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Shu Wang

Publications and source records attributed to Shu Wang.

At least 145 records · Page 8Linked to original sources
arXiv2023

Unveiling Optimal SDG Pathways: An Innovative Approach Leveraging Graph Pruning and Intent Graph for Effective Recommendations

The recommendation of appropriate development pathways, also known as ecological civilization patterns for achieving Sustainable Development Goals (namely, sustainable development patterns), are of utmost importance for promoting ecological, economic, social, and resource sustainability in a specific region. To achieve this, the recommendation process must carefully consider the region's natural, environmental, resource, and economic characteristics. However, current recommendation algorithms in the field of computer science fall short in adequately addressing the spatial heterogeneity related to environment and sparsity of regional historical interaction data, which limits their effectiveness in recommending sustainable development patterns. To overcome these challenges, this paper proposes a method called User Graph after Pruning and Intent Graph (UGPIG). Firstly, we utilize the high-density linking capability of the pruned User Graph to address the issue of spatial heterogeneity neglect in recommendation algorithms. Secondly, we construct an Intent Graph by incorporating the intent network, which captures the preferences for attributes including environmental elements of target regions. This approach effectively alleviates the problem of sparse historical interaction data in the region. Through extensive experiments, we demonstrate that UGPIG outperforms state-of-the-art recommendation algorithms like KGCN, KGAT, and KGIN in sustainable development pattern recommendations, with a maximum improvement of 9.61% in Top-3 recommendation performance.

cs.IR↗
arXiv2023

Compensating Removed Frequency Components: Thwarting Voice Spectrum Reduction Attacks

Automatic speech recognition (ASR) provides diverse audio-to-text services for humans to communicate with machines. However, recent research reveals ASR systems are vulnerable to various malicious audio attacks. In particular, by removing the non-essential frequency components, a new spectrum reduction attack can generate adversarial audios that can be perceived by humans but cannot be correctly interpreted by ASR systems. It raises a new challenge for content moderation solutions to detect harmful content in audio and video available on social media platforms. In this paper, we propose an acoustic compensation system named ACE to counter the spectrum reduction attacks over ASR systems. Our system design is based on two observations, namely, frequency component dependencies and perturbation sensitivity. First, since the Discrete Fourier Transform computation inevitably introduces spectral leakage and aliasing effects to the audio frequency spectrum, the frequency components with similar frequencies will have a high correlation. Thus, considering the intrinsic dependencies between neighboring frequency components, it is possible to recover more of the original audio by compensating for the removed components based on the remaining ones. Second, since the removed components in the spectrum reduction attacks can be regarded as an inverse of adversarial noise, the attack success rate will decrease when the adversarial audio is replayed in an over-the-air scenario. Hence, we can model the acoustic propagation process to add over-the-air perturbations into the attacked audio. We implement a prototype of ACE and the experiments show ACE can effectively reduce up to 87.9% of ASR inference errors caused by spectrum reduction attacks. Also, by analyzing residual errors, we summarize six general types of ASR inference errors and investigate the error causes and potential mitigation solutions.

cs.CR↗
arXiv2023

The Ultraviolet to Mid-infrared Extinction Law of the Taurus Molecular Cloud Based on the Gaia DR3, GALEX, APASS, Pan-STARRS1, 2MASS, and WISE Surveys

Interstellar dust extinction law is essential for interpreting observations. In this work, we investigate the ultraviolet (UV)--mid-infrared (IR) extinction law of the Taurus molecular cloud and its possible variations. We select 504,988 dwarf stars (4200 K < Teff < 8000 K) and 4,757 giant stars (4200 K < Teff < 5200 K) based on the stellar parameters of Gaia DR3 as tracers. We establish the Teff--intrinsic color relations and determine the intrinsic color indices and color excesses for different types of stars. In the determination of color excess ratios (CERs), we analyze and correct the curvature of CERs and derive the UV--mid-IR CERs of 16 bands. We consider different effective wavelengths for different types of stars when converting CERs to relative extinction, and obtain the extinction law with a better wavelength resolution. In addition, we analyze the possible regional variation of extinction law and derive the average extinction law of Rv=3.13+-0.32 for the Taurus molecular cloud. Only 0.9% of subregions have deviations >3sigma, indicating limited regional variation in the extinction law. We also discuss the effect of Gaia Teff overestimation on the determination of the Taurus extinction law and find that the effect is negligible.

