arXiv subjects

Zhiwei Wang

Publications and source records attributed to Zhiwei Wang.

At least 199 records · Page 11Linked to original sources

arXiv2021

Multiple Energy Scales and Anisotropic Energy Gap in the Charge-Density-Wave Phase of Kagome Superconductor CsV3Sb5

Kagome metals AV3Sb5 (A = K, Rb, and Cs) exhibit superconductivity at 0.9-2.5 K and charge-density wave (CDW) at 78-103 K. Key electronic states associated with the CDW and superconductivity remain elusive. Here, we investigate low-energy excitations of CsV3Sb5 by angle-resolved photoemission spectroscopy. We found an energy gap of 70-100 meV at the Dirac-crossing points of linearly dispersive bands, pointing to an importance of spin-orbit coupling. We also found a signature of strongly Fermi-surface and momentum-dependent CDW gap characterized by the larger energy gap of maximally 70 meV for a band forming a saddle point around the M point, the smaller (0-18 meV) gap for a band forming massive Dirac cones, and a zero gap at the Gamma-centered electron pocket. The observed highly anisotropic CDW gap which is enhanced around the M point signifies an importance of scattering channel connecting the saddle points, laying foundation for understanding the nature of CDW and superconductivity in AV3Sb5.

cond-mat.supr-con↗

arXiv2021

Intermediate anomalous Hall states induced by noncollinear spin structure in magnetic topological insulator MnBi2Te4

The combination of topology and magnetism is attractive to produce exotic quantum matters, such as the quantum anomalous Hall state, axion insulators and the magnetic Weyl semimetals. MnBi2Te4, as an intrinsic magnetic topological insulator, provides a platform for the realization of various topological phases. Here we report the intermediate Hall steps in the magnetic hysteresis of MnBi2Te4, where four distinguishable magnetic memory states at zero magnetic field are revealed. The gate and temperature dependence of the magnetic intermediate states indicates the noncollinear spin structure in MnBi2Te4, which can be attributed to the Dzyaloshinskii-Moriya interaction as the coexistence of strong spin-orbit coupling and local inversion symmetry breaking on the surface. Moreover, these multiple magnetic memory states can be programmatically switched among each other through applying designed pulses of magnetic field. Our results provide new insights of the influence of bulk topology on the magnetic states, and the multiple memory states should be promising for spintronic devices.

cond-mat.mtrl-sci↗

arXiv2021

Electronic nature of chiral charge order in the kagome superconductor CsV3Sb5

Kagome superconductors with Tc up to 7K have been discovered over 40 years. Recently, unconventional chiral charge order has been reported in kagome superconductor KV3Sb5, with an ordering temperature of one order of magnitude higher than the TC. However, the chirality of the charge order has not been reported in the cousin kagome superconductor CsV3Sb5, and the electronic nature of the chirality remains elusive. In this letter, we report the observation of electronic chiral charge order in CsV3Sb5 via scanning tunneling microscopy (STM). We observe a 2x2 charge modulation and a 1x4 superlattice in both topographic data and tunneling spectroscopy. 2x2 charge modulation is highly anticipated as a charge order by fundamental kagome lattice models at van Hove filling, and is shown to exhibit intrinsic chirality. We find that the 1x4 superlattices forms various small domain walls, and can be a surface effect as supported by our first-principles calculations. Crucially, we find that the amplitude of the energy gap opened by the charge order exhibits real space modulations, and features 2x2 wave vectors with chirality, highlighting the electronic nature of the chiral charge order. STM study at 0.4K reveals a superconducting energy gap with a gap size 2Δ=0.85meV, which estimates a moderate superconductivity coupling strength with 2Δ/kBTc=3.9. When further applying a c-axis magnetic field, vortex core bound states are observed within this gap, indicative of clean-limit superconductivity.

cond-mat.str-el↗

arXiv2021

Deep Natural Language Processing for LinkedIn Search

Many search systems work with large amounts of natural language data, e.g., search queries, user profiles, and documents. Building a successful search system requires a thorough understanding of textual data semantics, where deep learning based natural language processing techniques (deep NLP) can be of great help. In this paper, we introduce a comprehensive study for applying deep NLP techniques to five representative tasks in search systems: query intent prediction (classification), query tagging (sequential tagging), document ranking (ranking), query auto completion (language modeling), and query suggestion (sequence to sequence). We also introduce BERT pre-training as a sixth task that can be applied to many of the other tasks. Through the model design and experiments of the six tasks, readers can find answers to four important questions: (1). When is deep NLP helpful/not helpful in search systems? (2). How to address latency challenges? (3). How to ensure model robustness? This work builds on existing efforts of LinkedIn search, and is tested at scale on LinkedIn's commercial search engines. We believe our experiences can provide useful insights for the industry and research communities.

