• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Tools

Sorted by:
Try your query at:
Semantic Scholar Scholar Academic
Google Bing DBLP
Results 1 - 10 of 4,197
Next 10 →

Guaranteeing the Diversity of Number Generators

by Adi Shamir, Boaz Tsaban - Information and Computation , 2003
"... A major problem in using iterative number generators of the form x i = f(x i 1 ) is that they can enter unexpectedly short cycles. This is hard to analyze when the generator is designed, hard to detect in real time when the generator is used, and can have devastating cryptanalytic implications. ..."
Abstract - Cited by 13 (1 self) - Add to MetaCart
A major problem in using iterative number generators of the form x i = f(x i 1 ) is that they can enter unexpectedly short cycles. This is hard to analyze when the generator is designed, hard to detect in real time when the generator is used, and can have devastating cryptanalytic implications

The unity and diversity of executive functions and their contributions to complex “Frontal Lobe” tasks: a latent variable analysis

by Akira Miyake, Naomi P. Friedman, Michael J. Emerson, Alexander H. Witzki, Amy Howerter, Tor D. Wager - COGNIT PSYCHOL , 2000
"... This individual differences study examined the separability of three often postulated executive functions—mental set shifting ("Shifting"), information updating and monitoring ("Updating"), and inhibition of prepotent responses ("Inhibition")—and their roles in complex ..."
Abstract - Cited by 696 (9 self) - Add to MetaCart
Sorting Test (WCST), Tower of Hanoi (TOH), random number generation (RNG), operation span, and dual tasking. Confirmatory factor analysis indicated that the three target executive functions are moderately correlated with one another, but are clearly separable. Moreover, structural equation modeling

Training Support Vector Machines: an Application to Face Detection

by Edgar Osuna, Robert Freund, Federico Girosi , 1997
"... We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision sur ..."
Abstract - Cited by 727 (1 self) - Add to MetaCart
surfaces are found by solving a linearly constrained quadratic programming problem. This optimization problem is challenging because the quadratic form is completely dense and the memory requirements grow with the square of the number of data points. We present a decomposition algorithm that guarantees

Critical Power for Asymptotic Connectivity in Wireless Networks

by Piyush Gupta, P. R. Kumar , 1998
"... : In wireless data networks each transmitter's power needs to be high enough to reach the intended receivers, while generating minimum interference on other receivers sharing the same channel. In particular, if the nodes in the network are assumed to cooperate in routing each others ' pack ..."
Abstract - Cited by 541 (19 self) - Add to MetaCart
: In wireless data networks each transmitter's power needs to be high enough to reach the intended receivers, while generating minimum interference on other receivers sharing the same channel. In particular, if the nodes in the network are assumed to cooperate in routing each others &apos

Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach

by Eckart Zitzler, Lothar Thiele - IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION , 1999
"... Evolutionary algorithms (EA’s) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a singl ..."
Abstract - Cited by 813 (22 self) - Add to MetaCart
) evaluating an individual’s fitness dependent on the number of external nondominated points that dominate it, c) preserving population diversity using the Pareto dominance relationship, and d) incorporating a clustering procedure in order to reduce the nondominated set without destroying its characteristics

Loopy belief propagation for approximate inference: An empirical study. In:

by Kevin P Murphy , Yair Weiss , Michael I Jordan - Proceedings of Uncertainty in AI, , 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" -the use of Pearl's polytree algorithm in a Bayesian network with loops -can perform well in the context of error-correcting codes. The most dramatic instance of this is the near Shannon-limit performanc ..."
Abstract - Cited by 676 (15 self) - Add to MetaCart
with loops (undirected cycles). The algorithm is an exact inference algorithm for singly connected networks -the beliefs converge to the cor rect marginals in a number of iterations equal to the diameter of the graph.1 However, as Pearl noted, the same algorithm will not give the correct beliefs for mul

Coalition Structure Generation with Worst Case Guarantees

by Tuomas Sandholm, Kate Larson , Martin Andersson , Onn Shehory , Fernando Tohmé , 1999
"... Coalition formation is a key topic in multiagent systems. One may prefer a coalition structure that maximizes the sum of the values of the coalitions, but often the number of coalition structures is too large to allow exhaustive search for the optimal one. Furthermore, finding the optimal coalition ..."
Abstract - Cited by 270 (9 self) - Add to MetaCart
structure is NP-complete. But then, can the coalition structure found via a partial search be guaranteed to be within a bound from optimum? We show that none of the previous coalition structure generation algorithms can establish any bound because they search fewer nodes than a threshold that we show

Benchmarking cloud serving systems with ycsb

by Brian F. Cooper, Adam Silberstein, Erwin Tam, Raghu Ramakrishnan, Russell Sears - SoCC
"... While the use of MapReduce systems (such as Hadoop) for large scale data analysis has been widely recognized and studied, we have recently seen an explosion in the number of systems developed for cloud data serving. These newer systems address “cloud OLTP ” applications, though they typically do not ..."
Abstract - Cited by 329 (0 self) - Add to MetaCart
While the use of MapReduce systems (such as Hadoop) for large scale data analysis has been widely recognized and studied, we have recently seen an explosion in the number of systems developed for cloud data serving. These newer systems address “cloud OLTP ” applications, though they typically do

A Delaunay Refinement Algorithm for Quality 2-Dimensional Mesh Generation

by Jim Ruppert , 1995
"... We present a simple new algorithm for triangulating polygons and planar straightline graphs. It provides "shape" and "size" guarantees: All triangles have a bounded aspect ratio. The number of triangles is within a constant factor of optimal. Such "quality" triangulatio ..."
Abstract - Cited by 241 (0 self) - Add to MetaCart
We present a simple new algorithm for triangulating polygons and planar straightline graphs. It provides "shape" and "size" guarantees: All triangles have a bounded aspect ratio. The number of triangles is within a constant factor of optimal. Such "

Provably Good Mesh Generation

by Marshall Bern, David Eppstein, John Gilbert - J. COMPUT. SYST. SCI , 1990
"... We study several versions of the problem of generating triangular meshes for finite element methods. We show how to triangulate a planar point set or polygonally bounded domain with triangles of bounded aspect ratio; how to triangulate a planar point set with triangles having no obtuse angles; how t ..."
Abstract - Cited by 214 (10 self) - Add to MetaCart
, and run in optimal time O(n log n+k) with input of size n and output of size k. No previous work on mesh generation simultaneously guarantees well-shaped elements and small total size.
Next 10 →
Results 1 - 10 of 4,197
Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University