Structured Data Types: Array, File, Set and Record. The Pointer Data Type

1988 ◽  
pp. 107-138
Author(s):  
J. Attikiouzel
Keyword(s):  
Structured Data ◽  
Data Type ◽  
Data Types

scholarly journals Lifting the curse of stringly-typed code

2017 ◽  
Author(s):  
Eddie A Santos ◽  
Karim Ali
Keyword(s):  
Structured Data ◽  
Data Type ◽  
Data Types

How often do JavaScript programmers embed structured languages into strings literals? We conduct an empirical investigating mining nearly 500 thousand JavaScript source files from almost ten thousand repositories from GitHub. We parsed each string literal with seven separate common grammars, and found the most common data type that is hidden within the confines of string literals. To reduce the overuse of strings for structured data types, we present a simple static program analyzer that finds embedded languages and warns the developer, providing an optional fix.

scholarly journals Lifting the curse of stringly-typed code

2017 ◽  
Author(s):  
Eddie A Santos ◽  
Karim Ali
Keyword(s):  
Structured Data ◽  
Data Type ◽  
Data Types

How often do JavaScript programmers embed structured languages into strings literals? We conduct an empirical investigating mining nearly 500 thousand JavaScript source files from almost ten thousand repositories from GitHub. We parsed each string literal with seven separate common grammars, and found the most common data type that is hidden within the confines of string literals. To reduce the overuse of strings for structured data types, we present a simple static program analyzer that finds embedded languages and warns the developer, providing an optional fix.

Hydraulic tomography using joint inversion of head and flux data 

2021 ◽  
Author(s):  
Behzad Pouladiborj ◽  
Olivier Bour ◽  
Niklas Linde ◽  
Laurent Longuevergne
Keyword(s):  
Hydraulic Conductivity ◽  
Joint Inversion ◽  
Data Type ◽  
Data Types ◽  
Hydraulic Tomography ◽  
Flux Data ◽  
Planning And Design ◽  
Constant Rate ◽  
Resolution Analysis ◽  
Constant Head

<p>Hydraulic tomography is a state of the art method for inferring hydraulic conductivity fields using head data. Here, a numerical model is used to simulate a steady-state hydraulic tomography experiment by assuming a Gaussian hydraulic conductivity field (also constant storativity) and generating the head and flux data in different observation points. We employed geostatistical inversion using head and flux data individually and jointly to better understand the relative merits of each data type. For the typical case of a small number of observation points, we find that flux data provide a better resolved hydraulic conductivity field compared to head data when considering data with similar signal-to-noise ratios. In the case of a high number of observation points, we find the estimated fields to be of similar quality regardless of the data type. A resolution analysis for a small number of observations reveals that head data averages over a broader region than flux data, and flux data can better resolve the hydraulic conductivity field than head data. The inversions' performance depends on borehole boundary conditions, with the best performing setting for flux data and head data are constant head and constant rate, respectively. However, the joint inversion results of both data types are insensitive to the borehole boundary type. Considering the same number of observations, the joint inversion of head and flux data does not offer advantages over individual inversions. By increasing the hydraulic conductivity field variance, we find that the resulting increased non-linearity makes it more challenging to recover high-quality estimates of the reference hydraulic conductivity field. Our findings would be useful for future planning and design of hydraulic tomography tests comprising the flux and head data.</p>

Can we build a grammar on the basis of judgments?

2020 ◽  
pp. 165-188
Author(s):  
Sam Featherston
Keyword(s):  
Small Group ◽  
Data Type ◽  
Theory Building ◽  
Final Part ◽  
Data Types ◽  
Ongoing Debate ◽  
Single Person

This chapter is a contribution to the ongoing debate about the necessary quality of the database for theory building in research on syntax. In particular, the focus is upon introspective judgments as a data type or group of data types. In the first part, the chapter lays out some of the evidence for the view that the judgments of a single person or of a small group of people are much less valid than the judgments of a group. In the second part, the chapter criticizes what the author takes to be overstatements and overgeneralizations of findings by Sprouse, Almeida, and Schütze that are sometimes viewed as vindicating an “armchair method” in linguistics. The final part of the chapter attempts to sketch out a productive route forward that empirically grounded syntax could take.

