Programming Experience Research Articles

Applying the LATER model to reaction time data: an open-source toolkit.

Analyzing reaction time distributions can provide insights into decision-making processes in the brain. The Linear Approach to Threshold with Ergodic Rate (LATER) model is arguably the simplest model for predicting reaction time distributions and can summarize distributions with as few as two free parameters. However, the coordinates for visualizing and fitting distributions with LATER ("reciprobit" space) are irregular, making the application of this simple model inaccessible to those without a programming background. Here we describe an open-source R package, LATERmodel, that allows for easy visualization of reaction time distributions, along with fitting of these with the LATER model. Using canonical data from the literature, we show that our tool replicates key features from previous LATER analysis tools, while also providing more robust fitting procedures and a more flexible method for fitting subpopulations of very rapid, early responses. Although all features of LATERmodel can be used directly in the statistical programming language R, key features are also available through a RShiny graphical user interface to allow researchers without programming background to apply the LATER model to their reaction time data.NEW & NOTEWORTHY Analyzing reaction time distributions provides a powerful tool for investigating decision-making processes. Here we describe an open-source toolbox to allow the Linear Approach to Threshold with Ergodic Rate (LATER) model, the simplest principled model linking reaction times and decisions, to be applied to empirical reaction time data, including by clinicians and scientists without any programming experience.

The Place of Local Field Potentials in Deep Brain Stimulation Programming for Parkinson’s Disease: A Review

Background/Objections: The pharmacological management of Parkinson’s Disease (PD) is often supplemented by deep brain stimulation (DBS) to tackle problems of advanced disease such as motor fluctuation, dyskinesias or medication-resistant tremor. DBS uses high-frequency stimulation with spatially distributed electrodes to produce electrical fields that influence neuronal networks. The programming of such stimulation is complex and time-consuming. Recent technological advancements have enabled DBS systems to record local field potentials (LFPs). In conjunction with biomarker discovery, such as beta oscillations, this shows promise in streamlining the DBS programming process. This review aims to synthesize the current literature investigating LFP characteristics in PD in order to understand the place of LFPs in assisting with DBS programming. Methods: A comprehensive literature search was conducted using databases including OVID MEDLINE, Scopus, and Cochrane Library, resulting in 738 identified articles; 122 studies remained after screening and 87 studies were selected for detailed analysis. Results: Analyzing LFPs clearly has the potential to assist or streamline DBS programming in clinical practice, but there are knowledge gaps and challenges to overcome, especially in the utilization of intraoperative LFPs. Conclusions: More research is required to compare different algorithms that utilize LFPs in DBS programming to identify a simple, practical and time-saving algorithm incorporating reliable LFP biomarkers that will enhance the DBS programming experience for both patients and clinicians.

Effect of Visual Programming Instruction on Students' Flow Experience, Programming Self‐Efficacy, and Sustained Willingness to Learn

ABSTRACTBackgroundMany studies have highlighted the positive effects of visual programming instruction (VPI) on students' learning experiences, programming self‐efficacy and flow experience. However, there is a notable gap in the research on how these factors specifically impact programming achievement and learning intentions. Our study addresses this gap by focusing on flowchart‐based programming—a relatively underexplored area in educational research. To ensure relevance to the educational context, AbilixChart and Ability Storm SK902 were specifically selected for their alignment with the curriculum of the target school, where these tools are widely utilised in both teaching and extracurricular activities. This alignment allowed for a seamless integration of these tools into regular classroom practices after the study's conclusion, ensuring continued application and maximising the study's practical impact. Furthermore, integrating educational robots enhanced student engagement and provided a practical means to evaluate the accuracy of their programming skills. By doing so, our study not only contributes to filling a gap in the literature but also has the potential to influence educational practices by demonstrating the value of incorporating flowchart‐based programming and robotics into the curriculum.ObjectivesThe present study aims to conduct an instructional experiment utilising VPI with flowchart‐based programming tools. The main objective is to investigate how these tools influence 219 high school students' flow experience, programming self‐efficacy, and sustained learning willingness.MethodsThis study employed a pre‐ and post‐test design with a single group and conducted an 11‐week instructional experiment. The students used the Abilix Chart software and the Ability Storm SK902 kit to build an intelligent car and learn about visual programming. They were tasked with independently designing programs to solve practical problems in different scenarios. Research tools included Scales of Flow Experience, Computer Programming Self‐Efficacy, Sustained Learning Willingness and Program Achievement.ResultsThe findings revealed that VPI effectively improved students' programming achievement, flow experience and programming self‐efficacy. Students with programming learning experience surpassed their inexperienced peers in willingness to engage in sustained learning. Conversely, 4.57% of the group with lower pre‐test scores and no learning experience showed more positive emotional experience than those with learning experience.ConclusionsThe results indicated that VPI positively affected programming achievement, self‐efficacy and flow experience. Regardless of prior programming experience, all students benefited from VPI. Additionally, self‐efficacy and flow experience were key factors influencing sustained learning motivation and achievement.

