The Optical Velocity of the Antlia Dwarf Galaxy

We present the results of a VLT observing program carried out in service mode using FORS1 on ANTU in Long Slit mode to determine the optical velocities of nearby low surface brightness galaxies. Outlying Local Group galaxies are of paramount importance in placing constraints the dynamics and thus on both the age and the total mass of the Local Group. Optical velocities are also necessary to determine if the observations of HI gas in and around these systems are the result of gas associated with these galaxies or a chance superposition with high velocity HI clouds or the Magellanic Stream. The data were of sufficient signal-to-noise to obtain a reliable result in one of the galaxies we observed - Antlia - for which we have found an optical helio-centric radial velocity of 351 $\pm$ 15 km/s.Comment: 11 pages, 4 figures, 5 tables MNRAS, in pres

Sleep and inflammation in resilient aging.

Sleep quality is important to health, and increasingly viewed as critical in promoting successful, resilient aging. In this review, the interplay between sleep and mental and physical health is considered with a focus on the role of inflammation as a biological pathway that translates the effects of sleep on risk of depression, pain and chronic disease risk in aging. Given that sleep regulates inflammatory biologic mechanisms with effects on mental and physical health outcomes, the potential of interventions that target sleep to reduce inflammation and promote health in aging is also discussed

Learning to Rank Using Localized Geometric Mean Metrics

Many learning-to-rank (LtR) algorithms focus on query-independent model, in which query and document do not lie in the same feature space, and the rankers rely on the feature ensemble about query-document pair instead of the similarity between query instance and documents. However, existing algorithms do not consider local structures in query-document feature space, and are fragile to irrelevant noise features. In this paper, we propose a novel Riemannian metric learning algorithm to capture the local structures and develop a robust LtR algorithm. First, we design a concept called \textit{ideal candidate document} to introduce metric learning algorithm to query-independent model. Previous metric learning algorithms aiming to find an optimal metric space are only suitable for query-dependent model, in which query instance and documents belong to the same feature space and the similarity is directly computed from the metric space. Then we extend the new and extremely fast global Geometric Mean Metric Learning (GMML) algorithm to develop a localized GMML, namely L-GMML. Based on the combination of local learned metrics, we employ the popular Normalized Discounted Cumulative Gain~(NDCG) scorer and Weighted Approximate Rank Pairwise (WARP) loss to optimize the \textit{ideal candidate document} for each query candidate set. Finally, we can quickly evaluate all candidates via the similarity between the \textit{ideal candidate document} and other candidates. By leveraging the ability of metric learning algorithms to describe the complex structural information, our approach gives us a principled and efficient way to perform LtR tasks. The experiments on real-world datasets demonstrate that our proposed L-GMML algorithm outperforms the state-of-the-art metric learning to rank methods and the stylish query-independent LtR algorithms regarding accuracy and computational efficiency.Comment: To appear in SIGIR'1

Mindfulness meditation and improvement in depressive symptoms among Spanish- and English speaking adults: A randomized, controlled, comparative efficacy trial.

ObjectiveLatino immigrants experience acculturative stress and increased depression risk. Mindfulness meditation improves depressive symptoms, yet the vast majority of research has focused on English speaking populations.MethodsIn this randomized clinical trial with 2 parallel treatment groups, adults with moderate levels of perceived stress (n = 76) were recruited from the Los Angeles community from October 2015 to March 2016, stratified into Spanish- (n = 36) and English speaking (n = 40) language groups, and randomized for 6 weeks of treatment with standardized mindful awareness practices (MAPs) or health education (HE). Main outcome measure was depressive symptoms, measured by the Beck Depression Inventory.ResultsUsing an intent-to-treat analysis, the primary outcome, depressive symptoms as indexed by the Beck Depression Inventory, showed greater improvement in MAPs vs. HE, with a between-group post-intervention mean difference of -2.2 (95% CI -4.4 - -0.07) and effect size of 0.28; similar effect sizes were found in the the Spanish- (0.29) and English speaking (0.30) groups. MAPs showed significant improvement relative to HE on secondary outcome of mindfulness with between group difference of 10.7 (95% CI4.5-16.9), but not perceived stress.ConclusionThe comparable efficacy of Spanish and English formats of mindfulness meditation in improving depressive symptoms suggests that this community based intervention may mitigate depression risk in Latino adults who are experiencing social adversity.Trial registrationClinicalTrials.gov NCT03545074