arXiv subjects

Bo Zhang

Publications and source records attributed to Bo Zhang.

At least 667 records · Page 37Linked to original sources

arXiv2020

Near-field imaging of a locally rough interface and buried obstacles with the linear sampling method

Consider the problem of inverse scattering of time-harmonic point sources from an infinite, penetrable rough interface with bounded obstacles buried in the lower half-space, where the interface is assumed to be a local perturbation of a planar surface. A novel version of the sampling method is proposed to simultaneously reconstruct the local perturbation of the rough interface and buried obstacles by constructing a modified near-field equation associated with a special rough surface, yielding a fast imaging algorithm. Numerical examples are presented to illustrate the effectiveness of the inversion algorithm.

math.AP↗

arXiv2020

Coexistence of Ferromagnetism and Topology by Charge Carrier Engineering in intrinsic magnetic topological insulator MnBi4Te7

Intrinsic magnetic topological insulators (MTIs) MnBi2Te4 and MnBi2Te4/(Bi2Te3)n are expected to realize the high-temperature quantum anomalous Hall effect (QAHE) and dissipationless electrical transport. Extensive efforts have been made on this field but there is still lack of ideal MTI candidate with magnetic ordering of ferromagnetic (FM) ground state. Here, we demonstrate a MTI sample of Mn(Bi0.7Sb0.3)4Te7 which holds the coexistence of FM ground state and topological non-triviality. The dramatic modulation of the magnetism is induced by a charge carrier engineering process by the way of Sb substitution in MnBi4Te7 matrix with AFM ordering. The evolution of magnetism in Mn(Bi1-xSbx)4Te7 is systematically investigated by magnetic measurements and theoretical calculations. The clear topological surface states of the FM sample of x = 0.3 are also verified by angle-resolved photoemission spectra. We also aware that the FM sample of x = 0.3 is close to the charge neutral point. Therefore, the demonstration of intrinsic FM-MTI of Mn(Bi0.7Sb0.3)4Te7 in this work sheds light to the further studies of QAHE realization and optimizations.

cond-mat.mtrl-sci↗

arXiv2020

Integrated and Spectrally Selective Thermal Emitters Enabled by Layered Metamaterials

Nanophotonic engineering of light-matter interaction at subwavelength scale allows thermal radiation that is fundamentally different from that of traditional thermal emitters and provides exciting opportunities for various thermal-photonic applications. We propose a new kind of integrated and electrically controlled thermal emitter that exploits layered metamaterials with lithography-free and dielectric/metallic nanolayers. We demonstrate both theoretically and experimentally that the proposed concept can create a strong photonic bandgap in the visible regime and allow small impedance mismatch at the infrared wavelengths, which gives rise to optical features of significantly enhanced emissivity at the broad infrared wavelengths of 1.4-14 um as well as effectively suppressed emissivity in the visible region. The electrically driven metamaterial devices are optically and thermally stable at temperature up to ~800 K with electro-optical conversion efficiency reaching ~30%. We believe that the proposed high efficiency thermal emitters will pave the way towards integrated infrared light source platforms for various thermal-photonic applications and particularly provide a novel alternative for cost-effective, compact, low glare, and energy-efficient infrared heating.

physics.optics↗

arXiv2020

Neural Architecture Search on Acoustic Scene Classification

Convolutional neural networks are widely adopted in Acoustic Scene Classification (ASC) tasks, but they generally carry a heavy computational burden. In this work, we propose a lightweight yet high-performing baseline network inspired by MobileNetV2, which replaces square convolutional kernels with unidirectional ones to extract features alternately in temporal and frequency dimensions. Furthermore, we explore a dynamic architecture space built on the basis of the proposed baseline with the recent Neural Architecture Search (NAS) paradigm, which first trains a supernet that incorporates all candidate networks and then applies a well-known evolutionary algorithm NSGA-II to discover more efficient networks with higher accuracy and lower computational cost. Experimental results demonstrate that our searched network is competent in ASC tasks, which achieves 90.3% F1-score on the DCASE2018 task 5 evaluation set, marking a new state-of-the-art performance while saving 25% of FLOPs compared to our baseline network.

