arXiv subjects

Bo Zhang

Publications and source records attributed to Bo Zhang.

At least 757 records · Page 42Linked to original sources

arXiv2018

Message Passing Stein Variational Gradient Descent

Stein variational gradient descent (SVGD) is a recently proposed particle-based Bayesian inference method, which has attracted a lot of interest due to its remarkable approximation ability and particle efficiency compared to traditional variational inference and Markov Chain Monte Carlo methods. However, we observed that particles of SVGD tend to collapse to modes of the target distribution, and this particle degeneracy phenomenon becomes more severe with higher dimensions. Our theoretical analysis finds out that there exists a negative correlation between the dimensionality and the repulsive force of SVGD which should be blamed for this phenomenon. We propose Message Passing SVGD (MP-SVGD) to solve this problem. By leveraging the conditional independence structure of probabilistic graphical models (PGMs), MP-SVGD converts the original high-dimensional global inference problem into a set of local ones over the Markov blanket with lower dimensions. Experimental results show its advantages of preventing vanishing repulsive force in high-dimensional space over SVGD, and its particle efficiency and approximation flexibility over other inference methods on graphical models.

stat.ML↗

arXiv2018

Interlinked Convolutional Neural Networks for Face Parsing

Face parsing is a basic task in face image analysis. It amounts to labeling each pixel with appropriate facial parts such as eyes and nose. In the paper, we present a interlinked convolutional neural network (iCNN) for solving this problem in an end-to-end fashion. It consists of multiple convolutional neural networks (CNNs) taking input in different scales. A special interlinking layer is designed to allow the CNNs to exchange information, enabling them to integrate local and contextual information efficiently. The hallmark of iCNN is the extensive use of downsampling and upsampling in the interlinking layers, while traditional CNNs usually uses downsampling only. A two-stage pipeline is proposed for face parsing and both stages use iCNN. The first stage localizes facial parts in the size-reduced image and the second stage labels the pixels in the identified facial parts in the original image. On a benchmark dataset we have obtained better results than the state-of-the-art methods.

cs.CV↗

arXiv2018

Bearing fault diagnosis based on domain adaptation using transferable features under different working conditions

Bearing failure is the most common failure mode in rotating machinery and can result in large financial losses or even casualties. However, complex structures around bearing and actual variable working conditions can lead to large distribution difference of vibration signal between a training set and a test set, which causes the accuracy-dropping problem of fault diagnosis. Thus, how to improve efficiently the performance of bearing fault diagnosis under different working conditions is always a primary challenge. In this paper, a novel bearing fault diagnosis under different working conditions method is proposed based on domain adaptation using transferable features(DATF). The dataset of normal bearing and faulty bearings are obtained through the fast Fourier transformation(FFT) of raw vibration signals under different motor speeds and load conditions. Then we reduce marginal and conditional distributions simultaneously across domains based on maximum mean discrepancy(MMD) in feature space by refining pseudo test labels, which can be obtained by the Nearest-Neighbor(NN) classifier built on training data, and then a robust transferable feature representation for training and test domains is achieved after several iterations. With the help of the NN classifier trained on transferable features, bearing fault categories are identified accurately in final. Extensive experiment results show that the proposed method under different working conditions can identify the bearing faults accurately and outperforms obviously competitive approaches.

eess.SP↗

arXiv2018

An FFT-based algorithm for efficient computation of Green's functions for the Helmholtz and Maxwell's equations in periodic domains

The integral equation method is widely used in numerical simulations of 2D/3D acoustic and electromagnetic scattering problems, which needs a large number of values of the Green's functions. A significant topic is the scattering problems in periodic domains, where the corresponding Green's functions are quasi-periodic. The quasi-periodic Green's functions are defined by series that converge too slowly to be used for calculations. Many mathematicians have developed several efficient numerical methods to calculate quasi-periodic Green's functions. In this paper, we will propose a new FFT-based fast algorithm to compute the 2D/3D quasi-periodic Green's functions for both the Helmholtz equations and Maxwell's equations. The convergence results and error estimates are also investigated in this paper. Further, the numerical examples are given to show that, when a large number of values are needed, the new algorithm is very competitive.

math.NA↗

arXiv2018

Fast imaging of scattering obstacles from phaseless far-field measurements at a fixed frequency

