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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: J. B. Vielfaure; S. D. Vergani; J. Japelj; Johan P. U. Fynbo; +22 Authors

    Context. The identification of the sources that reionized the Universe and their specific contribution to this process are key missing pieces of our knowledge of the early Universe. Faint star-forming galaxies may be the main contributors to the ionizing photon budget during the epoch of reionization, but their escaping photons cannot be detected directly due to inter-galactic medium opacity. Hence, it is essential to characterize the properties of faint galaxies with significant Lyman continuum (LyC) photon leakage up to z ∼ 4 to define indirect indicators allowing analogs to be found at the highest redshift. Aims. Long gamma-ray bursts (LGRBs) typically explode in star-forming regions of faint, star-forming galaxies. Through LGRB afterglow spectroscopy it is possible to detect directly LyC photons. Our aim is to use LGRBs as tools to study LyC leakage from faint, star-forming galaxies at high redshift. Methods. Here we present the observations of LyC emission in the afterglow spectra of GRB 191004B at z = 3.5055, together with those of the other two previously known LyC-leaking LGRB host galaxies (GRB 050908 at z = 3.3467, and GRB 060607A at z = 3.0749), to determine their LyC escape fraction and compare their properties. Results. From the afterglow spectrum of GRB 191004B we determine a neutral hydrogen column density at the LGRB redshift of log(NH I/cm−2) = 17.2 ± 0.15, and negligible extinction (AV = 0.03 ± 0.02 mag). The only metal absorption lines detected are C IV and Si IV. In contrast to GRB 050908 and GRB 060607A, the host galaxy of GRB 191004B displays significant Lyman-alpha (Lyα) emission. From its Lyα emission and the non-detection of Balmer emission lines we constrain its star-formation rate (SFR) to 1 ≤ SFR ≤ 4.7 M⊙ yr−1. We fit the Lyα emission with a shell model and find parameters values consistent with the observed ones. The absolute (relative) LyC escape fractions we find for GRB 191004B, GRB 050908 and GRB 060607A are of 0.35−0.11+0.10 (0.43−0.13+0.12), 0.08−0.04+0.05 (0.08−0.04+0.05) and 0.20−0.05+0.05 (0.45−0.15+0.15), respectively. We compare the LyC escape fraction of LGRBs to the values of other LyC emitters found from the literature, showing that LGRB afterglows can be powerful tools to study LyC escape for faint high-redshift star-forming galaxies. Indeed we could push LyC leakage studies to much higher absolute magnitudes. The host galaxies of the three LGRBs presented here have all M1600 > −19.5 mag, with the GRB 060607A host at M1600 > −16 mag. LGRB hosts may therefore be particularly suitable for exploring the ionizing escape fraction in galaxies that are too faint or distant for conventional techniques. Furthermore, the time involved is minimal compared to galaxy studies. © J.-B. Vielfaure et al. 2020 This work is part of the BEaPro project (PI: S.D. Vergani) funded by the French Agence Nationale de la Recherche (ANR-16-CE31-0003). We thank Giancarlo Ghirlanda for providing useful information. JBV and SDV thank Anne Verhamme for useful discussions. SDV acknowledges financial support from the French Space Agency (CNES). MG was supported by NASA through the NASA Hubble Fellowship grant HST-HF2-51409 and acknowledges support from HST grants HST-GO-15643.017-A, HST-AR-15039.003-A, and XSEDE grant TG-AST180036. The Cosmic DAWN center is funded by the DNRF. JPUF thanks the Carlsberg foundation for support. DBM acknowledges support from VILLUM FONDEN research grant 19054. NRT acknowledges support from STFC via grant ST/N000757/1. DAK acknowledges support from Spanish research project RTI2018-098104-J-I00 (GRBPhot). The PanSTARRS1 Surveys (PS1) and the PS1 public science archive have been made possible through contributions by the Institute for Astronomy, the University of Hawaii, the Pan-STARRS Project O ffice, the Max-Planck Society and its participating institutes, the Max Planck Institute for Astronomy, Heidelberg and the Max Planck Institute for Extraterrestrial Physics, Garching, The Johns Hopkins University, Durham University, the University of Edinburgh, the Queen's University Belfast, the Harvard-Smithsonian Center for Astrophysics, the Las Cumbres Observatory Global Telescope Network Incorporated, the National Central University of Taiwan, the Space Telescope Science Institute, the National Aeronautics and Space Administration under Grant No. NNX08AR22G issued through the Planetary Science Division of the NASA Science Mission Directorate, the National Science Foundation Grant No. AST-1238877, the University of Maryland, Eotvos Lorand University (ELTE), the Los Alamos National Laboratory, and the Gordon and Betty Moore Foundation. This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium).Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. The NumPy (van der Walt et al. 2011), SciPy (Virtanen et al. 2020) and matplotlib (Hunter 2007) packages have been extensively used for the preparation and presentation of this work. Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Peer reviewed

