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Song Wang

Publications and source records attributed to Song Wang.

At least 415 records · Page 23Linked to original sources

Searching for Black Hole Candidates by LAMOST and ASAS-SN

Most dynamically confirmed stellar-mass black holes and the candidates were originally selected from X-ray outbursts. In the present work, we search for black hole candidates in the LAMOST survey by using the spectra along with photometry from the ASAS-SN survey, where the orbital period of the binary may be revealed by the periodic light curve, such as the ellipsoidal modulation type. Our sample consists of 9 binaries, where each source contains a giant star with large radial velocity variation ($ΔV_{\rm R} > 70~{\rm km~s^{-1}}$) and periods known from light curves. We focus on the 9 sources with long periods ($T_{\rm ph} > 5$ days) and evaluate the mass $M_2$ of the optically invisible companion. Since the observed $ΔV_{\rm R}$ from only a few repeating spectroscopic observations is a lower limit of the real amplitude, the real mass $M_2$ can be significantly higher than the current evaluation. It is likely an efficient method to place constraints on $M_2$ by combining $ΔV_{\rm R}$ from LAMOST and $T_{\rm ph}$ from ASAS-SN, particularly by the ongoing LAMOST Medium Resolution Survey.

astro-ph.SR↗

Long rotation period main-sequence stars from Kepler SAP light curves

Stellar rotation plays a key role in stellar activity. The rotation period could be detected through light curve variations caused by starspots. Kepler provides two types of light curves, one is the Pre-search Data Conditioning (PDC) light curves, the other is the Simple Aperture Photometer (SAP) light curves. Compared with the PDC light curves, the SAP light curves keep the long-term trend, relatively suitable for searches of long period signals. However, SAP data are inflicted by some artefacts such as quarterly rolls and instrumental errors, making it difficult to find the physical periods in the SAP light curves. We explore a systematic approach based on the light curve pre-processing, period detection and candidate selection. We also develop a simulated light curve test to estimate our detection limits for the SAP-like LCs. After applying our method to the raw SAP light curves, we found more than 1000 main-sequence stars with the period longer than 30 days, 165 are newly discovered. Considering the potential flaw of the SAP, we also inspect the newly found objects with photometry methods, and most of our periodical signals are confirmed.

astro-ph.SR↗

Effects of Blur and Deblurring to Visual Object Tracking

Intuitively, motion blur may hurt the performance of visual object tracking. However, we lack quantitative evaluation of tracker robustness to different levels of motion blur. Meanwhile, while image deblurring methods can produce visually clearer videos for pleasing human eyes, it is unknown whether visual object tracking can benefit from image deblurring or not. In this paper, we address these two problems by constructing a Blurred Video Tracking benchmark, which contains a variety of videos with different levels of motion blurs, as well as ground truth tracking results for evaluating trackers. We extensively evaluate 23 trackers on this benchmark and observe several new interesting results. Specifically, we find that light blur may improve the performance of many trackers, but heavy blur always hurts the tracking performance. We also find that image deblurring may help to improve tracking performance on heavily blurred videos but hurt the performance on lightly blurred videos. According to these observations, we propose a new GAN based scheme to improve the tracker robustness to motion blurs. In this scheme, a finetuned discriminator is used as an adaptive assessor to selectively deblur frames during the tracking process. We use this scheme to successfully improve the accuracy and robustness of 6 trackers.

cs.CV↗

Characterizing and Understanding Software Developer Networks in Security Development

To build secure software, developers often work together during software development and maintenance to find, fix, and prevent security vulnerabilities. Examining the nature of developer interactions during their security activities regarding security introducing and fixing activities can provide insights for improving current practices. In this work, we conduct a large-scale empirical study to characterize and understand developers' interactions during their security activities regarding security introducing and fixing, which involves more than 16K security fixing commits and over 28K security introducing commits from nine large-scale open-source software projects. For our analysis, we first examine whether a project is a hero-centric project when assessing developers' contribution in their security activities. Then we study the interaction patterns between developers, explore how the distribution of the patterns changes over time, and study the impact of developers' interactions on the quality of projects. In addition, we also characterize the nature of developer interaction in security activities in comparison to developer interaction in non-security activities (i.e., introducing and fixing non-security bugs). Among our findings we identify that: most of the experimental projects are non hero-centric projects when evaluating developers' contribution by using their security activities; there exist common dominating interaction patterns across our experimental projects; the distribution of interaction patterns has correlation with the quality of software projects. We believe the findings from this study can help developers understand how vulnerabilitiesoriginate and fix under the interactions of software developers.