cs.SD↗

arXiv2020

A linear sampling method for inverse acoustic scattering by a locally rough interface

This paper is concerned with the inverse problem of time-harmonic acoustic scattering by an unbounded, locally rough interface which is assumed to be a local perturbation of a plane. The purpose of this paper is to recover the local perturbation of the interface from the near-field measurement given on a straight line segment with a finite distance above the interface and generated by point sources. Precisely, we propose a novel version of the linear sampling method to recover the location and shape of the local perturbation of the interface numerically. Our method is based on a modified near-field operator equation associated with a special rough surface, constructed by reformulating the forward scattering problem into an equivalent integral equation formulation in a bounded domain, leading to a fast imaging algorithm. Numerical experiments are presented to illustrate the effectiveness of the imaging method.

math.AP↗

arXiv2020

Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search

Deep convolutional neural networks demonstrate impressive results in the super-resolution domain. A series of studies concentrate on improving peak signal noise ratio (PSNR) by using much deeper layers, which are not friendly to constrained resources. Pursuing a trade-off between the restoration capacity and the simplicity of models is still non-trivial. Recent contributions are struggling to manually maximize this balance, while our work achieves the same goal automatically with neural architecture search. Specifically, we handle super-resolution with a multi-objective approach. We also propose an elastic search tactic at both micro and macro level, based on a hybrid controller that profits from evolutionary computation and reinforcement learning. Quantitative experiments help us to draw a conclusion that our generated models dominate most of the state-of-the-art methods with respect to the individual FLOPS.

cs.CV↗

arXiv2020

Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search

Differentiable Architecture Search (DARTS) is now a widely disseminated weight-sharing neural architecture search method. However, it suffers from well-known performance collapse due to an inevitable aggregation of skip connections. In this paper, we first disclose that its root cause lies in an unfair advantage in exclusive competition. Through experiments, we show that if either of two conditions is broken, the collapse disappears. Thereby, we present a novel approach called Fair DARTS where the exclusive competition is relaxed to be collaborative. Specifically, we let each operation's architectural weight be independent of others. Yet there is still an important issue of discretization discrepancy. We then propose a zero-one loss to push architectural weights towards zero or one, which approximates an expected multi-hot solution. Our experiments are performed on two mainstream search spaces, and we derive new state-of-the-art results on CIFAR-10 and ImageNet. Our code is available on https://github.com/xiaomi-automl/fairdarts .

cs.LG↗

arXiv2020

A calibrated sensitivity analysis for matched observational studies with application to the effect of second-hand smoke exposure on blood lead levels in U.S. children

Matched observational studies are commonly used to study treatment effects in non-randomized data. After matching for observed confounders, there could remain bias from unobserved confounders. A standard way to address this problem is to do a sensitivity analysis. A sensitivity analysis asks how sensitive the result is to a hypothesized unmeasured confounder U. One method, known as simultaneous sensitivity analysis, has two sensitivity parameters: one relating U to treatment assignment and the other to response. This method assumes that in each matched set, U is distributed to make the bias worst. This approach has two concerning features. First, this worst case distribution of U in each matched set does not correspond to a realistic distribution of U in the population. Second, sensitivity parameters are in absolute scales which are hard to compare to observed covariates. We address these concerns by introducing a method that endows U with a probability distribution in the population and calibrates the unmeasured confounder to the observed covariates. We compare our method to simultaneous sensitivity analysis in simulations and in a study of the effect of second-hand smoke exposure on blood lead levels in U.S. children.

stat.ME↗

arXiv2020

Understanding and Stabilizing GANs' Training Dynamics with Control Theory

Generative adversarial networks (GANs) are effective in generating realistic images but the training is often unstable. There are existing efforts that model the training dynamics of GANs in the parameter space but the analysis cannot directly motivate practically effective stabilizing methods. To this end, we present a conceptually novel perspective from control theory to directly model the dynamics of GANs in the function space and provide simple yet effective methods to stabilize GANs' training. We first analyze the training dynamic of a prototypical Dirac GAN and adopt the widely-used closed-loop control (CLC) to improve its stability. We then extend CLC to stabilize the training dynamic of normal GANs, where CLC is implemented as a squared $L2$ regularizer on the output of the discriminator. Empirical results show that our method can effectively stabilize the training and obtain state-of-the-art performance on data generation tasks.

