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Kang Liu

Kang Liu appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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Computation and LanguageMachine LearningArtificial Intelligenceastro-ph.SRCryptography and SecurityInformation Retrievalastro-ph.GAComputer Visionphysics.soc-phastro-ph.EPcs.CYeess.SYFormal Languages and Automata TheoryInformation Theorymath.ITNeural and Evolutionary Computingq-fin.GNRoboticsSystems and Control

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Published work

24 published item(s)

preprint2026arXiv

DV-World: Benchmarking Data Visualization Agents in Real-World Scenarios

Real-world data visualization (DV) requires native environmental grounding, cross-platform evolution, and proactive intent alignment. Yet, existing benchmarks often suffer from code-sandbox confinement, single-language creation-only tasks, and assumption of perfect intent. To bridge these gaps, we introduce DV-World, a benchmark of 260 tasks designed to evaluate DV agents across real-world professional lifecycles. DV-World spans three domains: DV-Sheet for native spreadsheet manipulation including chart and dashboard creation as well as diagnostic repair; DV-Evolution for adapting and restructuring reference visual artifacts to fit new data across diverse programming paradigms and DV-Interact for proactive intent alignment with a user simulator that mimics real-world ambiguous requirements. Our hybrid evaluation framework integrates Table-value Alignment for numerical precision and MLLM-as-a-Judge with rubrics for semantic-visual assessment. Experiments reveal that state-of-the-art models achieve less than 50% overall performance, exposing critical deficits in handling the complex challenges of real-world data visualization. DV-World provides a realistic testbed to steer development toward the versatile expertise required in enterprise workflows. Our data and code are available at \href{https://github.com/DA-Open/DV-World}{this project page}.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules

Knowledge graphs (KGs) play a crucial role in many applications, such as question answering, but incompleteness is an urgent issue for their broad application. Much research in knowledge graph completion (KGC) has been performed to resolve this issue. The methods of KGC can be classified into two major categories: rule-based reasoning and embedding-based reasoning. The former has high accuracy and good interpretability, but a major challenge is to obtain effective rules on large-scale KGs. The latter has good efficiency and scalability, but it relies heavily on data richness and cannot fully use domain knowledge in the form of logical rules. We propose a novel method that injects rules and learns representations iteratively to take full advantage of rules and embeddings. Specifically, we model the conclusions of rule groundings as 0-1 variables and use a rule confidence regularizer to remove the uncertainty of the conclusions. The proposed approach has the following advantages: 1) It combines the benefits of both rules and knowledge graph embeddings (KGEs) and achieves a good balance between efficiency and scalability. 2) It uses an iterative method to continuously improve KGEs and remove incorrect rule conclusions. Evaluations on two public datasets show that our method outperforms the current state-of-the-art methods, improving performance by 2.7\% and 4.3\% in mean reciprocal rank (MRR).

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A New High Energy Efficiency Scheme Based on Two-Dimension Resource Blocks in Wireless Communication Systems

Energy efficiency (EE) plays a key role in future wireless communication network and it is easily to achieve high EE performance in low SNR regime. In this paper, a new high EE scheme is proposed for a MIMO wireless communication system working in the low SNR regime by using two dimension resource allocation. First, we define the high EE area based on the relationship between the transmission power and the SNR. To meet the constraint of the high EE area, both frequency and space dimension are needed. Besides analysing them separately, we decided to consider frequency and space dimensions as a unit and proposed a two-dimension scheme. Furthermore, considering communication in the high EE area may cause decline of the communication quality, we add quality-of-service(QoS) constraint into the consideration and derive the corresponding EE performance based on the effective capacity. We also derive an approximate expression to simplify the complex EE performance. Finally, our numerical results demonstrate the effectiveness of the proposed scheme.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Impact of initial outbreak locations on transmission risk of infectious diseases in an intra-urban area