cs.SE↗

Multiple Human Association between Top and Horizontal Views by Matching Subjects' Spatial Distributions

Video surveillance can be significantly enhanced by using both top-view data, e.g., those from drone-mounted cameras in the air, and horizontal-view data, e.g., those from wearable cameras on the ground. Collaborative analysis of different-view data can facilitate various kinds of applications, such as human tracking, person identification, and human activity recognition. However, for such collaborative analysis, the first step is to associate people, referred to as subjects in this paper, across these two views. This is a very challenging problem due to large human-appearance difference between top and horizontal views. In this paper, we present a new approach to address this problem by exploring and matching the subjects' spatial distributions between the two views. More specifically, on the top-view image, we model and match subjects' relative positions to the horizontal-view camera in both views and define a matching cost to decide the actual location of horizontal-view camera and its view angle in the top-view image. We collect a new dataset consisting of top-view and horizontal-view image pairs for performance evaluation and the experimental results show the effectiveness of the proposed method.

cs.CV↗

Machine Learning Regression of stellar effective temperatures in the second $Gaia$ Data Release

This paper reports on the application of the supervised machine-learning algorithm to the stellar effective temperature regression for the second $Gaia$ data release, based on the combination of the stars in four spectroscopic surveys: Large Sky Area Multi-Object Fiber Spectroscopic Telescope, Sloan Extension for Galactic Understanding and Exploration, the Apache Point Observatory Galactic Evolution Experiment and the RAdial Velocity Extension. This combination, about four million stars, enables us to construct one of the largest training sample for the regression, and further predict reliable stellar temperatures with a root-mean-squared error of 191 K. This result is more precise than that given by $Gaia$ second data release that is based on about sixty thousands stars. After a series of data cleaning processes, the input features that feed the regressor are carefully selected from the $Gaia$ parameters, including the colors, the 3D position and the proper motion. These $Gaia$ parameters is used to predict effective temperatures for 132,739,323 valid stars in the second $Gaia$ data release. We also present a new method for blind tests and a test for external regression without additional data. The machine-learning algorithm fed with the parameters only in one catalog provides us an effective approach to maximize sample size for prediction, and this methodology has a wide application prospect in future studies of astrophysics.

astro-ph.SR↗

CG X-1: an eclipsing Wolf-Rayet ULX in the Circinus galaxy

We investigated the time-variability and spectral properties of the eclipsing X-ray source Circinus Galaxy X-1 (GG X-1), using Chandra, XMM-Newton and ROSAT. We phase-connected the lightcurves observed over 20 years, and obtained a best-fitting period $P = (25,970.0 \pm 0.1)$ s $\approx$7.2 hr, and a period derivative $\dot{P}/P = ( 10.2\pm4.6) \times 10^{-7}$ yr$^{-1}$. The X-ray lightcurve shows asymmetric eclipses, with sharp ingresses and slow, irregular egresses. The eclipse profile and duration vary substantially from cycle to cycle. We show that the X-ray spectra are consistent with a power-law-like component, absorbed by neutral and ionized Compton-thin material, and by a Compton-thick, partial-covering medium, responsible for the irregular dips. The high X-ray/optical flux ratio rules out the possibility that CG X-1 is a foreground Cataclysmic Variable; in agreement with previous studies, we conclude that it is the first example of a compact ultraluminous X-ray source fed by a Wolf-Rayet star or stripped Helium star. Its unocculted luminosity varies between $\approx$4 $\times 10^{39}$ erg s$^{-1}$ and $\approx$3 $\times 10^{40}$ erg s$^{-1}$. Both the donor star and the super-Eddington compact object drive powerful outflows: we suggest that the occulting clouds are produced in the wind-wind collision region and in the bow shock in front of the compact object. Among the rare sample of Wolf-Rayet X-ray binaries, CG X-1 is an exceptional target for studies of super-critical accretion and close binary evolution; it is also a likely progenitor of gravitational wave events.

