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The Machine That Builds Itself: How the Strengths of Lisp Family
Khomtchouk et al. OPINION NOTE The Machine that Builds Itself: How the Strengths of Lisp Family Languages Facilitate Building Complex and Flexible Bioinformatic Models Bohdan B. Khomtchouk1*, Edmund Weitz2 and Claes Wahlestedt1 *Correspondence: [email protected] Abstract 1Center for Therapeutic Innovation and Department of We address the need for expanding the presence of the Lisp family of Psychiatry and Behavioral programming languages in bioinformatics and computational biology research. Sciences, University of Miami Languages of this family, like Common Lisp, Scheme, or Clojure, facilitate the Miller School of Medicine, 1120 NW 14th ST, Miami, FL, USA creation of powerful and flexible software models that are required for complex 33136 and rapidly evolving domains like biology. We will point out several important key Full list of author information is features that distinguish languages of the Lisp family from other programming available at the end of the article languages and we will explain how these features can aid researchers in becoming more productive and creating better code. We will also show how these features make these languages ideal tools for artificial intelligence and machine learning applications. We will specifically stress the advantages of domain-specific languages (DSL): languages which are specialized to a particular area and thus not only facilitate easier research problem formulation, but also aid in the establishment of standards and best programming practices as applied to the specific research field at hand. DSLs are particularly easy to build in Common Lisp, the most comprehensive Lisp dialect, which is commonly referred to as the “programmable programming language.” We are convinced that Lisp grants programmers unprecedented power to build increasingly sophisticated artificial intelligence systems that may ultimately transform machine learning and AI research in bioinformatics and computational biology. -
(Pdf) of from Push/Enter to Eval/Apply by Program Transformation
From Push/Enter to Eval/Apply by Program Transformation MaciejPir´og JeremyGibbons Department of Computer Science University of Oxford [email protected] [email protected] Push/enter and eval/apply are two calling conventions used in implementations of functional lan- guages. In this paper, we explore the following observation: when considering functions with mul- tiple arguments, the stack under the push/enter and eval/apply conventions behaves similarly to two particular implementations of the list datatype: the regular cons-list and a form of lists with lazy concatenation respectively. Along the lines of Danvy et al.’s functional correspondence between def- initional interpreters and abstract machines, we use this observation to transform an abstract machine that implements push/enter into an abstract machine that implements eval/apply. We show that our method is flexible enough to transform the push/enter Spineless Tagless G-machine (which is the semantic core of the GHC Haskell compiler) into its eval/apply variant. 1 Introduction There are two standard calling conventions used to efficiently compile curried multi-argument functions in higher-order languages: push/enter (PE) and eval/apply (EA). With the PE convention, the caller pushes the arguments on the stack, and jumps to the function body. It is the responsibility of the function to find its arguments, when they are needed, on the stack. With the EA convention, the caller first evaluates the function to a normal form, from which it can read the number and kinds of arguments the function expects, and then it calls the function body with the right arguments. -
From JSON to JSEN Through Virtual Languages of the Creative Commons Attribution License (
© 2021 by the authors; licensee RonPub, Lübeck, Germany. This article is an open access article distributed under the terms and conditions A.of Ceravola, the Creative F. Joublin:Commons From Attribution JSON to license JSEN through(http://c reativecommons.org/licenses/by/4Virtual Languages .0/). Open Access Open Journal of Web Technology (OJWT) Volume 8, Issue 1, 2021 www.ronpub.com/ojwt ISSN 2199-188X From JSON to JSEN through Virtual Languages Antonello Ceravola, Frank Joublin Honda Research Institute Europe GmbH, Carl Legien Str. 30, Offenbach/Main, Germany, {Antonello.Ceravola, Frank.Joublin}@honda-ri.de ABSTRACT In this paper we describe a data format suitable for storing and manipulating executable language statements that can be used for exchanging/storing programs, executing them concurrently and extending homoiconicity of the hosting language. We call it JSEN, JavaScript Executable Notation, which represents the counterpart of JSON, JavaScript Object Notation. JSON and JSEN complement each other. The former is a data format for storing and representing objects and data, while the latter has been created for exchanging/storing/executing and manipulating statements of programs. The two formats, JSON and JSEN, share some common properties, reviewed in this paper with a more extensive analysis on what the JSEN data format can provide. JSEN extends homoiconicity of the hosting language (in our case JavaScript), giving the possibility to manipulate programs in a finer grain manner than what is currently possible. This property makes definition of virtual languages (or DSL) simple and straightforward. Moreover, JSEN provides a base for implementing a type of concurrent multitasking for a single-threaded language like JavaScript. -
