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On the Xorshift Random Number Generators FRANCOIS PANNETON and PIERRE LECUYER Universite de Montreal G

On the Xorshift Random Number Generators FRANCOIS PANNETON and PIERRE LECUYER Universite de Montreal G

  • danika-pritchard
  • 16 Slides

Marsaglia introduced recently a class of very fas...

Random Sampling with a Reservoir JEFFREY SCOTT VITTER Brown University We introduce fast algorithms for selecting a random sample of n records without replacement from a pool of N records where the v

Random Sampling with a Reservoir JEFFREY SCOTT VITTER Brown University We introduce fast algorithms for selecting a random sample of n records without replacement from a pool of N records where the v

  • briana-ranney
  • 21 Slides

The main result of the paper is the design and an...

SemiMarkov Conditional Random Fields for Information Extraction Sunita Sarawagi Indian Institute of Technology Bombay India sunitaiitb

SemiMarkov Conditional Random Fields for Information Extraction Sunita Sarawagi Indian Institute of Technology Bombay India sunitaiitb

  • natalia-silvester
  • 8 Slides

acin William W Cohen Center for Automated Learning...

SIGN DETECTION IN NA TU RAL IM GES WITH CONDITIONAL RANDOM FIELDS Jero einman Allen Hanson and Andrew McCallum weinmanhansonmccallumcs

SIGN DETECTION IN NA TU RAL IM GES WITH CONDITIONAL RANDOM FIELDS Jero einman Allen Hanson and Andrew McCallum weinmanhansonmccallumcs

  • yoshiko-marsland
  • 10 Slides

Departmen of Computer Science Univ ersit of Massa...

Random Complex Zeroes Mikhail Sodin Abstract Random complex zeroes a

Random Complex Zeroes Mikhail Sodin Abstract Random complex zeroes a

  • sherrill-nordquist
  • 1 Slides

ka chaotic analytic zero point s are zeroes of a G...

October Kevin Costello Random Walks on Directed Graphs Comments on a Method of Santosh Ventala The Eigencluster Algorithm given by Santosh Ventala is essentially a recursive algorithm it uses spec

October Kevin Costello Random Walks on Directed Graphs Comments on a Method of Santosh Ventala The Eigencluster Algorithm given by Santosh Ventala is essentially a recursive algorithm it uses spec

  • pasty-toler
  • 5 Slides

This may be inef64257cient as you are forced to k...

Efciently Matching Sets of Features with Random Histograms Wei Dong Zhe Wang Moses Charikar Kai Li Department of Computer Science Princeton University Olden Street Princeton NJ USA wdongzhewangmose

Efciently Matching Sets of Features with Random Histograms Wei Dong Zhe Wang Moses Charikar Kai Li Department of Computer Science Princeton University Olden Street Princeton NJ USA wdongzhewangmose

  • alida-meadow
  • 10 Slides

princetonedu ABSTRACT As the commonly used represe...

Chapter Giant Sucking Sound Lyapunov Functions A system whether it be social biological or physical can go to equilibrium it can cycle it can produce random patterns or it can be complex

Chapter Giant Sucking Sound Lyapunov Functions A system whether it be social biological or physical can go to equilibrium it can cycle it can produce random patterns or it can be complex

  • liane-varnes
  • 11 Slides

In this chapter were going to learn one reason wh...

Predicting nancial markets with Go ogle Trends and not so random keywords Damien Challet Chaire de nance quantitative Laboratoire de mathmatiques appliques aux systmes cole Centrale Paris Grande Voie

Predicting nancial markets with Go ogle Trends and not so random keywords Damien Challet Chaire de nance quantitative Laboratoire de mathmatiques appliques aux systmes cole Centrale Paris Grande Voie

  • pasty-toler
  • 9 Slides

We rst review the many subtle and less subtle bia...

COMPRESSIVE SENSING BY RANDOM CONVOLUTION JUSTIN ROMBERG Abstract

COMPRESSIVE SENSING BY RANDOM CONVOLUTION JUSTIN ROMBERG Abstract

  • mitsue-stanley
  • 22 Slides

This paper outlines a new framework for compressi...

A Comparative Study of Energy Minimization Methods for Markov Random Fields Richard Szeliski RaminZabih DanielScharstein Olga Veksler Vladimir Kolmogorov Aseem Agarwala Marshall Tappen and Carsten

A Comparative Study of Energy Minimization Methods for Markov Random Fields Richard Szeliski RaminZabih DanielScharstein Olga Veksler Vladimir Kolmogorov Aseem Agarwala Marshall Tappen and Carsten

  • liane-varnes
  • 14 Slides

com Cornell University rdzcscornelledu Middlebury ...

INTRODUCTION TO DIGITAL FILTERS Analog and digital filters In signal processing the function of a filter is to remove unwanted parts of the signal such as random noise or to extract useful parts of t

INTRODUCTION TO DIGITAL FILTERS Analog and digital filters In signal processing the function of a filter is to remove unwanted parts of the signal such as random noise or to extract useful parts of t

  • danika-pritchard
  • 15 Slides

The following block diagram illustrates the basic...

