arXiv subjects

Yong Xu

Publications and source records attributed to Yong Xu.

At least 379 records · Page 21Linked to original sources

arXiv2020

Tunable interlayer magnetism and band topology in van der Waals heterostructures of MnBi2Te4-family materials

Manipulating the interlayer magnetic coupling in van der Waals magnetic materials and heterostructures is the key to tailoring their magnetic and electronic properties for various electronic applications and fundamental studies in condensed matter physics. By utilizing the MnBi2Te4-family compounds and their heterostructures as a model system, we systematically studied the dependence of the sign and strength of interlayer magnetic coupling on constituent elements by using first-principles calculations. It was found that the coupling is a long-range superexchange interaction mediated by the chains of p orbitals between the magnetic atoms of neighboring septuple-layers. The interlayer exchange is always antiferromagnetic in the pure compounds, but can be tuned to ferromagnetic in some combinations of heterostructures, dictated by d orbital occupations. Strong interlayer magnetic coupling can be realized if the medial p electrons are delocalized and the d bands of magnetic atoms are near the Fermi level. The knowledge on the interlayer coupling mechanism enables us to engineer magnetic and topological properties of MnBi2Te4-family materials as well as many other insulating van der Waals magnetic materials and heterostructures.

cond-mat.mtrl-sci↗

arXiv2020

Dedge-AGMNet:an effective stereo matching network optimized by depth edge auxiliary task

To improve the performance in ill-posed regions, this paper proposes an atrous granular multi-scale network based on depth edge subnetwork(Dedge-AGMNet). According to a general fact, the depth edge is the binary semantic edge of instance-sensitive. This paper innovatively generates the depth edge ground-truth by mining the semantic and instance dataset simultaneously. To incorporate the depth edge cues efficiently, our network employs the hard parameter sharing mechanism for the stereo matching branch and depth edge branch. The network modifies SPP to Dedge-SPP, which fuses the depth edge features to the disparity estimation network. The granular convolution is extracted and extends to 3D architecture. Then we design the AGM module to build a more suitable structure. This module could capture the multi-scale receptive field with fewer parameters. Integrating the ranks of different stereo datasets, our network outperforms other stereo matching networks and advances state-of-the-art performances on the Sceneflow, KITTI 2012 and KITTI 2015 benchmark datasets.

cs.CV↗

arXiv2020

Enhancing End-to-End Multi-channel Speech Separation via Spatial Feature Learning

Hand-crafted spatial features (e.g., inter-channel phase difference, IPD) play a fundamental role in recent deep learning based multi-channel speech separation (MCSS) methods. However, these manually designed spatial features are hard to incorporate into the end-to-end optimized MCSS framework. In this work, we propose an integrated architecture for learning spatial features directly from the multi-channel speech waveforms within an end-to-end speech separation framework. In this architecture, time-domain filters spanning signal channels are trained to perform adaptive spatial filtering. These filters are implemented by a 2d convolution (conv2d) layer and their parameters are optimized using a speech separation objective function in a purely data-driven fashion. Furthermore, inspired by the IPD formulation, we design a conv2d kernel to compute the inter-channel convolution differences (ICDs), which are expected to provide the spatial cues that help to distinguish the directional sources. Evaluation results on simulated multi-channel reverberant WSJ0 2-mix dataset demonstrate that our proposed ICD based MCSS model improves the overall signal-to-distortion ratio by 10.4% over the IPD based MCSS model.

eess.AS↗

arXiv2020

Enhancement of superconductivity in organic-inorganic hybrid topological materials