This paper is concerned with the inverse obstacle scattering problem with phaseless far-field data at a fixed frequency. The main difficulty of this problem is the so-called translation invariance property of the modulus of the far-field pattern or the phaseless far-field pattern generated by one plane wave as the incident field, which means that the location of the obstacle can not be recovered from such phaseless far-field data at a fixed frequency. It was recently proved in our previous work \cite{XZZ18} that the obstacle can be uniquely determined by the phaseless far-field patterns generated by infinitely many sets of superpositions of two plane waves with different directions at a fixed frequency if the obstacle is a priori known to be a sound-soft or an impedance obstacle with real-valued impedance function. The purpose of this paper is to develop a direct imaging algorithm to reconstruct the location and shape of the obstacle from the phaseless far-field data corresponding to infinitely many sets of superpositions of two plane waves with a fixed frequency as the incident fields. Our imaging algorithm only involves the calculation of the products of the measurement data with two exponential functions at each sampling point and is thus fast and easy to implement. Further, the proposed imaging algorithm does not need to know the type of boundary conditions on the obstacle in advance and is capable to reconstruct multiple obstacles with different boundary conditions. Numerical experiments are also carried out to illustrate that our imaging method is stable, accurate and robust to noise.

math.NA↗

arXiv2018

Study of the rare earth effects on the magnetic fluctuations in RbLn$_2$Fe$_4$As$_4$O$_2$ (Ln = Tb, Dy and Ho) by Mössbauer spectroscopy

In the current work, we report the investigation of RbLn$_2$Fe$_4$As$_4$O$_2$ (Ln = Tb, Dy and Ho) and RbFe$_2$As$_2$ by $^{57}$Fe Mössbauer spectroscopy. Singlet pattern has been observed for all the samples indicating the absence of static magnetic ordering down to 5.9\,K on the Fe sublattice. The observed intermediate value of the isomer shift confirms the effective self charge transfer effect for the studied superconductors. Debye temperatures of these samples have been determined by the temperature dependence of the isomer shift. Most importantly, we observe different spectral line width broadening behaviors for samples with different rare earth elements, and no line width broadenings for samples Ln=Tb and Ho. These results suggest that the observed magnetic fluctuations at the Fe site might be sensitive to the magnetic behaviors of the rare earth magnetic moments.

cond-mat.str-el↗

arXiv2018

Study of the abundance features of the metal-poor star HD 94028

Recent work has found that the metal-poor star HD 94028 shows interesting abundance features. The s-process material (e.g., Ba, La, Ce, and Pb) and r-process material (e.g., Eu, Os, Pt) are mildly overabundant while the element C is not enhanced. However, the observed supersolar ratio [As/Ge](= $0.99 \pm 0.23$) and subsolar ratio [Se/As](= $-0.16 \pm 0.22$) could not be fitted by the combination of s- and r-process abundances. In this work, adopting the abundance decomposition method, we investigate the astrophysical origins of the elements in this star. We find that the primary process of massive stars dominates the production of the light elements and iron-group elements. The lighter neutron-capture elements from Ge to Te mainly come from the weak r-process. The elements from Ba to Pb are synthesized dominantly by the main s- and main r-process. The observed abundance features of Ge, As, and Se in HD 94028 are mainly produced by the weak r-process, which possesses the features of the supersolar ratio [As/Ge] and subsolar ratio [Se/As]. Because Ge is not synthesized solely by the neutron-capture process, As should be the lightest neutron-capture element. Although the i-process has been suggested in several astrophysical environments, it should be superfluous to explaining the abundances of the lighter neutron-capture elements in HD 94028.

astro-ph.SR↗

arXiv2018

Formation of the Abundance Boundaries of the Heavier Neutron-capture Elements in Metal-poor Stars

The abundance scatter of heavier r-process elements (Z > 56) relative to Fe ([r/Fe]) in metal-poor stars preserves excellent information of the star formation history and provides important insights into the various situations of the Galactic chemical enrichment. In this respect, the upper and lower boundaries of [r/Fe] could present useful clues for investigating the extreme situations of the star formation history and the early Galactic chemical evolution. In this paper, we investigate the formation of the upper and lower boundaries of [r/Fe] for the gas clouds. We find that, for a cloud from which metal-poor stars formed, the formation of the upper limits of [r/Fe] is mainly due to the pollution from a single main r-process event. For a cloud from which metal-poor stars formed, the formation of the lower limits of [r/Fe] is mainly due to the pollution from a single SN II event that ejects primary Fe.

