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Active galactic nuclei (AGN) are prominent environments for stellar capture, growth and formation. These environments may catalyze stellar mergers and explosive transients, such as thermonuclear and core-collapse supernovae (SNe). SN explosions in AGN discs generate strong shocks, leading to unique observable signatures. We develop an analytical model which follows the evolution of the shock propagating in the disc until it eventually breaks out. We derive the peak luminosity, bolometric lightcurve, and breakout time. The peak luminosities may exceed $10^{45}$ erg s$^{-1}$ and last from hours to days. The brightest explosions occur in regions of reduced density; either off-plane, or in discs around low-mass central black holes ($\sim 10^6\ M_\odot$), or in starved subluminous AGNs. Explosions in the latter two sites are easier to observe due to a reduced AGN background luminosity. We perform suites of 1D Lagrangian radiative hydrodynamics SNEC code simulations to validate our results and obtain the luminosity in different bands, and 2D axisymmetric Eulerian hydrodynamics code HORMONE simulations to study the morphology of the ejecta and its deviation from spherical symmetry. The observed signature is expected to be a bright blue, UV, or X-ray flare on top of the AGN luminosity from the initial shock breakout, while the subsequent red part of the lightcurve will largely be unobservable. We estimate the upper limit for the total event rate to be $\mathcal{R}\lesssim 100\ \rm yr^{-1}\ Gpc^{-3}$ for optimal conditions and discuss the large uncertainties in this estimate. Future high-cadence transient searches may reveal these events. Some existing tidal disruption event candidates may originate from AGN supernovae. Accepted to MNRAS

Abstract We present an open-source MATLAB package, entitled OpenPIV-MATLAB, for analyzing particle image velocimetry (PIV) data. We extend the PIV analysis with additional tools for post-processing the PIV results including the estimation of aero/hydrodynamic forces from the PIV data of a wake behind an immersed (bluff or streamlined) body. The paper presents a detailed description of the packages, covering the three main parts: generating two-dimensional two component velocity fields from pairs of images (OpenPIV-MATLAB), spatial and temporal flow analysis based on the velocity fields (Spatial and Temporal Analysis Toolbox), and wake flow analysis along with the force estimates (getWAKE Toolbox). A complete analysis with a variety of post-processing capabilities is demonstrated using time-resolved PIV wake data of a freely flying European starling (Sturnus vulgaris) in a wind tunnel.

In this paper we present a deployed, scalable optical character recognition (OCR) system, which we call Rosetta, designed to process images uploaded daily at Facebook scale. Sharing of image content has become one of the primary ways to communicate information among internet users within social networks such as Facebook and Instagram, and the understanding of such media, including its textual information, is of paramount importance to facilitate search and recommendation applications. We present modeling techniques for efficient detection and recognition of text in images and describe Rosetta's system architecture. We perform extensive evaluation of presented technologies, explain useful practical approaches to build an OCR system at scale, and provide insightful intuitions as to why and how certain components work based on the lessons learnt during the development and deployment of the system. Comment: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) 2018, London, United Kingdom

Recent work on adversarial learning has focused mainly on neural networks and domains where those networks excel, such as computer vision, or audio processing. The data in these domains is typically homogeneous, whereas heterogeneous tabular datasets domains remain underexplored despite their prevalence. When searching for adversarial patterns within heterogeneous input spaces, an attacker must simultaneously preserve the complex domain-specific validity rules of the data, as well as the adversarial nature of the identified samples. As such, applying adversarial manipulations to heterogeneous datasets has proved to be a challenging task, and no generic attack method was suggested thus far. We, however, argue that machine learning models trained on heterogeneous tabular data are as susceptible to adversarial manipulations as those trained on continuous or homogeneous data such as images. To support our claim, we introduce a generic optimization framework for identifying adversarial perturbations in heterogeneous input spaces. We define distribution-aware constraints for preserving the consistency of the adversarial examples and incorporate them by embedding the heterogeneous input into a continuous latent space. Due to the nature of the underlying datasets We focus on $\ell_0$ perturbations, and demonstrate their applicability in real life. We demonstrate the effectiveness of our approach using three datasets from different content domains. Our results demonstrate that despite the constraints imposed on input validity in heterogeneous datasets, machine learning models trained using such data are still equally susceptible to adversarial examples.

