Utah Suicide Prevention Plan 2017-2021

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

Spherical distribution vectors

In this paper we consider a locally compact second countable unimodular group $G$ and a closed unimodular subgroup $H$. Let $\rho$ be a finite dimensional unitary representation of $H$ with closed image. For the unitary representation of $G$ obtained by inducing $\rho$ from $H$ to $G$ a decomposition in Hilbert subspaces of a certain space of distributions is given. It is shown that the representations relevant for this decomposition are determined by so-called $(\rho,H)$ spherical distributions, which leads to a description of the decomposition on the level of these distributions. \u

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

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)

Sharp error bounds for Ritz vectors and approximate singular vectors

We derive sharp bounds for the accuracy of approximate eigenvectors (Ritz vectors) obtained by the Rayleigh-Ritz process for symmetric eigenvalue problems. Using information that is available or easy to estimate, our bounds improve the classical Davis-Kahan $\sin\theta$ theorem by a factor that can be arbitrarily large, and can give nontrivial information even when the $\sin\theta$ theorem suggests that a Ritz vector might have no accuracy at all. We also present extensions in three directions, deriving error bounds for invariant subspaces, singular vectors and subspaces computed by a (Petrov-Galerkin) projection SVD method, and eigenvectors of self-adjoint operators on a Hilbert space

Skip-Thought Vectors

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

A Neurobiologically Motivated Analysis of Distributional Semantic Models

The pervasive use of distributional semantic models or word embeddings in a variety of research fields is due to their remarkable ability to represent the meanings of words for both practical application and cognitive modeling. However, little has been known about what kind of information is encoded in text-based word vectors. This lack of understanding is particularly problematic when word vectors are regarded as a model of semantic representation for abstract concepts. This paper attempts to reveal the internal information of distributional word vectors by the analysis using Binder et al.'s (2016) brain-based vectors, explicitly structured conceptual representations based on neurobiologically motivated attributes. In the analysis, the mapping from text-based vectors to brain-based vectors is trained and prediction performance is evaluated by comparing the estimated and original brain-based vectors. The analysis demonstrates that social and cognitive information is better encoded in text-based word vectors, but emotional information is not. This result is discussed in terms of embodied theories for abstract concepts.Comment: submitted to CogSci 201