University of Texas at Austin

About: University of Texas at Austin is a education organization based out in Austin, Texas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 94352 authors who have published 206297 publications receiving 9070052 citations. The organization is also known as: UT-Austin & UT Austin.

Adolescent psychopathology: I. Prevalence and incidence of depression and other DSM-III-R disorders in high school students.

Abstract: Data were collected on the point and lifetime prevalences, 1-year incidence, and comorbidity of depression with other disorders (Diagnostic and Statistical Manual of Mental Disorders [3rd ed., rev.]) in a randomly selected sample (n = 1,710) of high school students at point of entry and at 1-year follow-up (n = 1,508). The Schedule for Affective Disorders and Schizophrenia for School-Age Children was used to collect diagnostic information; 9.6% met criteria for a current disorder, more than 33% had experienced a disorder over their lifetimes, and 31.7% of the latter had experienced a second disorder. High relapse rates were found for all disorders, especially for unipolar depression (18.4%) and substance use (15.0%). Female subjects had significantly higher rates at all age levels for unipolar depression, anxiety disorders, eating disorders, and adjustment disorders; male subjects had higher rates of disruptive behavior disorders.

Product Development Decisions: A Review of the Literature

Abstract: This paper is a review of research in product development, which we define as the transformation of a market opportunity into a product available for sale. Our review is broad, encompassing work in the academic fields of marketing, operations management, and engineering design. The value of this breadth is in conveying the shape of the entire research landscape. We focus on product development projects within a single firm. We also devote our attention to the development of physical goods, although much of the work we describe applies to products of all kinds. We look inside the "black box" of product development at the fundamentaldecisions that are made by intention or default. In doing so, we adopt the perspective of product development as a deliberate business process involving hundreds of decisions, many of which can be usefully supported by knowledge and tools. We contrast this approach to prior reviews of the literature, which tend to examine the importance of environmental and contextual variables, such as market growth rate, the competitive environment, or the level of top-management support.

Clustering with Bregman Divergences

Abstract: A wide variety of distortion functions, such as squared Euclidean distance, Mahalanobis distance, Itakura-Saito distance and relative entropy, have been used for clustering. In this paper, we propose and analyze parametric hard and soft clustering algorithms based on a large class of distortion functions known as Bregman divergences. The proposed algorithms unify centroid-based parametric clustering approaches, such as classical kmeans , the Linde-Buzo-Gray (LBG) algorithm and information-theoretic clustering, which arise by special choices of the Bregman divergence. The algorithms maintain the simplicity and scalability of the classical kmeans algorithm, while generalizing the method to a large class of clustering loss functions. This is achieved by first posing the hard clustering problem in terms of minimizing the loss in Bregman information, a quantity motivated by rate distortion theory, and then deriving an iterative algorithm that monotonically decreases this loss. In addition, we show that there is a bijection between regular exponential families and a large class of Bregman divergences, that we call regular Bregman divergences. This result enables the development of an alternative interpretation of an efficient EM scheme for learning mixtures of exponential family distributions, and leads to a simple soft clustering algorithm for regular Bregman divergences. Finally, we discuss the connection between rate distortion theory and Bregman clustering and present an information theoretic analysis of Bregman clustering algorithms in terms of a trade-off between compression and loss in Bregman information.