arXiv subjects

Yuting Feng

Publications and source records attributed to Yuting Feng.

9 recordsLinked to original sources

arXiv2026

The Next Generation Virgo Cluster Survey (NGVS). II. A Catalog of Galaxies in the Virgo Cluster

The Next Generation Virgo Cluster Survey (NGVS) is a deep, high resolution imaging campaign that used the 1 deg$^2$ MegaCam instrument on the Canada-France-Hawaii Telescope to carry out a comprehensive optical survey of the Virgo cluster, from its core to its virial radius. The NGVS covers a contiguous area of 104 deg$^2$ (8.63 Mpc$^2$ at the 16.5 Mpc distance of Virgo) in the $u^*$-,$g$-,$i$-, and $z$-band, with additional limited coverage in $r$. In this paper, we present the final catalog of Virgo galaxies across the entire NGVS area. The catalog includes 3680 galaxies considered to be $bona~fide$ members of the cluster, spanning a factor of 2.5 million in luminosity, from $g = 8.42$ mag to $g = 24.41$ mag ($M_g = -22.67$ mag to $M_g = -6.68$ mag). With 2100 previously uncataloged galaxies, the NGVS catalog augments the number of known Virgo members by a factor 2.3. The catalog is complete down to $g = 18.6$ mag ($M_g=-12.5$ mag, corresponding to a stellar mass $M_* \sim 1.6\times10^7~M_{\odot}$ for an old stellar population) and 50% complete at $g = 22.0$ mag ($M_g=-9.1$ mag, $M_* \sim 6.2\times10^5~M_{\odot}$), three magnitudes deeper than the venerable Virgo Cluster Catalog (VCC), which for over 40 years has served as the reference standard for Virgo. Photometric and structural parameters are derived for all NGVS galaxies and presented in a series of tables, alongside nuclear and morphological classification, as well as stellar masses and, when available, radial velocities.

astro-ph.GA↗

arXiv2026

CASISR: Circular Arbitrary-Scale Image Super-Resolution

The generalization performance (GP) of deep learning-based arbitrary-scale image super-resolution (ASISR) methods is subject to limited training datasets and unlimited testing datasets. It is vitally significant to enhance the GP of the pretrained ASISR models by making full use of the testing samples. The ASISR models usually employ an open-loop architecture from low-resolution (LR) images to super-resolution (SR) images. The degradation model from SR samples to LR samples is known bicubic down-sampling for the classical ASISR, is supposed down-sampling with additive random noise for the blind ASISR, and is learnable for the real-world ASISR. Combining the ASISR and degradation models, it is potentially possible to adopt a closed-loop architecture based on the automatic control theory for strengthening the GP of the ASISR methods. Therefore, this paper proposes a closed-loop architecture, circular ASISR (CASISR), to lift the capability of image reconstruction. A mathematical nonlinear loop equation is established to describe the CASISR, the reasonability of the CASISR is proven by conditional probability theory, and the stability of the CASISR is proven by Taylor series approximation. The first-order and second-order absolute difference images are defined to compare the image reconstruction performance of the ASISR and the CASISR methods. Comprehensive simulation experiments show that the proposed CASISR approach outperforms the eight state-of-the-art ASISR approaches in the quality of image reconstruction. Especially, the proposed CASISR is extraordinarily suitable for fractional SR scale factors and is extremely effective for text and stripe images with drastically changed edges.

cs.CV↗

arXiv2025

Kinematics of Distant Milky Way Halo RR Lyrae Stars out to 160 kpc

We present a kinematical study of the outer halo (r_GC approximately 60 to 160 kpc) of the Milky Way based on spectroscopy of 55 RR Lyrae stars obtained with the ESI instrument on the Keck II telescope. Our spectroscopic targets were selected from three photometric surveys: NGVS, DES, and Pan-STARRS1. We derive center-of-mass radial velocities with uncertainties of 6 to 35 km s^-1. The halo velocity dispersion measured from our sample is 70 plus/minus 7 km s^-1. The velocity field shows a possible dipole-like structure, with redshifted northern and blueshifted southern hemispheres. Fitting a Milky Way - Large Magellanic Cloud dipole perturbation model yields a weak or marginal dipole signal with amplitude -30 (+16, -20) km s^-1 and apex direction (l, b) = (-38.2 (+42.4, -31.5), -41.3 (+27.9, -23.8)) deg, along with a bulk compression velocity of -16 plus/minus 11 km s^-1. Although limited by sky coverage and sample size, our results are consistent with the presence of LMC-induced disequilibrium in the distant halo beyond 100 kpc. In addition to the 55 RR Lyrae stars, our spectroscopy reveals that 10 additional photometrically selected RR Lyrae candidates are actually quasar or blazar contaminants, highlighting the need for caution regarding such contaminants in sparsely sampled photometric surveys. Our study demonstrates that single-epoch spectroscopy of RR Lyrae stars is a viable method for probing the kinematics of the outer halo, and future surveys such as Rubin LSST and DESI-II have the potential to significantly advance this effort.

