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.

Generalizing motion edits with Gaussian processes

Abstract: One way that artists create compelling character animations is by manipulating details of a character's motion. This process is expensive and repetitive. We show that we can make such motion editing more efficient by generalizing the edits an animator makes on short sequences of motion to other sequences. Our method predicts frames for the motion using Gaussian process models of kinematics and dynamics. These estimates are combined with probabilistic inference. Our method can be used to propagate edits from examples to an entire sequence for an existing character, and it can also be used to map a motion from a control character to a very different target character. The technique shows good generalization. For example, we show that an estimator, learned from a few seconds of edited example animation using our methods, generalizes well enough to edit minutes of character animation in a high-quality fashion. Learning is interactive: An animator who wants to improve the output can provide small, correcting examples and the system will produce improved estimates of motion. We make this interactive learning process efficient and natural with a fast, full-body IK system with novel features. Finally, we present data from interviews with professional character animators that indicate that generalizing and propagating animator edits can save artists significant time and work.

Content-boosted collaborative filtering for improved recommendations

Abstract: Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor tc enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, Content-Boosted Collaborative Filtering, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.

Bioerodible hydrogels based on photopolymerized poly(ethylene glycol)-co-poly(.alpha.-hydroxy acid) diacrylate macromers

Abstract: Macromers having a poly(ethylene glycol) central block, extended with oligomers of α-hydroxy acids such as oligo(dl-lactic acid) or oligo(glycolic acid) and terminated with acrylate groups, were synthesized and characterized with the goal of obtaining a bioerodible hydrogel that could be formed in direct contact with tissues or proteins by photopolymerization of aqueous solutions of the macromer. The amphiphilic nature of the macromers causes them to assume a micellar conformation, which enables them to undergo rapid photopolymerization, resulting in the formation of crosslinked gels

Auxin regulates SCF(TIR1)-dependent degradation of AUX/IAA proteins.

Abstract: The plant hormone auxin is central in many aspects of plant development. Previous studies have implicated the ubiquitin-ligase SCFTIR1 and the AUX/IAA proteins in auxin response. Dominant mutations in several AUX/IAA genes confer pleiotropic auxin-related phenotypes, whereas recessive mutations affecting the function of SCFTIR1 decrease auxin response. Here we show that SCFTIR1 is required for AUX/IAA degradation. We demonstrate that SCFTIR1 interacts with AXR2/IAA7 and AXR3/IAA17, and that domain II of these proteins is necessary and sufficient for this interaction. Further, auxin stimulates binding of SCFTIR1 to the AUX/IAA proteins, and their degradation. Because domain II is conserved in nearly all AUX/IAA proteins in Arabidopsis, we propose that auxin promotes the degradation of this large family of transcriptional regulators, leading to diverse downstream effects.

The horseshoe estimator for sparse signals

Abstract: This paper proposes a new approach to sparsity called the horseshoe estimator. The horseshoe is a close cousin of other widely used Bayes rules arising from, for example, double-exponential and Cauchy priors, in that it is a member of the same family of multivariate scale mixtures of normals. But the horseshoe enjoys a number of advantages over existing approaches, including its robustness, its adaptivity to dierent sparsity patterns, and its analytical tractability. We prove two theorems that formally characterize both the horseshoe’s adeptness at large outlying signals, and its super-ecient rate of convergence to the correct estimate of the sampling density in sparse situations. Finally, using a combination of real and simulated data, we show that the horseshoe estimator corresponds quite closely to the answers one would get by pursuing a full Bayesian model-averaging approach using a discrete mixture prior to model signals and noise.