A Milky Way-like barred spiral galaxy at a redshift of 3

The majority of massive disk galaxies in the local Universe show a stellar barred structure in their central regions, including our Milky Way. Bars are supposed to develop in dynamically cold stellar disks at low redshift, as the strong gas turbulence typical of disk galaxies at high redshift suppresses or delays bar formation. Moreover, simulations predict bars to be almost absent beyond z = 1.5 in the progenitors of Milky Way-like galaxies. Here we report observations of ceers-2112, a barred spiral galaxy at redshift zphot ≈ 3, which was already mature when the Universe was only 2 Gyr old. The stellar mass (M★ = 3.9 × 109 M⊙) and barred morphology mean that ceers-2112 can be considered a progenitor of the Milky Way, in terms of both structure and massassembly history in the frst 2 Gyr of the Universe, and was the closest in mass in the frst 4 Gyr. We infer that baryons in galaxies could have already dominated over dark matter at z ≈ 3, that high-redshift bars could form in approximately 400 Myr and that dynamically cold stellar disks could have been in place by redshift z = 4–5 (more than 12 Gyrs ago)

Review of Post-Multicultural Writers as Neo-Cosmopolitan Mediators by Sneja Gunew

Review of Post-Multicultural Writers as Neo-Cosmopolitan Mediators by Sneja Gune

The Differential Assembly History of the Centers and Outskirts of Main Sequence Galaxies at z∼2.3z\sim2.3

We present a study of spatially-resolved star formation histories (SFHs) for 60 $z\sim2.3$ main-sequence, star-forming galaxies selected from the MOSDEF spectroscopic survey in the GOODS-N field. Photometry is decomposed into a central and outer spatial component using observed $z_\mathrm{F850LP}-H_\mathrm{F160W}$ colors. The Prospector code is used to model spectral energy distributions for the centers, outskirts, and integrated galaxy using HST/ACS and WFC3, Spitzer/IRAC, and ground-based photometry, with additional constraints on metallicity and spectroscopic redshift from MOSDEF spectroscopy. For the low-resolution bands, spatially-resolved photometry is determined with an iterative approach. The reconstructed SFHs indicate that the majority of galaxies with $\log(M_\star/M_\odot)<10.5$ are observed while their central regions undergo relatively recent ($<100$ Myr) bursts of star formation, while the outskirts have a smooth, quasi-steady SFH. The enhanced star formation activity of the central parts is broadly consistent with the idea that it is produced by highly dissipative gas compaction and accretion. The broad dispersion of central density and size observed in the sample suggests that for the selected galaxies this process has started but is still far from being completed. The implication would be that selecting star-forming galaxies at cosmic noon frequently includes systems in an "evolved" evolutionary phase where the centers have recently started a burst of star formation activity that will likely initiate inside-out quenching in the next several hundred million years.Comment: 25 pages, 18 figures. Submitted for publication in Ap

Sharper Asymptotically Optimal CDC Schemes via Combinatorial Designs

Coded distributed computing (CDC) was introduced to greatly reduce the communication load for MapReduce computing systems. Such a system has $K$ nodes, $N$ input files, and $Q$ Reduce functions. Each input file is mapped by $r$ nodes and each Reduce function is computed by $s$ nodes. The architecture must allow for coding techniques that achieve the maximum multicast gain. Some CDC schemes that achieve optimal communication load have been proposed before. The parameters $N$ and $Q$ in those schemes, however, grow too fast with respect to $K$ to be of great practical value. To improve the situation, researchers have come up with some asymptotically optimal cascaded CDC schemes with $s+r=K$ from symmetric designs. In this paper, we propose new asymptotically optimal cascaded CDC schemes. Akin to known schemes, ours have $r+s=K$ and make use of symmetric designs as construction tools. Unlike previous schemes, ours have much smaller communication loads, given the same set of parameters $K$, $r$, $N$, and $Q$. We also expand the construction tools to include almost difference sets. Using them, we have managed to construct a new asymptotically optimal cascaded CDC scheme

A Construction of Asymptotically Optimal Cascaded CDC Schemes via Combinatorial Designs

A coded distributed computing (CDC) system aims to reduce the communication load in the MapReduce framework. Such a system has $K$ nodes, $N$ input files, and $Q$ Reduce functions. Each input file is mapped by $r$ nodes and each Reduce function is computed by $s$ nodes. The objective is to achieve the maximum multicast gain. There are known CDC schemes that achieve optimal communication load. In some prominent known schemes, however, $N$ and $Q$ grow too fast in terms of $K$, greatly reducing their gains in practical scenarios. To mitigate the situation, some asymptotically optimal cascaded CDC schemes with $r=s$ have been proposed by using symmetric designs. In this paper, we put forward new asymptotically optimal cascaded CDC schemes with $r=s$ by using $1$-designs. Compared with earlier schemes from symmetric designs, ours have much smaller computation loads while keeping the other relevant parameters the same. We also obtain new asymptotically optimal cascaded CDC schemes with more flexible parameters compared with previously best-performing schemes

Demographic information prediction: a portrait of smartphone application Users

Demographic information is usually treated as private data (e.g., gender and age), but has been shown great values in personalized services, advertisement, behavior study and other aspects. In this paper, we propose a novel approach to make efficient demographic prediction based on smartphone application usage. Specifically, we firstly consider to characterize the data set by building a matrix to correlate users with types of categories from the log file of smartphone applications. Then, by considering the category-unbalance problem, we make use of the correlation between users’ demographic information and their requested Internet resources to make the prediction, and propose an optimal method to further smooth the obtained results with category neighbors and user neighbors. The evaluation is supplemented by the dataset from real world workload. The results show advantages of the proposed prediction approach compared with baseline prediction. In particular, the proposed approach can achieve 81.21% of Accuracy in gender prediction. While in dealing with a more challenging multi-class problem, the proposed approach can still achieve good performance (e.g., 73.84% of Accuracy in the prediction of age group and 66.42% of Accuracy in the prediction of phone level)