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    Authors: Achar, Pramod,; Henderson, Anthony; Juteau, Daniel; Riche, Simon;

    We construct a modular generalized Springer correspondence for any classical group, by generalizing to the modular setting various results of Lusztig in the case of characteristic-$0$ coefficients. We determine the cuspidal pairs in all classical types, and compute the correspondence explicitly for $\mathrm{SL}(n)$ with coefficients of arbitrary characteristic and for $\mathrm{SO}(n)$ and $\mathrm{Sp}(2n)$ with characteristic-$2$ coefficients. Comment: 52 pages. Version 2 corrects a minor mistake in the combinatorics of the type D case; no numbered statements are affected. Version 3 has minor additions, mostly in Section 2; final version, to appear in J. Eur. Math. Soc

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    Other literature type . 2017
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    Journal of the European Mathematical Society
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    Authors: Gabrielle Fack; Julien Grenet; Yinghua He;

    We propose novel approaches to estimating student preferences with data from matching mechanisms, especially the Gale-Shapley deferred acceptance. Even if the mechanism is strategy-proof, assuming that students truthfully rank schools in applications may be restrictive. We show that when students are ranked strictly by some ex ante known priority index (e.g., test scores), stability is a plausible and weaker assumption, implying that every student is matched with her favorite school/college among those she qualifies for ex post. The methods are illustrated in simulations and applied to school choice in Paris. We discuss when each approach is more appropriate in real-life settings. (JEL D11, D12, D82, I23)

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    The American Economic Review
    Article . 2019 . Peer-reviewed
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    Authors: M. Beccaria; G. P. Korchemsky; A. A. Tseytlin;

    We apply the localization technique to compute the free energy on four-sphere and the circular BPS Wilson loop in the four-dimensional $\cal N$=2 superconformal $Sp(2N)$ gauge theory containing vector multiplet coupled to four hypermultiplets in fundamental representation and one hypermultiplet in rank-2 antisymmetric representation. This theory is unique among similar $\cal N$=2 superconformal models that are planar-equivalent to $\cal N$=4 SYM in that the corresponding localization matrix model has the interaction potential containing single-trace terms only. We exploit this property to show that, to any order in large $N$ expansion and an arbitrary 't Hooft coupling $\lambda$, the free energy and the Wilson loop satisfy Toda lattice equations. Solving these equations at strong coupling, we find remarkably simple expressions for these observables which include all corrections in $1/N$ and $1/\sqrt{\lambda}$. We also compute the leading exponentially suppressed ${\cal O}(e^{-\sqrt{\lambda}})$ corrections and consider a generalization to the case when the fundamental hypermultiplets have a non-zero mass. The string theory dual of this $\cal N$=2 gauge theory should be a particular orientifold of AdS$_5 \times S^5$ type IIB string and we discuss the string theory interpretation of the obtained strong-coupling results. Comment: 41 pages. v2: minor correction; v3: eqs.(2.41) and (2.43) corrected

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    Journal of High Energy Physics
    Article . 2023 . Peer-reviewed
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      Journal of High Energy Physics
      Article . 2023 . Peer-reviewed
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    Authors: Katrina L. Grasby*† and Neda Jahanshad*† et al.;

    The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson’s disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

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    Authors: David, Guy; Engelstein, Max; Smit, Mariana; Toro, Tatiana;

    In [David-Toro 15] and [David-Engelstein-Toro 19], (some of) the authors studied almost minimizers for functionals of the type first studied by Alt and Caffarelli in [Alt-Caffarelli 81] and Alt, Caffarelli and Friedman in [Alt-Caffarelli-Friedman 84]. In this paper we study the regularity of almost minimizers to energy functionals with variable coefficients (as opposed to [DT15, DET19. AC 81] and [ACF84] which deal only with the "Laplacian" setting). We prove Lipschitz regularity up to, and across, the free boundary, generalizing the results of [David-Toro 15] to the variable coefficient setting. Comment: 41 pages. Revised version has minor corrections and additions. To appear in Math Z

