arXiv subjects

Ming Yang

Publications and source records attributed to Ming Yang.

At least 307 records · Page 17Linked to original sources

arXiv2020

Tunable spin and orbital polarization in SrTiO3-based heterostructures

We formulate the effective Hamiltonian of Rashba spin-orbit coupling (RSOC) in $\mathrm{LaAlO_3/SrTiO_3}$ (LAO/STO) heterostructures. We derive analytical expressions of properties, e.g., Rashba parameter, effective mass, band edge energy and orbital occupancy, as functions of material and tunable heterostructure parameters. While linear RSOC is dominant around the $Γ$-point, cubic RSOC becomes significant at the higher-energy anti-crossing region. We find that linear RSOC stems from the structural inversion asymmetry (SIA), while the cubic term is induced by both SIA and bulk asymmetry. Furthermore, the SOC strength shows a striking dependence on the tunable heterostructure parameters such as STO thickness and the interfacial electric field which is ascribed to the quantum confinement effect near the LAO/STO interface. The calculated values of the linear and cubic RSOC are in agreement with previous experimental results.

cond-mat.mes-hall↗

arXiv2020

The Zwicky Transient Facility Catalog of Periodic Variable Stars

The number of known periodic variables has grown rapidly in recent years. Thanks to its large field of view and faint limiting magnitude, the Zwicky Transient Facility (ZTF) offers a unique opportunity to detect variable stars in the northern sky. Here, we exploit ZTF Data Release 2 (DR2) to search for and classify variables down to r ~ 20.6 mag. We classify 781,602 periodic variables into 11 main types using an improved classification method. Comparison with previously published catalogs shows that 621,702 objects (79.5%) are newly discovered or newly classified, including ~700 Cepheids, ~5000 RR Lyrae stars, ~15,000 Delta Scuti variables, ~350,000 eclipsing binaries, ~100,000 long-period variables, and about 150,000 rotational variables. The typical misclassification rate and period accuracy are on the order of 2% and 99%, respectively. 74% of our variables are located at Galactic latitudes, $|b|<10^\circ$. This large sample of Cepheids, RR Lyrae, Delta Scuti stars, and contact (EW-type) eclipsing binaries is helpful to investigate the Galaxy's disk structure and evolution with an improved completeness, areal coverage, and age resolution. Specifically, the northern warp and the disk's edge at distances of 15--20 kpc are significantly better covered than previously. Among rotational variables, RS Canum Venaticorum and BY Draconis-type variables can be separated easily. Our knowledge of stellar chromospheric activity would benefit greatly from a statistical analysis of these types of variables.

astro-ph.SR↗

arXiv2020

Evolved Massive Stars at Low-metallicity II. Red Supergiant Stars in the Small Magellanic Cloud

We present the most comprehensive RSG sample for the SMC up to now, including 1,239 RSG candidates. The initial sample is derived based on a source catalog for the SMC with conservative ranking. Additional spectroscopic RSGs are retrieved from the literature, as well as RSG candidates selected from the inspection of CMDs. We estimate that there are in total $\sim$ 1,800 or more RSGs in the SMC. We purify the sample by studying the infrared CMDs and the variability of the objects, though there is still an ambiguity between AGBs and RSGs. There are much less RSGs candidates ($\sim$4\%) showing PAH emission features compared to the Milky Way and LMC ($\sim$15\%). The MIR variability of RSG sample increases with luminosity. We separate the RSG sample into two subsamples ("risky" and "safe") and identify one M5e AGB star in the "risky" subsample. Most of the targets with large variability are also the bright ones with large MLR. Some targets show excessive dust emission, which may be related to previous episodic mass loss events. We also roughly estimate the total gas and dust budget produced by entire RSG population as $\rm \sim1.9^{+2.4}_{-1.1}\times10^{-6}~M_{\odot}/yr$ in the most conservative case. Based on the MIST models, we derive a linear relation between $T_{\rm eff}$ and observed $\rm J-K_S$ color with reddening correction for the RSG sample. By using a constant bolometric correction and this relation, the Geneva evolutionary model is compared with our RSG sample, showing a good agreement and a lower initial mass limit of $\sim$7 $\rm M_\odot $ for the RSG population. Finally, we compare the RSG sample in the SMC and the LMC. Despite the incompleteness of LMC sample in the faint end, the result indicates that the LMC sample always shows redder color (except for the $\rm IRAC1-IRAC2$ and $\rm WISE1-WISE2$ colors due to CO absorption) and larger variability than the SMC sample.