THE C++ EMBEDDABLE RULE SYSTEM

1993 ◽  
Vol 02 (01) ◽  
pp. 33-46 ◽  
Author(s):  
DANIEL P. MIRANKER ◽  
FREDERIC H. BURKE ◽  
JERI J. STEELE ◽  
JOHN KOLTS ◽  
DAVID R. HAUG
Keyword(s):  
Data Type ◽  
Third Party ◽  
Abstract Data Type ◽  
Data Types ◽  
Forward Chaining ◽  
Abstract Data ◽  
Data Objects ◽  
Rule System

Most the execution environments, having been derived from LISP, inference on internally defined data-types and come packaged with stand-alone development environments. Data derived from outside these systems must be reformatted before it can be evaluated. This mismatch leads to a duplicate representation of data, which, in turn, introduces both performance and semantic problems. This paper describes a C++ Embeddable Rule System (CERS) which avoids this mismatch. CERS is a compiled, forward-chaining rule system that inferences directly on arbitrary C++ objects. CERS can be viewed as an extension of C++, where the methods associated with a ruleset class can be defined either procedurally or declaratively. CERS is unique in that rules may match against and manipulate arbitrary, user-defined C++ objects. There is no requirement that the developer anticipated using CERS when defining the class. Thus CERS rules can inference over data objects instantiated in persistent object stores and third-party. C++ abstract data-type libraries.

Towards a quantum programming language

2004 ◽  
Vol 14 (4) ◽  
pp. 527-586 ◽  
Author(s):  
PETER SELINGER
Keyword(s):  
Programming Language ◽  
Quantum Algorithms ◽  
Denotational Semantics ◽  
Quantum Circuit ◽  
Structured Data ◽  
Data Types ◽  
Simple Quantum ◽  
Recursive Procedures ◽  
Quantum Programming ◽  
High Level

We propose the design of a programming language for quantum computing. Traditionally, quantum algorithms are frequently expressed at the hardware level, for instance in terms of the quantum circuit model or quantum Turing machines. These approaches do not encourage structured programming or abstractions such as data types. In this paper, we describe the syntax and semantics of a simple quantum programming language with high-level features such as loops, recursive procedures, and structured data types. The language is functional in nature, statically typed, free of run-time errors, and has an interesting denotational semantics in terms of complete partial orders of superoperators.

Depth estimates from ratios of gravity, geoid, and gravity gradient anomalies

Geophysics ◽  
1986 ◽  
Vol 51 (1) ◽  
pp. 123-136 ◽  
Author(s):  
Carl Bowin ◽  
Edward Scheer ◽  
Woollcott Smith
Keyword(s):  
Frequency Domain ◽  
Spectral Methods ◽  
Depth Estimation ◽  
Data Type ◽  
Gravity Gradient ◽  
Data Types ◽  
Vertical Gravity Gradient ◽  
Frequency Domain Methods ◽  
Gradient Measurements ◽  
Vertical Gravity

The utility of combining geoid, gravity, and vertical gravity gradient measurements for delineation of causative mass anomalies is explained and compared with spatial and spectral methods for depth estimation. Depth rules for various source geometries are reviewed and new rules developed for geoid, gravity, and vertical gravity‐gradient data. Both spatial and frequency‐domain methods are discussed. Simple ratios of single observations of different data types (e.g., geoid, gravity, or vertical gravity gradient) are shown to provide information comparable to the traditional spatial and frequency analyses of one data type alone.

scholarly journals Data Types as Functions

1978 ◽  
Vol 7 (89) ◽  
Author(s):  
Brian H. Mayoh
Keyword(s):  
Data Type ◽  
Extended Version ◽  
Data Types ◽  
Formal Parameter ◽  
Actual Parameter ◽  
Definition Of ◽  
Simple Definition