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    Mathematische Zeitschrift
    Article . 2021 . Peer-reviewed
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      Mathematische Zeitschrift
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    Authors: Cacciari, Matteo; Salam, Gavin P.; Strassler, Matthew J.;

    It has recently been pointed out that CDF data for the cross section of high-p_t charged particles show an excess of up to three orders of magnitude over QCD predictions, a feature tentatively ascribed to possible violations of factorisation. We observe that for p_t > 80 GeV the measured charged-particle cross sections become of the same order as jet cross sections. Combining this information with data on charged particle distributions within jets allows us to rule out the hypothesis that the CDF data could be interpreted in terms of QCD factorisation violation. We also comment on the difficulty of interpreting the excess in terms of new physics scenarios. 8 pages, 4 figures

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    https://doi.org/10.48550/arxiv...
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    Authors: Aubin, Benjamin; Krzakala, Florent; Lu, Yue M.; Zdeborová, Lenka;

    We consider a commonly studied supervised classification of a synthetic dataset whose labels are generated by feeding a one-layer neural network with random iid inputs. We study the generalization performances of standard classifiers in the high-dimensional regime where $\alpha=n/d$ is kept finite in the limit of a high dimension $d$ and number of samples $n$. Our contribution is three-fold: First, we prove a formula for the generalization error achieved by $\ell_2$ regularized classifiers that minimize a convex loss. This formula was first obtained by the heuristic replica method of statistical physics. Secondly, focussing on commonly used loss functions and optimizing the $\ell_2$ regularization strength, we observe that while ridge regression performance is poor, logistic and hinge regression are surprisingly able to approach the Bayes-optimal generalization error extremely closely. As $\alpha \to \infty$ they lead to Bayes-optimal rates, a fact that does not follow from predictions of margin-based generalization error bounds. Third, we design an optimal loss and regularizer that provably leads to Bayes-optimal generalization error. Comment: 11 pages + 45 pages Supplementary Material / 5 figures, v2 revised and accepted at NeurIPS

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    Authors: Lia Papadopoulos; Christopher W. Lynn; Demian Battaglia; Danielle S. Bassett;

    At the macroscale, the brain operates as a network of interconnected neuronal populations, which display coordinated rhythmic dynamics that support interareal communication. Understanding how stimulation of different brain areas impacts such activity is important for gaining basic insights into brain function and for further developing therapeutic neurmodulation. However, the complexity of brain structure and dynamics hinders predictions regarding the downstream effects of focal stimulation. More specifically, little is known about how the collective oscillatory regime of brain network activity—in concert with network structure—affects the outcomes of perturbations. Here, we combine human connectome data and biophysical modeling to begin filling these gaps. By tuning parameters that control collective system dynamics, we identify distinct states of simulated brain activity and investigate how the distributed effects of stimulation manifest at different dynamical working points. When baseline oscillations are weak, the stimulated area exhibits enhanced power and frequency, and due to network interactions, activity in this excited frequency band propagates to nearby regions. Notably, beyond these linear effects, we further find that focal stimulation causes more distributed modifications to interareal coherence in a band containing regions’ baseline oscillation frequencies. Importantly, depending on the dynamical state of the system, these broadband effects can be better predicted by functional rather than structural connectivity, emphasizing a complex interplay between anatomical organization, dynamics, and response to perturbation. In contrast, when the network operates in a regime of strong regional oscillations, stimulation causes only slight shifts in power and frequency, and structural connectivity becomes most predictive of stimulation-induced changes in network activity patterns. In sum, this work builds upon and extends previous computational studies investigating the impacts of stimulation, and underscores the fact that both the stimulation site, and, crucially, the regime of brain network dynamics, can influence the network-wide responses to local perturbations. Author summary Stimulation can be used to alter brain activity and is a therapeutic option for certain neurological conditions. However, predicting the distributed effects of local perturbations is difficult. Previous studies show that responses to stimulation depend on anatomical (or structural) coupling. In addition to structure, here we consider how stimulation effects also depend on the brain’s collective dynamical (or functional) state, arising from the coordination of rhythmic activity across large-scale networks. In a whole-brain computational model, we show that global responses to regional stimulation can indeed be contingent upon and differ across various dynamical working points. Notably, depending on the network’s oscillatory regime, stimulation can accelerate the activity of the stimulated site, and lead to widespread effects at both the new, excited frequency, as well as in a much broader frequency range including areas’ baseline frequencies. While structural connectivity is a good predictor of “excited band” changes, in some states “baseline band” effects can be better predicted by functional connectivity, which depends upon the system’s oscillatory regime. By integrating and extending past efforts, our results thus indicate that dynamical—in additional to structural—brain organization plays a role in governing how focal stimulation modulates interactions between distributed network elements.

