arXiv subjects

Lu Zhang

Publications and source records attributed to Lu Zhang.

At least 379 records · Page 21Linked to original sources

arXiv2021

Elastic wave propagation in curvilinear coordinates with mesh refinement interfaces by a fourth order finite difference method

We develop a fourth order accurate finite difference method for the three dimensional elastic wave equation in isotropic media with the piecewise smooth material property. In our model, the material property can be discontinuous at curved interfaces. The governing equations are discretized in second order form on curvilinear meshes by using a fourth order finite difference operator satisfying a summation-by-parts property. The method is energy stable and high order accurate. The highlight is that mesh sizes can be chosen according to the velocity structure of the material so that computational efficiency is improved. At the mesh refinement interfaces with hanging nodes, physical interface conditions are imposed by using ghost points and interpolation. With a fourth order predictor-corrector time integrator, the fully discrete scheme is energy conserving. Numerical experiments are presented to verify the fourth order convergence rate and the energy conserving property.

math.NA↗

arXiv2021

Learning to Inference with Early Exit in the Progressive Speech Enhancement

In real scenarios, it is often necessary and significant to control the inference speed of speech enhancement systems under different conditions. To this end, we propose a stage-wise adaptive inference approach with early exit mechanism for progressive speech enhancement. Specifically, in each stage, once the spectral distance between adjacent stages lowers the empirically preset threshold, the inference will terminate and output the estimation, which can effectively accelerate the inference speed. To further improve the performance of existing speech enhancement systems, PL-CRN++ is proposed, which is an improved version over our preliminary work PL-CRN and combines stage recurrent mechanism and complex spectral mapping. Extensive experiments are conducted on the TIMIT corpus, the results demonstrate the superiority of our system over state-of-the-art baselines in terms of PESQ, ESTOI and DNSMOS. Moreover, by adjusting the threshold, we can easily control the inference efficiency while sustaining the system performance.

cs.SD↗

arXiv2021

Glance and Gaze: A Collaborative Learning Framework for Single-channel Speech Enhancement

The capability of the human to pay attention to both coarse and fine-grained regions has been applied to computer vision tasks. Motivated by that, we propose a collaborative learning framework in the complex domain for monaural noise suppression. The proposed system consists of two principal modules, namely spectral feature extraction module (FEM) and stacked glance-gaze modules (GGMs). In FEM, the UNet-block is introduced after each convolution layer, enabling the feature recalibration from multiple scales. In each GGM, we decompose the multi-target optimization in the complex spectrum into two sub-tasks. Specifically, the glance path aims to suppress the noise in the magnitude domain to obtain a coarse estimation, and meanwhile, the gaze path attempts to compensate for the lost spectral detail in the complex domain. The two paths work collaboratively and facilitate spectral estimation from complementary perspectives. Besides, by repeatedly unfolding the GGMs, the intermediate result can be iteratively refined across stages and lead to the ultimate estimation of the spectrum. The experiments are conducted on the WSJ0-SI84, DNS-Challenge dataset, and Voicebank+Demand dataset. Results show that the proposed approach achieves state-of-the-art performance over previous advanced systems on the WSJ0-SI84 and DNS-Challenge dataset, and meanwhile, competitive performance is achieved on the Voicebank+Demand corpus.

cs.SD↗

arXiv2021

Evidences for the random singlet phase in a new honeycomb iridate SrIr$_2$O$_{6-δ}$

Strong spin-orbital-coupling magnetic systems with the honeycomb structure can provide bond-directional interactions which may result in Kitaev quantum spin liquids and exotic anyonic excitations. However, one of the key ingredients in real materials$-$disorders$-$has been much less studied in Kitaev systems. Here we synthesized a trigonal SrIr$_2$O$_{6-δ}$ with $δ\approx 0.25$, which consists of two-dimensional honeycomb Ir planes with edge-sharing IrO$_6$ octahedra. First-principles computation and experimental measurements suggest that the electronic system is gapped, and there should be no magnetic moment as the Ir$^{5+}$ ion has no unpaired electrons. However, significant magnetism has been observed in the material, and it can be attributed to disorders that are most likely from oxygen vacancies. No magnetic order is found down to 0.05 K, and the low-temperature magnetic properties exhibit power-law behaviors in magnetic susceptibility and zero-field specific heat, and a single-parameter scaling of the specific heat under magnetic fields. These results provide strong evidence for the existence of the random singlet phase in SrIr$_2$O$_{6-δ}$, which offers a different member to the family of spin-orbital entangled iridates and Kitaev materials.

cond-mat.str-el↗

arXiv2021

Representative Functional Connectivity Learning for Multiple Clinical groups in Alzheimer's Disease

