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Dong Wang

Publications and source records attributed to Dong Wang.

At least 523 records · Page 29Linked to original sources
arXiv2021

A vector Riemann-Hilbert approach to the Muttalib-Borodin ensembles

In this paper, we consider Muttalib-Borodin ensemble of Laguerre type, a determinantal point process over $[0,\infty)$ which depends on the varying weights $x^αe^{-nV(x)}$, $α>-1$, and a parameter $θ$. For $θ$ being a positive integer, we derive asymptotics of the associated biorthogonal polynomials near the origin for a large class of potential functions $V$ as $n\to \infty$. This further allows us to establish the hard edge scaling limit of the correlation kernel, which is previously only known in the special cases and conjectured to be universal. Our proof is based on the Deift/Zhou nonlinear steepest descent analysis of two $1 \times 2$ vector-valued Riemann-Hilbert problems that characterize the biorthogonal polynomials and the explicit constructions of $(θ+1)\times(θ+1)$ local parametrices near the origin in terms of the Meijer G-functions.

math.PR↗
arXiv2021

Optimized entropic uncertainty relations for multiple measurements

Recently, an entropic uncertainty relation for multiple measurements has been proposed by Liu et al. in [Phys. Rev. A 91, 042133 (2015)]. However, the lower bound of the relation is not always tight with respect to different measurements. Herein, we improve the lower bound of the entropic uncertainty relation for multiple measurements, termed as simply constructed bound (SCB). We verify that the SCB is tighter than Liu et al.'s result for arbitrary mutually unbiased basis measurements, which might play a fundamental and crucial role in practical quantum information processing. Moreover, we optimize the SCB by considering mutual information and the Holevo quantity, and propose an optimized SCB (OSCB). Notably, the proposed bounds are extrapolations of the behavior of two measurements to a larger collection of measurements. It is believed that our findings would shed light on entropy-based uncertainty relations in the multiple measurement scenario and will be beneficial for security analysis in quantum key distributions.

quant-ph↗
arXiv2021

Future Study of Dense Superconducting Hydrides at High Pressure

The discovery of a record high superconducting transition temperature (Tc) of 288 K in a pressurized hydride inspires new hope to realize ambient condition superconductivity. Here, we give a perspective on the theoretical and experimental studies of hydride superconductivity. Predictions based on the BCS-Eliashberg-Midgal theory with the aid of density functional theory have been playing a leading role in the research and guiding the experimental realizations. To date, about twenty hydrides experiments have been reported to exhibit high-Tc superconductivity and their Tc agree well with the predicted values. However, there are still some controversies existing between the predictions and experiments, such as no significant transition temperature broadening observed in the magnetic field, the experimental electron-phonon coupling beyond the Eliashberg-Midgal limit, and the energy dependence of density of states around the Fermi level. To investigate these controversies and the origin of the highest Tc in hydrides, key experiments are required to determine the structure, bonding, and vibrational properties associated with H atoms in these hydrides.

cond-mat.supr-con↗
arXiv2021

Painleve analysis, Backlund transformation, Lax pair and periodic wave solutions for a generalized (2+1)-dimensional Hirota-Satsuma-Ito equation in fluid mechanics

In this paper, we investigate a generalized (2+1)-dimensional Hirota-Satsuma-Ito (HSI) equation in fluid mechanics. Via the Painleve analysis, we find that the HSI equation is Painleve integrable under certain condition. Bilinear form, Bell-polynomial-type Backlund transformation and Lax pair are constructed with the binary Bell polynomials. One periodic-wave solutions are derived via the Hirota-Riemann method and displayed graphically.

nlin.SI↗
arXiv2021

CN-Celeb: multi-genre speaker recognition

Research on speaker recognition is extending to address the vulnerability in the wild conditions, among which genre mismatch is perhaps the most challenging, for instance, enrollment with reading speech while testing with conversational or singing audio. This mismatch leads to complex and composite inter-session variations, both intrinsic (i.e., speaking style, physiological status) and extrinsic (i.e., recording device, background noise). Unfortunately, the few existing multi-genre corpora are not only limited in size but are also recorded under controlled conditions, which cannot support conclusive research on the multi-genre problem. In this work, we firstly publish CN-Celeb, a large-scale multi-genre corpus that includes in-the-wild speech utterances of 3,000 speakers in 11 different genres. Secondly, using this dataset, we conduct a comprehensive study on the multi-genre phenomenon, in particular the impact of the multi-genre challenge on speaker recognition and the performance gain when the new dataset is used to conduct multi-genre training.

