arXiv subjects

Bo Zhang

Publications and source records attributed to Bo Zhang.

At least 613 records · Page 34Linked to original sources

arXiv2021

Diagnostics for ultrashort X-ray pulses using silicon trackers

The spectrum of laser-plasma generated X-rays is very important, it characterizes electron dynamics in plasma and is basic for applications. However, the accuracies and efficiencies of existing methods to diagnose the spectrum of laser-plasma based X-ray pulse are not very high, especially in the range of several hundred keV. In this study, a new method based on electron tracks detection to measure the spectrum of laser-plasma produced X-ray pulses is proposed and demonstrated. Laser-plasma generated X-rays are scattered in a multi-pixel silicon tracker. Energies and scattering directions of Compton electrons can be extracted from the response of the detector, and then the spectrum of X-rays can be reconstructed. Simulations indicate that the energy resolution of this method is approximately 20% for X-rays from 200 to 550 keV for a silicon-on-insulator pixel detector with 12 $\rm μ$m pixel pitch and 500 $\rm μ$m depletion region thickness. The results of a proof-of-principle experiment based on a Timepix3 detector are also shown.

physics.ins-det↗

arXiv2021

Object-aware Long-short-range Spatial Alignment for Few-Shot Fine-Grained Image Classification

The goal of few-shot fine-grained image classification is to recognize rarely seen fine-grained objects in the query set, given only a few samples of this class in the support set. Previous works focus on learning discriminative image features from a limited number of training samples for distinguishing various fine-grained classes, but ignore one important fact that spatial alignment of the discriminative semantic features between the query image with arbitrary changes and the support image, is also critical for computing the semantic similarity between each support-query pair. In this work, we propose an object-aware long-short-range spatial alignment approach, which is composed of a foreground object feature enhancement (FOE) module, a long-range semantic correspondence (LSC) module and a short-range spatial manipulation (SSM) module. The FOE is developed to weaken background disturbance and encourage higher foreground object response. To address the problem of long-range object feature misalignment between support-query image pairs, the LSC is proposed to learn the transferable long-range semantic correspondence by a designed feature similarity metric. Further, the SSM module is developed to refine the transformed support feature after the long-range step to align short-range misaligned features (or local details) with the query features. Extensive experiments have been conducted on four benchmark datasets, and the results show superior performance over most state-of-the-art methods under both 1-shot and 5-shot classification scenarios.

cs.CV↗

arXiv2021

Densely Semantic Enhancement for Domain Adaptive Region-free Detectors

Unsupervised domain adaptive object detection aims to adapt a well-trained detector from its original source domain with rich labeled data to a new target domain with unlabeled data. Previous works focus on improving the domain adaptability of region-based detectors, e.g., Faster-RCNN, through matching cross-domain instance-level features that are explicitly extracted from a region proposal network (RPN). However, this is unsuitable for region-free detectors such as single shot detector (SSD), which perform a dense prediction from all possible locations in an image and do not have the RPN to encode such instance-level features. As a result, they fail to align important image regions and crucial instance-level features between the domains of region-free detectors. In this work, we propose an adversarial module to strengthen the cross-domain matching of instance-level features for region-free detectors. Firstly, to emphasize the important regions of image, the DSEM learns to predict a transferable foreground enhancement mask that can be utilized to suppress the background disturbance in an image. Secondly, considering that region-free detectors recognize objects of different scales using multi-scale feature maps, the DSEM encodes both multi-level semantic representations and multi-instance spatial-contextual relationships across different domains. Finally, the DSEM is pluggable into different region-free detectors, ultimately achieving the densely semantic feature matching via adversarial learning. Extensive experiments have been conducted on PASCAL VOC, Clipart, Comic, Watercolor, and FoggyCityscape benchmarks, and their results well demonstrate that the proposed approach not only improves the domain adaptability of region-free detectors but also outperforms existing domain adaptive region-based detectors under various domain shift settings.

cs.CV↗

arXiv2021

The Relative Calibration of Radial Velocity for LAMOST Medium Resolution Stellar Spectra