Infectious diseases usually originate from a specific location within a city. Due to the heterogenous distribution of population and public facilities, and the structural heterogeneity of human mobility network embedded in space, infectious diseases break out at different locations would cause different transmission risk and control difficulty. This study aims to investigate the impact of initial outbreak locations on the risk of spatiotemporal transmission and reveal the driving force behind high-risk outbreak locations. First, integrating mobile phone location data, we built a SLIR (susceptible-latent-infectious-removed)-based meta-population model to simulate the spreading process of an infectious disease (i.e., COVID-19) across fine-grained intra-urban regions (i.e., 649 communities of Shenzhen City, China). Based on the simulation model, we evaluated the transmission risk caused by different initial outbreak locations by proposing three indexes including the number of infected cases (CaseNum), the number of affected regions (RegionNum), and the spatial diffusion range (SpatialRange). Finally, we investigated the contribution of different influential factors to the transmission risk via machine learning models. Results indicates that different initial outbreak locations would cause similar CaseNum but different RegionNum and SpatialRange. To avoid the epidemic spread quickly to more regions, it is necessary to prevent epidemic breaking out in locations with high population-mobility flow density. While to avoid epidemic spread to larger spatial range, remote regions with long daily trip distance of residents need attention. Those findings can help understand the transmission risk and driving force of initial outbreak locations within cities and make precise prevention and control strategies in advance.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

MALICE: Manipulation Attacks on Learned Image ComprEssion

Deep learning techniques have shown promising results in image compression, with competitive bitrate and image reconstruction quality from compressed latent. However, while image compression has progressed towards a higher peak signal-to-noise ratio (PSNR) and fewer bits per pixel (bpp), their robustness to adversarial images has never received deliberation. In this work, we, for the first time, investigate the robustness of image compression systems where imperceptible perturbation of input images can precipitate a significant increase in the bitrate of their compressed latent. To characterize the robustness of state-of-the-art learned image compression, we mount white-box and black-box attacks. Our white-box attack employs fast gradient sign method on the entropy estimation of the bitstream as its bitrate approximation. We propose DCT-Net simulating JPEG compression with architectural simplicity and lightweight training as the substitute in the black-box attack and enable fast adversarial transferability. Our results on six image compression models, each with six different bitrate qualities (thirty-six models in total), show that they are surprisingly fragile, where the white-box attack achieves up to 56.326x and black-box 1.947x bpp change. To improve robustness, we propose a novel compression architecture factorAtn which incorporates attention modules and a basic factorized entropy model, resulting in a promising trade-off between the rate-distortion performance and robustness to adversarial attacks that surpasses existing learned image compressors.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

A Novel Actuation Strategy for an Agile Bio-inspired FWAV Performing a Morphing-coupled Wingbeat Pattern

Flying vertebrates exhibit sophisticated wingbeat kinematics. Their specialized forelimbs allow for the wing morphing motion to couple with the flapping motion during their level flight, Previous flyable bionic platforms have successfully applied bio-inspired wing morphing but cannot yet be propelled by the morphing-coupled wingbeat pattern. Spurred by this, we develop a bio-inspired flapping-wing aerial vehicle (FWAV) entitled RoboFalcon, which is equipped with a novel mechanism to drive the bat-style morphing wings, performs a morphing-coupled wingbeat pattern, and overall manages an appealing flight. The novel mechanism of RoboFalcon allows coupling the morphing and flapping during level flight and decoupling these when maneuvering is required, producing a bilateral asymmetric downstroke affording high rolling agility. The bat-style morphing wing is designed with a tilted mounting angle around the radius at the wrist joint to mimic the wrist supination and pronation effect of flying vertebrates' forelimbs. The agility of RoboFalcon is assessed through several rolling maneuver flight tests, and we demonstrate its well-performing agility capability compared to flying creatures and current flapping-wing platforms. Wind tunnel tests indicate that the roll moment of the asymmetric downstroke is correlated with the flapping frequency, and the wrist mounting angle can be used for tuning the angle of attack and lift-thrust configuration of the equilibrium flight state. We believe that this work yields a well-performing bionic platform and provides a new actuation strategy for the morphing-coupled flapping flight.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Bias Busters: Robustifying DL-based Lithographic Hotspot Detectors Against Backdooring Attacks

Deep learning (DL) offers potential improvements throughout the CAD tool-flow, one promising application being lithographic hotspot detection. However, DL techniques have been shown to be especially vulnerable to inference and training time adversarial attacks. Recent work has demonstrated that a small fraction of malicious physical designers can stealthily "backdoor" a DL-based hotspot detector during its training phase such that it accurately classifies regular layout clips but predicts hotspots containing a specially crafted trigger shape as non-hotspots. We propose a novel training data augmentation strategy as a powerful defense against such backdooring attacks. The defense works by eliminating the intentional biases introduced in the training data but does not require knowledge of which training samples are poisoned or the nature of the backdoor trigger. Our results show that the defense can drastically reduce the attack success rate from 84% to ~0%.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Connecting Embeddings for Knowledge Graph Entity Typing