astro-ph.HE↗

A Method to Search for Black Hole Candidates with Giant Companions by LAMOST

We propose a method to search for stellar-mass black hole (BH) candidates with giant companions from spectroscopic observations. Based on the stellar spectra of LAMOST Data Release 6, we obtain a sample of seven giants in binaries with large radial velocity variation $ΔV_R > 80~{\rm km~s^{-1}}$. With the effective temperature, surface gravity, and metallicity provided by LAMOST, and the parallax given by {\it Gaia}, we can estimate the mass and radius of the giant, and therefore evaluate the possible mass of the optically invisible star in the binary. We show that the sources in our sample are potential BH candidates, and are worthy of dynamical measurement by further spectroscopic observations. Our method may be particularly valid for the selection of BH candidates in binaries with unknown orbital periods.

astro-ph.SR↗

X-ray Activity from Different Types of Stars

X-ray emission is an important indicator of stellar activity. In this paper, we study stellar X-ray activity using the XMM-Newton and LAMOST data for different types of stars. We provide a sample including 1259 X-ray emitting stars, of which 1090 have accurate stellar parameter estimations. Our sample size is much larger than those in previous works. We find a bimodal distribution of X-ray to optical flux ratio (log(fX/fV)) for G and K stars. We interpret that this bimodality is due to two subpopulations with different coronal heating rates. Furthermore, using the full widths at half maxima calculated from Hα and H\b{eta} lines, we show that these stars in the inactive peaks have smaller rotational velocities. This is consistent with the magnetic dynamo theory that stars with low rotational velocities have low levels of stellar activity. We also examine the correlation between log(fX/fV) and luminosity of the excess emission in the Hα line, and find a tight relation between the coronal and chromospheric activity indicators.

astro-ph.SR↗

Globular Clusters in the Outer Halo of M31

In this paper, we present photometry of 53 globular clusters (GCs) in the M31 outer halo, including the {\sl GALEX} FUV and NUV, SDSS $ugriz$, 15 intermediate-band filters of BATC, and 2MASS $JHK_{\rm s}$ bands. By comparing the multicolour photometry with stellar population synthesis models, we determine the metallicities, ages, and masses for these GCs, aiming to probe the merging/accretion history of M31. We find no clear trend of metallicity and mass with the de-projected radius. The halo GCs with age younger than $\approx$ 8 Gyr are mostly located at the de-projected radii around 100 kpc, but this may be due to a selection effect. We also find that the halo GCs have consistent metallicities with their spatially-associated substructures, which provides further evidence of the physical association between them. Both the disk and halo GCs in M31 show a bimodal luminosity distribution. However, we should emphasize that there are more faint halo GCs which are not being seen in the disk. The bimodal luminosity function of the halo GCs may reflect different origin or evolution environment in their original hosts. The M31 halo GCs includes one intermediate metallicity group ($-1.5 <$ [Fe/H] $< -0.4$) and one metal-poor group ([Fe/H] $<-1.5$), while the disk GCs have one metal-rich group more. There are considerable differences between the halo GCs in M31 and the Milky Way (MW). The total number of M31 GCs is approximately three times more numerous than that of the MW, however, M31 has about six times the number of halo GCs in the MW. Compared to M31 halo GCs, the Galactic halo ones are mostly metal-poor. Both the numerous halo GCs and the higher-metallicity component are suggestive of an active merger history of M31.

astro-ph.GA↗

A Combined Chandra and LAMOST Study of Stellar Activity

We probed stellar X-ray activity over a wide range of stellar parameters, using {\it Chandra} and LAMOST data. We measured the X-ray-to-bolometric luminosity ratio ($R_X = L_X/L_{\rm bol}$) for 484 main-sequence stars, and found a bimodal distribution for G and K types. We interpret this bimodality as evidence of two sub-populations with different coronal temperatures, which are caused by different coronal heating rates. Using the metallicity and velocity information, we find both of the two sub-populations are mostly located in the thin disk. We find no trend of $R_X$ with stellar age for stars older than $\sim$ 4 Gyr; there is a trough in the $R_X$ vs age distribution, with the lowest range of $R_X$ appearing at ages around 2 Gyr. We then examined the correlation between $R_X$ and $R_{\rm Hα}$ (proxy of chromospheric activity): we find that the two quantities are well correlated, as found in many earlier studies. Finally, we selected a sample of twelve stars with X-ray flares, and studied the light-curve morphology of the flares. The variety of flare profiles and timescales observed in our sample suggests the contribution of different processes of energy release.

