arXiv subjects

Jian Wu

Publications and source records attributed to Jian Wu.

At least 379 records · Page 21Linked to original sources

arXiv2018

Improved Dynamic Memory Network for Dialogue Act Classification with Adversarial Training

Dialogue Act (DA) classification is a challenging problem in dialogue interpretation, which aims to attach semantic labels to utterances and characterize the speaker's intention. Currently, many existing approaches formulate the DA classification problem ranging from multi-classification to structured prediction, which suffer from two limitations: a) these methods are either handcrafted feature-based or have limited memories. b) adversarial examples can't be correctly classified by traditional training methods. To address these issues, in this paper we first cast the problem into a question and answering problem and proposed an improved dynamic memory networks with hierarchical pyramidal utterance encoder. Moreover, we apply adversarial training to train our proposed model. We evaluate our model on two public datasets, i.e., Switchboard dialogue act corpus and the MapTask corpus. Extensive experiments show that our proposed model is not only robust, but also achieves better performance when compared with some state-of-the-art baselines.

cs.CL↗

arXiv2018

Learned Neural Iterative Decoding for Lossy Image Compression Systems

For lossy image compression systems, we develop an algorithm, iterative refinement, to improve the decoder's reconstruction compared to standard decoding techniques. Specifically, we propose a recurrent neural network approach for nonlinear, iterative decoding. Our decoder, which works with any encoder, employs self-connected memory units that make use of causal and non-causal spatial context information to progressively reduce reconstruction error over a fixed number of steps. We experiment with variants of our estimator and find that iterative refinement consistently creates lower distortion images of higher perceptual quality compared to other approaches. Specifically, on the Kodak Lossless True Color Image Suite, we observe as much as a 0.871 decibel (dB) gain over JPEG, a 1.095 dB gain over JPEG 2000, and a 0.971 dB gain over a competitive neural model.

cs.CV↗

arXiv2018

Charge Measurement of Cosmic Ray Nuclei with the Plastic Scintillator Detector of DAMPE

One of the main purposes of the DArk Matter Particle Explorer (DAMPE) is to measure the cosmic ray nuclei up to several tens of TeV or beyond, whose origin and propagation remains a hot topic in astrophysics. The Plastic Scintillator Detector (PSD) on top of DAMPE is designed to measure the charges of cosmic ray nuclei from H to Fe and serves as a veto detector for discriminating gamma-rays from charged particles. We propose in this paper a charge reconstruction procedure to optimize the PSD performance in charge measurement. Essentials of our approach, including track finding, alignment of PSD, light attenuation correction, quenching and equalization correction are described detailedly in this paper after a brief description of the structure and operational principle of the PSD. Our results show that the PSD works very well and almost all the elements in cosmic rays from H to Fe are clearly identified in the charge spectrum.

astro-ph.IM↗

arXiv2018

Optical rogue wave in random distributed feedback fiber laser

The famous demonstration of optical rogue wave (RW)-rarely and unexpectedly event with extremely high intensity-had opened a flourishing time for temporal statistic investigation as a powerful tool to reveal the fundamental physics in different laser scenarios. However, up to now, optical RW behavior with temporally localized structure has yet not been presented in random fiber laser (RFL) characterized with mirrorless open cavity, whose feedback arises from distinctive distributed multiple scattering. Here, thanks to the participation of sustained and crucial stimulated Brillouin scattering (SBS) process, experimental explorations of optical RW are done in the highly-skewed transient intensity of an incoherently-pumped standard-telecom-fiber-constructed RFL. Furthermore, threshold-like beating peak behavior can also been resolved in the radiofrequency spectroscopy. Bringing the concept of optical RW to RFL domain without fixed cavity may greatly extend our comprehension of the rich and complex kinetics such as photon propagation and localization in disordered amplifying media with multiple scattering.

physics.optics↗

arXiv2018

A Hierarchical Recurrent Neural Network for Symbolic Melody Generation

In recent years, neural networks have been used to generate symbolic melodies. However, the long-term structure in the melody has posed great difficulty for designing a good model. In this paper, we present a hierarchical recurrent neural network for melody generation, which consists of three Long-Short-Term-Memory (LSTM) subnetworks working in a coarse-to-fine manner along time. Specifically, the three subnetworks generate bar profiles, beat profiles and notes in turn, and the output of the high-level subnetworks are fed into the low-level subnetworks, serving as guidance for generating the finer time-scale melody components in low-level subnetworks. Two human behavior experiments demonstrate the advantage of this structure over the single-layer LSTM which attempts to learn all hidden structures in melodies. Compared with the state-of-the-art models MidiNet and MusicVAE, the hierarchical recurrent neural network produces better melodies evaluated by humans.