SIGN DETECTION IN NA TU RAL IM GES WITH CONDITIONAL RANDOM FIELDS Jero einman Allen Hanson and Andrew McCallum weinmanhansonmccallumcs

SIGN DETECTION IN NA TU RAL IM GES WITH CONDITIONAL RANDOM FIELDS Jero einman Allen Hanson and Andrew McCallum weinmanhansonmccallumcs

  • jane-oiler
  • 10 Slides

Departmen of Computer Science Univ ersit of Massa...

Sparse Recovery Using Sparse Matrices Anna Gilbert Piotr Indyk Abstract We survey algorithms for sparse recovery prob lems that are based on sparse random matrices

Sparse Recovery Using Sparse Matrices Anna Gilbert Piotr Indyk Abstract We survey algorithms for sparse recovery prob lems that are based on sparse random matrices

  • briana-ranney
  • 10 Slides

Such matrices has several attractive properties t...

Conceptualizing and Testing Random Indirect Effects and Moderated Mediation in Multilevel Models New Procedures and Recommendations Daniel J

Conceptualizing and Testing Random Indirect Effects and Moderated Mediation in Multilevel Models New Procedures and Recommendations Daniel J

  • celsa-spraggs
  • 22 Slides

Bauer Kristopher J Preacher and Karen M Gil Unive...

Hiddenstate Conditional Random Fields A

Hiddenstate Conditional Random Fields A

  • tatiana-dople
  • 17 Slides

Quattoni S Wang LP Morency M Collins T Darrell MI...

opic er sp ectral densit and TI systems The er sp ectral densit of WSS random pro cess Resp onse of an TI system to random signals Linear MSE estimation ES Ha rva rd SEAS The auto correlation functi

opic er sp ectral densit and TI systems The er sp ectral densit of WSS random pro cess Resp onse of an TI system to random signals Linear MSE estimation ES Ha rva rd SEAS The auto correlation functi

  • phoebe-click
  • 10 Slides

If drops quic kly with then pro cess hanges quic ...

Magnetoresistive Random Access Memory MRAM By James Daughton Copyright Contact Jim Daughton tel

Magnetoresistive Random Access Memory MRAM By James Daughton Copyright Contact Jim Daughton tel

  • liane-varnes
  • 13 Slides

952 9961607 or email daughtonnvecom Page of 13 I ...

Renewal ProcessesCounting ProcessesPoisson Processes Nt number of arrivals in Epoch of th arrival interarrival intervals Renewal Process Interarrival intervals ar positive IID random variables Ren

Renewal ProcessesCounting ProcessesPoisson Processes Nt number of arrivals in Epoch of th arrival interarrival intervals Renewal Process Interarrival intervals ar positive IID random variables Ren

  • tatiana-dople
  • 2 Slides

Equivalent or or Note Poisson Process Renewal pr...

SIGN DETECTION IN NA TU RAL IM GES WITH CONDITIONAL RANDOM FIELDS Jero einman Allen Hanson and Andrew McCallum weinmanhansonmccallumcs

SIGN DETECTION IN NA TU RAL IM GES WITH CONDITIONAL RANDOM FIELDS Jero einman Allen Hanson and Andrew McCallum weinmanhansonmccallumcs

  • min-jolicoeur
  • 10 Slides

Departmen of Computer Science Univ ersit of Massa...

Predicting nancial markets with Go ogle Trends and not so random keywords Damien Challet Chaire de nance quantitative Laboratoire de mathmatiques appliques aux systmes cole Centrale Paris Grande Voie

Predicting nancial markets with Go ogle Trends and not so random keywords Damien Challet Chaire de nance quantitative Laboratoire de mathmatiques appliques aux systmes cole Centrale Paris Grande Voie

  • calandra-battersby
  • 9 Slides

We rst review the many subtle and less subtle bia...

Eigenvalues and Condition Numbers of Complex Random Matrices T

Eigenvalues and Condition Numbers of Complex Random Matrices T

  • pamella-moone
  • 16 Slides

Ratnarajah R Vaillancourt M Alvo CRM3022 April 20...

Optimal DistortionPo wer radeof fs in Sensor Netw orks GaussMark Random Processes Nan Liu Sennur Ulukus Department of Electrical and Computer Engineering Uni ersity of Maryland Colle ge ark MD nkanc

Optimal DistortionPo wer radeof fs in Sensor Netw orks GaussMark Random Processes Nan Liu Sennur Ulukus Department of Electrical and Computer Engineering Uni ersity of Maryland Colle ge ark MD nkanc

  • sherrill-nordquist
  • 6 Slides

edu ulukusumdedu Abstract in estigate the optimal ...

EECS POWER SPECTRAL DENSITY Fall Given is a realvalued mean WSS random process RP

EECS POWER SPECTRAL DENSITY Fall Given is a realvalued mean WSS random process RP

  • faustina-dinatale
  • 8 Slides

With Autocorrelation for any time Note 0mean ...