cs.SD↗
arXiv2021

A Principle Solution for Enroll-Test Mismatch in Speaker Recognition

Mismatch between enrollment and test conditions causes serious performance degradation on speaker recognition systems. This paper presents a statistics decomposition (SD) approach to solve this problem. This approach decomposes the PLDA score into three components that corresponding to enrollment, prediction and normalization respectively. Given that correct statistics are used in each component, the resultant score is theoretically optimal. A comprehensive experimental study was conducted on three datasets with different types of mismatch: (1) physical channel mismatch, (2) speaking behavior mismatch, (3) near-far recording mismatch. The results demonstrated that the proposed SD approach is highly effective, and outperforms the ad-hoc multi-condition training approach that is commonly adopted but not optimal in theory.

cs.SD↗
arXiv2021

How Speech is Recognized to Be Emotional - A Study Based on Information Decomposition

The way that humans encode their emotion into speech signals is complex. For instance, an angry man may increase his pitch and speaking rate, and use impolite words. In this paper, we present a preliminary study on various emotional factors and investigate how each of them impacts modern emotion recognition systems. The key tool of our study is the SpeechFlow model presented recently, by which we are able to decompose speech signals into separate information factors (content, pitch, rhythm). Based on this decomposition, we carefully studied the performance of each information component and their combinations. We conducted the study on three different speech emotion corpora and chose an attention-based convolutional RNN as the emotion classifier. Our results show that rhythm is the most important component for emotional expression. Moreover, the cross-corpus results are very bad (even worse than guess), demonstrating that the present speech emotion recognition model is rather weak. Interestingly, by removing one or several unimportant components, the cross-corpus results can be improved. This demonstrates the potential of the decomposition approach towards a generalizable emotion recognition.

cs.SD↗
arXiv2021

A Study on Decoupled Probabilistic Linear Discriminant Analysis

Probabilistic linear discriminant analysis (PLDA) has broad application in open-set verification tasks, such as speaker verification. A key concern for PLDA is that the model is too simple (linear Gaussian) to deal with complicated data; however, the simplicity by itself is a major advantage of PLDA, as it leads to desirable generalization. An interesting research therefore is how to improve modeling capacity of PLDA while retaining the simplicity. This paper presents a decoupling approach, which involves a global model that is simple and generalizable, and a local model that is complex and expressive. While the global model holds a bird view on the entire data, the local model represents the details of individual classes. We conduct a preliminary study towards this direction and investigate a simple decoupling model including both the global and local models. The new model, which we call decoupled PLDA, is tested on a speaker verification task. Experimental results show that it consistently outperforms the vanilla PLDA when the model is based on raw speaker vectors. However, when the speaker vectors are processed by length normalization, the advantage of decoupled PLDA will be largely lost, suggesting future research on non-linear local models.

cs.SD↗
arXiv2021

An MAP Estimation for Between-Class Variance

Probabilistic linear discriminant analysis (PLDA) has been widely used in open-set verification tasks, such as speaker verification. A potential issue of this model is that the training set often contains limited number of classes, which makes the estimation for the between-class variance unreliable. This unreliable estimation often leads to degraded generalization. In this paper, we present an MAP estimation for the between-class variance, by employing an Inverse-Wishart prior. A key problem is that with hierarchical models such as PLDA, the prior is placed on the variance of class means while the likelihood is based on class members, which makes the posterior inference intractable. We derive a simple MAP estimation for such a model, and test it in both PLDA scoring and length normalization. In both cases, the MAP-based estimation delivers interesting performance improvement.