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) started median-resolution spectroscopic (MRS, R$\sim$7500) survey since October 2018. The main scientific goals of MRS, including binary stars, pulsators, and other variable stars are launched with a time-domain spectroscopic survey. However, the systematic errors, including the bias induced from wavelength calibration and the systematic difference between different spectrographs have to be carefully considered during radial velocity measurement. In this work, we provide a technique to correct the systematics in the wavelength calibration based on the relative radial velocity measurements from LAMOST MRS spectra. We show that, for the stars with multi-epoch spectra, the systematic bias which is induced from the exposures of different nights can be well corrected for LAMOST MRS in each spectrograph. And the precision of radial velocity zero-point of multi-epoch time-domain observations reaches below 0.5 km/s . As a by-product, we also give the constant star candidates, which can be the secondary radial-velocity standard star candidates of LAMOST MRS time-domain surveys.

astro-ph.SR↗

arXiv2021

The Flare and Warp of the Young Stellar Disk traced with LAMOST DR5 OB-type stars

We present analysis of the spatial density structure for the outer disk from 8$-$14 \,kpc with the LAMOST DR5 13534 OB-type stars and observe similar flaring on north and south sides of the disk implying that the flaring structure is symmetrical about the Galactic plane, for which the scale height at different Galactocentric distance is from 0.14 to 0.5 \,kpc. By using the average slope to characterize the flaring strength we find that the thickness of the OB stellar disk is similar but flaring is slightly stronger compared to the thin disk as traced by red giant branch stars, possibly implying that secular evolution is not the main contributor to the flaring but perturbation scenarios such as interactions with passing dwarf galaxies should be more possible. When comparing the scale height of OB stellar disk of the north and south sides with the gas disk, the former one is slightly thicker than the later one by $\approx$ 33 and 9 \,pc, meaning that one could tentatively use young OB-type stars to trace the gas properties. Meanwhile, we unravel that the radial scale length of the young OB stellar disk is 1.17 $\pm$ 0.05 \,kpc, which is shorter than that of the gas disk, confirming that the gas disk is more extended than stellar disk. What is more, by considering the mid-plane displacements ($Z_{0}$) in our density model we find that almost all of $Z_{0}$ are within 100 \,pc with the increasing trend as Galactocentric distance increases.

astro-ph.GA↗

arXiv2021

SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search

To discover powerful yet compact models is an important goal of neural architecture search. Previous two-stage one-shot approaches are limited by search space with a fixed depth. It seems handy to include an additional skip connection in the search space to make depths variable. However, it creates a large range of perturbation during supernet training and it has difficulty giving a confident ranking for subnetworks. In this paper, we discover that skip connections bring about significant feature inconsistency compared with other operations, which potentially degrades the supernet performance. Based on this observation, we tackle the problem by imposing an equivariant learnable stabilizer to homogenize such disparities. Experiments show that our proposed stabilizer helps to improve the supernet's convergence as well as ranking performance. With an evolutionary search backend that incorporates the stabilized supernet as an evaluator, we derive a family of state-of-the-art architectures, the SCARLET series of several depths, especially SCARLET-A obtains 76.9% top-1 accuracy on ImageNet. Code is available at https://github.com/xiaomi-automl/ScarletNAS.

cs.LG↗

arXiv2021

Social Distancing and COVID-19: Randomization Inference for a Structured Dose-Response Relationship

Social distancing is widely acknowledged as an effective public health policy combating the novel coronavirus. But extreme social distancing has costs and it is not clear how much social distancing is needed to achieve public health effects. In this article, we develop a design-based framework to make inference about the dose-response relationship between social distancing and COVID-19 related death toll and case numbers. We first discuss how to embed observational data with a time-independent, continuous treatment dose into an approximate randomized experiment, and develop a randomization-based procedure that tests if a structured dose-response relationship fits the data. We then generalize the design and testing procedure to accommodate a time-dependent, treatment dose trajectory, and generalize a dose-response relationship to a longitudinal setting. Finally, we apply the proposed design and testing procedures to investigate the effect of social distancing during the phased reopening in the United States on public health outcomes using data compiled from sources including Unacast, the United States Census Bureau, and the County Health Rankings and Roadmaps Program. We rejected a primary analysis null hypothesis that stated the social distancing from April 27, 2020, to June 28, 2020, had no effect on the COVID-19-related death toll from June 29, 2020, to August 2, 2020 (p-value < 0.001), and found that it took more reduction in mobility to prevent exponential growth in case numbers for non-rural counties compared to rural counties.

