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We present an explicit analysis of wave-resonant instability of swirling flows inside fast rotating cylindrical containers. The linear dynamics are decomposed into the interaction between the horizontal inner centrifugal edge waves, the outer vertical gravity waves with the aim of understanding the dynamics of the centrifugal waves. We show how the far field velocity induced respectively by the centrifugal and the gravity waves affect each other's propagation rates and amplitude growth. We follow this with an analysis of the instability in terms of a four wave interaction, two centrifugal and two gravity ones, and explain why the resonant instability can be obtained only between a pair of two counter-propagating waves, one centrifugal and one gravity. Furthermore, a near resonant regime which does not yield instability is shown to result from a phase-locking configuration between a pair of a counter-propagating centrifugal wave and a pro-propagating gravity one, where the interaction affects the waves' propagation rates but not the amplitude growth.

Mode-dependent loss (MDL) is known to be a major issue in space-division multiplexed (SMD) systems. Its effect on performance is complex as it affects both the data carrying signal and the accumulated amplification noise. In this paper we propose a procedure for characterizing the MDL of SDM systems by means of standard measurements that are routinely performed on SDM setups. The figure of merit that we present for quantifying MDL incorporates the effect on the transmitted signal and the noise and is directly related to the spectral efficiency reduction.

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

We introduce a discrete model for binary spin-orbit-coupled (SOC) Bose-Einstein condensates (BEC) trapped in a deep one-dimensional optical lattice. Two different types of the couplings are considered, with spatial derivatives acting inside each species, or between the species. The discrete system with inter-site couplings dominated by the SOC, while the usual hopping is negligible, \textit{emulates} condensates composed of extremely heavy atoms, as well as those with opposite signs of the effective atomic masses in the two components.\ Stable localized composite states of miscible and immiscible types are constructed. The effect of the SOC on the immiscibility-miscibility transition in the localized complexes, which emulates the phase transition between insulating and conducting states in semiconductors, is studied. Comment: Journal of Physics B , in press

We discuss the spectrum phenomenon for Lipschitz functions on the infinite-dimensional torus. Suppose that [Formula: see text] is a measurable, real-valued, Lipschitz function on the torus [Formula: see text]. We prove that there exists a number [Formula: see text] with the following property: For any [Formula: see text], there exists a parallel, infinite-dimensional subtorus [Formula: see text] such that the restriction of the function [Formula: see text] to the subtorus [Formula: see text] has an [Formula: see text]-norm of at most [Formula: see text].

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.

© 2016 Elyashiv et al. Natural selection at one site shapes patterns of genetic variation at linked sites. Quantifying the effects of “linked selection” on levels of genetic diversity is key to making reliable inference about demography, building a null model in scans for targets of adaptation, and learning about the dynamics of natural selection. Here, we introduce the first method that jointly infers parameters of distinct modes of linked selection, notably background selection and selective sweeps, from genome-wide diversity data, functional annotations and genetic maps. The central idea is to calculate the probability that a neutral site is polymorphic given local annotations, substitution patterns, and recombination rates. Information is then combined across sites and samples using composite likelihood in order to estimate genome-wide parameters of distinct modes of selection. In addition to parameter estimation, this approach yields a map of the expected neutral diversity levels along the genome. To illustrate the utility of our approach, we apply it to genome-wide resequencing data from 125 lines in Drosophila melanogaster and reliably predict diversity levels at the 1Mb scale. Our results corroborate estimates of a high fraction of beneficial substitutions in proteins and untranslated regions (UTR). They allow us to distinguish between the contribution of sweeps and other modes of selection around amino acid substitutions and to uncover evidence for pervasive sweeps in untranslated regions (UTRs). Our inference further suggests a substantial effect of other modes of linked selection and of adaptation in particular. More generally, we demonstrate that linked selection has had a larger effect in reducing diversity levels and increasing their variance in D. melanogaster than previously appreciated.

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