cs.SD↗

arXiv2018

A Method of Alignment of the Plastic Scintillator Detector of DAMPE

The Plastic Scintillator Detector (PSD) of the DArk Matter Particle Explorer (DAMPE) is designed to measure cosmic ray charge (Z) and to act as a veto detector for gamma-ray identification. In order to fully exploit the charge identification potential of the PSD and to enhance its capability to identify the gamma ray events, we develop a PSD detector alignment method. The path length of a given track in the volume of a PSD bar is derived taking into account the shift and rotation alignment corrections. By examining energy spectra of corner-passing events and fully contained events, position shifts and rotations of all PSD bars are obtained, and found to be on average about 1mm and 0.0015 radian respectively. To validate the alignment method, we introduce the artificial shifts and rotations of PSD bars in the detector simulation. These shift and rotation parameters can be recovered successfully by the alignment procedure. As a result of the PSD alignment procedure, the charge resolution of the PSD is improved from $4\%$ to $8\%$ depending on the nuclei.

astro-ph.IM↗

arXiv2018

The role of interstitial hydrogen in SrCoO$_{2.5}$ antiferromagnetic insulator

Hydrogen exhibits qualitatively different charge states depending on the host material, as nicely explained by the state-of-the-art impurity-state calculation. Motivated by a recent experiment [Nature 546, 124 (2017)], we show that the complex oxide SrCoO$_{2.5}$ represents an interesting example, in which the interstitial H appears as a deep-level center according to the commonly-used transition level calculation, but no bound electron can be found around the impurity. Via a combination of charge difference analysis, density of states projection and constraint magnetization calculation, it turns out that the H-doped electron is spontaneously trapped by a nonunique Co ion and is fully spin-polarized by the onsite Hund's rule coupling. Consequently, the doped system remains insulating, whereas the antiferromagnetic exchange is slightly perturbed locally.

cond-mat.mtrl-sci↗

arXiv2018

The Parallel Knowledge Gradient Method for Batch Bayesian Optimization

In many applications of black-box optimization, one can evaluate multiple points simultaneously, e.g. when evaluating the performances of several different neural network architectures in a parallel computing environment. In this paper, we develop a novel batch Bayesian optimization algorithm --- the parallel knowledge gradient method. By construction, this method provides the one-step Bayes-optimal batch of points to sample. We provide an efficient strategy for computing this Bayes-optimal batch of points, and we demonstrate that the parallel knowledge gradient method finds global optima significantly faster than previous batch Bayesian optimization algorithms on both synthetic test functions and when tuning hyperparameters of practical machine learning algorithms, especially when function evaluations are noisy.

stat.ML↗

arXiv2018

Field-free Three-Dimensional Orientation of Asymmetric-Top Molecules

Alignment and orientation of molecules by intense, ultrashort laser fields are crucial for a variety of applications in physics and chemistry. These include control of high harmonics generation, molecular orbitals tomography, control of molecular photoionization and dissociation processes, production of "molecular movies" with the help of X-ray free-electron laser sources and ultrafast electron diffraction of relativistic electrons. While the dynamics of laser-induced molecular alignment has been extensively studied and demonstrated, molecular orientation is a much more challenging task, especially for asymmetric-top molecules. Here we report the first experimental demonstration of a field-free, all-optical three-dimensional orientation of asymmetric-top molecules by means of phase-locked cross-polarized two-color laser pulses. In addition to the conventional integrated orientation factor, we report the differential degree of orientation which is not amenable to optical measurements, but is readily accessible in our angle-resolved imaging technique. Our scheme applies to a wide class of asymmetric molecules and opens new ways towards controlling their orientation, eventually leading to direct imaging of structure of gas-phase molecules using advanced free electron laser beams with extremely high spatiotemporal resolution.

physics.chem-ph↗

arXiv2018

Searching for spectral oscillations due to photon-axionlike particle conversion using the Fermi-LAT observations of bright supernova remnants

