arXiv subjects

Bin Liu

Publications and source records attributed to Bin Liu.

At least 397 records · Page 22Linked to original sources

arXiv2020

2.5-kV AlGaN/GaN Schottky Barrier Diode on Silicon Substrate with Recessed-anode Structure

In this letter, we demonstrate high-performance lateral AlGaN/GaN Schottky barrier diodes (SBD) on Si substrate with a recessed-anode structure. The optimized rapid etch process provides results in improving etching quality with a 0.26-nm roughness of the anode recessed surface. By using the high work function metal Pt as the Schottky electrode, a low Von of 0.71 V is obtained with a high uniformity of 0.023 V for 40 devices. Supported by the flat anode recess surface and related field plate design, the SBD device with the anode-cathode spacing of 15 um show the Ron,sp of 1.53 mOhm.cm2 only, the breakdown voltage can reach 1592 V with a high power FOM (Figure-of-Merit) of 1656 MW/cm2. For the SBD device with the anode-cathode spacing of 30 um, the breakdown voltage can be as high as 2521 V and the power FOM is 1244 MW/cm2.

physics.app-ph↗

arXiv2020

Numerical stability and three dimensionality of a streamline hyperbolic critical point in wake at low Reynolds number

In this work the numerical stability of a streamline singular hyperbolic/saddle critical point (HSP) and its relationship with the divergence of pressure force/fluid flux are numerically investigated at low Reynolds numbers. Three canonical configurations at different Reynolds numbers are considered: (a) an isolated circular cylinder; (b) side-by-side circular cylinders and (c) a near-wall circular cylinder. The objective is to investigate the behavior of a HSP subjecting to imbalanced shear-layer interaction and different Reynolds numbers. It is found that a HSP evolves along the shear-layer interfaces and imposes adverse pressure gradients, which potentially deteriorates near-wake stability. The inherent characteristics of a HSP is linked with net positive fluid-fluid divergence and fluid three dimensionality. A vorticity-free stagnant zone is formed around a HSP, which cut the kinetic energy supply of shear layers in wake, project third-dimensional fluid fluxes and develops three-dimensional streamwise braids. These findings are confirmed and explained via the quantification of the fluid-flux divergence, the hydrodynamic responses of cylinder(s) and the secondary enstrophy. The primary focus in this article is to reveal the subtle analytical relationships between HSP, divergence of pressure force/fluid flux, fluid three dimensionality and imbalanced shear-layer interaction. To the knowledge of authors, so far these analytical relationships have not been reported in literature.

physics.flu-dyn↗

arXiv2020

AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction

Learning feature interactions is crucial for click-through rate (CTR) prediction in recommender systems. In most existing deep learning models, feature interactions are either manually designed or simply enumerated. However, enumerating all feature interactions brings large memory and computation cost. Even worse, useless interactions may introduce noise and complicate the training process. In this work, we propose a two-stage algorithm called Automatic Feature Interaction Selection (AutoFIS). AutoFIS can automatically identify important feature interactions for factorization models with computational cost just equivalent to training the target model to convergence. In the \emph{search stage}, instead of searching over a discrete set of candidate feature interactions, we relax the choices to be continuous by introducing the architecture parameters. By implementing a regularized optimizer over the architecture parameters, the model can automatically identify and remove the redundant feature interactions during the training process of the model. In the \emph{re-train stage}, we keep the architecture parameters serving as an attention unit to further boost the performance. Offline experiments on three large-scale datasets (two public benchmarks, one private) demonstrate that AutoFIS can significantly improve various FM based models. AutoFIS has been deployed onto the training platform of Huawei App Store recommendation service, where a 10-day online A/B test demonstrated that AutoFIS improved the DeepFM model by 20.3\% and 20.1\% in terms of CTR and CVR respectively.

