arXiv subjects

Dong Wang

Publications and source records attributed to Dong Wang.

At least 757 records · Page 42Linked to original sources

arXiv2016

OC16-CE80: A Chinese-English Mixlingual Database and A Speech Recognition Baseline

We present the OC16-CE80 Chinese-English mixlingual speech database which was released as a main resource for training, development and test for the Chinese-English mixlingual speech recognition (MixASR-CHEN) challenge on O-COCOSDA 2016. This database consists of 80 hours of speech signals recorded from more than 1,400 speakers, where the utterances are in Chinese but each involves one or several English words. Based on the database and another two free data resources (THCHS30 and the CMU dictionary), a speech recognition (ASR) baseline was constructed with the deep neural network-hidden Markov model (DNN-HMM) hybrid system. We then report the baseline results following the MixASR-CHEN evaluation rules and demonstrate that OC16-CE80 is a reasonable data resource for mixlingual research.

cs.CL↗

arXiv2016

Local Training for PLDA in Speaker Verification

PLDA is a popular normalization approach for the i-vector model, and it has delivered state-of-the-art performance in speaker verification. However, PLDA training requires a large amount of labeled development data, which is highly expensive in most cases. A possible approach to mitigate the problem is various unsupervised adaptation methods, which use unlabeled data to adapt the PLDA scattering matrices to the target domain. In this paper, we present a new `local training' approach that utilizes inaccurate but much cheaper local labels to train the PLDA model. These local labels discriminate speakers within a single conversion only, and so are much easier to obtain compared to the normal `global labels'. Our experiments show that the proposed approach can deliver significant performance improvement, particularly with limited globally-labeled data.

cs.SD↗

arXiv2016

AP16-OL7: A Multilingual Database for Oriental Languages and A Language Recognition Baseline

We present the AP16-OL7 database which was released as the training and test data for the oriental language recognition (OLR) challenge on APSIPA 2016. Based on the database, a baseline system was constructed on the basis of the i-vector model. We report the baseline results evaluated in various metrics defined by the AP16-OLR evaluation plan and demonstrate that AP16-OL7 is a reasonable data resource for multilingual research.

cs.CL↗

arXiv2016

Deflecting cavity dynamics for time-resolved machine studies of SXFEL user facility

Radio frequency deflectors are widely used for time-resolved electron beam energy, emittance and radiation profile measurements in modern free electron laser facilities. In this paper, we present the beam dynamics aspects of the deflecting cavity of SXFEL user facility, which is located at the exit of the undulator. With a targeted time resolution around 10 fs, it is expected to be an important tool for time-resolved commissioning and machine studies for SXFEL user facility.

physics.acc-ph↗

arXiv2016

Generating coherent soft x-ray pulses in the water window with a high-brightness seeded free-electron laser

We propose a new scheme to generate high-brightness and temporal coherent soft x-ray radiation in a seeded free-electron laser. The proposed scheme is based the coherent harmonic generation (CHG) and superradiant principles. A CHG scheme is first used to generate coherent signal at ultra-high harmonics of the seed. This coherent signal is then amplified by a series of chicane-undulator modules via the fresh bunch and superradiant processes in the following radiator. Using a representative of realistic set of parameters, three-dimensional simulations have been carried out and the simulations results demonstrated that 10 GW-level ultra-short coherent radiation pulses in the water window can be achieved by using the proposed technique.

physics.acc-ph↗

arXiv2016

Nonintersecting Brownian bridges between reflecting or absorbing walls

We study a model of nonintersecting Brownian bridges on an interval with either absorbing or reflecting walls at the boundaries, focusing on the point in space-time at which the particles meet the wall. These processes are determinantal, and in different scaling limits when the particles approach the reflecting (resp. absorbing) walls we obtain hard-edge limiting kernels which are the even (resp. odd) parts of the Pearcey and tacnode kernels. We also show that in the single time case, our hard-edge tacnode kernels are equivalent to the ones studied by Delvaux [16], defined in terms of a $4\times 4$ Lax pair for the inhomogeneous Painlevé II equation (PII). As a technical ingredient in the proof, we construct a Schlesinger transform for the $4 \times 4$ Lax pair in [16] which preserves the Hastings--McLeod solutions to PII.

math.PR↗

arXiv2016

Propagation of singular behavior for Gaussian perturbations of random matrices

We study the asymptotic behavior of the eigenvalues of Gaussian perturbations of large Hermitian random matrices for which the limiting eigenvalue density vanishes at a singular interior point or vanishes faster than a square root at a singular edge point. First, we show that the singular behavior propagates macroscopically for sufficiently small Gaussian perturbations, and we describe the macroscopic eigenvalue behavior for Gaussian perturbations of critical size. Secondly, for sufficiently small Gaussian perturbations of unitary invariant random matrices, we prove that the microscopic eigenvalue correlations near the singular point are described by the same limiting kernel as in the unperturbed case. We also interpret our results in terms of nonintersecting Brownian paths with random starting positions, and we establish multi-time generalizations of the microscopic results.

