Range-Efficient Counting of Distinct Elements in a Massive Data
... † Department of Computer Science, Iowa State University, Ames, IA 50010 ([email protected]). This author’s research was supported in part by NSF grant CCF-0430807. ‡ Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50010 ([email protected]). This author’s research ...
... † Department of Computer Science, Iowa State University, Ames, IA 50010 ([email protected]). This author’s research was supported in part by NSF grant CCF-0430807. ‡ Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50010 ([email protected]). This author’s research ...
Ten Challenges Redux: Recent Progress in Propositional
... graph that has all decision variables on one side, called the reason side, and false as well as at least one conflict literal on the other side, called the conflict side. All nodes on the reason side that have at least one edge going to the conflict side form a cause of the conflict. The negations o ...
... graph that has all decision variables on one side, called the reason side, and false as well as at least one conflict literal on the other side, called the conflict side. All nodes on the reason side that have at least one edge going to the conflict side form a cause of the conflict. The negations o ...
Semi-supervised collaborative clustering with partial background
... involved in the conflict, an operator is chosen and applied as follows: But the application of the two operators is not always relevant. Indeed, it does not always increase the similarity of the results implied in the conflict treated, and especially, the iteration of conflict resolutions may lead t ...
... involved in the conflict, an operator is chosen and applied as follows: But the application of the two operators is not always relevant. Indeed, it does not always increase the similarity of the results implied in the conflict treated, and especially, the iteration of conflict resolutions may lead t ...
Artificial Intelligence in Networking: Ant Colony Optimization
... Ant Colony Optimization has been researched for and applied to some core areas of Computer Science. One main area of interest is networking; because of ant’s natural ability to go from their starting point to their destination with the most limited amount of cost. We are also interested in their qui ...
... Ant Colony Optimization has been researched for and applied to some core areas of Computer Science. One main area of interest is networking; because of ant’s natural ability to go from their starting point to their destination with the most limited amount of cost. We are also interested in their qui ...
High Dimensional Similarity Joins: Algorithms and Performance
... two sets of n points on a line, we are to report all pairs of points, one from each set, within distance from each other. We can do this by sorting both sets (an O(n log n) operation), and performing a scan of both les by treating portions of each le corresponding to a range of values of the att ...
... two sets of n points on a line, we are to report all pairs of points, one from each set, within distance from each other. We can do this by sorting both sets (an O(n log n) operation), and performing a scan of both les by treating portions of each le corresponding to a range of values of the att ...
A New Non-oscillatory Numerical Approach for Structural Dynamics
... smoothed particle hydrodynamics (SPH) method and others. Many different numerical methods have been developed for the time integration of Eq. (1). However, for wave propagation problems, the integration of Eq. (1) leads to the appearance of spurious high-frequency oscillations. Both the spatial disc ...
... smoothed particle hydrodynamics (SPH) method and others. Many different numerical methods have been developed for the time integration of Eq. (1). However, for wave propagation problems, the integration of Eq. (1) leads to the appearance of spurious high-frequency oscillations. Both the spatial disc ...
An Extension of the ICP Algorithm Considering Scale Factor
... Fig. 2. The convergence of ICP and SICP on the Stanford Bunny In Table 3, SICP with bounded scale and ICP are similar in accuracy on the Stanford Bunny. Though RMS of SICP ( s j is any arbitrary number) is 0.6854, seemingly smaller than those of others, its scale S is close to 03u3 , an unreasonable ...
... Fig. 2. The convergence of ICP and SICP on the Stanford Bunny In Table 3, SICP with bounded scale and ICP are similar in accuracy on the Stanford Bunny. Though RMS of SICP ( s j is any arbitrary number) is 0.6854, seemingly smaller than those of others, its scale S is close to 03u3 , an unreasonable ...
Automating Operational Business Decisions Using Artificial
... Making business decisions nowadays is increasingly reliant upon analyzing very large data-sets and the complex relations between them. This makes the task time consuming and complex for humans to carry out accurately. Algorithms could support or even take over this task by learning to make certain d ...
... Making business decisions nowadays is increasingly reliant upon analyzing very large data-sets and the complex relations between them. This makes the task time consuming and complex for humans to carry out accurately. Algorithms could support or even take over this task by learning to make certain d ...
(G5AIAI) - 2001/02
... game. These are shown in bold in the search tree above. (d) Given the following search tree, apply the alpha-beta pruning algorithm to it and show the search tree that would be built by this algorithm. Make sure that you show where the alpha and beta cuts are applied and which parts of the search tr ...
... game. These are shown in bold in the search tree above. (d) Given the following search tree, apply the alpha-beta pruning algorithm to it and show the search tree that would be built by this algorithm. Make sure that you show where the alpha and beta cuts are applied and which parts of the search tr ...
Genetic algorithm
In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.