Py.Aroma: An Intuitive Graphical User Interface for Diverse Aromaticity Analyses

The nucleus-independent chemical shift (NICS) criterion plays a significant role in evaluating (anti-)aromaticity. While being readily accessible even for non-computational chemists, adding ghost atoms for multi-points NICS evaluations poses a significant challenge. In this article, I introduce py.Aroma 4, a freely available and open-source Python package designed specifically for analyzing (anti-)aromaticity. Through its user-friendly graphical interface, py.Aroma simplifies and enhances aromaticity analyses by offering key features such as HOMA/HOMER index computation, Gaussian-type input file generation for diverse NICS calculations and corresponding output processing, NMR spectra plotting, and computational supporting information (SI) generation for scientific manuscripts. Additionally, NICS⊥ is suggested for evaluating (anti-)aromaticity for non-planar or tilted rings. Pre-compiled executables for macOS and Windows are freely available online. Facilitate accessibility for users lacking programming experience or time constraints.

Intentionally Equitable: Translating the Universal Design for Learning Principles to Academic Library Outreach

Equity in student learning experiences is a key concern in academic libraries, yet there is a striking lack of literature and frameworks addressing equitable academic library outreach. This gap exists despite the important intersection of library outreach with student learning, success, and wellness across multiple university units. This paper addresses this challenge by examining Universal Design for Learning (UDL)—a standard increasingly accepted in academic libraries for promoting equity in information literacy instruction and pedagogy—for its potential applicability in the domain of library outreach. Although originally intended for instructional contexts, the UDL principles are shown in this paper to be relevant to, and practicable in, library outreach initiatives. The paper reviews a selection of the literature on academic library outreach, accessibility, and equity, highlighting the need for more structured consideration of these critical areas. It also presents a detailed examination of UDL guidelines and checkpoints, providing outreach-related examples and short case studies from the author's own programming and outreach experiences that exemplify the effective application of UDL principles in library outreach. This exploration not only extends the potential use of UDL beyond traditional instructional settings but also serves as a foundation for academic librarians and researchers to further develop and refine practices promoting equity, accessibility, and inclusivity in library outreach efforts.

The effectiveness of intelligent tutoring systems in supporting students with varying levels of programming experience

This study investigates the effectiveness of Intelligent Tutoring Systems (ITS) in supporting students with varying levels of programming experience. Through a mixed-methods research design, the study explores the impact of ITS on student performance, adaptability to different skill levels, and best practices for utilizing ITS in heterogeneous programming classrooms. The quantitative analysis reveals significant differences in the influence of ITS on improving programming skills, the usefulness of ITS feedback, and satisfaction levels with the interface based on students’ programming experience levels. Advanced students show the most significant improvement in programming skills and find the feedback more valuable, while intermediate and advanced students report higher satisfaction with the ITS interface. The qualitative analysis highlights positive impacts on the learning process, challenges faced, and suggestions for improvement. This study contributes to the literature by emphasizing the importance of tailoring ITS interventions to meet the unique needs of students with varying levels of programming experience, offering insights for educators, curriculum developers, and educational technology designers.

