Spectral Properties of Galaxies in Void Regions

We present a study of spectral properties of galaxies in underdense large-scale structures, voids. Our void galaxy sample (75,939 galaxies) is selected from the Sloan Digital Sky Survey (SDSS) Data Release 7 (DR7) with $\rm z < 0.107$. We find that there are no significant differences in the luminosities, stellar masses, stellar populations, and specific star formation rates between void galaxies of specific spectral types and their wall counterparts. However, the fraction of star-forming galaxies in voids is significantly higher ($\ge 9\%$) than that in walls. Void galaxies, when considering all spectral types, are slightly fainter, less massive, have younger stellar populations and of higher specific star formation rates than wall galaxies. These minor differences are totally caused by the higher fraction of star-forming galaxies in voids. We confirm that AGNs exist in voids, already found by \cite{co08}, with similar abundance as in walls. Type I AGNs contribute $\sim$ 1\%-2\% of void galaxies, similar to their fraction in walls. The intrinsic [O III] luminosities , spanning over 10^6\ L_{\sun} \sim 10^9\ L_{\sun}, and Eddington ratios are similar comparing our void AGNs versus wall AGNs. Small scale statistics show that all spectral types of void galaxies are less clustered than their counterparts in walls. Major merger may not be the dominant trigger of black hole accretion in the luminosity range we probe. Our study implies that the growth of black holes relies weakly on large scale structures.Comment: 14 pages,16 figures, accepted for publication in Ap

The HI Mass Function and Velocity Width Function of Void Galaxies in the Arecibo Legacy Fast ALFA Survey

We measure the HI mass function (HIMF) and velocity width function (WF) across environments over a range of masses $7.2<\log(M_{HI}/M_{\odot})<10.8$, and profile widths $1.3\log(km/s)<\log(W)<2.9\log(km/s)$, using a catalog of ~7,300 HI-selected galaxies from the ALFALFA Survey, located in the region of sky where ALFALFA and SDSS (Data Release 7) North overlap. We divide our galaxy sample into those that reside in large-scale voids (void galaxies) and those that live in denser regions (wall galaxies). We find the void HIMF to be well fit by a Schechter function with normalization $\Phi^*=(1.37\pm0.1)\times10^{-2} h^3Mpc^{-3}$, characteristic mass $\log(M^*/M_{\odot})+2\log h_{70}=9.86\pm0.02$, and low-mass-end slope $\alpha=-1.29\pm0.02$. Similarly, for wall galaxies, we find best-fitting parameters $\Phi^*=(1.82\pm0.03)\times10^{-2} h^3Mpc^{-3}$, $\log(M^*/M_{\odot})+2\log h_{70}=10.00\pm0.01$, and $\alpha=-1.35\pm0.01$. We conclude that void galaxies typically have slightly lower HI masses than their non-void counterparts, which is in agreement with the dark matter halo mass function shift in voids assuming a simple relationship between DM mass and HI mass. We also find that the low-mass slope of the void HIMF is similar to that of the wall HIMF suggesting that there is either no excess of low-mass galaxies in voids or there is an abundance of intermediate HI mass galaxies. We fit a modified Schechter function to the ALFALFA void WF and determine its best-fitting parameters to be $\Phi^*=0.21\pm0.1 h^3Mpc^{-3}$, $\log(W^*)=2.13\pm0.3$, $\alpha=0.52\pm0.5$ and high-width slope $\beta=1.3\pm0.4$. For wall galaxies, the WF parameters are: $\Phi^*=0.022\pm0.009 h^3Mpc^{-3}$, $\log(W^*)=2.62\pm0.5$, $\alpha=-0.64\pm0.2$ and $\beta=3.58\pm1.5$. Because of large uncertainties on the void and wall width functions, we cannot conclude whether the WF is dependent on the environment.Comment: Accepted for publication at MNRAS, 14 pages, 12 figure

6G Mobile-Edge Empowered Metaverse: Requirements, Technologies, Challenges and Research Directions

The Metaverse has emerged as the successor of the conventional mobile internet to change people's lifestyles. It has strict visual and physical requirements to ensure an immersive experience (i.e., high visual quality, low motion-to-photon latency, and real-time tactile and control experience). However, the current communication systems fall short to satisfy these requirements. Mobile edge computing (MEC) has been indispensable to enable low latency and powerful computing. Moreover, the sixth generation (6G) networks promise to provide end users with high-capacity communications to MEC servers. In this paper, we bring together the primary components into a 6G mobile-edge framework to empower the Metaverse. This includes the usage of heterogeneous radios, intelligent reflecting surfaces (IRS), non-orthogonal multiple access (NOMA), and digital twins (DTs). We also discuss novel communication paradigms (i.e., semantic communication, holographic-type communication, and haptic communication) to further satisfy the demand for human-type communications and fulfil user preferences and immersive experiences in the Metaverse