Axionlike-particles (ALPs) are one promising type of dark matter candidate particle that may generate detectable effects on $γ$-ray spectra other than the canonical weakly interacting massive particles. In this work we search for such oscillation effects in the spectra of supernova remnants caused by the photon-ALP conversion, using the Fermi Large Area Telescope data. Three bright supernova remnants, IC443, W44, and W51C, are analyzed. The inclusion of photon-ALP oscillations yields an improved fit to the $γ$-ray spectrum of IC443, which gives a statistical significance of $4.2σ$ in favor of such spectral oscillation. However, the best-fit parameters of ALPs ($m_{a}=6.6\,{\rm neV}$, $g_{aγ}=13.4 \times 10^{-11}\,{\rm GeV}^{-1}$) are in tension with the upper bound ($g_{aγ}< 6.6 \times 10^{-11}\,{\rm GeV}^{-1}$) set by the CAST experiment. It is difficult to explain the results using the systematic uncertainties of the flux measurements. We speculate that the "irregularity" displayed in the spectrum of IC443 may be due to the superposition of the emission from different parts of the remnant.

astro-ph.HE↗

arXiv2018

Bayesian Optimization with Gradients

Bayesian optimization has been successful at global optimization of expensive-to-evaluate multimodal objective functions. However, unlike most optimization methods, Bayesian optimization typically does not use derivative information. In this paper we show how Bayesian optimization can exploit derivative information to decrease the number of objective function evaluations required for good performance. In particular, we develop a novel Bayesian optimization algorithm, the derivative-enabled knowledge-gradient (dKG), for which we show one-step Bayes-optimality, asymptotic consistency, and greater one-step value of information than is possible in the derivative-free setting. Our procedure accommodates noisy and incomplete derivative information, comes in both sequential and batch forms, and can optionally reduce the computational cost of inference through automatically selected retention of a single directional derivative. We also compute the d-KG acquisition function and its gradient using a novel fast discretization-free technique. We show d-KG provides state-of-the-art performance compared to a wide range of optimization procedures with and without gradients, on benchmarks including logistic regression, deep learning, kernel learning, and k-nearest neighbors.

stat.ML↗

arXiv2017

Large deformation and instability of soft hollow cylinder with surface effects

Surface stress, which is always neglected in classical elastic theories, has recently emerged as a key role in the mechanics of highly deformable soft solids. In this paper, the effect of surface stress on the deformation and instability of soft hollow cylinder are analyzed. By incorporating surface energy density function into the constitutive model of a hyper-elastic theory, explicit solutions are obtained for the deformation of soft hollow cylinder under the conditions of uniform pressure loading and geometric everting. It is found that surface tension evidently alters the deformation of the soft cylinder. Specifically, the surface stiffness resists the deformation, but the residual surface stress is inclined to larger deformation. Effects of surface stress on the instability of the soft hollow cylinder is also explored. For both the pressure loading and geometric everting conditions, significant changes in critical condition of the creases are found by varying the surface parameter. The results in this work reveal that surface energy obviously influences both the deformation and the instability of soft hollow cylinder at finite deformation. The obtained results will be helpful for understanding and predicting the mechanical behavior of soft structures accurately.

cond-mat.soft↗

arXiv2017

Spectral adjustment of a powerful random fiber laser

Random fiber laser (RFL) based on random distributed feedback and Raman gain has earned much attention in recent years. In this presentation, we demonstrate a powerful linearly polarized RFL with spectral adjustment, in which the central wavelength and the linewidth of the spectrum can be tuned independently through a bandwidth-adjustable tunable optical filter (BA-TOF). As a result, the central wavelength can be continuously tuned from 1095 to 1115 nm, while the full width at half-maximum (FWHM) linewidth has a maximal tuning range from ~0.6 to more than 2 nm. This laser provides a flexible tool for scenes where the temporal coherence property accounts, such as coherent sensing/communication and nonlinear frequency conversion. To the best of our knowledge, this is the first demonstration of a high power linearly polarized RFL with both wavelength and linewidth tunability.