An Introduction to Quantitative Text Analysis for Linguistics: Reproducible Research Using R

Jerid Francom’s book An Introduction to Quantitative Text Analysis for Linguistics: Reproducible Research Using R is an essential textbook for researchers and students alike, who are exploring quantitative text analysis. This book is designed with beginners in mind, it emphasizes reproducible research, offering a structured approach to text analysis through the programming language R. Spanning five interconnected parts, beginning with foundational concepts like the Data-Information-Knowledge-Insight (DIKI) hierarchy, corpus creation, and data curation, advancing to topics like tokenization, dimensionality reduction, vector space modeling, and hypothesis testing with the {infer} package. This book contains practical exercises alongside detailed explanations that guide readers through the entire process of text analysis, starting from data acquisition to predictive modeling and statistical designs. Computational methods including readability measures, sentiment analysis, semantic modeling, and topic modeling are highlighted within this book, ensuring that readers are equipped to extract meaningful insights from linguistic data. Through the incorporation of Tidyverse tools and additional resources like GitHub repositories, Francom successfully bridges theoretical understanding with hands-on application. Transparency and reproducibility have been prioritized within the text, and meticulous data documentation and open-source methodologies have been meticulously advocated by the author. Although the book is an accessible resource for English-language data, readers might be challenged due to its focus on breadth over depth when their focus might be on seeking advanced exploration or on the other hand for those without basic programming experience. Regardless of this, Francom’s pedagogical approach combines clarity with practical guidance, making this book a valuable resource for students, researchers, and professionals who aim to integrate quantitative methods into their linguistic research.

Survey of common deep brain stimulation programming practices by experts in Parkinson's Disease.

Identify consensus and variability in deep brain stimulation (DBS) programming practices for Parkinson's disease. DBS programming relies on the personal experience and skills of programmers. Despite consensus statements, there aren't official guidelines for DBS programming, making it likely for protocols to vary among providers. We administered an online survey to the Functional Neurosurgery Working Group of the Parkinson's Study Group to capture those actively programming DBS patients. We performed descriptive statistics and comparisons of responses based on career stage: early (0-10years) versus later (>10years). Boston Scientific (n=15/31, 48%) and Medtronic (n=14/35, 40%) are the two DBS systems ranked as most used, with less reported frequency of Abbott devices (n=4/32, 12.5). Traditional monopolar review ranked as the most common initial programming strategy by 23/29 (79%) respondents, regardless of the device type implanted. Monopolar omnidirectional testing was the most often used approach for contact configuration at initial programming (24/33, 73%).For treating dyskinesia, tremor, bradykinesia, rigidity, speech-related side effects, non-motor adverse effects, or swallowing-related side effects, the most likely optimization strategy selected was to modify amplitude of the active contact. When treating freezing of gait, there was a divergence between first modifying amplitude (n=11/29, 38%) or frequency (n=12/33, 36%). Initial programming practices generally align with published recommendations, which can reassure less experienced clinicians in practices with near consensus and allow them to devote more time to areas with wider variety of practice. Our data also highlights aspects of DBS programming with less consensus, demonstrating the need for future evidence.

Pioneering Computational Culture Within Pharmacy Schools by Empowering Students With Data Science and Bioinformatics Skills.

As advancements in digital health lead to the generation of increasingly diverse and voluminous pharmaceutical data, it is increasingly critical that we teach trainee pharmaceutical scientists how to leverage this data to lead future innovations in healthcare and pharmaceutical research. To address this need, the University of Southern California Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences (USC Mann) is incorporating data science and bioinformatics into the graduate and undergraduate curricula through introductory courses tailored for students without prior programming experience. These courses feature a teaching framework designed to make the fundamentals of data science and bioinformatics accessible to pharmacy students through step-by-step, Jupyter-based coding assignments with examples relevant to the pharmaceutical sciences. The framework supports PharmD students by focusing on the practical applications of data science in clinical settings, while for PhD and MS students, the emphasis is on research methodologies and advanced data analysis techniques. Here, we outline the design of this framework, highlighting the strategies we developed and the opportunities it provides to cultivate a computational culture within our institution and beyond.

Unlocking AutoML: Enhancing Data with Deep Learning Algorithms for Medical Imaging

Deep learning algorithms have become increasingly popular over the years, having proved their efficiency in input-output functions for distinct types of data. This technology is particularly useful in medical imaging, where complex image structures often generate disagreements between medical staff. These technologies can streamline the diagnostic process by performing automatic image analysis, which results in more accurate and reproducible diagnoses. Additionally, these technologies can enhance content retrieval systems by automatically labeling the images based on the structures they possess. Despite the benefits, the mathematical complexity of deep learning algorithms and their training optimizations can be challenging. Automated machine learning provides a solution to this challenge by offering tools that automate the development and training of these algorithms. This makes it possible for users with limited programming experience to take advantage of these powerful technologies to quickly develop and prototype analysis algorithms for their specific needs. This article presents a management platform for deep learning services on the cloud that provides a code-free experience through automated machine learning. The evaluation was done in one of the most demanding scenarios, where the service was integrated into a research pathology PACS to annotate mitotic cells in breast cancer tissue automatically. The annotations are processed by an open-source PACS archive and stored directly on the files, enhancing the image metadata and consequently content retrieval systems. The results of the developed algorithms were compared to the state-of-the-art to evaluate the competitiveness of the solution.

