of the most challenging WIMP scenarios given its minimal collider signature and high thermal target mass scale of 1 TeV$-$23 TeV. We perform a first study of the reach of high energy muon colliders, focusing on the simple, inclusive and conservative signals with large missing mass, through the mono-photon, VBF di-muon and a novel mono-muon channel. Using these inclusive signals, it is possible to cover the thermal targets of doublet and triplet with a 10 TeV muon collider. Higher energies, 14 TeV$-$75 TeV, would ensure a $5 σ$ reach above the thermal targets for the higher EW multiplets. We also estimate the reach of a search for disappearing tracks, demonstrating the potential significant enhancement of the sensitivity.

hep-ph↗

arXiv2021

Factorization at Subleading Power, Sudakov Resummation and Endpoint Divergences in Soft-Collinear Effective Theory

Starting from the first renormalized factorization theorem for a process described at subleading power in soft-collinear effective theory, we discuss the resummation of Sudakov logarithms for such processes in renormalization-group improved perturbation theory. Endpoint divergences in convolution integrals, which arise generically beyond leading power, are regularized and removed by systematically rearranging the factorization formula. We study in detail the example of the $b$-quark induced $h\toγγ$ decay of the Higgs boson, for which we resum large logarithms of the ratio $M_h/m_b$ at next-to-leading logarithmic order. We also briefly discuss the related $gg\to h$ amplitude.

hep-ph↗

arXiv2021

Robust large-gap topological insulator phase in transition-metal chalcogenide ZrTe$_4$Se

Based on density functional theory (DFT), we investigate the electronic properties of bulk and single-layer ZrTe$_4$Se. The band structure of bulk ZrTe$_4$Se can produce a semimetal-to-topological insulator (TI) phase transition under uniaxial strain. The maximum global band gap is 0.189 eV at the 7\% tensile strain. Meanwhile, the Z$_2$ invariants (0; 110) demonstrate conclusively it is a weak topological insulator (WTI). The two Dirac cones for the (001) surface further confirm the nontrivial topological nature. The single-layer ZrTe$_4$Se is a quantum spin Hall (QSH) insulator with a band gap 86.4 meV and Z$_2$=1, the nontrivial metallic edge states further confirm the nontrivial topological nature. The maximum global band gap is 0.211 eV at the tensile strain 8\%. When the compressive strain is more than 1\%, the band structure of single-layer ZrTe$_4$Se produces a TI-to-semimetal transition. These theoretical analysis may provide a method for searching large band gap TIs and platform for topological nanoelectronic device applications.

cond-mat.mtrl-sci↗

arXiv2021

Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation

Self-training has proven effective for improving NMT performance by augmenting model training with synthetic parallel data. The common practice is to construct synthetic data based on a randomly sampled subset of large-scale monolingual data, which we empirically show is sub-optimal. In this work, we propose to improve the sampling procedure by selecting the most informative monolingual sentences to complement the parallel data. To this end, we compute the uncertainty of monolingual sentences using the bilingual dictionary extracted from the parallel data. Intuitively, monolingual sentences with lower uncertainty generally correspond to easy-to-translate patterns which may not provide additional gains. Accordingly, we design an uncertainty-based sampling strategy to efficiently exploit the monolingual data for self-training, in which monolingual sentences with higher uncertainty would be sampled with higher probability. Experimental results on large-scale WMT English$\Rightarrow$German and English$\Rightarrow$Chinese datasets demonstrate the effectiveness of the proposed approach. Extensive analyses suggest that emphasizing the learning on uncertain monolingual sentences by our approach does improve the translation quality of high-uncertainty sentences and also benefits the prediction of low-frequency words at the target side.

cs.CL↗

arXiv2021

TranSmart: A Practical Interactive Machine Translation System

Automatic machine translation is super efficient to produce translations yet their quality is not guaranteed. This technique report introduces TranSmart, a practical human-machine interactive translation system that is able to trade off translation quality and efficiency. Compared to existing publicly available interactive translation systems, TranSmart supports three key features, word-level autocompletion, sentence-level autocompletion and translation memory. By word-level and sentence-level autocompletion, TranSmart allows users to interactively translate words in their own manners rather than the strict manner from left to right. In addition, TranSmart has the potential to avoid similar translation mistakes by using translated sentences in history as its memory. This report presents major functions of TranSmart, algorithms for achieving these functions, how to use the TranSmart APIs, and evaluation results of some key functions. TranSmart is publicly available at its homepage (https://transmart.qq.com).