cs.LG↗

arXiv2020

Deep-Learning Inversion of Seismic Data

We propose a new method to tackle the mapping challenge from time-series data to spatial image in the field of seismic exploration, i.e., reconstructing the velocity model directly from seismic data by deep neural networks (DNNs). The conventional way of addressing this ill-posed inversion problem is through iterative algorithms, which suffer from poor nonlinear mapping and strong nonuniqueness. Other attempts may either import human intervention errors or underuse seismic data. The challenge for DNNs mainly lies in the weak spatial correspondence, the uncertain reflection-reception relationship between seismic data and velocity model, as well as the time-varying property of seismic data. To tackle these challenges, we propose end-to-end seismic inversion networks (SeisInvNets) with novel components to make the best use of all seismic data. Specifically, we start with every seismic trace and enhance it with its neighborhood information, its observation setup, and the global context of its corresponding seismic profile. From the enhanced seismic traces, the spatially aligned feature maps can be learned and further concatenated to reconstruct a velocity model. In general, we let every seismic trace contribute to the reconstruction of the whole velocity model by finding spatial correspondence. The proposed SeisInvNet consistently produces improvements over the baselines and achieves promising performance on our synthesized and proposed SeisInv data set according to various evaluation metrics. The inversion results are more consistent with the target from the aspects of velocity values, subsurface structures, and geological interfaces. Moreover, the mechanism and the generalization of the proposed method are discussed and verified. Nevertheless, the generalization of deep-learning-based inversion methods on real data is still challenging and considering physics may be one potential solution.

cs.CV↗

arXiv2020

Deep Learning Inversion of Electrical Resistivity Data

The inverse problem of electrical resistivity surveys (ERSs) is difficult because of its nonlinear and ill-posed nature. For this task, traditional linear inversion methods still face challenges such as suboptimal approximation and initial model selection. Inspired by the remarkable nonlinear mapping ability of deep learning approaches, in this article, we propose to build the mapping from apparent resistivity data (input) to resistivity model (output) directly by convolutional neural networks (CNNs). However, the vertically varying characteristic of patterns in the apparent resistivity data may cause ambiguity when using CNNs with the weight sharing and effective receptive field properties. To address the potential issue, we supply an additional tier feature map to CNNs to help those aware of the relationship between input and output. Based on the prevalent U-Net architecture, we design our network (ERSInvNet) that can be trained end-to-end and can reach a very fast inference speed during testing. We further introduce a depth weighting function and a smooth constraint into loss function to improve inversion accuracy for the deep region and suppress false anomalies. Six groups of experiments are considered to demonstrate the feasibility and efficiency of the proposed methods. According to the comprehensive qualitative analysis and quantitative comparison, ERSInvNet with tier feature map, smooth constraints, and depth weighting function together achieve the best performance.

cs.CV↗

arXiv2020

Parametric Instance Classification for Unsupervised Visual Feature Learning

This paper presents parametric instance classification (PIC) for unsupervised visual feature learning. Unlike the state-of-the-art approaches which do instance discrimination in a dual-branch non-parametric fashion, PIC directly performs a one-branch parametric instance classification, revealing a simple framework similar to supervised classification and without the need to address the information leakage issue. We show that the simple PIC framework can be as effective as the state-of-the-art approaches, i.e. SimCLR and MoCo v2, by adapting several common component settings used in the state-of-the-art approaches. We also propose two novel techniques to further improve effectiveness and practicality of PIC: 1) a sliding-window data scheduler, instead of the previous epoch-based data scheduler, which addresses the extremely infrequent instance visiting issue in PIC and improves the effectiveness; 2) a negative sampling and weight update correction approach to reduce the training time and GPU memory consumption, which also enables application of PIC to almost unlimited training images. We hope that the PIC framework can serve as a simple baseline to facilitate future study.

cs.CV↗

arXiv2020

Compact Differences of Weighted Composition Operators

Compact differences of two weighted composition operators acting from the weighted Bergman space $A^p_ω$ to another weighted Bergman space $A^q_ν$, where $0<p\le q<\infty$ and $ω,ν$ belong to the class $\mathcal{D}$ of radial weights satisfying two-sided doubling conditions, are characterized. On the way to the proof a new description of $q$-Carleson measures for $A^p_ω$, with $ω\in\mathcal{D}$, in terms of pseudohyperbolic discs is established. This last-mentioned result generalizes the well-known characterization of $q$-Carleson measures for the classical weighted Bergman space $A^p_α$ with $-1<α<\infty$ to the setting of doubling weights.