cs.LG↗

arXiv2020

Differential rotation of the halo traced by the K-giant stars

We use K-giant stars selected from the LAMOST DR5 to study the variation of the rotational velocity of the galactic halo at different space positions. Modelling the rotational velocity distribution with both the halo and disk components, we find that the rotational velocity of the halo population decreases almost linearly with increasing vertical distance to the galactic disk plane, $Z$, at fixed galactocentric radius, $R$. The samples are separated into two parts with $6<R<12$ kpc and $12<R<20$ kpc. We derive that the decreasing rates along $Z$ for the two subsamples are $-3.07\pm0.63$ and $-1.89\pm0.37$ km s$^{-1}$ kpc$^{-1}$, respectively. Compared with the TNG simulations, we suggest that this trend is probably caused by the interaction between the disk and halo. The results from the simulations show that only the oblate halo can provide a decreasing rotational velocity with an increasing $Z$. This indicates that the Galactic halo is oblate with galactocentric radius $R<20$ kpc. On the other hand, the flaring of the disk component (mainly the thick disk) is clearly traced by this study, with $R$ between 12 and 20 kpc, the disk can vertically extend to $6\sim10$ kpc above the disk plane. What is more interesting is that, we find the Gaia-Enceladus-Sausage (GES) component has a significant contribution only in the halo with $R<12$ kpc, i.e. a fraction of 23$-$47\%. While in the outer subsample, the contribution is too low to be well constrained.

astro-ph.GA↗

arXiv2020

Learning Implicit Generative Models by Teaching Explicit Ones

Implicit generative models are difficult to train as no explicit density functions are defined. Generative adversarial nets (GANs) present a minimax framework to train such models, which however can suffer from mode collapse due to the nature of the JS-divergence. This paper presents a learning by teaching (LBT) approach to learning implicit models, which intrinsically avoids the mode collapse problem by optimizing a KL-divergence rather than the JS-divergence in GANs. In LBT, an auxiliary density estimator is introduced to fit the implicit model's distribution while the implicit model teaches the density estimator to match the data distribution. LBT is formulated as a bilevel optimization problem, whose optimal generator matches the true data distribution. LBT can be naturally integrated with GANs to derive a hybrid LBT-GAN that enjoys complimentary benefits. Finally, we present a stochastic gradient ascent algorithm with unrolling to solve the challenging learning problems. Experimental results demonstrate the effectiveness of our method.

cs.LG↗

arXiv2020

The atomic gas of star-forming galaxies at z$\sim$0.05 as revealed by the Five-hundred-meter Aperture Spherical Radio Telescope

We report new HI observations of four z$\sim$0.05 star-forming galaxies undertaken during the commissioning phase of the Five-hundred-meter Aperture Spherical Radio Telescope (FAST). FAST is the largest single-dish telescope with a 500 meter aperture and a 19-Beam receiver. Exploiting the unprecedented sensitivity provided by FAST, we aim to study the atomic gas, via the HI 21cm emission line, in low-$z$ star-forming galaxies taken from the Valparaíso ALMA/APEX Line Emission Survey (VALES) project. Together with previous ALMA CO($J=1-0$) observations, the HI data provides crucial information to measure the gas mass and dynamics. As a pilot HI survey, we targeted four local star-forming galaxies at $z\sim0.05$. In particular, one of them has already been detected in HI by the Arecibo Legacy Fast ALFA survey (ALFALFA), allowing a careful comparison. We use an ON-OFF observing approach that allowed us to reach an rms of 0.7mJy/beam at a 1.7km/s velocity resolution within only 20 minutes ON-target integration time. We demonstrate the great capabilities of the FAST 19-beam receiver for pushing the detectability of the HI emission line of extra-galactic sources. The HI emission line detected by FAST shows good consistency with the previous ALFALFA results. Our observations are put in context with previous multi-wavelength data to reveal the physical properties of these low-$z$ galaxies. We find that the CO($J=1-0$) and HI emission line profiles are similar. The dynamical mass estimated from the HI data is an order of magnitude higher than the baryon mass and the dynamical mass derived from the CO observations, implying that the mass probed by dynamics of HI is dominated by the dark matter halo. In one case, a target shows an excess of CO($J=1-0$) in the line centre, which can be explained by an enhanced CO($J=1-0$) emission induced by a nuclear starburst showing high velocity dispersion.