astro-ph.SR↗

arXiv2020

Class Distribution Alignment for Adversarial Domain Adaptation

Most existing unsupervised domain adaptation methods mainly focused on aligning the marginal distributions of samples between the source and target domains. This setting does not sufficiently consider the class distribution information between the two domains, which could adversely affect the reduction of domain gap. To address this issue, we propose a novel approach called Conditional ADversarial Image Translation (CADIT) to explicitly align the class distributions given samples between the two domains. It integrates a discriminative structure-preserving loss and a joint adversarial generation loss. The former effectively prevents undesired label-flipping during the whole process of image translation, while the latter maintains the joint distribution alignment of images and labels. Furthermore, our approach enforces the classification consistence of target domain images before and after adaptation to aid the classifier training in both domains. Extensive experiments were conducted on multiple benchmark datasets including Digits, Faces, Scenes and Office31, showing that our approach achieved superior classification in the target domain when compared to the state-of-the-art methods. Also, both qualitative and quantitative results well supported our motivation that aligning the class distributions can indeed improve domain adaptation.

cs.CV↗

arXiv2020

Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios

As one of the most important tasks in autonomous driving systems, ego-lane detection has been extensively studied and has achieved impressive results in many scenarios. However, ego-lane detection in the missing feature scenarios is still an unsolved problem. To address this problem, previous methods have been devoted to proposing more complicated feature extraction algorithms, but they are very time-consuming and cannot deal with extreme scenarios. Different from others, this paper exploits prior knowledge contained in digital maps, which has a strong capability to enhance the performance of detection algorithms. Specifically, we employ the road shape extracted from OpenStreetMap as lane model, which is highly consistent with the real lane shape and irrelevant to lane features. In this way, only a few lane features are needed to eliminate the position error between the road shape and the real lane, and a search-based optimization algorithm is proposed. Experiments show that the proposed method can be applied to various scenarios and can run in real-time at a frequency of 20 Hz. At the same time, we evaluated the proposed method on the public KITTI Lane dataset where it achieves state-of-the-art performance. Moreover, our code will be open source after publication.

cs.CV↗

arXiv2020

Superflares on solar-type stars from the first year observation of TESS

Superflares, as strong explosions on stars, have been well studied with the progress of space time-domain astronomy. In this work, we present the study of superflares on solar-type stars using Transiting Exoplanet Survey Satellite ({\em{TESS}}) data. 13 sectors of observations during the first year of the {\em TESS} mission have covered the southern hemisphere of the sky, containing 25,734 solar-type stars. We verified 1,216 superflares on 400 solar-type stars through automatic search and visual inspection with 2-minute cadence data. Our result suggests a higher superflare frequency distribution than the result from {\em Kepler}. The reason may be that the majority of {\em TESS} solar-type stars in our dataset are rapidly rotating stars. The power-law index $γ$ of the superflare frequency distribution ($dN/dE\propto E^{-γ}$) is constrained to be $γ= 2.16\pm 0.10$, which is a little larger than that of solar flares but consistent with the results from {\em Kepler}. Because only 7 superflares of Sun-like stars are detected, we may not give a robust superflare occurrence frequency. And four stars are accompanied by unconfirmed hot planet candidates. Therefore, superflares are possibly caused by stellar magnetic activities instead of planet-star interactions. We also find an extraordinary star TIC43472154, which exhibits about 200 superflares per year. In addition, the correlation between energy and duration of superflares ($T_{\text {duration }} \propto E^β$) is analyzed. We derive the power-law index to be $β=0.42\pm0.01$, which is a little larger than $β=1/3$ from the prediction according to magnetic reconnection theory.

