arXiv subjects

Yong Xu

Publications and source records attributed to Yong Xu.

At least 415 records · Page 23Linked to original sources

arXiv2019

Type-II Ising Pairing in Few-Layer Stanene

Spin-orbit coupling has proven indispensable in realizing topological materials and more recently Ising pairing in two-dimensional superconductors. This pairing mechanism relies on inversion symmetry breaking and sustains anomalously large in-plane polarizing magnetic fields whose upper limit is expected to diverge at low temperatures, although experimental demonstration of this has remained elusive due to the required fields. In this work, the recently discovered superconductor few-layer stanene, i.e. epitaxially strained $α$-Sn, is shown to exhibit a new type of Ising pairing between carriers residing in bands with different orbital indices near the $Γ$-point. The bands are split as a result of spin-orbit locking without the participation of inversion symmetry breaking. The in-plane upper critical field is strongly enhanced at ultra-low temperature and reveals the sought for upturn.

cond-mat.supr-con↗

arXiv2019

Enhanced CNN for image denoising

Owing to flexible architectures of deep convolutional neural networks (CNNs), CNNs are successfully used for image denoising. However, they suffer from the following drawbacks: (i) deep network architecture is very difficult to train. (ii) Deeper networks face the challenge of performance saturation. In this study, the authors propose a novel method called enhanced convolutional neural denoising network (ECNDNet). Specifically, they use residual learning and batch normalisation techniques to address the problem of training difficulties and accelerate the convergence of the network. In addition, dilated convolutions are used in the proposed network to enlarge the context information and reduce the computational cost. Extensive experiments demonstrate that the ECNDNet outperforms the state-of-the-art methods for image denoising.

cs.CV↗

arXiv2019

Sound Event Detection and Time-Frequency Segmentation from Weakly Labelled Data

Sound event detection (SED) aims to detect when and recognize what sound events happen in an audio clip. Many supervised SED algorithms rely on strongly labelled data which contains the onset and offset annotations of sound events. However, many audio tagging datasets are weakly labelled, that is, only the presence of the sound events is known, without knowing their onset and offset annotations. In this paper, we propose a time-frequency (T-F) segmentation framework trained on weakly labelled data to tackle the sound event detection and separation problem. In training, a segmentation mapping is applied on a T-F representation, such as log mel spectrogram of an audio clip to obtain T-F segmentation masks of sound events. The T-F segmentation masks can be used for separating the sound events from the background scenes in the time-frequency domain. Then a classification mapping is applied on the T-F segmentation masks to estimate the presence probabilities of the sound events. We model the segmentation mapping using a convolutional neural network and the classification mapping using a global weighted rank pooling (GWRP). In SED, predicted onset and offset times can be obtained from the T-F segmentation masks. As a byproduct, separated waveforms of sound events can be obtained from the T-F segmentation masks. We remixed the DCASE 2018 Task 1 acoustic scene data with the DCASE 2018 Task 2 sound events data. When mixing under 0 dB, the proposed method achieved F1 scores of 0.534, 0.398 and 0.167 in audio tagging, frame-wise SED and event-wise SED, outperforming the fully connected deep neural network baseline of 0.331, 0.237 and 0.120, respectively. In T-F segmentation, we achieved an F1 score of 0.218, where previous methods were not able to do T-F segmentation.

cs.SD↗

arXiv2019

Magnetization reversal more rapidly by using an ultrashort square-wave laser pulse

With the feature of low-power magnetization manipulation at an ultrashort time scale, all optical switching (AOS) has been propelled to the forefront in investigations. To further speed up the magnetization reversal by manipulating ultrashort optical pulses, in this paper, one single square-wave laser pulse (SWLP) vie the combination of heating and Inverse Faraday Effect (IFE) is explored to excite the reversal of magnetization in a Co/Pt system. Simulation results show that the switching time of magnetization is 3 times faster than the using of a traditional Gaussian wave laser pulse (GWLP) under the same laser energy and pulse duration, and the threshold of AOS for the ferromagnet is 0.67 mJ/cm2. We furthermore demonstrate that the "heat accumulating effect" of laser-pulse is an important factor that influences the switching time, and a SWLP has a larger effect of heat accumulating than a GWLP.