physics.optics↗

arXiv2017

Temperature effects on MIPs in the BGO calorimeters of DAMPE

In this paper, we presented a study of temperature effects on BGO calorimeters using proton MIP's collected in the first year operation of DAMPE. By directly comparing MIP calibration constants used by DAMPE data production pipe line, we found an experimental relation between temperature and signal amplitudes of each BGO bar: a general deviation of -1.162%/$^{\circ}$C,and -0.47%/$^{\circ}$C to -1.60%/$^{\circ}$C statistically for each detector element. During 2016, DAMPE's temperature changed by about 7 degrees due to solar elevation angle and the corresponding energy scale bias is about 8%. By frequent MIP calibration operation, this kind of bias is eliminated to an acceptable value.

physics.ins-det↗

arXiv2017

A Broad Learning Approach for Context-Aware Mobile Application Recommendation

With the rapid development of mobile apps, the availability of a large number of mobile apps in application stores brings challenge to locate appropriate apps for users. Providing accurate mobile app recommendation for users becomes an imperative task. Conventional approaches mainly focus on learning users' preferences and app features to predict the user-app ratings. However, most of them did not consider the interactions among the context information of apps. To address this issue, we propose a broad learning approach for \textbf{C}ontext-\textbf{A}ware app recommendation with \textbf{T}ensor \textbf{A}nalysis (CATA). Specifically, we utilize a tensor-based framework to effectively integrate user's preference, app category information and multi-view features to facilitate the performance of app rating prediction. The multidimensional structure is employed to capture the hidden relationships between multiple app categories with multi-view features. We develop an efficient factorization method which applies Tucker decomposition to learn the full-order interactions within multiple categories and features. Furthermore, we employ a group $\ell_{1}-$norm regularization to learn the group-wise feature importance of each view with respect to each app category. Experiments on two real-world mobile app datasets demonstrate the effectiveness of the proposed method.

cs.IR↗

arXiv2017

Discretization-free Knowledge Gradient Methods for Bayesian Optimization

This paper studies Bayesian ranking and selection (R&S) problems with correlated prior beliefs and continuous domains, i.e. Bayesian optimization (BO). Knowledge gradient methods [Frazier et al., 2008, 2009] have been widely studied for discrete R&S problems, which sample the one-step Bayes-optimal point. When used over continuous domains, previous work on the knowledge gradient [Scott et al., 2011, Wu and Frazier, 2016, Wu et al., 2017] often rely on a discretized finite approximation. However, the discretization introduces error and scales poorly as the dimension of domain grows. In this paper, we develop a fast discretization-free knowledge gradient method for Bayesian optimization. Our method is not restricted to the fully sequential setting, but useful in all settings where knowledge gradient can be used over continuous domains. We show how our method can be generalized to handle (i) batch of points suggestion (parallel knowledge gradient); (ii) the setting where derivative information is available in the optimization process (derivative-enabled knowledge gradient). In numerical experiments, we demonstrate that the discretization-free knowledge gradient method finds global optima significantly faster than previous Bayesian optimization algorithms on both synthetic test functions and real-world applications, especially when function evaluations are noisy; and derivative-enabled knowledge gradient can further improve the performances, even outperforming the gradient-based optimizer such as BFGS when derivative information is available.

stat.ML↗

arXiv2017

Preconditioned wire array Z-pinches driven by a double pulse current generator

Suppressing of the core-corona structures shows a strong potential as a new breakthrough in the X-ray power production of the wire array Z-pinches. In this letter, the demonstration of suppressing the core-corona structures and its ablation using a novel double pulse current generator "Qin-1" facility is presented. The "Qin-1" facility coupled a ~10 kA 20 ns prepulse generator to a ~ 1 MA 170 ns main current generator. Driven by the prepulse current, the two aluminum wire array were mostly heated to gaseous state rather than the core-corona structures, and the implosion of the aluminum vapors driven by the main current showed no ablation, and no trailing mass. The seeds for the MRT instability formed from the inhomogeneous ablation were suppressed, however, the magneto Rayleigh-Taylor instability during the implosion was still significant and further researches on the generation and development of the magneto Rayleigh-Taylor instabilities of this gasified wire array are needed.

physics.plasm-ph↗

arXiv2017

Offline software for the DAMPE experiment

A software system has been developed for the DArk Matter Particle Explorer (DAMPE) mission, a satellite-based experiment. The DAMPE software is mainly written in C++ and steered using Python script. This article presents an overview of the DAMPE offline software, including the major architecture design and specific implementation for simulation, calibration and reconstruction. The whole system has been successfully applied to DAMPE data analysis, based on which some results from simulation and beam test experiments are obtained and presented.

astro-ph.IM↗