The Semantics of Syntax Applying Denotational Semantics to Hygienic Macro Systems
The Semantics of Syntax Applying Denotational Semantics to Hygienic Macro Systems Neelakantan R. Krishnaswami University of Birmingham <[email protected]> 1. Introduction body of a macro definition do not interfere with names oc- Typically, when semanticists hear the words “Scheme” or curring in the macro’s arguments. Consider this definition of and “Lisp”, what comes to mind is “untyped lambda calculus a short-circuiting operator: plus higher-order state and first-class control”. Given our (define-syntax and typical concerns, this seems to be the essence of Scheme: it (syntax-rules () is a dynamically typed applied lambda calculus that sup- ((and e1 e2) (let ((tmp e1)) ports mutable data and exposes first-class continuations to (if tmp the programmer. These features expose a complete com- e2 putational substrate to programmers so elegant that it can tmp))))) even be characterized mathematically; every monadically- representable effect can be implemented with state and first- In this definition, even if the variable tmp occurs freely class control [4]. in e2, it will not be in the scope of the variable definition However, these days even mundane languages like Java in the body of the and macro. As a result, it is important to support higher-order functions and state. So from the point interpret the body of the macro not merely as a piece of raw of view of a working programmer, the most distinctive fea- syntax, but as an alpha-equivalence class. ture of Scheme is something quite different – its support for 2.2 Open Recursion macros. The intuitive explanation is that a macro is a way of defining rewrites on abstract syntax trees. -
Stepping Ocaml
Stepping OCaml TsukinoFurukawa YouyouCong KenichiAsai Ochanomizu University Tokyo, Japan {furukawa.tsukino, so.yuyu, asai}@is.ocha.ac.jp Steppers, which display all the reduction steps of a given program, are a novice-friendly tool for un- derstanding program behavior. Unfortunately, steppers are not as popular as they ought to be; indeed, the tool is only available in the pedagogical languages of the DrRacket programming environment. We present a stepper for a practical fragment of OCaml. Similarly to the DrRacket stepper, we keep track of evaluation contexts in order to reconstruct the whole program at each reduction step. The difference is that we support effectful constructs, such as exception handling and printing primitives, allowing the stepper to assist a wider range of users. In this paper, we describe the implementationof the stepper, share the feedback from our students, and show an attempt at assessing the educational impact of our stepper. 1 Introduction Programmers spend a considerable amount of time and effort on debugging. In particular, novice pro- grammers may find this process extremely painful, since existing debuggers are usually not friendly enough to beginners. To use a debugger, we have to first learn what kinds of commands are available, and figure out which would be useful for the current purpose. It would be even harder to use the com- mand in a meaningful manner: for instance, to spot the source of an unexpected behavior, we must be able to find the right places to insert breakpoints, which requires some programming experience. Then, is there any debugging tool that is accessible to first-day programmers? In our view, the algebraic stepper [1] of DrRacket, a pedagogical programming environment for the Racket language, serves as such a tool. -
A Practical Introduction to Python Programming
A Practical Introduction to Python Programming Brian Heinold Department of Mathematics and Computer Science Mount St. Mary’s University ii ©2012 Brian Heinold Licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported Li- cense Contents I Basics1 1 Getting Started 3 1.1 Installing Python..............................................3 1.2 IDLE......................................................3 1.3 A first program...............................................4 1.4 Typing things in...............................................5 1.5 Getting input.................................................6 1.6 Printing....................................................6 1.7 Variables...................................................7 1.8 Exercises...................................................9 2 For loops 11 2.1 Examples................................................... 11 2.2 The loop variable.............................................. 13 2.3 The range function............................................ 13 2.4 A Trickier Example............................................. 14 2.5 Exercises................................................... 15 3 Numbers 19 3.1 Integers and Decimal Numbers.................................... 19 3.2 Math Operators............................................... 19 3.3 Order of operations............................................ 21 3.4 Random numbers............................................. 21 3.5 Math functions............................................... 21 3.6 Getting -