astro-ph.SR↗

arXiv2020

Monocular Pedestrian Orientation Estimation Based on Deep 2D-3D Feedforward

Accurate pedestrian orientation estimation of autonomous driving helps the ego vehicle obtain the intentions of pedestrians in the related environment, which are the base of safety measures such as collision avoidance and prewarning. However, because of relatively small sizes and high-level deformation of pedestrians, common pedestrian orientation estimation models fail to extract sufficient and comprehensive information from them, thus having their performance restricted, especially monocular ones which fail to obtain depth information of objects and related environment. In this paper, a novel monocular pedestrian orientation estimation model, called FFNet, is proposed. Apart from camera captures, the model adds the 2D and 3D dimensions of pedestrians as two other inputs according to the logic relationship between orientation and them. The 2D and 3D dimensions of pedestrians are determined from the camera captures and further utilized through two feedforward links connected to the orientation estimator. The feedforward links strengthen the logicality and interpretability of the network structure of the proposed model. Experiments show that the proposed model has at least 1.72% AOS increase than most state-of-the-art models after identical training processes. The model also has competitive results in orientation estimation evaluation on KITTI dataset.

cs.CV↗

arXiv2019

Optimal Incentive Contract with Endogenous Monitoring Technology

Recent technology advances have enabled firms to flexibly process and analyze sophisticated employee performance data at a reduced and yet significant cost. We develop a theory of optimal incentive contracting where the monitoring technology that governs the above procedure is part of the designer's strategic planning. In otherwise standard principal-agent models with moral hazard, we allow the principal to partition agents' performance data into any finite categories and to pay for the amount of information the output signal carries. Through analysis of the trade-off between giving incentives to agents and saving the monitoring cost, we obtain characterizations of optimal monitoring technologies such as information aggregation, strict MLRP, likelihood ratio-convex performance classification, group evaluation in response to rising monitoring costs, and assessing multiple task performances according to agents' endogenous tendencies to shirk. We examine the implications of these results for workforce management and firms' internal organizations.

econ.TH↗

arXiv2019

Attention Guided Network for Retinal Image Segmentation

Learning structural information is critical for producing an ideal result in retinal image segmentation. Recently, convolutional neural networks have shown a powerful ability to extract effective representations. However, convolutional and pooling operations filter out some useful structural information. In this paper, we propose an Attention Guided Network (AG-Net) to preserve the structural information and guide the expanding operation. In our AG-Net, the guided filter is exploited as a structure sensitive expanding path to transfer structural information from previous feature maps, and an attention block is introduced to exclude the noise and reduce the negative influence of background further. The extensive experiments on two retinal image segmentation tasks (i.e., blood vessel segmentation, optic disc and cup segmentation) demonstrate the effectiveness of our proposed method.

eess.IV↗

arXiv2019

RFBNet: Deep Multimodal Networks with Residual Fusion Blocks for RGB-D Semantic Segmentation

RGB-D semantic segmentation methods conventionally use two independent encoders to extract features from the RGB and depth data. However, there lacks an effective fusion mechanism to bridge the encoders, for the purpose of fully exploiting the complementary information from multiple modalities. This paper proposes a novel bottom-up interactive fusion structure to model the interdependencies between the encoders. The structure introduces an interaction stream to interconnect the encoders. The interaction stream not only progressively aggregates modality-specific features from the encoders but also computes complementary features for them. To instantiate this structure, the paper proposes a residual fusion block (RFB) to formulate the interdependences of the encoders. The RFB consists of two residual units and one fusion unit with gate mechanism. It learns complementary features for the modality-specific encoders and extracts modality-specific features as well as cross-modal features. Based on the RFB, the paper presents the deep multimodal networks for RGB-D semantic segmentation called RFBNet. The experiments on two datasets demonstrate the effectiveness of modeling the interdependencies and that the RFBNet achieved state-of-the-art performance.

cs.CV↗

arXiv2019

Restricted Deformable Convolution based Road Scene Semantic Segmentation Using Surround View Cameras

Understanding the surrounding environment of the vehicle is still one of the challenges for autonomous driving. This paper addresses 360-degree road scene semantic segmentation using surround view cameras, which are widely equipped in existing production cars. First, in order to address large distortion problem in the fisheye images, Restricted Deformable Convolution (RDC) is proposed for semantic segmentation, which can effectively model geometric transformations by learning the shapes of convolutional filters conditioned on the input feature map. Second, in order to obtain a large-scale training set of surround view images, a novel method called zoom augmentation is proposed to transform conventional images to fisheye images. Finally, an RDC based semantic segmentation model is built; the model is trained for real-world surround view images through a multi-task learning architecture by combining real-world images with transformed images. Experiments demonstrate the effectiveness of the RDC to handle images with large distortions, and that the proposed approach shows a good performance using surround view cameras with the help of the transformed images.

