No Arabic abstract
The Gaia space project, planned for launch in 2011, is one of the ESA cornerstone missions, and will provide astrometric, photometric and spectroscopic data of very high quality for about one billion stars brighter than V=20. This will allow to reach an unprecedented level of information and knowledge on several of the most fundamental astrophysical issues, such as mapping of the Milky Way, stellar physics (classification and parameterization), Galactic kinematics and dynamics, study of the resolved stellar populations in the Local Group, distance scale and age of the Universe, dark matter distribution (potential tracers), reference frame (quasars, astrometry), planet detection, fundamental physics, Solar physics, Solar system science. I will present a description of the instrument and its main characteristics, and discuss a few specific science cases where Gaia data promise to contribute fundamental improvement within the scope of this Symposium.
Natural language processing systems often struggle with out-of-vocabulary (OOV) terms, which do not appear in training data. Blends, such as innoventor, are one particularly challenging class of OOV, as they are formed by fusing together two or more bases that relate to the intended meaning in unpredictable manners and degrees. In this work, we run experiments on a novel dataset of English OOV blends to quantify the difficulty of interpreting the meanings of blends by large-scale contextual language models such as BERT. We first show that BERTs processing of these blends does not fully access the component meanings, leaving their contextual representations semantically impoverished. We find this is mostly due to the loss of characters resulting from blend formation. Then, we assess how easily different models can recognize the structure and recover the origin of blends, and find that context-aware embedding systems outperform character-level and context-free embeddings, although their results are still far from satisfactory.
Knowledge of ages for stars formed over a galaxys lifetime is fundamental to understand its formation and evolution. However, stellar ages are difficult to obtain since they cannot be measured from observations, being comparison with stellar models (Soderblom 2010) required. Alternatively, age distributions can be derived applying the robust technique of colour-magnitude diagram fitting (Gallart et al. 2005), till now mainly employed to study nearby galaxies. The new distances to individual Milky Way stars from the Gaia mission (Brown et al. 2018) have allowed us to use this technique to derive ages from a thick disk colour-magnitude diagram, and from the enigmatic, two-sequenced colour-magnitude diagram of the kinematically hot local halo (Babusiaux et al. 2018), which blue-sequence has been linked to a major accretion event (Haywood et al. 2018, Helmi et al. 2018). Because accurate ages were lacking, the time of the merger and its role on our Galaxys early evolution remained unclear. We show that the stars in both halo sequences share identical age distributions, and are older than the bulk of thick disc stars. The sharp halo age cut 10 Gyr ago can be identified with the accretion of Gaia-Enceladus. Along with state-of-the-art cosmological simulations of galaxy formation (Brook et al. 2012), these robust ages allow us to order the early sequence of events that shaped our Galaxy, identifying the red-sequence as the first stars formed within the Milky Way progenitor which, because of their kinematics, can be described as its long sought in-situ halo.
In this paper, we build the case that 5G and concomitant emerging technologies (such as IoT, big data, artificial intelligence, and machine learning) will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of AI and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system, and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution.
We use the framework developed as part of the MESA Isochrones and Stellar Tracks (MIST) project to assess the utility of several types of observables in jointly measuring the age and 1D stellar model parameters in star clusters. We begin with a pedagogical overview summarizing the effects of stellar model parameters, such as the helium abundance, mass-loss efficiency, and the mixing length parameter, on observational diagnostics including the color-magnitude diagram, mass-radius relation, and surface abundances, amongst others. We find that these parameters and the stellar age influence observables in qualitatively distinctive, degeneracy-breaking ways. To assess the current state of affairs, we use the recent Gaia Data Release 2 (DR2) along with data from the literature to investigate three well-studied old open clusters---NGC6819, M67, NGC6791---as case studies. Although there is no obvious tension between the existing observations and the MIST models for NGC6819, there are interesting discrepancies in the cases of M67 and NGC6791. At this time, parallax zero point uncertainties in Gaia DR2 remain one of the limiting factors in the analysis of these clusters. With a combination of exquisite photometry, parallax distances, and cluster memberships from Gaia at the end of its mission, we anticipate precise and accurate ages for these and other star clusters in the Galaxy.
We simulate the evolutions of the stellar wind and the supernova remnant (SNR) originating from a runaway massive star in an uniform Galactic environment based on the three-dimensional magnetohydrodynamics models. Taking the stellar wind into consideration, we can explain the radio morphologies of many supernova remnants. The directions of the kinematic velocity of the progenitor, the magnetic field and the line of sight are the most important factors influencing the morphologies. If the velocity is perpendicular to the magnetic field, the simulation will give us two different unilateral SNRs and a bilateral symmetric SNR. If the velocity is parallel to the magnetic field, we can obtain a bilateral asymmetric SNR and a quasi-circular SNR. Our simulations show the stellar wind plays a key role in the radio evolution of a SNR, which implies the Galactic global density and magnetic field distribution play a secondary role in shaping a SNR.