stat.AP↗

arXiv2021

The Nyström method for elastic wave scattering by unbounded rough surfaces

We consider the numerical algorithm for the two-dimensional time-harmonic elastic wave scattering by unbounded rough surfaces with Dirichlet boundary condition. A Nyström method is proposed for the scattering problem based on the integral equation method. Convergence of the Nyström method is established with convergence rate depending on the smoothness of the rough surfaces. In doing so, a crucial role is played by analyzing the singularities of the kernels of the relevant boundary integral operators. Numerical experiments are presented to demonstrate the effectiveness of the method.

math.NA↗

arXiv2021

FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search

One of the most critical problems in weight-sharing neural architecture search is the evaluation of candidate models within a predefined search space. In practice, a one-shot supernet is trained to serve as an evaluator. A faithful ranking certainly leads to more accurate searching results. However, current methods are prone to making misjudgments. In this paper, we prove that their biased evaluation is due to inherent unfairness in the supernet training. In view of this, we propose two levels of constraints: expectation fairness and strict fairness. Particularly, strict fairness ensures equal optimization opportunities for all choice blocks throughout the training, which neither overestimates nor underestimates their capacity. We demonstrate that this is crucial for improving the confidence of models' ranking. Incorporating the one-shot supernet trained under the proposed fairness constraints with a multi-objective evolutionary search algorithm, we obtain various state-of-the-art models, e.g., FairNAS-A attains 77.5% top-1 validation accuracy on ImageNet. The models and their evaluation codes are made publicly available online http://github.com/fairnas/FairNAS .

cs.LG↗

arXiv2021

Coexistence of ferromagnetism, antiferromagnetism, and superconductivity in magnetically anisotropic (Eu,La)FeAs2

Materials with exceptional magnetism and superconductivity usually conceive emergent physical phenomena. Here, we investigate the physical properties of the (Eu,La)FeAs2 system with double magnetic sublattices. The parent EuFeAs2 shows anisotropy-associated magnetic behaviors, such as Eu-related moment canting and exchange bias. Through La doping, the magnetic anisotropy is enhanced with ferromagnetism of Eu2+ realized in the overdoped region, and a special exchange bias of the superposed ferromagnetic/superconducting loop revealed in Eu0.8La0.2FeAs2. Meanwhile, the Fe-related antiferromagnetism shows unusual robustness against La doping. Theoretical calculation and 57Fe Mössbauer spectroscopy investigation reveal a doping-tunable dual itinerant/localized nature of the Fe-related antiferromagnetism. Coexistence of the Eu-related ferromagnetism, Fe-related robust antiferromagnetism, and superconductivity is further revealed in Eu0.8La0.2FeAs2, providing a platform for further exploration of potential applications and emergent physics. Finally, an electronic phase diagram is established for (Eu,La)FeAs2 with the whole superconducting dome adjacent to the Fe-related antiferromagnetic phase, which is of benefit for seeking underlying clues to high-temperature superconductivity.

cond-mat.supr-con↗

arXiv2021

Polar state memory in active fluids

Spontaneous emergence of correlated states such as flocks and vortices are prime examples of remarkable collective dynamics and self-organization observed in active matter. The formation of globally correlated polar states in geometrically confined systems proceeds through the emergence of a macroscopic steadily rotating vortex that spontaneously selects a clockwise or counterclockwise global chiral state. Here, we reveal that a global vortex formed by colloidal rollers exhibits state memory. The information remains stored even when the energy injection is ceased and the activity is terminated. We show that a subsequent formation of the collective states upon re-energizing the system is not random. We combine experiments and simulations to elucidate how a combination of hydrodynamic and electrostatic interactions leads to hidden asymmetries in the local particle positional order encoding the chiral state of the system. The stored information can be accessed and exploited to systematically command subsequent polar states of active liquid through temporal control of the activity. With the chirality of the emergent collective states controlled on-demand, active liquids offer new possibilities for flow manipulation, transport, and mixing at the microscale.

