X-Ray Flashes in Recurrent Novae: M31N 2008-12a and the Implications of the Swift Non-detection

Models of nova outbursts suggest that an X-ray flash should occur just after hydrogen ignition. However, this X-ray flash has never been observationally confirmed. We present four theoretical light curves of the X-ray flash for two very massive white dwarfs (WDs) of 1.380 and 1.385 M_sun and for two recurrence periods of 0.5 and 1 years. The duration of the X-ray flash is shorter for a more massive WD and for a longer recurrence period. The shortest duration of 14 hours (0.6 days) among the four cases is obtained for the 1.385 M_sun WD with one year recurrence period. In general, a nova explosion is relatively weak for a very short recurrence period, which results in a rather slow evolution toward the optical peak. This slow timescale and the predictability of very short recurrence period novae give us a chance to observe X-ray flashes of recurrent novae. In this context, we report the first attempt, using the Swift observatory, to detect an X-ray flash of the recurrent nova M31N 2008-12a (0.5 or 1 year recurrence period), which resulted in the non-detection of X-ray emission during the period of 8 days before the optical detection. We discuss the impact of these observations on nova outburst theory. The X-ray flash is one of the last frontiers of nova studies and its detection is essentially important to understand the pre-optical-maximum phase. We encourage further observations.Comment: 12 pages, including 9 figures and 3 tables. To appear in the Astrophysical Journa

Aerospace Medicine and Biology: A continuing bibliography, supplement 191

A bibliographical list of 182 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1979 is presented

Emerging Linguistic Functions in Early Infancy

This paper presents results from experimental studies on early language acquisition in infants and attempts to interpret the experimental results within the framework of the Ecological Theory of Language Acquisition (ETLA) recently proposed by (Lacerda et al., 2004a). From this perspective, the infant’s first steps in the acquisition of the ambient language are seen as a consequence of the infant’s general capacity to represent sensory input and the infant’s interaction with other actors in its immediate ecological environment. On the basis of available experimental evidence, it will be argued that ETLA offers a productive alternative to traditional descriptive views of the language acquisition process by presenting an operative model of how early linguistic function may emerge through interaction

The FALCON concept: multi-object spectroscopy combined with MCAO in near-IR

A large fraction of the present-day stellar mass was formed between z=0.5 and z~3 and our understanding of the formation mechanisms at work at these epochs requires both high spatial and high spectral resolution: one shall simultaneously} obtain images of objects with typical sizes as small as 1-2kpc(~0''.1), while achieving 20-50 km/s (R >= 5000) spectral resolution. The obvious instrumental solution to adopt in order to tackle the science goal is therefore a combination of multi-object 3D spectrograph with multi-conjugate adaptive optics in large fields. A partial, but still competitive correction shall be prefered, over a much wider field of view. This can be done by estimating the turbulent volume from sets of natural guide stars, by optimizing the correction to several and discrete small areas of few arcsec2 selected in a large field (Nasmyth field of 25 arcmin) and by correcting up to the 6th, and eventually, up to the 60th Zernike modes. Simulations on real extragalactic fields, show that for most sources (>80%), the recovered resolution could reach 0".15-0".25 in the J and H bands. Detection of point-like objects is improved by factors from 3 to >10, when compared with an instrument without adaptive correction. The proposed instrument concept, FALCON, is equiped with deployable mini-integral field units (IFUs), achieving spectral resolutions between R=5000 and 20000. Its multiplex capability, combined with high spatial and spectral resolution characteristics, is a natural ground based complement to the next generation of space telescopes.Comment: ESO Workshop Proceedings: Scientific Drivers for ESO Future VLT/VLTI Instrumentation, 10 pages and 5 figure

Holistic Dynamic Frequency Transformer for Image Fusion and Exposure Correction

The correction of exposure-related issues is a pivotal component in enhancing the quality of images, offering substantial implications for various computer vision tasks. Historically, most methodologies have predominantly utilized spatial domain recovery, offering limited consideration to the potentialities of the frequency domain. Additionally, there has been a lack of a unified perspective towards low-light enhancement, exposure correction, and multi-exposure fusion, complicating and impeding the optimization of image processing. In response to these challenges, this paper proposes a novel methodology that leverages the frequency domain to improve and unify the handling of exposure correction tasks. Our method introduces Holistic Frequency Attention and Dynamic Frequency Feed-Forward Network, which replace conventional correlation computation in the spatial-domain. They form a foundational building block that facilitates a U-shaped Holistic Dynamic Frequency Transformer as a filter to extract global information and dynamically select important frequency bands for image restoration. Complementing this, we employ a Laplacian pyramid to decompose images into distinct frequency bands, followed by multiple restorers, each tuned to recover specific frequency-band information. The pyramid fusion allows a more detailed and nuanced image restoration process. Ultimately, our structure unifies the three tasks of low-light enhancement, exposure correction, and multi-exposure fusion, enabling comprehensive treatment of all classical exposure errors. Benchmarking on mainstream datasets for these tasks, our proposed method achieves state-of-the-art results, paving the way for more sophisticated and unified solutions in exposure correction

Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network

The application of deep learning to symbolic domains remains an active research endeavour. Graph neural networks (GNN), consisting of trained neural modules which can be arranged in different topologies at run time, are sound alternatives to tackle relational problems which lend themselves to graph representations. In this paper, we show that GNNs are capable of multitask learning, which can be naturally enforced by training the model to refine a single set of multidimensional embeddings $\in \mathbb{R}^d$ and decode them into multiple outputs by connecting MLPs at the end of the pipeline. We demonstrate the multitask learning capability of the model in the relevant relational problem of estimating network centrality measures, focusing primarily on producing rankings based on these measures, i.e. is vertex $v_1$ more central than vertex $v_2$ given centrality $c$?. We then show that a GNN can be trained to develop a \emph{lingua franca} of vertex embeddings from which all relevant information about any of the trained centrality measures can be decoded. The proposed model achieves $89\%$ accuracy on a test dataset of random instances with up to 128 vertices and is shown to generalise to larger problem sizes. The model is also shown to obtain reasonable accuracy on a dataset of real world instances with up to 4k vertices, vastly surpassing the sizes of the largest instances with which the model was trained ($n=128$). Finally, we believe that our contributions attest to the potential of GNNs in symbolic domains in general and in relational learning in particular.Comment: Published at ICANN2019. 10 pages, 3 Figure