math.PR↗

arXiv2016

Two Lax systems for the Painlevé II equation, and two related kernels in random matrix theory

We consider two Lax systems for the homogeneous Painlevé II equation: one of size $2\times 2$ studied by Flaschka and Newell in the early 1980's, and one of size $4\times 4$ introduced by Delvaux-Kuijlaars-Zhang and Duits-Geudens in the early 2010's. We prove that solutions to the $4\times 4$ system can be derived from those to the $2\times 2$ system via an integral transform, and consequently relate the Stokes multipliers for the two systems. As corollaries we are able to express two kernels for determinantal processes as contour integrals involving the Flaschka-Newell Lax system: the tacnode kernel arising in models of nonintersecting paths, and a critical kernel arising in a two-matrix model.

nlin.SI↗

arXiv2016

An efficient iterative thresholding method for image segmentation

We proposed an efficient iterative thresholding method for multi-phase image segmentation. The algorithm is based on minimizing piecewise constant Mumford-Shah functional in which the contour length (or perimeter) is approximated by a non-local multi-phase energy. The minimization problem is solved by an iterative method. Each iteration consists of computing simple convolutions followed by a thresholding step. The algorithm is easy to implement and has the optimal complexity $O(N \log N)$ per iteration. We also show that the iterative algorithm has the total energy decaying property. We present some numerical results to show the efficiency of our method.

cs.CV↗

arXiv2016

Chinese Song Iambics Generation with Neural Attention-based Model

Learning and generating Chinese poems is a charming yet challenging task. Traditional approaches involve various language modeling and machine translation techniques, however, they perform not as well when generating poems with complex pattern constraints, for example Song iambics, a famous type of poems that involve variable-length sentences and strict rhythmic patterns. This paper applies the attention-based sequence-to-sequence model to generate Chinese Song iambics. Specifically, we encode the cue sentences by a bi-directional Long-Short Term Memory (LSTM) model and then predict the entire iambic with the information provided by the encoder, in the form of an attention-based LSTM that can regularize the generation process by the fine structure of the input cues. Several techniques are investigated to improve the model, including global context integration, hybrid style training, character vector initialization and adaptation. Both the automatic and subjective evaluation results show that our model indeed can learn the complex structural and rhythmic patterns of Song iambics, and the generation is rather successful.

cs.CL↗

arXiv2016

Can Machine Generate Traditional Chinese Poetry? A Feigenbaum Test

Recent progress in neural learning demonstrated that machines can do well in regularized tasks, e.g., the game of Go. However, artistic activities such as poem generation are still widely regarded as human's special capability. In this paper, we demonstrate that a simple neural model can imitate human in some tasks of art generation. We particularly focus on traditional Chinese poetry, and show that machines can do as well as many contemporary poets and weakly pass the Feigenbaum Test, a variant of Turing test in professional domains. Our method is based on an attention-based recurrent neural network, which accepts a set of keywords as the theme and generates poems by looking at each keyword during the generation. A number of techniques are proposed to improve the model, including character vector initialization, attention to input and hybrid-style training. Compared to existing poetry generation methods, our model can generate much more theme-consistent and semantic-rich poems.

cs.CL↗

arXiv2016

Test of \textit{Topmetal-${II}^-$} In Liquid Nitrogen For Cryogenic Temperature TPCs

\textit{Topmetal-${II}^-$} is a highly pixelated direct charge sensor that contains a 72${\times}$72 pixel array of 83$μ$m pitch size. The key feature of \textit{Topmetal-${II}^-$} is that it can directly collect charges via metal nodes of each pixel to form two-dimensional images of charge cloud distributions. \textit{Topmetal-${II}^-$} was proved to measure charged particles without amplification at room temperature. To measure its performance at cryogenic temperature, a \textit{Topmetal-${II}^-$} sensor is embedded into a liquid nitrogen dewar. The results presented in this paper show that \textit{Topmetal-${II}^-$} can also operate well at this low temperature with a noise (ENC) of 12 e$^-$ lower than that at room temperature (13 e$^-$). From the noise perspective, \textit{Topmetal-${II}^-$} is a promising candidate for the next generation readout of liquid argon and xenon Time Projection Chamber (TPC) used in experiments searching for neutrinoless double beta decay and dark matter.

physics.ins-det↗

arXiv2016

Gender Differences in Communication Behaviors, Spatial Proximity Patterns, and Mobility Habits

The existence of gender differences in the structure and composition of social networks is a well established finding in the social and behavioral sciences, but researchers continue to debate whether structural, dispositional, or life course factors are the primary driver of these differences. In this paper we extend work on gender differences in social networks to patterns of interaction, propinquity, and connectivity captured via a social sensing platform comprised of an ensemble of individuals' phone calls, text messaging, face-to-face interactions, and traces of their mobility activities. We attempt to isolate dispositional from other factors by focusing on a relatively homogeneous population on a relatively closed setting at the same stage in the life course. Analysis across three different networks along with mobility data reveals systematic gender differences in terms of communicative, distributional, mobility, and spatial proximity tendencies. Most importantly, some patterns observed in the communication network (e.g. stronger same-gender preference for women) are found to be reversed in the spatial proximity context, with men displaying a greater tendency to spend time in a narrower (and thus more predictable) range of spaces with same-gender peers than women. These findings provide robust and novel evidence for the powerful effect of gender in structuring behavior across a wide range of communication and mobility behaviors, validating and extending recent work pointing to differences of socio-cultural and evolutionary origin in the styles of sociability and interaction characteristic of men and women.