astro-ph.GA↗
arXiv2023

Double-mode RR Lyrae star -- robust distance and metallicity indicators

RR Lyrae (RR Lyr) stars are a well-known and useful distance indicator for old stellar populations such as globular clusters and dwarf galaxies. Fundamental-mode RR Lyr (RRab) stars are commonly used to measure distances, and the accuracy of the determined distance is strongly constrained by metallicity. Here, we investigate the metallicity dependence in the period-luminosity (PL) relation of double-mode RR Lyr (RRd) stars. We find and establish a linear relation between metallicity and period or period ratio for RRd stars. This relation can predict the metallicity as accurately as the low-resolution spectra. Based on this relation, we establish a metallicity-independent PL relation for RRd stars. Combining the distance of the Large Magellanic Cloud and Gaia parallaxes, we calibrate the zero point of the derived PL relation to an error of 0.022 mag. Using RRd stars, we measure the distances of globular clusters and dwarf galaxies with an accuracy of 2-3% and 1-2%, respectively. In the future, RRd stars could anchor galaxy distances to an accuracy of 1.0% and become an independent distance ladder in the Local Group.

astro-ph.SR↗
arXiv2023

Exploring Security Commits in Python

Python has become the most popular programming language as it is friendly to work with for beginners. However, a recent study has found that most security issues in Python have not been indexed by CVE and may only be fixed by 'silent' security commits, which pose a threat to software security and hinder the security fixes to downstream software. It is critical to identify the hidden security commits; however, the existing datasets and methods are insufficient for security commit detection in Python, due to the limited data variety, non-comprehensive code semantics, and uninterpretable learned features. In this paper, we construct the first security commit dataset in Python, namely PySecDB, which consists of three subsets including a base dataset, a pilot dataset, and an augmented dataset. The base dataset contains the security commits associated with CVE records provided by MITRE. To increase the variety of security commits, we build the pilot dataset from GitHub by filtering keywords within the commit messages. Since not all commits provide commit messages, we further construct the augmented dataset by understanding the semantics of code changes. To build the augmented dataset, we propose a new graph representation named CommitCPG and a multi-attributed graph learning model named SCOPY to identify the security commit candidates through both sequential and structural code semantics. The evaluation shows our proposed algorithms can improve the data collection efficiency by up to 40 percentage points. After manual verification by three security experts, PySecDB consists of 1,258 security commits and 2,791 non-security commits. Furthermore, we conduct an extensive case study on PySecDB and discover four common security fix patterns that cover over 85% of security commits in Python, providing insight into secure software maintenance, vulnerability detection, and automated program repair.

cs.CR↗
arXiv2023

The Seoul National University AGN Monitoring Project IV: H$α$ reverberation mapping of 6 AGNs and the H$α$ Size-Luminosity Relation

The broad line region (BLR) size-luminosity relation has paramount importance for estimating the mass of black holes in active galactic nuclei (AGNs). Traditionally, the size of the H$β$ BLR is often estimated from the optical continuum luminosity at 5100\angstrom{} , while the size of the H$α$ BLR and its correlation with the luminosity is much less constrained. As a part of the Seoul National University AGN Monitoring Project (SAMP) which provides six-year photometric and spectroscopic monitoring data, we present our measurements of the H$α$ lags of 6 high-luminosity AGNs. Combined with the measurements for 42 AGNs from the literature, we derive the size-luminosity relations of H$α$ BLR against broad H$α$ and 5100\angstrom{} continuum luminosities. We find the slope of the relations to be $0.61\pm0.04$ and $0.59\pm0.04$, respectively, which are consistent with the \hb{} size-luminosity relation. Moreover, we find a linear relation between the 5100\angstrom{} continuum luminosity and the broad H$α$ luminosity across 7 orders of magnitude. Using these results, we propose a new virial mass estimator based on the H$α$ broad emission line, finding that the previous mass estimates based on the scaling relations in the literature are overestimated by up to 0.7 dex at masses lower than $10^7$~M$_{\odot}$.