Mild cognitive impairment (MCI) is a high-risk dementia condition which progresses to probable Alzheimer's disease (AD) at approximately 10% to 15% per year. Characterization of group-level differences between two subtypes of MCI - stable MCI (sMCI) and progressive MCI (pMCI) is the key step to understand the mechanisms of MCI progression and enable possible delay of transition from MCI to AD. Functional connectivity (FC) is considered as a promising way to study MCI progression since which may show alterations even in preclinical stages and provide substrates for AD progression. However, the representative FC patterns during AD development for different clinical groups, especially for sMCI and pMCI, have been understudied. In this work, we integrated autoencoder and multi-class classification into a single deep model and successfully learned a set of clinical group related feature vectors. Specifically, we trained two non-linear mappings which realized the mutual transformations between original FC space and the feature space. By mapping the learned clinical group related feature vectors to the original FC space, representative FCs were constructed for each group. Moreover, based on these feature vectors, our model achieves a high classification accuracy - 68% for multi-class classification (NC vs SMC vs sMCI vs pMCI vs AD).

q-bio.NC↗

arXiv2021

Deep Interaction between Masking and Mapping Targets for Single-Channel Speech Enhancement

The most recent deep neural network (DNN) models exhibit impressive denoising performance in the time-frequency (T-F) magnitude domain. However, the phase is also a critical component of the speech signal that is easily overlooked. In this paper, we propose a multi-branch dilated convolutional network (DCN) to simultaneously enhance the magnitude and phase of noisy speech. A causal and robust monaural speech enhancement system is achieved based on the multi-objective learning framework of the complex spectrum and the ideal ratio mask (IRM) targets. In the process of joint learning, the intermediate estimation of IRM targets is used as a way of generating feature attention factors to realize the information interaction between the two targets. Moreover, the proposed multi-scale dilated convolution enables the DCN model to have a more efficient temporal modeling capability. Experimental results show that compared with other state-of-the-art models, this model achieves better speech quality and intelligibility with less computation.

eess.AS↗

arXiv2021

Recyclable flame-retardant epoxy composites based on disulfide bonds. Flammability and recyclability

A series of recyclable epoxy resin (REP) composites are successfully prepared in this study, aiming to improve flame retardancy while keep good recyclability and mechanical properties. The commercial 9,10-Dihydro-9-oxa-10-phosphaphenanthrene 10-Oxide (DOPO) with high reactive P-H bond is used as highly-efficient flame retardant in this work. Compared with pure epoxy resin with a limited oxygen index of 21.7 % and no rating in vertical burning test, recyclable epoxy resin only with 3 wt% DOPO acquires a limited oxygen index value of 27.8% and V-0 rating. In addition, REP composites containing 4 wt% DOPO displays quick self-extinguishing behavior, which shows the 43.5% drop of the peak heat release rate. The main reason might be that traditional phosphorous-containing flame retardant existing in REP composites can play a positive role in both condensed phase and gas phase when burning. On the other hand, both scanning electron microscopy and Raman results also further affirm the improved flame retardancy. Moreover, flame-retardant REP composites can also maintain good transparency and mechanical properties. As well as the favorable recyclability originated from the special exchangeable disulfide crosslinks. High-Performance REP composites possessing expected flame retardancy, recyclability and good mechanical properties pave the way to broaden applications of thermoset epoxy materials.

physics.app-ph↗

arXiv2021

Energy-based discontinuous Galerkin difference methods for second-order wave equations

We combine the newly-constructed Galerkin difference basis with the energy-based discontinuous Galerkin method for wave equations in second order form. The approximation properties of the resulting method are excellent and the allowable time steps are large compared to traditional discontinuous Galerkin methods. The one drawback of the combined approach is the cost of inversion of the local mass matrix. We demonstrate that for constant coefficient problems on Cartesian meshes this bottleneck can be removed by the use of a modified Galerkin difference basis. For variable coefficients or non-Cartesian meshes this technique is not possible and we instead use the preconditioned conjugate gradient method to iteratively invert the mass matrices. With a careful choice of preconditioner we can demonstrate optimal complexity, albeit with a larger constant.

math.NA↗

arXiv2021

Conditional Coding for Flexible Learned Video Compression

This paper introduces a novel framework for end-to-end learned video coding. Image compression is generalized through conditional coding to exploit information from reference frames, allowing to process intra and inter frames with the same coder. The system is trained through the minimization of a rate-distortion cost, with no pre-training or proxy loss. Its flexibility is assessed under three coding configurations (All Intra, Low-delay P and Random Access), where it is shown to achieve performance competitive with the state-of-the-art video codec HEVC.

