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.

The Unhealthy = Tasty Intuition and Its Effects on Taste Inferences, Enjoyment, and Choice of Food Products

Abstract: Across four experiments, the authors find that when information pertaining to the assessment of the healthiness of food items is provided, the less healthy the item is portrayed to be, (1) the better is its inferred taste, (2) the more it is enjoyed during actual consumption, and (3) the greater is the preference for it in choice tasks when a hedonic goal is more (versus less) salient. The authors obtain these effects both among consumers who report that they believe that healthiness and tastiness are negatively correlated and, to a lesser degree, among those who do not report such a belief. The authors also provide evidence that the association between the concepts of “unhealthy” and “tasty” operates at an implicit level. The authors discuss possibilities for controlling the effect of the unhealthy = tasty intuition (and its potential for causing negative health consequences), including controlling the volume of unhealthy but tasty food eaten, changing unhealthy foods to make them less unhealthy...

Pilot Contamination and Precoding in Multi-Cell TDD Systems

Abstract: This paper considers a multi-cell multiple antenna system with precoding used at the base stations for downlink transmission. For precoding at the base stations, channel state information (CSI) is essential at the base stations. A popular technique for obtaining this CSI in time division duplex (TDD) systems is uplink training by utilizing the reciprocity of the wireless medium. This paper mathematically characterizes the impact that uplink training has on the performance of such multi-cell multiple antenna systems. When non-orthogonal training sequences are used for uplink training, the paper shows that the precoding matrix used by the base station in one cell becomes corrupted by the channel between that base station and the users in other cells in an undesirable manner. This paper analyzes this fundamental problem of pilot contamination in multi-cell systems. Furthermore, it develops a new multi-cell MMSE-based precoding method that mitigate this problem. In addition to being a linear precoding method, this precoding method has a simple closed-form expression that results from an intuitive optimization problem formulation. Numerical results show significant performance gains compared to certain popular single-cell precoding methods.

Facebook Profiles Reflect Actual Personality, Not Self-Idealization

Abstract: More than 700 million people worldwide now have profiles on on-line social networking sites (OSNs), such as MySpace and Facebook (ComScore, 2008); OSNs have become integrated into the milieu of modern-day social interactions and are widely used as a primary medium for communication and networking (boyd & Ellison, 2007; Valkenburg & Peter, 2009). Despite the increasing integration of OSN activity into everyday life, however, there has been no research on the most fundamental question about OSN profiles: Do they convey accurate impressions of profile owners? A widely held assumption, supported by content analyses, suggests that OSN profiles are used to create and communicate idealized selves (Manago, Graham, Greenfield, & Salimkhan, 2008). According to this idealized virtual-identity hypothesis, profile owners display idealized characteristics that do not reflect their actual personalities. Thus, personality impressions based on OSN profiles should reflect profile owners’ ideal-self views rather than what the owners are actually like. A contrasting view holds that OSNs may constitute an extended social context in which to express one’s actual personality characteristics, thus fostering accurate interpersonal perceptions. OSNs integrate various sources of personal information that mirror those found in personal environments, private thoughts, facial images, and social behavior, all of which are known to contain valid information about personality (Ambady & Skowronski, 2008; Funder, 1999; Hall & Bernieri, 2001; Kenny, 1994; Vazire & Gosling, 2004). Moreover, creating idealized identities should be hard to accomplish because (a) OSN profiles include information about one’s reputation that is difficult to control (e.g., wall posts) and (b) friends provide accountability and subtle feedback on one’s profile. Accordingly, the extended real-life hypothesis predicts that people use OSNs to communicate their real personality. If this supposition is true, lay observers should be able to accurately infer the personality characteristics of OSN profile owners. In the present study, we tested the two competing hypotheses. Method Participants

Weighted Graph Cuts without Eigenvectors A Multilevel Approach

Abstract: A variety of clustering algorithms have recently been proposed to handle data that is not linearly separable; spectral clustering and kernel k-means are two of the main methods In this paper, we discuss an equivalence between the objective functions used in these seemingly different methods - in particular, a general weighted kernel k-means objective is mathematically equivalent to a weighted graph clustering objective We exploit this equivalence to develop a fast high-quality multilevel algorithm that directly optimizes various weighted graph clustering objectives, such as the popular ratio cut, normalized cut, and ratio association criteria This eliminates the need for any eigenvector computation for graph clustering problems, which can be prohibitive for very large graphs Previous multilevel graph partitioning methods such as Metis have suffered from the restriction of equal-sized clusters; our multilevel algorithm removes this restriction by using kernel k-means to optimize weighted graph cuts Experimental results show that our multilevel algorithm outperforms a state-of-the-art spectral clustering algorithm in terms of speed, memory usage, and quality We demonstrate that our algorithm is applicable to large-scale clustering tasks such as image segmentation, social network analysis, and gene network analysis