cs.CV↗

arXiv2019

SSAP: Single-Shot Instance Segmentation With Affinity Pyramid

Recently, proposal-free instance segmentation has received increasing attention due to its concise and efficient pipeline. Generally, proposal-free methods generate instance-agnostic semantic segmentation labels and instance-aware features to group pixels into different object instances. However, previous methods mostly employ separate modules for these two sub-tasks and require multiple passes for inference. We argue that treating these two sub-tasks separately is suboptimal. In fact, employing multiple separate modules significantly reduces the potential for application. The mutual benefits between the two complementary sub-tasks are also unexplored. To this end, this work proposes a single-shot proposal-free instance segmentation method that requires only one single pass for prediction. Our method is based on a pixel-pair affinity pyramid, which computes the probability that two pixels belong to the same instance in a hierarchical manner. The affinity pyramid can also be jointly learned with the semantic class labeling and achieve mutual benefits. Moreover, incorporating with the learned affinity pyramid, a novel cascaded graph partition module is presented to sequentially generate instances from coarse to fine. Unlike previous time-consuming graph partition methods, this module achieves $5\times$ speedup and 9% relative improvement on Average-Precision (AP). Our approach achieves state-of-the-art results on the challenging Cityscapes dataset.

cs.CV↗

arXiv2019

A Feasibility Study on Deep Learning-Based Radiotherapy Dose Calculation

Purpose: Various dose calculation algorithms are available for radiation therapy for cancer patients. However, these algorithms are faced with the tradeoff between efficiency and accuracy. The fast algorithms are generally less accurate, while the accurate dose engines are often time consuming. In this work, we try to resolve this dilemma by exploring deep learning (DL) for dose calculation. Methods: We developed a new radiotherapy dose calculation engine based on a modified Hierarchically Densely Connected U-net (HD U-net) model and tested its feasibility with prostate intensity-modulated radiation therapy (IMRT) cases. Mapping from an IMRT fluence map domain to a 3D dose domain requires a deep neural network of complicated architecture and a huge training dataset. To solve this problem, we first project the fluence maps to the dose domain using a modified ray-tracing algorithm, and then we use the HD U-net to map the ray-tracing dose distribution into an accurate dose distribution calculated using a collapsed cone convolution/superposition (CS) algorithm. Results: It takes about one second to compute a 3D dose distribution for a typical 7-field prostate IMRT plan, which can be further reduced to achieve real-time dose calculation by optimizing the network. For all eight testing patients, evaluation with Gamma Index and various clinical goals for IMRT optimization shows that the DL dose distributions are clinically identical to the CS dose distributions. Conclusions: We have shown the feasibility of using DL for calculating radiotherapy dose distribution with high accuracy and efficiency.

physics.med-ph↗

arXiv2019

Evolved Massive Stars at Low-metallicity I. A Source Catalog for the Small Magellanic Cloud

We present a clean, magnitude-limited (IRAC1 or WISE1 $\leq$ 15.0 mag) multiwavelength source catalog for the SMC with 45,466 targets in total, with the purpose of building an anchor for future studies, especially for the massive star populations at low-metallicity. The catalog contains data in 50 different bands including 21 optical and 29 infrared bands, ranging from the ultraviolet to the far-infrared. Additionally, radial velocities and spectral classifications were collected from the literature, as well as infrared and optical variability statistics were retrieved from different projects. The catalog was essentially built upon a $1''$ crossmatching and a $3''$ deblending between the SEIP source list and Gaia DR2 photometric data. Further constraints on the proper motions and parallaxes from Gaia DR2 allowed us to remove the foreground contamination. We estimated that about 99.5\% of the targets in our catalog were most likely genuine members of the SMC. By using the evolutionary tracks and synthetic photometry from MIST and the theoretical $\rm J-K_S$ color cuts, we identified 1,405 RSG, 217 YSG and 1,369 BSG candidates in the SMC in five different CMDs, where attention should also be paid to the incompleteness of our sample. We ranked the candidates based on the intersection of different CMDs. A comparison between the models and observational data shows that the lower limit of initial mass for the RSGs population may be as low as 7 or even 6 $M_{\odot}$ and the RSG is well separated from the AGB population even at faint magnitude, making RSGs a unique population connecting the evolved massive and intermediate stars, since stars with initial mass around 6 to 8 $M_{\odot}$ are thought to go through a second dredge-up to become AGBs. We encourage the interested reader to further exploit the potential of our catalog.