The use of cloud based machine learning to predict outcome in intracerebral haemorrhage without explicit programming expertise

Machine Learning (ML) techniques require novel computer programming skills along with clinical domain knowledge to produce a useful model. We demonstrate the use of a cloud-based ML tool that does not require any programming expertise to develop, validate and deploy a prognostic model for Intracerebral Haemorrhage (ICH). The data of patients admitted with Spontaneous Intracerebral haemorrhage from January 2015 to December 2019 was accessed from our prospectively maintained hospital stroke registry. 80% of the dataset was used for training, 10% for validation, and 10% for testing. Seventeen input variables were used to predict the dichotomized outcomes (Good outcome mRS 0–3/ Bad outcome mRS 4–6), using machine learning (ML) and logistic regression (LR) models. The two different approaches were evaluated using Area Under the Curve (AUC) for Receiver Operating Characteristic (ROC), Precision recall and accuracy. Our data set comprised of a cohort of 1000 patients. The data was split 8:1 for training & testing respectively. The AUC ROC of the ML model was 0.86 with an accuracy of 75.7%. With LR AUC ROC was 0.74 with an accuracy of 73.8%. Feature importance chart showed that Glasgow coma score (GCS) at presentation had the highest relative importance, followed by hematoma volume and age in both approaches. Machine learning models perform better when compared to logistic regression. Models can be developed by clinicians possessing domain expertise and no programming experience using cloud based tools. The models so developed lend themselves to be incorporated into clinical workflow.

Applications of the Circle-Inspired Optimization Algorithm: Comparison between MatLab and Python versions

This paper presents applications of the Circle Inspired Optimization Algorithm (CIOA), a modern optimization algorithm developed by the authors, in optimization problems from different areas of study implemented in MatLab and Python, with the main objective of making a comparison between versions of each computer language. Different sets of optimization problems were selected, involving classical benchmark functions, traditional truss structural optimization problems, and real-world optimization problems obtained in the 'CEC2020 Real-World One-Objective Constrained Optimization Competition'. Each optimization problem was solved multiple times on the same computer in MatLab and Python, to make comparisons between the accuracy and robustness of each version of the algorithm, evaluating the best solution and the mean and standard deviation between several solutions. Furthermore, a comparison of computational time was also performed. The results obtained attest to the efficiency of CIOA in both computational languages. In terms of accuracy and robustness, it was not possible to clearly identify a better version (MatLab or Python) for CIOA, as the differences obtained in the results were, in most cases, very small, and may be caused by the random characteristics of the algorithm. In the comparison of computational time, the MatLab version performed better in most optimization problems. This fact may be due to the efficiency of the specific libraries used and the programming experience of the authors in each computer language.

Computational Thinking Integrated in School Subjects – A Cross-Case Analysis of Students’ Experiences

The integration of computational thinking (CT) into K-12 education offers substantial potential to improve digital literacy and deepen students’ understanding across various subject areas. By embedding computational procedures and solution-oriented approaches into traditional curricula, students can develop essential skills that are critical for thriving in an increasingly digital world. However, the effective integration of CT into subject-specific learning poses challenges for educators, largely due to a lack of training and experience in implementing interdisciplinary instructional strategies. This study explores the impact of integrating CT into K-12 education through diverse instructional approaches, including modelling, storytelling, and unplugged activities. The research examines how students' subject knowledge, CT concepts, and practices evolved under each CT integration practice, as well as their acceptance toward CT integrated lessons. The study was conducted in different subjects, including language arts, biology, and physics, and at different grade levels. The technology acceptance model was used as a theoretical framework to understand students' adoption towards the different integration practices. The study revealed that different CT integration practices yield varying impacts on students' attitudes, particularly in relation to their prior programming experiences. Namely, primary school students with prior programming experience display a higher behavioural intention due to a lack of early biases toward programming. Conversely, secondary school students, less familiar with interdisciplinary approaches, initially show less enthusiasm for future engagement. These insights underscore the need for tailored instructional strategies that consider students' prior experiences and the broader goal of preparing them to contribute to a technologically driven society.