cond-mat.soft↗

arXiv2021

Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters

Convolutional neural networks (CNNs) have been successfully used in a range of tasks. However, CNNs are often viewed as "black-box" and lack of interpretability. One main reason is due to the filter-class entanglement -- an intricate many-to-many correspondence between filters and classes. Most existing works attempt post-hoc interpretation on a pre-trained model, while neglecting to reduce the entanglement underlying the model. In contrast, we focus on alleviating filter-class entanglement during training. Inspired by cellular differentiation, we propose a novel strategy to train interpretable CNNs by encouraging class-specific filters, among which each filter responds to only one (or few) class. Concretely, we design a learnable sparse Class-Specific Gate (CSG) structure to assign each filter with one (or few) class in a flexible way. The gate allows a filter's activation to pass only when the input samples come from the specific class. Extensive experiments demonstrate the fabulous performance of our method in generating a sparse and highly class-related representation of the input, which leads to stronger interpretability. Moreover, comparing with the standard training strategy, our model displays benefits in applications like object localization and adversarial sample detection. Code link: https://github.com/hyliang96/CSGCNN.

cs.CV↗

arXiv2021

Modified sampling method with near field measurements

This paper investigates the inverse scattering problems using sampling methods with near field measurements. The near field measurements appear in two classical inverse scattering problems: the inverse scattering for obstacles and the interior inverse scattering for cavities. We propose modified sampling methods to treat these two classical problems using near field measurements without making any asymptotic assumptions on the distance between the measurement surface and the scatterers. We provide theoretical justifications based on the factorization of the near field operator in both symmetric factorization case and non-symmetric factorization case. Furthermore, we introduce a data completion algorithm which allows us to apply the modified sampling methods to treat the limited-aperture inverse scattering problems. Finally numerical examples are provided to illustrate the modified sampling methods with both full- and limited- aperture near field measurements.

math.NA↗

arXiv2021

VeriDL: Integrity Verification of Outsourced Deep Learning Services (Extended Version)

Deep neural networks (DNNs) are prominent due to their superior performance in many fields. The deep-learning-as-a-service (DLaaS) paradigm enables individuals and organizations (clients) to outsource their DNN learning tasks to the cloud-based platforms. However, the DLaaS server may return incorrect DNN models due to various reasons (e.g., Byzantine failures). This raises the serious concern of how to verify if the DNN models trained by potentially untrusted DLaaS servers are indeed correct. To address this concern, in this paper, we design VeriDL, a framework that supports efficient correctness verification of DNN models in the DLaaS paradigm. The key idea of VeriDL is the design of a small-size cryptographic proof of the training process of the DNN model, which is associated with the model and returned to the client. Through the proof, VeriDL can verify the correctness of the DNN model returned by the DLaaS server with a deterministic guarantee and cheap overhead. Our experiments on four real-world datasets demonstrate the efficiency and effectiveness of VeriDL.

cs.CR↗

arXiv2021

Data completion algorithms and their applications in inverse acoustic scattering with limited-aperture backscattering data

We introduce two data completion algorithms for the limited-aperture problems in inverse acoustic scattering. Both completion algorithms are independent of the topological and physical properties of the unknown scatterers. The main idea is to relate the limited-aperture data to the full-aperture data via the prolate matrix. The data completion algorithms are simple and fast since only the approximate inversion of the prolate matrix is involved. We then combine the data completion algorithms with imaging methods such as factorization method and direct sampling method for the object reconstructions. A variety of numerical examples are presented to illustrate the effectiveness and robustness of the proposed algorithms.

math.AP↗

arXiv2021

Asteroseismology of RRab variable star EZ Cnc from K2 photometry and LAMOST spectroscopy

