Parameter passing commutes with implementation of parameterized data types

1982 ◽  
pp. 197-211 ◽  
Author(s):  
H. Ehrig ◽  
H. -J. Kreowski
Keyword(s):  
Data Types ◽  
Parameter Passing

Compositionality and compatibility of parameterization and parameter passing in specification languages

1995 ◽  
Vol 5 (2) ◽  
pp. 283-314 ◽  
Author(s):  
Rosa M. Jiménez ◽  
Fernando Orejas ◽  
Hartmut Ehrig
Keyword(s):  
Specification Languages ◽  
Data Types ◽  
Initial Approach ◽  
Categorical Constructions ◽  
Initial Framework ◽  
Parameter Passing

In this paper we continue previous work by Sannella, Sokolowski and Tarlecki on parameterization in specification languages. Within the loose approach, we define specification and model level semantics for two kinds of parameterizations (parameterized specifications and specifications of parameterized data types) and describe, in a compositional manner, parameter passing at both levels. Moreover, the specification and the model level semantics of parameter passing are shown to be compatible. We also show that the results obtained do not only apply to the loose approach but can also be directly applicable to the initial framework, and in general to any other kind of monomorphic framework (i.e., a framework where all specifications are monomorphic). In particular, the results obtained generalize and extend previous results for the initial approach. Finally, to obtain our results, new categorical constructions of multiple pushouts, amalgamations and extensions, which generalize standard notions of pushouts, amalgamations and extensions, had to be introduced.

scholarly journals Compatibility of parameter passing and implementation of parameterized data types

1983 ◽  
Vol 27 (3) ◽  
pp. 255-286 ◽  
Author(s):  
H. Ehrig ◽  
H.-J. Kreowski
Keyword(s):  
Data Types ◽  
Parameter Passing

Integrating Visual and Bayesian Statistical Analyses in Single Case Experimental Research to Evaluate the Effectiveness and Magnitude of a Comprehensive Behavioral Intervention

2018 ◽  
Author(s):  
Prathiba Natesan ◽  
Smita Mehta
Keyword(s):  
Behavioral Intervention ◽  
Effect Size ◽  
Rate Ratio ◽  
Visual Analysis ◽  
Single Case ◽  
Small Sample ◽  
Statistical Analyses ◽  
Data Types ◽  
Ratio Effect ◽  
Small Sample Sizes

Single case experimental designs (SCEDs) have become an indispensable methodology where randomized control trials may be impossible or even inappropriate. However, the nature of SCED data presents challenges for both visual and statistical analyses. Small sample sizes, autocorrelations, data types, and design types render many parametric statistical analyses and maximum likelihood approaches ineffective. The presence of autocorrelation decreases interrater reliability in visual analysis. The purpose of the present study is to demonstrate a newly developed model called the Bayesian unknown change-point (BUCP) model which overcomes all the above-mentioned data analytic challenges. This is the first study to formulate and demonstrate rate ratio effect size for autocorrelated data, which has remained an open question in SCED research until now. This expository study also compares and contrasts the results from BUCP model with visual analysis, and rate ratio effect size with nonoverlap of all pairs (NAP) effect size. Data from a comprehensive behavioral intervention are used for the demonstration.

Digital cinema (D-cinema). XML Data types

2010 ◽  
Keyword(s):  
Data Types ◽  
Xml Data ◽  
Digital Cinema

Ordered versus Unordered Map for Primitive Data Types

2015 ◽  
Author(s):  
Tom Nealis
Keyword(s):  
Data Types

GAADT (Genetic Algorithm based on Abstracts Data Types): Uma Abordagem Teórica e Aplicativa

2012 ◽  
Vol 10 (4) ◽  
pp. 202-215
Author(s):  
Manoel Agamemnon Lopes ◽  
Roberta Vilhena Vieira Lopes
Keyword(s):  
Genetic Algorithm ◽  
Data Types

Deep Learning in Disease Diagnosis: Models and Datasets

2020 ◽  
Vol 15 ◽  
Author(s):  
Deeksha Saxena ◽  
Mohammed Haris Siddiqui ◽  
Rajnish Kumar
Keyword(s):  
Biological Sciences ◽  
Machine Learning ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Learning Models ◽  
Data Types ◽  
Related Data ◽  
Abstract Level ◽  
Experimental Validations ◽  
Selection Of

Background: Deep learning (DL) is an Artificial neural network-driven framework with multiple levels of representation for which non-linear modules combined in such a way that the levels of representation can be enhanced from lower to a much abstract level. Though DL is used widely in almost every field, it has largely brought a breakthrough in biological sciences as it is used in disease diagnosis and clinical trials. DL can be clubbed with machine learning, but at times both are used individually as well. DL seems to be a better platform than machine learning as the former does not require an intermediate feature extraction and works well with larger datasets. DL is one of the most discussed fields among the scientists and researchers these days for diagnosing and solving various biological problems. However, deep learning models need some improvisation and experimental validations to be more productive. Objective: To review the available DL models and datasets that are used in disease diagnosis. Methods: Available DL models and their applications in disease diagnosis were reviewed discussed and tabulated. Types of datasets and some of the popular disease related data sources for DL were highlighted. Results: We have analyzed the frequently used DL methods, data types and discussed some of the recent deep learning models used for solving different biological problems. Conclusion: The review presents useful insights about DL methods, data types, selection of DL models for the disease diagnosis.

Advanced type parameter features

2018 ◽  
Author(s):  
Michael Metcalf ◽  
John Reid ◽  
Malcolm Cohen
Keyword(s):  
Data Types ◽  
Derived Data ◽  
Type Parameter

The advanced type parameter features consist of type parameter enquiry and the ability to parameterize derived-data types.

scholarly journals Some ideas on data types in high level languages

1976 ◽  
Author(s):  
David Gries ◽  
Narain Gehani
Keyword(s):  
Data Types ◽  
High Level
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