cs.SD↗
arXiv2021

Characterizing and Mitigating Self-Admitted Technical Debt in Build Systems

Technical Debt is a metaphor used to describe the situation in which long-term software artifact quality is traded for short-term goals in software projects. In recent years, the concept of self-admitted technical debt (SATD) was proposed, which focuses on debt that is intentionally introduced and described by developers. Although prior work has made important observations about admitted technical debt in source code, little is known about SATD in build systems. In this paper, we set out to better understand the characteristics of SATD in build systems. To do so, through a qualitative analysis of 500 SATD comments in the Maven build system of 291 projects, we characterize SATD by location and rationale (reason and purpose). Our results show that limitations in tools and libraries, and complexities of dependency management are the most frequent causes, accounting for 50% and 24% of the comments. We also find that developers often document SATD as issues to be fixed later. As a first step towards the automatic detection of SATD rationale, we train classifiers to detect the two most frequently occurring reasons and the four most frequently occurring purposes of SATD in the content of comments in Maven build systems. The classifier performance is promising, achieving an F1-score of 0.71-0.79. Finally, within 16 identified 'ready-to-be-addressed' SATD instances, the three SATD submitted by pull requests and the five SATD submitted by issue reports were resolved after developers were made aware. Our work presents the first step towards understanding technical debt in build systems and opens up avenues for future work, such as tool support to track and manage SATD backlogs.

cs.SE↗
arXiv2021

MOS: A Low Latency and Lightweight Framework for Face Detection, Landmark Localization, and Head Pose Estimation

With the emergence of service robots and surveillance cameras, dynamic face recognition (DFR) in wild has received much attention in recent years. Face detection and head pose estimation are two important steps for DFR. Very often, the pose is estimated after the face detection. However, such sequential computations lead to higher latency. In this paper, we propose a low latency and lightweight network for simultaneous face detection, landmark localization and head pose estimation. Inspired by the observation that it is more challenging to locate the facial landmarks for faces with large angles, a pose loss is proposed to constrain the learning. Moreover, we also propose an uncertainty multi-task loss to learn the weights of individual tasks automatically. Another challenge is that robots often use low computational units like ARM based computing core and we often need to use lightweight networks instead of the heavy ones, which lead to performance drop especially for small and hard faces. In this paper, we propose online feedback sampling to augment the training samples across different scales, which increases the diversity of training data automatically. Through validation in commonly used WIDER FACE, AFLW and AFLW2000 datasets, the results show that the proposed method achieves the state-of-the-art performance in low computational resources. The code and data will be available at https://github.com/lyp-deeplearning/MOS-Multi-Task-Face-Detect.

cs.CV↗
arXiv2021

Terrain-perception-free Quadrupedal Spinning Locomotion on Versatile Terrains: Modeling, Analysis, and Experimental Validation

Dynamic quadrupedal locomotion over rough terrains reveals remarkable progress over the last few decades. Small-scale quadruped robots are adequately flexible and adaptable to traverse uneven terrains along sagittal direction, such as slopes and stairs. To accomplish autonomous locomotion navigation in complex environments, spinning is a fundamental yet indispensable functionality for legged robots. However, spinning behaviors of quadruped robots on uneven terrain often exhibit position drifts. Motivated by this problem, this study presents an algorithmic method to enable accurate spinning motions over uneven terrain and constrain the spinning radius of the Center of Mass (CoM) to be bounded within a small range to minimize the drift risks. A modified spherical foot kinematics representation is proposed to improve the foot kinematic model and rolling dynamics of the quadruped during locomotion. A CoM planner is proposed to generate stable spinning motion based on projected stability margins. Accurate motion tracking is accomplished with Linear Quadratic Regulator (LQR) to bound the position drift during the spinning movement. Experiments are conducted on a small-scale quadruped robot and the effectiveness of the proposed method is verified on versatile terrains including flat ground, stairs and slopes.

cs.RO↗
arXiv2021

Single-shot fast 3D imaging through scattering media using structured illumination

Conventional approaches for 3D imaging in or through scattering media are usually limited to 2D reconstruction of objects at some discontinuous locations, although the time-consuming iteration, guide-star, or complex system are implemented. How to quickly visualize dynamic 3D objects behind scattering media is still an open issue. Here, by using structured light illumination, we propose a single-shot technique that can quickly acquire continuous 3D surfaces of objects hidden behind the diffuser. The proposed method can realize the 3D imaging of single, multiple, and dynamic targets from the speckled structured light patterns under broad or narrow band light illumination, in which only once calibration of the imaging setup is needed before conducting the imaging. Our approach paves the way to quickly visualize dynamic objects behind scattering media in 3D and multispectral.