math.FA↗

arXiv2020

Multi-Label Sampling based on Local Label Imbalance

Class imbalance is an inherent characteristic of multi-label data that hinders most multi-label learning methods. One efficient and flexible strategy to deal with this problem is to employ sampling techniques before training a multi-label learning model. Although existing multi-label sampling approaches alleviate the global imbalance of multi-label datasets, it is actually the imbalance level within the local neighbourhood of minority class examples that plays a key role in performance degradation. To address this issue, we propose a novel measure to assess the local label imbalance of multi-label datasets, as well as two multi-label sampling approaches based on the local label imbalance, namely MLSOL and MLUL. By considering all informative labels, MLSOL creates more diverse and better labeled synthetic instances for difficult examples, while MLUL eliminates instances that are harmful to their local region. Experimental results on 13 multi-label datasets demonstrate the effectiveness of the proposed measure and sampling approaches for a variety of evaluation metrics, particularly in the case of an ensemble of classifiers trained on repeated samples of the original data.

cs.LG↗

arXiv2020

Nonlinear modes in spatially confined spin-orbit-coupled Bose-Einstein condensates with repulsive nonlinearity

It was found that spatially confined spin-orbit (SO) coupling, which can be induced by illuminating Bose-Einstein condensates (BECs) with a Gaussian laser beam, can help trap a spinor Bose gas in multi-dimensional space. Previous works on this topic were all based on a Boson gas featuring an attractive interaction. In this paper, we consider the trapping effect in the case in which the Boson gas features a repulsive interaction. After replacing the repulsive effect, stable excited modes of semi-vortex (SV) type and mixed-mode (MM) type, which cannot be created in a boson gas with attractive interactions, can be found in the current setting. The trapping ability and the capacity of the confined SO coupling versus the degree of the repulsive strength as well as the order of the excited mode are systematically discussed firstly through the paper. Moreover, the stability of the nonlinear mode trapped in this system with a moving reference frame is also discussed. Unlike the system with homogeneous SO coupling, two different types of stationary mobility modes can be stabilized when the SO coupling moves in the x- and y- directions, respectively. This finding indicates that the system with moving confined SO coupling features a typical anisotropic character that differs from the system with moving homogeneous SO coupling.

nlin.PS↗

arXiv2020

NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results

This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new version of the previous NTIRE 2019 challenge on real image denoising that was based on the SIDD benchmark. This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+. This challenge has two tracks for quantitatively evaluating image denoising performance in (1) the Bayer-pattern rawRGB and (2) the standard RGB (sRGB) color spaces. Each track ~250 registered participants. A total of 22 teams, proposing 24 methods, competed in the final phase of the challenge. The proposed methods by the participating teams represent the current state-of-the-art performance in image denoising targeting real noisy images. The newly collected SIDD+ datasets are publicly available at: https://bit.ly/siddplus_data.

cs.CV↗

arXiv2020

Two-Dimensional Rare Earth -- Gold Intermetallic Compounds on Au(111) by Surface Alloying

Surface alloying is a straightforward route to control and modify the structure and electronic properties of surfaces. Here, We present a systematical study on the structural and electronic properties of three novel rare earth-based intermetallic compounds, namely ReAu2 (Re = Tb, Ho, and Er), on Au(111) via directly depositing rare-earth metals onto the hot Au(111) surface. Scanning tunneling microscopy/spectroscopy measurements reveal the very similar atomic structures and electronic properties, e.g. electronic states, and surface work functions, for all these intermetallic compound systems due to the physical and chemical similarities between these rare earth elements. Further, these electronic properties are periodically modulated by the moiré structures caused by the lattice mismatches between ReAu2 and Au(111). These periodically modulated surfaces could serve as templates for the self-assembly of nanostructures. Besides, these two-dimensional rare earth-based intermetallic compounds provide platforms to investigate the rare earth related catalysis, magnetisms, etc., in the lower dimensions.