astro-ph.SR↗

arXiv2018

Adversarial adaptive 1-D convolutional neural networks for bearing fault diagnosis under varying working condition

Traditional intelligent fault diagnosis of rolling bearings work well only under a common assumption that the labeled training data (source domain) and unlabeled testing data (target domain) are drawn from the same distribution. However, in many real-world applications, this assumption does not hold, especially when the working condition varies. In this paper, a new adversarial adaptive 1-D CNN called A2CNN is proposed to address this problem. A2CNN consists of four parts, namely, a source feature extractor, a target feature extractor, a label classifier and a domain discriminator. The layers between the source and target feature extractor are partially untied during the training stage to take both training efficiency and domain adaptation into consideration. Experiments show that A2CNN has strong fault-discriminative and domain-invariant capacity, and therefore can achieve high accuracy under different working conditions. We also visualize the learned features and the networks to explore the reasons behind the high performance of our proposed model.

eess.SP↗

arXiv2018

A Generalized Matrix Inverse with Applications to Robotic Systems

It is well-understood that the robustness of mechanical and robotic control systems depends critically on minimizing sensitivity to arbitrary application-specific details whenever possible. For example, if a system is defined and performs well in one particular Euclidean coordinate frame then it should be expected to perform identically if that coordinate frame is arbitrarily rotated or scaled. Similarly, the performance of the system should not be affected if its key parameters are all consistently defined in metric units or in imperial units. In this paper we show that a recently introduced generalized matrix inverse permits performance consistency to be rigorously guaranteed in control systems that require solutions to underdetermined and/or overdetermined systems of equations.

eess.SP↗

arXiv2018

A direct imaging method for inverse scattering by unbounded rough surfaces

This paper is concerned with the inverse scattering problem by an unbounded rough surface. A direct imaging method is proposed to reconstruct the rough surface from the scattered near-field Cauchy data generating by point sources and measured on a horizontal straight line segment at a finite distance above the rough surface. Theoretical analysis of the imaging algorithm is given for the case of a penetrable rough surface, but the imaging algorithm also works for impenetrable surfaces with Dirichlet or impedance boundary conditions. Numerical experiments are presented to show that the direct imaging algorithm is fast, accurate and very robust with respect to noise in the data.

math.NA↗

arXiv2018

A deep convolutional encoder-decoder neural network in assisting seismic horizon tracking

Seismic horizons are geologically significant surfaces that can be used for building geology structure and stratigraphy models. However, horizon tracking in 3D seismic data is a time-consuming and challenging problem. Relief human from the tedious seismic interpretation is one of the hot research topics. We proposed a novel automatically seismic horizon tracking method by using a deep convolutional neural network. We employ a state-of-art end-to-end semantic segmentation method to track the seismic horizons automatically. Experiment result shows that our proposed neural network can automatically track multiple horizons simultaneously. We validate the effectiveness and robustness of our proposed method by comparing automatically tracked horizons with manually picked horizons.

physics.geo-ph↗

arXiv2018

Near-field imaging of an unbounded elastic rough surface with a direct imaging method

This paper is concerned with the inverse scattering problem of time-harmonic elastic waves by an unbounded rigid rough surface. A direct imaging method is developed to reconstruct the unbounded rough surface from the elastic scattered near-field Cauchy data generated by point sources. A Helmholtz-Kirchhoff-type identity is derived and then used to provide a theoretical analysis of the direct imaging algorithm. Numerical experiments are presented to show that the direct imaging algorithm is fast, accurate and robust with respect to noise in the data.

math.NA↗

arXiv2018

Mesoscopic magnetic resonance spectroscopy with a remote spin sensor

Quantum sensing based on nitrogen-vacancy (NV) centers in diamond has been developed as a powerful tool for microscopic magnetic resonance. However, the reported sensor-to-sample distance is limited within tens of nanometers because the signal of spin fluctuation decreases cubically with the increasing distance. Here we extend the sensing distance to tens of micrometers by detecting spin polarization rather than spin fluctuation. We detected the mesoscopic magnetic resonance spectra of polarized electrons of a pentacene-doped crystal, measured its two typical decay times and observed the optically enhanced spin polarization. This work paves the way for the NV-based mesoscopic magnetic resonance spectroscopy and imaging at ambient conditions.