Knowledge graph (KG) entity typing aims at inferring possible missing entity type instances in KG, which is a very significant but still under-explored subtask of knowledge graph completion. In this paper, we propose a novel approach for KG entity typing which is trained by jointly utilizing local typing knowledge from existing entity type assertions and global triple knowledge from KGs. Specifically, we present two distinct knowledge-driven effective mechanisms of entity type inference. Accordingly, we build two novel embedding models to realize the mechanisms. Afterward, a joint model with them is used to infer missing entity type instances, which favors inferences that agree with both entity type instances and triple knowledge in KGs. Experimental results on two real-world datasets (Freebase and YAGO) demonstrate the effectiveness of our proposed mechanisms and models for improving KG entity typing. The source code and data of this paper can be obtained from: https://github.com/ Adam1679/ConnectE

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Distantly Supervised Relation Extraction in Federated Settings

This paper investigates distantly supervised relation extraction in federated settings. Previous studies focus on distant supervision under the assumption of centralized training, which requires collecting texts from different platforms and storing them on one machine. However, centralized training is challenged by two issues, namely, data barriers and privacy protection, which make it almost impossible or cost-prohibitive to centralize data from multiple platforms. Therefore, it is worthy to investigate distant supervision in the federated learning paradigm, which decouples the model training from the need for direct access to the raw data. Overcoming label noise of distant supervision, however, becomes more difficult in federated settings, since the sentences containing the same entity pair may scatter around different platforms. In this paper, we propose a federated denoising framework to suppress label noise in federated settings. The core of this framework is a multiple instance learning based denoising method that is able to select reliable instances via cross-platform collaboration. Various experimental results on New York Times dataset and miRNA gene regulation relation dataset demonstrate the effectiveness of the proposed method.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Event Coreference Resolution via a Multi-loss Neural Network without Using Argument Information

Event coreference resolution(ECR) is an important task in Natural Language Processing (NLP) and nearly all the existing approaches to this task rely on event argument information. However, these methods tend to suffer from error propagation from the stage of event argument extraction. Besides, not every event mention contains all arguments of an event, and argument information may confuse the model that events have arguments to detect event coreference in real text. Furthermore, the context information of an event is useful to infer the coreference between events. Thus, in order to reduce the errors propagated from event argument extraction and use context information effectively, we propose a multi-loss neural network model that does not need any argument information to do the within-document event coreference resolution task and achieve a significant performance than the state-of-the-art methods.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Knowledge Guided Metric Learning for Few-Shot Text Classification

The training of deep-learning-based text classification models relies heavily on a huge amount of annotation data, which is difficult to obtain. When the labeled data is scarce, models tend to struggle to achieve satisfactory performance. However, human beings can distinguish new categories very efficiently with few examples. This is mainly due to the fact that human beings can leverage knowledge obtained from relevant tasks. Inspired by human intelligence, we propose to introduce external knowledge into few-shot learning to imitate human knowledge. A novel parameter generator network is investigated to this end, which is able to use the external knowledge to generate relation network parameters. Metrics can be transferred among tasks when equipped with these generated parameters, so that similar tasks use similar metrics while different tasks use different metrics. Through experiments, we demonstrate that our method outperforms the state-of-the-art few-shot text classification models.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Magnetic Activity of F-, G-, and K-type Stars in the LAMOST-Kepler Field

Monitoring chromospheric and photospheric indexes of magnetic activity can provide valuable information, especially the interaction between different parts of the atmosphere and their response to magnetic fields. We extract chromospheric indexes, S and Rhk+, for 59,816 stars from LAMOST spectra in the LAMOST-Kepler program, and photospheric index, Reff, for 5575 stars from Kepler light curves. The log Reff shows positive correlation with log Rhk+. We estimate the power-law indexes between Reff and Rhk+ for F-, G-, and K-type stars, respectively. We also confirm the dependence of both chromospheric and photospheric activity on stellar rotation. Ca II H and K emissions and photospheric variations generally decrease with increasing rotation periods for stars with rotation periods exceeding a few days. The power-law indexes in exponential decay regimes show different characteristics in the two activity-rotation relations. The updated largest sample including the activity proxies and reported rotation periods provides more information to understand the magnetic activity for cool stars.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA

Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks. CCKS 2019 held an evaluation track with 6 tasks and attracted more than 1,600 teams. In this paper, we give an overview of the knowledge graph evaluation tract at CCKS 2019. By reviewing the task definition, successful methods, useful resources, good strategies and research challenges associated with each task in CCKS 2019, this paper can provide a helpful reference for developing knowledge graph applications and conducting future knowledge graph researches.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

RGCF: Refined Graph Convolution Collaborative Filtering with concise and expressive embedding

Graph Convolution Network (GCN) has attracted significant attention and become the most popular method for learning graph representations. In recent years, many efforts have been focused on integrating GCN into the recommender tasks and have made remarkable progress. At its core is to explicitly capture high-order connectivities between the nodes in user-item bipartite graph. However, we theoretically and empirically find an inherent drawback existed in these GCN-based recommendation methods, where GCN is directly applied to aggregate neighboring nodes will introduce noise and information redundancy. Consequently, the these models' capability of capturing high-order connectivities among different nodes is limited, leading to suboptimal performance of the recommender tasks. The main reason is that the the nonlinear network layer inside GCN structure is not suitable for extracting non-sematic features(such as one-hot ID feature) in the collaborative filtering scenarios. In this work, we develop a new GCN-based Collaborative Filtering model, named Refined Graph convolution Collaborative Filtering(RGCF), where the construction of the embeddings of users (items) are delicately redesigned from several aspects during the aggregation on the graph. Compared to the state-of-the-art GCN-based recommendation, RGCF is more capable for capturing the implicit high-order connectivities inside the graph and the resultant vector representations are more expressive. We conduct extensive experiments on three public million-size datasets, demonstrating that our RGCF significantly outperforms state-of-the-art models. We release our code at https://github.com/hfutmars/RGCF.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Subverting Privacy-Preserving GANs: Hiding Secrets in Sanitized Images

Unprecedented data collection and sharing have exacerbated privacy concerns and led to increasing interest in privacy-preserving tools that remove sensitive attributes from images while maintaining useful information for other tasks. Currently, state-of-the-art approaches use privacy-preserving generative adversarial networks (PP-GANs) for this purpose, for instance, to enable reliable facial expression recognition without leaking users' identity. However, PP-GANs do not offer formal proofs of privacy and instead rely on experimentally measuring information leakage using classification accuracy on the sensitive attributes of deep learning (DL)-based discriminators. In this work, we question the rigor of such checks by subverting existing privacy-preserving GANs for facial expression recognition. We show that it is possible to hide the sensitive identification data in the sanitized output images of such PP-GANs for later extraction, which can even allow for reconstruction of the entire input images, while satisfying privacy checks. We demonstrate our approach via a PP-GAN-based architecture and provide qualitative and quantitative evaluations using two public datasets. Our experimental results raise fundamental questions about the need for more rigorous privacy checks of PP-GANs, and we provide insights into the social impact of these.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Are Adversarial Perturbations a Showstopper for ML-Based CAD? A Case Study on CNN-Based Lithographic Hotspot Detection

There is substantial interest in the use of machine learning (ML) based techniques throughout the electronic computer-aided design (CAD) flow, particularly those based on deep learning. However, while deep learning methods have surpassed state-of-the-art performance in several applications, they have exhibited intrinsic susceptibility to adversarial perturbations --- small but deliberate alterations to the input of a neural network, precipitating incorrect predictions. In this paper, we seek to investigate whether adversarial perturbations pose risks to ML-based CAD tools, and if so, how these risks can be mitigated. To this end, we use a motivating case study of lithographic hotspot detection, for which convolutional neural networks (CNN) have shown great promise. In this context, we show the first adversarial perturbation attacks on state-of-the-art CNN-based hotspot detectors; specifically, we show that small (on average 0.5% modified area), functionality preserving and design-constraint satisfying changes to a layout can nonetheless trick a CNN-based hotspot detector into predicting the modified layout as hotspot free (with up to 99.7% success). We propose an adversarial retraining strategy to improve the robustness of CNN-based hotspot detection and show that this strategy significantly improves robustness (by a factor of ~3) against adversarial attacks without compromising classification accuracy.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

