scholarly journals A NOVEL DATA ASSOCIATION TECHNIQUE TO IMPROVE RUN-TIME EFFICIENCY OF SLAM ALGORITHMS

2019 ◽  
Author(s):  
Volkan Sezer ◽  
Ziya uygar Yengin
Keyword(s):  
Data Association ◽  
Time Efficiency ◽  
Run Time

How does Migrating to Kotlin Impact the Run-time Efficiency of Android Apps?

2021 ◽  
Author(s):  
Michael Peters ◽  
Gian Luca Scoccia ◽  
Ivano Malavolta
Keyword(s):  
Time Efficiency ◽  
Android Apps ◽  
Run Time

scholarly journals Unfolding for CHR programs

2013 ◽  
Vol 15 (3) ◽  
pp. 264-311 ◽  
Author(s):  
MAURIZIO GABBRIELLI ◽  
MARIA CHIARA MEO ◽  
PAOLO TACCHELLA ◽  
HERBERT WIKLICKY
Keyword(s):  
Program Transformation ◽  
Large Body ◽  
Constraint Handling ◽  
Sequential Programs ◽  
Time Efficiency ◽  
Semantic Equivalence ◽  
Procedure Call ◽  
Run Time ◽  
Transformation Systems

AbstractProgram transformation is an appealing technique which allows to improve run-time efficiency, space-consumption, and more generally to optimize a given program. Essentially, it consists of a sequence of syntactic program manipulations which preserves some kind of semantic equivalence. Unfolding is one of the basic operations used by most program transformation systems and consists of the replacement of a procedure call by its definition. While there is a large body of literature on the transformation and unfolding of sequential programs, very few papers have addressed this issue for concurrent languages. This paper defines an unfolding system for Constraint Handling Rules programs. We define an unfolding rule, show its correctness and discuss some conditions that can be used to delete an unfolded rule while preserving the program meaning. We also prove that, under some suitable conditions, confluence and termination are preserved by the above transformation.

scholarly journals CranSLIK v2.0: improving the stochastic prediction of oil spill transport and fate using approximation methods

2015 ◽  
Vol 8 (10) ◽  
pp. 3365-3377 ◽  
Author(s):  
R. Rutherford ◽  
I. Moulitsas ◽  
B. J. Snow ◽  
A. J. Kolios ◽  
M. De Dominicis
Keyword(s):  
Multiple Regression ◽  
Oil Spill ◽  
Stochastic Approach ◽  
Approximation Methods ◽  
Time Efficiency ◽  
Run Time

Abstract. Oil spill models are used to forecast the transport and fate of oil after it has been released. CranSLIK is a model that predicts the movement and spread of a surface oil spill at sea via a stochastic approach. The aim of this work is to identify parameters that can further improve the forecasting algorithms and expand the functionality of CranSLIK, while maintaining the run-time efficiency of the method. The results from multiple simulations performed using the operational, validated oil spill model, MEDSLIK-II, were analysed using multiple regression in order to identify improvements which could be incorporated into CranSLIK. This has led to a revised model, namely CranSLIK v2.0, which was validated against MEDSLIK-II forecasts for real oil spill cases. The new version of CranSLIK demonstrated significant forecasting improvements by capturing the oil spill accurately in real validation cases and also proved capable of simulating a broader range of oil spill scenarios.

scholarly journals CranSLIK v2.0: improvements on the stochastic prediction of oil spill transport and fate using approximation methods

2015 ◽  
Vol 8 (6) ◽  
pp. 4949-4977
Author(s):  
R. Rutherford ◽  
I. Moulitsas ◽  
B. J. Snow ◽  
A. J. Kolios ◽  
M. De Dominicis
Keyword(s):  
Multiple Regression ◽  
Oil Spill ◽  
Stochastic Approach ◽  
Approximation Methods ◽  
Time Efficiency ◽  
Run Time

Abstract. Oil spill models are used to forecast the transport and fate of oil after it has been released. CranSLIK is a model that predicts the movement and spread of a surface oil spill at sea via a stochastic approach. The aim of this work is to identify parameters that can further improve the forecasting algorithms and expand the functionality of CranSLIK, while maintaining the run time efficiency of the method. The results from multiple simulations performed using the operational, validated oil spill model, MEDSLIK-II, were analysed using multiple regression in order to identify improvements which could be incorporated into CranSLIK. This has led to a revised model, namely CranSLIK v2.0, which was validated against MEDSLIK-II forecasts for real oil spill cases. The new version of CranSLIK demonstrated significant forecasting improvements by capturing the oil spill accurately in real validation cases and also proved capable of simulating a broader range of oil spill scenarios.

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