We present $^{56}$Ni mass estimates for 110 normal Type II supernovae (SNe II), computed here from their luminosity in the radioactive tail. This sample consists of SNe from the literature, with at least three photometric measurements in a single optical band within 95-320 d since explosion. To convert apparent magnitudes to bolometric ones, we compute bolometric corrections (BCs) using 15 SNe in our sample having optical and near-IR photometry, along with three sets of SN II atmosphere models to account for the unobserved flux. We find that the $I$- and $i$-band are best suited to estimate luminosities through the BC technique. The $^{56}$Ni mass distribution of our SN sample has a minimum and maximum of 0.005 and 0.177 M$_{\odot}$, respectively, and a selection-bias-corrected average of $0.037\pm0.005$ M$_{\odot}$. Using the latter value together with iron isotope ratios of two sets of core-collapse (CC) nucleosynthesis models, we calculate a mean iron yield of $0.040\pm0.005$ M$_{\odot}$ for normal SNe II. Combining this result with recent mean $^{56}$Ni mass measurements for other CC SN subtypes, we estimate a mean iron yield $$36 per cent. We also find that the empirical relation between $^{56}$Ni mass and steepness parameter ($S$) is poorly suited to measure the $^{56}$Ni mass of normal SNe II. Instead, we present a correlation between $^{56}$Ni mass, $S$, and absolute magnitude at 50 d since explosion. The latter allows to measure $^{56}$Ni masses of normal SNe II with a precision around 30 per cent. Comment: 33 pages, 20 figures, 6 figures in appendix, accepted for publication to MNRAS

We consider the problem of distributed source simulation with no communication, in which Alice and Bob observe sequences $U^{n}$ and $V^{n}$ respectively, drawn from a joint distribution $p_{UV}^ {\otimes n}$ , and wish to locally generate sequences $X^{n}$ and $Y^{n}$ respectively with a joint distribution that is close (in KL divergence) to $p_{XY}^ {\otimes n}$ . We provide a single-letter condition under which such a simulation is asymptotically possible with a vanishing KL divergence. Our condition is nontrivial only in the case where the Gacs-Korner (GK) common information between $U$ and $V$ is nonzero, and we conjecture that only scalar Markov chains $X-U-V-Y$ can be simulated otherwise. Motivated by this conjecture, we further examine the case where both $p_{UV}$ and $p_{XY}$ are doubly symmetric binary sources with parameters $p,q\leq 1/2$ respectively. While it is trivial that in this case $p\leq q$ is both necessary and sufficient, we use Fourier analytic tools to show that when $p$ is close to $q$ then any successful simulation is close to being scalar in the total variation sense.

We review the current status of the radioisotopes program at the Soreq Applied Research Accelerator Facility (SARAF), where we utilize an electrostatic-ion-beam trap and a magneto-optical trap for studying the nuclear $\beta$-decay from trapped radioactive atoms and ions. The differential energy spectra of $\beta$'s and recoil ions emerging from the decay is sensitive to beyond standard model interactions and is complementary to high energy searches. The completed facility SARAF-II will be one of the world's most powerful deuteron, proton and fast neutron sources, producing light radioactive isotopes in unprecedented amounts, needed for obtaining enough statistics for a high precision measurement.