cond-mat.mtrl-sci↗

arXiv2020

Discrete solitons in zigzag waveguide arrays with different types of linear mixing between nearest-neighbor and next-nearest-neighbor couplings

We study discrete solitons in zigzag discrete waveguide arrays with different types of linear mixing between nearest-neighbor and next-nearest-neighbor couplings. The waveguide array is constructed from two layers of one-dimensional (1D) waveguide arrays arranged in zigzag form. If we alternately label the number of waveguides between the two layers, the cross-layer couplings (which couple one waveguide in one layer with two adjacent waveguides in the other layer) construct the nearestneighbor couplings, while the couplings that couple this waveguide with the two nearest-neighbor waveguides in the same layer, i.e., self-layer couplings, contribute the next-nearest-neighbor couplings. Two families of discrete solitons are found when these couplings feature different types of linear mixing. As the total power is increased, a phase transition of the second kind occurs for discrete solitons in one type of setting, which is formed when the nearest-neighbor coupling and next-nearest-neighbor coupling feature positive and negative linear mixing, respectively. The mobilities and collisions of these two families of solitons are discussed systematically throughout the paper, revealing that the width of the soliton plays an important role in its

nlin.PS↗

arXiv2020

Holding and transferring matter-wave solitons against gravity by spin-orbit-coupling tweezers

We consider possibilities to grasp and drag one-dimensional solitons in two-component Bose- Einstein condensates (BECs), under the action of gravity, by tweezers induced by spatially confined spin-orbit (SO) coupling applied to the BEC, with the help of focused laser illumination. Solitons of two types are considered, semi-dipoles and mixed modes. We find critical values of the gravity force, up to which the solitons may be held or transferred by the tweezers. The dependence of the critical force on the magnitude and spatial extension of the localized SO interaction, as well as on the solitons norm and speed (in the transfer regime), are systematically studied by means of numerical methods, and analytically with the help of a quasi-particle approximation for the soliton. In particular, a noteworthy finding is that the critical gravity force increases with the increase of the transfer speed (i.e., moving solitons are more robust than quiescent ones). Nonstationary regimes are addressed too, by considering abrupt application of gravity to solitons created in the weightless setting. In that case, solitons feature damped shuttle motion, provided that the gravity force does not exceed a dynamical critical value, which is smaller than its static counterpart. The results may help to design gravimeters based on ultracold atoms.

nlin.PS↗

arXiv2020

Simultaneous Denoising and Dereverberation Using Deep Embedding Features

Monaural speech dereverberation is a very challenging task because no spatial cues can be used. When the additive noises exist, this task becomes more challenging. In this paper, we propose a joint training method for simultaneous speech denoising and dereverberation using deep embedding features, which is based on the deep clustering (DC). DC is a state-of-the-art method for speech separation that includes embedding learning and K-means clustering. As for our proposed method, it contains two stages: denoising and dereverberation. At the denoising stage, the DC network is leveraged to extract noise-free deep embedding features. These embedding features are generated from the anechoic speech and residual reverberation signals. They can represent the inferred spectral masking patterns of the desired signals, which are discriminative features. At the dereverberation stage, instead of using the unsupervised K-means clustering algorithm, another supervised neural network is utilized to estimate the anechoic speech from these deep embedding features. Finally, the denoising stage and dereverberation stage are optimized by the joint training method. Experimental results show that the proposed method outperforms the WPE and BLSTM baselines, especially in the low SNR condition.