A Comparative Study of Handheld Augmented Reality Interaction Techniques for Developing AR Instructions using AR Authoring Tools

Augmented Reality (AR) instructions offer companies tremendous savings potential. However, developing these AR instructions has traditionally been challenging due to the need for programming skills and spatial knowledge. To address this complexity, industry and academia are working to simplify AR development. A crucial aspect of this process is the accurate positioning of AR content within the physical environment, which requires effective AR interaction techniques that enable full 3D manipulation of AR elements. In this study, we conducted an experimental comparison of three different AR interaction techniques with 55 participants to empirically assess their performance, workload, and user satisfaction across tasks related to AR instruction development. Our findings contribute to the design of future AR instructions and AR authoring tools, emphasizing the importance of evaluating AR interaction techniques that can be utilized by users without programming experience tailored to the specific needs of the intended application domain.

Mechanic Maker: Accessible Game Development via Symbolic Learning Program Synthesis

Game development is a highly technical practice that traditionally requires programming skills. This serves as a barrier to entry for would-be developers or those hoping to use games as part of their creative expression. While there have been prior game development tools focused on accessibility, they generally still require programming, or have major limitations in terms of the kinds of games they can make. In this paper we introduce Mechanic Maker, a tool for creating a wide-range of game mechanics without programming. It instead relies on a backend symbolic learning system to synthesize game mechanics from examples. We conducted a user study to evaluate the benefits of the tool for participants with a variety of programming and game development experience. Our results demonstrated that participants' ability to use the tool was unrelated to programming ability. We conclude that tools like ours could help democratize game development, making the practice accessible regardless of programming skills.

Closha 2.0: a bio-workflow design system for massive genome data analysis on high performance cluster infrastructure

BackgroundThe explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and significant computational challenges. As the cost of next-generation sequencing (NGS) has decreased, the amount of genomic data has surged globally. However, the cost and complexity of the computational resources required continue to be substantial barriers to leveraging big data. A promising solution to these computational challenges is cloud computing, which provides researchers with the necessary CPUs, memory, storage, and software tools.ResultsHere, we present Closha 2.0, a cloud computing service that offers a user-friendly platform for analyzing massive genomic datasets. Closha 2.0 is designed to provide a cloud-based environment that enables all genomic researchers, including those with limited or no programming experience, to easily analyze their genomic data. The new 2.0 version of Closha has more user-friendly features than the previous 1.0 version. Firstly, the workbench features a script editor that supports Python, R, and shell script programming, enabling users to write scripts and integrate them into their pipelines. This functionality is particularly useful for downstream analysis. Second, Closha 2.0 runs on containers, which execute each tool in an independent environment. This provides a stable environment and prevents dependency issues and version conflicts among tools. Additionally, users can execute each step of a pipeline individually, allowing them to test applications at each stage and adjust parameters to achieve the desired results. We also updated a high-speed data transmission tool called GBox that facilitates the rapid transfer of large datasets.ConclusionsThe analysis pipelines on Closha 2.0 are reproducible, with all analysis parameters and inputs being permanently recorded. Closha 2.0 simplifies multi-step analysis with drag-and-drop functionality and provides a user-friendly interface for genomic scientists to obtain accurate results from NGS data. Closha 2.0 is freely available at https://www.kobic.re.kr/closha2.

Coding learning innovation: Interactive programming experience with virtual lab platform

Information and communication technology development in recent years has brought significant educational changes. Virtual learning environments (VLEs) provide students with interactive and flexible online learning. This study aims to quantitatively evaluate the effectiveness of the virtual lab platform (VLP) in improving students' programming skills, focusing on aspects of logic and algorithms, syntax and code structure, and variables and functions. The total sample of 64 students was chosen using a purposive sampling technique and was divided into the experimental group (34 students) and the control group (30 students). Data collection in this study involved several methods: programming skill tests, questionnaires, and monitoring VLP activity logs. According to research results, VLP improves students' programming skills more significantly than conventional methods. Data analysis shows significant differences between the experimental group with VLP and the control group with conventional methods, supporting the effectiveness of VLP as an alternative to learning. In addition, students' positive perception of VLP contributes significantly to learning motivation and ease of use, suggesting that learning technologies such as VLP can improve overall learning motivation. This study provides an empirical basis for educational institutions to consider VLP as a teaching strategy to increase students' learning potential in programming.

