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    MICRO
    1996
    IEEE
    106views Hardware» more  MICRO 1996»
    Optimization of Machine Descriptions for Efficient Use
    14 years 1 months ago
    Optimization of Machine Descriptions for Efficient Use
    Download  www.cs.princeton.edu
    A machine description facility allows compiler writers to specify machine execution constraints to the optimization and scheduling phases of an instruction-level parallelism (ILP)...
    John C. Gyllenhaal, Wen-mei W. Hwu, B. Ramakrishna...
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    AOSD
    2007
    ACM
    137views Software Engineering» more  AOSD 2007»
    A machine code model for efficient advice dispatch
    14 years 1 months ago
    A machine code model for efficient advice dispatch
    Download  www.cs.iastate.edu
    The primary implementations of AspectJ to date are based on a compile- or load-time weaving process that produces Java byte code. Although this implementation strategy has been cr...
    Ryan M. Golbeck, Gregor Kiczales
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    ASM
    2008
    ASM
    135views Computational Biology» more  ASM 2008»
    Using EventB to Create a Virtual Machine Instruction Set Architecture
    13 years 11 months ago
    Using EventB to Create a Virtual Machine Instruction Set Architecture
    Download  www.cs.bris.ac.uk
    A Virtual Machine (VM) is a program running on a conventional microprocessor that emulates the binary instruction set, registers, and memory space of an idealized computing machine...
    Stephen Wright
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    FMOODS
    2008
    187views Formal Methods» more  FMOODS 2008»
    Symbolic Step Encodings for Object Based Communicating State Machines
    13 years 11 months ago
    Symbolic Step Encodings for Object Based Communicating State Machines
    Download  www.tcs.hut.fi
    In this work, novel symbolic step encodings of the transition relation for object based communicating state machines are presented. This class of systems is tailored to capture the...
    Jori Dubrovin, Tommi A. Junttila, Keijo Heljanko
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    ICMLA
    2008
    116views Machine Learning» more  ICMLA 2008»
    Ensemble Machine Methods for DNA Binding
    13 years 11 months ago
    Ensemble Machine Methods for DNA Binding
    Download  math.bu.edu
    We introduce three ensemble machine learning methods for analysis of biological DNA binding by transcription factors (TFs). The goal is to identify both TF target genes and their ...
    Yue Fan, Mark A. Kon, Charles DeLisi
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