astro-ph.SR↗

Which One to Go: Security and Usability Evaluation of Mid-Air Gestures

With the emerging of touch-less human-computer interaction techniques and gadgets, mid-air hand gestures have been widely used for authentication. Much literature examined either the usability or security of a handful of gestures. This paper aims at quantifying usability and security of gestures as well as understanding their relationship across multiple gestures. To study gesture-based authentication, we design an authentication method that combines Dynamic Time Warping (DTW) and Support Vector Machine (SVM), and conducted a user study with 42 participants over a period of 6 weeks. We objectively quantify the usability of a gesture by the number of corners and the frame length of all gesture samples, quantify the security using the equal error rate (EER), and the consistency by EER over a period of time. Meanwhile, we obtain subjective evaluation of usability and security by conducting a survey. By examining the responses, we found that the subjective evaluation confirms with the objective ones, and usability is in inverse relationship with security. We studied the consistency of gestures and found that most participants forgot gestures to some degree and reinforcing the memorization of gestures is necessary to improve the authentication performance. Finally, we performed a study with another 17 participants on shoulder surfing attacks, where attackers can observe the victims multiple times. The results show that shoulder surfing does not help to boost the attacks.

cs.CR↗

Validating the Contextual Information of Outdoor Images for Photo Misuse Detection

The contextual information (i.e., the time and location) in which a photo is taken can be easily tampered with or falsely claimed by forgers to achieve malicious purposes, e.g., creating fear among the general public. A rich body of work has focused on detecting photo tampering and manipulation by verifying the integrity of image content. Instead, we aim to detect photo misuse by verifying the capture time and location of photos. This paper is motivated by the law of nature that sun position varies with the time and location, which can be used to determine whether the claimed contextual information corresponds with the sun position that the image content actually indicates. Prior approaches to inferring sun position from images mainly rely on vanishing points associated with at least two shadows, while we propose novel algorithms which utilize only one shadow in the image to infer the sun position. Meanwhile, we compute the sun position by applying astronomical algorithms which take as input the claimed capture time and location. Only when the two estimated sun positions are consistent can the claimed contextual information be genuine. We have developed a prototype called IMAGEGUARD. The experimental results show that our method can successfully estimate sun position and detect the time-location inconsistency with high accuracy. By setting the thresholds to be 9.4 degrees and 5 degrees for the sun position distance and the altitude angle distance, respectively, our system can correctly identify 91.5% of falsified photos with fake contextual information.

cs.CR↗

Global and Local Sensitivity Guided Key Salient Object Re-augmentation for Video Saliency Detection

The existing still-static deep learning based saliency researches do not consider the weighting and highlighting of extracted features from different layers, all features contribute equally to the final saliency decision-making. Such methods always evenly detect all "potentially significant regions" and unable to highlight the key salient object, resulting in detection failure of dynamic scenes. In this paper, based on the fact that salient areas in videos are relatively small and concentrated, we propose a \textbf{key salient object re-augmentation method (KSORA) using top-down semantic knowledge and bottom-up feature guidance} to improve detection accuracy in video scenes. KSORA includes two sub-modules (WFE and KOS): WFE processes local salient feature selection using bottom-up strategy, while KOS ranks each object in global fashion by top-down statistical knowledge, and chooses the most critical object area for local enhancement. The proposed KSORA can not only strengthen the saliency value of the local key salient object but also ensure global saliency consistency. Results on three benchmark datasets suggest that our model has the capability of improving the detection accuracy on complex scenes. The significant performance of KSORA, with a speed of 17FPS on modern GPUs, has been verified by comparisons with other ten state-of-the-art algorithms.

