arXiv subjects

Junhui Liu

Publications and source records attributed to Junhui Liu.

26 records · Page 2Linked to original sources

arXiv2021

KIC 12602250: A low-amplitude Double-mode $δ$ Scuti star with Amplitude Modulation

We propose for the first time that KIC 12602250 is a low-amplitude radial double-mode $δ$ Scuti star with amplitude modulation. The detailed frequency analysis is given for the light curve of KIC 12602250 which is delivered from the Kepler mission. The Fourier analysis of the long cadence data (i.e. Q0 - Q17, spanning 1471 days) reveals that the variations of the light curve are dominated by the strongest mode with frequency F0 = 11.6141 $\rm{d^{-1}}$, suggesting that KIC 12602250 is a $δ$ Scuti star. The other independent mode F1 = 14.9741 $\rm{d^{-1}}$ is newly detected. The amplitude of the light variations of \target is $\sim$ 0.06 mag, which indicates that this is a low-amplitude $δ$ Scuti star, but the ratio of F0/F1 is estimated as 0.7756 which is typical of HADS, and a slow amplitude growth is detected in F1 and $f_{3}$, which could be due to stellar evolution, suggesting that KIC 12602250 could be a post main sequence $δ$ Scuti which is crossing the instability strip for the first time.

astro-ph.SR↗

arXiv2020

Investigating Variable Stars in the Open Cluster NGC 1912 and Its Surrounding Field

In this work, we studied the variable stars in the open cluster NGC 1912 based on the photometric observations and $Gaia$ DR2 data. More than 3600 CCD frames in B, V, R filters were reduced, and we obtained the light curves that span about 63 hours. By analyzing these light curves, we detected 24 variable stars, including 16 periodic variable stars, seven eclipsing binaries and one star whose type is unclear. Among these 24 variable stars, 11 are newly-discovered, which are classified as six gamma Doradus stars, one delta Scuti star, three detached binaries and one contact binaries. We also confirmed 13 previously known variable stars. Based on cluster members identified by Cantat-Gaudin et al. (2018), we inferred cluster memberships for these detected variable stars. Using Gaia DR2 data, we plotted a new color-magnitude diagram for NGC 1912, and showed the nature of variable cluster members in kinematical properties and heliocentric distance. Among the 24 variable stars, seven variables are probable cluster members, which show homogeneity in kinematic characters and space position with the established cluster members. Four of the seven variable cluster members are the previously discovered stars, consisting of two gamma Dor stars and two delta Sct stars. The remaining three variable cluster members, which are all gamma Dor stars, are firstly detected in this work. The main physical parameters of these variable cluster members estimated from the color-magnitude diagram are log(age/yr)=8.75, [Fe/H]=-0.1, m-M=10.03 mag, and E(B-V)=0.307.

astro-ph.SR↗

arXiv2020

Boundary Content Graph Neural Network for Temporal Action Proposal Generation

Temporal action proposal generation plays an important role in video action understanding, which requires localizing high-quality action content precisely. However, generating temporal proposals with both precise boundaries and high-quality action content is extremely challenging. To address this issue, we propose a novel Boundary Content Graph Neural Network (BC-GNN) to model the insightful relations between the boundary and action content of temporal proposals by the graph neural networks. In BC-GNN, the boundaries and content of temporal proposals are taken as the nodes and edges of the graph neural network, respectively, where they are spontaneously linked. Then a novel graph computation operation is proposed to update features of edges and nodes. After that, one updated edge and two nodes it connects are used to predict boundary probabilities and content confidence score, which will be combined to generate a final high-quality proposal. Experiments are conducted on two mainstream datasets: ActivityNet-1.3 and THUMOS14. Without the bells and whistles, BC-GNN outperforms previous state-of-the-art methods in both temporal action proposal and temporal action detection tasks.

cs.CV↗

arXiv2020

Cartoon Face Recognition: A Benchmark Dataset

Recent years have witnessed increasing attention in cartoon media, powered by the strong demands of industrial applications. As the first step to understand this media, cartoon face recognition is a crucial but less-explored task with few datasets proposed. In this work, we first present a new challenging benchmark dataset, consisting of 389,678 images of 5,013 cartoon characters annotated with identity, bounding box, pose, and other auxiliary attributes. The dataset, named iCartoonFace, is currently the largest-scale, high-quality, richannotated, and spanning multiple occurrences in the field of image recognition, including near-duplications, occlusions, and appearance changes. In addition, we provide two types of annotations for cartoon media, i.e., face recognition, and face detection, with the help of a semi-automatic labeling algorithm. To further investigate this challenging dataset, we propose a multi-task domain adaptation approach that jointly utilizes the human and cartoon domain knowledge with three discriminative regularizations. We hence perform a benchmark analysis of the proposed dataset and verify the superiority of the proposed approach in the cartoon face recognition task. We believe this public availability will attract more research attention in broad practical application scenarios.

