60,148 research outputs found

    'Fat people and bombs':HPA axis cognition, structured stress, and the US obesity epidemic

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    We examine the accelerating 'obesity epidemic' in the US from the perspective of recently developed theory relating a cognitive hypothalamic-pituitary-adrenal axis to an embedding 'language' of structured psychosocial stress. Using a Rate Distortion argument, the obesity epidemic is found to represent the literal writing of an image of a ratcheting pathological social hierarchy onto the bodies of American adults and children. This process, while stratified by the usual divisions of class and ethnicity, is nonetheless relentlessly engulfing even affluent majority populations. Our perspective places the common explanation that 'obesity occurs when people eat too much and get too little exercise' in the same category as the remark by US President Calvin Coolidge on the eve of the Great Depression that 'unemployment occurs when large numbers of people are out of work'. Both statements ignore profound structural determinants of great population suffering which must be addressed by collective actions of equally great reform

    Semi-supervised sequence tagging with bidirectional language models

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    Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks. However, in most cases, the recurrent network that operates on word-level representations to produce context sensitive representations is trained on relatively little labeled data. In this paper, we demonstrate a general semi-supervised approach for adding pre- trained context embeddings from bidirectional language models to NLP systems and apply it to sequence labeling tasks. We evaluate our model on two standard datasets for named entity recognition (NER) and chunking, and in both cases achieve state of the art results, surpassing previous systems that use other forms of transfer or joint learning with additional labeled data and task specific gazetteers.Comment: To appear in ACL 201

    Sliced Wasserstein Kernel for Persistence Diagrams

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    Persistence diagrams (PDs) play a key role in topological data analysis (TDA), in which they are routinely used to describe topological properties of complicated shapes. PDs enjoy strong stability properties and have proven their utility in various learning contexts. They do not, however, live in a space naturally endowed with a Hilbert structure and are usually compared with specific distances, such as the bottleneck distance. To incorporate PDs in a learning pipeline, several kernels have been proposed for PDs with a strong emphasis on the stability of the RKHS distance w.r.t. perturbations of the PDs. In this article, we use the Sliced Wasserstein approximation SW of the Wasserstein distance to define a new kernel for PDs, which is not only provably stable but also provably discriminative (depending on the number of points in the PDs) w.r.t. the Wasserstein distance d1d_1 between PDs. We also demonstrate its practicality, by developing an approximation technique to reduce kernel computation time, and show that our proposal compares favorably to existing kernels for PDs on several benchmarks.Comment: Minor modification

    Aerospace medicine and biology: A continuing bibliography with indexes, supplement 125

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    This special bibliography lists 323 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1974
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