eess.IV↗

arXiv2021

Quality Assessment of DIBR-synthesized views: An Overview

The Depth-Image-Based-Rendering (DIBR) is one of the main fundamental technique to generate new views in 3D video applications, such as Multi-View Videos (MVV), Free-Viewpoint Videos (FVV) and Virtual Reality (VR). However, the quality assessment of DIBR-synthesized views is quite different from the traditional 2D images/videos. In recent years, several efforts have been made towards this topic, but there {is a lack of} detailed survey in {the} literature. In this paper, we provide a comprehensive survey on various current approaches for DIBR-synthesized views. The current accessible datasets of DIBR-synthesized views are firstly reviewed{, followed} by a summary analysis of the representative state-of-the-art objective metrics. Then, the performances of different objective metrics are evaluated and discussed on all available datasets. Finally, we discuss the potential challenges and suggest possible directions for future research.

eess.IV↗

arXiv2021

Conditional Coding and Variable Bitrate for Practical Learned Video Coding

This paper introduces a practical learned video codec. Conditional coding and quantization gain vectors are used to provide flexibility to a single encoder/decoder pair, which is able to compress video sequences at a variable bitrate. The flexibility is leveraged at test time by choosing the rate and GOP structure to optimize a rate-distortion cost. Using the CLIC21 video test conditions, the proposed approach shows performance on par with HEVC.

cs.NE↗

arXiv2021

Towards Better Graph Representation: Two-Branch Collaborative Graph Neural Networks for Multimodal Marketing Intention Detection

Inspired by the fact that spreading and collecting information through the Internet becomes the norm, more and more people choose to post for-profit contents (images and texts) in social networks. Due to the difficulty of network censors, malicious marketing may be capable of harming society. Therefore, it is meaningful to detect marketing intentions online automatically. However, gaps between multimodal data make it difficult to fuse images and texts for content marketing detection. To this end, this paper proposes Two-Branch Collaborative Graph Neural Networks to collaboratively represent multimodal data by Graph Convolution Networks (GCNs) in an end-to-end fashion. Experimental results demonstrate that our proposed method achieves superior graph classification performance for marketing intention detection.

cs.CV↗

arXiv2021

Determining the absolute value of magnetic penetration depth in small-sized superconducting films

In the previous four decades, a two-coil mutual inductance (MI) technique has been widely employed in characterizing magnetic penetration depth, $λ$, of superconducting films. However, the conventional methods used to obtain $λ$ are not applicable to small-sized films with common shapes, which limits the application of the MI technique in superconductivity research. Here, we first employed the fast wavelet collocation (FWC) method to a two-coil system and then proposed the possibility of directly obtaining the absolute $λ$ of polygonal superconducting films with arbitrary sizes. To verify its accuracy and advantages, we extracted the $λ$ values of square NbN films with different sizes using the FWC and conventional flux leakage subtraction (FLS) methods. Notably, the FLS method fails for a $5\times 5 \, \rm mm^2$ film, which is attributed to the significant current peak at the film edge. In contrast, the absolute $λ$ extracted using the FWC method was independent of the film size. Finally, we established the applicability of the FWC method to large coil spacings, which may pave the way for integrating high-accuracy $λ$ measurements with the ionic liquid gating technique.

cond-mat.supr-con↗

arXiv2021

Extracting Concise Bug-Fixing Patches from Human-Written Patches in Version Control Systems

High-quality and large-scale repositories of real bugs and their concise patches collected from real-world applications are critical for research in software engineering community. In such a repository, each real bug is explicitly associated with its fix. Therefore, on one side, the real bugs and their fixes} may inspire novel approaches for finding, locating, and repairing software bugs; on the other side, the real bugs and their fixes are indispensable for rigorous and meaningful evaluation of approaches for software testing, fault localization, and program repair. To this end, a number of such repositories, e.g., Defects4J, have been proposed. However, such repositories are rather small because their construction involves expensive human intervention. Although bug-fixing code commits as well as associated test cases could be retrieved from version control systems automatically, existing approaches could not yet automatically extract concise bug-fixing patches from bug-fixing commits because such commits often involve bug-irrelevant changes. In this paper, we propose an automatic approach, called BugBuilder, to extracting complete and concise bug-fixing patches from human-written patches in version control systems. It excludes refactorings by detecting refactorings involved in bug-fixing commits, and reapplying detected refactorings on the faulty version. It enumerates all subsets of the remaining part and validates them on test cases. If none of the subsets has the potential to be a complete bug-fixing patch, the remaining part as a whole is taken as a complete and concise bug-fixing patch. Evaluation results on 809 real bug-fixing commits in Defects4J suggest that BugBuilder successfully generated complete and concise bug-fixing patches for forty percent of the bug-fixing commits, and its precision (99%) was even higher than human experts.

