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Distributed Snapshots: Determining Global States of Distributed Systems

by K. Mani Chandy, LESLIE LAMPORT - ACM TRANSACTIONS ON COMPUTER SYSTEMS , 1985
"... This paper presents an algorithm by which a process in a distributed system determines a global state of the system during a computation. Many problems in distributed systems can be cast in terms of the problem of detecting global states. For instance, the global state detection algorithm helps to s ..."
Abstract - Cited by 1208 (6 self) - Add to MetaCart
This paper presents an algorithm by which a process in a distributed system determines a global state of the system during a computation. Many problems in distributed systems can be cast in terms of the problem of detecting global states. For instance, the global state detection algorithm helps

Freenet: A Distributed Anonymous Information Storage and Retrieval System

by Ian Clarke, Oskar Sandberg, Brandon Wiley, Theodore W. Hong - INTERNATIONAL WORKSHOP ON DESIGNING PRIVACY ENHANCING TECHNOLOGIES: DESIGN ISSUES IN ANONYMITY AND UNOBSERVABILITY , 2001
"... We describe Freenet, an adaptive peer-to-peer network application that permits the publication, replication, and retrieval of data while protecting the anonymity of both authors and readers. Freenet operates as a network of identical nodes that collectively pool their storage space to store data ..."
Abstract - Cited by 1064 (12 self) - Add to MetaCart
where there is no interest. It is infeasible to discover the true origin or destination of a file passing through the network, and difficult for a node operator to determine or be held responsible for the actual physical contents of her own node.

Multiresolution grayscale and rotation invariant texture classification with local binary patterns

by Timo Ojala, Matti Pietikäinen, Topi Mäenpää - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2002
"... This paper presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain ..."
Abstract - Cited by 1299 (39 self) - Add to MetaCart
be further improved by combining them with rotation invariant variance measures that characterize the contrast of local image texture. The joint distributions of these orthogonal measures are shown to be very powerful tools for rotation invariant texture analysis.

Matching words and pictures

by Kobus Barnard, Pinar Duygulu, David Forsyth, Nando De Freitas, David M. Blei, Michael I. Jordan - JOURNAL OF MACHINE LEARNING RESEARCH , 2003
"... We present a new approach for modeling multi-modal data sets, focusing on the specific case of segmented images with associated text. Learning the joint distribution of image regions and words has many applications. We consider in detail predicting words associated with whole images (auto-annotation ..."
Abstract - Cited by 665 (40 self) - Add to MetaCart
We present a new approach for modeling multi-modal data sets, focusing on the specific case of segmented images with associated text. Learning the joint distribution of image regions and words has many applications. We consider in detail predicting words associated with whole images (auto

Longitudinal data analysis using generalized linear models”.

by Kung-Yee Liang , Scott L Zeger - Biometrika, , 1986
"... SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating ..."
Abstract - Cited by 1526 (8 self) - Add to MetaCart
. The estimating equations are derived without specifying the joint distribution of a subject's observations yet they reduce to the score equations for multivariate Gaussian outcomes. Asymptotic theory is presented for the general class of estimators. Specific cases in which we assume independence, m

Transform Analysis and Asset Pricing for Affine Jump-Diffusions

by Darrell Duffie, Jun Pan, Kenneth Singleton - Econometrica , 2000
"... In the setting of ‘‘affine’ ’ jump-diffusion state processes, this paper provides an analytical treatment of a class of transforms, including various Laplace and Fourier transforms as special cases, that allow an analytical treatment of a range of valuation and econometric problems. Example applicat ..."
Abstract - Cited by 710 (38 self) - Add to MetaCart
‘smirks ’ of the joint distribution of jumps in volatility and jumps in the underlying asset price, through both jump amplitude as well as jump timing.

Calibrating noise to sensitivity in private data analysis

by Cynthia Dwork, Frank Mcsherry, Kobbi Nissim, Adam Smith - In Proceedings of the 3rd Theory of Cryptography Conference , 2006
"... Abstract. We continue a line of research initiated in [10, 11] on privacypreserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function f mapping databases to reals, the so-called true answer is the result of applying f to the datab ..."
Abstract - Cited by 649 (60 self) - Add to MetaCart
to the database. To protect privacy, the true answer is perturbed by the addition of random noise generated according to a carefully chosen distribution, and this response, the true answer plus noise, is returned to the user. Previous work focused on the case of noisy sums, in which f =P i g(xi), where xi denotes

Convolution Kernels on Discrete Structures

by David Haussler , 1999
"... We introduce a new method of constructing kernels on sets whose elements are discrete structures like strings, trees and graphs. The method can be applied iteratively to build a kernel on an infinite set from kernels involving generators of the set. The family of kernels generated generalizes the fa ..."
Abstract - Cited by 506 (0 self) - Add to MetaCart
the family of radial basis kernels. It can also be used to define kernels in the form of joint Gibbs probability distributions. Kernels can be built from hidden Markov random elds, generalized regular expressions, pair-HMMs, or ANOVA decompositions. Uses of the method lead to open problems involving

Reversible jump Markov chain Monte Carlo computation and Bayesian model determination

by Peter J. Green - Biometrika , 1995
"... Markov chain Monte Carlo methods for Bayesian computation have until recently been restricted to problems where the joint distribution of all variables has a density with respect to some xed standard underlying measure. They have therefore not been available for application to Bayesian model determi ..."
Abstract - Cited by 1345 (23 self) - Add to MetaCart
Markov chain Monte Carlo methods for Bayesian computation have until recently been restricted to problems where the joint distribution of all variables has a density with respect to some xed standard underlying measure. They have therefore not been available for application to Bayesian model

Real-Time Obstacle Avoidance for Manipulators and Mobile Robots

by Oussama Khatib - INT. JOUR OF ROBOTIC RESEARCH , 1986
"... This paper presents a unique real-time obstacle avoidance approach for manipulators and mobile robots based on the artificial potential field concept. Collision avoidance, tradi-tionally considered a high level planning problem, can be effectively distributed between different levels of control, al- ..."
Abstract - Cited by 1345 (28 self) - Add to MetaCart
This paper presents a unique real-time obstacle avoidance approach for manipulators and mobile robots based on the artificial potential field concept. Collision avoidance, tradi-tionally considered a high level planning problem, can be effectively distributed between different levels of control, al
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