Inducing or enhancing superconductivity in topological materials is an important route toward topological superconductivity. Reducing the thickness of transition metal dichalcogenides (e.g. WTe2 and MoTe2) has provided an important pathway to engineer superconductivity in topological matters; for instance, emergent superconductivity with Tc=0.82 K was observed in monolayer WTe2 which also hosts intriguing quantum spin Hall effect, although the bulk crystal is nonsuperconducting. However, such monolayer sample is difficult to obtain, unstable in air, and with extremely low Tc, which could pose a grand challenge for practical applications. Here we report an experimentally convenient approach to control the interlayer coupling to achieve tailored topological properties, enhanced superconductivity and good sample stability through organic cation intercalation of the Weyl semimetals MoTe2 and WTe2. The as-formed organic-inorganic hybrid crystals are weak topological insulators with enhanced Tc of 7.0 K for intercalated MoTe2 (0.25 K for pristine crystal) and 2.3 K for intercalated WTe2 (2.8 times compared to monolayer WTe2). Such organic-cationintercalation method can be readily applied to many other layered crystals, providing a new pathway for manipulating their electronic, topological and superconducting properties.

cond-mat.mtrl-sci↗

arXiv2020

Self-supervised learning for audio-visual speaker diarization

Speaker diarization, which is to find the speech segments of specific speakers, has been widely used in human-centered applications such as video conferences or human-computer interaction systems. In this paper, we propose a self-supervised audio-video synchronization learning method to address the problem of speaker diarization without massive labeling effort. We improve the previous approaches by introducing two new loss functions: the dynamic triplet loss and the multinomial loss. We test them on a real-world human-computer interaction system and the results show our best model yields a remarkable gain of +8%F1-scoresas well as diarization error rate reduction. Finally, we introduce a new large scale audio-video corpus designed to fill the vacancy of audio-video datasets in Chinese.

eess.AS↗

arXiv2020

Rate-dependent tipping-delay phenomenon in a thermoacoustic system with colored noise

Tipping is a phenomenon in multistable systems where small changes in inputs cause huge changes in outputs. When the parameter varies within a certain time scale, the rate will affect the tipping behaviors. These behaviors are undesirable in thermoacoustic systems, which are widely used in aviation, power generation and other industries. Thus, this paper aims at considering the tipping behaviors of the thermoacoustic system with the time-varying parameters and the combined excitations of additive and multiplicative colored noises. Transient dynamical behaviors for the proposed thermoacoustic model are implemented through the reduced Fokker-Planck-Kolmogorov equation derived by a standard stochastic averaging method. Then, the tipping problems of the rate-dependent thermoacoustic systems with random fluctuations are studied by virtue of the obtained probability density functions. Our results show that the rate delays the value of the tipping parameter compared to the one with the quasi-steady assumption, which is called as a rate-dependent tipping-delay phenomenon. Besides, the influences of the initial values, the rate, the changing time of the parameters, and the correlation time of the noises on the rate-dependent tipping-delay phenomenon are analyzed in detail. These results are of great significance for research in related fields such as aviation and land gas turbines.

nlin.AO↗

arXiv2020

Multiparty Quantum Sealed-Bid Auction Using Single Photons as Message Carrier

In this study, a novel multiparty quantum sealed-bid auction protocol using the single photons as the message carrier of bids is proposed, followed by an example of three-party auction. Compared with those protocols based on the entangled states (GHZ state, EPR pairs, etc.), the present protocol is more economic and feasible within present technology. In order to guarantee the security and the fairness of the auction, the decoy photon checking technique and an improved post-confirmation mechanism with EPR pairs are introduced respectively.

quant-ph↗

arXiv2020

Robust axion insulator and Chern insulator phases in a two-dimensional antiferromagnetic topological insulator

