University of Texas at Arlington

About: University of Texas at Arlington is a education organization based out in Arlington, Texas, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 11758 authors who have published 28598 publications receiving 801626 citations. The organization is also known as: UT Arlington & University of Texas-Arlington.

Pose-Free Facial Landmark Fitting via Optimized Part Mixtures and Cascaded Deformable Shape Model

Abstract: This paper addresses the problem of facial landmark localization and tracking from a single camera. We present a two-stage cascaded deformable shape model to effectively and efficiently localize facial landmarks with large head pose variations. For face detection, we propose a group sparse learning method to automatically select the most salient facial landmarks. By introducing 3D face shape model, we use procrustes analysis to achieve pose-free facial landmark initialization. For deformation, the first step uses mean-shift local search with constrained local model to rapidly approach the global optimum. The second step uses component-wise active contours to discriminatively refine the subtle shape variation. Our framework can simultaneously handle face detection, pose-free landmark localization and tracking in real time. Extensive experiments are conducted on both laboratory environmental face databases and face-in-the-wild databases. All results demonstrate that our approach has certain advantages over state-of-the-art methods in handling pose variations.

Commitment, Procedural Fairness, and Organizational Citizenship Behavior: A Multifoci Analysis

Abstract: Research on commitment, procedural fairness, and organizational citizenship behavior (OCB) suggests that employees maintain distinct beliefs about, and direct behaviors towards, multiple targets in the workplace (e.g., the organization as a whole, their supervisor, and fellow workgroup members). The present studies were designed to test for “target similarity effects,” in which the relationships between commitment, procedural fairness, and OCB were expected to be stronger when they referred to the same target than when they referred to different targets. As predicted, we found that: (1) the positive relationship between commitment and OCB, and (2) the mediating effect of commitment on the positive relationship between procedural fairness and OCB, was particularly likely to emerge when the constructs were in reference to the same target. Support for these target similarity effects was found among layoff survivors (Study 1) and student project teams (Study 2). Theoretical and practical implications are discussed, as are limitations of the studies and suggestions for future research. Copyright © 2008 John Wiley & Sons, Ltd.

A New Version of Reimers' Law of Mass Loss Based on a Physical Approach

Abstract: We present a new semiempirical relation for the mass loss of cool stellar winds, which so far has frequently been described by "Reimers' law." Originally, this relation was based solely on dimensional scaling arguments without any physical interpretation. In our approach, the wind is assumed to result from the spillover of the extended chromosphere, possibly associated with the action of waves, especially Alfven waves, which are used as guidance in the derivation of the new formula. We obtain a relation akin to the original Reimers law, but which includes two new factors. They reflect how the chromospheric height depends on gravity and how the mechanical energy flux depends, mainly, on the effective temperature. The new relation is tested and sensitively calibrated by modeling the blue end of the horizontal branch of globular clusters. The most significant difference from mass-loss rates predicted by the Reimers relation is an increase by up to a factor of 3 for luminous late-type (super)giants, in good agreement with observations.

Collaborative filtering: Weighted nonnegative matrix factorization incorporating user and item graphs

Abstract: Collaborative filtering is an important topic in data mining and has been widely used in recommendation system. In this paper, we proposed a unified model for collaborative filtering based on graph regularized weighted nonnegative matrix factorization. In our model, two graphs are constructed on users and items, which exploit the internal information (e.g. neighborhood information in the user-item rating matrix) and external information (e.g. content information such as user’s occupation and item’s genre, or other kind of knowledge such as social trust network). The proposed method not only inherits the advantages of model-based method, but also owns the merits of memory-based method which considers the neighborhood information. Moreover, it has the ability to make use of content information and any additional information regarding user-user such as social trust network. Due to the use of these internal and external information, the proposed method is able to find more interpretable lowdimensional representations for users and items, which is helpful for improving the recommendation accuracy. Experimental results on benchmark collaborative filtering data sets demonstrate that the proposed methods outperform the state of the art collaborative filtering methods a lot.