cs.CL↗

arXiv2021

Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism

In this paper, we consider hybrid parallelism -- a paradigm that employs both Data Parallelism (DP) and Model Parallelism (MP) -- to scale distributed training of large recommendation models. We propose a compression framework called Dynamic Communication Thresholding (DCT) for communication-efficient hybrid training. DCT filters the entities to be communicated across the network through a simple hard-thresholding function, allowing only the most relevant information to pass through. For communication efficient DP, DCT compresses the parameter gradients sent to the parameter server during model synchronization. The threshold is updated only once every few thousand iterations to reduce the computational overhead of compression. For communication efficient MP, DCT incorporates a novel technique to compress the activations and gradients sent across the network during the forward and backward propagation, respectively. This is done by identifying and updating only the most relevant neurons of the neural network for each training sample in the data. We evaluate DCT on publicly available natural language processing and recommender models and datasets, as well as recommendation systems used in production at Facebook. DCT reduces communication by at least $100\times$ and $20\times$ during DP and MP, respectively. The algorithm has been deployed in production, and it improves end-to-end training time for a state-of-the-art industrial recommender model by 37\%, without any loss in performance.

cs.LG↗

arXiv2021

Multi-Task Learning with Shared Encoder for Non-Autoregressive Machine Translation

Non-Autoregressive machine Translation (NAT) models have demonstrated significant inference speedup but suffer from inferior translation accuracy. The common practice to tackle the problem is transferring the Autoregressive machine Translation (AT) knowledge to NAT models, e.g., with knowledge distillation. In this work, we hypothesize and empirically verify that AT and NAT encoders capture different linguistic properties of source sentences. Therefore, we propose to adopt Multi-Task learning to transfer the AT knowledge to NAT models through encoder sharing. Specifically, we take the AT model as an auxiliary task to enhance NAT model performance. Experimental results on WMT14 English-German and WMT16 English-Romanian datasets show that the proposed Multi-Task NAT achieves significant improvements over the baseline NAT models. Furthermore, the performance on large-scale WMT19 and WMT20 English-German datasets confirm the consistency of our proposed method. In addition, experimental results demonstrate that our Multi-Task NAT is complementary to knowledge distillation, the standard knowledge transfer method for NAT.

cs.CL↗

arXiv2021

Perfect absorption in GaAs metasurfaces by degenerate critical coupling

Enhancing absorption in optically thin semiconductors is the key in the development of high-performance optical and optoelectronic devices. In this paper, we resort to the concept of degenerate critical coupling and design an ultra-thin semiconductor absorber composed of free-standing GaAs nanocylinder metasurfaces in the near infrared. The numerical results show that perfect absorption can be achieved through overlapping two Mie modes with opposite symmetry, with each mode contributing a theoretical maximum of 50% in their respective critical coupling state. The absorption also shows the polarization-independent and angle-insensitive robustness. This work, together with the design concept, opens up great opportunities for the realization of high-efficiency metasurface devices, including optical emitters, modulators, detectors, and sensors.

physics.optics↗

arXiv2021

Structural Controllability on Graphs for Drifted Bilinear Systems over Lie Groups

In this paper, we study graphical conditions for structural controllability and accessibility of drifted bilinear systems over Lie groups. We consider a bilinear control system with drift and controlled terms that evolves over the special orthogonal group, the general linear group, and the special unitary group. Zero patterns are prescribed for the drift and controlled dynamics with respect to a set of base elements in the corresponding Lie algebra. The drift dynamics must respect a rigid zero-pattern in the sense that the drift takes values as a linear combination of base elements with strictly non-zero coefficients; the controlled dynamics are allowed to follow a free zero pattern with potentially zero coefficients in the configuration of the controlled term by linear combination of the controlled base elements. First of all, for such bilinear systems over the special orthogonal group or the special unitary group, the zero patterns are shown to be associated with two undirected or directed graphs whose connectivity and connected components ensure structural controllability/accessibility. Next, for bilinear systems over the special unitary group, we introduce two edge-colored graphs associated with the drift and controlled zero patterns, and prove structural controllability conditions related to connectivity and the number of edges of a particular color.

