Ghosts in the Machine
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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. -
Agent-Based Modeling with Netlogo
Agent-Based Modeling with NetLogo Uri Wilensky Center for Connected Learning and Computer-Based Modeling Northwestern Institute on Complex Systems Departments of Computer Science & Learning Sciences Northwestern University Agent-Based Modeling in NetLogo SFI MOOC, Summer 2016 1 History: Roman to Hindu-Arabic Europe – at the turn of the first millenium • Before widespread adoption of Hindu-Arabic, very few could do multiplication/division • Scientists recognized superiority immediately • But widespread adoption took a very long time • Was in surreptitious use, but not official 2 Restructurations Structurations -- the encoding of the knowledge in a domain as a function of the representational infrastructure used to express the knowledge Restructurations -- A change from one structuration of a domain to another resulting from a change in representational infrastructure --- Wilensky & Papert 2006;2010 What is important and hard for people today? Similar to numeracy importance for science but difficulties in understanding, today we need to make sense of complex systems yet we find it difficult. What are Complex Systems? • Systems with a large number of interacting parts, evolving over time • Decentralized decisions vs. centralized control • Emergent global patterns from local interactions and decisions • Examples: ecosystems, economies, immune systems, molecular systems, minds, stock market, democratic government... Emergent Phenomena • Structure (Rules) at Micro- level leads to pattern at Macro- level • Order without Design • No leader or orchestrator -
An Evaluation of Go and Clojure
An evaluation of Go and Clojure A thesis submitted in partial satisfaction of the requirements for the degree Bachelors of Science in Computer Science Fall 2010 Robert Stimpfling Department of Computer Science University of Colorado, Boulder Advisor: Kenneth M. Anderson, PhD Department of Computer Science University of Colorado, Boulder 1. Introduction Concurrent programming languages are not new, but they have been getting a lot of attention more recently due to their potential with multiple processors. Processors have gone from growing exponentially in terms of speed, to growing in terms of quantity. This means processes that are completely serial in execution will soon be seeing a plateau in performance gains since they can only rely on one processor. A popular approach to using these extra processors is to make programs multi-threaded. The threads can execute in parallel and use shared memory to speed up execution times. These multithreaded processes can significantly speed up performance, as long as the number of dependencies remains low. Amdahl‘s law states that these performance gains can only be relative to the amount of processing that can be parallelized [1]. However, the performance gains are significant enough to be looked into. These gains not only come from the processing being divvied up into sections that run in parallel, but from the inherent gains from sharing memory and data structures. Passing new threads a copy of a data structure can be demanding on the processor because it requires the processor to delve into memory and make an exact copy in a new location in memory. Indeed some studies have shown that the problem with optimizing concurrent threads is not in utilizing the processors optimally, but in the need for technical improvements in memory performance [2]. -
Implementation Notes
IMPLEMENTATION NOTES XEROX 3102464 lyric Release June 1987 XEROX COMMON LISP IMPLEMENTATION NOTES 3102464 Lyric Release June 1987 The information in this document is subject to change without notice and should not be construed as a commitment by Xerox Corporation. While every effort has been made to ensure the accuracy of this document, Xerox Corporation assumes no responsibility for any errors that may appear. Copyright @ 1987 by Xerox Corporation. Xerox Common Lisp is a trademark. All rights reserved. "Copyright protection claimed includes all forms and matters of copyrightable material and information now allowed by statutory or judicial law or hereinafter granted, including, without limitation, material generated from the software programs which are displayed on the screen, such as icons, screen display looks, etc. " This manual is set in Modern typeface with text written and formatted on Xerox Artificial Intelligence workstations. Xerox laser printers were used to produce text masters. PREFACE The Xerox Common Lisp Implementation Notes cover several aspects of the Lyric release. In these notes you will find: • An explanation of how Xerox Common Lisp extends the Common Lisp standard. For example, in Xerox Common Lisp the Common Lisp array-constructing function make-array has additional keyword arguments that enhance its functionality. • An explanation of how several ambiguities in Steele's Common Lisp: the Language were resolved. • A description of additional features that provide far more than extensions to Common Lisp. How the Implementation Notes are Organized . These notes are intended to accompany the Guy L. Steele book, Common Lisp: the Language which represents the current standard for Co~mon Lisp. -
