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  • Publication . Article . Other literature type . Preprint . 2022
    Open Access English
    Authors: 
    Laura Sberna; Stanislav Babak; Sylvain Marsat; Andrea Caputo; Giulia Cusin; Alexandre Toubiana; Enrico Barausse; Chiara Caprini; Tito Dal Canton; Alberto Sesana; +1 more
    Project: EC | GRU (101007855), EC | LDMThExp (682676), EC | GRAMS (815673), EC | B Massive (818691)

    Binaries of relatively massive black holes like GW190521 have been proposed to form in dense gas environments, such as the disks of Active Galactic Nuclei (AGNs), and they might be associated with transient electromagnetic counterparts. The interactions of this putative environment with the binary could leave a significant imprint at the low gravitational wave frequencies observable with the Laser Interferometer Space Antenna (LISA). We show that LISA will be able to detect up to ten GW190521-like black hole binaries, with sky position errors $\lesssim1$ deg$^2$. Moreover, it will measure directly various effects due to the orbital motion around the supermassive black hole at the center of the AGN, especially the Doppler modulation and the Shapiro time delay. Thanks to a careful treatment of their frequency domain signal, we were able to perform the full parameter estimation of Doppler and Shapiro-modulated binaries as seen by LISA. We find that the Doppler and Shapiro effects will allow for measuring the AGN parameters (radius and inclination of the orbit around the AGN, central black hole mass) with up to percent-level precision. Properly modeling these low-frequency environmental effects is crucial to determine the binary formation history, as well as to avoid biases in the reconstruction of the source parameters and in tests of general relativity with gravitational waves. 13+4 pages, 7+1 figures v3: corrected typo in Fig 5

  • Open Access English
    Authors: 
    Gal Dalal; Balázs Szörényi; Gugan Thoppe;
    Project: NSF | RoL: FELS: RAISE: Does ev... (1840223)

    Policy evaluation in reinforcement learning is often conducted using two-timescale stochastic approximation, which results in various gradient temporal difference methods such as GTD(0), GTD2, and TDC. Here, we provide convergence rate bounds for this suite of algorithms. Algorithms such as these have two iterates, $\theta_n$ and $w_n,$ which are updated using two distinct stepsize sequences, $\alpha_n$ and $\beta_n,$ respectively. Assuming $\alpha_n = n^{-\alpha}$ and $\beta_n = n^{-\beta}$ with $1 > \alpha > \beta > 0,$ we show that, with high probability, the two iterates converge to their respective solutions $\theta^*$ and $w^*$ at rates given by $\|\theta_n - \theta^*\| = \tilde{O}( n^{-\alpha/2})$ and $\|w_n - w^*\| = \tilde{O}(n^{-\beta/2});$ here, $\tilde{O}$ hides logarithmic terms. Via comparable lower bounds, we show that these bounds are, in fact, tight. To the best of our knowledge, ours is the first finite-time analysis which achieves these rates. While it was known that the two timescale components decouple asymptotically, our results depict this phenomenon more explicitly by showing that it in fact happens from some finite time onwards. Lastly, compared to existing works, our result applies to a broader family of stepsizes, including non-square summable ones.

  • Open Access English
    Authors: 
    Frédéric Paulin; Uri Shapira;
    Country: France
    Project: UKRI | Isaac Newton Institute fo... (EP/K032208/1), NSF | Mathematical Sciences Res... (1440140)

    Let $P$ be a prime polynomial in the variable $Y$ over a finite field and let $f$ be a quadratic irrational in the field of formal Laurant series in the variable $Y^{-1}$. We study the asymptotic properties of the degrees of the coefficients of the continued fraction expansion of quadratic irrationals such as $P^nf$ and prove results that are in sharp contrast to the analogue situation in zero characteristic.

