The Definition of Standard ML, Revised
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Type-Safe Multi-Tier Programming with Standard ML Modules
Experience Report: Type-Safe Multi-Tier Programming with Standard ML Modules Martin Elsman Philip Munksgaard Ken Friis Larsen Department of Computer Science, iAlpha AG, Switzerland Department of Computer Science, University of Copenhagen, Denmark [email protected] University of Copenhagen, Denmark [email protected] kfl[email protected] Abstract shared module, it is now possible, on the server, to implement a We describe our experience with using Standard ML modules and module that for each function responds to a request by first deseri- Standard ML’s core language typing features for ensuring type- alising the argument into a value, calls the particular function and safety across physically separated tiers in an extensive web applica- replies to the client with a serialised version of the result value. tion built around a database-backed server platform and advanced Dually, on the client side, for each function, the client code can client code that accesses the server through asynchronous server serialise the argument and send the request asynchronously to the requests. server. When the server responds, the client will deserialise the re- sult value and apply the handler function to the value. Both for the server part and for the client part, the code can be implemented in 1. Introduction a type-safe fashion. In particular, if an interface type changes, the The iAlpha Platform is an advanced asset management system programmer will be notified about all type-inconsistencies at com- featuring a number of analytics modules for combining trading pile -
A Networked Robotic System and Its Use in an Oil Spill Monitoring Exercise
A Networked Robotic System and its Use in an Oil Spill Monitoring Exercise El´oiPereira∗† Co-authors: Pedro Silvay, Clemens Krainerz, Christoph Kirschz, Jos´eMorgadoy, and Raja Sengupta∗ cpcc.berkeley.edu, eloipereira.com [email protected] Swarm at the Edge of the Could - ESWeek'13, Montreal, Canada September 29, 2013 ∗ - Systems Engineering, UC Berkeley y - Research Center, Portuguese Air Force Academy z - Computer Science Dept., University of Salzburg Programming the Ubiquitous World • In networked mobile systems (e.g. teams of robots, smartphones, etc.) the location and connectivity of \machines" may vary during the execution of its \programs" (computation specifications) • We investigate models for bridging \programs" and \machines" with dynamic structure (location and connectivity) • BigActors [PKSBdS13, PPKS13, PS13] are actors [Agh86] hosted by entities of the physical structure denoted as bigraph nodes [Mil09] E. Pereira, C. M. Kirsch, R. Sengupta, and J. B. de Sousa, \Bigactors - A Model for Structure-aware Computation," in ACM/IEEE 4th International Conference on Cyber-Physical Systems, 2013, pp. 199-208. Case study: Oil spill monitoring scenario • \Bilge dumping" is an environmental problem of great relevance for countries with large area of jurisdictional waters • EC created the European Maritime Safety Agency to \...prevent and respond to pollution by ships within the EU" • How to use networked robotics to monitor and take evidences of \bilge dumping" Figure: Portuguese Jurisdiction waters and evidences of \Bilge dumping". -
Four Lectures on Standard ML
Mads Tofte March Lab oratory for Foundations of Computer Science Department of Computer Science Edinburgh University Four Lectures on Standard ML The following notes give an overview of Standard ML with emphasis placed on the Mo dules part of the language The notes are to the b est of my knowledge faithful to The Denition of Standard ML Version as regards syntax semantics and terminology They have b een written so as to b e indep endent of any particular implemen tation The exercises in the rst lectures can b e tackled without the use of a machine although having access to an implementation will no doubt b e b enecial The pro ject in Lecture presupp oses access to an implementation of the full language including mo dules At present the Edinburgh compiler do es not fall into this category the author used the New Jersey Standard ML compiler Lecture gives an introduction to ML aimed at the reader who is familiar with some programming language but do es not know ML Both the Core Language and the Mo dules are covered by way of example Lecture discusses the use of ML mo dules in the development of large programs A useful metho dology for programming with functors signatures and structures is presented Lecture gives a fairly detailed account of the static semantics of ML mo dules for those who really want to understand the crucial notions of sharing and signature matching Lecture presents a one day pro ject intended to give the student an opp ortunity of mo difying a nontrivial piece of software using functors signatures and structures -
The Space and Motion of Communicating Agents