cond-mat.mes-hall↗

arXiv2019

Valley filtering effect of phonons in graphene with a grain boundary

Due to their possibility to encode information and realize low-energy-consumption quantum devices, control and manipulation of the valley degree of freedom have been widely studied in electronic systems. In contrast, the phononic counterpart--valley phononics--has been largely unexplored, despite the importance in both fundamental science and practical applications. In this work, we demonstrate that the control of "valleys" is also applicable for phonons in graphene by using a grain boundary. In particular, perfect valley filtering effect is observed at certain energy windows for flexural modes and found to be closely related to the anisotropy of phonon valley pockets. Moreover, valley filtering may be further improved using Fano-like resonance. Our findings reveal the possibility of valley phononics, paving the road towards purposeful phonon engineering and future valley phononics.

cond-mat.mes-hall↗

arXiv2019

Energy-efficient domain wall motion governed by the interplay of helicity-dependent optical effect and spin-orbit torque

Spin-orbit torque provides a powerful means of manipulating domain walls along magnetic wires. However, the current density required for domain wall motion is still too high to realize low power devices. Here we experimentally demonstrate helicity-dependent domain wall motion by combining synchronized femtosecond laser pulses and short current pulses in Co/Ni/Co ultra-thin film wires with perpendicular magnetization. Domain wall can remain pinned under one laser circular helicity while depinned by the opposite circular helicity. Thanks to the all-optical helicity-dependent effect, the threshold current density due to spin-orbit torque can be reduced by more than 50%. Based on this joint effect combining spin-orbit torque and helicity-dependent laser pulses, an optoelectronic logic-in-memory device has been experimentally demonstrated. This work enables a new class of low power spintronic-photonic devices beyond the conventional approach of all-optical switching or all-current switching for data storage.

cond-mat.mes-hall↗

arXiv2019

Domain-wall motion induced by spin transfer torque delivered by helicity-dependent femtosecond laser

In magnetic wires with perpendicular anisotropy, moving domain with only current or only circularly polarized light requires a high power. Here, we propose to reduce it by using both short current pulses and femtosecond laser pulses simultaneously. The wires were made out of perpendicularly magnetized film of Pt/Co/Ni/Co/Pt. The displacement of the domain wall is found to be dependent on the laser helicity. Based on a quantitative analysis of the current-induced domain wall motion, the spin orbit torque contribution can be neglected when compared to the spin transfer torque contribution. The effective field of the spin transfer torque is extracted from the pulsed field domain wall measurements. Finally, our result can be described using the Fatuzzo-Labrune model and considering the effective field due to the polarized laser beam, the effective field due to spin transfer torque, and the Gaussian temperature distribution of the laser spot.

cond-mat.mes-hall↗

arXiv2019

Dual Graph-Laplacian PCA: A Closed-Form Solution for Bi-clustering to Find "Checkerboard" Structures on Gene Expression Data

In the context of cancer, internal "checkerboard" structures are normally found in the matrices of gene expression data, which correspond to genes that are significantly up- or down-regulated in patients with specific types of tumors. In this paper, we propose a novel method, called dual graph-regularization principal component analysis (DGPCA). The main innovation of this method is that it simultaneously considers the internal geometric structures of the condition manifold and the gene manifold. Specifically, we obtain principal components (PCs) to represent the data and approximate the cluster membership indicators through Laplacian embedding. This new method is endowed with internal geometric structures, such as the condition manifold and gene manifold, which are both suitable for bi-clustering. A closed-form solution is provided for DGPCA. We apply this new method to simultaneously cluster genes and conditions (e.g., different samples) with the aim of finding internal "checkerboard" structures on gene expression data, if they exist. Then, we use this new method to identify regulatory genes under the particular conditions and to compare the results with those of other state-of-the-art PCA-based methods. Promising results on gene expression data have been verified by extensive experiments

q-bio.GN↗

arXiv2019

Enhanced magneto-optical response due to the flat band in nanoribbons made from the $α-T_3$ lattice

We study the optical response of nanoribbons made from the $α-T_3$ lattice under a weak magnetic field in the terahertz to far-infrared regime. It is found that the magnetic field can open a gap in the band structure and induce a new absorption peak with much reduced frequency in metallic armchair ribbons and a class of zigzag ribbons with particular boundaries. This tunable magneto-optical modulation effect is attributed to the interband transitions between the flat band and the propagating bands. By contrast, this magnetic modulation of gap opening and optical conductance is much weaker in metallic armchair graphene ribbons (the case of $α=0$) in which the flat band is absent. The enhancement in the $α-T_3$ model is analytically investigated and explained within the perturbation theory for metallic armchair ribbons. The magnetic field induced valley degeneracy lifting and valley splitting of the absorption peak are also discussed in the case of zigzag ribbons. These findings pave the way for magneto-optics devices based on the $α-T_3$ model materials.

