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Institution

AT&T Labs

Company
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.


Papers
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Journal ArticleDOI
17 Jun 2013
TL;DR: In this article, vector fields are used to induce a notion of similarity between trajectories, letting the vector fields themselves define and represent each cluster, and an efficient algorithm to find a locally optimal clustering of trajectories into vector fields, and demonstrate how vector field k-means can find patterns missed by previous methods.
Abstract: Scientists study trajectory data to understand trends in movement patterns, such as human mobility for traffic analysis and urban planning. In this paper, we introduce a novel trajectory clustering technique whose central idea is to use vector fields to induce a notion of similarity between trajectories, letting the vector fields themselves define and represent each cluster. We present an efficient algorithm to find a locally optimal clustering of trajectories into vector fields, and demonstrate how vector-field k-means can find patterns missed by previous methods. We present experimental evidence of its effectiveness and efficiency using several datasets, including historical hurricane data, GPS tracks of people and vehicles, and anonymous cellular radio handoffs from a large service provider.

105 citations

Proceedings ArticleDOI
27 May 2018
TL;DR: In this article, the authors provide a set of algorithms for materializing marginal statistics under the strong model of local differential privacy, and prove tight theoretical bounds on the accuracy of marginals compiled under each approach, perform empirical evaluation to confirm these bounds, and evaluate them for tasks such as modeling and correlation testing.
Abstract: Many analysis and machine learning tasks require the availability of marginal statistics on multidimensional datasets while providing strong privacy guarantees for the data subjects. Applications for these statistics range from finding correlations in the data to fitting sophisticated prediction models. In this paper, we provide a set of algorithms for materializing marginal statistics under the strong model of local differential privacy. We prove the first tight theoretical bounds on the accuracy of marginals compiled under each approach, perform empirical evaluation to confirm these bounds, and evaluate them for tasks such as modeling and correlation testing. Our results show that releasing information based on (local) Fourier transformations of the input is preferable to alternatives based directly on (local) marginals.

105 citations

Proceedings ArticleDOI
05 Apr 2005
TL;DR: This paper proposes an architecture and adaptive algorithms for efficiently computing top-k matches to XML queries that can be used to evaluate both exact and approximate matches where approximation is defined by relaxing XPath axes.
Abstract: The ability to compute top-k matches to XML queries is gaining importance due to the increasing number of large XML repositories. The efficiency of top-k query evaluation relies on using scores to prune irrelevant answers as early as possible in the evaluation process. In this context, evaluating the same query plan for all answers might be too rigid because, at any time in the evaluation, answers have gone through the same number and sequence of operations, which limits the speed at which scores grow. Therefore, adaptive query processing that permits different plans for different partial matches and maximizes the best scores is more appropriate. In this paper, we propose an architecture and adaptive algorithms for efficiently computing top-k matches to XML queries. Our techniques can be used to evaluate both exact and approximate matches where approximation is defined by relaxing XPath axes. In order to compute the scores of query answers, we extend the traditional tf*idf measure to account for document structure. We conduct extensive experiments on a variety of benchmark data and queries, and demonstrate the usefulness of the adaptive approach for computing top-k queries in XML.

105 citations

Journal ArticleDOI
TL;DR: In this paper, the authors considered four simple but representative initial access protocols that use various combinations of directional beamforming and omnidirectional transmission and reception at the mobile and the BS, during the cell search (CS) and random access (RA) phases.
Abstract: Initial access is the process which allows a mobile user to first connect to a cellular network. It consists of two main steps: cell search (CS) on the downlink and random access (RA) on the uplink. Millimeter wave (mm-wave) cellular systems typically must rely on directional beamforming (BF) in order to create a viable connection. The BF direction must, therefore, be learned—as well as used—in the initial access process for mm-wave cellular networks. This paper considers four simple but representative initial access protocols that use various combinations of directional BF and omnidirectional transmission and reception at the mobile and the BS, during the CS and RA phases. We provide a system-level analysis of the success probability for CS and RA for each one, as well as of the initial access delay and user-perceived downlink throughput (UPT). For a baseline exhaustive search protocol, we find the optimal BS beamwidth and observe that in terms of initial access delay it is decreasing as blockage becomes more severe, but is relatively constant (about $\pi /12$ ) for UPT. Of the considered protocols, the best tradeoff between initial access delay and UPT is achieved under a fast CS protocol.

105 citations

Journal ArticleDOI
TL;DR: This paper shows how the Wolman model is applied to large-scale caching systems in which the interior nodes belong to third-party content distribution services and correlates the model's predictions of interior cache behavior with empirical observations from the root caches of the NLANR cache hierarchy.

105 citations


Authors

Showing all 1881 results

NameH-indexPapersCitations
Yoshua Bengio2021033420313
Scott Shenker150454118017
Paul Shala Henry13731835971
Peter Stone130122979713
Yann LeCun121369171211
Louis E. Brus11334763052
Jennifer Rexford10239445277
Andreas F. Molisch9677747530
Vern Paxson9326748382
Lorrie Faith Cranor9232628728
Ward Whitt8942429938
Lawrence R. Rabiner8837870445
Thomas E. Graedel8634827860
William W. Cohen8538431495
Michael K. Reiter8438030267
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20225
202133
202069
201971
2018100
201791