Efficient Algorithms for Solving Facility Problems with Disruptions

This study investigates facility location problems in the presence of facility disruptions. Two types of problems are investigated. Firstly, we study a facility location problem considering random disruptions. Secondly, we study a facility fortification problem considering disruptions caused by random failures and intelligent attacks.We first study a reliable facility location problem in which facilities are faced with the risk of random disruptions. In the literature, reliable facility location models and solution methods have been proposed under different assumptions of the disruption distribution. In most of these models, the disruption distribution is assumed to be completely known, that is, the disruptions are known to be uncorrelated or to follow a certain distribution. In practice, we may have only limited information about the distribution. In this work, we propose a robust reliable facility location model that considers the worst-case distribution with incomplete information. Because the model imposes fewer distributional assumptions, it includes several important reliable facility location problems as special cases. We propose an effective cutting plane algorithm based on the supermodularity of the problem. For the case in which the distribution is completely known, we develop a heuristic algorithm called multi-start tabu search to solve very large instances.In the second part of the work, we study an r-interdiction median problem with fortification that simultaneously considers two types of disruption risks: random disruptions that happen probabilistically and disruptions caused by intentional attacks. The problem is to determine the allocation of limited facility fortification resources to an existing network. The problem is modeled as a bi-level programming model that generalizes the r-interdiction median problem with probabilistic fortification. The lower level problem, that is, the interdiction problem, is a challenging high-degree non-linear model. In the literature, only the enumeration method is applied to solve a special case of the problem. By exploring the special structure property of the problem, we propose an exact cutting plane method for the problem. For the fortification problem, an effective logic based Benders decomposition algorithm is proposed

DyTed: Disentangled Representation Learning for Discrete-time Dynamic Graph

Unsupervised representation learning for dynamic graphs has attracted a lot of research attention in recent years. Compared with static graph, the dynamic graph is a comprehensive embodiment of both the intrinsic stable characteristics of nodes and the time-related dynamic preference. However, existing methods generally mix these two types of information into a single representation space, which may lead to poor explanation, less robustness, and a limited ability when applied to different downstream tasks. To solve the above problems, in this paper, we propose a novel disenTangled representation learning framework for discrete-time Dynamic graphs, namely DyTed. We specially design a temporal-clips contrastive learning task together with a structure contrastive learning to effectively identify the time-invariant and time-varying representations respectively. To further enhance the disentanglement of these two types of representation, we propose a disentanglement-aware discriminator under an adversarial learning framework from the perspective of information theory. Extensive experiments on Tencent and five commonly used public datasets demonstrate that DyTed, as a general framework that can be applied to existing methods, achieves state-of-the-art performance on various downstream tasks, as well as be more robust against noise

Robust Recommender System: A Survey and Future Directions

With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload. However, their practical deployment often encounters "dirty" data, where noise or malicious information can lead to abnormal recommendations. Research on improving recommender systems' robustness against such dirty data has thus gained significant attention. This survey provides a comprehensive review of recent work on recommender systems' robustness. We first present a taxonomy to organize current techniques for withstanding malicious attacks and natural noise. We then explore state-of-the-art methods in each category, including fraudster detection, adversarial training, certifiable robust training against malicious attacks, and regularization, purification, self-supervised learning against natural noise. Additionally, we summarize evaluation metrics and common datasets used to assess robustness. We discuss robustness across varying recommendation scenarios and its interplay with other properties like accuracy, interpretability, privacy, and fairness. Finally, we delve into open issues and future research directions in this emerging field. Our goal is to equip readers with a holistic understanding of robust recommender systems and spotlight pathways for future research and development

What drives the velocity dispersion of ionized gas in star-forming galaxies?

We analyze the intrinsic velocity dispersion properties of 648 star-forming galaxies observed by the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, to explore the relation of intrinsic gas velocity dispersions with star formation rates (SFRs), SFR surface densities ($\rm{\Sigma_{SFR}}$), stellar masses and stellar mass surface densities ($\rm{\Sigma_{*}}$). By combining with high z galaxies, we found that there is a good correlation between the velocity dispersion and the SFR as well as $\rm{\Sigma_{SFR}}$. But the correlation between the velocity dispersion and the stellar mass as well as $\rm{\Sigma_{*}}$ is moderate. By comparing our results with predictions of theoretical models, we found that the energy feedback from star formation processes alone and the gravitational instability alone can not fully explain simultaneously the observed velocity-dispersion/SFR and velocity-dispersion/$\rm{\Sigma_{SFR}}$ relationships.Comment: 11 pages, 11 figures. Accepted for publication in MNRA

The site conditions of the Guo Shou Jing Telescope

The weather at Xinglong Observing Station, where the Guo Shou Jing Telescope (GSJT) is located, is strongly affected by the monsoon climate in north-east China. The LAMOST survey strategy is constrained by these weather patterns. In this paper, we present a statistics on observing hours from 2004 to 2007, and the sky brightness, seeing, and sky transparency from 1995 to 2011 at the site. We investigate effects of the site conditions on the survey plan. Operable hours each month shows strong correlation with season: on average there are 8 operable hours per night available in December, but only 1-2 hours in July and August. The seeing and the sky transparency also vary with seasons. Although the seeing is worse in windy winters, and the atmospheric extinction is worse in the spring and summer, the site is adequate for the proposed scientific program of LAMOST survey. With a Monte Carlo simulation using historical data on the site condition, we find that the available observation hours constrain the survey footprint from 22h to 16h in right ascension; the sky brightness allows LAMOST to obtain the limit magnitude of V = 19.5mag with S/N = 10.Comment: 10 pages, 8 figures, accepted for publication in RA

P-MaNGA : full spectral fitting and stellar population maps from prototype observations