astro-ph.GA↗
arXiv2023

Investigation of Stellar Kinematics and Ionized gas Outflows in Local (U)LIRGs

We explore properties of stellar kinematics and ionized gas in a sample of 1106 local (U)LIRGs from the AKARI telescope. We combine data from $Wide-field\ Infrared\ Survey\ Explorer$ (WISE) and Sloan Digital Sky Survey (SDSS) Data Release 13 (DR13) to fit the spectral energy distribution (SED) of each source to constrain the contribution of active galactic nuclei (AGNs) to the total IR luminosity and estimate physical parameters such as stellar mass and star-formation rate (SFR). We split our sample into AGNs and weak/non-AGNs. We find that our sample is considerably above the main sequence. The highest SFRs and stellar masses are associated with ULIRGs. We also fit the H$β$ and H$α$ regions to characterize the outflows. We find that the incidence of ionized gas outflows in AGN (U)LIRGs ($\sim$ 72\%) is much higher than that in weak/non-AGN ones ($\sim$ 39\%). The AGN ULIRGs have extreme outflow velocities (up to $\sim$ 2300 km s$^{-1}$) and high mass-outflow rates (up to $\sim$ 60 \solarm~yr$^{-1}$). Our results suggest that starbursts are insufficient to produce such powerful outflows. We explore the correlations of SFR and specific SFR (sSFR) with ionized gas outflows. We find that AGN hosts with the highest SFRs exhibit a negative correlation between outflow velocity and sSFR. Therefore, in AGNs containing large amounts of gas, the negative feedback scenario might be suggested.

astro-ph.GA↗
arXiv2023

The use of double-mode RR Lyrae stars as robust distance and metallicity indicators

RR Lyrae stars are one of the primary distance indicators for old stellar populations such as globular clusters, dwarf galaxies and galaxies. Typically, fundamental-mode RR Lyr stars are used for distance measurements, and their accuracy is strongly limited by the dependence of absolute magnitudes on metallicity, in both the optical and infrared bands. Here, we report the discovery of a period-(period ratio)-metallicity relation for double-mode RR Lyr stars, which can predict metallicity as accurately as the low-resolution spectra. With theoretical and observational evidence, we propose that the period-luminosity relation of double-mode RR Lyr stars is not affected by the metallicity. Combining the Large Magellanic Cloud distance and Gaia parallaxes, we calibrate the zero point of the period-luminosity relation to an error of 0.022 mag, which means that in the best case double-mode RR Lyr stars can anchor galaxy distances to an accuracy of 1.0%. For four globular clusters and two dwarf galaxies, we obtain distances using double-mode RR Lyr stars with a distance accuracy of 2-3% and 1-2%, respectively. With future telescopes such as the China Space Station Telescope and the Vera C. Rubin Observatory, double-mode RR Lyr stars will be established as an independent distance ladder in the near-field universe.

astro-ph.SR↗
arXiv2023

Asymmetric Patch Sampling for Contrastive Learning

Asymmetric appearance between positive pair effectively reduces the risk of representation degradation in contrastive learning. However, there are still a mass of appearance similarities between positive pair constructed by the existing methods, which inhibits the further representation improvement. In this paper, we propose a novel asymmetric patch sampling strategy for contrastive learning, to further boost the appearance asymmetry for better representations. Specifically, dual patch sampling strategies are applied to the given image, to obtain asymmetric positive pairs. First, sparse patch sampling is conducted to obtain the first view, which reduces spatial redundancy of image and allows a more asymmetric view. Second, a selective patch sampling is proposed to construct another view with large appearance discrepancy relative to the first one. Due to the inappreciable appearance similarity between positive pair, the trained model is encouraged to capture the similarity on semantics, instead of low-level ones. Experimental results demonstrate that our proposed method significantly outperforms the existing self-supervised methods on both ImageNet-1K and CIFAR dataset, e.g., 2.5% finetune accuracy improvement on CIFAR100. Furthermore, our method achieves state-of-the-art performance on downstream tasks, object detection and instance segmentation on COCO.Additionally, compared to other self-supervised methods, our method is more efficient on both memory and computation during training. The source code is available at https://github.com/visresearch/aps.

