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Institution

University of Colorado Colorado Springs

EducationColorado Springs, Colorado, United States
About: University of Colorado Colorado Springs is a education organization based out in Colorado Springs, Colorado, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 6664 authors who have published 10872 publications receiving 323416 citations. The organization is also known as: UCCS & University of Colorado at Colorado Springs.


Papers
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Journal ArticleDOI
TL;DR: A daily dose of 600 mg ursodiol is effective prophylaxis for gallstone formation following GBP-induced rapid weight loss.
Abstract: Background Previous studies have documented a high incidence of gallstone formation following gastric-bypass (GBP)-induced rapid weight loss in morbidly obese patients. This study was designed to determine if a 6-month regimen of prophylactic ursodiol might prevent the development of gallstones. Methods A multicenter, randomized, doubleblind, prospective trial evaluated 3 oral doses of ursodiol: 300, 600, and 1,200 mg versus placebo beginning within 10 days after surgery and continuing for 6 months or until gallstone development, for patients with a body mass index (BMI) ≥40 kg/m2. All patients had normal intraoperative gallbladder sonography. Transabdominal sonography was obtained at 2, 4, and 6 months following surgery, or until gallstone formation. Results Of 233 patients with at least one postoperative sonogram, 56 were randomized to placebo, 53 to 300 mg ursodiol, 61 to 600 mg ursodiol, and 63 to 1,200 mg ursodiol. Preoperative age, sex, race, weight, BMI, and postoperative weight loss were not significantly different between groups. Gallstone formation occurred at 6 months in 32%, 13%, 2%, and 6% of the patients on the respective doses. Gallstones were significantly (P Conclusion A daily dose of 600 mg ursodiol is effective prophylaxis for gallstone formation following GBP-induced rapid weight loss.

371 citations

Journal ArticleDOI
TL;DR: In this article, a review shows that a well-balanced combination of an appropriate catalytic system together with an adapted regeneration process could put large-scale industrial applications within reach, which is the main obstacle in the way of large scale industrial applications.

369 citations

Reference EntryDOI
30 Jan 2010
TL;DR: The Diagnostic Interview Schedule for DSM-IV (DIS-IV; Robins, Cottler, Bucholz, Compton, North, & Rourke, 2000) is a structured interview designed to diagnose in a reliable and valid fashion the major psychiatric disorders according to the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) (American Psychiatric Association, 2000).
Abstract: The Diagnostic Interview Schedule for DSM-IV (DIS-IV; Robins, Cottler, Bucholz, Compton, North, & Rourke, 2000) is a structured interview designed to diagnose in a reliable and valid fashion the major psychiatric disorders according to the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) (American Psychiatric Association, 2000). The DIS-IV is unique among the multidisorder structured diagnostic interviews in that it is a fully structured interview specifically designed for use by nonclinician interviewers, whereas the other interviews are semi-structured. By definition, a fully structured interview specifies clearly all questions and probes and does not permit deviations. Keywords: psychiatric diagnosis; structured interview; assessment

368 citations

Posted Content
TL;DR: OpenMax as mentioned in this paper adapts Meta-Recognition concepts to the activation patterns in the penultimate layer of the network to estimate the probability of an input being from an unknown class.
Abstract: Deep networks have produced significant gains for various visual recognition problems, leading to high impact academic and commercial applications. Recent work in deep networks highlighted that it is easy to generate images that humans would never classify as a particular object class, yet networks classify such images high confidence as that given class - deep network are easily fooled with images humans do not consider meaningful. The closed set nature of deep networks forces them to choose from one of the known classes leading to such artifacts. Recognition in the real world is open set, i.e. the recognition system should reject unknown/unseen classes at test time. We present a methodology to adapt deep networks for open set recognition, by introducing a new model layer, OpenMax, which estimates the probability of an input being from an unknown class. A key element of estimating the unknown probability is adapting Meta-Recognition concepts to the activation patterns in the penultimate layer of the network. OpenMax allows rejection of "fooling" and unrelated open set images presented to the system; OpenMax greatly reduces the number of obvious errors made by a deep network. We prove that the OpenMax concept provides bounded open space risk, thereby formally providing an open set recognition solution. We evaluate the resulting open set deep networks using pre-trained networks from the Caffe Model-zoo on ImageNet 2012 validation data, and thousands of fooling and open set images. The proposed OpenMax model significantly outperforms open set recognition accuracy of basic deep networks as well as deep networks with thresholding of SoftMax probabilities.

368 citations

Patent
13 Nov 2001
TL;DR: In this paper, the authors present an Authoring System for Data Definition File (DDF) that allows the creation and editing of DDFs using a set of parameters used by the server to customize the resulting keyed data file for the client's purposes.
Abstract: A computer system provides the ability to construct and edit a Data Definition File (DDF) containing hierarchically related elements of data, some of which are dynamic in that they must execute in order to produce or retrieve data. A client computer system having knowledge of a DDF appropriate for its uses sends a request to a server, which contains or can retrieve the DDF requested by the client. The request contains parameters used by the server to customize the resulting keyed data file for the client's purposes. Upon receipt of the request, the server copies the DDF into a coupled memory, performs requested parameter substitutions, and executes dynamic elements to produce resulting data elements. The process is repeated recursively for all elements of the hierarchical structure, until no dynamic elements remain, then the resulting keyed data file is returned to the client for its uses. Data elements may be derived from a plurality of sources, and these sources may be combined and manipulated using a plurality of data operations, including relational algebra or structured query language, enabling joins and merges between multiple sources and formats. An Authoring System is provided which assists in the construction and validation of DDFs.

362 citations


Authors

Showing all 6706 results

NameH-indexPapersCitations
Jeff Greenberg10554243600
James F. Scott9971458515
Martin Wikelski8942025821
Neil W. Kowall8927934943
Ananth Dodabalapur8539427246
Tom Pyszczynski8224630590
Patrick S. Kamath7846631281
Connie M. Weaver7747330985
Alejandro Lucia7568023967
Michael J. McKenna7035616227
Timothy J. Craig6945818340
Sheldon Solomon6715023916
Michael H. Stone6537016355
Christopher J. Gostout6533413593
Edward T. Ryan6030311822
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202325
202246
2021568
2020543
2019479
2018454