179 resultados para K-pop


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Massive amount of data that are geo-tagged and associated with text information are being generated at an unprecedented scale. These geo-textual data cover a wide range of topics. Users are interested in receiving up-to-date tweets such that their locations are close to a user specified location and their texts are interesting to users. For example, a user may want to be updated with tweets near her home on the topic “food poisoning vomiting.” We consider the Temporal Spatial-Keyword Top-k Subscription (TaSK) query. Given a TaSK query, we continuously maintain up-to-date top-k most relevant results over a stream of geo-textual objects (e.g., geo-tagged Tweets) for the query. The TaSK query takes into account text relevance, spatial proximity, and recency of geo-textual objects in evaluating its relevance with a geo-textual object. We propose a novel solution to efficiently process a large number of TaSK queries over a stream of geotextual objects. We evaluate the efficiency of our approach on two real-world datasets and the experimental results show that our solution is able to achieve a reduction of the processing time by 70-80% compared with two baselines.

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The 2014 Research Excellence Framework sought for the first time to assess the impact that research was having beyond the boundaries of the university and the wider academic sphere. While the REF continued the approach of previous research assessment exercises in attempting to measure the overall quality of research and teaching within the higher-education sector, it also expected institutions to evidence how some of their research had had ‘an effect on, change or benefit to the economy, society, culture, public policy or services, health, the environment or quality of life, beyond academia’ (REF 2012: 48). This article provides a case study in how researchers in one U.K. anthropology department were able to demonstrate the impact of their work in the public sphere successfully as part of this major audit exercise.

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We show that the X-ray line flux of the Mn Kα line at 5.9 keV from the decay of 55Fe is a promising diagnostic to distinguish between Type Ia supernova (SN Ia) explosion models. Using radiation transport calculations, we compute the line flux for two three-dimensional explosion models: a near-Chandrasekhar mass delayed detonation and a violent merger of two (1.1 and 0.9 M⊙) white dwarfs. Both models are based on solar metallicity zero-age main-sequence progenitors. Due to explosive nuclear burning at higher density, the delayed-detonation model synthesizes ˜3.5 times more radioactive 55Fe than the merger model. As a result, we find that the peak Mn Kα line flux of the delayed-detonation model exceeds that of the merger model by a factor of ˜4.5. Since in both models the 5.9-keV X-ray flux peaks five to six years after the explosion, a single measurement of the X-ray line emission at this time can place a constraint on the explosion physics that is complementary to those derived from earlier phase optical spectra or light curves. We perform detector simulations of current and future X-ray telescopes to investigate the possibilities of detecting the X-ray line at 5.9 keV. Of the currently existing telescopes, XMM-Newton/pn is the best instrument for close (≲1-2 Mpc), non-background limited SNe Ia because of its large effective area. Due to its low instrumental background, Chandra/ACIS is currently the best choice for SNe Ia at distances above ˜2 Mpc. For the delayed-detonation scenario, a line detection is feasible with Chandra up to ˜3 Mpc for an exposure time of 106 s. We find that it should be possible with currently existing X-ray instruments (with exposure times ≲5 × 105 s) to detect both of our models at sufficiently high S/N to distinguish between them for hypothetical events within the Local Group. The prospects for detection will be better with future missions. For example, the proposed Athena/X-IFU instrument could detect our delayed-detonation model out to a distance of ˜5 Mpc. This would make it possible to study future events occurring during its operational life at distances comparable to those of the recent supernovae SN 2011fe (˜6.4 Mpc) and SN 2014J (˜3.5 Mpc).