The space and motion of communicating agents Robin Milner December 1, 2008 ii to my family: Lucy, Barney, Chloe,¨ and in memory of Gabriel iv Contents Prologue vii Part I : Space 1 1 The idea of bigraphs 3 2 Defining bigraphs 15 2.1 Bigraphsandtheirassembly . 15 2.2 Mathematicalframework . 19 2.3 Bigraphicalcategories . 24 3 Algebra for bigraphs 27 3.1 Elementarybigraphsandnormalforms. 27 3.2 Derivedoperations ........................... 31 4 Relative and minimal bounds 37 5 Bigraphical structure 43 5.1 RPOsforbigraphs............................ 43 5.2 IPOsinbigraphs ............................ 48 5.3 AbstractbigraphslackRPOs . 53 6 Sorting 55 6.1 PlacesortingandCCS ......................... 55 6.2 Linksorting,arithmeticnetsandPetrinets . ..... 60 6.3 Theimpactofsorting .......................... 64 Part II : Motion 66 7 Reactions and transitions 67 7.1 Reactivesystems ............................ 68 7.2 Transitionsystems ........................... 71 7.3 Subtransitionsystems .. .... ... .... .... .... .... 76 v vi CONTENTS 7.4 Abstracttransitionsystems . 77 8 Bigraphical reactive systems 81 8.1 DynamicsforaBRS .......................... 82 8.2 DynamicsforaniceBRS.... ... .... .... .... ... .. 87 9 Behaviour in link graphs 93 9.1 Arithmeticnets ............................. 93 9.2 Condition-eventnets .. .... ... .... .... .... ... .. 95 10 Behavioural theory for CCS 103 10.1 SyntaxandreactionsforCCSinbigraphs . 103 10.2 TransitionsforCCSinbigraphs . 107 Part III : Development 113 11 Further topics 115 11.1Tracking................................ -
Understanding and Evolving the ML Module System
Understanding and Evolving the ML Module System Derek Dreyer May 2005 CMU-CS-05-131 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Thesis Committee: Robert Harper (co-chair) Karl Crary (co-chair) Peter Lee David MacQueen (University of Chicago) Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Copyright c 2005 Derek Dreyer This research was sponsored in part by the National Science Foundation under grant CCR-0121633 and EIA- 9706572, and the US Air Force under grant F19628-95-C-0050 and a generous fellowship. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity. Keywords: ML, module systems, type systems, functors, abstract data types, lambda calculus, recursive modules, singleton kinds Abstract The ML module system stands as a high-water mark of programming language support for data abstraction. Nevertheless, it is not in a fully evolved state. One prominent weakness is that module interdependencies in ML are restricted to be acyclic, which means that mutually recursive functions and data types must be written in the same module even if they belong conceptually in different modules. Existing efforts to remedy this limitation either involve drastic changes to the notion of what a module is, or fail to allow mutually recursive modules to hide type information from one another. Another issue is that there are several dialects of ML, and the module systems of these dialects differ in subtle yet semantically significant ways that have been difficult to account for in any rigorous way. -
The Origins of Structural Operational Semantics
The Origins of Structural Operational Semantics Gordon D. Plotkin Laboratory for Foundations of Computer Science, School of Informatics, University of Edinburgh, King’s Buildings, Edinburgh EH9 3JZ, Scotland I am delighted to see my Aarhus notes [59] on SOS, Structural Operational Semantics, published as part of this special issue. The notes already contain some historical remarks, but the reader may be interested to know more of the personal intellectual context in which they arose. I must straightaway admit that at this distance in time I do not claim total accuracy or completeness: what I write should rather be considered as a reconstruction, based on (possibly faulty) memory, papers, old notes and consultations with colleagues. As a postgraduate I learnt the untyped λ-calculus from Rod Burstall. I was further deeply impressed by the work of Peter Landin on the semantics of pro- gramming languages [34–37] which includes his abstract SECD machine. One should also single out John McCarthy’s contributions [45–48], which include his 1962 introduction of abstract syntax, an essential tool, then and since, for all approaches to the semantics of programming languages. The IBM Vienna school [42, 41] were interested in specifying real programming languages, and, in particular, worked on an abstract interpreting machine for PL/I using VDL, their Vienna Definition Language; they were influenced by the ideas of McCarthy, Landin and Elgot [18]. I remember attending a seminar at Edinburgh where the intricacies of their PL/I abstract machine were explained. The states of these machines are tuples of various kinds of complex trees and there is also a stack of environments; the transition rules involve much tree traversal to access syntactical control points, handle jumps, and to manage concurrency. -
Curriculum Vitae Thomas A
