New cluster members and halo stars of the Galactic globular cluster NGC 1851

NGC 1851 is an intriguing Galactic globular cluster, with multiple stellar evolutionary sequences, light and heavy element abundance variations and indications of a surrounding stellar halo. We present the first results of a spectroscopic study of red giant stars within and outside of the tidal radius of this cluster. Our results identify nine probable new cluster members (inside the tidal radius) with heliocentric radial velocities consistent with that of NGC 1851. We also identify, based on their radial velocities, four probable extratidal cluster halo stars at distances up to ~3.1 times the tidal radius, which are supportive of previous findings that NGC 1851 is surrounded by an extended stellar halo. Proper motions were available for 12 of these 13 stars and all are consistent with that of NGC 1851. Apart from the cluster members and cluster halo stars, our observed radial velocity distribution agrees with the expected distribution from a Besancon disk/N-body stellar halo Milky Way model generated by the Galaxia code, suggesting that no other structures at different radial velocities are present in our field. The metallicities of these stars are estimated using equivalent width measurements of the near infrared calcium triplet absorption lines and are found, within the limitations of this method, to be consistent with that of NGC 1851. In addition we recover 110 red giant cluster members from previous studies based on their radial velocities and identify three stars with unusually high radial velocities.Comment: 10 pages, 8 figures. Accepted for publication in MNRA

New halo stars of the Galactic globular clusters M3 and M13 in the LAMOST DR1 Catalog

M3 and M13 are Galactic globular clusters with previous reports of surrounding stellar halos. We present the results of a search for members and extratidal cluster halo stars within and outside of the tidal radius of these clusters in the LAMOST Data Release 1. We find seven candidate cluster members (inside the tidal radius) of both M3 and M13 respectively. In M3 we also identify eight candidate extratidal cluster halo stars at distances up to ~9.8 times the tidal radius, and in M13 we identify 12 candidate extratidal cluster halo stars at distances up to ~13.8 times the tidal radius. These results support previous indications that both M3 and M13 are surrounded by extended stellar halos, and we find that the GC destruction rates corresponding to the observed mass loss are generally significantly higher than theoretical studies predict.Comment: 21 pages, 10 figures. Accepted for publication in Ap

MCA, Inc. v. United States: Judicial Recognition of the Separate Interests Theory

For United States federal tax purposes, the classificaiton of an entity as a partnership or a corporation has significant ramifications, particularly with respect to entities in foreign countries. Classification is especially important to the owners - whether shareholders or partners - of the entity because the question of whether they are taxed on their share of the profits or only upon repartriation will often depend on how the entity, set up under foreign law, is recognized by the Internal Revenue Service (Service). While entity classification in the domestic area has always been vulnerable to challenge, foreign entities face an additional problem in view of the Service\u27s application of a rather complicated separate interests test. While in theory the classification of a foreign entity embodies the same tests as the classification of a domestic entity, recent cases and revenue rulings have created uncertainty and confusion for United States taxpayers wishing to conduct some portion of their operations abroad. In MCA, Inc. v. United States, the United States Court of Appeals for the Ninth Circuit examined this problem of classifying foreign entities for federal tax purposes

Deep Learning for Metagenomic Data: using 2D Embeddings and Convolutional Neural Networks

Deep learning (DL) techniques have had unprecedented success when applied to images, waveforms, and texts to cite a few. In general, when the sample size (N) is much greater than the number of features (d), DL outperforms previous machine learning (ML) techniques, often through the use of convolution neural networks (CNNs). However, in many bioinformatics ML tasks, we encounter the opposite situation where d is greater than N. In these situations, applying DL techniques (such as feed-forward networks) would lead to severe overfitting. Thus, sparse ML techniques (such as LASSO e.g.) usually yield the best results on these tasks. In this paper, we show how to apply CNNs on data which do not have originally an image structure (in particular on metagenomic data). Our first contribution is to show how to map metagenomic data in a meaningful way to 1D or 2D images. Based on this representation, we then apply a CNN, with the aim of predicting various diseases. The proposed approach is applied on six different datasets including in total over 1000 samples from various diseases. This approach could be a promising one for prediction tasks in the bioinformatics field.Comment: Accepted at NIPS 2017 Workshop on Machine Learning for Health (https://ml4health.github.io/2017/); In Proceedings of the NIPS ML4H 2017 Workshop in Long Beach, CA, USA