Making a Faster Curry with Extensional Types
Making a Faster Curry with Extensional Types Paul Downen Simon Peyton Jones Zachary Sullivan Microsoft Research Zena M. Ariola Cambridge, UK University of Oregon [email protected] Eugene, Oregon, USA [email protected] [email protected] [email protected] Abstract 1 Introduction Curried functions apparently take one argument at a time, Consider these two function definitions: which is slow. So optimizing compilers for higher-order lan- guages invariably have some mechanism for working around f1 = λx: let z = h x x in λy:e y z currying by passing several arguments at once, as many as f = λx:λy: let z = h x x in e y z the function can handle, which is known as its arity. But 2 such mechanisms are often ad-hoc, and do not work at all in higher-order functions. We show how extensional, call- It is highly desirable for an optimizing compiler to η ex- by-name functions have the correct behavior for directly pand f1 into f2. The function f1 takes only a single argu- expressing the arity of curried functions. And these exten- ment before returning a heap-allocated function closure; sional functions can stand side-by-side with functions native then that closure must subsequently be called by passing the to practical programming languages, which do not use call- second argument. In contrast, f2 can take both arguments by-name evaluation. Integrating call-by-name with other at once, without constructing an intermediate closure, and evaluation strategies in the same intermediate language ex- this can make a huge difference to run-time performance in presses the arity of a function in its type and gives a princi- practice [Marlow and Peyton Jones 2004]. -
Advanced Tcl E D
PART II I I . A d v a n c Advanced Tcl e d T c l Part II describes advanced programming techniques that support sophisticated applications. The Tcl interfaces remain simple, so you can quickly construct pow- erful applications. Chapter 10 describes eval, which lets you create Tcl programs on the fly. There are tricks with using eval correctly, and a few rules of thumb to make your life easier. Chapter 11 describes regular expressions. This is the most powerful string processing facility in Tcl. This chapter includes a cookbook of useful regular expressions. Chapter 12 describes the library and package facility used to organize your code into reusable modules. Chapter 13 describes introspection and debugging. Introspection provides information about the state of the Tcl interpreter. Chapter 14 describes namespaces that partition the global scope for vari- ables and procedures. Namespaces help you structure large Tcl applications. Chapter 15 describes the features that support Internationalization, includ- ing Unicode, other character set encodings, and message catalogs. Chapter 16 describes event-driven I/O programming. This lets you run pro- cess pipelines in the background. It is also very useful with network socket pro- gramming, which is the topic of Chapter 17. Chapter 18 describes TclHttpd, a Web server built entirely in Tcl. You can build applications on top of TclHttpd, or integrate the server into existing appli- cations to give them a web interface. TclHttpd also supports regular Web sites. Chapter 19 describes Safe-Tcl and using multiple Tcl interpreters. You can create multiple Tcl interpreters for your application. If an interpreter is safe, then you can grant it restricted functionality. -
Star TEX: the Next Generation Several Ideas on How to Modernize TEX Already Exist
TUGboat, Volume 33 (2012), No. 2 199 Star TEX: The Next Generation Several ideas on how to modernize TEX already exist. Some of them have actually been implemented. Didier Verna In this paper, we present ours. The possible future Abstract that we would like to see happening for TEX is some- what different from the current direction(s) TEX's While T X is unanimously praised for its typesetting E evolution has been following. In our view, modern- capabilities, it is also regularly blamed for its poor izing TEX can start with grounding it in an old yet programmatic offerings. A macro-expansion system very modern programming language: Common Lisp. is indeed far from the best choice in terms of general- Section 2 clarifies what our notion of a \better", or purpose programming. Several solutions have been \more modern" TEX is. Section 3 on the next page proposed to modernize T X on the programming E presents several approaches sharing a similar goal. side. All of them currently involve a heterogeneous Section 4 on the following page justifies the choice approach in which T X is mixed with a full-blown pro- E of Common Lisp. Finally, section 5 on page 205 gramming language. This paper advocates another, outlines the projected final shape of the project. homogeneous approach in which TEX is first rewrit- ten in a modern language, Common Lisp, which 2 A better TEX serves both at the core of the program and at the The T X community has this particularity of being scripting level. All programmatic macros of T X are E E a mix of two populations: people coming from the hence rendered obsolete, as the underlying language world of typography, who love it for its beautiful itself can be used for user-level programming. -