cs.IR↗

arXiv2021

Origin of the Charge Density Wave in the Kagome Metal CsV$_{3}$Sb$_{5}$ as Revealed by Optical Spectroscopy

We report on a detailed study of the optical properties of CsV$_{3}$Sb$_{5}$ at a large number of temperatures above and below the charge-density-wave (CDW) transition. Above the CDW transition, the low-frequency optical conductivity reveals two Drude components with distinct widths. An examination of the band structure allows us to ascribe the narrow Drude to multiple light and Dirac bands, and the broad Drude to the heavy bands near the $M$ points which form saddle points near the Fermi level. Upon entering the CDW state, the opening of the CDW gap is clearly observed. A large portion of the broad Drude is removed by the gap, whereas the narrow Drude is not affected. Meanwhile, an absorption peak associated with interband transitions near the saddle points shifts to higher energy and grows in weight. These observations are consistent with the scenario that the CDW in CsV$_{3}$Sb$_{5}$ is driven by nesting of Fermi surfaces near the saddle points at $M$.

cond-mat.supr-con↗

arXiv2021

Power System Transient Modeling and Simulation using Integrated Circuit

Transient stability analysis (TSA) plays an important role in power system analysis to investigate the stability of power system. Traditionally, transient stability analysis methods have been developed using time domain simulation by means of numerical integration method. In this paper, a new approach is proposed to model power systems as an integrated circuit and simulate the power system dynamic behavior by integrated circuit simulator. The proposed method modeled power grid, generator, governor, and exciter with high fidelity. The power system dynamic simulation accuracy and efficiency of the proposed approach are verified and demonstrated by case study on an IEEE standard system.

eess.SY↗

arXiv2021

On the extension of Kähler currents on compact Kähler manifolds: holomorphic retraction case

In the present paper, we show that given a compact Kähler manifold $(X,ω)$ with a Kähler metric $ω$, and a complex submanifold $V\subset X$ of positive dimension, if $V$ has a holomorphic retraction structure in $X$, then any quasi-plurisubharmonic function $φ$ on $V$ such that $ω|_V+\sqrt{-1}\partial\bar\partialφ\geq \varepsilonω|_V$ with $\varepsilon>0$ can be extended to a quasi-plurisubharmonic function $Φ$ on $X$, such that $ω+\sqrt{-1}\partial\bar\partial Φ\geq \varepsilon'ω$ for some $\varepsilon'>0$. This is an improvement of results in \cite{WZ20}. Examples satisfying the assumption that there exists a holomorphic retraction structure contain product manifolds, thus contains many compact Kähler manifolds which are not necessarily projective.

math.CV↗

arXiv2021

Twofold symmetry of $c$-axis resistivity in topological kagome superconductor CsV$_3$Sb$_5$ with in-plane rotating magnetic field

In transition metal compounds, due to the interplay of charge, spin, lattice and orbital degrees of freedom, many intertwined orders exist with close energies. One of the commonly observed states is the so-called nematic electron state, which breaks the in-plane rotational symmetry. This nematic state appears in cuprates, iron-based superconductor, etc. Nematicity may coexist, affect, cooperate or compete with other orders. Here we show the anisotropic in-plane electronic state and superconductivity in a recently discovered kagome metal CsV$_3$Sb$_5$ by measuring $c$-axis resistivity with the in-plane rotation of magnetic field. We observe a twofold symmetry of superconductivity in the superconducting state and a unique in-plane nematic electronic state in normal state when rotating the in-plane magnetic field. Interestingly these two orders are orthogonal to each other in terms of the field direction of the minimum resistivity. Our results shed new light in understanding non-trivial physical properties of CsV$_3$Sb$_5$.