The new science of metagenomics and the challenges of its use in both developed and developing countries

Our view of the microbial world and its impact on human health is changing radically with the ability to sequence uncultured or unculturable microbes sampled directly from their habitats, ability made possible by fast and cheap next generation sequencing technologies. Such recent developments represents a paradigmatic shift in the analysis of habitat biodiversity, be it the human, soil or ocean microbiome. We review here some research examples and results that indicate the importance of the microbiome in our lives and then discus some of the challenges faced by metagenomic experiments and the subsequent analysis of the generated data. We then analyze the economic and social impact on genomic-medicine and research in both developing and developed countries. We support the idea that there are significant benefits in building capacities for developing high-level scientific research in metagenomics in developing countries. Indeed, the notion that developing countries should wait for developed countries to make advances in science and technology that they later import at great cost has recently been challenged

ESO452-SC11: The lowest mass globular cluster with a potential chemical inhomogeneity

We present the largest spectroscopic investigation of one of the faintest and least studied stellar clusters of the Milky Way, ESO452-SC11. Using the Anglo-Australian Telescope AAOmega and Keck HIRES spectrographs we have identified 11 members of the cluster and found indications of star-to-star light element abundance variation, primarily using the blue cyanogen (CN) absorption features. From a stellar density profile, we estimate a total cluster mass of $(6.8\pm3.4)\times10^3$ solar masses. This would make ESO452-SC11 the lowest mass cluster with evidence for multiple populations. These data were also used to measure the radial velocity of the cluster ($16.7\pm0.3$ km s$^{-1}$) and confirm that ESO452-SC11 is relatively metal-rich for a globular cluster ([Fe/H]$=-0.81\pm0.13$). All known massive clusters studied in detail show multiple populations of stars each with a different chemical composition, but many low-mass globular clusters appear to be chemically homogeneous. ESO452-SC11 sets a lower mass limit for the multiple stellar population phenomenon.Comment: 13 pages, 11 figures. Accepted for publication in MNRA

Perceptual Learning and Abstraction in Machine Learning : an application to autonomous robotics

This paper deals with the possible benefits of Perceptual Learning in Artificial Intelligence. On the one hand, Perceptual Learning is more and more studied in neurobiology and is now considered as an essential part of any living system. In fact, Perceptual Learning and Cognitive Learning are both necessary for learning and often depends on each other. On the other hand, many works in Machine Learning are concerned with "Abstraction" in order to reduce the amount of complexity related to some learning tasks. In the Abstraction framework, Perceptual Learning can be seen as a specific process that learns how to transform the data before the traditional learning task itself takes place. In this paper, we argue that biologically-inspired Perceptual Learning mechanisms could be used to build efficient low-level Abstraction operators that deal with real world data. To illustrate this, we present an application where perceptual learning inspired meta-operators are used to perform an abstraction on an autonomous robot visual perception. The goal of this work is to enable the robot to learn how to identify objects it encounters in its environment

Rounding Methods for Discrete Linear Classification (Extended Version)

Learning discrete linear classifiers is known as a difficult challenge. In this paper, this learning task is cast as combinatorial optimization problem: given a training sample formed by positive and negative feature vectors in the Euclidean space, the goal is to find a discrete linear function that minimizes the cumulative hinge loss of the sample. Since this problem is NP-hard, we examine two simple rounding algorithms that discretize the fractional solution of the problem. Generalization bounds are derived for several classes of binary-weighted linear functions, by analyzing the Rademacher complexity of these classes and by establishing approximation bounds for our rounding algorithms. Our methods are evaluated on both synthetic and real-world data

WISE circumstellar discs in the young Sco-Cen association

We present an analysis of the WISE photometric data for 829 stars in the Sco-Cen OB2 association, using thelatest high-mass membership probabilities. We detect infrared excesses associated with 135 BAF-type stars, 99 ofwhich are secure Sco-Cen members. There is a clear increase in excess fraction with membership probability, which can be fitted linearly.We infer that 41 ± 5 per cent of Sco-Cen OB2 BAF stars have excesses, while the field star excess fraction is consistent with zero. This is the first time that the probability of non-membership has been used in the calculation of excess fractions for young stars. We do not observe any significant change in excess fraction between the three subgroups.Within our sample, we have observed that B-type association members have a significantly smaller excess fraction than Aand F-type association members