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    Europe PubMed Central
    Article . 2020
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    PLoS Computational Biology
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    PLoS Computational Biology
    Article . 2020 . Peer-reviewed
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    Authors: Guiomar Niso; Rotem Botvinik-Nezer; Stefan Appelhoff; Alejandro De La Vega; +19 Authors

    G.N. was supported by the AXA Research Fund and by NIH CR-CNS: US-France Data Sharing Proposal (NIBIB (USA) R01 EB030896 and ANR-20-NEUC-0004-01). R.B-N. is an Awardee of the Weizmann Institute of Science -Israel National Postdoctoral Award Program for Advancing Women in Science. A.d.l.V was supported by NIH grant 5R01MH109682. O.E. was supported by the Swiss National Science Foundation (SNSF) Project 185872 and NIMH grant (RF1MH121867, O.E.). J.A.E was supported by NIH grant R37MH066078 to Todd S. Braver. K.F. was supported by the Polish National Agency for Academic Exchange (the Bekker programme; PPN/BEK/2020/1/00279/U/00001). M.G. was supported by the Elsass foundation (18-3-0147). Y.O.H. was supported by NIH grant 1P41EB019936-01A1. P.H. was supported in parts by funding from the Canada First Research Excellence Fund, awarded to McGill University for the Healthy Brains for Healthy Lives initiative, the National Institutes of Health (NIH) NIH-NIBIB P41 EB019936 ReproNim), the National Institute Of Mental Health of the NIH under Award Number R01MH096906, a research scholar award from Brain Canada, in partnership with Health Canada, for the Canadian Open Neuroscience Platform initiative, as well as an Excellence Scholarship from Unifying Neuroscience and Artificial Intelligence - Québec. A.K. was supported by the TransMedTech Institute Postdoc Fellowship. D.B.K. was supported by the National Institute of Mental Health under grant RF1 MH120021 (PI: Keator), and acknowledges the International Neuroinformatics Coordinating Facility (INCF). C.R.P. was supported by the Novo Nordisk Fonden NNF20OC0063277. F.P. is supported by NSF grants IIS 1636893, IIS 1912270, and BCS 1734853, NIH National Institute of Biomedical Imaging and Bioengineering (NIBIB) grant 1R01EB029272, NIH NIMH 1R01MH126699, and a Microsoft Investigator Fellowship. N.Q. acknowledges the NIDM-Terms grant (National Institutes of Mental Health (NIMH) grant 1RF1MH120021 PI: David B Keator). K.J.W. was supported by The UKRI Strategic Priorities Fund under EPSRC Grant EP/T001569/1, particularly the "Tools, Practices and Systems" theme within that grant, and by The Alan Turing Institute under the EPSRC grant EP/N510129/1. J.W.R. was supported by the DFG Device Center Grant INST 184/216-1 “Tools and infrastructure for open and reproducible neuroimaging”, and the DFG grant 390895286 of the Excellence Strategy (EXC 2177/1, Hearing4All). Empirical observations of how labs conduct research indicate that the adoption rate of open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with the overwhelming evidence for the necessity of these practices and their benefits for individual researchers, scientific progress, and society in general. To date, information required for implementing open science practices throughout the different steps of a research project is scattered among many different sources. Even experienced researchers in the topic find it hard to navigate the ecosystem of tools and to make sustainable choices. Here, we provide an integrated overview of community-developed resources that can support collaborative, open, reproducible, replicable, robust and generalizable neuroimaging throughout the entire research cycle from inception to publication and across different neuroimaging modalities. We review tools and practices supporting study inception and planning, data acquisition, research data management, data processing and analysis, and research dissemination. An online version of this resource can be found at https://oreoni.github.io. We believe it will prove helpful for researchers and institutions to make a successful and sustainable move towards open and reproducible science and to eventually take an active role in its future development.

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    https://doi.org/10.31219/osf.i...
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