cs.CV↗

arXiv2020

Cross-modal supervised learning for better acoustic representations

Obtaining large-scale human-labeled datasets to train acoustic representation models is a very challenging task. On the contrary, we can easily collect data with machine-generated labels. In this work, we propose to exploit machine-generated labels to learn better acoustic representations, based on the synchronization between vision and audio. Firstly, we collect a large-scale video dataset with 15 million samples, which totally last 16,320 hours. Each video is 3 to 5 seconds in length and annotated automatically by publicly available visual and audio classification models. Secondly, we train various classical convolutional neural networks (CNNs) including VGGish, ResNet 50 and Mobilenet v2. We also make several improvements to VGGish and achieve better results. Finally, we transfer our models on three external standard benchmarks for audio classification task, and achieve significant performance boost over the state-of-the-art results. Models and codes are available at: https://github.com/Deeperjia/vgg-like-audio-models.

cs.CV↗

arXiv2019

Unknown Identity Rejection Loss: Utilizing Unlabeled Data for Face Recognition

Face recognition has advanced considerably with the availability of large-scale labeled datasets. However, how to further improve the performance with the easily accessible unlabeled dataset remains a challenge. In this paper, we propose the novel Unknown Identity Rejection (UIR) loss to utilize the unlabeled data. We categorize identities in unconstrained environment into the known set and the unknown set. The former corresponds to the identities that appear in the labeled training dataset while the latter is its complementary set. Besides training the model to accurately classify the known identities, we also force the model to reject unknown identities provided by the unlabeled dataset via our proposed UIR loss. In order to 'reject' faces of unknown identities, centers of the known identities are forced to keep enough margin from centers of unknown identities which are assumed to be approximated by the features of their samples. By this means, the discriminativeness of the face representations can be enhanced. Experimental results demonstrate that our approach can provide obvious performance improvement by utilizing the unlabeled data.

cs.CV↗

arXiv2019

An optical and X-ray study of the contact binary, BH Cassiopeiae

We present the revised and high quality CCD time series observations in 2017 and 2018 of the W UMa-type contact binary BH~Cas. The combination of our new determinations of minimum times and those collected from the literatures reveal a long-term period increase with a rate of $\textit{dP/dt}\,=\,+3.27 \times 10^{-7} \textrm{d}\,\textrm{yr}^{-1}$, which can be accounted for by angular momentum transfer as the less-massive component loses its mass to the more massive one. One cyclical oscillation ($A'=0.00300$~days and $P'=20.09$~yrs) superimposed on the long-term period increase tendency is attributed to one additional object that orbits BH Cas. The low-resolution spectra of this source are observed at phase $\sim$0.0 and $\sim$0.5 in 2018 and 2019, and the spectral types are identified as K3V$\,\pm\,$1 and G8V$\,\pm\,$2. According to the luminosity contributions and spectra at these phases, the spectral type of the primary star is close to K3V$\,\pm\,$1. We infer a cool spot on the hotter and less-massive component to fit the asymmetry of light curves. In addition, changes in the location, temperature, and area of spots on the secondary star in different observations may indicate that the magnetic field activity of the secondary is more active than that of the primary. Such strong spot activities and optical flare are both detected at the same time from a W UMa-type star for the first time. With the \emph{XMM-Newton} observations taken in 2003 and 2012, the X-ray light curve is obtained and the X-ray spectra can be well described with a black-body or a bremsstrahlung model. The soft X-ray luminosity of BH Cas is estimated as 8.8\,--\,9.7$\,\times\,10^{30}$\,erg\,s$^{-1}$.

astro-ph.SR↗

arXiv2018

A Weak Modulation Effect Detected in the Light Curves of KIC 5950759: Intrinsic or Instrumental Effect?

In this paper, the high-precision light curves of $Kepler$ target KIC 5950759 are analyzed. The Fourier analysis of the long cadence light curve reveals 3 independent frequencies. Two of them are main pulsation modes: F0 = 14.221373(21) $\rm{d^{-1}}$ and F1 = 18.337249(44) $\rm{d^{-1}}$. The third independent frequency $f_m$ = 0.3193 d$^{-1}$ is found in long cadence data with a signal-to-noise ratio of 6.2. A weak modulation of $f_m$ to F0 and F1 modes (triplet structures centred on F0 and F1) are detected both in long and short cadence data. This is the first detection of the modulation effect in a double-mode HADS star. The most possible cause of the modulation effect in the light curves is amplitude modulation with the star's rotation frequency of 0.3193 d$^{-1}$. The preliminary analysis suggests that KIC 5950759 is in the bottom of the HADS instability strip and likely situated in the main sequence. Spectroscopic observations are necessary to verify the true nature of the modulation terms.

astro-ph.SR↗