Hybrid Prompt Learning for Generating Justifications of Security Risks in Automation Rules

Trigger-action platforms (TAPs) enable users without programming experience to personalize the behavior of Internet of Things applications and services through IF-THEN rules. Unfortunately, the arbitrary connection of smart devices and online services, even with simple rules, such as “IF the entrance Netatmo Wheather Station detects a temperature above 30 \({}^{\circ}C\) ( \(86^{\circ}F\) ) THEN open the shutters in the living room,” might expose users to potential security and privacy risks (e.g., the execution of the previous rule might provide an easy entry point for thieves, especially during the summer vacation period). The goal of our research is to make the users capable of understanding and mitigating the threats and risks associated with the execution of IF-THEN rules. To this end, we define a new challenging task, namely generating post hoc justifications of privacy and security risks associated with automation rules, and propose a novel natural language generation strategy based on hybrid prompt learning producing justifications in the form of real-life threat scenarios. The proposed strategy allows for prompt customization with task-specific information, providing contextual details enabling to grasp the nuances and subtleties of the domain language, resulting in more coherent justifications. The experiments conducted on the if-this-then-that (IFTTT) platform show that our method produces effective justifications, improving the explainability of discrete and hybrid prompting methods up to 27% in BLEURT score. The code of the software is publicly available on GitHub 1 .

Utilising Blended Learning for Large Classes to Deliver an Introductory Programming Course

Higher education institutions are constantly pushed to increase the student intake and produce industry-ready graduates. The pressure generally arises from the governing bodies, originating from socio-political factors. It is particularly evident in the field of Information Communication Technology (ICT) where there is a growing need for quality graduates. Undergraduate programmes in the field of ICT generally offer introductory computer programming courses that are mandatory at early stages of the degree programme. Consequently, the number of students registering for such courses can increase drastically. The students are coming from diversified backgrounds. While some students may have prior experience in computer programming, a larger majority may not have any prior knowledge or experience. Increased class size meets the constraint of the limited availability of physical classroom space and would require the class to be separated into smaller groups for face-to-face activities. Large classes are also challenged with the increased man-power requirement in terms of lecturers, instructors, and teaching assistants. Maintaining student engagement and interactivity becomes increasingly difficult in a large class. Grading of assessments becomes time-consuming and needs and requires increased manpower. Based on the observations and learning from an ongoing introductory programming course, this study evaluates the procedure for overcoming the challenges faced by the teaching staff, especially with a class size exceeding 1000. To cover the theoretical components, recorded video lectures are provided. The class is divided into groups consisting of approximately 100 students and live, online, interactive group discussions are conducted. The live, online smaller group discussions allow students to clarify doubts regarding the content. This also allows the distribution of the limited teaching and assistant staff. Students who need further assistance are encouraged to meet the teaching assistants physically. Thus, the workload is distributed effectively, and it helps ensure the dedicated support and attention is provided to the students. Auto graded programming assignments are effectively employed for formative and summative assessments, which reduces the grading workload significantly. The findings of this study provide insights on course design to effectively deliver the content and conduct assessments overcoming the challenges presented by increased class size.

AUTOMAP: Inferring Rank-Polymorphic Function Applications with Integer Linear Programming

Dynamically typed array languages such as Python, APL, and Matlab lift scalar operations to arrays and replicate scalars to fit applications. We present a mechanism for automatically inferring map and replicate operations in a statically-typed language in a way that resembles the programming experience of a dynamically-typed language while preserving the static typing guarantees. Our type system---which supports parametric polymorphism, higher-order functions, and top-level let-generalization---makes use of integer linear programming in order to find the minimum number of operations needed to elaborate to a well-typed program. We argue that the inference system provides useful and unsurprising guarantees to the programmer. We demonstrate important theoretical properties of the mechanism and report on the implementation of the mechanism in the statically-typed array programming language Futhark.