math.OC↗

arXiv2021

Benchmarking Graph Neural Networks on Link Prediction

In this paper, we benchmark several existing graph neural network (GNN) models on different datasets for link predictions. In particular, the graph convolutional network (GCN), GraphSAGE, graph attention network (GAT) as well as variational graph auto-encoder (VGAE) are implemented dedicated to link prediction tasks, in-depth analysis are performed, and results from several different papers are replicated, also a more fair and systematic comparison are provided. Our experiments show these GNN architectures perform similarly on various benchmarks for link prediction tasks.

cs.LG↗

arXiv2021

Top quark pair production near threshold: single/double distributions and mass determination

We investigate top quark pair production near the threshold where the pair invariant mass $M_{t\bar{t}}$ approaches $2m_t$, which provides sensitive observables to extract the top quark mass $m_t$. Using the effective field theory methods, we derive a factorization and resummation formula for kinematic distributions in the threshold limit up to the next-to-leading power, which resums higher order Coulomb corrections to all orders in the strong coupling constant. Our formula is similar to those in the literature but differs in several important aspects. We apply our formula to the $M_{t\bar{t}}$ distribution, as well as to the double differential cross section with respect to $M_{t\bar{t}}$ and the rapidity of the $t\bar{t}$ pair. We find that the resummation effects significantly increase the cross sections near the threshold, and lead to predictions better compatible with experimental data than the fixed-order ones. We demonstrate that incorporating resummation effects in the top quark mass determination can shift the extracted value of $m_t$ by as large as 1.4 GeV. The shift is much larger than the estimated uncertainties in previous experimental studies, and leads to a value of the top quark pole mass more consistent with the current world average.

hep-ph↗

arXiv2021

Invariant-mass distribution of top-quark pairs and top-quark mass determination

We investigate the invariant-mass distribution of top-quark pairs near the $2m_t$ threshold, which has strong impact on the determination of the top-quark mass $m_t$. We show that higher-order non-relativistic corrections lead to large contributions which are not included in the state-of-the-art theoretical predictions. We derive a factorization formula to resum such corrections to all orders in the strong-coupling, and calculate necessary ingredients to perform the resummation at next-to-leading power. We combine the resummation with fixed-order results and present phenomenologically relevant numeric results. We find that the resummation effect significantly enhances the differential cross section in the threshold region, and makes the theoretical prediction more compatible with experimental data. We estimate that using our prediction in the determination of $m_t$ will lead to a value closer to the result of direct measurement.

hep-ph↗

arXiv2021

Factorization at Subleading Power and Endpoint Divergences in $h\toγγ$ Decay: II. Renormalization and Scale Evolution

Building on the recent derivation of a bare factorization theorem for the $b$-quark induced contribution to the $h\toγγ$ decay amplitude based on soft-collinear effective theory, we derive the first renormalized factorization theorem for a process described at subleading power in scale ratios, where $λ=m_b/M_h\ll 1$ in our case. We prove two refactorization conditions for a matching coefficient and an operator matrix element in the endpoint region, where they exhibit singularities giving rise to divergent convolution integrals. The refactorization conditions ensure that the dependence of the decay amplitude on the rapidity regulator, which regularizes the endpoint singularities, cancels out to all orders of perturbation theory. We establish the renormalized form of the factorization formula, proving that extra contributions arising from the fact that "endpoint regularization" does not commute with renormalization can be absorbed, to all orders, by a redefinition of one of the matching coefficients. We derive the renormalization-group evolution equation satisfied by all quantities in the factorization formula and use them to predict the large logarithms of order $α{\hspace{0.3mm}}α_s^2{\hspace{0.3mm}} L^k$ in the three-loop decay amplitude, where $L=\ln(-M_h^2/m_b^2)$ and $k=6,5,4,3$. We find perfect agreement with existing numerical results for the amplitude and analytical results for the three-loop contributions involving a massless quark loop. On the other hand, we disagree with the results of previous attempts to predict the series of subleading logarithms $\simα{\hspace{0.3mm}}α_s^n{\hspace{0.3mm}} L^{2n+1}$.