astro-ph.GA↗

arXiv2025

CECGSR: Circular ECG Super-Resolution

Background and Objective: The electrocardiogram (ECG) plays a crucial role in the diagnosis and treatment of various cardiac diseases. ECG signals suffer from low-resolution (LR) due to the use of convenient acquisition devices, as well as internal and external noises and artifacts. Classical ECG super-resolution (ECGSR) methods adopt an open-loop architecture that converts LR ECG signals to super-resolution (SR) ones. According to the theory of automatic control, a closed-loop framework exhibits superior dynamic and static performance compared with its open-loop counterpart. Methods: This paper proposes a closed-loop approach, termed circular ECGSR (CECGSR), which models the degradation process from SR ECG signals to LR ones. The negative feedback mechanism of the closed-loop system is based on the differences between the LR ECG signals. A mathematical loop equation is constructed to characterize the closed-loop infrastructure. The Taylor series expansion is employed to demonstrate the near-zero steady-state error of the proposed method. A Plug-and-Play strategy is considered to establish the SR unit of the proposed architecture, leveraging any existing advanced open-loop ECGSR methods. This paper also presents Transformer model based open-loop ECGSR and closed-loop CECGSR algorithms. Results: Simulation experiments on both noiseless and noisy subsets of the Physikalisch-Technische Bundesanstalt-Extra Large (PTB-XL) datasets demonstrate that the proposed CECGSR outperforms state-of-the-art open-loop ECGSR algorithms in the reconstruction performance of ECG signals. Conclusions: The proposed method will efficiently enrich ECG signal details and remove ECG signal artifacts in clinical applications.

eess.SP↗

arXiv2024

Influence Maximization via Graph Neural Bandits

We consider a ubiquitous scenario in the study of Influence Maximization (IM), in which there is limited knowledge about the topology of the diffusion network. We set the IM problem in a multi-round diffusion campaign, aiming to maximize the number of distinct users that are influenced. Leveraging the capability of bandit algorithms to effectively balance the objectives of exploration and exploitation, as well as the expressivity of neural networks, our study explores the application of neural bandit algorithms to the IM problem. We propose the framework IM-GNB (Influence Maximization with Graph Neural Bandits), where we provide an estimate of the users' probabilities of being influenced by influencers (also known as diffusion seeds). This initial estimate forms the basis for constructing both an exploitation graph and an exploration one. Subsequently, IM-GNB handles the exploration-exploitation tradeoff, by selecting seed nodes in real-time using Graph Convolutional Networks (GCN), in which the pre-estimated graphs are employed to refine the influencers' estimated rewards in each contextual setting. Through extensive experiments on two large real-world datasets, we demonstrate the effectiveness of IM-GNB compared with other baseline methods, significantly improving the spread outcome of such diffusion campaigns, when the underlying network is unknown.

cs.LG↗

arXiv2024

The Next Generation Virgo Cluster Survey. XXXVII. Distant RR Lyrae Stars and the Milky Way Stellar Halo out to 300 kpc

RR Lyrae stars are standard candles with characteristic photometric variability and serve as powerful tracers of Galactic structure, substructure, accretion history, and dark matter content. Here we report the discovery of distant RR Lyrae stars, including some of the most distant stars known in the Milky Way halo, with Galactocentric distances of approximately 300 kpc. We use time-series u*g'i'z' Canada-France-Hawaii Telescope/MegaCam photometry from the Next Generation Virgo Cluster Survey (NGVS). We employ a template light curve fitting method based on empirical Sloan Digital Sky Survey (SDSS) Stripe 82 RR Lyrae data to identify RR Lyrae candidates in the NGVS data set. We eliminate several hundred suspected quasars and identify 180 RR Lyrae candidates, with heliocentric distances of approximately 20--300 kpc. The halo stellar density distribution is consistent with an r^(-4.09 +/- 0.10) power-law radial profile over most of this distance range with no signs of a break. The distribution of ab-type RR Lyrae in a period-amplitude plot (Bailey diagram) suggests that the mean metallicity of the halo decreases outwards. Compared to other recent RR Lyrae surveys, like Pan-STARRS1 (PS1), the High Cadence Transient Survey (HiTS), and the Dark Energy Survey (DES), our NGVS study has better single-epoch photometric precision and a comparable number of epochs but smaller sky coverage. At large distances, our RR Lyrae sample appears to be relatively pure and complete, with well-measured periods and amplitudes. These newly discovered distant RR Lyrae stars are important additions to the few secure stellar tracers beyond 150 kpc in the Milky Way halo.

