AT&T Labs

About: AT&T Labs is a based out in . It is known for research contribution in the topics: Network packet & The Internet. The organization has 1879 authors who have published 5595 publications receiving 483151 citations.

Resource sharing for book-ahead and instantaneous-request calls

Abstract: In order to provide an adequate quality of service to large-bandwidth calls, such as video conference calls, service providers of integrated services networks may want to allow some customers to book their calls ahead, i.e., make advance reservations. We propose a scheme for sharing resources among book-ahead (BA) calls (that announce their call holding times as well as their call initiation times upon arrival) and non-BA calls (that do not announce their holding times). It is possible to share resources without allowing any calls in progress to be interrupted, but in order to achieve a more efficient use of resources, we think that it may be desirable to occasionally allow a call in progress to be interrupted. (In practice, it may be possible to substitute service degradation, such as bit dropping or coarser encoding of video, for interruption.) Thus, we propose an admission control algorithm in which a call is admitted if an approximate interrupt probability (computed in real time) is below a threshold. Simulation experiments show that the proposed admission control algorithm can be better (i.e., yield higher total utilization or higher revenue) than alternative schemes that do not allow interruption, such as a strict partitioning of resources.

Implementing XQuery 1.0: the Galax experience

Abstract: Galax is a light-weight, portable, open-source implementation of XQuery 1.0. Started in December 2000 as a small prototype designed to test the XQuery static type system, Galax has now become a solid implementation, aiming at full conformance with the family of XQuery 1.0 specifications. Because of its completeness and open architecture, Galax also turns out to be a very convenient platform for researchers interested in experimenting with XQuery optimization. We demonstrate the Galax system as well as its most advanced features, including support for XPath 2.0, XML Schema and static type-checking. We also present some of our first experiments with optimization. Notably, we demonstrate query rewriting capabilities in the Galax compiler, and the ability to run queries on documents up to a Gigabyte without the need for preindexing. Although early versions of Galax have been shown in industrial conferences over the last two years, this is the first time it is demonstrated in the database community.

POC plots: calibrating species distribution models with presence-only data

Abstract: Statistical models are widely used for predicting species' geographic distributions and for analyzing species' responses to climatic and other predictor variables. Their predictive performance can be characterized in two complementary ways: discrimination, the ability to distinguish between occupied and unoccupied sites, and calibration, the extent to which a model correctly predicts conditional probability of presence. The most common measures of model performance, such as the area under the receiver operating characteristic curve (AUC), measure only discrimination. In contrast, we introduce a new tool for measuring model calibration: the presence-only calibration plot, or POC plot. This tool relies on presence-only evaluation data, which are more widely available than presence-absence evaluation data, to determine whether predictions are proportional to conditional probability of presence. We generalize the predicted/expected curves of Hirzel et al. to produce a presence-only analogue of traditional (presence-absence) calibration curves. POC plots facilitate visual exploration of model calibration, and can be used to recalibrate badly calibrated models. We demonstrate their use by recalibrating models made by the DOMAIN modeling method on a comprehensive set of 226 species from six regions of the world, significantly improving DOMAIN's predictive performance.