CSE341, Fall 2011, Lecture 17 Summary
CSE341, Fall 2011, Lecture 17 Summary Standard Disclaimer: This lecture summary is not necessarily a complete substitute for attending class, reading the associated code, etc. It is designed to be a useful resource for students who attended class and are later reviewing the material. This lecture covers three topics that are all directly relevant to the upcoming homework assignment in which you will use Racket to write an interpreter for a small programming language: • Racket's structs for defining new (dynamic) types in Racket and contrasting them with manually tagged lists [This material was actually covered in the \Monday section" and only briefly reviewed in the other materials for this lecture.] • How a programming language can be implemented in another programming language, including key stages/concepts like parsing, compilation, interpretation, and variants thereof • How to implement higher-order functions and closures by explicitly managing and storing environments at run-time One-of types with lists and dynamic typing In ML, we studied one-of types (in the form of datatype bindings) almost from the beginning, since they were essential for defining many kinds of data. Racket's dynamic typing makes the issue less central since we can simply use primitives like booleans and cons cells to build anything we want. Building lists and trees out of dynamically typed pairs is particularly straightforward. However, the concept of one-of types is still prevalent. For example, a list is null or a cons cell where there cdr holds a list. Moreover, Racket extends its Scheme predecessor with something very similar to ML's constructors | a special form called struct | and it is instructive to contrast the two language's approaches. -
Specialising Dynamic Techniques for Implementing the Ruby Programming Language
SPECIALISING DYNAMIC TECHNIQUES FOR IMPLEMENTING THE RUBY PROGRAMMING LANGUAGE A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the Faculty of Engineering and Physical Sciences 2015 By Chris Seaton School of Computer Science This published copy of the thesis contains a couple of minor typographical corrections from the version deposited in the University of Manchester Library. [email protected] chrisseaton.com/phd 2 Contents List of Listings7 List of Tables9 List of Figures 11 Abstract 15 Declaration 17 Copyright 19 Acknowledgements 21 1 Introduction 23 1.1 Dynamic Programming Languages.................. 23 1.2 Idiomatic Ruby............................ 25 1.3 Research Questions.......................... 27 1.4 Implementation Work......................... 27 1.5 Contributions............................. 28 1.6 Publications.............................. 29 1.7 Thesis Structure............................ 31 2 Characteristics of Dynamic Languages 35 2.1 Ruby.................................. 35 2.2 Ruby on Rails............................. 36 2.3 Case Study: Idiomatic Ruby..................... 37 2.4 Summary............................... 49 3 3 Implementation of Dynamic Languages 51 3.1 Foundational Techniques....................... 51 3.2 Applied Techniques.......................... 59 3.3 Implementations of Ruby....................... 65 3.4 Parallelism and Concurrency..................... 72 3.5 Summary............................... 73 4 Evaluation Methodology 75 4.1 Evaluation Philosophy -
Language Design and Implementation Using Ruby and the Interpreter Pattern
Language Design and Implementation using Ruby and the Interpreter Pattern Ariel Ortiz Information Technology Department Tecnológico de Monterrey, Campus Estado de México Atizapán de Zaragoza, Estado de México, Mexico. 52926 [email protected] ABSTRACT to explore different language concepts and programming styles. In this paper, the S-expression Interpreter Framework (SIF) is Section 4 describes briefly how the SIF has been used in class. presented as a tool for teaching language design and The conclusions are found in section 5. implementation. The SIF is based on the interpreter design pattern and is written in the Ruby programming language. Its core is quite 1.1 S-Expressions S-expressions (symbolic expressions) are a parenthesized prefix small, but it can be easily extended by adding primitive notation, mainly used in the Lisp family languages. They were procedures and special forms. The SIF can be used to demonstrate selected as the framework’s source language notation because advanced language concepts (variable scopes, continuations, etc.) they offer important advantages: as well as different programming styles (functional, imperative, and object oriented). • Both code and data can be easily represented. Adding a new language construct is pretty straightforward. • Categories and Subject Descriptors The number of program elements is minimal, thus a parser is − relatively easy to write. D.3.4 [Programming Languages]: Processors interpreters, • run-time environments. The arity of operators doesn’t need to be fixed, so, for example, the + operator can be defined to accept any number of arguments. General Terms • There are no problems regarding operator precedence or Design, Languages. associativity, because every operator has its own pair of parentheses.