1,430,039 research outputs found

    Utah Suicide Prevention Plan 2017-2021

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    Living in Utah has many advantages including the best snow on Earth and many beautiful national and state parks in which the opportunity for outdoor adventure is almost unlimited. Utah also ranks high in a number of health and happiness related outcomes. In spite of all that Utah has to offer, Utah continually ranks in the top ten states for high suicide rates in the U.S. People in Utah also experience higher rates of associated mood disorders. The Utah Suicide Prevention Coalition is dedicated to better understanding this paradox and implementing prevention, intervention and postvention strategies to decrease suicide and the associated suffering it brings.Suicide is a major preventable public health problem in Utah and the 8th leading cause of death (2010-2015 inclusive). Every suicide death causes a ripple effect of immeasurable pain to individuals, families, and communities throughout the state. From 2009 to 2015, Utah's age-adjusted suicide rate was 19.9 per 100,000 persons. This is an average of 525 suicide deaths per year. Suicide was the second-leading cause of death for Utahns ages 10 to 39 years old in 2013 and the number one cause of death for youth ages 10-17. Many more people attempt suicide than die by suicide. The most recent data show that 6,039 Utahns were seen in emergency departments (2014) and 2,314 Utahns were hospitalized for self-inflicted injuries including suicide attempts (UDOH Indicator-based Information System for Public Health, 2014). One in fifteen Utah adults report having had serious thoughts of suicide in the past year (SAMHSA National Survey on Drug Use and Health, 2008-2009). According to the Student Health and Risk Prevention Survey, 14.4 % of youth grades 6-12 report seriously considering suicide, 6.7% of Utah youth grades 6-12 students attempted suicide one or more times and 13.9% of students report harming themselves without the intention of dying in the prior year.While suicide is a leading cause of death and many people report thoughts of suicide, the topic is still largely met with silence and shame. It is critical for all of us to challenge this silence using both research and personal stories of recovery. Everyone plays a role in suicide prevention and it is up to each one of us to help create communities in which people are able to feel safe and supported in disclosing suicide risk, including mental illness and substance use problems. We need to break down the barriers that keep people from accessing care and support for prevention, early intervention and crisis services. As you review this plan, we encourage you to identify how you can implement any of the strategies and help create suicide safer communities

    Permanental Vectors

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    A permanental vector is a generalization of a vector with components that are squares of the components of a Gaussian vector, in the sense that the matrix that appears in the Laplace transform of the vector of Gaussian squares is not required to be either symmetric or positive definite. In addition the power of the determinant in the Laplace transform of the vector of Gaussian squares, which is -1/2, is allowed to be any number less than zero. It was not at all clear what vectors are permanental vectors. In this paper we characterize all permanental vectors in R+3R^{3}_{+} and give applications to permanental vectors in R+nR^{n}_{+} and to the study of permanental processes

    Computing covariant vectors, Lyapunov vectors, Oseledets vectors, and dichotomy projectors: a comparative numerical study

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    Covariant vectors, Lyapunov vectors, or Oseledets vectors are increasingly being used for a variety of model analyses in areas such as partial differential equations, nonautonomous differentiable dynamical systems, and random dynamical systems. These vectors identify spatially varying directions of specific asymptotic growth rates and obey equivariance principles. In recent years new computational methods for approximating Oseledets vectors have been developed, motivated by increasing model complexity and greater demands for accuracy. In this numerical study we introduce two new approaches based on singular value decomposition and exponential dichotomies and comparatively review and improve two recent popular approaches of Ginelli et al. (2007) and Wolfe and Samelson (2007). We compare the performance of the four approaches via three case studies with very different dynamics in terms of symmetry, spectral separation, and dimension. We also investigate which methods perform well with limited data

    Skip-Thought Vectors

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    We describe an approach for unsupervised learning of a generic, distributed sentence encoder. Using the continuity of text from books, we train an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded passage. Sentences that share semantic and syntactic properties are thus mapped to similar vector representations. We next introduce a simple vocabulary expansion method to encode words that were not seen as part of training, allowing us to expand our vocabulary to a million words. After training our model, we extract and evaluate our vectors with linear models on 8 tasks: semantic relatedness, paraphrase detection, image-sentence ranking, question-type classification and 4 benchmark sentiment and subjectivity datasets. The end result is an off-the-shelf encoder that can produce highly generic sentence representations that are robust and perform well in practice. We will make our encoder publicly available.Comment: 11 page

    Co-occurrence Vectors from Corpora vs. Distance Vectors from Dictionaries

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    A comparison was made of vectors derived by using ordinary co-occurrence statistics from large text corpora and of vectors derived by measuring the inter-word distances in dictionary definitions. The precision of word sense disambiguation by using co-occurrence vectors from the 1987 Wall Street Journal (20M total words) was higher than that by using distance vectors from the Collins English Dictionary (60K head words + 1.6M definition words). However, other experimental results suggest that distance vectors contain some different semantic information from co-occurrence vectors.Comment: 6 pages, appeared in the Proc. of COLING94 (pp. 304-309)
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