Programming languages for CSE: the state of the art

1998 ◽  
Vol 5 (2) ◽  
pp. 18-26 ◽  
Author(s):  
L.V. Kale
Keyword(s):  
Programming Languages ◽  
State Of The Art ◽  
The State

scholarly journals Knowledge Compilation meets Uniform Sampling

10.29007/h4p9 ◽  
2018 ◽  
Author(s):  
Shubham Sharma ◽  
Rahul Gupta ◽  
Subhajit Roy ◽  
Kuldeep S. Meel
Keyword(s):  
Programming Languages ◽  
State Of The Art ◽  
The State ◽  
Knowledge Compilation ◽  
New Approach ◽  
Uniform Sampling ◽  
Incremental Sampling ◽  
Uniform Generation ◽  
Constrained Random Verification ◽  
Primary Contribution

Uniform sampling has drawn diverse applications in programming languages and software engineering, like in constrained-random verification (CRV), constrained-fuzzing and bug synthesis. The effectiveness of these applications depend on the uniformity of test stimuli generated from a given set of constraints. Despite significant progress over the past few years, the performance of the state of the art techniques still falls short of those of heuristic methods employed in the industry which sacrifice either uniformity or scalability when generating stimuli.In this paper, we propose a new approach to the uniform generation that builds on recent progress in knowledge compilation. The primary contribution of this paper is marrying knowledge compilation with uniform sampling: our algorithm, KUS, employs the state-of-the-art knowledge compilers to first compile constraints into d-DNNF form, and then, generates samples by making two passes over the compiled representation.We show that KUS is able to significantly outperform existing state-of-the-art algorithms, SPUR and UniGen2, by up to 3 orders of magnitude in terms of runtime while achieving a geometric speedup of 1.7× and 8.3× over SPUR and UniGen2 respectively. Also, KUS achieves a lower PAR-21 score, around 0.82× that of SPUR and 0.38× that of UniGen2. Furthermore, KUS achieves speedups of up to 3 orders of magnitude for incremental sampling. The distribution generated by KUS is statistically indistinguishable from that generated by an ideal uniform sampler. Moreover, KUS is almost oblivious to the number of samples requested.

Practical Picture Processing

1974 ◽  
Vol 32 ◽  
pp. 338-339
Author(s):  
T. A. Welton
Keyword(s):  
Radiation Damage ◽  
Coherence Length ◽  
Spatial Information ◽  
State Of The Art ◽  
Coherent Radiation ◽  
The State ◽  
Energy Spread ◽  
Electron Micrograph ◽  
Picture Processing ◽  
Molecular Skeleton

Various authors have emphasized the spatial information resident in an electron micrograph taken with adequately coherent radiation. In view of the completion of at least one such instrument, this opportunity is taken to summarize the state of the art of processing such micrographs. We use the usual symbols for the aberration coefficients, and supplement these with £ and 6 for the transverse coherence length and the fractional energy spread respectively. He also assume a weak, biologically interesting sample, with principal interest lying in the molecular skeleton remaining after obvious hydrogen loss and other radiation damage has occurred.

Hypothesis-Evidence Coordination: The State of the Art

2003 ◽  
Vol 48 (6) ◽  
pp. 826-829 ◽  
Author(s):  
Eric Amsel
Keyword(s):  
State Of The Art ◽  
The State

The State of the Art

1968 ◽  
Vol 13 (9) ◽  
pp. 479-480
Author(s):  
LEWIS PETRINOVICH
Keyword(s):  
State Of The Art ◽  
The State

Needs Assessment: The State of the Art

1984 ◽  
Vol 29 (5) ◽  
pp. 426-428
Author(s):  
Anthony R. D'Augelli
Keyword(s):  
Needs Assessment ◽  
State Of The Art ◽  
The State

Biofeedback: The State of the Art

1991 ◽  
Vol 36 (2) ◽  
pp. 140-140
Author(s):  
John A. Corson
Keyword(s):  
State Of The Art ◽  
The State
Sign in / Sign up

Export Citation Format

Share Document