astro-ph.GA↗

arXiv2020

Galaxy Optical Variability of Virgo Cluster: New Tracer for Environmental Influences on Galaxies

We investigate the relationship between the optical variability of galaxies and their distances from the centre of the Virgo Cluster using Palomar Transient Factory data. We define the ratio between the standard deviation of the galaxy brightness and the mean value of the standard deviation as a measure of a galaxy's optical variability. A sample of 814 Virgo galaxies with 230263 observations shows a monotonically decreasing trend of optical variability with increasing clustercentric distance. The variability level inside the cluster is 3.2$σ$ higher than the level outside. We fit the variability with a linear function and find that the data reject a distance-independent model. We examine 217 background galaxies for comparison and find no significant trend in galaxy variability. We assess the relation with Monte Carlo simulation by rebuilding the brightness of each galaxy. The simulation shows a monotonically decreasing relation for member galaxy variability and a distance-independent relation for background galaxies. Our result is consistent with the theory that the cold gas flowing inwards the cluster centre fuels AGN activity. This work is a new implementation of the method using optical variability to investigate the relation between galaxies evolution and their environment.

astro-ph.GA↗

arXiv2020

A Catalog of Short Period Spectroscopic and Eclipsing Binaries Identified from the LAMOST & PTF Surveys

Binaries play key roles in determining stellar parameters and exploring stellar evolution models. We build a catalog of 88 eclipsing binaries with spectroscopic information, taking advantage of observations from both the Large Sky Area Multi-Object fiber Spectroscopic Telescope (LAMOST) and the Palomar Transient Factory (PTF) surveys. A software pipeline is constructed to identify binary candidates by examining their light curves. The orbital periods of binaries are derived from the Lomb-Scargle method. The key distinguishing features of eclipsing binaries are recognized by a new filter \textit{Flat Test}. We classify the eclipsing binaries by applying Fourier analysis on the light curves. Among all the binary stars, 13 binaries are identified as eclipsing binaries for the first time. The catalog contains information: position, primary eclipsing magnitude and time, eclipsing depth, the number of photometry and radial velocity observations, largest radial velocity difference, binary type, the effective temperature of observable star $T_{\rm eff}$, and surface gravity of observable star log \emph{g}. The false-positive probability is calculated by using both a Monte Carlo simulation and real data from the SDSS Stripe 82 Standard Catalog. The binaries in the catalog are mostly with a period of less than one day. The period distribution shows a 0.22-day cut-off which is consistent with the low probability of an eclipsing binary rotating with such a period.

astro-ph.SR↗

arXiv2020

Modeling and Detailed Numerical Simulation of the Primary Breakup of a Gasoline Surrogate Jet under Non-Evaporative Operating Conditions