cs.CV↗
arXiv2023

Estimating AGN Black Hole Masses via Continuum Reverberation Mapping in the Era of LSST

Spectroscopic reverberation mapping (RM) is a direct approach widely used to estimate the mass of black holes (BHs) in active galactic nuclei (AGNs). However, it is very time consuming and difficult to apply to a large AGN sample. The empirical relation between the broad-line region size and luminosity (H$β$ $R_{\rm BLR}\unicode{x2013}L$) provides a practical alternative yet is subject to large scatter and systematic bias. Based on the relation between the continuum emitting region size and luminosity ($R_{\rm CER}\unicode{x2013}L$) reported by Netzer (2022), we present a new BH mass estimator via continuum RM (CRM) by comparing $R_{\rm CER}$ and $R_{\rm BLR}$, assuming that the continuum lags are dominated by the diffuse continuum emission. Using a sample of 21 AGNs, we find a tight $R_{\rm BLR}\unicode{x2013}R_{\rm CER}$ relation (scatter$\sim$0.28 dex) and that $R_{\rm BLR}$ is larger than $R_{\rm CER}$ at 5100 Å by an average factor of 8.1. This tight relation enables the BH mass estimation based on the CRM combined with the velocity information. Applying the relation to rest objects in our CRM sample, we demonstrate that the predicted $R_{\rm BLR,CRM}$ follows the existing H$β$ $R_{\rm BLR}\unicode{x2013}L$ relation well and the estimated CRM BH masses are consistent with the RM/single-epoch BH masses using H$β$. This method will provide significant applications for BH mass estimation thanks to the short continuum lags and the easily accessible high-cadence, large-area photometric data, especially in the era of Legacy Survey of Space and Time.

astro-ph.GA↗
arXiv2023

Evolved Massive Stars at Low-metallicity V. Mass-Loss Rate of Red Supergiant Stars in the Small Magellanic Cloud

We assemble the most complete and clean red supergiant (RSG) sample (2,121 targets) so far in the Small Magellanic Cloud (SMC) with 53 different bands of data to study the MLR of RSGs. In order to match the observed spectral energy distributions (SEDs), a theoretical grid of 17,820 Oxygen-rich models (``normal'' and ``dusty'' grids are half-and-half) is created by the radiatively-driven wind model of the DUSTY code, covering a wide range of dust parameters. We select the best model for each target by calculating the minimal modified chi-square and visual inspection. The resulting MLRs from DUSTY are converted to real MLRs based on the scaling relation, for which a total MLR of $6.16\times10^{-3}$ $M_\odot$ yr$^{-1}$ is measured (corresponding to a dust-production rate of $\sim6\times10^{-6}$ $M_\odot$ yr$^{-1}$), with a typical MLR of $\sim10^{-6}$ $M_\odot$ yr$^{-1}$ for the general population of the RSGs. The complexity of mass-loss estimation based on the SED is fully discussed for the first time, indicating large uncertainties based on the photometric data (potentially up to one order of magnitude or more). The Hertzsprung-Russell and luminosity versus median absolute deviation diagrams of the sample indicate the positive relation between luminosity and MLR. Meanwhile, the luminosity versus MLR diagrams show a ``knee-like'' shape with enhanced mass-loss occurring above $\log_{10}(L/L_\odot)\approx4.6$, which may be due to the degeneracy of luminosity, pulsation, low surface gravity, convection, and other factors. We derive our MLR relation by using a third-order polynomial to fit the sample and compare our result with previous empirical MLR prescriptions. Given that our MLR prescription is based on a much larger sample than previous determinations, it provides a more accurate relation at the cool and luminous region of the H-R diagram at low-metallicity compared to previous studies.