MC acknowledges support from a Royal Society University Research Fellowship.MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) is a 6-yearSDSS-IV survey that will obtain resolved spectroscopy from 3600 Å to10300 Å for a representative sample of over 10,000 nearby galaxies.In this paper, we derive spatially resolved stellar population properties and radial gradients by performing full spectral fitting of observed galaxy spectra from P-MaNGA, a prototype of the MaNGA instrument. These data include spectra for eighteen galaxies, covering a large range of morphological type. We derive age, metallicity, dust and stellar mass maps, and their radial gradients, using high spectral-resolution stellar population models, and assess the impact of varying the stellar library input to the models. We introduce a method to determine dust extinction which is able to give smooth stellar mass maps even in cases of high and spatially non-uniform dust attenuation.With the spectral fitting we produce detailed maps of stellar population properties which allow us to identify galactic features among this diverse sample such as spiral structure, smooth radial profiles with little azimuthal structure in spheroidal galaxies, and spatially distinct galaxy sub-components. In agreement with the literature, we find the gradients for galaxies identified as early-type to be on average flat in age, and negative (- 0.15 dex / Re ) in metallicity,whereas the gradients for late-type galaxies are on average negative in age (- 0.39 dex / Re ) and flat in metallicity. We demonstrate howdifferent levels of data quality change the precision with which radialgradients can be measured. We show how this analysis, extended to thelarge numbers of MaNGA galaxies, will have the potential to shed lighton galaxy structure and evolution.PostprintPeer reviewe

Low metallicities and old ages for three ultra-diffuse galaxies in the Coma cluster

A.W. acknowledges support of a Leverhulme Trust Early Career Fellowship.A large population of ultra-diffuse galaxies (UDGs) was recently discovered in the Coma cluster. Here we present optical spectra of three such UDGs, DF 7, DF 44, and DF 17, which have central surface brightnesses of μ g ≈ 24.4–25.1 mag arcsec−2. The spectra were acquired as part of an ancillary program within the SDSS-IV MaNGA Survey. We stacked 19 fibers in the central regions from larger integral field units (IFUs) per source. With over 13.5 hr of on-source integration, we achieved a mean signal-to-noise ratio in the optical of 9.5 Å−1, 7.9 Å−1, and 5.0 Å−1, respectively, for DF 7, DF 44, and DF 17. Stellar population models applied to these spectra enable measurements of recession velocities, ages, and metallicities. The recession velocities of DF 7, DF 44, and DF 17 are ${6599}_{-25}^{+40}$ km s−1, ${6402}_{-39}^{+41}$ km s−1, and ${8315}_{-43}^{+43}$ km s−1, spectroscopically confirming that all of them reside in the Coma cluster. The stellar populations of these three galaxies are old and metal-poor, with ages of ${7.9}_{-2.5}^{+3.6}$ Gyr, ${8.9}_{-3.3}^{+4.3}$ Gyr, and ${9.1}_{-5.5}^{+3.9}$ Gyr, and iron abundances of [Fe/H] $-{1.0}_{-0.4}^{+0.3}$, $-{1.3}_{-0.4}^{+0.4}$, and $-{0.8}_{-0.5}^{+0.5}$, respectively. Their stellar masses are (3–6) × 108M⊙. The UDGs in our sample are as old or older than galaxies at similar stellar mass or velocity dispersion (only DF 44 has an independently measured dispersion). They all follow the well-established stellar mass–stellar metallicity relation, while DF 44 lies below the velocity dispersion-metallicity relation. These results, combined with the fact that UDGs are unusually large for their stellar masses, suggest that stellar mass plays a more important role in setting stellar population properties for these galaxies than either size or surface brightness.Publisher PDFPeer reviewe

Spectroscopic Constraints on the Buildup of Intracluster Light in the Coma Cluster

The stellar content of the intracluster light (ICL) provides unique insight into the hierarchical assembly process of galaxy clusters. We present optical spectra of three ICL regions (μg ≈25.3-26.2 mag arcsec-²) in the Coma cluster, located between 100 and 180 kpc from their nearest brightest cluster galaxies (BCGs): NGC 4889 and NGC 4874. Integral-field unit (IFU) spectroscopy with 13.5 hr on-source integration was acquired in an ancillary program within the Sloan Digital Sky Survey-IV MaNGA survey. We stacked the 127 individual fiber spectra in each IFU to achieve a 1σ limiting surface brightness of 27.9 mag arcsec-², corresponding to a mean signal-to-noise ratio in the optical of 21.7, 9.0, and 11.7 Å−1, for each ICL region. We apply stellar population models to the stacked spectra. Our results show that the velocity dispersions of ICL regions are very high (σ ~ 630 km s-¹), indicating the stars in these regions are tracing the gravitational potential of the cluster, instead of any individual galaxy. The line-of-sight velocities are different from each other by ~700 km s-¹, while the velocity of each region is similar to the closest BCG. This suggests that the ICL regions are associated with two distinct subclusters centered on NGC 4889 and NGC 4874. The stellar populations of these regions are old and metal-poor, with ages of ${12.7}_{-3.4}^{+1.0}$, ${6.7}_{-2.0}^{+2.9}$, and ${9.7}_{-3.5}^{+3.5}$ Gyr, and [Fe/H] of $-{1.0}_{-0.3}^{+0.2}$, $-{0.6}_{-0.5}^{+0.3}$, and $-{0.8}_{-0.4}^{+0.3}$. From the derived age and metallicity, the buildup of ICL in Coma is likely to be through the accretion of low-mass galaxies or the tidal stripping of the outskirts of massive galaxies that have ended their star formation early on, instead of directly from major mergers of massive galaxies