physics.optics↗
arXiv2021

Characterization of the measurement uncertainty dynamics in an open system

Uncertainty principle plays a crucial role in quantum mechanics, because it captures the essence of the inevitable randomness associated with the outcomes of two incompatible quantum measurements. Information entropy can perfectly describe this type of randomness in information theory, because entropy can measure the degree of chaos in a given quantum system. However, the quantum-assisted uncertainty of entropy eventually inflate inevitably as the quantum correlations of the system are progressively corrupted by noise from the surrounding environment. In this paper, we investigate the dynamical features of the von Neumann entropic uncertainty in the presence of quantum memory, exploring the time evolution of entropic uncertainty suffer noise from the surrounding. Noteworthily, how the environmental noises affect the uncertainty of entropy is revealed, and specifically we verify how two types of noise environments, AD channel and BPF channel, influence the uncertainty. Meanwhile, we put forward some effective operation strategies to reduce the magnitude of the measurement uncertainty under the open systems. Furthermore, we explore the applications of the uncertainty relation investigated on entanglement witness and quantum channel capacity. Therefore , in open quantum correlation systems, our investigations could provide an insight into quantum measurement estimation

quant-ph↗
arXiv2021

CycleFlow: Purify Information Factors by Cycle Loss

SpeechFlow is a powerful factorization model based on information bottleneck (IB), and its effectiveness has been reported by several studies. A potential problem of SpeechFlow, however, is that if the IB channels are not well designed, the resultant factors cannot be well disentangled. In this study, we propose a CycleFlow model that combines random factor substitution and cycle loss to solve this problem. Experiments on voice conversion tasks demonstrate that this simple technique can effectively reduce mutual information among individual factors, and produce clearly better conversion than the IB-based SpeechFlow. CycleFlow can also be used as a powerful tool for speech editing. We demonstrate this usage by an emotion perception experiment.

eess.AS↗
arXiv2021

Real Additive Margin Softmax for Speaker Verification

The additive margin softmax (AM-Softmax) loss has delivered remarkable performance in speaker verification. A supposed behavior of AM-Softmax is that it can shrink within-class variation by putting emphasis on target logits, which in turn improves margin between target and non-target classes. In this paper, we conduct a careful analysis on the behavior of AM-Softmax loss, and show that this loss does not implement real max-margin training. Based on this observation, we present a Real AM-Softmax loss which involves a true margin function in the softmax training. Experiments conducted on VoxCeleb1, SITW and CNCeleb demonstrated that the corrected AM-Softmax loss consistently outperforms the original one. The code has been released at https://gitlab.com/csltstu/sunine.

cs.SD↗
arXiv2021

Extracting Attentive Social Temporal Excitation for Sequential Recommendation

In collaborative filtering, it is an important way to make full use of social information to improve the recommendation quality, which has been proved to be effective because user behavior will be affected by her friends. However, existing works leverage the social relationship to aggregate user features from friends' historical behavior sequences in a user-level indirect paradigm. A significant defect of the indirect paradigm is that it ignores the temporal relationships between behavior events across users. In this paper, we propose a novel time-aware sequential recommendation framework called Social Temporal Excitation Networks (STEN), which introduces temporal point processes to model the fine-grained impact of friends' behaviors on the user s dynamic interests in an event-level direct paradigm. Moreover, we propose to decompose the temporal effect in sequential recommendation into social mutual temporal effect and ego temporal effect. Specifically, we employ a social heterogeneous graph embedding layer to refine user representation via structural information. To enhance temporal information propagation, STEN directly extracts the fine-grained temporal mutual influence of friends' behaviors through the mutually exciting temporal network. Besides, the user s dynamic interests are captured through the self-exciting temporal network. Extensive experiments on three real-world datasets show that STEN outperforms state-of-the-art baseline methods. Moreover, STEN provides event-level recommendation explainability, which is also illustrated experimentally.

cs.SI↗