University of North Texas

About: University of North Texas is a education organization based out in Denton, Texas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 11866 authors who have published 26984 publications receiving 705376 citations. The organization is also known as: Fight, North Texas & UNT.

Third-Party Intervention and the Civil War Process

Abstract: What effect do third parties have on the evolution of civil wars? The authors argue that intervention by third parties is central to the civil war process, a process that is characterized by the duration of hostilities and the type of outcome. The authors examine empirically the effect of third-party intervention into civil wars during the period 1816—1997, using the event history framework of competing risks. From the perspective of competing risks, as a civil war endures, it is at risk of experiencing a transition to one of three civil war outcomes in our sample: military victory by the government, military victory by the opposition group, and negotiated settlement. The competing risks approach provides considerably better leverage on the dynamic qualities of civil wars and, in particular, the influence of interventions by third parties. The analysis suggests that third-party interventions can be decisive in the evolution of civil wars and that third-party interventions have a different effect on the du...

Validation of the New Interpretive Guidelines for the Clinical Dementia Rating Scale Sum of Boxes Score in the National Alzheimer's Coordinating Center Database

Abstract: Background It was recently demonstrated that the Clinical Dementia Rating scale Sum of Boxes (CDR-SB) score can be used to accurately stage severity of Alzheimer dementia and mild cognitive impairment (MCI). However, to our knowledge, the utility of those interpretive guidelines has not been cross-validated or applied to a heterogeneous sample of dementia cases. Objective To cross-validate the staging guidelines proposed in a previous study using the National Alzheimer's Coordinating Center (NACC) database. Design The previously published cut scores were applied to the NACC sample and diagnostic accuracy estimates obtained. Next, analyses were restricted to NACC participants with a CDR global score (CDR-GS) of 0.5 and receiver operating characteristic curves generated to determine optimal CDR-SB cut scores for distinguishing MCI from very early dementia. Setting The 2008 NACC uniform data set. Participants There were 12 462 participants (5115 controls; 2551 patients with MCI; 4796 patients with dementia, all etiologies) in the NACC data set used for the current analysis. Main Outcome Measure Accurate prediction of diagnoses (MCI or dementia) using the CDR-SB score. Results The previously proposed CDR-SB ranges successfully classified the vast majority of patients across all impairment ranges with a κ of 0.91 and 94% overall correct classification rate. Additionally, the CDR-SB score discriminated between patients diagnosed with MCI and dementia when CDR-GS was restricted to 0.5 (overall area under the curve = 0.83). Conclusions These findings cross-validate the previously published CDR-SB interpretative guidelines for staging dementia severity and extend those findings to a large heterogeneous sample of patients with dementia. Additionally, the CDR-SB scores distinguished MCI from dementia in patients with reasonable accuracy when CDR-GS was restricted to 0.5.

Three-dimensional metal-graphene-nanotube multifunctional hybrid materials.

Abstract: Graphene was grown directly on porous nickel films, followed by the growth of controlled lengths of vertical carbon nanotube (CNT) forests that seamlessly emanate from the graphene surface. The metal–graphene–CNT structure is used to directly fabricate field-emitter devices and double-layer capacitors. The three-dimensional nanostructured hybrid materials, with better interfacial contacts and volume utilization, can stimulate the development of several energy-efficient technologies.

Putting pieces together: Combining FrameNet, VerbNet and WordNet for robust semantic parsing

Abstract: This paper describes our work in integrating three different lexical resources: FrameNet, VerbNet, and WordNet, into a unified, richer knowledge-base, to the end of enabling more robust semantic parsing. The construction of each of these lexical resources has required many years of laborious human effort, and they all have their strengths and shortcomings. By linking them together, we build an improved resource in which (1) the coverage of FrameNet is extended, (2) the VerbNet lexicon is augmented with frame semantics, and (3) selectional restrictions are implemented using WordNet semantic classes. The synergistic exploitation of various lexical resources is crucial for many complex language processing applications, and we prove it once again effective in building a robust semantic parser.

A Language Independent Algorithm for Single and Multiple Document Summarization

Abstract: This paper discusses a language independent algorithm for single and multiple document summarization.