cs.SE↗

arXiv2021

Spatial Factor Modeling: A Bayesian Matrix-Normal Approach for Misaligned Data

Multivariate spatially-oriented data sets are prevalent in the environmental and physical sciences. Scientists seek to jointly model multiple variables, each indexed by a spatial location, to capture any underlying spatial association for each variable and associations among the different dependent variables. Multivariate latent spatial process models have proved effective in driving statistical inference and rendering better predictive inference at arbitrary locations for the spatial process. High-dimensional multivariate spatial data, which is the theme of this article, refers to data sets where the number of spatial locations and the number of spatially dependent variables is very large. The field has witnessed substantial developments in scalable models for univariate spatial processes, but such methods for multivariate spatial processes, especially when the number of outcomes are moderately large, are limited in comparison. Here, we extend scalable modeling strategies for a single process to multivariate processes. We pursue Bayesian inference which is attractive for full uncertainty quantification of the latent spatial process. Our approach exploits distribution theory for the Matrix-Normal distribution, which we use to construct scalable versions of a hierarchical linear model of coregionalization (LMC) and spatial factor models that deliver inference over a high-dimensional parameter space including the latent spatial process. We illustrate the computational and inferential benefits of our algorithms over competing methods using simulation studies and an analysis of a massive vegetation index data set.

stat.ME↗

arXiv2021

A comprehensive study on the semileptonic decay of heavy flavor mesons

The semileptonic decay of heavy flavor mesons offers a clean environment for extraction of the Cabibbo-Kobayashi-Maskawa (CKM) matrix elements, which describes the CP-violating and flavor changing process in the Standard Model. The involved form factors where the dynamical information is encoded play an essential role in achieving any conclusive statement. That is, the knowledge of the form factors should be under good control, requiring one to examine more observables in addition to the branching fraction. In this paper, we provide the mean value and the $q^2$-dependent shape for further observables [differential decay distribution ($dΓ/dq^2$), forward-backward asymmetry ($\mathcal{A}_{FB}^l$), longitudinal ($P_L^l$) and transverse ($P_T^l$) polarization of a charged lepton, longitudinal polarization of a vector meson in the final state ($F_L^l(V)$), leptonic convexity parameter ($C_F^l$), and trigonometric moments ($W_i^l$) in the decay of $D_{(s)}$ and $B_{(s)}$ to $P/V l^+ ν_l$ ($l=e$, $μ$ or $τ$)], based on the predictions of the relevant form factors from the covariant light-front quark model. $P$ and $V$ denote the pseudoscalar and vector meson, respectively. As a comparison, we find a good agreement with the results from the covariant confining quark model and the relativistic quark model in the literature. As it has been observed that the $P_L^l$ and $F_L^l(V)$ are crucial quantities to discriminate various New Physics models, the reexamination of these observables from a different method is also essential and necessary.

hep-ph↗

arXiv2020

NLocalSAT: Boosting Local Search with Solution Prediction

The Boolean satisfiability problem (SAT) is a famous NP-complete problem in computer science. An effective way for solving a satisfiable SAT problem is the stochastic local search (SLS). However, in this method, the initialization is assigned in a random manner, which impacts the effectiveness of SLS solvers. To address this problem, we propose NLocalSAT. NLocalSAT combines SLS with a solution prediction model, which boosts SLS by changing initialization assignments with a neural network. We evaluated NLocalSAT on five SLS solvers (CCAnr, Sparrow, CPSparrow, YalSAT, and probSAT) with instances in the random track of SAT Competition 2018. The experimental results show that solvers with NLocalSAT achieve 27% ~ 62% improvement over the original SLS solvers.

cs.AI↗

arXiv2020

A Unified Model for Gate Level Propagation Analysis

Classic hardware verification techniques (e.g., X-propagation and fault-propagation) and more recent hardware security verification techniques based on information flow tracking (IFT) aim to understand how information passes, affects, and otherwise modifies a circuit. These techniques all have separate usage scenarios, but when dissected into their core functionality, they relate in a fundamental manner. In this paper, we develop a common framework for gate level propagation analysis. We use our model to generate synthesizable propagation logic to use in standard EDA tools. To justify our model, we prove that Precise Hardware IFT is equivalent to gate level X-propagation and imprecise fault propagation. We also show that the difference between Precise Hardware IFT and fault propagation is not significant for 74X-series and '85 ISCAS benchmarks with more than 313 gates and the difference between imprecise hardware IFT and Precise Hardware IFT is almost always significant regardless of size.

cs.AR↗