Clojure, Given the Pun on Closure, Representing Anything Specific
dynamic, functional programming for the JVM “It (the logo) was designed by my brother, Tom Hickey. “It I wanted to involve c (c#), l (lisp) and j (java). I don't think we ever really discussed the colors Once I came up with Clojure, given the pun on closure, representing anything specific. I always vaguely the available domains and vast emptiness of the thought of them as earth and sky.” - Rich Hickey googlespace, it was an easy decision..” - Rich Hickey Mark Volkmann [email protected] Functional Programming (FP) In the spirit of saying OO is is ... encapsulation, inheritance and polymorphism ... • Pure Functions • produce results that only depend on inputs, not any global state • do not have side effects such as Real applications need some changing global state, file I/O or database updates side effects, but they should be clearly identified and isolated. • First Class Functions • can be held in variables • can be passed to and returned from other functions • Higher Order Functions • functions that do one or both of these: • accept other functions as arguments and execute them zero or more times • return another function 2 ... FP is ... Closures • main use is to pass • special functions that retain access to variables a block of code that were in their scope when the closure was created to a function • Partial Application • ability to create new functions from existing ones that take fewer arguments • Currying • transforming a function of n arguments into a chain of n one argument functions • Continuations ability to save execution state and return to it later think browser • back button 3 .. -
PUB DAM Oct 67 CONTRACT N00014-83-6-0148; N00014-83-K-0655 NOTE 63P
DOCUMENT RESUME ED 290 438 IR 012 986 AUTHOR Cunningham, Robert E.; And Others TITLE Chips: A Tool for Developing Software Interfaces Interactively. INSTITUTION Pittsburgh Univ., Pa. Learning Research and Development Center. SPANS AGENCY Office of Naval Research, Arlington, Va. REPORT NO TR-LEP-4 PUB DAM Oct 67 CONTRACT N00014-83-6-0148; N00014-83-K-0655 NOTE 63p. PUB TYPE Reports - Research/Technical (143) EDRS PRICE MF01/PC03 Plus Postage. DESCRIPTORS *Computer Graphics; *Man Machine Systems; Menu Driven Software; Programing; *Programing Languages IDENTIF7ERS Direct Manipulation Interface' Interface Design Theory; *Learning Research and Development Center; LISP Programing Language; Object Oriented Programing ABSTRACT This report provides a detailed description of Chips, an interactive tool for developing software employing graphical/computer interfaces on Xerox Lisp machines. It is noted that Chips, which is implemented as a collection of customizable classes, provides the programmer with a rich graphical interface for the creation of rich graphical interfaces, and the end-user with classes for modeling the graphical relationships of objects on the screen and maintaining constraints between them. This description of the system is divided into five main sections: () the introduction, which provides background material and a general description of the system; (2) a brief overview of the report; (3) detailed explanations of the major features of Chips;(4) an in-depth discussion of the interactive aspects of the Chips development environment; and (5) an example session using Chips to develop and modify a small portion of an interface. Appended materials include descriptions of four programming techniques that have been sound useful in the development of Chips; descriptions of several systems developed at the Learning Research and Development Centel tsing Chips; and a glossary of key terms used in the report. -
The Evolution of Lisp
1 The Evolution of Lisp Guy L. Steele Jr. Richard P. Gabriel Thinking Machines Corporation Lucid, Inc. 245 First Street 707 Laurel Street Cambridge, Massachusetts 02142 Menlo Park, California 94025 Phone: (617) 234-2860 Phone: (415) 329-8400 FAX: (617) 243-4444 FAX: (415) 329-8480 E-mail: [email protected] E-mail: [email protected] Abstract Lisp is the world’s greatest programming language—or so its proponents think. The structure of Lisp makes it easy to extend the language or even to implement entirely new dialects without starting from scratch. Overall, the evolution of Lisp has been guided more by institutional rivalry, one-upsmanship, and the glee born of technical cleverness that is characteristic of the “hacker culture” than by sober assessments of technical requirements. Nevertheless this