46,516 research outputs found
MedlinePlus??: The National Library of Medicine?? Brings Quality Information to Health Consumers
The National Library of Medicine???s (NLM??) MedlinePlus?? is a high-quality
gateway to consumer health information from NLM, the National Institutes
of Health (NIH), and other authoritative organizations. For decades,
NLM has been a leader in indexing, organizing, and distributing health
information to health professionals. In creating MedlinePlus, NLM uses
years of accumulated expertise and technical knowledge to produce an
authoritative, reliable consumer health Web site. This article describes the
development of MedlinePlus???its quality control processes, the integration
of NLM and NIH information, NLM???s relationship to other institutions,
the technical and staffing infrastructures, the use of feedback for quality
improvement, and future plans.published or submitted for publicatio
Enriching Rare Word Representations in Neural Language Models by Embedding Matrix Augmentation
The neural language models (NLM) achieve strong generalization capability by
learning the dense representation of words and using them to estimate
probability distribution function. However, learning the representation of rare
words is a challenging problem causing the NLM to produce unreliable
probability estimates. To address this problem, we propose a method to enrich
representations of rare words in pre-trained NLM and consequently improve its
probability estimation performance. The proposed method augments the word
embedding matrices of pre-trained NLM while keeping other parameters unchanged.
Specifically, our method updates the embedding vectors of rare words using
embedding vectors of other semantically and syntactically similar words. To
evaluate the proposed method, we enrich the rare street names in the
pre-trained NLM and use it to rescore 100-best hypotheses output from the
Singapore English speech recognition system. The enriched NLM reduces the word
error rate by 6% relative and improves the recognition accuracy of the rare
words by 16% absolute as compared to the baseline NLM.Comment: 5 pages, 2 figures, accepted to INTERSPEECH 201
Fast Separable Non-Local Means
We propose a simple and fast algorithm called PatchLift for computing
distances between patches (contiguous block of samples) extracted from a given
one-dimensional signal. PatchLift is based on the observation that the patch
distances can be efficiently computed from a matrix that is derived from the
one-dimensional signal using lifting; importantly, the number of operations
required to compute the patch distances using this approach does not scale with
the patch length. We next demonstrate how PatchLift can be used for patch-based
denoising of images corrupted with Gaussian noise. In particular, we propose a
separable formulation of the classical Non-Local Means (NLM) algorithm that can
be implemented using PatchLift. We demonstrate that the PatchLift-based
implementation of separable NLM is few orders faster than standard NLM, and is
competitive with existing fast implementations of NLM. Moreover, its denoising
performance is shown to be consistently superior to that of NLM and some of its
variants, both in terms of PSNR/SSIM and visual quality
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