David Berry · The International Encyclopedia of Communication Theory and Philosophy · 2016
Abstract Machine code is the basic level of machinic description that is directly executable by a computer. It is stored internally in the computer, in the form of binary code with a specific numerical value. Machine code is the level at which the instructions that are executed by a computer are directly transferred to its processor and actuated. It is very rare for programmers to write in machine code directly; instead a programmer works at the level of assembly language, which converts the numeric code of machine code into a quasi‐symbolic form, while still being operative at the machinic level.
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Martin H. Weik · 2000
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biomechanoid (UCN) · Bulletin of Miscellaneous Information (Royal Gardens Kew) · 2006
An album of eclectic drum & bass from biomechanoid. Recorded 2003-2005 Artist page: biomechanoid www.universal-communication.net
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Andrew W. Appel · Cambridge University Press eBooks · 1991
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Martin H. Weik · 2000
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Torben Ægidius Mogensen · Undergraduate topics in computer science · 2023
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Shehryaar Ali, Zeeshan, Muhammad · 2019
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Liñares, Jose, Fernandez-Lozano, Carlos · Figshare · 2018
Script of Machine Learning experiment. For benchmark experiment it was used bmr R package. In addition, Feature Selection techniques were carried out. <br>It is about a supervised binary classification problem.
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Elsevier eBooks · 1977
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Goodhew, Anthony · Figshare · 2021
Machine code simulation
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Martin H. Weik · 2000
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Ics Spring, Object Files · 2010
High-level code char *tmpfilename; int num_schedulers=0; int num_request_submitters=0; int i,j; if (!(f = fopen(filename,&quot;r&quot;))) { xbt_assert1(0,&quot;Cannot open file %s&quot;,filename); while(fgets(buffer,256,f)) { if (!strncmp(buffer,&quot;SCHEDULER&quot;,9)) num_schedulers++; if (!strncmp(buffer,&quot;REQUESTSUBMITTER&quot;,16)) num_request_submitters++; fclose(f); tmpfilename = strdup(&quot;/tmp/jobsimulator_
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Liñares, Jose, Fernandez-Lozano, Carlos · Figshare · 2018
Script of Machine Learning experiment. For benchmark experiment it was used bmr R package. In addition, Feature Selection techniques were carried out. <br>It is about a supervised binary classification problem.
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Clinton F. Goss · 1986
This dissertation explores classes of compiler optimization techniques which are applicable late in the compilation process, after all executable code for a program has been linked. We concentrate on techniques which, for various reasons, cannot be applied earlier in the compilation process.
We begin by demonstrating the need for optimizations at this level in the UNIX('(REGTM)) programming environment. We then describe a Machine Code Optimizer which improves code in executable task files in that environment. The specific details of certain algorithms are then described: code elimination to remove unreachable code, code distribution to re-order sections of code, operand reduction which converts operands to use more advantageous addressing modes available on the target architecture, and macro compression which collapses common sequences of instructions. We show that the problem of finding optimal solutions for code distribution is NP-Complete and discuss heuristics for practical solutions.
We then describe the implementation of a Machine Code Optimizer containing the code elimination, code distribution, and operand reduction algorithms. This optimizer operates in a production environment and incorporates a machine independent architecture representation which allows it to be ported across a large class of machines. We demonstrate the portability of the Machine Code Optimizer to the Motorola MC68000('(REGTM)) and the Digital VAX-11('(REGTM)) instruction sets. Finally, metrics on the improvements obtained across architectures and the optimization techniques are provided along with proposed lines of further research. The methods demonstrate that substantial reductions in code space and more modest improvements in execution speed can be obtained using these techniques.
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Thomas Johnsson · Lecture notes in computer science · 1987
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Bulletin of Miscellaneous Information (Royal Gardens Kew)
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Cleomar Pereira da Silva, Douglas Mota Dias, Cristiana Bentes et al. · HAL (Le Centre pour la Communication Scientifique Directe) · 2015
International audience
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Marco Scutari, Mauro Malvestio · 2023
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Jun Jiang · Zenodo (CERN European Organization for Nuclear Research) · 2020
README.md This project "A Machine Learning Protocol for Predicting Protein Infrared Spectra" was supported by Prof. Shaul Mukamel(the University of California, Irvine), Prof. Jonathan D. Hirst(University of Nottingham),and Prof.Jun Jiang(University of Science and Technology of China). Simulation data and code of ML protocl for IR spectra of proteins. Any researchers who interested in protein spectroscopy can use our ML protcol online service: http://dcaiku.com:12880/platform/first For the machine learning protocol source code written in Python and Bash language which including: 1.1.py: Split the protein into individual peptide bonds and dipeptides. 1.2.py: Calculate the center of mass for each each peptide bond and dipeptide. 1.3.sh: Convert the pdb format file to xyz format 2.py: Extracte the Coulomb Matrix (CM) descriptors of peptide bond and dipeptide.. 3.py: Predict the vibrational frequency and vibrational transition dipole moment of each peptide bond from trained NMA Neural Networks (NN) model. 4.py: Predict the neighboring coupling of each dipeptide from trained GLDP NN model. 5.sh: Generate the input file for SPECTRON program to calculate the IR spectra of proteins. 6.py: Construct the model Hamiltonian for amide I vibrations in a protein based on vibration exciton model theory. IR.sh: Diagonalize the Hamilton matrix calculate the IR spectra by using the SPECTRON.
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Annie Kelly · 2019
My research motivation is to empower performing artists, particularly women and non-binary adults, to have more equity in the technological implementations of computational systems used in their performances. Many women have missed out on current youth CS Education initiatives, requiring artists to frequently contract technical work to male engineers. To address this inequity, I am working on a design-based research project. My work involves understanding artists' technological needs and challenges they face working with DIY "beginner-friendly'' technologies, in order to iteratively develop design theories for more novice accessible and expressive tools develop interactive artistic systems. Currently, while novice-targeted beginner-friendly physical computing tools can simplify some aspects of learning programming and electronics, these tools are still limited in supporting novices who want to go beyond the creation of simple projects and create sophisticated artistic performance systems.
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