process has eventually produced both an industrial- strength programming language, messy but powerful, and a technically pure dialect, small but powerful, that is suitable for use by programming-language theoreticians. We pick up where McCarthy’s paper in the first HOPL conference left off. We trace the development chronologically from the era of the PDP-6, through the heyday of Interlisp and MacLisp, past the ascension and decline of special purpose Lisp machines, to the present era of standardization activities. We then examine the technical evolution of a few representative language features, including both some notable successes and some notable failures, that illuminate design issues that distinguish Lisp from other programming languages. We also discuss the use of Lisp as a laboratory for designing other programming languages. We conclude with some reflections on the forces that have driven the evolution of Lisp. -
Allegro CL User Guide
Allegro CL User Guide Volume 1 (of 2) version 4.3 March, 1996 Copyright and other notices: This is revision 6 of this manual. This manual has Franz Inc. document number D-U-00-000-01-60320-1-6. Copyright 1985-1996 by Franz Inc. All rights reserved. No part of this pub- lication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means electronic, mechanical, by photocopying or recording, or otherwise, without the prior and explicit written permission of Franz incorpo- rated. Restricted rights legend: Use, duplication, and disclosure by the United States Government are subject to Restricted Rights for Commercial Software devel- oped at private expense as specified in DOD FAR 52.227-7013 (c) (1) (ii). Allegro CL and Allegro Composer are registered trademarks of Franz Inc. Allegro Common Windows, Allegro Presto, Allegro Runtime, and Allegro Matrix are trademarks of Franz inc. Unix is a trademark of AT&T. The Allegro CL software as provided may contain material copyright Xerox Corp. and the Open Systems Foundation. All such material is used and distrib- uted with permission. Other, uncopyrighted material originally developed at MIT and at CMU is also included. Appendix B is a reproduction of chapters 5 and 6 of The Art of the Metaobject Protocol by G. Kiczales, J. des Rivieres, and D. Bobrow. All this material is used with permission and we thank the authors and their publishers for letting us reproduce their material. Contents Volume 1 Preface 1 Introduction 1.1 The language 1-1 1.2 History 1-1 1.3 Format -
Proceedings of the 8Th European Lisp Symposium Goldsmiths, University of London, April 20-21, 2015 Julian Padget (Ed.) Sponsors
Proceedings of the 8th European Lisp Symposium Goldsmiths, University of London, April 20-21, 2015 Julian Padget (ed.) Sponsors We gratefully acknowledge the support given to the 8th European Lisp Symposium by the following sponsors: WWWLISPWORKSCOM i Organization Programme Committee Julian Padget – University of Bath, UK (chair) Giuseppe Attardi — University of Pisa, Italy Sacha Chua — Toronto, Canada Stephen Eglen — University of Cambridge, UK Marc Feeley — University of Montreal, Canada Matthew Flatt — University of Utah, USA Rainer Joswig — Hamburg, Germany Nick Levine — RavenPack, Spain Henry Lieberman — MIT, USA Christian Queinnec — University Pierre et Marie Curie, Paris 6, France Robert Strandh — University of Bordeaux, France Edmund Weitz — University of Applied Sciences, Hamburg, Germany Local Organization Christophe Rhodes – Goldsmiths, University of London, UK (chair) Richard Lewis – Goldsmiths, University of London, UK Shivi Hotwani – Goldsmiths, University of London, UK Didier Verna – EPITA Research and Development Laboratory, France ii Contents Acknowledgments i Messages from the chairs v Invited contributions Quicklisp: On Beyond Beta 2 Zach Beane µKanren: Running the Little Things Backwards 3 Bodil Stokke Escaping the Heap 4 Ahmon Dancy Unwanted Memory Retention 5 Martin Cracauer Peer-reviewed papers Efficient Applicative Programming Environments for Computer Vision Applications 7 Benjamin Seppke and Leonie Dreschler-Fischer Keyboard? How quaint. Visual Dataflow Implemented in Lisp 15 Donald Fisk P2R: Implementation of -
A Simplified Introduction to Virus Propagation Using Maple's Turtle Graphics Package