FGK 22 $μ$m Excess Stars in LAMOST DR2 Stellar Catalog

Since the release of LAMOST (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope) catalog, we have the opportunity to use the LAMOST DR2 stellar catalog and \emph{WISE All-sky catalog} to search for 22 $μ$m excess candidates. In this paper, we present 10 FGK candidates which show an excess in the infrared (IR) at 22 $μ$m. The ten sources are all the newly identified 22 $μ$m excess candidates. Of these 10 stars, 5 stars are F type and 5 stars are G type. The criterion for selecting candidates is $K_s-[22]_{μm}\geq0.387$. In addition, we present the spectral energy distributions (SEDs) covering wavelength from optical to mid-infrared band. Most of them show an obvious excess from 12 $μ$m band and three candidates even show excess from 3.4 $μ$m. To characterize the amount of dust, we also estimate the fractional luminosity of ten 22 $μ$m excess candidates.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Question Answering over Knowledge Base with Neural Attention Combining Global Knowledge Information

With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important. Knowledge base-based question answering (KB-QA) is one of the most promising approaches to access the substantial knowledge. Meantime, as the neural network-based (NN-based) methods develop, NN-based KB-QA has already achieved impressive results. However, previous work did not put emphasis on question representation, and the question is converted into a fixed vector regardless of its candidate answers. This simple representation strategy is unable to express the proper information of the question. Hence, we present a neural attention-based model to represent the questions dynamically according to the different focuses of various candidate answer aspects. In addition, we leverage the global knowledge inside the underlying KB, aiming at integrating the rich KB information into the representation of the answers. And it also alleviates the out of vocabulary (OOV) problem, which helps the attention model to represent the question more precisely. The experimental results on WEBQUESTIONS demonstrate the effectiveness of the proposed approach.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

How to Generate a Good Word Embedding?

We analyze three critical components of word embedding training: the model, the corpus, and the training parameters. We systematize existing neural-network-based word embedding algorithms and compare them using the same corpus. We evaluate each word embedding in three ways: analyzing its semantic properties, using it as a feature for supervised tasks and using it to initialize neural networks. We also provide several simple guidelines for training word embeddings. First, we discover that corpus domain is more important than corpus size. We recommend choosing a corpus in a suitable domain for the desired task, after that, using a larger corpus yields better results. Second, we find that faster models provide sufficient performance in most cases, and more complex models can be used if the training corpus is sufficiently large. Third, the early stopping metric for iterating should rely on the development set of the desired task rather than the validation loss of training embedding.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Exploring the sources of p-mode frequency shifts in the CoRoT target HD 49933

The oscillations of the solar-like star HD 49933 have been observed thoroughly by CoRot. Two dozens of frequency shifts, which are closely related with the change in magnetic activity, have been measured. To explore the effects of the magnetic activity on the frequency shifts, we calculate frequency shifts for the radial and $l = 1$ p-modes of HD 49933 with the general variational method, which evaluates the shifts using a spatial integral of the product of a kernel and some sources. The theoretical frequency shifts well reproduce the observation. The magnitudes and positions of the sources are determined according to the $χ^2$ criterion. We predict the source that contributes to both $l = 0$ and $l = 1$ modes is located at $0.48 - 0.62$Mm below the stellar surface. In addition, based on the assumption that $A_{0}$ is proportional to the change in the MgII activity index $Δ{i}_{MgII}$, we obtained that the change of MgII index between minimum and maximum of HD 49933 cycle period is about 0.665. The magnitude of the frequency shifts compared to the Sun already told us that HD 49933 is much more active than the Sun, which is further confirmed in this paper. Furthermore, our calculation on the frequency shifts of $l = 1$ modes indicates the variation of turbulent velocity in the stellar convective zone may be an important source for the $l = 1$ shifts.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Asteroseismic analysis of the CoRoT target HD49933