The intricate interplay between nontrivial topology and magnetism in two-dimensional (2D) materials has led to the emergence of many novel phenomena and functionalities. An outstanding example is the quantum anomalous Hall (QAH) effect, which was realized in magnetically doped topological insulators (TIs) in the absence of magnetic field. Recently, the layered van der Waals compound MnBi2Te4 has been theoretically predicted and experimentally verified to be a TI with interlayer antiferromagnetic (AFM) order. It is a rare stoichiometric material with coexisting topology and magnetism, thus represents a perfect building block for complex topological-magnetic structures. Here we investigate the quantum transport behaviors of both bulk crystal and exfoliated MnBi2Te4 flakes in a field effect transistor geometry. In the 6 septuple layers (SLs) device tuned into the insulating regime, we observe a large longitudinal resistance and zero Hall plateau, which are characteristic of the axion insulator state. The robust axion insulator state occurs in zero magnetic field, over a wide magnetic field range, and at relatively high temperatures. Moreover, a moderate magnetic field drives a quantum phase transition from the axion insulator phase to a Chern insulator phase with zero longitudinal resistance and quantized Hall resistance h/e2 (h is the Plank constant and e is the elemental charge). These results pave the road for using even-number-SL MnBi2Te4 to realize the quantized topological magnetoelectric effect and axion electrodynamics in condensed matter systems.

cond-mat.mes-hall↗

arXiv2020

Electronic states and magnetic response of MnBi2Te4 by scanning tunneling microscopy and spectroscopy

Exotic quantum phenomena have been demonstrated in recently discovered intrinsic magnetic topological insulator MnBi2Te4. At its two-dimensional limit, quantum anomalous Hall (QAH) effect and axion insulator state are observed in odd and even layers of MnBi2Te4, respectively. The measured band structures exhibit intriguing and complex properties. Here we employ low-temperature scanning tunneling microscopy to study its surface states and magnetic response. The quasiparticle interference patterns indicate that the electronic structures on the topmost layer of MnBi2Te4 is different from that of the expected out-of-plane A-type antiferromagnetic phase. The topological surface states may be embedded in deeper layers beneath the topmost surface. Such novel electronic structure presumably related to the modification of crystalline structure during sample cleaving and re-orientation of magnetic moment of Mn atoms near the surface. Mn dopants substituted at the Bi site on the second atomic layer are observed. The ratio of Mn/Bi substitutions is 5%. The electronic structures are fluctuating at atomic scale on the surface, which can affect the magnetism of MnBi2Te4. Our findings shed new lights on the magnetic property of MnBi2Te4 and thus the design of magnetic topological insulators.

cond-mat.mtrl-sci↗

arXiv2019

A Unified Framework for Speech Separation

Speech separation refers to extracting each individual speech source in a given mixed signal. Recent advancements in speech separation and ongoing research in this area, have made these approaches as promising techniques for pre-processing of naturalistic audio streams. After incorporating deep learning techniques into speech separation, performance on these systems is improving faster. The initial solutions introduced for deep learning based speech separation analyzed the speech signals into time-frequency domain with STFT; and then encoded mixed signals were fed into a deep neural network based separator. Most recently, new methods are introduced to separate waveform of the mixed signal directly without analyzing them using STFT. Here, we introduce a unified framework to include both spectrogram and waveform separations into a single structure, while being only different in the kernel function used to encode and decode the data; where, both can achieve competitive performance. This new framework provides flexibility; in addition, depending on the characteristics of the data, or limitations of the memory and latency can set the hyper-parameters to flow in a pipeline of the framework which fits the task properly. We extend single-channel speech separation into multi-channel framework with end-to-end training of the network while optimizing the speech separation criterion (i.e., Si-SNR) directly. We emphasize on how tied kernel functions for calculating spatial features, encoder, and decoder in multi-channel framework can be effective. We simulate spatialized reverberate data for both WSJ0 and LibriSpeech corpora here, and while these two sets of data are different in the matter of size and duration, the effect of capturing shorter and longer dependencies of previous/+future samples are studied in detail. We report SDR, Si-SNR and PESQ to evaluate the performance of developed solutions.

