arXiv subjects

Yong Xu

Publications and source records attributed to Yong Xu.

At least 397 records · Page 22Linked to original sources

arXiv2019

Topological Amorphous Metals

We study amorphous systems with completely random sites and find that, through constructing and exploring a concrete model Hamiltonian, such a system can host an exotic phase of topological amorphous metal in three dimensions. In contrast to the traditional Weyl semimetals, topological amorphous metals break translational symmetry, and thus cannot be characterized by the first Chern number defined based on the momentum space band structures. Instead, their topological properties will manifest in the Bott index and the Hall conductivity as well as the surface states. By studying the energy band and quantum transport properties, we find that topological amorphous metals exhibit a diffusive metal behavior. We further introduce a practical experimental proposal with electric circuits where the predicted phenomena can be observed using state-of-the-art technologies. Our results open a door for exploring topological gapless phenomena in amorphous systems.

cond-mat.mes-hall↗

arXiv2019

CloudDet: Interactive Visual Analysis of Anomalous Performances in Cloud Computing Systems

Detecting and analyzing potential anomalous performances in cloud computing systems is essential for avoiding losses to customers and ensuring the efficient operation of the systems. To this end, a variety of automated techniques have been developed to identify anomalies in cloud computing performance. These techniques are usually adopted to track the performance metrics of the system (e.g., CPU, memory, and disk I/O), represented by a multivariate time series. However, given the complex characteristics of cloud computing data, the effectiveness of these automated methods is affected. Thus, substantial human judgment on the automated analysis results is required for anomaly interpretation. In this paper, we present a unified visual analytics system named CloudDet to interactively detect, inspect, and diagnose anomalies in cloud computing systems. A novel unsupervised anomaly detection algorithm is developed to identify anomalies based on the specific temporal patterns of the given metrics data (e.g., the periodic pattern), the results of which are visualized in our system to indicate the occurrences of anomalies. Rich visualization and interaction designs are used to help understand the anomalies in the spatial and temporal context. We demonstrate the effectiveness of CloudDet through a quantitative evaluation, two case studies with real-world data, and interviews with domain experts.

cs.HC↗

arXiv2019

Antiferromagnetic Topological Insulator MnBi2Te4: Synthesis and Magnetic properties

Recently, MnBi2Te4 has been discovered as the first intrinsic antiferromagnetic topological insulator (AFM TI), and will become a promising material to discover exotic topological quantum phenomena. In this work, we have realized the successful synthesis of high-quality MnBi2Te4 single crystals by solid-state reactions. The as-grown MnBi2Te4 single crystal exhibits a van der Waals layered structure, which is composed of septuple Te-Bi-Te-Mn-Te-Bi-Te sequences as determined by powder X-ray diffraction (PXRD) and high-resolution high-angle annular dark field scanning transmission electron microscopy (HAADF-STEM). The magnetic order below 25 K as a consequence of A-type antiferromagnetic interaction between Mn layers in the MnBi2Te4 crystal suggests the unique interplay between antiferromagnetism and topological quantum states. The transport measurements of MnBi2Te4 single crystals further confirm its magnetic transition. Moreover, the unstable surface of MnBi2Te4, which is found to be easily oxidized in air, deserves attention for onging research on few-layer samples. This study on the first AFM TI of MnBi2Te4 will guide the future research on other potential candidates in the MBixTey family (M = Ni, V, Ti, etc.).

cond-mat.mtrl-sci↗

arXiv2019

A comprehensive study of speech separation: spectrogram vs waveform separation

Speech separation has been studied widely for single-channel close-talk microphone recordings over the past few years; developed solutions are mostly in frequency-domain. Recently, a raw audio waveform separation network (TasNet) is introduced for single-channel data, with achieving high Si-SNR (scale-invariant source-to-noise ratio) and SDR (source-to-distortion ratio) comparing against the state-of-the-art solution in frequency-domain. In this study, we incorporate effective components of the TasNet into a frequency-domain separation method. We compare both for alternative scenarios. We introduce a solution for directly optimizing the separation criterion in frequency-domain networks. In addition to speech separation objective and subjective measurements, we evaluate the separation performance on a speech recognition task as well. We study the speech separation problem for far-field data (more similar to naturalistic audio streams) and develop multi-channel solutions for both frequency and time-domain separators with utilizing spectral, spatial and speaker location information. For our experiments, we simulated multi-channel spatialized reverberate WSJ0-2mix dataset. Our experimental results show that spectrogram separation can achieve competitive performance with better network design. Multi-channel framework as well is shown to improve the single-channel performance relatively up to +35.5% and +46% in terms of WER and SDR, respectively.