  • Open Access English
    Authors: 
    Liam Connor; J. van Leeuwen; L. C. Oostrum; Emily Petroff; Yogesh Maan; Elizabeth A. K. Adams; Jisk Attema; J. E. Bast; Oliver M. Boersma; H. Dénes; +31 more
    Country: Netherlands
    Project: EC | ALERT (617199), EC | RadioNet (730562), NWO | Microporous membranes fro... (2300159022), NWO | ARTS - the Apertif Radio ... (2300177746)

    ABSTRACT We report the detection of a bright fast radio burst, FRB 191108, with Apertif on the Westerbork Synthesis Radio Telescope. The interferometer allows us to localize the FRB to a narrow 5 arcsec × 7 arcmin ellipse by employing both multibeam information within the Apertif phased-array feed beam pattern, and across different tied-array beams. The resulting sightline passes close to Local Group galaxy M33, with an impact parameter of only 18 kpc with respect to the core. It also traverses the much larger circumgalactic medium (CGM) of M31, the Andromeda Galaxy. We find that the shared plasma of the Local Group galaxies could contribute ∼10 per cent of its dispersion measure of 588 pc cm−3. FRB 191108 has a Faraday rotation measure (RM) of +474 $\pm \, 3$ rad m−2, which is too large to be explained by either the Milky Way or the intergalactic medium. Based on the more moderate RMs of other extragalactic sources that traverse the halo of M33, we conclude that the dense magnetized plasma resides in the host galaxy. The FRB exhibits frequency structure on two scales, one that is consistent with quenched Galactic scintillation and broader spectral structure with Δν ≈ 40 MHz. If the latter is due to scattering in the shared M33/M31 CGM, our results constrain the Local Group plasma environment. We found no accompanying persistent radio sources in the Apertif imaging survey data.

  • Publication . Preprint . Conference object . Article . 2020
    Open Access English
    Authors: 
    Susanna F. de Rezende; Or Meir; Jakob Nordström; Toniann Pitassi; Robert Robere; Marc Vinyals;
    Project: EC | UTHOTP (279611), NSERC

    We significantly strengthen and generalize the theorem lifting Nullstellensatz degree to monotone span program size by Pitassi and Robere (2018) so that it works for any gadget with high enough rank, in particular, for useful gadgets such as equality and greater-than. We apply our generalized theorem to solve three open problems: •We present the first result that demonstrates a separation in proof power for cutting planes with unbounded versus polynomially bounded coefficients. Specifically, we exhibit CNF formulas that can be refuted in quadratic length and constant line space in cutting planes with unbounded coefficients, but for which there are no refutations in subexponential length and subpolynomial line space if coefficients are restricted to be of polynomial magnitude. •We give the first explicit separation between monotone Boolean formulas and monotone real formulas. Specifically, we give an explicit family of functions that can be computed with monotone real formulas of nearly linear size but require monotone Boolean formulas of exponential size. Previously only a non-explicit separation was known. •We give the strongest separation to-date between monotone Boolean formulas and monotone Boolean circuits. Namely, we show that the classical GEN problem, which has polynomial-size monotone Boolean circuits, requires monotone Boolean formulas of size $2^{\Omega(n/\text{polylog}(n))}$ . An important technical ingredient, which may be of independent interest, is that we show that the Nullstellensatz degree of refuting the pebbling formula over a DAG $G$ over any field coincides exactly with the reversible pebbling price of $G$ . In particular, this implies that the standard decision tree complexity and the parity decision tree complexity of the corresponding falsified clause search problem are equal. This is an extended abstract. The full version of the paper is available at https://arxiv.org/abs/2001.02144.