cs.LG↗

arXiv2019

Weakly Labelled AudioSet Tagging with Attention Neural Networks

Audio tagging is the task of predicting the presence or absence of sound classes within an audio clip. Previous work in audio tagging focused on relatively small datasets limited to recognising a small number of sound classes. We investigate audio tagging on AudioSet, which is a dataset consisting of over 2 million audio clips and 527 classes. AudioSet is weakly labelled, in that only the presence or absence of sound classes is known for each clip, while the onset and offset times are unknown. To address the weakly-labelled audio tagging problem, we propose attention neural networks as a way to attend the most salient parts of an audio clip. We bridge the connection between attention neural networks and multiple instance learning (MIL) methods, and propose decision-level and feature-level attention neural networks for audio tagging. We investigate attention neural networks modeled by different functions, depths and widths. Experiments on AudioSet show that the feature-level attention neural network achieves a state-of-the-art mean average precision (mAP) of 0.369, outperforming the best multiple instance learning (MIL) method of 0.317 and Google's deep neural network baseline of 0.314. In addition, we discover that the audio tagging performance on AudioSet embedding features has a weak correlation with the number of training samples and the quality of labels of each sound class.

cs.SD↗

arXiv2019

Solving Fokker-Planck equation using deep learning

The probability density function of stochastic differential equations is governed by the Fokker-Planck (FP) equation. A novel machine learning method is developed to solve the general FP equations based on deep neural networks. The proposed algorithm does not require any interpolation and coordinate transformation, which is different from the traditional numercial methods. The main novelty of this paper is that penalty factors are introduced to overcome the local optimization for the deep learning approach, and the corresponding setting rules are given. Meanwhile, we consider a normalization condition as a supervision condition to effectively avoid that the trial solution is zero. Several numerical examples are presented to illustrate performances of the proposed algorithm, including one- and two-dimensional systems. All the results suggest that the deep learning is quite feasible and effective to calculate the FP equation. Further, influences of the number of hidden layers, the penalty factors, and the optimization algorithm are discussed in detail. These results indicate that the performances of the machine learning technique can be improved through constructing the neural networks appropriately.

physics.comp-ph↗

arXiv2019

Time Domain Audio Visual Speech Separation

Audio-visual multi-modal modeling has been demonstrated to be effective in many speech related tasks, such as speech recognition and speech enhancement. This paper introduces a new time-domain audio-visual architecture for target speaker extraction from monaural mixtures. The architecture generalizes the previous TasNet (time-domain speech separation network) to enable multi-modal learning and at meanwhile it extends the classical audio-visual speech separation from frequency-domain to time-domain. The main components of proposed architecture include an audio encoder, a video encoder that extracts lip embedding from video streams, a multi-modal separation network and an audio decoder. Experiments on simulated mixtures based on recently released LRS2 dataset show that our method can bring 3dB+ and 4dB+ Si-SNR improvements on two- and three-speaker cases respectively, compared to audio-only TasNet and frequency-domain audio-visual networks

eess.AS↗

arXiv2019

Quantum Spin Hall Insulators in Tin Films: Beyond Stanene

Large-gap quantum spin Hall (QSH) insulators were previously predicted in stanene and its derivatives. Beyond stanene that is the thinnest $α$-Sn(111) film, we propose to explore QSH insulators in $α$-Sn films with different crystallographic orientations. Our first-principles calculations reveal that the thickness-dependent band gap of $α$-Sn (100) and (110) films does not show a monotonic decrease as typically expected by quantum confinement, but displays an oscillating change behavior, an indicative of topological quantum phase transition. While these films are normal insulators in the ultrathin limit, the QSH phase emerges above a critical film thickness of around 10 layers. Remarkably, the QSH insulators are obtainable within a wide thickness range and their energy gaps are sizable (even $>$0.1 eV), which facilitates experimental realization of the high-temperature QSH effect.

cond-mat.mtrl-sci↗

arXiv2019

First-principles calculation of optical responses based on nonorthogonal localized orbitals