cs.SD↗

arXiv2019

Coupled-Projection Residual Network for MRI Super-Resolution

Magnetic Resonance Imaging(MRI) has been widely used in clinical application and pathology research by helping doctors make more accurate diagnoses. On the other hand, accurate diagnosis by MRI remains a great challenge as images obtained via present MRI techniques usually have low resolutions. Improving MRI image quality and resolution thus becomes a critically important task. This paper presents an innovative Coupled-Projection Residual Network (CPRN) for MRI super-resolution. The CPRN consists of two complementary sub-networks: a shallow network and a deep network that keep the content consistency while learning high frequency differences between low-resolution and high-resolution images. The shallow sub-network employs coupled-projection for better retaining the MRI image details, where a novel feedback mechanism is introduced to guide the reconstruction of high-resolution images. The deep sub-network learns from the residuals of the high-frequency image information, where multiple residual blocks are cascaded to magnify the MRI images at the last network layer. Finally, the features from the shallow and deep sub-networks are fused for the reconstruction of high-resolution MRI images. For effective fusion of features from the deep and shallow sub-networks, a step-wise connection (CPRN S) is designed as inspired by the human cognitive processes (from simple to complex). Experiments over three public MRI datasets show that our proposed CPRN achieves superior MRI super-resolution performance as compared with the state-of-the-art. Our source code will be publicly available at http://www.yongxu.org/lunwen.html.

eess.IV↗

arXiv2019

Single-Channel Signal Separation and Deconvolution with Generative Adversarial Networks

Single-channel signal separation and deconvolution aims to separate and deconvolve individual sources from a single-channel mixture and is a challenging problem in which no prior knowledge of the mixing filters is available. Both individual sources and mixing filters need to be estimated. In addition, a mixture may contain non-stationary noise which is unseen in the training set. We propose a synthesizing-decomposition (S-D) approach to solve the single-channel separation and deconvolution problem. In synthesizing, a generative model for sources is built using a generative adversarial network (GAN). In decomposition, both mixing filters and sources are optimized to minimize the reconstruction error of the mixture. The proposed S-D approach achieves a peak-to-noise-ratio (PSNR) of 18.9 dB and 15.4 dB in image inpainting and completion, outperforming a baseline convolutional neural network PSNR of 15.3 dB and 12.2 dB, respectively and achieves a PSNR of 13.2 dB in source separation together with deconvolution, outperforming a convolutive non-negative matrix factorization (NMF) baseline of 10.1 dB.

eess.AS↗

arXiv2019

Robust Classification with Sparse Representation Fusion on Diverse Data Subsets

Sparse Representation (SR) techniques encode the test samples into a sparse linear combination of all training samples and then classify the test samples into the class with the minimum residual. The classification of SR techniques depends on the representation capability on the test samples. However, most of these models view the representation problem of the test samples as a deterministic problem, ignoring the uncertainty of the representation. The uncertainty is caused by two factors, random noise in the samples and the intrinsic randomness of the sample set, which means that if we capture a group of samples, the obtained set of samples will be different in different conditions. In this paper, we propose a novel method based upon Collaborative Representation that is a special instance of SR and has closed-form solution. It performs Sparse Representation Fusion based on the Diverse Subset of training samples (SRFDS), which reduces the impact of randomness of the sample set and enhances the robustness of classification results. The proposed method is suitable for multiple types of data and has no requirement on the pattern type of the tasks. In addition, SRFDS not only preserves a closed-form solution but also greatly improves the classification performance. Promising results on various datasets serve as the evidence of better performance of SRFDS than other SR-based methods. The Matlab code of SRFDS will be accessible at http://www.yongxu.org/lunwen.html.

cs.CV↗

arXiv2019

Cross-task learning for audio tagging, sound event detection and spatial localization: DCASE 2019 baseline systems

The Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 challenge focuses on audio tagging, sound event detection and spatial localisation. DCASE 2019 consists of five tasks: 1) acoustic scene classification, 2) audio tagging with noisy labels and minimal supervision, 3) sound event localisation and detection, 4) sound event detection in domestic environments, and 5) urban sound tagging. In this paper, we propose generic cross-task baseline systems based on convolutional neural networks (CNNs). The motivation is to investigate the performance of a variety of models across several audio recognition tasks without exploiting the specific characteristics of the tasks. We looked at CNNs with 5, 9, and 13 layers, and found that the optimal architecture is task-dependent. For the systems we considered, we found that the 9-layer CNN with average pooling after convolutional layers is a good model for a majority of the DCASE 2019 tasks.