cond-mat.supr-con↗

arXiv2021

PanGu-$α$: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation

Large-scale Pretrained Language Models (PLMs) have become the new paradigm for Natural Language Processing (NLP). PLMs with hundreds of billions parameters such as GPT-3 have demonstrated strong performances on natural language understanding and generation with \textit{few-shot in-context} learning. In this work, we present our practice on training large-scale autoregressive language models named PanGu-$α$, with up to 200 billion parameters. PanGu-$α$ is developed under the MindSpore and trained on a cluster of 2048 Ascend 910 AI processors. The training parallelism strategy is implemented based on MindSpore Auto-parallel, which composes five parallelism dimensions to scale the training task to 2048 processors efficiently, including data parallelism, op-level model parallelism, pipeline model parallelism, optimizer model parallelism and rematerialization. To enhance the generalization ability of PanGu-$α$, we collect 1.1TB high-quality Chinese data from a wide range of domains to pretrain the model. We empirically test the generation ability of PanGu-$α$ in various scenarios including text summarization, question answering, dialogue generation, etc. Moreover, we investigate the effect of model scales on the few-shot performances across a broad range of Chinese NLP tasks. The experimental results demonstrate the superior capabilities of PanGu-$α$ in performing various tasks under few-shot or zero-shot settings.

cs.CL↗

arXiv2020

Fourier-domain Variational Formulation and Its Well-posedness for Supervised Learning

A supervised learning problem is to find a function in a hypothesis function space given values on isolated data points. Inspired by the frequency principle in neural networks, we propose a Fourier-domain variational formulation for supervised learning problem. This formulation circumvents the difficulty of imposing the constraints of given values on isolated data points in continuum modelling. Under a necessary and sufficient condition within our unified framework, we establish the well-posedness of the Fourier-domain variational problem, by showing a critical exponent depending on the data dimension. In practice, a neural network can be a convenient way to implement our formulation, which automatically satisfies the well-posedness condition.

math.NA↗

arXiv2020

Defocus Blur Detection via Salient Region Detection Prior

Defocus blur always occurred in photos when people take photos by Digital Single Lens Reflex Camera(DSLR), giving salient region and aesthetic pleasure. Defocus blur Detection aims to separate the out-of-focus and depth-of-field areas in photos, which is an important work in computer vision. Current works for defocus blur detection mainly focus on the designing of networks, the optimizing of the loss function, and the application of multi-stream strategy, meanwhile, these works do not pay attention to the shortage of training data. In this work, to address the above data-shortage problem, we turn to rethink the relationship between two tasks: defocus blur detection and salient region detection. In an image with bokeh effect, it is obvious that the salient region and the depth-of-field area overlap in most cases. So we first train our network on the salient region detection tasks, then transfer the pre-trained model to the defocus blur detection tasks. Besides, we propose a novel network for defocus blur detection. Experiments show that our transfer strategy works well on many current models, and demonstrate the superiority of our network.

cs.CV↗

arXiv2020

2D-Dirac surface states and bulk gap probed via quantum capacitance in a 3D topological insulator

BiSbTeSe$_2$ is a 3D topological insulator (3D-TI) with Dirac type surface states and low bulk carrier density, as donors and acceptors compensate each other. Dominating low temperature surface transport in this material is heralded by Shubnikov-de Haas oscillations and the quantum Hall effect. Here, we experimentally probe and model the electronic density of states (DOS) in thin layers of BiSbTeSe$_2$ by capacitance experiments both without and in quantizing magnetic fields. By probing the lowest Landau levels, we show that a large fraction of the electrons filled via field effect into the system ends up in (localized) bulk states and appears as a background DOS. The surprisingly strong temperature dependence of such background DOS can be traced back to Coulomb interactions. Our results point at the coexistence and intimate coupling of Dirac surface states with a bulk many-body phase (a Coulomb glass) in 3D-TIs.

cond-mat.mes-hall↗

arXiv2020

On the extensions of Kähler currents on compact Kähler manifolds

Let $(X,ω)$ be a compact Kähler manifold with a Kähler form $ω$ of complex dimension $n$, and $V\subset X$ is a compact complex submanifold of positive dimension $k<n$. Suppose that $V$ can be embedded in $X$ as a zero section of a holomorphic vector bundle or rank $n-k$ over $V$. Let $φ$ be a strictly $ω|_V$-psh function on $V$. In this paper, we prove that there is a strictly $ω$-psh function $Φ$ on $X$, such that $Φ|_V=φ$. This result gives a partial answer to an open problem raised by Collins-Tosatti and Dinew-Guedj-Zeriahi, for the case of Kähler currents. We also discuss possible extensions of Kähler currents in a big class.

math.CV↗

arXiv2020

Signature of band inversion in the antiferromagnetic phase of axion insulator candidate EuIn2As2