A hierarchical Bayesian model is applied to the Cosmicflows-3 catalog of galaxy distances in order to derive the peculiar velocity field and distribution of matter within $z \sim 0.054$. The model assumes the $\Lambda$CDM model within the linear regime and includes the fit of the galaxy distances together with the underlying density field. By forward modeling the data, the method is able to mitigate biases inherent to peculiar velocity analyses, such as the Homogeneous Malmquist bias or the log-normal distribution of peculiar velocities. The statistical uncertainty on the recovered velocity field is about 150 km/s depending on the location, and we study systematics coming from the selection function and calibration of distance indicators. The resulting velocity field and related density fields recover the cosmography of the Local Universe which is presented in an unprecedented volume of universe 10 times larger than previously reached. This methodology open the doors to reconstruction of initial conditions for larger and more accurate constrained cosmological simulations. This work is also preparatory to larger peculiar velocity datasets coming from Wallaby, TAIPAN or LSST. Comment: 15 pages, 14 figures, 1 table. Accepted by MNRAS

For a sample of star forming galaxies in the redshift interval 0.15$<$z$<$0.3, we study how both the relative strength of the AGN infra-red emission, compared to that due to the star formation (SF), and the numerical fraction of AGNs, change as a function of the total stellar mass of the hosting galaxy group (M$^{*}_{\mathrm{group}}$), between $10^{10.25}$ and $10^{11.9}$M$_{\odot}$. Using a multi-component SED fitting analysis, we separate the contribution of stars, AGN torus and star formation to the total emission at different wavelengths. This technique is applied to a new multi-wavelength data-set in the SIMES field (23 not redundant photometric bands), spanning the wavelength range from the UV (GALEX) to the far-IR (Herschel) and including crucial AKARI and WISE mid-IR observations (4.5 \mu m$<\lambda<$24 \mu m), where the BH thermal emission is stronger. This new photometric catalog, that includes our best photo-z estimates, is released through the NASA/IPAC Infrared Science Archive (IRSA). Groups are identified through a friends of friends algorithm ($\sim$62% purity, $\sim$51% completeness). We identified a total of 45 galaxies requiring an AGN emission component, 35 of which in groups and 10 in the field. We find BHAR$\propto ($M$^{*}_{\mathrm{group}})^{1.21\pm0.27}$ and (BHAR/SFR)$\propto ($M$^{*}_{\mathrm{group}})^{1.04\pm0.24}$ while, in the same range of M$^{*}_{\mathrm{group}}$, we do not observe any sensible change in the numerical fraction of AGNs. Our results indicate that the nuclear activity (i.e. the BHAR and the BHAR/SFR ratio) is enhanced when galaxies are located in more massive and richer groups. Comment: 31 pages, 23 figures

Context. The stars in the Milky Way thin and thick disks can be distinguished by several properties such as metallicity and kinematics. It is not clear whether the two populations also differ in the properties of planets orbiting the stars. In order to study this, a careful analysis of both the chemical composition and mass detection limits is required for a sufficiently large sample. Currently, this information is still limited only to large radial-velocity (RV) programs. Based on the recently published archival database of the High Accuracy Radial velocity Planet Searcher (HARPS) spectrograph, we present a first analysis of low-mass (small) planet occurrence rates in a sample of thin- and thick-disk stars. Aims. We aim to assess the effects of stellar properties on planet occurrence rates and to obtain first estimates of planet occurrence rates in the thin and thick disks of the Galaxy. As a baseline for comparison, we also aim to provide an updated value for the small close-in planet occurrence rate and compare it to results of previous RV and transit ($\textit{Kepler}$) works. Methods. We used archival HARPS RV datasets to calculate detection limits of a sample of stars that were previously analysed for their elemental abundances. For stars with known planets we first subtracted the Keplerian orbit. We then used this information to calculate planet occurrence rates according to a simplified Bayesian model in different regimes of stellar and planet properties. Results. Our results suggest that metal-poor stars and more massive stars host fewer low-mass close-in planets. We find the occurrence rates of these planets in the thin and thick disks to be comparable. In the iron-poor regimes, we find these occurrence rates to be significantly larger at the high-$\alpha$ region (thick-disk stars) as compared with the low-$\alpha$ region (thin-disk stars). In general, we find the... Comment: 10 pages, 6 figures, accepted for publication in A&A