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.

Mechanical Properties of Monolayer Graphene Oxide

Abstract: Mechanical properties of ultrathin membranes consisting of one layer, two overlapped layers, and three overlapped layers of graphene oxide platelets were investigated by atomic force microscopy (AFM) imaging in contact mode. In order to evaluate both the elastic modulus and prestress of thin membranes, the AFM measurement was combined with the finite element method (FEM) in a new approach for evaluating the mechanics of ultrathin membranes. Monolayer graphene oxide was found to have a lower effective Young’s modulus (207.6 ± 23.4 GPa when a thickness of 0.7 nm is used) as compared to the value reported for “pristine” graphene. The prestress (39.7−76.8 MPa) of the graphene oxide membranes obtained by solution-based deposition was found to be 1 order of magnitude lower than that obtained by others for mechanically cleaved graphene. The novel AFM imaging and FEM-based mapping methods presented here are of general utility for obtaining the elastic modulus and prestress of thin membranes.

Emotional Brand Attachment and Brand Personality: The Relative Importance of the Actual and the Ideal Self:

Abstract: Creating emotional brand attachment is a key branding issue in today's marketing world. One way to accomplish this is to match the brand's personality with the consumer's self. A key question, however, is whether the brand's personality should match the consumer's actual self or the consumer's ideal self. On the basis of two empirical studies of 167 brands (evaluated by 1329 and 980 consumers), the authors show that the implications of self-congruence for consumers' emotional brand attachment are complex and differ by consumers' product involvement, consumers' individual difference variables, and the type of self-congruence (fit of the brand's personality with the consumer's actual self versus with the consumer's ideal self). On a general level, actual self-congruence has the greatest impact on emotional brand attachment. Product involvement, self-esteem, and public self-consciousness increase the positive impact of actual self-congruence but decrease the impact of ideal self-congruence on emotio...

A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers

Abstract: High-dimensional statistical inference deals with models in which the the number of parameters p is comparable to or larger than the sample size n. Since it is usually impossible to obtain consistent procedures unless p/n → 0, a line of recent work has studied models with various types of structure (e.g., sparse vectors; block-structured matrices; low-rank matrices; Markov assumptions). In such settings, a general approach to estimation is to solve a regularized convex program (known as a regularized M-estimator) which combines a loss function (measuring how well the model fits the data) with some regularization function that encourages the assumed structure. The goal of this paper is to provide a unified framework for establishing consistency and convergence rates for such regularized M-estimators under high-dimensional scaling. We state one main theorem and show how it can be used to re-derive several existing results, and also to obtain several new results on consistency and convergence rates. Our analysis also identifies two key properties of loss and regularization functions, referred to as restricted strong convexity and decomposability, that ensure the corresponding regularized M-estimators have fast convergence rates.

Introduction to Data Envelopment Analysis and Its Uses: With DEA-Solver Software and References

Abstract: General Discussion.- The Basic CCR Model.- The CCR Model and Production Correspondence.- Alternative Dea Models.- Returns To Scale.- Models with Restricted Multipliers.- Discretionary, non-Discretionary and Categorical Variables.- Allocation Models.- Data Variations.- Super-Efficiency Models.