Curriculum Vitae Thomas A. Henzinger February 6, 2021 Address IST Austria (Institute of Science and Technology Austria) Phone: +43 2243 9000 1033 Am Campus 1 Fax: +43 2243 9000 2000 A-3400 Klosterneuburg Email: [email protected] Austria Web: pub.ist.ac.at/~tah Research Mathematical logic, automata and game theory, models of computation. Analysis of reactive, stochastic, real-time, and hybrid systems. Formal software and hardware verification, especially model checking. Design and implementation of concurrent and embedded software. Executable modeling of biological systems. Education September 1991 Ph.D., Computer Science Stanford University July 1987 Dipl.-Ing., Computer Science Kepler University, Linz August 1986 M.S., Computer and Information Sciences University of Delaware Employment September 2009 President IST Austria April 2004 to Adjunct Professor, University of California, June 2011 Electrical Engineering and Computer Sciences Berkeley April 2004 to Professor, EPFL August 2009 Computer and Communication Sciences January 1999 to Director Max-Planck Institute March 2000 for Computer Science, Saarbr¨ucken July 1998 to Professor, University of California, March 2004 Electrical Engineering and Computer Sciences Berkeley July 1997 to Associate Professor, University of California, June 1998 Electrical Engineering and Computer Sciences Berkeley January 1996 to Assistant Professor, University of California, June 1997 Electrical Engineering and Computer Sciences Berkeley January 1992 to Assistant Professor, Cornell University December 1996 Computer Science October 1991 to Postdoctoral Scientist, Universit´eJoseph Fourier, December 1991 IMAG Laboratory Grenoble 1 Honors Member, US National Academy of Sciences, 2020. Member, American Academy of Arts and Sciences, 2020. ESWEEK (Embedded Systems Week) Test-of-Time Award, 2020. LICS (Logic in Computer Science) Test-of-Time Award, 2020. -
Lisp: Final Thoughts
20 Lisp: Final Thoughts Both Lisp and Prolog are based on formal mathematical models of computation: Prolog on logic and theorem proving, Lisp on the theory of recursive functions. This sets these languages apart from more traditional languages whose architecture is just an abstraction across the architecture of the underlying computing (von Neumann) hardware. By deriving their syntax and semantics from mathematical notations, Lisp and Prolog inherit both expressive power and clarity. Although Prolog, the newer of the two languages, has remained close to its theoretical roots, Lisp has been extended until it is no longer a purely functional programming language. The primary culprit for this diaspora was the Lisp community itself. The pure lisp core of the language is primarily an assembly language for building more complex data structures and search algorithms. Thus it was natural that each group of researchers or developers would “assemble” the Lisp environment that best suited their needs. After several decades of this the various dialects of Lisp were basically incompatible. The 1980s saw the desire to replace these multiple dialects with a core Common Lisp, which also included an object system, CLOS. Common Lisp is the Lisp language used in Part III. But the primary power of Lisp is the fact, as pointed out many times in Part III, that the data and commands of this language have a uniform structure. This supports the building of what we call meta-interpreters, or similarly, the use of meta-linguistic abstraction. This, simply put, is the ability of the program designer to build interpreters within Lisp (or Prolog) to interpret other suitably designed structures in the language. -
A Separate Compilation Extension to Standard ML
A Separate Compilation Extension to Standard ML David Swasey Tom Murphy VII Karl Crary Robert Harper Carnegie Mellon {swasey,tom7,crary,rwh}@cs.cmu.edu Abstract The goal of this work is to consolidate and synthesize previ- We present an extension to Standard ML, called SMLSC, to support ous work on compilation management for ML into a formally de- separate compilation. The system gives meaning to individual pro- fined extension to the Standard ML language. The extension itself gram fragments, called units. Units may depend on one another in a is syntactically and conceptually very simple. A unit is a series of way specified by the programmer. A dependency may be mediated Standard ML top-level declarations, given a name. To the current by an interface (the type of a unit); if so, the units can be compiled top-level declarations such as structure and functor we add an separately. Otherwise, they must be compiled in sequence. We also import declaration that is to units what the open declaration is to propose a methodology for programming in SMLSC that reflects structures. An import declaration may optionally specify an inter- code development practice and avoids syntactic repetition of inter- face for the unit, in which case we are able to compile separately faces. The language is given a formal semantics, and we argue that against that dependency; with no interface we must compile incre- this semantics is implementable in a variety of compilers. mentally. Compatibility with existing compilers, including whole- program compilers, is assured by making no commitment to the Categories and Subject Descriptors D.3.3 [Programming Lan- precise meaning of “compile” and “link”—a compiler is free to guages]: Language Constructs and Features—Modules, pack- limit compilation to elaboration and type checking, and to perform ages; D.3.1 [Programming Languages]: Formal Definitions and code generation as part of linking. -