This paper introduces a new, simple definition of what a data type is. This definition gives one possible solution of the theoretical problems: when can an actual parameter of type T be substituted for a formal parameter of type T'? When can a type T' be implemented as another type T''? The preprint is an extended version of a paper presented at MFCS 78, Zakopane.

scholarly journals Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis (Preprint)

2017 ◽  
Author(s):  
Brett K Beaulieu-Jones ◽  
Daniel R Lavage ◽  
John W Snyder ◽  
Jason H Moore ◽  
Sarah A Pendergrass ◽  
...  
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Clinical Laboratory ◽  
Real Data ◽  
Missing At Random ◽  
Theoretical Work ◽  
Structured Data ◽  
Data Types ◽  
Imputation Methods ◽  
Electronic Health

BACKGROUND Missing data is a challenge for all studies; however, this is especially true for electronic health record (EHR)-based analyses. Failure to appropriately consider missing data can lead to biased results. While there has been extensive theoretical work on imputation, and many sophisticated methods are now available, it remains quite challenging for researchers to implement these methods appropriately. Here, we provide detailed procedures for when and how to conduct imputation of EHR laboratory results. OBJECTIVE The objective of this study was to demonstrate how the mechanism of missingness can be assessed, evaluate the performance of a variety of imputation methods, and describe some of the most frequent problems that can be encountered. METHODS We analyzed clinical laboratory measures from 602,366 patients in the EHR of Geisinger Health System in Pennsylvania, USA. Using these data, we constructed a representative set of complete cases and assessed the performance of 12 different imputation methods for missing data that was simulated based on 4 mechanisms of missingness (missing completely at random, missing not at random, missing at random, and real data modelling). RESULTS Our results showed that several methods, including variations of Multivariate Imputation by Chained Equations (MICE) and softImpute, consistently imputed missing values with low error; however, only a subset of the MICE methods was suitable for multiple imputation. CONCLUSIONS The analyses we describe provide an outline of considerations for dealing with missing EHR data, steps that researchers can perform to characterize missingness within their own data, and an evaluation of methods that can be applied to impute clinical data. While the performance of methods may vary between datasets, the process we describe can be generalized to the majority of structured data types that exist in EHRs, and all of our methods and code are publicly available.

scholarly journals Scalable Micro-planned Generation of Discourse from Structured Data

2020 ◽  
Vol 45 (4) ◽  
pp. 737-763 ◽  
Author(s):  
Anirban Laha ◽  
Parag Jain ◽  
Abhijit Mishra ◽  
Karthik Sankaranarayanan
Keyword(s):  
Natural Language ◽  
Structured Data ◽  
Data Sets ◽  
Data Types ◽  
Data Set ◽  
Language Generation ◽  
Parallel Data ◽  
Simple Sentences ◽  
Diverse Data ◽  
Existing Data

We present a framework for generating natural language description from structured data such as tables; the problem comes under the category of data-to-text natural language generation (NLG). Modern data-to-text NLG systems typically use end-to-end statistical and neural architectures that learn from a limited amount of task-specific labeled data, and therefore exhibit limited scalability, domain-adaptability, and interpretability. Unlike these systems, ours is a modular, pipeline-based approach, and does not require task-specific parallel data. Rather, it relies on monolingual corpora and basic off-the-shelf NLP tools. This makes our system more scalable and easily adaptable to newer domains. Our system utilizes a three-staged pipeline that: (i) converts entries in the structured data to canonical form, (ii) generates simple sentences for each atomic entry in the canonicalized representation, and (iii) combines the sentences to produce a coherent, fluent, and adequate paragraph description through sentence compounding and co-reference replacement modules. Experiments on a benchmark mixed-domain data set curated for paragraph description from tables reveals the superiority of our system over existing data-to-text approaches. We also demonstrate the robustness of our system in accepting other popular data sets covering diverse data types such as knowledge graphs and key-value maps.

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