cond-mat.mes-hall↗

arXiv2018

Image Cartoon-Texture Decomposition Using Isotropic Patch Recurrence

Aiming at separating the cartoon and texture layers from an image, cartoon-texture decomposition approaches resort to image priors to model cartoon and texture respectively. In recent years, patch recurrence has emerged as a powerful prior for image recovery. However, the existing strategies of using patch recurrence are ineffective to cartoon-texture decomposition, as both cartoon contours and texture patterns exhibit strong patch recurrence in images. To address this issue, we introduce the isotropy prior of patch recurrence, that the spatial configuration of similar patches in texture exhibits the isotropic structure which is different from that in cartoon, to model the texture component. Based on the isotropic patch recurrence, we construct a nonlocal sparsification system which can effectively distinguish well-patterned features from contour edges. Incorporating the constructed nonlocal system into morphology component analysis, we develop an effective method to both noiseless and noisy cartoon-texture decomposition. The experimental results have demonstrated the superior performance of the proposed method to the existing ones, as well as the effectiveness of the isotropic patch recurrence prior.

cs.CV↗

arXiv2018

Deep Learning for Image Denoising: A Survey

Since the proposal of big data analysis and Graphic Processing Unit (GPU), the deep learning technology has received a great deal of attention and has been widely applied in the field of imaging processing. In this paper, we have an aim to completely review and summarize the deep learning technologies for image denoising proposed in recent years. Morever, we systematically analyze the conventional machine learning methods for image denoising. Finally, we point out some research directions for the deep learning technologies in image denoising.

cs.CV↗

arXiv2018

Realizing an Epitaxial Stanene with an Insulating Bandgap

Stanene, a single atomic layer of Sn in a honeycomb lattice, is predicted a candidate wide bandgap two-dimensional (2D) topological insulator and can host intriguing topological states of matter such as quantum anomalous Hall effect and topological superconductivity with different surface modifications. Despite intensive research efforts, one still cannot obtain bulk-insulating stanene samples-a prerequisite for any transport studies and applications of stanene. Here we show the experimental realization of an epitaxial stanene with an insulating bulk bandgap by using PbTe(111) substrates. With low-temperature molecular beam epitaxy, we are able to grow single layer stanene on PbTe(111). In-situ angle-resolved photoemission spectroscopy shows the characteristic stanene bands with its Fermi level lying in the bandgap. Doping Sr in PbTe removes the substrate states located in the stanene gap, resulting in a stanene sample with truly insulating bulk. This experimental progress paves the way for studies of stanene-based topological quantum effects.

cond-mat.mtrl-sci↗

arXiv2018

Superconductivity in Few-Layer Stanene

A single atomic slice of α-tin-stanene-has been predicted to host quantum spin Hall effect at room temperature, offering an ideal platform to study low-dimensional and topological physics. While recent research has intensively focused on monolayer stanene, the quantum size effect in few-layer stanene could profoundly change material properties, but remains unexplored. By exploring the layer degree of freedom, we unexpectedly discover superconductivity in few-layer stanene down to a bilayer grown on PbTe, while bulk α-tin is not superconductive. Through substrate engineering, we further realize a transition from a single-band to a two-band superconductor with a doubling of the transition temperature. In-situ angle resolved photoemission spectroscopy (ARPES) together with first-principles calculations elucidate the corresponding band structure. Interestingly, the theory also indicates the existence of a topologically nontrivial band. Our experimental findings open up novel strategies for constructing two-dimensional topological superconductors.

cond-mat.mtrl-sci↗

arXiv2018

DCASE 2018 Challenge Surrey Cross-Task convolutional neural network baseline

The Detection and Classification of Acoustic Scenes and Events (DCASE) consists of five audio classification and sound event detection tasks: 1) Acoustic scene classification, 2) General-purpose audio tagging of Freesound, 3) Bird audio detection, 4) Weakly-labeled semi-supervised sound event detection and 5) Multi-channel audio classification. In this paper, we create a cross-task baseline system for all five tasks based on a convlutional neural network (CNN): a "CNN Baseline" system. We implemented CNNs with 4 layers and 8 layers originating from AlexNet and VGG from computer vision. We investigated how the performance varies from task to task with the same configuration of neural networks. Experiments show that deeper CNN with 8 layers performs better than CNN with 4 layers on all tasks except Task 1. Using CNN with 8 layers, we achieve an accuracy of 0.680 on Task 1, an accuracy of 0.895 and a mean average precision (MAP) of 0.928 on Task 2, an accuracy of 0.751 and an area under the curve (AUC) of 0.854 on Task 3, a sound event detection F1 score of 20.8% on Task 4, and an F1 score of 87.75% on Task 5. We released the Python source code of the baseline systems under the MIT license for further research.