Cosmic voids and void properties

The cosmic energy budget of the standard model of cosmology ( CDM) dictates that 72% of the Universe is Dark Energy (undetected, unknown), 23% Dark Matter (undetected, some candidates, largely unknown), and 4% baryons. Everything we have seen and detected including galaxies, stars, white dwarves, supernovae, and black holes make up just 4% of the known Universe. The predictions of CDM has held up surprisingly well to various studies of the observable Universe, including Hubble Space Telescope observations of supernovae, Sloan Digital Sky Survey observations of the baryon acoustic oscillations, and Wilkinson Micro Anisotropy Probe studies of the cosmic microwave background. In my thesis, I test the predictions of CDM on the large scale structure ofthe Universe, speci cally voids. Using a void catalog generated from the Sloan Digital Sky Survey, I study the sizes and shapes of voids, the small scale distribution of void galaxies, and the distribution of Ly (neutral hydrogen) clouds. I nd that voids in the Universe have characteristic sizes and shapes based on cosmology, voids can be modeled as mini-universes where void galaxies are much less clustered than their wall counterparts, and the surprising result that Ly clouds do not trace the large scale distribution of baryons or dark matter in the Universe.Ph.D., Physics -- Drexel University, 201

Bi-directional Digital Twin and Edge Computing in the Metaverse

The Metaverse has emerged to extend our lifestyle beyond physical limitations. As essential components in the Metaverse, digital twins (DTs) are the digital replicas of physical items. DTs enable emulation of real-world scenarios and prediction for energy and resource-efficient operation, resulting in sustainable applications. End users access the Metaverse using a variety of devices (e.g., head-mounted devices (HMDs)), mostly lightweight. Multi-access edge computing (MEC) provides responsive services to the end users, leading to an immersive Metaverse experience. With the anticipation to represent physical objects, end users, and edge computing systems as DTs in the Metaverse, the construction of these DTs and the interplay between them have not been investigated. In this paper, we discuss the bidirectional reliance between the DT and the MEC system and investigate the creation of DTs of objects and users on the MEC servers and DT-assisted edge computing (DTEC). We also study the interplay between the DTs and DTECs to allocate the resources fairly and optimally and provide an immersive experience in the Metaverse. Owing to the dynamic network states (e.g., channel states) and mobility of the users, we discuss the interplay between local DTECs (on local MEC servers) and the global DTEC (on cloud server) to cope with the handover among MEC servers and avoid intermittent Metaverse services

Pregnancy outcomes in women with active anorexia nervosa: a systematic review

Background: It is a common misconception that women with active anorexia nervosa (AN) are less likely to conceive. Pregnancies in women with AN are considered high risk. The purpose of this systematic review was to explore pregnancy complications in women with active AN, including maternal, fetal, and neonatal complications. Methods: The authors conducted a systematic review in accordance with PRISMA statement guidelines with stringent selection criteria to include studies on patients with active AN during pregnancy. Results: There were 21 studies included in our review. Anaemia, caesarean section, concurrent recreational substance use, intrauterine growth restriction, preterm birth, small-for-gestation (SGA) birth, and low birth weight were the most reported pregnancy complications in women with active AN, while the rates of gestational diabetes and postpartum haemorrhage were lower. Discussion: Women with active AN have a different profile of pregnancy complications comparing to malnourished women and women in starvation. We recommend early discussion with women diagnosed with AN regarding their fertility and pregnancy complications. We recommend clinicians to aim to improve physical and psychological symptoms of AN as well as correction of any nutritional deficiency ideally prior to conception. Management of pregnancies in women with active AN requires regular monitoring, active involvement of obstetricians and psychiatrist. Paediatric follow-up postpartum is recommended to ensure adequate feeding, wellbeing and general health of the infants. Psychiatric follow-up is recommended for mothers due to risk of worsening symptoms of AN during perinatal period

Federated Graph Representation Learning using Self-Supervision

Federated graph representation learning (FedGRL) brings the benefits of distributed training to graph structured data while simultaneously addressing some privacy and compliance concerns related to data curation. However, several interesting real-world graph data characteristics viz. label deficiency and downstream task heterogeneity are not taken into consideration in current FedGRL setups. In this paper, we consider a realistic and novel problem setting, wherein cross-silo clients have access to vast amounts of unlabeled data with limited or no labeled data and additionally have diverse downstream class label domains. We then propose a novel FedGRL formulation based on model interpolation where we aim to learn a shared global model that is optimized collaboratively using a self-supervised objective and gets downstream task supervision through local client models. We provide a specific instantiation of our general formulation using BGRL a SoTA self-supervised graph representation learning method and we empirically verify its effectiveness through realistic cross-slio datasets: (1) we adapt the Twitch Gamer Network which naturally simulates a cross-geo scenario and show that our formulation can provide consistent and avg. 6.1% gains over traditional supervised federated learning objectives and on avg. 1.7% gains compared to individual client specific self-supervised training and (2) we construct and introduce a new cross-silo dataset called Amazon Co-purchase Networks that have both the characteristics of the motivated problem setting. And, we witness on avg. 11.5% gains over traditional supervised federated learning and on avg. 1.9% gains over individually trained self-supervised models. Both experimental results point to the effectiveness of our proposed formulation. Finally, both our novel problem setting and dataset contributions provide new avenues for the research in FedGRL.Comment: FedGraph'22 workshop (non archival) version. (https://sites.google.com/view/fedgraph2022/accepted-papers