The frequency ratios and of HD 49933 exhibit an increase at high frequencies. This behavior also exists in the ratios of other stars, which is considered to result from the low signal-to-noise ratio and the larger line width at the high-frequency end and could not be predicted by stellar models in previous work. Our calculations show that the behavior not only can be reproduced by stellar models, but can be predicted by asymptotic formulas of the ratios. The frequency ratios of the Sun, too, can be reproduced well by the asymptotic formulas. The increased behavior derives from the fact that the gradient of mean molecular weight at the bottom of the radiative region hinders the propagation of p-modes, while the hindrance does not exist in the convective core. This behavior should exist in the ratios of stars with a large convective core. The characteristic of the ratios at high frequencies provides a strict constraint on stellar models and aids in determining the size of the convective core and the extent of overshooting. Observational constraints point to a star with $M=1.28\pm0.01 M_{\odot}$, $R=1.458\pm0.005 R_{\odot}$, $t=1.83\pm0.1$ Gyr, $r_{cc}=0.16\pm0.02 R_{\odot}$, $α=1.85\pm0.05$, and $δ_{ov}=0.6\pm0.2$ for HD 49933.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

The Effect of Growth On Equality in Models of the Economy

We investigate the relation between economic growth and equality in a modified version of the agent-based asset exchange model (AEM). The modified model is a driven system that for a range of parameter space is effectively ergodic in the limit of an infinite system. We find that the belief that "a rising tide lifts all boats" does not always apply, but the effect of growth on the wealth distribution depends on the nature of the growth. In particular, we find that the rate of growth, the way the growth is distributed, and the percentage of wealth exchange determine the degree of equality. We find strong numerical evidence that there is a phase transition in the modified model, and for a part of parameter space the modified AEM acts like a geometric random walk.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Characteristics of solar-like oscillations of secondary red clump stars

We calculated the populations of core-helium-burning (CHeB) stars and found that the secondary red clump (SRC) stars can form an SRC peak in the distributions of the frequency of maximum seismic amplitude ($ν_{max}$) and mean large-frequency separation ($Δν$) of CHeB stars when metallicity $Z \geq$ 0.02. The $ν_{max}$ and $Δν$ of CHeB stars are dependent not only on He core mass but on H-shell burning. The SRC peak is composed of the CHeB stars with mass roughly between the critical mass M_{Hef} and M_{Hef}+0.2 while He core mass is between about 0.33 and 0.36 M_{sun}. The location of the SRC peak can be affected by the mixing-length parameter $α$, metallicity $Z$, and overshooting parameter $δ_{ov}$. A decrease in $α$ or increase in $Z$ or $δ_{ov}$ leads to a movement of the SRC peak towards a lower frequency. However, the change in $Z$ and $α$ only slightly affects the value of M_{Hef} but the variation in $δ_{ov}$ can significantly affects the value of M_{Hef}. Thus the SRC peak might aid in determining the value of M_{Hef} and calibrating $δ_{ov}$. In addition, the effects of convective acceleration of SRC stars and the $ν_{max}$ of `semi-degenerate' stars decreasing with mass result in the appearance of a shoulder between about 40 and 50 $μ$hz in the \dnu{} distribution. However, the convective acceleration of stars with M < M_{Hef} leads to the deficit in the $ν_{max}$ distribution between about 9 and 20 $μ$hz{}. Moreover, the value of the parameter $b$ of the relation between $ν_{max}$ and $Δν$ for the populations with M > M_{Hef} is obviously larger than that for the populations with $M <$ \dmhef{}.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

The Contributions of Interactive Binary Stars to Double Main Sequence Turn-Offs and Dual Red Clump of Intermediate-Age Star Clusters

Double or extended main-sequence turn-offs (DMSTOs) and dual red clump (RC) were observed in intermediate-age clusters, such as in NGC 1846 and 419. the DMSTOs are interpreted as that the cluster has two distinct stellar popula- tions with differences in age of about 200-300 Myr but with the same metallicity. The dual RC is interpreted as a result of a prolonged star formation. Using a stellar population-synthesis method, we calculated the evolutions of binary-star stellar population (BSP). We found that binary interactions and merging can reproduce the dual RC in the color-magnitude diagrams of an intermediate-age cluster, whereas in actuality only a single population exists. Moreover, the binary interactions can lead to an extended MSTO rather than DMSTOs. However, the rest of main sequence, subgiant branch and first giant branch are hardly spread by the binary interactions. Part of the observed dual RC and extended MSTO may be the results of binary interactions and merger.

0 citations0 reviewsNo code linkedOpen access