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We present the prospects for the pre-merger detection and localization of binary neutron star mergers with third generation gravitational-wave observatories. We consider a wide variety of gravitational-wave networks which may be operating in the 2030's and beyond; these networks include up to two Cosmic Explorer sites, the Einstein Telescope, and continued observation with the existing second generation ground-based detectors. For a fiducial local merger rate of 300 Gpc$^{-3}$yr$^{-1}$, we find that the Einstein Telescope on its own is able to detect 6 and 2 sources per year at 5 and 30 minutes before merger, respectively, while providing a localization of $<10~\textrm{deg}^2$. A single Cosmic Explorer would detect but be unable to localize sources on its own. A two-detector Cosmic Explorer network, however, would detect 22 and 0.4 mergers per year using the same criteria. A full three-detector network with the operation of dual Cosmic Explorers and the Einstein Telescope would allow for $<1~\textrm{deg}^2$ source localization at 5 minutes before merger for $\sim7$ sources per year. Given the dramatic increase in localization and detection capabilities, third generation observatories will enable the regular observation of the prompt emission of mergers by a broad array of observatories including gamma-ray, x-ray, and optical telescopes. Moreover, sub-degree localizations minutes before merger, combined with narrow-field-of-view high-energy telescopes, could strongly constrain the high-energy pre-merger emission models proposed in the last decade., Comment: 10 pages, 4 Figures, updated to match accepted ApJL version, supplementary materials at https://github.com/gwastro/gw-3g-merger-forecasting

This work investigates application of different machine learning based prediction methodologies to estimate the performance of silicon based textured cells. Concept of confidence bound regions is introduced and advantages of this concept are discussed in detail. Results show that reflection profiles and depth dependent optical generation profiles can be accurately estimated using Gaussian processes with exact knowledge of uncertainty in the prediction values.It is also shown that cell design parameters can be estimated for a desired performance metric.

We present a novel framework to solve simultaneously the electroweak hierarchy problem and the strong-CP problem. A small but finite Higgs vacuum expectation value and a small $\theta$-angle are selected after the QCD phase transition, without relying on the Peccei-Quinn mechanism or other traditional solutions. We predict a distinctive pattern of correlated signals at hadronic EDM, fuzzy dark matter and axion experiments., Comment: 9 pages, 3 figures. Version published on PRL

This paper introduces a new automata-theoretic class of string-to-string functions with polynomial growth. Several equivalent definitions are provided: a machine model which is a restricted variant of pebble transducers, and a few inductive definitions that close the class of regular functions under certain operations. Our motivation for studying this class comes from another characterization, which we merely mention here but prove elsewhere, based on a $\lambda$-calculus with a linear type system.As their name suggests, these comparison-free polyregular functions form a subclass of polyregular functions; we prove that the inclusion is strict. We also show that they are incomparable with HDT0L transductions, closed under usual function composition -- but not under a certain ``map'' combinator -- and satisfy a comparison-free version of the pebble minimization theorem.On the broader topic of polynomial growth transductions, we also consider the recently introduced layered streaming string transducers (SSTs), or equivalently k-marble transducers. We prove that a function can be obtained by composing such transducers together if and only if it is polyregular, and that k-layered SSTs (or k-marble transducers) are closed under ``map'' and equivalent to a corresponding notion of (k+1)-layered HDT0L systems.

Current tasks and methods in Document Understanding aims to process documents as single elements. However, documents are usually organized in collections (historical records, purchase invoices), that provide context useful for their interpretation. To address this problem, we introduce Document Collection Visual Question Answering (DocCVQA) a new dataset and related task, where questions are posed over a whole collection of document images and the goal is not only to provide the answer to the given question, but also to retrieve the set of documents that contain the information needed to infer the answer. Along with the dataset we propose a new evaluation metric and baselines which provide further insights to the new dataset and task.

Infographics are documents designed to effectively communicate information using a combination of textual, graphical and visual elements. In this work, we explore the automatic understanding of infographic images by using Visual Question Answering technique.To this end, we present InfographicVQA, a new dataset that comprises a diverse collection of infographics along with natural language questions and answers annotations. The collected questions require methods to jointly reason over the document layout, textual content, graphical elements, and data visualizations. We curate the dataset with emphasis on questions that require elementary reasoning and basic arithmetic skills. Finally, we evaluate two strong baselines based on state of the art multi-modal VQA models, and establish baseline performance for the new task. The dataset, code and leaderboard will be made available at http://docvqa.org

In expansion of our recent proposal (Physics, 2020, 2, 213-276) that the solar system's evolution occurred in two stages -- during the first stage, the gaseous giants formed (via disk instability), and, during the second stage (caused by an encounter with a particular stellar-object leading to "in-system" fission-driven nucleogenesis), the terrestrial planets formed (via accretion) -- we emphasize here that the mechanism of formation of such stellar-objects is generally universal and therefore encounters of such objects with stellar-systems may have occurred elsewhere across galaxies. If so, their aftereffects may perhaps be observed as puzzling features in the spectra of individual stars (such as idiosyncratic chemical enrichments) and/or in the structures of exoplanetary systems (such as unusually high planet densities or short orbital periods). This paper reviews and reinterprets astronomical data within the "fission-events framework." Classification of stellar systems as "pristine" or "impacted" is offered., Comment: Review, 26 pages, 13 figures

Forecasting and decision-making are generally modeled as two sequential steps with no feedback, following an open-loop approach. In this paper, we present application-driven learning, a new closed-loop framework in which the processes of forecasting and decision-making are merged and co-optimized through a bilevel optimization problem. We present our methodology in a general format and prove that the solution converges to the best estimator in terms of the expected cost of the selected application. Then, we propose two solution methods: an exact method based on the KKT conditions of the second-level problem and a scalable heuristic approach suitable for decomposition methods. The proposed methodology is applied to the relevant problem of defining dynamic reserve requirements and conditional load forecasts, offering an alternative approach to current ad hoc procedures implemented in industry practices. We benchmark our methodology with the standard sequential least-squares forecast and dispatch planning process. We apply the proposed methodology to an illustrative system and to a wide range of instances, from dozens of buses to large-scale realistic systems with thousands of buses. Our results show that the proposed methodology is scalable and yields consistently better performance than the standard open-loop approach.

Gravitational-wave observations became commonplace in Advanced LIGO-Virgo's recently concluded third observing run. 56 non-retracted candidates were identified and publicly announced in near real time. Gravitational waves from binary neutron star mergers, however, remain of special interest since they can be precursors to high-energy astrophysical phenomena like $\gamma$-ray bursts and kilonovae. While late-time electromagnetic emissions provide important information about the astrophysical processes within, the prompt emission along with gravitational waves uniquely reveals the extreme matter and gravity during - and in the seconds following - merger. Rapid communication of source location and properties from the gravitational-wave data is crucial to facilitate multi-messenger follow-up of such sources. This is especially enabled if the partner facilities are forewarned via an early-warning (pre-merger) alert. Here we describe the commissioning and performance of such a low-latency infrastructure within LIGO-Virgo. We present results from an end-to-end mock data challenge that detects binary neutron star mergers and alerts partner facilities before merger. We set expectations for these alerts in future observing runs.