In the present study, detailed numerical simulations are performed to investigate the primary breakup of a gasoline surrogate jet under non-evaporative "Spray G" operating conditions. The Spray G injector and operating conditions, developed by the Engine Combustion Network (ECN), represent the early phase of spray-guided gasoline injection. To focus the computational resources on resolving the primary breakup, simplifications have been made on the injector geometry. The effect of the internal flow on the primary breakup is modeled by specifying a nonzero injection angle at the inlet. The nonzero injection angle results in an increase of the jet penetration speed and also a deflection of the liquid jet. A parametric study on the injection angle is performed, and the numerical results are compared to the experimental data to identify the injection angle that best represents the Spray G conditions. The nonzero injection angle introduces an azimuthally non-uniform velocity in the liquid jet, which in turn influences the instability development on the jet surfaces and also the deformation and breakup of the jet head. The asymmetric primary breakup dynamics eventually lead to an azimuthal variation of droplet size distributions. The number of droplets varies significantly with the azimuthal angle, but interestingly, the probability density functions (PDF) of droplet size for different azimuthal angles collapse to a self-similar profile. Analysis has also been conducted to estimate the percentage and statistics of the tiny droplets that are under resolved in the present simulation. The PDF of the azimuthal angle is also presented, which is also shown to exhibit a self-similar form that varies little over time. Finally, a model is developed to predict the droplet number as a function of droplet diameter, azimuthal angle where a droplet is located, and time.

physics.flu-dyn↗

arXiv2020

LAMOST Medium-Resolution Spectroscopic Survey (LAMOST-MRS): Scientific goals and survey plan

Since September 2018, LAMOST starts a new 5-year medium-resolution spectroscopic survey (MRS) using bright/gray nights. We present the scientific goals of LAMOST-MRS and propose a near optimistic strategy of the survey. A complete footprint is also provided. Not only the regular medium-resolution survey, but also a time-domain spectroscopic survey is being conducted since 2018 and will be end in 2023. According to the detailed survey plan, we expect that LAMOST-MRS can observe about 2 million stellar spectra with ~7500 and limiting magnitude of around G=15 mag. Moreover, it will also provide about 200 thousand stars with averagely 60-epoch observations and limiting magnitude of G~14 mag. These high quality spectra will give around 20 elemental abundances, rotational velocities, emission line profiles as well as precise radial velocity with uncertainty less than 1 km/s. With these data, we expect that LAMOST can effectively leverage sciences on stellar physics, e.g. exotic binary stars, detailed observation of many types of variable stars etc., planet host stars, emission nebulae, open clusters, young pre-main-sequence stars etc.

astro-ph.SR↗

arXiv2020

Convergence analysis of the PML method for time-domain electromagnetic scattering problems

In this paper, a perfectly matched layer (PML) method is proposed to solve the time-domain electromagnetic scattering problems in 3D effectively. The PML problem is defined in a spherical layer and derived by using the Laplace transform and real coordinate stretching in the frequency domain. The well-posedness and the stability estimate of the PML problem are first proved based on the Laplace transform and the energy method. The exponential convergence of the PML method is then established in terms of the thickness of the layer and the PML absorbing parameter. As far as we know, this is the first convergence result for the time-domain PML method for the three-dimensional Maxwell equations. Our proof is mainly based on the stability estimates of solutions of the truncated PML problem and the exponential decay estimates of the stretched dyadic Green's function for the Maxwell equations in the free space.

math.AP↗

arXiv2020

The radio properties of the OH megamaser galaxy IRAS 02524+2046

We present results from VLBI observations of continuum and OH line emission in IRAS 02524+2046 and also arcsecond-scale radio properties of this galaxy using VLA archive data. We found that there is no significant detection of radio continuum emission from VLBI observations. The arcsecond-scale radio images of this source show no clear extended emission, the total radio flux density at L and C band are around 2.9 mJy and 1.0 mJy respectively, which indicate a steep radio spectral index between the two band. Steep spectral index, low brightness temperature and high $q$-ratio (the FIR to the radio flux density), which are three critical indicators in classification of radio activity in the nuclei of galaxies, are all consistent with the classification of this source as a starburst galaxy from its optical spectrum. The high-resolution line profile show that both of \textbf{the 1665 and 1667 MHz OH maser} line have been detected which show three and two clear components respectively. The channel maps show that the maser emission are distributed in a region $\sim$ 210 pc $\times$ 90 pc, the detected maser components at different region show similar double spectral feature, which might be an evidence that this galaxy is at a stage of major merger as seen from the optical morphology.

astro-ph.GA↗