Results 1  10
of
4,299
Proof of a Fundamental Result in SelfSimilar Traffic Modeling
 COMPUTER COMMUNICATION REVIEW
, 1997
"... We state and prove the following key mathematical result in selfsimilar traffic modeling: the superposition of many ON/OFF sources (also known as packet trains) with strictly alternating ON and OFFperiods and whose ONperiods or OFFperiods exhibit the Noah Effect (i.e., have high variability or ..."
Abstract

Cited by 287 (9 self)
 Add to MetaCart
We state and prove the following key mathematical result in selfsimilar traffic modeling: the superposition of many ON/OFF sources (also known as packet trains) with strictly alternating ON and OFFperiods and whose ONperiods or OFFperiods exhibit the Noah Effect (i.e., have high variability or infinite variance) can produce aggregate network traffic that exhibits the Joseph Effect (i.e., is selfsimilar or longrange dependent). There is, moreover, a simple relation between the parameters describing the intensities of the Noah Effect (high variability) and the Joseph Effect (selfsimilarity). This provides a simple physical explanation for the presence of selfsimilar traffic patterns in modern highspeed network traffic that is consistent with traffic measurements at the source level. We illustrate how this mathematical result can be combined with modern highperformance computing capabilities to yield a simple and efficient lineartime algorithm for generating selfsimilar traf...
Operations for Learning with Graphical Models
 Journal of Artificial Intelligence Research
, 1994
"... This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Wellknown examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models ..."
Abstract

Cited by 277 (13 self)
 Add to MetaCart
This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Wellknown examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feedforward networks, and learning Gaussian and discrete Bayesian networks from data. The paper conclu...
The electronic properties of graphene
 Rev. Mod. Phys. 2009
"... This article reviews the basic theoretical aspects of graphene, a oneatomthick allotrope of carbon, with unusual twodimensional Diraclike electronic excitations. The Dirac electrons can be controlled by application of external electric and magnetic fields, or by altering sample geometry and/or t ..."
Abstract

Cited by 212 (1 self)
 Add to MetaCart
This article reviews the basic theoretical aspects of graphene, a oneatomthick allotrope of carbon, with unusual twodimensional Diraclike electronic excitations. The Dirac electrons can be controlled by application of external electric and magnetic fields, or by altering sample geometry and/or topology. The Dirac electrons behave in unusual ways in tunneling, confinement, and the integer quantum Hall effect. The electronic properties of graphene stacks are discussed and vary with stacking order and number of layers. Edge �surface � states in graphene depend on the edge termination �zigzag or armchair � and affect the physical properties of nanoribbons. Different types of disorder modify the Dirac equation leading to unusual spectroscopic and transport properties. The effects of electronelectron and electronphonon interactions in single layer and multilayer graphene are also
The MONK's Problems A Performance Comparison of Different Learning Algorithms
, 1991
"... This report summarizes a comparison of different learning techniques which was performed at the 2nd European Summer School on Machine Learning, held in Belgium during summer 1991. A variety of symbolic and nonsymbolic learning techniques  namely AQ17DCI, AQ17HCI, AQ17FCLS, AQ14NT, AQ15GA, Ass ..."
Abstract

Cited by 198 (15 self)
 Add to MetaCart
, Assistant Professional, mFOIL, ID5R, IDL, ID5Rhat, TDIDT, ID3, AQR, CN2, CLASS WEB, ECOBWEB, PRISM, Backpropagation, and Cascade Correlation  are compared on three classification problems, the MONK's problems. The MONK's problems are derived from a domain in which each training example
Linear Programming: Foundations and Extensions
, 1996
"... under the United States Copyright Act of 1976, no part of this publication may be reproduced or distributed in any form or by any means, or stored in a data base or retrieval system, without the prior written permission of the publisher. ISBN 0000000000 The text for this book was formated in Time ..."
Abstract

Cited by 196 (0 self)
 Add to MetaCart
under the United States Copyright Act of 1976, no part of this publication may be reproduced or distributed in any form or by any means, or stored in a data base or retrieval system, without the prior written permission of the publisher. ISBN 0000000000 The text for this book was formated in TimesRoman and the mathematics was formated in Michael Spivak’s Mathtimes using AMSL ATEX(which is a macro package for Leslie Lamport’s L ATEX, which itself is a macro package for Donald Knuth’s TEXtext formatting system) and converted from deviceindependent to postscript format using DVIPS. The figures were produced using SHOWCASE on a Silicon Graphics, Inc. workstation and were incorporated into the text as encapsulated postscript files with the macro package called PSFIG.TEX. To my parents, Howard and Marilyn, my dear wife, Krisadee, and the babes, Marisa and Diana Contents
Numerical Recipes in C: The Art of Scientific Computing. Second Edition
, 1992
"... This reprinting is corrected to software version 2.10 ..."
Abstract

Cited by 177 (0 self)
 Add to MetaCart
This reprinting is corrected to software version 2.10
Nonlinear Dynamic Structures
 Econometrica
, 1993
"... We describe three methods for analyzing the dynamics of a nonlinear time series that is represented by a nonparametric estimate of its onestep ahead conditional density. These strategies are based on examination of conditional moment profiles corresponding to certain shocks; a conditional moment pr ..."
Abstract

Cited by 130 (10 self)
 Add to MetaCart
We describe three methods for analyzing the dynamics of a nonlinear time series that is represented by a nonparametric estimate of its onestep ahead conditional density. These strategies are based on examination of conditional moment profiles corresponding to certain shocks; a conditional moment profile is the conditional expectation evaluated at time t of a time invariant function evaluated at time t + j regarded as a function of j. The first method, which compares conditional moment profiles to baseline profiles, is the nonlinear analog of conventional impulseresponse analysis. The second assesses the significance of a profile by comparing its supnorm confidence band to a null profile. The third examines profile bundles for evidence of damping or persistence. Experimental designs for choosing an appropriate set of shocks are discussed. These methods are applied to a bivariate series comprised of daily changes in the Standard and Poor's composite price index and daily NYSE transactions volume from 1928 to 1987. The findings from these data are: (i) The multistep ahead conditional volatility profile exhibits a symmetric response to both positive and negative price shocks. In contrast, the conditional volatility profile of the univariate price change process exhibits an asymmetric response. (ii) The onestep ahead response of volume to price shocks is different than the multistep ahead response. Price shocks produce an increase in volume onestep ahead but decrease it in subsequent steps. (iii) There is little evidence for longterm persistence in either the conditional mean or volatility of the bivariate process. o 1
Results 1  10
of
4,299