hep-ph↗

arXiv2020

Hierarchical Representation via Message Propagation for Robust Model Fitting

In this paper, we propose a novel hierarchical representation via message propagation (HRMP) method for robust model fitting, which simultaneously takes advantages of both the consensus analysis and the preference analysis to estimate the parameters of multiple model instances from data corrupted by outliers, for robust model fitting. Instead of analyzing the information of each data point or each model hypothesis independently, we formulate the consensus information and the preference information as a hierarchical representation to alleviate the sensitivity to gross outliers. Specifically, we firstly construct a hierarchical representation, which consists of a model hypothesis layer and a data point layer. The model hypothesis layer is used to remove insignificant model hypotheses and the data point layer is used to remove gross outliers. Then, based on the hierarchical representation, we propose an effective hierarchical message propagation (HMP) algorithm and an improved affinity propagation (IAP) algorithm to prune insignificant vertices and cluster the remaining data points, respectively. The proposed HRMP can not only accurately estimate the number and parameters of multiple model instances, but also handle multi-structural data contaminated with a large number of outliers. Experimental results on both synthetic data and real images show that the proposed HRMP significantly outperforms several state-of-the-art model fitting methods in terms of fitting accuracy and speed.

cs.CV↗

arXiv2020

Non-Newtonian and poroelastic effects in simulations of arterial flows

In this paper, we focus on investigating the influence on hydrodynamic factors of different coupled computational models describing the interaction between an incompressible fluid and two symmetric elastic or poroelastic structures. The fluid region is governed by time dependent Navier-Stokes equations; while for the structure region, we employ two different types of fully dynamic models to study the effects of elasticity and poroelasticity. It is known that blood flow shows a non- Newtonian property in small vessels and in situations of complex geometries. On one hand, we perform numerical experiments for blood flow using the Carreau-Yasuda model to simulate the viscosity and study the influence of non-Newtonian blood rheology as well as the poroelasticity on a benchmark vessel, by means of comparing computational results with models with Newtonian fluids or elastic structures. On the other hand, we present a two-dimensional simulation of blood flow in an axisymmetric stenosis artery, considering not only the non-Newtonian fluids properties but also the fluid-structure interaction. The results of this study demonstrate that the flow characteristics, including velocity and pressure fields, wall shear stress, relative residence time, displacement and filtration velocity, are affected by different models, geometries and parameters, such as permeability and Lamé coefficients.

physics.flu-dyn↗

arXiv2020

Controlling light absorption of graphene at critical coupling through magnetic dipole quasi-bound states in the continuum resonance

Enhancing the light-matter interaction in two-dimensional (2D) materials with high-$Q$ resonances in photonic structures has boosted the development of optical and photonic devices. Herein, we intend to build a bridge between the radiation engineering and the bound states in the continuum (BIC), and present a general method to control light absorption at critical coupling through the quasi-BIC resonance. In a single-mode two-port system composed of graphene coupled with silicon nanodisk metasurfaces, the maximum absorption of 0.5 can be achieved when the radiation rate of the magnetic dipole resonance equals to the dissipate loss rate of graphene. Furthermore, the absorption bandwidth can be adjusted more than two orders of magnitude from 0.9 nm to 94 nm by simultaneously changing the asymmetric parameter of metasurfaces, the Fermi level and the layer number of graphene. This work reveals out the essential role of BIC in radiation engineering and provides promising strategies in controlling light absorption of 2D materials for the next-generation optical and photonic devices, e.g., light emitters, detectors, modulators, and sensors.

physics.optics↗

arXiv2020

Data Rejuvenation: Exploiting Inactive Training Examples for Neural Machine Translation

Large-scale training datasets lie at the core of the recent success of neural machine translation (NMT) models. However, the complex patterns and potential noises in the large-scale data make training NMT models difficult. In this work, we explore to identify the inactive training examples which contribute less to the model performance, and show that the existence of inactive examples depends on the data distribution. We further introduce data rejuvenation to improve the training of NMT models on large-scale datasets by exploiting inactive examples. The proposed framework consists of three phases. First, we train an identification model on the original training data, and use it to distinguish inactive examples and active examples by their sentence-level output probabilities. Then, we train a rejuvenation model on the active examples, which is used to re-label the inactive examples with forward-translation. Finally, the rejuvenated examples and the active examples are combined to train the final NMT model. Experimental results on WMT14 English-German and English-French datasets show that the proposed data rejuvenation consistently and significantly improves performance for several strong NMT models. Extensive analyses reveal that our approach stabilizes and accelerates the training process of NMT models, resulting in final models with better generalization capability.

cs.CL↗