cs.CV↗

Cutting Away the Confusion From Crowdtesting

Crowdtesting is effective especially when it comes to the feedback on GUI systems, or subjective opinions about features. Despite of this, we find crowdtesting reports are highly replicated, i.e., 82% of them are replicates of others. Hence automatically detecting replicate reports could help reduce triaging efforts. Most of the existing approaches mainly adopted textual information for replicate detection, and suffered from low accuracy because of the expression gap. Our observation on real industrial crowdtesting data found that when dealing with crowdtesting reports of GUI systems, the reports would accompanied with images, i.e., the screenshots of the app. We assume the screenshot to be valuable for replicate crowdtesting report detection because it reflects the real scenario of the failure and is not affected by the variety of natural languages. In this work, we propose a replicate detection approach, TSDetector, which combines information from the screenshots and the textual descriptions to detect replicate crowdtesting reports. We extract four types of features to characterize the screenshots and the textual descriptions, and design an algorithm to detect replicates based on four similarity scores derived from the four different features respectively. We investigate the effectiveness and advantage of TSDetector on 15 commercial projects with 4,172 reports from one of the Chinese largest crowdtesting platforms.Results show that TSDetector can outperform existing state-of-the-art approaches significantly. In addition, we also evaluate its usefulness using real-world case studies. The feedback from real-world testers demonstrates its practical value

cs.SE↗

Machine learning Applied to Star-Galaxy-QSO Classification and Stellar Effective Temperature Regression

In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised machine-learning algorithm, random forests (RF), to the star/galaxy/QSO classification and the stellar effective temperature regression based on the combination of LAMOST and SDSS spectroscopic data. This combination enable us to obtain reliable predictions with one of the largest training sample ever used. The training samples are built with nine-color data set of about three million objects for the classification and seven-color data set of over one million stars for the regression. The performance of the classification and regression is examined with the validation and the blind tests on the objects in the RAVE, 6dFGS, UVQS and APOGEE surveys. We demonstrate that the RF is an effective algorithm with the classification accuracies higher than 99\% for the stars and the galaxies, and higher than 94\% for the QSOs. These accuracies are higher than the machine-learning results in the former studies. The total standard deviations of the regression are smaller than 200 K that is similar to those of some spectrum-based methods. The machine-learning algorithm with the broad-band photometry provides us a more efficient approach to deal with massive amounts of astrophysical data than traditional color-cuts and SED fit.

astro-ph.GA↗

Dating Ancient Paintings of Mogao Grottoes Using Deeply Learnt Visual Codes

Cultural heritage is the asset of all the peoples of the world. The preservation and inheritance of cultural heritage is conducive to the progress of human civilization. In northwestern China, there is a world heritage site -- Mogao Grottoes -- that has a plenty of mural paintings showing the historical cultures of ancient China. To study these historical cultures, one critical procedure is to date the mural paintings, i.e., determining the era when they were created. Until now, most mural paintings at Mogao Grottoes have been dated by directly referring to the mural texts or historical documents. However, some are still left with creation-era undetermined due to the lack of reference materials. Considering that the drawing style of mural paintings was changing along the history and the drawing style can be learned and quantified through painting data, we formulate the problem of mural-painting dating into a problem of drawing-style classification. In fact, drawing styles can be expressed not only in color or curvature, but also in some unknown forms -- the forms that have not been observed. To this end, besides sophisticated color and shape descriptors, a deep convolution neural network is designed to encode the implicit drawing styles. 3860 mural paintings collected from 194 different grottoes with determined creation-era labels are used to train the classification model and build the dating method. In experiments, the proposed dating method is applied to seven mural paintings which were previously dated with controversies, and the exciting new dating results are approved by the Dunhuang expert.

cs.CV↗

Effects of Image Degradations to CNN-based Image Classification

Just like many other topics in computer vision, image classification has achieved significant progress recently by using deep-learning neural networks, especially the Convolutional Neural Networks (CNN). Most of the existing works are focused on classifying very clear natural images, evidenced by the widely used image databases such as Caltech-256, PASCAL VOCs and ImageNet. However, in many real applications, the acquired images may contain certain degradations that lead to various kinds of blurring, noise, and distortions. One important and interesting problem is the effect of such degradations to the performance of CNN-based image classification. More specifically, we wonder whether image-classification performance drops with each kind of degradation, whether this drop can be avoided by including degraded images into training, and whether existing computer vision algorithms that attempt to remove such degradations can help improve the image-classification performance. In this paper, we empirically study this problem for four kinds of degraded images -- hazy images, underwater images, motion-blurred images and fish-eye images. For this study, we synthesize a large number of such degraded images by applying respective physical models to the clear natural images and collect a new hazy image dataset from the Internet. We expect this work can draw more interests from the community to study the classification of degraded images.

cs.CV↗