Statistical Testing of Random Number Generators Juan Soto National Institute of Standards Technology Bureau Drive Stop Gaithersburg MD sotonist

Statistical Testing of Random Number Generators Juan Soto National Institute of Standards Technology Bureau Drive Stop Gaithersburg MD sotonist

  • karlyn-bohler
  • 12 Slides

gov 301 9754641 Abstract Random Number Generators ...

Approximations for Mean and Variance of a Ratio Consider random variables and where either has no mass at discrete or has support

Approximations for Mean and Variance of a Ratio Consider random variables and where either has no mass at discrete or has support

  • lois-ondreau
  • 2 Slides

Let RS RS Find approximations for EG and Var us...

Learning with Partially Absorbing Random Walks XiaoMing Wu Zhenguo Li Anthony ManCho So John Wright and ShihFu Chang Department of Electrical Engineering Columbia University Department of Computer S

Learning with Partially Absorbing Random Walks XiaoMing Wu Zhenguo Li Anthony ManCho So John Wright and ShihFu Chang Department of Electrical Engineering Columbia University Department of Computer S

  • conchita-marotz
  • 9 Slides

columbiaedu manchososecuhkeduhk Abstract We propos...

Certiable Quantum Dice Or True Random Number Generation Secure Against Quantum Advers aries Extended Abstract Umesh Vazirani Computer Science division UC Berkeley USA vaziranieecs

Certiable Quantum Dice Or True Random Number Generation Secure Against Quantum Advers aries Extended Abstract Umesh Vazirani Computer Science division UC Berkeley USA vaziranieecs

  • tatyana-admore
  • 16 Slides

berkeleyedu Thomas Vidick Computer Science and Art...

Journal of Scientific Exploration Vol

Journal of Scientific Exploration Vol

  • olivia-moreira
  • 16 Slides

16 No 1 pp 3 18 2002 08923310 02 2002 Society for...

Finding Large Hidden Clique in Random Graph Noga lon Michael Krivelevich and Benny Sudakov Department of Mathematics Raymond and Beverly Sackler Faculty of Exact Sciences Tel Aviv University Tel Aviv

Finding Large Hidden Clique in Random Graph Noga lon Michael Krivelevich and Benny Sudakov Department of Mathematics Raymond and Beverly Sackler Faculty of Exact Sciences Tel Aviv University Tel Aviv

  • jane-oiler
  • 10 Slides

tauacil krivelev mathtauacil sudakov mathtauacil R...

Convincing Evidence Andrew Gelman Keith ORourke June Abstract Textbooks on statistics emphasize care and precision via concepts such as reliability and validity in measurement random sampling and t

Convincing Evidence Andrew Gelman Keith ORourke June Abstract Textbooks on statistics emphasize care and precision via concepts such as reliability and validity in measurement random sampling and t

  • alexa-scheidler
  • 5 Slides

But how do researchers decide what to believe and...

The dramatic appearance of crackle takes on the characteristics of aged metal patina because each unique application produces random splitting and blistering that result in interesting multilayer dep

The dramatic appearance of crackle takes on the characteristics of aged metal patina because each unique application produces random splitting and blistering that result in interesting multilayer dep

  • danika-pritchard
  • 6 Slides

Sophisticated Bold Adventurous Great for a firepl...

Conditional Random Field utoencoders or Unsuper vised

Conditional Random Field utoencoders or Unsuper vised

  • cheryl-pisano
  • 9 Slides

Smith School of Computer Science Carne gie Mellon...

Akaike Information Criterion Shuhua Hu Center for Rese

Akaike Information Criterion Shuhua Hu Center for Rese

  • kittie-lecroy
  • 19 Slides

random variable with some probability distributi...

THE INTERPLAY OF CLASSES OF ALGORITHMICALLY RANDOM OBJ

THE INTERPLAY OF CLASSES OF ALGORITHMICALLY RANDOM OBJ

  • alexa-scheidler
  • 18 Slides

PORTER Abstract We study algorithmically random c...

where N rms is the rms value of some random variable n

where N rms is the rms value of some random variable n

  • faustina-dinatale
  • 6 Slides

In the case of amplifier noise nt represents eith...

Random variable RV a variable that assumes numerical v

Random variable RV a variable that assumes numerical v

  • ellena-manuel
  • 4 Slides

The diameter of a tree 2 The number of chapters i...

Mean and Variance of Binomial Random Variables The pro

Mean and Variance of Binomial Random Variables The pro

  • lindy-dunigan
  • 2 Slides

If is a random variable with this probability dis...

Analysis of spontaneous bursting activity in random ne

Analysis of spontaneous bursting activity in random ne

  • test
  • 5 Slides

Latham Laboratory of Neural Control Section on De...

CircularlySymmetric Gaussian random vectors Robert G

CircularlySymmetric Gaussian random vectors Robert G

  • myesha-ticknor
  • 9 Slides

Gallager January 1 2008 Abstract A number of basi...

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