cs.SD↗

arXiv2019

Topological Phases in Non-Hermitian Aubry-André-Harper Models

Topological phases have recently witnessed a rapid progress in non-Hermitian systems. Here we study a one-dimensional non-Hermitian Aubry-André-Harper model with imaginary periodic or quasiperiodic modulations. We demonstrate that the non-Hermitian off-diagonal AAH models can host zero-energy modes at the edges. In contrast to the Hermitian case, the zero-energy mode can be localized only at one edge. Such a topological phase corresponds to the existence of a quarter winding number defined by eigenenergy in momentum space. We further find the coexistence of a zero-energy mode located only at one edge and topological nonzero energy edge modes characterized by a generalized Bott index. In the incommensurate case, a topological non-Hermitian quasicrystal is predicted where all bulk states and two topological edge states are localized at one edge. Such topological edge modes are protected by the generalized Bott index. Finally, we propose an experimental scheme to realize these non-Hermitian models in electric circuits. Our findings add a new direction for exploring topological properties in Aubry-André-Harper models.

cond-mat.mes-hall↗

arXiv2019

End-to-End Multi-Channel Speech Separation

The end-to-end approach for single-channel speech separation has been studied recently and shown promising results. This paper extended the previous approach and proposed a new end-to-end model for multi-channel speech separation. The primary contributions of this work include 1) an integrated waveform-in waveform-out separation system in a single neural network architecture. 2) We reformulate the traditional short time Fourier transform (STFT) and inter-channel phase difference (IPD) as a function of time-domain convolution with a special kernel. 3) We further relaxed those fixed kernels to be learnable, so that the entire architecture becomes purely data-driven and can be trained from end-to-end. We demonstrate on the WSJ0 far-field speech separation task that, with the benefit of learnable spatial features, our proposed end-to-end multi-channel model significantly improved the performance of previous end-to-end single-channel method and traditional multi-channel methods.

cs.SD↗

arXiv2019

Supervised Discriminative Sparse PCA for Com-Characteristic Gene Selection and Tumor Classification on Multiview Biological Data

Principal Component Analysis (PCA) has been used to study the pathogenesis of diseases. To enhance the interpretability of classical PCA, various improved PCA methods have been proposed to date. Among these, a typical method is the so-called sparse PCA, which focuses on seeking sparse loadings. However, the performance of these methods is still far from satisfactory due to their limitation of using unsupervised learning methods; moreover, the class ambiguity within the sample is high. To overcome this problem, this study developed a new PCA method, which is named the Supervised Discriminative Sparse PCA (SDSPCA). The main innovation of this method is the incorporation of discriminative information and sparsity into the PCA model. Specifically, in contrast to the traditional sparse PCA, which imposes sparsity on the loadings, here, sparse components are obtained to represent the data. Furthermore, via linear transformation, the sparse components approximate the given label information. On the one hand, sparse components improve interpretability over traditional PCA, while on the other hand, they are have discriminative abilities suitable for classification purposes. A simple algorithm is developed and its convergence proof is provided. The SDSPCA has been applied to common characteristic gene selection (com-characteristic gene) and tumor classification on multi-view biological data. The sparsity and classification performance of the SDSPCA are empirically verified via abundant, reasonable, and effective experiments, and the obtained results demonstrate that SDSPCA outperforms other state-of-the-art methods.

cs.LG↗

arXiv2019

Magnetically Controllable Topological Quantum Phase Transitions in Antiferromagnetic Topological Insulator MnBi$_2$Te$_4$

The recent discovery of antiferromagnetic (AFM) topological insulator (TI) MnBi$_2$Te$_4$ has triggered great research efforts on exploring novel magnetic topological physics. Based on first-principles calculations, we find that the manipulation of magnetic orientation and order not only significantly affects material symmetries and orbital hybridizations, but also results in variant new magnetic topological phases in MnBi$_2$Te$_4$. We thus predict a series of unusual topological quantum phase transitions that are magnetically controllable in the material, including phase transitions from AFM TI to AFM mirror topological crystalline insulator, from type-II to type-I topological Weyl semimetal, and from axion insulator to Chern insulator. The findings open new opportunities for future research and applications of magnetic topological materials.

cond-mat.mes-hall↗

arXiv2019

Proximity-induced Majorana hinge modes in antiferromagnetic topological insulators