cs.SD↗

arXiv2018

Experimental realization of an intrinsic magnetic topological insulator

Intrinsic magnetic topological insulator (TI) is a stoichiometric magnetic compound possessing both inherent magnetic order and topological electronic states. Such a material can provide a shortcut to various novel topological quantum effects but remains elusive experimentally so far. Here, we report the experimental realization of high-quality thin films of an intrinsic magnetic TI---MnBi$_2$Te$_4$---by alternate growth of a Bi$_2$Te$_3$ quintuple-layer and a MnTe bilayer with molecular beam epitaxy. The material shows the archetypical Dirac surface states in angle-resolved photoemission spectroscopy and is demonstrated to be an antiferromagnetic topological insulator with ferromagnetic surfaces by magnetic and transport measurements as well as first-principles calculations. The unique magnetic and topological electronic structures and their interplays enable the material to embody rich quantum phases such as quantum anomalous Hall insulators and axion insulators in a well-controlled way.

cond-mat.mtrl-sci↗

arXiv2018

Highly-Economized Multi-View Binary Compression for Scalable Image Clustering

How to economically cluster large-scale multi-view images is a long-standing problem in computer vision. To tackle this challenge, we introduce a novel approach named Highly-economized Scalable Image Clustering (HSIC) that radically surpasses conventional image clustering methods via binary compression. We intuitively unify the binary representation learning and efficient binary cluster structure learning into a joint framework. In particular, common binary representations are learned by exploiting both sharable and individual information across multiple views to capture their underlying correlations. Meanwhile, cluster assignment with robust binary centroids is also performed via effective discrete optimization under L21-norm constraint. By this means, heavy continuous-valued Euclidean distance computations can be successfully reduced by efficient binary XOR operations during the clustering procedure. To our best knowledge, HSIC is the first binary clustering work specifically designed for scalable multi-view image clustering. Extensive experimental results on four large-scale image datasets show that HSIC consistently outperforms the state-of-the-art approaches, whilst significantly reducing computational time and memory footprint.

cs.CV↗

arXiv2018

Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization

Clustering with incomplete views is a challenge in multi-view clustering. In this paper, we provide a novel and simple method to address this issue. Specifically, the proposed method simultaneously exploits the local information of each view and the complementary information among views to learn the common latent representation for all samples, which can greatly improve the compactness and discriminability of the obtained representation. Compared with the conventional graph embedding methods, the proposed method does not introduce any extra regularization term and corresponding penalty parameter to preserve the local structure of data, and thus does not increase the burden of extra parameter selection. By imposing the orthogonal constraint on the basis matrix of each view, the proposed method is able to handle the out-of-sample. Moreover, the proposed method can be viewed as a unified framework for multi-view learning since it can handle both incomplete and complete multi-view clustering and classification tasks. Extensive experiments conducted on several multi-view datasets prove that the proposed method can significantly improve the clustering performance.

cs.CV↗

arXiv2018

Phononic topological insulators with tunable pseudospin physics

Efficient control of phonons is crucial to energy-information technology, but limited by the lacking of tunable degrees of freedom like charge or spin. Here we suggest to utilize crystalline symmetry-protected pseudospins as new quantum degrees of freedom to manipulate phonons. Remarkably, we reveal a duality between phonon pseudospins and electron spins by presenting Kramers-like degeneracy and pseudospin counterparts of spin-orbit coupling, which lays the foundation for "pseudospin phononics". Furthermore, we report two types of three-dimensional phononic topological insulators, which give topologically protected, gapless surface states with linear and quadratic band degeneracies, respectively. These topological surface states display unconventional phonon transport behaviors attributed to the unique pseudospin-momentum locking, which are useful for phononic circuits, transistors, antennas, etc. The emerging pseudospin physics offers new opportunities to develop future phononics.

cond-mat.mes-hall↗