Based on ab initio software packages using nonorthogonal localized orbitals, we develop a general scheme of calculating response functions. We test the performance of this method by calculating nonlinear optical responses of materials, like the shift current conductivity of monolayer WS2, and achieve good agreement with previous calculations. This method bears many similarities to Wannier interpolation, which requires a challenging optimization of Wannier functions due to the conflicting requirements of orthogonality and localization. Although computationally heavier compared to Wannier interpolation, our procedure avoids the construction of Wannier functions and thus enables automated high throughput calculations of linear and nonlinear responses related to electrical, magnetic and optical material properties.

cond-mat.mtrl-sci↗

arXiv2019

Type-II Ising superconductivity in two-dimensional materials with strong spin-orbit coupling

Recent discovery of Ising superconductivity protected against in-plane magnetic field by spin-orbit coupling (SOC) has stimulated intensive research interests. The effect, however, was only expected to appear in two-dimensional (2D) noncentrosymmetric materials with spin-valley locking. In this work, we proposed a new type of Ising superconductivity in 2D materials with $C_{nz}$ rotational symmetry ($n=3,4,6$). This mechanism, dubbed as type-II Ising superconductivity, is applicable for centrosymmetric materials. Type-II Ising superconductivity relies on the SOC-induced spin-orbital locking characterized by Ising-type Zeeman-like fields displaying opposite signs for opposing orbitals. We found that type-II Ising superconductivity are most prominent around time-reversal invariant momenta and is not sensitive to inversion symmetry breaking. By performing high-throughput first-principles calculations, about one hundred candidate materials were identified. Our work significantly enriches the physics and materials of Ising superconductor, opening new opportunities for fundamental research and practical applications of 2D materials.

cond-mat.supr-con↗

arXiv2019

Berry Curvature Engineering by Gating Two-Dimensional Antiferromagnets

Recent advances in tuning electronic, magnetic, and topological properties of two-dimensional (2D) magnets have opened a new frontier in the study of quantum physics and promised exciting possibilities for future quantum technologies. In this study, we find that the dual-gate technology can well tune the electronic and topological properties of antiferromagnetic (AFM) even septuple-layer (SL) MnBi$_2$Te$_4$ thin films. Under an out-of-plane electric field that breaks $\mathcal{PT}$ symmetry, the Berry curvature of the thin film could be engineered efficiently, resulting in a huge change of anomalous Hall (AH) signal. Beyond the critical electric field, the double-SL MnBi$_2$Te$_4$ thin film becomes a Chern insulator with a high Chern number of 3. We further demonstrate that such 2D material can be used as an AFM switch via electric-field control of the AH signal. These discoveries inspire the design of low-power memory prototype for future AFM spintronic applications.

cond-mat.mtrl-sci↗

arXiv2019

Dimensional Crossover and Topological Phase Transition in Dirac Semimetal Na3Bi Films

Three-dimensional (3D) topological Dirac semimetal, when thinned down to 2D few layers, is expected to possess gapped Dirac nodes via quantum confinement effect and concomitantly display the intriguing quantum spin Hall (QSH) insulator phase. However, the 3D-to-2D crossover and the associated topological phase transition, which is valuable for understanding the topological quantum phases, remain unexplored. Here, we synthesize high-quality Na3Bi thin films with R3*R3 reconstruction on graphene, and systematically characterize their thickness-dependent electronic and topological properties by scanning tunneling microscopy/spectroscopy in combination with first-principles calculations. We demonstrate that Dirac gaps emerge in Na3Bi films, providing spectroscopic evidences of dimensional crossover from a 3D semimetal to a 2D topological insulator. Importantly, the Dirac gaps are revealed to be of sizable magnitudes on 3 and 4 monolayers (72 and 65 meV, respectively) with topologically nontrivial edge states. Moreover, the Fermi energy of a Na3Bi film can be tuned via certain growth process, thus offering a viable way for achieving charge neutrality in transport. The feasibility of controlling Dirac gap opening and charge neutrality enables realizing intrinsic high-temperature QSH effect in Na3Bi films and achieving potential applications in topological devices.

cond-mat.mtrl-sci↗