Drscheme: a Pedagogic Programming Environment for Scheme
DrScheme A Pedagogic Programming Environment for Scheme Rob ert Bruce Findler Cormac Flanagan Matthew Flatt Shriram Krishnamurthi and Matthias Felleisen Department of Computer Science Rice University Houston Texas Abstract Teaching intro ductory computing courses with Scheme el evates the intellectual level of the course and thus makes the sub ject more app ealing to students with scientic interests Unfortunatelythe p o or quality of the available programming environments negates many of the p edagogic advantages Toovercome this problem wehavedevel op ed DrScheme a comprehensive programming environmentforScheme It fully integrates a graphicsenriched editor a multilingua l parser that can pro cess a hierarchyofsyntactically restrictivevariants of Scheme a functional readevalprint lo op and an algebraical ly sensible printer The environment catches the typical syntactic mistakes of b eginners and pinp oints the exact source lo cation of runtime exceptions DrScheme also provides an algebraic stepp er a syntax checker and a static debugger The rst reduces Scheme programs including programs with assignment and control eects to values and eects The to ol is useful for explainin g the semantics of linguistic facilities and for studying the b ehavior of small programs The syntax c hecker annotates programs with fontandcolorchanges based on the syntactic structure of the pro gram It also draws arrows on demand that p oint from b ound to binding o ccurrences of identiers The static debugger roughly sp eaking pro vides a typ e inference system -
Purely Functional Data Structures
Purely Functional Data Structures Chris Okasaki September 1996 CMU-CS-96-177 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy. Thesis Committee: Peter Lee, Chair Robert Harper Daniel Sleator Robert Tarjan, Princeton University Copyright c 1996 Chris Okasaki This research was sponsored by the Advanced Research Projects Agency (ARPA) under Contract No. F19628- 95-C-0050. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of ARPA or the U.S. Government. Keywords: functional programming, data structures, lazy evaluation, amortization For Maria Abstract When a C programmer needs an efficient data structure for a particular prob- lem, he or she can often simply look one up in any of a number of good text- books or handbooks. Unfortunately, programmers in functional languages such as Standard ML or Haskell do not have this luxury. Although some data struc- tures designed for imperative languages such as C can be quite easily adapted to a functional setting, most cannot, usually because they depend in crucial ways on as- signments, which are disallowed, or at least discouraged, in functional languages. To address this imbalance, we describe several techniques for designing functional data structures, and numerous original data structures based on these techniques, including multiple variations of lists, queues, double-ended queues, and heaps, many supporting more exotic features such as random access or efficient catena- tion. In addition, we expose the fundamental role of lazy evaluation in amortized functional data structures. -
Functional Programming (ML)
Functional Programming CSE 215, Foundations of Computer Science Stony Brook University http://www.cs.stonybrook.edu/~cse215 Functional Programming Function evaluation is the basic concept for a programming paradigm that has been implemented in functional programming languages. The language ML (“Meta Language”) was originally introduced in the 1970’s as part of a theorem proving system, and was intended for describing and implementing proof strategies. Standard ML of New Jersey (SML) is an implementation of ML. The basic mode of computation in SML is the use of the definition and application of functions. 2 (c) Paul Fodor (CS Stony Brook) Install Standard ML Download from: http://www.smlnj.org Start Standard ML: Type ''sml'' from the shell (run command line in Windows) Exit Standard ML: Ctrl-Z under Windows Ctrl-D under Unix/Mac 3 (c) Paul Fodor (CS Stony Brook) Standard ML The basic cycle of SML activity has three parts: read input from the user, evaluate it, print the computed value (or an error message). 4 (c) Paul Fodor (CS Stony Brook) First SML example SML prompt: - Simple example: - 3; val it = 3 : int The first line contains the SML prompt, followed by an expression typed in by the user and ended by a semicolon. The second line is SML’s response, indicating the value of the input expression and its type. 5 (c) Paul Fodor (CS Stony Brook) Interacting with SML SML has a number of built-in operators and data types. it provides the standard arithmetic operators - 3+2; val it = 5 : int The Boolean values true and false are available, as are logical operators such as not (negation), andalso (conjunction), and orelse (disjunction).