We have performed angle-resolved photoemission spectroscopy on EuIn2As2 which is predicted to be an axion insulator in the antiferromagnetic state. By utilizing soft-x-ray and vacuum-ultraviolet photons, we revealed a three-dimensional hole pocket centered at the Gamma point of bulk Brillouin zone together with a heavily hole-doped surface state in the paramagnetic phase. Upon entering the antiferromagnetic phase, the band structure exhibits a marked reconstruction characterized by the emergence of a "M"-shaped bulk band near the Fermi level. The qualitative agreement with first-principles band-structure calculations suggests the occurrence of bulk-band inversion at the Gamma point in the antiferromagnetic phase. We suggest that EuIn2As2 provides a good opportunity to study the exotic quantum phases associated with possible axion-insulator phase.

cond-mat.mes-hall↗

arXiv2020

Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection

Discrete event sequences are ubiquitous, such as an ordered event series of process interactions in Information and Communication Technology systems. Recent years have witnessed increasing efforts in detecting anomalies with discrete-event sequences. However, it still remains an extremely difficult task due to several intrinsic challenges including data imbalance issues, the discrete property of the events, and sequential nature of the data. To address these challenges, in this paper, we propose OC4Seq, a multi-scale one-class recurrent neural network for detecting anomalies in discrete event sequences. Specifically, OC4Seq integrates the anomaly detection objective with recurrent neural networks (RNNs) to embed the discrete event sequences into latent spaces, where anomalies can be easily detected. In addition, given that an anomalous sequence could be caused by either individual events, subsequences of events, or the whole sequence, we design a multi-scale RNN framework to capture different levels of sequential patterns simultaneously. Experimental results on three benchmark datasets show that OC4Seq consistently outperforms various representative baselines by a large margin. Moreover, through both quantitative and qualitative analysis, the importance of capturing multi-scale sequential patterns for event anomaly detection is verified.

cs.LG↗

arXiv2020

Evaluating Load Models and Their Impacts on Power Transfer Limits

Power transfer limits or transfer capability (TC) directly relate to the system operation and control as well as electricity markets. As a consequence, their assessment has to comply with static constraints, such as line thermal limits, and dynamic constraints, such as transient stability limits, voltage stability limits and small-signal stability limits. Since the load dynamics have substantial impacts on power system transient stability, load models are one critical factor that affects the power transfer limits. Currently, multiple load models have been proposed and adopted in the industry and academia, including the ZIP model, ZIP plus induction motor composite model (ZIP + IM) and WECC composite load model (WECC CLM). Each of them has its unique advantages, but their impacts on the power transfer limits are not yet adequately addressed. One existing challenge is fitting the high-order nonlinear models such as WECC CLM. In this study, we innovatively adopt double deep Q-learning Network (DDQN) agent as a general load modeling tool in the dynamic assessment procedure and fit the same transient field measurements into different load models. A comprehensive evaluation is then conducted to quantify the load models' impacts on the power transfer limits. The simulation environment is the IEEE-39 bus system constructed in Transient Security Assessment Tool (TSAT).

eess.SP↗

arXiv2020

Observation of inverted band structure in topological Dirac-semimetal candidate CaAuAs

We have performed high-resolution angle-resolved photoemission spectroscopy of ternary pnictide CaAuAs which is predicted to be a three-dimensional topological Dirac semimetal (TDS). By accurately determining the bulk-band structure, we have revealed the coexistence of three-dimensional and quasi-two-dimensional Fermi surfaces with dominant hole carriers. The band structure around the Brillouin-zone center is characterized by an energy overlap between hole and electron pockets, in excellent agreement with first-principles band-structure calculations. This indicates the occurrence of bulk-band inversion, supporting the TDS state in CaAuAs. Because of the high tunability in the chemical composition besides the TDS nature, CaAuAs provides a precious opportunity for investigating the quantum phase transition from TDS to other exotic topological phases.

cond-mat.mes-hall↗

arXiv2020

Chat as Expected: Learning to Manipulate Black-box Neural Dialogue Models

Recently, neural network based dialogue systems have become ubiquitous in our increasingly digitalized society. However, due to their inherent opaqueness, some recently raised concerns about using neural models are starting to be taken seriously. In fact, intentional or unintentional behaviors could lead to a dialogue system to generate inappropriate responses. Thus, in this paper, we investigate whether we can learn to craft input sentences that result in a black-box neural dialogue model being manipulated into having its outputs contain target words or match target sentences. We propose a reinforcement learning based model that can generate such desired inputs automatically. Extensive experiments on a popular well-trained state-of-the-art neural dialogue model show that our method can successfully seek out desired inputs that lead to the target outputs in a considerable portion of cases. Consequently, our work reveals the potential of neural dialogue models to be manipulated, which inspires and opens the door towards developing strategies to defend them.

cs.CL↗