astro-ph.SR↗
arXiv2023

The Optical to Infrared Extinction Law of Magellanic Clouds Based on Red Supergiant and Classical Cepheid

Precise interstellar dust extinction laws are important to infer the intrinsic properties of reddened objects and correctly interpret observations. In this work, we attempt to measure the optical--infrared extinction laws of the Large and Small Magellanic Clouds (LMC and SMC) by using red supergiant (RSG) stars and classical Cepheids as extinction tracers. The spectroscopic RSG samples are constructed based on the APOGEE spectral parameters, Gaia astrometric data, and multi-band photometry. We establish the effective temperature--intrinsic color relations for RSG stars and determine the color excess ratio (CER) E(lambda-GRP)/E(GBP-GRP) for LMC and SMC. We use classical Cepheids to derive base relative extinction A_GRP/E(GBP-GRP). The results are 1.589+-0.014 and 1.412+-0.041 for LMC and SMC. By combining CERs with A_GRP /E(GBP-GRP), the optical--infrared extinction coefficients A_lambda/A_GRP are determined for 16 bands. We adjust the parameters of Rv-dependent extinction laws and obtain the average extinction law of LMC and SMC as Rv=3.40+-0.07 and Rv=2.53+-0.10, which are consistent with Gordon et al. (2003). In the optical bands, the adjusted Rv extinction curves agree with the observations with deviations less than 3%.

astro-ph.GA↗
arXiv2023

PatchRNN: A Deep Learning-Based System for Security Patch Identification

With the increasing usage of open-source software (OSS) components, vulnerabilities embedded within them are propagated to a huge number of underlying applications. In practice, the timely application of security patches in downstream software is challenging. The main reason is that such patches do not explicitly indicate their security impacts in the documentation, which would be difficult to recognize for software maintainers and users. However, attackers can still identify these "secret" security patches by analyzing the source code and generate corresponding exploits to compromise not only unpatched versions of the current software, but also other similar software packages that may contain the same vulnerability due to code cloning or similar design/implementation logic. Therefore, it is critical to identify these secret security patches to enable timely fixes. To this end, we propose a deep learning-based defense system called PatchRNN to automatically identify secret security patches in OSS. Besides considering descriptive keywords in the commit message (i.e., at the text level), we leverage both syntactic and semantic features at the source-code level. To evaluate the performance of our system, we apply it on a large-scale real-world patch dataset and conduct a case study on a popular open-source web server software - NGINX. Experimental results show that the PatchRNN can successfully detect secret security patches with a low false positive rate.

cs.CR↗
arXiv2022

Demographics of z ~ 6 Quasars in the Black Hole Mass-Luminosity Plane

We study the demographics of z ~ 6 broad-line quasars in the black hole (BH) mass-luminosity plane using a sample of more than 100 quasars at 5.7 < z < 6.5. These quasars have well quantified selection functions and nearly one third of them also have virial BH masses estimated from near-IR spectroscopy. We use forward modeling of parameterized intrinsic distributions of BH masses and Eddington ratios, and account for the sample flux limits and measurement uncertainties of the BH masses and luminosities. We find significant differences between the intrinsic and observed distributions of the quantities due to measurement uncertainties and sample flux limits. There is also marginal evidence that the virial BH masses are susceptible to a positive luminosity-dependent bias (BH mass is overestimated when luminosity is above the average), and that the mean Eddington ratio increases with BH mass. Our models provide reliable constraints on the z ~ 6 black hole mass function at M_BH > 10^8.5 M_Sun, with a median 1-sigma uncertainty of ~0.5 dex in abundance. The intrinsic Eddington ratio distribution of M_BH > 10^8.5 M_Sun quasars can be approximated by a mass-dependent Schechter model, with a broad peak around log(L_bol/L_Edd}) ~ -0.9. We also find that, at 4.5 < z < 6, the number densities of more massive BHs tend to decline more rapidly with increasing redshift, contrary to the trend at 2.5 < z < 4.5 reported previously.

astro-ph.GA↗
arXiv2022

AGN Continuum Reverberation Mapping Based on Zwicky Transient Facility Light Curves