astro-ph.GA↗

arXiv2023

Influence Maximization with Fairness at Scale (Extended Version)

In this paper, we revisit the problem of influence maximization with fairness, which aims to select k influential nodes to maximise the spread of information in a network, while ensuring that selected sensitive user attributes are fairly affected, i.e., are proportionally similar between the original network and the affected users. Recent studies on this problem focused only on extremely small networks, hence the challenge remains on how to achieve a scalable solution, applicable to networks with millions or billions of nodes. We propose an approach that is based on learning node representations for fair spread from diffusion cascades, instead of the social connectivity s.t. we can deal with very large graphs. We propose two data-driven approaches: (a) fairness-based participant sampling (FPS), and (b) fairness as context (FAC). Spread related user features, such as the probability of diffusing information to others, are derived from the historical information cascades, using a deep neural network. The extracted features are then used in selecting influencers that maximize the influence spread, while being also fair with respect to the chosen sensitive attributes. In FPS, fairness and cascade length information are considered independently in the decision-making process, while FAC considers these information facets jointly and considers correlations between them. The proposed algorithms are generic and represent the first policy-driven solutions that can be applied to arbitrary sets of sensitive attributes at scale. We evaluate the performance of our solutions on a real-world public dataset (Sina Weibo) and on a hybrid real-synthethic dataset (Digg), which exhibit all the facets that we exploit, namely diffusion network, diffusion traces, and user profiles. These experiments show that our methods outperform the state-the-art solutions in terms of spread, fairness, and scalability.

cs.SI↗

arXiv2022

IGNiteR: News Recommendation in Microblogging Applications (Extended Version)

News recommendation is one of the most challenging tasks in recommender systems, mainly due to the ephemeral relevance of news to users. As social media, and particularly microblogging applications like Twitter or Weibo, gains popularity as platforms for news dissemination, personalized news recommendation in this context becomes a significant challenge. We revisit news recommendation in the microblogging scenario, by taking into consideration social interactions and observations tracing how the information that is up for recommendation spreads in an underlying network. We propose a deep-learning based approach that is diffusion and influence-aware, called Influence-Graph News Recommender (IGNiteR). It is a content-based deep recommendation model that jointly exploits all the data facets that may impact adoption decisions, namely semantics, diffusion-related features pertaining to local and global influence among users, temporal attractiveness, and timeliness, as well as dynamic user preferences. To represent the news, a multi-level attention-based encoder is used to reveal the different interests of users. This news encoder relies on a CNN for the news content and on an attentive LSTM for the diffusion traces. For the latter, by exploiting previously observed news diffusions (cascades) in the microblogging medium, users are mapped to a latent space that captures potential influence on others or susceptibility of being influenced for news adoptions. Similarly, a time-sensitive user encoder enables us to capture the dynamic preferences of users with an attention-based bidirectional LSTM. We perform extensive experiments on two real-world datasets, showing that IGNiteR outperforms the state-of-the-art deep-learning based news recommendation methods.

cs.IR↗

arXiv2018

Optical-Mid-Infrared Period-Luminosity Relations for W UMa-type Contact Binaries Based on Gaia DR 1: 8% Distance Accuracy

W Ursa Majoris (W UMa)-type contact binary systems (CBs) are useful statistical distance indicators because of their large numbers. Here, we establish (orbital) period-luminosity relations (PLRs) in 12 optical-to-mid-infrared bands (GBV RIJHKsW1W2W3W4) based on 183 nearby W UMa-type CBs with accurate Tycho-Gaia parallaxes. The 1{\sigma} dispersion of the PLRs decreases from optical to near- and mid-infrared wavelengths. The minimum scatter, 0.16 mag, implies that W UMa-type CBs can be used to recover distances to 7% precision. Applying our newly determined PLRs to 19 open clusters containing W UMa-type CBs demonstrates that the PLR and open cluster CB distance scales are mutually consistent to within 1%. Adopting our PLRs as secondary distance indicators, we compiled a catalog of 55,603 CBs candidates, of which 80% have distance estimates based on a combination of optical, near-, and mid-infrared photometry. Using Fourier decomposition, 27,318 high-probability W UMa-type CBs were selected. The resulting 8% distance accuracy implies that our sam- ple encompasses the largest number of objects with accurate distances within a local volume with a radius of 3 kpc available to date. The distribution of W UMa-type CBs in the Galaxy suggests that in different environments, the CB luminosity function may be different: larger numbers of brighter (longer-period) W UMa-type CBs are found in younger environments.

astro-ph.GA↗