  • Open Access English
    Authors: 
    Ivano Baronchelli; G. Rodighiero; Harry I. Teplitz; Claudia Scarlata; Alberto Franceschini; S. Berta; Laia Barrufet; Mattia Vaccari; Matteo Bonato; Laure Ciesla; +15 more
    Publisher: HAL CCSD
    Countries: United States, France, Italy
    Project: EC | HELP (607254)

    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

  • Open Access English
    Authors: 
    Gurel-Gurevich, Ori; Jerison, Daniel C.; Nachmias, Asaf;
    Project: EC | RANDGEOM (676970)

    Given a finite simple triangulation, we estimate the sizes of circles in its circle packing in terms of Cannon's vertex extremal length. Our estimates provide control over the size of the largest circle in the packing. We use them, combined with results from [12], to prove that in a proper circle packing of the discrete mating-of-trees random map model of Duplantier, Gwynne, Miller and Sheffield, the size of the largest circle goes to zero with high probability. 13 pages, 3 figures

  • Publication . Conference object . Preprint . Article . 2021
    Open Access English
    Authors: 
    Yonatan Yehezkeally; Sagi Marcovich; Eitan Yaakobi;
    Publisher: IEEE
    Project: EC | inCREASE (801434)

    The problem of string reconstruction based on its substrings spectrum has received significant attention recently due to its applicability to DNA data storage and sequencing. In contrast to previous works, we consider in this paper a setup of this problem where multiple strings are reconstructed together. Given a multiset $S$ of strings, all their substrings of some fixed length $\ell$, defined as the $\ell$-profile of $S$, are received and the goal is to reconstruct all strings in $S$. A multi-strand $\ell$-reconstruction code is a set of multisets such that every element $S$ can be reconstructed from its $\ell$-profile. Given the number of strings~$k$ and their length~$n$, we first find a lower bound on the value of $\ell$ necessary for existence of multi-strand $\ell$-reconstruction codes with non-vanishing asymptotic rate. We then present two constructions of such codes and show that their rates approach~$1$ for values of $\ell$ that asymptotically behave like the lower bound. 5 pages + 1 reference page. Version accepted for presentation at ITW2021

  • Publication . Preprint . Conference object . Article . 2020
    Open Access English
    Authors: 
    Matt Gardner; Yoav Artzi; Victoria Basmov; Jonathan Berant; Ben Bogin; Sihao Chen; Pradeep Dasigi; Dheeru Dua; Yanai Elazar; Ananth Gottumukkala; +16 more
    Publisher: Association for Computational Linguistics
    Project: EC | DELPHI (802800)

    Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture the abilities a dataset is intended to test. We propose a more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data. In particular, after a dataset is constructed, we recommend that the dataset authors manually perturb the test instances in small but meaningful ways that (typically) change the gold label, creating contrast sets. Contrast sets provide a local view of a model’s decision boundary, which can be used to more accurately evaluate a model’s true linguistic capabilities. We demonstrate the efficacy of contrast sets by creating them for 10 diverse NLP datasets (e.g., DROP reading comprehension, UD parsing, and IMDb sentiment analysis). Although our contrast sets are not explicitly adversarial, model performance is significantly lower on them than on the original test sets—up to 25% in some cases. We release our contrast sets as new evaluation benchmarks and encourage future dataset construction efforts to follow similar annotation processes.

  • Publication . Article . Preprint . Other literature type . 2020
    Open Access English
    Authors: 
    Or Sharir; Yoav Levine; Noam Wies; Giuseppe Carleo; Amnon Shashua;
    Country: Switzerland

    Artificial neural networks were recently shown to be an efficient representation of highly entangled many-body quantum states. In practical applications, neural-network states inherit numerical schemes used in variational Monte Carlo method, most notably the use of Markov-chain Monte Carlo (MCMC) sampling to estimate quantum expectations. The local stochastic sampling in MCMC caps the potential advantages of neural networks in two ways: (i) Its intrinsic computational cost sets stringent practical limits on the width and depth of the networks, and therefore limits their expressive capacity; (ii) its difficulty in generating precise and uncorrelated samples can result in estimations of observables that are very far from their true value. Inspired by the state-of-the-art generative models used in machine learning, we propose a specialized neural-network architecture that supports efficient and exact sampling, completely circumventing the need for Markov-chain sampling. We demonstrate our approach for two-dimensional interacting spin models, showcasing the ability to obtain accurate results on larger system sizes than those currently accessible to neural-network quantum states.

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