We perform a systematic survey of active galactic nucleus (AGN) continuum lags using $\sim$3 day cadence $gri$-band light curves from the Zwicky Transient Facility. We select a sample of 94 type 1 AGN at $z<0.8$ with significant and consistent inter-band lags based on the interpolated cross-correlation function method and the Bayesian method JAVELIN. Within the framework of the lamp-post reprocessing model, our findings are: 1) The continuum emission (CE) sizes inferred from the data are larger than the disk sizes predicted by the standard thin disk model; 2) For a subset of the sample, the CE size exceeds the theoretical limit of the self-gravity radius (12 lt-days) for geometrically thin disks; 3) The CE size scales with continuum luminosity as $R_{\mathrm{CE}} \propto L^{0.48\pm0.04}$ with a scatter of 0.2 dex, analogous to the well-known radius-luminosity relation of broad H$\mathrmβ$. These findings suggest a significant contribution of diffuse continuum emission from the broad-line region (BLR) to AGN continuum lags. We find that the $R_{\mathrm{CE}}-L$ relation can be explained by a photoionization model that assumes $\sim$23% of the total flux comes from the diffuse BLR emission. In addition, the ratio of the CE size and model-predicted disk size anti-correlates with the continuum luminosity, indicative of a potential non-disk BLR lag contribution evolving with luminosity. Finally, a robust positive correlation between CE size and black hole mass is detected.

astro-ph.GA↗
arXiv2022

Multi Receptive Field Network for Semantic Segmentation

Semantic segmentation is one of the key tasks in computer vision, which is to assign a category label to each pixel in an image. Despite significant progress achieved recently, most existing methods still suffer from two challenging issues: 1) the size of objects and stuff in an image can be very diverse, demanding for incorporating multi-scale features into the fully convolutional networks (FCNs); 2) the pixels close to or at the boundaries of object/stuff are hard to classify due to the intrinsic weakness of convolutional networks. To address the first issue, we propose a new Multi-Receptive Field Module (MRFM), explicitly taking multi-scale features into account. For the second issue, we design an edge-aware loss which is effective in distinguishing the boundaries of object/stuff. With these two designs, our Multi Receptive Field Network achieves new state-of-the-art results on two widely-used semantic segmentation benchmark datasets. Specifically, we achieve a mean IoU of 83.0 on the Cityscapes dataset and 88.4 mean IoU on the Pascal VOC2012 dataset.

cs.CV↗
arXiv2022

Mean-field limit of a particle approximation for the parabolic-parabolic Keller-Segel model

In this paper, we study propagation of chaos for the parabolic-parabolic Keller-Segel model with a logarithmic cut-off by establishing a rigorous convergence analysis from a stochastic particle system to the parabolic-parabolic Keller-Segel (KS) equation for any dimension case. Under the assumption that the initial data are independent and identically distributed (i.i.d.) with a common probability density function $ρ_0$, we rigorously prove the propagation of chaos for this interacting system with a cut-off parameter $\varepsilon\sim (\ln N)^{-\frac{2}{d+2}}$: when $N\rightarrow \infty$, the joint distribution of the particle system is $f$-chaotic and the measure $f$ possesses a density which is a weak solution to the mean-field parabolic-parabolic KS equation.

math.AP↗
arXiv2022

A Simulation Study Evaluating Phase I Clinical Trial Designs for Combinational Agents

Nowadays, more and more clinical trials choose combinational agents as the intervention to achieve better therapeutic responses. However, dose-finding for combinational agents is much more complicated than single agent as the full order of combination dose toxicity is unknown. Therefore, regular phase I designs are not able to identify the maximum tolerated dose (MTD) of combinational agents. Motivated by such needs, plenty of novel phase I clinical trial designs for combinational agents were proposed. With so many available designs, research that compare their performances, explore parameters' impacts, and provide recommendations is very limited. Therefore, we conducted a simulation study to evaluate multiple phase I designs that proposed to identify single MTD for combinational agents under various scenarios. We also explored influences of different design parameters. In the end, we summarized the pros and cons of each design, and provided a general guideline in design selection.

stat.AP↗