arXiv subjects

Shuang Xu

Publications and source records attributed to Shuang Xu.

66 records · Page 4Linked to original sources

arXiv2020

Variational Bayesian Weighted Complex Network Reconstruction

Complex network reconstruction is a hot topic in many fields. Currently, the most popular data-driven reconstruction framework is based on lasso. However, it is found that, in the presence of noise, lasso loses efficiency for weighted networks. This paper builds a new framework to cope with this problem. The key idea is to employ a series of linear regression problems to model the relationship between network nodes, and then to use an efficient variational Bayesian algorithm to infer the unknown coefficients. The numerical experiments conducted on both synthetic and real data demonstrate that the new method outperforms lasso with regard to both reconstruction accuracy and running speed.

stat.ML↗

arXiv2020

MFFW: A new dataset for multi-focus image fusion

Multi-focus image fusion (MFF) is a fundamental task in the field of computational photography. Current methods have achieved significant performance improvement. It is found that current methods are evaluated on simulated image sets or Lytro dataset. Recently, a growing number of researchers pay attention to defocus spread effect, a phenomenon of real-world multi-focus images. Nonetheless, defocus spread effect is not obvious in simulated or Lytro datasets, where popular methods perform very similar. To compare their performance on images with defocus spread effect, this paper constructs a new dataset called MFF in the wild (MFFW). It contains 19 pairs of multi-focus images collected on the Internet. We register all pairs of source images, and provide focus maps and reference images for part of pairs. Compared with Lytro dataset, images in MFFW significantly suffer from defocus spread effect. In addition, the scenes of MFFW are more complex. The experiments demonstrate that most state-of-the-art methods on MFFW dataset cannot robustly generate satisfactory fusion images. MFFW can be a new baseline dataset to test whether an MMF algorithm is able to deal with defocus spread effect.

cs.CV↗

arXiv2019

BLAS: Broadcast Relative Localization and Clock Synchronization for Dynamic Dense Multi-Agent Systems

The spatiotemporal information plays crucial roles in a multi-agent system (MAS). However, for a highly dynamic and dense MAS in unknown environments, estimating its spatiotemporal states is a difficult problem. In this paper, we present BLAS: a wireless broadcast relative localization and clock synchronization system to address these challenges. Our BLAS system exploits a broadcast architecture, under which a MAS is categorized into parent agents that broadcast wireless packets and child agents that are passive receivers, to reduce the number of required packets among agents for relative localization and clock synchronization. We first propose an asynchronous broadcasting and passively receiving (ABPR) protocol. The protocol schedules the broadcast of parent agents using a distributed time division multiple access (D-TDMA) scheme and delivers inter-agent information used for joint relative localization and clock synchronization. We then present distributed state estimation approaches in parent and child agents that utilize the broadcast inter-agent information for joint estimation of spatiotemporal states. The simulations and real-world experiments based on ultra-wideband (UWB) illustrate that our proposed BLAS cannot only enable accurate, high-frequency and real-time estimation of relative position and clock parameters but also support theoretically an unlimited number of agents.

cs.RO↗

arXiv2019

Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition

Sparse representation based classification (SRC) methods have achieved remarkable results. SRC, however, still suffer from requiring enough training samples, insufficient use of test samples and instability of representation. In this paper, a stable inverse projection representation based classification (IPRC) is presented to tackle these problems by effectively using test samples. An IPR is firstly proposed and its feasibility and stability are analyzed. A classification criterion named category contribution rate is constructed to match the IPR and complete classification. Moreover, a statistical measure is introduced to quantify the stability of representation-based classification methods. Based on the IPRC technique, a robust tumor recognition framework is presented by interpreting microarray gene expression data, where a two-stage hybrid gene selection method is introduced to select informative genes. Finally, the functional analysis of candidate's pathogenicity-related genes is given. Extensive experiments on six public tumor microarray gene expression datasets demonstrate the proposed technique is competitive with state-of-the-art methods.

q-bio.QM↗

arXiv2018

Single-channel Speech Dereverberation via Generative Adversarial Training

In this paper, we propose a single-channel speech dereverberation system (DeReGAT) based on convolutional, bidirectional long short-term memory and deep feed-forward neural network (CBLDNN) with generative adversarial training (GAT). In order to obtain better speech quality instead of only minimizing a mean square error (MSE), GAT is employed to make the dereverberated speech indistinguishable form the clean samples. Besides, our system can deal with wide range reverberation and be well adapted to variant environments. The experimental results show that the proposed model outperforms weighted prediction error (WPE) and deep neural network-based systems. In addition, DeReGAT is extended to an online speech dereverberation scenario, which reports comparable performance with the offline case.

cs.SD↗

arXiv2018

Multilingual End-to-End Speech Recognition with A Single Transformer on Low-Resource Languages

Sequence-to-sequence attention-based models integrate an acoustic, pronunciation and language model into a single neural network, which make them very suitable for multilingual automatic speech recognition (ASR). In this paper, we are concerned with multilingual speech recognition on low-resource languages by a single Transformer, one of sequence-to-sequence attention-based models. Sub-words are employed as the multilingual modeling unit without using any pronunciation lexicon. First, we show that a single multilingual ASR Transformer performs well on low-resource languages despite of some language confusion. We then look at incorporating language information into the model by inserting the language symbol at the beginning or at the end of the original sub-words sequence under the condition of language information being known during training. Experiments on CALLHOME datasets demonstrate that the multilingual ASR Transformer with the language symbol at the end performs better and can obtain relatively 10.5\% average word error rate (WER) reduction compared to SHL-MLSTM with residual learning. We go on to show that, assuming the language information being known during training and testing, about relatively 12.4\% average WER reduction can be observed compared to SHL-MLSTM with residual learning through giving the language symbol as the sentence start token.