eess.AS↗

arXiv2020

Density-Aware Graph for Deep Semi-Supervised Visual Recognition

Semi-supervised learning (SSL) has been extensively studied to improve the generalization ability of deep neural networks for visual recognition. To involve the unlabelled data, most existing SSL methods are based on common density-based cluster assumption: samples lying in the same high-density region are likely to belong to the same class, including the methods performing consistency regularization or generating pseudo-labels for the unlabelled images. Despite their impressive performance, we argue three limitations exist: 1) Though the density information is demonstrated to be an important clue, they all use it in an implicit way and have not exploited it in depth. 2) For feature learning, they often learn the feature embedding based on the single data sample and ignore the neighborhood information. 3) For label-propagation based pseudo-label generation, it is often done offline and difficult to be end-to-end trained with feature learning. Motivated by these limitations, this paper proposes to solve the SSL problem by building a novel density-aware graph, based on which the neighborhood information can be easily leveraged and the feature learning and label propagation can also be trained in an end-to-end way. Specifically, we first propose a new Density-aware Neighborhood Aggregation(DNA) module to learn more discriminative features by incorporating the neighborhood information in a density-aware manner. Then a novel Density-ascending Path based Label Propagation(DPLP) module is proposed to generate the pseudo-labels for unlabeled samples more efficiently according to the feature distribution characterized by density. Finally, the DNA module and DPLP module evolve and improve each other end-to-end.

cs.CV↗

arXiv2020

Negative Margin Matters: Understanding Margin in Few-shot Classification

This paper introduces a negative margin loss to metric learning based few-shot learning methods. The negative margin loss significantly outperforms regular softmax loss, and achieves state-of-the-art accuracy on three standard few-shot classification benchmarks with few bells and whistles. These results are contrary to the common practice in the metric learning field, that the margin is zero or positive. To understand why the negative margin loss performs well for the few-shot classification, we analyze the discriminability of learned features w.r.t different margins for training and novel classes, both empirically and theoretically. We find that although negative margin reduces the feature discriminability for training classes, it may also avoid falsely mapping samples of the same novel class to multiple peaks or clusters, and thus benefit the discrimination of novel classes. Code is available at https://github.com/bl0/negative-margin.few-shot.

cs.CV↗

arXiv2020

Superconductivity in hexagonal Nb-Mo-Ru-Rh-Pd high-entropy alloys

We report the superconducting properties of new hexagonal Nb$_{10+2x}$Mo$_{35-x}$Ru$_{35-x}$Rh$_{10}$Pd$_{10}$ high-entropy alloys (HEAs) (0 $\leq$ $x$ $\leq$ 5). With increasing $x$, the superconducting transition temperature $T_{\rm c}$ shows a maximum of 6.19 K at $x$ = 2.5, while the zero-temperature upper critical field $B_{\rm c2}$(0) increases monotonically, reaching 8.3 T at $x$ = 5. For all $x$ values, the specific heat jump deviates from the Bardeen-Cooper-Schreiffer behavior. In addition, we show that $T_{\rm c}$ of these HEAs is not determined mainly by the density of states at the Fermi level and would be enhanced by lowering the valence electron concentration.

cond-mat.supr-con↗

arXiv2020

Deep Attention Fusion Feature for Speech Separation with End-to-End Post-filter Method

In this paper, we propose an end-to-end post-filter method with deep attention fusion features for monaural speaker-independent speech separation. At first, a time-frequency domain speech separation method is applied as the pre-separation stage. The aim of pre-separation stage is to separate the mixture preliminarily. Although this stage can separate the mixture, it still contains the residual interference. In order to enhance the pre-separated speech and improve the separation performance further, the end-to-end post-filter (E2EPF) with deep attention fusion features is proposed. The E2EPF can make full use of the prior knowledge of the pre-separated speech, which contributes to speech separation. It is a fully convolutional speech separation network and uses the waveform as the input features. Firstly, the 1-D convolutional layer is utilized to extract the deep representation features for the mixture and pre-separated signals in the time domain. Secondly, to pay more attention to the outputs of the pre-separation stage, an attention module is applied to acquire deep attention fusion features, which are extracted by computing the similarity between the mixture and the pre-separated speech. These deep attention fusion features are conducive to reduce the interference and enhance the pre-separated speech. Finally, these features are sent to the post-filter to estimate each target signals. Experimental results on the WSJ0-2mix dataset show that the proposed method outperforms the state-of-the-art speech separation method. Compared with the pre-separation method, our proposed method can acquire 64.1%, 60.2%, 25.6% and 7.5% relative improvements in scale-invariant source-to-noise ratio (SI-SNR), the signal-to-distortion ratio (SDR), the perceptual evaluation of speech quality (PESQ) and the short-time objective intelligibility (STOI) measures, respectively.

eess.AS↗