The Experts below are selected from a list of 9 Experts worldwide ranked by ideXlab platform
Janet M. Laubenstein - One of the best experts on this subject based on the ideXlab platform.
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Data structures for data flow analysis and the elimination of common subexpressions
1993Co-Authors: Janet M. LaubensteinAbstract:CHAPTER 1 -INTERMEDIATE CODE GENERATION Source To Grammar Symbol Translation Emitting Quadruple Code Quadruple Code Structure Temporary Symbol Usage Representing Expressions Representing Constants Symbol Table Structure Representing Control Structures CHAPTER 2 -THE FLOW GRAPH Basic Block Determination Basic Block Structure Branching Statements Building the Flow Graph Predecessor and Successor Functions Predecessor Block Determination Building a Predecessor Block List CHAPTER 3 -GLOBAL DATA FLOW ANALYSIS Use-Definition of Symbols Reaching Definitions Definitions Generated within Blocks
Dan Klein - One of the best experts on this subject based on the ideXlab platform.
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ACL - Simple, Accurate Parsing with an All-Fragments Grammar
2010Co-Authors: Mohit Bansal, Dan KleinAbstract:We present a simple but accurate parser which exploits both large tree fragments and Symbol refinement. We parse with all fragments of the training set, in contrast to much recent work on tree selection in data-oriented parsing and tree-substitution Grammar learning. We require only simple, deterministic Grammar Symbol refinement, in contrast to recent work on latent Symbol refinement. Moreover, our parser requires no explicit lexicon machinery, instead parsing input sentences as character streams. Despite its simplicity, our parser achieves accuracies of over 88% F1 on the standard English WSJ task, which is competitive with substantially more complicated state-of-the-art lexicalized and latent-variable parsers. Additional specific contributions center on making implicit all-fragments parsing efficient, including a coarse-to-fine inference scheme and a new graph encoding.
Mohit Bansal - One of the best experts on this subject based on the ideXlab platform.
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ACL - Simple, Accurate Parsing with an All-Fragments Grammar
2010Co-Authors: Mohit Bansal, Dan KleinAbstract:We present a simple but accurate parser which exploits both large tree fragments and Symbol refinement. We parse with all fragments of the training set, in contrast to much recent work on tree selection in data-oriented parsing and tree-substitution Grammar learning. We require only simple, deterministic Grammar Symbol refinement, in contrast to recent work on latent Symbol refinement. Moreover, our parser requires no explicit lexicon machinery, instead parsing input sentences as character streams. Despite its simplicity, our parser achieves accuracies of over 88% F1 on the standard English WSJ task, which is competitive with substantially more complicated state-of-the-art lexicalized and latent-variable parsers. Additional specific contributions center on making implicit all-fragments parsing efficient, including a coarse-to-fine inference scheme and a new graph encoding.
Christoph Traxler - One of the best experts on this subject based on the ideXlab platform.
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Representation and realistic rendering of natural phenomena with cyclic CSG graphs
The Visual Computer, 1996Co-Authors: Michael Gervautz, Christoph TraxlerAbstract:A method for ray tracing recursive objects defined by parametric rewriting systems using constructive solid geometry (CSG) as the underlying method of object representation is introduced. Thus, the formal languages of our rewriting systems are subsets of the infinite set of CSG expressions. Instead of deriving such expressions to build up large CSG trees, we translate the systems into cyclic CSG graphs, which can be used directly as an object representation for ray tracing. For this purpose the CSG concept is extended by three new nodes. Selection nodes join all the rules for one Grammar Symbol, control flow by selecting proper rules, and are end-points of cyclic edges. Transformation nodes map the rays in affine space. Calculation nodes evaluate a finite set of arithmetic expressions to modify global parameters, which effect flow control and transformations. The CSG graphs introduced here are a very compact data structure, much like the describing data set. This property meets our intention to avoid both restrictions of the complexity of the scenes by computer memory and the approximation accuracy of objects.
Michael Gervautz - One of the best experts on this subject based on the ideXlab platform.
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Representation and realistic rendering of natural phenomena with cyclic CSG graphs
The Visual Computer, 1996Co-Authors: Michael Gervautz, Christoph TraxlerAbstract:A method for ray tracing recursive objects defined by parametric rewriting systems using constructive solid geometry (CSG) as the underlying method of object representation is introduced. Thus, the formal languages of our rewriting systems are subsets of the infinite set of CSG expressions. Instead of deriving such expressions to build up large CSG trees, we translate the systems into cyclic CSG graphs, which can be used directly as an object representation for ray tracing. For this purpose the CSG concept is extended by three new nodes. Selection nodes join all the rules for one Grammar Symbol, control flow by selecting proper rules, and are end-points of cyclic edges. Transformation nodes map the rays in affine space. Calculation nodes evaluate a finite set of arithmetic expressions to modify global parameters, which effect flow control and transformations. The CSG graphs introduced here are a very compact data structure, much like the describing data set. This property meets our intention to avoid both restrictions of the complexity of the scenes by computer memory and the approximation accuracy of objects.
