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

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

Code Completion with Neural Attention and Pointer Networks

Intelligent code completion has become an essential research task to accelerate modern software development. To facilitate effective code completion for dynamically-typed programming languages, we apply neural language models by learning from large codebases, and develop a tailored attention mechanism for code completion. However, standard neural language models even with attention mechanism cannot correctly predict the out-of-vocabulary (OoV) words that restrict the code completion performance. In this paper, inspired by the prevalence of locally repeated terms in program source code, and the recently proposed pointer copy mechanism, we propose a pointer mixture network for better predicting OoV words in code completion. Based on the context, the pointer mixture network learns to either generate a within-vocabulary word through an RNN component, or regenerate an OoV word from local context through a pointer component. Experiments on two benchmarked datasets demonstrate the effectiveness of our attention mechanism and pointer mixture network on the code completion task.Comment: Accepted in IJCAI 201

DDFlow: Learning Optical Flow with Unlabeled Data Distillation

We present DDFlow, a data distillation approach to learning optical flow estimation from unlabeled data. The approach distills reliable predictions from a teacher network, and uses these predictions as annotations to guide a student network to learn optical flow. Unlike existing work relying on hand-crafted energy terms to handle occlusion, our approach is data-driven, and learns optical flow for occluded pixels. This enables us to train our model with a much simpler loss function, and achieve a much higher accuracy. We conduct a rigorous evaluation on the challenging Flying Chairs, MPI Sintel, KITTI 2012 and 2015 benchmarks, and show that our approach significantly outperforms all existing unsupervised learning methods, while running at real time.Comment: 8 pages, AAAI 1

Title-Guided Encoding for Keyphrase Generation

Keyphrase generation (KG) aims to generate a set of keyphrases given a document, which is a fundamental task in natural language processing (NLP). Most previous methods solve this problem in an extractive manner, while recently, several attempts are made under the generative setting using deep neural networks. However, the state-of-the-art generative methods simply treat the document title and the document main body equally, ignoring the leading role of the title to the overall document. To solve this problem, we introduce a new model called Title-Guided Network (TG-Net) for automatic keyphrase generation task based on the encoder-decoder architecture with two new features: (i) the title is additionally employed as a query-like input, and (ii) a title-guided encoder gathers the relevant information from the title to each word in the document. Experiments on a range of KG datasets demonstrate that our model outperforms the state-of-the-art models with a large margin, especially for documents with either very low or very high title length ratios.Comment: AAAI 1