EZ Cnc, or EPIC 212182292, is a non-Blazhko RRab variable star located in the field of K2 Campaign 16. Its atmospheric parameters ($T_{\rm eff}$, $\log{g}$, [M/H]) and radial velocities are measured from the 55 high-quality LAMOST medium-resolution spectra. The fundamental frequency of pulsation is derived as $f=1.8323(17)$ d$^{-1}$ from the K2 light curves. The amplitude ratios $R_{21} = 0.5115(15), 0.490(8)$, $R_{31} = 0.3249(20), 0.279(7)$ and Fourier phase differences $φ_{21}=2.7550(20), 2.764(16)$, $φ_{31}=5.7194(25), 5.719(31)$ are determined from the Fourier decomposition of K2 light curve and LAMOST radial velocity curve, respectively. Through the constraints of the parameters, four optimal models are obtained in a time-dependent turbulent convection model survey for EPIC 212182292. The parameters of EPIC 212182292 are derived as $M=0.48\pm0.03$ M$_{\odot}$, $L = 42\pm2$ L$_{\odot}$, $T_{\rm eff}=6846\pm50$ K, $\log{g}=2.79\pm0.01$ dex, and $Z = 0.006\pm0.002$, respectively. The precisely determined parameters for RRab variable stars like EPIC 212180092 might help to better understand the period-luminosity relationship of RR Lyrae stars.

astro-ph.SR↗

arXiv2021

Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models

The learning and evaluation of energy-based latent variable models (EBLVMs) without any structural assumptions are highly challenging, because the true posteriors and the partition functions in such models are generally intractable. This paper presents variational estimates of the score function and its gradient with respect to the model parameters in a general EBLVM, referred to as VaES and VaGES respectively. The variational posterior is trained to minimize a certain divergence to the true model posterior and the bias in both estimates can be bounded by the divergence theoretically. With a minimal model assumption, VaES and VaGES can be applied to the kernelized Stein discrepancy (KSD) and score matching (SM)-based methods to learn EBLVMs. Besides, VaES can also be used to estimate the exact Fisher divergence between the data and general EBLVMs.

cs.LG↗

arXiv2021

Self-consistent Stellar Radial Velocities from LAMOST Medium-Resolution Survey (MRS) DR7

Radial velocity (RV) is among the most fundamental physical quantities obtainable from stellar spectra and is rather important in the analysis of time-domain phenomena. The LAMOST Medium-Resolution Survey (MRS) DR7 contains 5 million single-exposure stellar spectra at spectral resolution $R\sim7\,500$. However, the temporal variation of the RV zero-points (RVZPs) of the MRS survey, which makes the RVs from multiple epochs inconsistent, has not been addressed. In this paper, we measure the RVs of the 3.8 million single-exposure spectra (for 0.6 million stars) with signal-to-noise ratio (SNR) higher than 5 based on cross-correlation function (CCF) method, and propose a robust method to self-consistently determine the RVZPs exposure-by-exposure for each spectrograph with the help of \textit{Gaia} DR2 RVs. Such RVZPs are estimated for 3.6 million RVs and can reach a mean precision of $\sim 0.38\,\mathrm{km\,s}^{-1}$. The result of the temporal variation of RVZPs indicates that our algorithm is efficient and necessary before we use the absolute RVs to perform time-domain analysis. Validating the results with APOGEE DR16 shows that our absolute RVs can reach an overall precision of 0.84/0.80 $\mathrm{km\,s}^{-1}$ in the blue/red arm at $50<\mathrm{SNR}<100$, while 1.26/1.99 $\mathrm{km\,s}^{-1}$ at $5<\mathrm{SNR}<10$. The cumulative distribution function (CDF) of the standard deviations of multiple RVs ($N_\mathrm{obs}\geq 8$) for 678 standard stars reach 0.45/0.54, 1.07/1.39, and 1.45/1.86 $\mathrm{km\,s}^{-1}$ in the blue/red arm at 50\%, 90\%, and 95\% levels, respectively. The catalogs of the RVs, RVZPs, and selected candidate RV standard stars are available at \url{https://github.com/hypergravity/paperdata}.

astro-ph.SR↗