quant-ph↗

arXiv2018

A direct imaging method for inverse elastic scattering by unbounded rigid rough surfaces

This paper is concerned with the inverse time-harmonic elastic scattering problem of recovering unbounded rough surfaces in two dimensions. We assume that elastic plane waves with different directions are incident onto a rigid rough surface in a half plane. The elastic scattered field is measured on a horizontal straight line segment within a finite distance above the rough surface. A direct imaging algorithm is proposed to recover the unbounded rough surface from the scattered near-field data, which involves only inner products between the data. Numerical experiments are presented to show that the inversion scheme is not only efficient but also accurate and robust with respect to noise.

math.NA↗

arXiv2018

The Parallax of the Red Hypergiant VX Sgr with Accurate Tropospheric Delay Calibration

We report astrometric results of VLBI phase-referencing observations of 22 GHz \hho\ masers emission toward the red hypergiant \vxsgr, one of most massive and luminous red hypergiant stars in our Galaxy, using the Very Long Baseline Array. A background source, \Jtwoze, projected 4\d4 from the target \vxsgr, was used as the phase reference. For the low declinations of these sources, such a large separation normally would seriously degrade the relative astrometry. We use a two-step method of tropospheric delay calibration, which combines the VLBI geodetic-block (or GPS) calibration with an image-optimization calibration, to obtain a trigonometric parallax of $0.64\pm0.04$ mas, corresponding to a distance of 1.56$^{+0.11}_{-0.10}$ kpc. The measured proper motion of \vxsgr\ is $0.36\pm0.76$ and $-2.92\pm0.78$ \masy\ in the eastward and northward directions. The parallax and proper motion confirms that \vxsgr\ belong to the Sgr OB1 association. Rescaling bolometric luminosities in the literature to our parallax distance, we find the luminosity of \vxsgr~is $(1.95 \pm 0.62) \times 10^5$ \Lsun, where the uncertainty is dominated by differing photometry measurements.

astro-ph.IM↗

arXiv2018

Smooth Neighbors on Teacher Graphs for Semi-supervised Learning

The recently proposed self-ensembling methods have achieved promising results in deep semi-supervised learning, which penalize inconsistent predictions of unlabeled data under different perturbations. However, they only consider adding perturbations to each single data point, while ignoring the connections between data samples. In this paper, we propose a novel method, called Smooth Neighbors on Teacher Graphs (SNTG). In SNTG, a graph is constructed based on the predictions of the teacher model, i.e., the implicit self-ensemble of models. Then the graph serves as a similarity measure with respect to which the representations of "similar" neighboring points are learned to be smooth on the low-dimensional manifold. We achieve state-of-the-art results on semi-supervised learning benchmarks. The error rates are 9.89%, 3.99% for CIFAR-10 with 4000 labels, SVHN with 500 labels, respectively. In particular, the improvements are significant when the labels are fewer. For the non-augmented MNIST with only 20 labels, the error rate is reduced from previous 4.81% to 1.36%. Our method also shows robustness to noisy labels.

cs.LG↗

arXiv2018

Mössbauer spectroscopy study of magnetic fluctuations in superconducting RbGd$_2$Fe$_4$As$_4$O$_2$

$^{57}$Fe Mössbauer spectra were measured at different temperatures between 5.9 K and 300 K on the recently discovered self-doped superconducting RbGd$_2$Fe$_4$As$_4$O$_2$ with T$_c$ as high as 35 K. Singlet pattern was observed down to the lowest temperature measured in this work, indicating the absence of static magnetic order on the Fe site. The intermediate isomer shift in comparison with that of the samples RbFe$_2$As$_2$ and GdFeAsO confirms the self doping induced local electronic structure change. Surprisingly, we observe two magnetic fluctuation induced spectral broadenings below $\sim$15 K and $\sim$100 K which are believed to be originated from the transferred magnetic fluctuations of the Gd$^{3+}$ moments and that of the magnetic fluctuations of the Fe atoms, respectively.

cond-mat.supr-con↗