astro-ph.SR↗

arXiv2019

Resolution-invariant Person Re-Identification

Exploiting resolution invariant representation is critical for person Re-Identification (ReID) in real applications, where the resolutions of captured person images may vary dramatically. This paper learns person representations robust to resolution variance through jointly training a Foreground-Focus Super-Resolution (FFSR) module and a Resolution-Invariant Feature Extractor (RIFE) by end-to-end CNN learning. FFSR upscales the person foreground using a fully convolutional auto-encoder with skip connections learned with a foreground focus training loss. RIFE adopts two feature extraction streams weighted by a dual-attention block to learn features for low and high resolution images, respectively. These two complementary modules are jointly trained, leading to a strong resolution invariant representation. We evaluate our methods on five datasets containing person images at a large range of resolutions, where our methods show substantial superiority to existing solutions. For instance, we achieve Rank-1 accuracy of 36.4% and 73.3% on CAVIAR and MLR-CUHK03, outperforming the state-of-the art by 2.9% and 2.6%, respectively.

cs.CV↗

arXiv2019

A Novel Demodulation and Estimation Algorithm for Blackout Communication: Extract Principal Components with Deep Learning

For reentry or near space communication, owing to the influence of the time-varying plasma sheath channel environment, the received IQ baseband signals are severely rotated on the constellation. Researches have shown that the frequency of electron density varies from 20kHz to 100 kHz which is on the same order as the symbol rate of most TT\&C communication systems and a mass of bandwidth will be consumed to track the time-varying channel with traditional estimation. In this paper, motivated by principal curve analysis, we propose a deep learning (DL) algorithm which called symmetric manifold network (SMN) to extract the curves on the constellation and classify the signals based on the curves. The key advantage is that SMN can achieve joint optimization of demodulation and channel estimation. From our simulation results, the new algorithm significantly reduces the symbol error rate (SER) compared to existing algorithms and enables accurate estimation of fading with extremely high bandwith utilization rate.

eess.SP↗

arXiv2019

The Period-Luminosity Relations of Red Supergiants in M33 and M31

Based on previously selected preliminary samples of Red Supergiants (RSGs) in M33 and M31, the foreground stars and luminous Asymptotic Giant Branch stars (AGBs) are further excluded, which leads to the samples of 717 RSGs in M33 and 420 RSGs in M31. With the time-series data from the iPTF survey spanning nearly 2000 days, the period and amplitude of RSGs are analyzed. According to the lightcurves characteristics, they are classified into four categories in which 84 and 56 objects in M33 and M31 respectively are semi-regular variables. For these semi-regular variables, the pulsation mode is identified by comparing with the theoretical model, which yielded 19 (7) sources in the first overtone mode in M33 (M31), and the other 65 (49) RSGs in M33 (M31) in the fundamental mode. The period-luminosity (P-L) relation is analyzed for the RSGs in the fundamental mode. It is found the P-L relation is tight in the infrared, i.e. the 2MASS $JHK_{\rm S}$ bands and the short-wavelength bands of Spitzer. Meanwhile, the inhomogeneous extinction causes the P-L relation scattering in the $V$ band, and the dust emission causes the less tight P-L relation in the Spitzer/[8.0] and [24] bands. The derived P-L relations in the 2MASS/$K_{\rm S}$ band are in agreement with those of RSGs in SMC, LMC and the Milky Way within the uncertainty range. It is found that the number ratio of RSGs pulsating in the fundamental mode to the first overtone mode increases with metallicity.

astro-ph.SR↗

arXiv2019

EPIC 202843107 : A Close Eclipsing Binary Containing a δ Scuti Variable

This paper reports on the discovery that an eclipsing binary system, EPIC 202843107 , has a δ Scuti variable component. The phased light curve from Kepler space telescope presents a detached configuration. The binary modelling indicates that the two component stars have almost the same radius and may have experienced orbital circularization. Frequency analyses are performed for the residual light curve after subtracting the binary variations. The frequency spectrum reveals that one component star is a δ Scuti variable. A large frequency separation is cross-identified with the histogram graph, the Fourier transform, and the echelle diagram method. The mean density of the δ Scuti component is estimated to be 0.09 g/cm3 based on the large separation and density relation. Systems like EPIC 202843107 are helpful to study the stellar evolution and physical state for binary stars.

astro-ph.SR↗