E. Roanes-Lozano, C. Solano-Macías & E. Roanes-Macías.: A simplified introduction to virus propagation using Maple's Turtle Graphics package A simplified introduction to virus propagation using Maple's Turtle Graphics package Eugenio Roanes-Lozano Instituto de Matemática Interdisciplinar & Departamento de Didáctica de las Ciencias Experimentales, Sociales y Matemáticas Facultad de Educación, Universidad Complutense de Madrid, Spain Carmen Solano-Macías Departamento de Información y Comunicación Facultad de CC. de la Documentación y Comunicación, Universidad de Extremadura, Spain Eugenio Roanes-Macías Departamento de Álgebra, Universidad Complutense de Madrid, Spain [email protected] ; [email protected] ; [email protected] Partially funded by the research project PGC2018-096509-B-100 (Government of Spain) 1 E. Roanes-Lozano, C. Solano-Macías & E. Roanes-Macías.: A simplified introduction to virus propagation using Maple's Turtle Graphics package 1. INTRODUCTION: TURTLE GEOMETRY AND LOGO • Logo language: developed at the end of the ‘60s • Characterized by the use of Turtle Geometry (a.k.a. as Turtle Graphics). • Oriented to introduce kids to programming (Papert, 1980). • Basic movements of the turtle (graphic cursor): FD, BK RT, LT. • It is not based on a Cartesian Coordinate system. 2 E. Roanes-Lozano, C. Solano-Macías & E. Roanes-Macías.: A simplified introduction to virus propagation using Maple's Turtle Graphics package • Initially robots were used to plot the trail of the turtle. http://cyberneticzoo.com/cyberneticanimals/1969-the-logo-turtle-seymour-papert-marvin-minsky-et-al-american/ 3 E. Roanes-Lozano, C. Solano-Macías & E. Roanes-Macías.: A simplified introduction to virus propagation using Maple's Turtle Graphics package • IBM Logo / LCSI Logo (’80) 4 E. -
1960 1970 1980 1990 Japan California………………… UT IL in PA NJ NY Massachusetts………… Europe Lisp 1.5 TX
Japan UT IL PA NY Massachusetts………… Europe California………………… TX IN NJ Lisp 1.5 1960 1970 1980 1990 Direct Relationships Lisp 1.5 LISP Family Graph Lisp 1.5 LISP 1.5 Family Lisp 1.5 Basic PDP-1 LISP M-460 LISP 7090 LISP 1.5 BBN PDP-1 LISP 7094 LISP 1.5 Stanford PDP-1 LISP SDS-940 LISP Q-32 LISP PDP-6 LISP LISP 2 MLISP MIT PDP-1 LISP Stanford LISP 1.6 Cambridge LISP Standard LISP UCI-LISP MacLisp Family PDP-6 LISP MacLisp Multics MacLisp CMU SAIL MacLisp MacLisp VLISP Franz Lisp Machine Lisp LISP S-1 Lisp NIL Spice Lisp Zetalisp IBM Lisp Family 7090 LISP 1.5 Lisp360 Lisp370 Interlisp Illinois SAIL MacLisp VM LISP Interlisp Family SDS-940 LISP BBN-LISP Interlisp 370 Spaghetti stacks Interlisp LOOPS Common LOOPS Lisp Machines Spice Lisp Machine Lisp Interlisp Lisp (MIT CONS) (Xerox (BBN Jericho) (PERQ) (MIT CADR) Alto, Dorado, (LMI Lambda) Dolphin, Dandelion) Lisp Zetalisp TAO (3600) (ELIS) Machine Lisp (TI Explorer) Common Lisp Family APL APL\360 ECL Interlisp ARPA S-1 Lisp Scheme Meeting Spice at SRI, Lisp April 1991 PSL KCL NIL CLTL1 Zetalisp X3J13 CLTL2 CLOS ISLISP X3J13 Scheme Extended Family COMIT Algol 60 SNOBOL METEOR CONVERT Simula 67 Planner LOGO DAISY, Muddle Scheme 311, Microplanner Scheme 84 Conniver PLASMA (actors) Scheme Revised Scheme T 2 CLTL1 Revised Scheme Revised2 Scheme 3 CScheme Revised Scheme MacScheme PC Scheme Chez Scheme IEEE Revised4 Scheme Scheme Object-Oriented Influence Simula 67 Smalltalk-71 LOGO Smalltalk-72 CLU PLASMA (actors) Flavors Common Objects LOOPS ObjectLisp CommonLOOPS EuLisp New Flavors CLTL2 CLOS Dylan X3J13 ISLISP Lambda Calculus Church, 1941 λ Influence Lisp 1.5 Landin (SECD, ISWIM) Evans (PAL) Reynolds (Definitional Interpreters) Lisp370 Scheme HOPE ML Standard ML Standard ML of New Jersey Haskell FORTRAN Influence FORTRAN FLPL Lisp 1.5 S-1 Lisp Lisp Machine Lisp CLTL1 Revised3 Zetalisp Scheme X3J13 CLTL2 IEEE Scheme X3J13 John McCarthy Lisp 1.5 Danny Bobrow Richard Gabriel Guy Steele Dave Moon Jon L White. -
Programming Languages As Operating Systems (Or Revenge of the Son of the Lisp Machine)
Programming Languages as Operating Systems (or Revenge of the Son of the Lisp Machine) Matthew Flatt Robert Bruce Findler Shriram Krishnamurthi Matthias Felleisen Department of Computer Science∗ Rice University Houston, Texas 77005-1892 Abstract reclaim the program’s resources—even though the program and DrScheme share a single virtual machine. The MrEd virtual machine serves both as the implementa- To address this problem, MrEd provides a small set of tion platform for the DrScheme programming environment, new language constructs. These constructs allow a program- and as the underlying Scheme engine for executing expres- running program, such as DrScheme, to run nested programs sions and programs entered into DrScheme’s read-eval-print directly on the MrEd virtual machine without sacrificing loop. We describe the key elements of the MrEd virtual control over the nested programs. As a result, DrScheme machine for building a programming environment, and we can execute a copy of DrScheme that is executing its own step through the implementation of a miniature version of copy of DrScheme (see Figure 1). The inner and middle DrScheme in MrEd. More generally, we show how MrEd de- DrSchemes cannot interfere with the operation of the outer fines a high-level operating system for graphical programs. DrScheme, and the middle DrScheme cannot interfere with the outer DrScheme’s control over the inner DrScheme. 1 MrEd: A Scheme Machine In this paper, we describe the key elements of the MrEd virtual machine, and we step through the implementation The DrScheme programming environment [10] provides stu- of a miniature version of DrScheme in MrEd.