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Persistent organic pollutants (POPs) are toxic substances, highly resistant to environmental degradation, which can bio-accumulate and have long-range atmospheric transport potential (UNEP 2001). The majority of studies on endocrine disruption have focused on interferences on the sexual steroid hormones and so have overlooked disruption to glucocorticoid hormones. Here the endocrine disrupting potential of individual POPs and their mixtures has been investigated in vitro to identify any disruption to glucocorticoid nuclear receptor transcriptional activity. POP mixtures were screened for glucocorticoid receptor (GR) translocation using a GR redistribution assay (RA) on a CellInsight(TM) NXT High Content Screening (HCS) platform. A mammalian reporter gene assay (RGA) was then used to assess the individual POPs, and their mixtures, for effects on glucocorticoid nuclear receptor transactivation. POP mixtures did not induce GR translocation in the GR RA or produce an agonist response in the GR RGA. However, in the antagonist test, in the presence of cortisol, an individual POP, p,p'-dichlorodiphenyldichloroethylene (DDE), was found to decrease glucocorticoid nuclear receptor transcriptional activity to 72.5% (in comparison to the positive cortisol control). Enhanced nuclear transcriptional activity, in the presence of cortisol, was evident for the two lowest concentrations of perfluorodecanoic acid (PFOS) potassium salt (0.0147mg/ml and 0.0294mg/ml), the two highest concentrations of perfluorodecanoic acid (PFDA) (0.0025mg/ml and 0.005mg/ml) and the highest concentration of 2,2',4,4'-tetrabromodiphenyl ether (BDE 47) (0.0000858mg/ml). It is important to gain a better understanding of how POPs can interact with GRs as the disruption of glucocorticoid action is thought to contribute to complex diseases.

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Persistent organic pollutants (POPs) are toxic substances, highly resistant to environmental degradation, which can bio-accumulate and have long-range atmospheric transport potential. Most studies focus on single compound effects, however as humans are exposed to several POPs simultaneously, investigating exposure effects of real life POP mixtures on human health is necessary. A defined mixture of POPs was used, where the compound concentration reflected its contribution to the levels seen in Scandinavian human serum (total mix). Several sub mixtures representing different classes of POP were also constructed. The perfluorinated (PFC) mixture contained six perfluorinated compounds, brominated (Br) mixture contained seven brominated compounds, chlorinated (Cl) mixture contained polychlorinated biphenyls and also p,p'-dichlorodiphenyldichloroethylene, hexachlorobenzene, three chlordanes, three hexachlorocyclohexanes and dieldrin. Human hepatocarcinoma (HepG2) cells were used for 2h and 48h exposures to the seven mixtures and analysis on a CellInsight™ NXT High Content Screening platform. Multiple cytotoxic endpoints were investigated: cell number, nuclear intensity and area, mitochondrial mass and membrane potential (MMP) and reactive oxygen species (ROS). Both the Br and Cl mixtures induced ROS production but did not lead to apoptosis. The PFC mixture induced the ROS production and likely induced cell apoptosis accompanied by the dissipation of MMP. Synergistic effects were evident for ROS induction when cells were exposed to the PFC+Br mixture. No significant effects were detected in the Br+Cl, PFC+Cl or total mixtures, which contain the same concentrations of chlorinated compounds as the Cl mixture plus additional compounds; highlighting the need for further exploration of POP mixtures in risk assessment.

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We present a new wrapper feature selection algorithm for human detection. This algorithm is a hybrid featureselection approach combining the benefits of filter and wrapper methods. It allows the selection of an optimalfeature vector that well represents the shapes of the subjects in the images. In detail, the proposed featureselection algorithm adopts the k-fold subsampling and sequential backward elimination approach, while thestandard linear support vector machine (SVM) is used as the classifier for human detection. We apply theproposed algorithm to the publicly accessible INRIA and ETH pedestrian full image datasets with the PASCALVOC evaluation criteria. Compared to other state of the arts algorithms, our feature selection based approachcan improve the detection speed of the SVM classifier by over 50% with up to 2% better detection accuracy.Our algorithm also outperforms the equivalent systems introduced in the deformable part model approach witharound 9% improvement in the detection accuracy

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We investigate modules over “systematic” rings. Such rings are “almost graded” and have appeared under various names in the literature; they are special cases of the G-systems of Grzeszczuk. We analyse their K-theory in the presence of conditions on the support, and explain how this generalises and unifies calculations of graded and filtered K-theory scattered in the literature. Our treatment makes systematic use of the formalism of idempotent completion and a theory of triangular objects in additive categories, leading to elementary and transparent proofs throughout.

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This chapter provides an overview of some of the key findings from a large mixed methods study regarding disabled children in care in Northern Ireland