We propose a realization of chiral Majorana modes propagating on the hinges of a 3D antiferromagnetic topological insulator, which was recently theoretically predicted and experimentally confirmed in the tetradymite-type $\mathrm{MnBi_2Te_4}$-related ternary chalgogenides. These materials consist of ferromagnetically ordered 2D layers, whose magnetization direction alternates between neighboring layers, forming an antiferromagnetic order. Besides surfaces with a magnetic gap, there also exsist gapless surfaces with a single Dirac cone, which can be gapped out when proximity coupled to an $s$-wave superconductor. On the sharing edges between the two types of gapped surfaces, the chiral Majorana modes emerge. We further propose experimental signatures of these Majoana hinge modes in terms of two-terminal conductance measurements.

cond-mat.mes-hall↗

arXiv2019

Topological Gapless Matters in Three-dimensional Ultracold Atomic Gases

Three-dimensional topological gapless matters with gapless degeneracies protected by a topological invariant defined over a closed manifold in momentum space have attracted considerable interest in various fields ranging from condensed matter materials to ultracold atomic gases. As a highly controllable and disorder free system, ultracold atomic gases provide a versatile platform to simulate topological gapless matters. Here, the current progress in studies of topological gapless phenomena in three-dimensional cold atom systems is summarized in the review. It is mainly focused on Weyl points, structured (type-II) Weyl points, Dirac points, nodal rings and Weyl exceptional rings in cold atoms. Since interactions in cold atoms can be controlled via Feshbach resonances, the progress in both superfluids for attractive interactions and non-interacting cold atom gases is reviewed.

cond-mat.quant-gas↗

arXiv2019

Averaging principles for non-autonomous two-time-scale stochastic reaction-diffusion equations with polynomial growth

In this paper, we develop the averaging principle for a class of two-time-scale stochastic reaction-diffusion equations driven by Wiener processes and Poisson random measures. We assume that all coefficients of the equation have polynomial growth, and the drift term of the equation is non-Lipschitz. Hence, the classical formulation of the averaging principle under the Lipschitz condition is no longer available. To prove the validity of the averaging principle, the existence and uniqueness of the mild solution are proved firstly. Then, the existence of time-dependent evolution family of measures associated with the fast equation is studied, by which the averaged coefficient is obtained. Finally, the validity of the averaging principle is verified.

math.DS↗

arXiv2019

Cross-task learning for audio tagging, sound event detection spatial localization: DCASE 2019 baseline systems

The Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 challenge focuses on audio tagging, sound event detection and spatial localisation. DCASE 2019 consists of five tasks: 1) acoustic scene classification, 2) audio tagging with noisy labels and minimal supervision, 3) sound event localisation and detection, 4) sound event detection in domestic environments, and 5) urban sound tagging. In this paper, we propose generic cross-task baseline systems based on convolutional neural networks (CNNs). The motivation is to investigate the performance of a variety of models across several tasks without exploiting the specific characteristics of the tasks. We looked at CNNs with 5, 9, and 13 layers, and found that the optimal architecture is task-dependent. For the systems we considered, we found that the 9-layer CNN with average pooling is a good model for a majority of the DCASE 2019 tasks.

cs.SD↗

arXiv2019

Improved Speaker-Dependent Separation for CHiME-5 Challenge

This paper summarizes several follow-up contributions for improving our submitted NWPU speaker-dependent system for CHiME-5 challenge, which aims to solve the problem of multi-channel, highly-overlapped conversational speech recognition in a dinner party scenario with reverberations and non-stationary noises. We adopt a speaker-aware training method by using i-vector as the target speaker information for multi-talker speech separation. With only one unified separation model for all speakers, we achieve a 10\% absolute improvement in terms of word error rate (WER) over the previous baseline of 80.28\% on the development set by leveraging our newly proposed data processing techniques and beamforming approach. With our improved back-end acoustic model, we further reduce WER to 60.15\% which surpasses the result of our submitted CHiME-5 challenge system without applying any fusion techniques.

eess.AS↗

arXiv2019

LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking

In this paper, we present LaSOT, a high-quality benchmark for Large-scale Single Object Tracking. LaSOT consists of 1,400 sequences with more than 3.5M frames in total. Each frame in these sequences is carefully and manually annotated with a bounding box, making LaSOT the largest, to the best of our knowledge, densely annotated tracking benchmark. The average video length of LaSOT is more than 2,500 frames, and each sequence comprises various challenges deriving from the wild where target objects may disappear and re-appear again in the view. By releasing LaSOT, we expect to provide the community with a large-scale dedicated benchmark with high quality for both the training of deep trackers and the veritable evaluation of tracking algorithms. Moreover, considering the close connections of visual appearance and natural language, we enrich LaSOT by providing additional language specification, aiming at encouraging the exploration of natural linguistic feature for tracking. A thorough experimental evaluation of 35 tracking algorithms on LaSOT is presented with detailed analysis, and the results demonstrate that there is still a big room for improvements.

cs.CV↗