eess.AS↗

arXiv2018

Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin Chinese

Sequence-to-sequence attention-based models have recently shown very promising results on automatic speech recognition (ASR) tasks, which integrate an acoustic, pronunciation and language model into a single neural network. In these models, the Transformer, a new sequence-to-sequence attention-based model relying entirely on self-attention without using RNNs or convolutions, achieves a new single-model state-of-the-art BLEU on neural machine translation (NMT) tasks. Since the outstanding performance of the Transformer, we extend it to speech and concentrate on it as the basic architecture of sequence-to-sequence attention-based model on Mandarin Chinese ASR tasks. Furthermore, we investigate a comparison between syllable based model and context-independent phoneme (CI-phoneme) based model with the Transformer in Mandarin Chinese. Additionally, a greedy cascading decoder with the Transformer is proposed for mapping CI-phoneme sequences and syllable sequences into word sequences. Experiments on HKUST datasets demonstrate that syllable based model with the Transformer performs better than CI-phoneme based counterpart, and achieves a character error rate (CER) of \emph{$28.77\%$}, which is competitive to the state-of-the-art CER of $28.0\%$ by the joint CTC-attention based encoder-decoder network.

eess.AS↗

arXiv2018

A Comparison of Modeling Units in Sequence-to-Sequence Speech Recognition with the Transformer on Mandarin Chinese

The choice of modeling units is critical to automatic speech recognition (ASR) tasks. Conventional ASR systems typically choose context-dependent states (CD-states) or context-dependent phonemes (CD-phonemes) as their modeling units. However, it has been challenged by sequence-to-sequence attention-based models, which integrate an acoustic, pronunciation and language model into a single neural network. On English ASR tasks, previous attempts have already shown that the modeling unit of graphemes can outperform that of phonemes by sequence-to-sequence attention-based model. In this paper, we are concerned with modeling units on Mandarin Chinese ASR tasks using sequence-to-sequence attention-based models with the Transformer. Five modeling units are explored including context-independent phonemes (CI-phonemes), syllables, words, sub-words and characters. Experiments on HKUST datasets demonstrate that the lexicon free modeling units can outperform lexicon related modeling units in terms of character error rate (CER). Among five modeling units, character based model performs best and establishes a new state-of-the-art CER of $26.64\%$ on HKUST datasets without a hand-designed lexicon and an extra language model integration, which corresponds to a $4.8\%$ relative improvement over the existing best CER of $28.0\%$ by the joint CTC-attention based encoder-decoder network.

eess.AS↗

arXiv2018

TOA Positioning for a TDMA Localization System

Positioning with one single communication between base stations and user devices can effectively save air time and thus expand the user volume to infinite. However, this usually demands accurate synchronization between base stations. Wireless synchronization between base stations can simplify the deployment of the positioning system but requires accurate clock offset estimation between base stations. A time division multiple access (TDMA) localization system in which user devices only receive signals from base stations to generate time of arrival (TOA) measurements to position themselves and no cables are needed to interconnect base stations for clock synchronization is proposed, implemented and tested. In this system, the user devices can easily join in or exit without influence to other users and the update rate of each user can be easily adjusted independently according to its specific requirement.

eess.SP↗

arXiv2016

The original nucleation and migration of the basal/prismatic interfaces in Mg single crystals

The formation of basal/prismatic (BP) interfaces accompanying with the nucleation and growth of a reoriented crystal in Mg single-crystals under c-axis tension is investigated by molecular dynamics simulations. The BP interfaces nucleate by shuffling mechanism via local rearrangements of atoms. Both two-layer disconnections and one-layer disconnections contribute to the migration of BP interfaces. In a three-dimensional view, the BP interfaces relatively tend to migrate towards the [1-210] direction rather than the [-1010]/[0001] direction since the misfit disconnection or misfit dislocation caused by the accumulation of mismatch along the [-1010] /[0001] direction impedes the disconnection movement. The BP interfaces can transform to the {10-12} twin boundary (TB) and vice versa. While the process from BP interface to TB is described as the linear pile-up of interface disconnections, the versa transformation is proposed as the upright pile-up process. Both BP transformation and {10-12} twinning can efficiently accommodate the strain along the c-axis, and the conjugate BP interfaces and {10-12} TBs account for the large deviations of twin interfaces from the {10-12} twin plane.

cond-mat.mtrl-sci↗

arXiv2013

Size effect on deformation twinning at the nanoscale in hcp metals

Deformation twinning is generally considered to be the primary mechanism for hexagonal close-packed (hcp) metals due to their limited slip systems. Recent microcompression experiments on hcp metals point to intriguing strong size effects on twinning mechanisms, indicating that pyramidal slips dominate compression.In this work we analyze size effects on twinning due to lattice-rotation strain in hcp single crystals. A criterion for deformation twinning is derived at the nanoscale and tested by molecular dynamics simulations of magnesium and titanium single crystals. The results show pyramidal slip dominates the compression deformation at the nanoscale, consistent with experimental observations as well as our analysis. Our finding clarifies the nature of size effects in deformation twinning, while also providing an explanation for the so-called strength differential (SD) effect.

cond-mat.mtrl-sci↗

arXiv2010

Landau-Zener-Stückelberg Spectroscopy of a Superconducting Flux Qubit

We proposed a new method to measure the energy spectrum of a superconducting flux qubit. Different from the conventional frequency spectroscopy, a short triangle pulse is used to drive the qubit through the anticrossing and generates Landau-Zener-Stückelberg interference patterns, from which the information of the energy spectrum can be extracted. Without installing microwave lines one can simplify the experimental setup and reduce the unwanted effects of noise. Moreover, the method can be applied to other quantum systems, opening the possibility of calibrating and manipulating qubits with linear pulses.

quant-ph↗