physics.soc-ph↗

arXiv2016

Recurrent Neural Network Training with Dark Knowledge Transfer

Recurrent neural networks (RNNs), particularly long short-term memory (LSTM), have gained much attention in automatic speech recognition (ASR). Although some successful stories have been reported, training RNNs remains highly challenging, especially with limited training data. Recent research found that a well-trained model can be used as a teacher to train other child models, by using the predictions generated by the teacher model as supervision. This knowledge transfer learning has been employed to train simple neural nets with a complex one, so that the final performance can reach a level that is infeasible to obtain by regular training. In this paper, we employ the knowledge transfer learning approach to train RNNs (precisely LSTM) using a deep neural network (DNN) model as the teacher. This is different from most of the existing research on knowledge transfer learning, since the teacher (DNN) is assumed to be weaker than the child (RNN); however, our experiments on an ASR task showed that it works fairly well: without applying any tricks on the learning scheme, this approach can train RNNs successfully even with limited training data.

stat.ML↗

arXiv2016

Modeling the disequilibrium species for Jupiter and Saturn: Implications for Juno and Saturn entry probe

Disequilibrium species have been used previously to probe the deep water abundances and the eddy diffusion coefficient for giant planets. In this paper, we present a diffusion-kinetics code that predicts the abundances of disequilibrium species in the tropospheres of Jupiter and Saturn with updated thermodynamic and kinetic data. The dependence on the deep water abundance and the eddy diffusion coefficient is investigated. We quantified the disagreements in CO kinetics that comes from using different reaction networks and identified C$_2$H$_6$ as a useful tracer for the eddy diffusion coefficient. We first apply a H/P/O reaction network to Jupiter and Saturn's atmospheres and suggest a new PH$_3$ destruction pathway. New chemical pathways for SiH$_4$ and GeH$_4$ destruction are also suggested, and another AsH$_3$ destruction pathway is investigated thanks to new thermodynamic and kinetic data. These new models should enhance the interpretation of the measurement of disequilibrium species by JIRAM on board Juno and allow disentangling between methods for constraining the Saturn's deep water abundance with the Saturn entry probes envisaged by NASA or ESA.

astro-ph.EP↗

arXiv2016

The Transition Probability of the $q$-TAZRP ($q$-Bosons) with Inhomogeneous Jump Rates

In this paper we consider the $q$-deformed totally asymmetric zero range process ($q$-TAZRP), also known as the $q$-boson (stochastic) particle system, on the ${\mathbb Z}$ lattice, such that the jump rate of a particle depends on the site where it is on the lattice. We derive the transition probability for an $n$ particle process in Bethe ansatz form as a sum of $n!$ $n$-fold contour integrals. Our result generalizes the transition probability formula by Korhonen and Lee for $q$-TAZRP with a homogeneous lattice, and our method follows the same approach as theirs.

math.PR↗

arXiv2016

Bayesian Neighbourhood Component Analysis

Learning a good distance metric in feature space potentially improves the performance of the KNN classifier and is useful in many real-world applications. Many metric learning algorithms are however based on the point estimation of a quadratic optimization problem, which is time-consuming, susceptible to overfitting, and lack a natural mechanism to reason with parameter uncertainty, an important property useful especially when the training set is small and/or noisy. To deal with these issues, we present a novel Bayesian metric learning method, called Bayesian NCA, based on the well-known Neighbourhood Component Analysis method, in which the metric posterior is characterized by the local label consistency constraints of observations, encoded with a similarity graph instead of independent pairwise constraints. For efficient Bayesian optimization, we explore the variational lower bound over the log-likelihood of the original NCA objective. Experiments on several publicly available datasets demonstrate that the proposed method is able to learn robust metric measures from small size dataset and/or from challenging training set with labels contaminated by errors. The proposed method is also shown to outperform a previous pairwise constrained Bayesian metric learning method.

cs.CV↗

arXiv2016

Binary Speaker Embedding

The popular i-vector model represents speakers as low-dimensional continuous vectors (i-vectors), and hence it is a way of continuous speaker embedding. In this paper, we investigate binary speaker embedding, which transforms i-vectors to binary vectors (codes) by a hash function. We start from locality sensitive hashing (LSH), a simple binarization approach where binary codes are derived from a set of random hash functions. A potential problem of LSH is that the randomly sampled hash functions might be suboptimal. We therefore propose an improved Hamming distance learning approach, where the hash function is learned by a variable-sized block training that projects each dimension of the original i-vectors to variable-sized binary codes independently. Our experiments show that binary speaker embedding can deliver competitive or even better results on both speaker verification and identification tasks, while the memory usage and the computation cost are significantly reduced.

cs.SD↗