Pattern Matching in Concatenative Programming Languages
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Administering Unidata on UNIX Platforms
C:\Program Files\Adobe\FrameMaker8\UniData 7.2\7.2rebranded\ADMINUNIX\ADMINUNIXTITLE.fm March 5, 2010 1:34 pm Beta Beta Beta Beta Beta Beta Beta Beta Beta Beta Beta Beta Beta Beta Beta Beta UniData Administering UniData on UNIX Platforms UDT-720-ADMU-1 C:\Program Files\Adobe\FrameMaker8\UniData 7.2\7.2rebranded\ADMINUNIX\ADMINUNIXTITLE.fm March 5, 2010 1:34 pm Beta Beta Beta Beta Beta Beta Beta Beta Beta Beta Beta Beta Beta Notices Edition Publication date: July, 2008 Book number: UDT-720-ADMU-1 Product version: UniData 7.2 Copyright © Rocket Software, Inc. 1988-2010. All Rights Reserved. Trademarks The following trademarks appear in this publication: Trademark Trademark Owner Rocket Software™ Rocket Software, Inc. Dynamic Connect® Rocket Software, Inc. RedBack® Rocket Software, Inc. SystemBuilder™ Rocket Software, Inc. UniData® Rocket Software, Inc. UniVerse™ Rocket Software, Inc. U2™ Rocket Software, Inc. U2.NET™ Rocket Software, Inc. U2 Web Development Environment™ Rocket Software, Inc. wIntegrate® Rocket Software, Inc. Microsoft® .NET Microsoft Corporation Microsoft® Office Excel®, Outlook®, Word Microsoft Corporation Windows® Microsoft Corporation Windows® 7 Microsoft Corporation Windows Vista® Microsoft Corporation Java™ and all Java-based trademarks and logos Sun Microsystems, Inc. UNIX® X/Open Company Limited ii SB/XA Getting Started The above trademarks are property of the specified companies in the United States, other countries, or both. All other products or services mentioned in this document may be covered by the trademarks, service marks, or product names as designated by the companies who own or market them. License agreement This software and the associated documentation are proprietary and confidential to Rocket Software, Inc., are furnished under license, and may be used and copied only in accordance with the terms of such license and with the inclusion of the copyright notice. -
A New Macro-Programming Paradigm in Data Collection Sensor Networks
SpaceTime Oriented Programming: A New Macro-Programming Paradigm in Data Collection Sensor Networks Hiroshi Wada and Junichi Suzuki Department of Computer Science University of Massachusetts, Boston Program Insert This paper proposes a new programming paradigm for wireless sensor networks (WSNs). It is designed to significantly reduce the complexity of WSN programming by providing a new high- level programming abstraction to specify spatio-temporal data collections in a WSN from a global viewpoint as a whole rather than sensor nodes as individuals. Abstract SpaceTime Oriented Programming (STOP) is new macro-programming paradigm for wireless sensor networks (WSNs) in that it supports both spatial and temporal aspects of sensor data and it provides a high-level abstraction to express data collections from a global viewpoint for WSNs as a whole rather than sensor nodes as individuals. STOP treats space and time as first-class citizens and combines them as spacetime continuum. A spacetime is a three dimensional object that consists of a two special dimensions and a time playing the role of the third dimension. STOP allows application developers to program data collections to spacetime, and abstracts away the details in WSNs, such as how many nodes are deployed in a WSN, how nodes are connected with each other, and how to route data queries in a WSN. Moreover, STOP provides a uniform means to specify data collections for the past and future. Using the STOP application programming interfaces (APIs), application programs (STOP macro programs) can obtain a “snapshot” space at a given time in a given spatial resolutions (e.g., a space containing data on at least 60% of nodes in a give spacetime at 30 minutes ago.) and a discrete set of spaces that meet specified spatial and temporal resolutions. -
LATEX for Beginners
LATEX for Beginners Workbook Edition 5, March 2014 Document Reference: 3722-2014 Preface This is an absolute beginners guide to writing documents in LATEX using TeXworks. It assumes no prior knowledge of LATEX, or any other computing language. This workbook is designed to be used at the `LATEX for Beginners' student iSkills seminar, and also for self-paced study. Its aim is to introduce an absolute beginner to LATEX and teach the basic commands, so that they can create a simple document and find out whether LATEX will be useful to them. If you require this document in an alternative format, such as large print, please email [email protected]. Copyright c IS 2014 Permission is granted to any individual or institution to use, copy or redis- tribute this document whole or in part, so long as it is not sold for profit and provided that the above copyright notice and this permission notice appear in all copies. Where any part of this document is included in another document, due ac- knowledgement is required. i ii Contents 1 Introduction 1 1.1 What is LATEX?..........................1 1.2 Before You Start . .2 2 Document Structure 3 2.1 Essentials . .3 2.2 Troubleshooting . .5 2.3 Creating a Title . .5 2.4 Sections . .6 2.5 Labelling . .7 2.6 Table of Contents . .8 3 Typesetting Text 11 3.1 Font Effects . 11 3.2 Coloured Text . 11 3.3 Font Sizes . 12 3.4 Lists . 13 3.5 Comments & Spacing . 14 3.6 Special Characters . 15 4 Tables 17 4.1 Practical . -
Factor — Factor Analysis
Title stata.com factor — Factor analysis Description Quick start Menu Syntax Options for factor and factormat Options unique to factormat Remarks and examples Stored results Methods and formulas References Also see Description factor and factormat perform a factor analysis of a correlation matrix. The commands produce principal factor, iterated principal factor, principal-component factor, and maximum-likelihood factor analyses. factor and factormat display the eigenvalues of the correlation matrix, the factor loadings, and the uniqueness of the variables. factor expects data in the form of variables, allows weights, and can be run for subgroups. factormat is for use with a correlation or covariance matrix. Quick start Principal-factor analysis using variables v1 to v5 factor v1 v2 v3 v4 v5 As above, but retain at most 3 factors factor v1-v5, factors(3) Principal-component factor analysis using variables v1 to v5 factor v1-v5, pcf Maximum-likelihood factor analysis factor v1-v5, ml As above, but perform 50 maximizations with different starting values factor v1-v5, ml protect(50) As above, but set the seed for reproducibility factor v1-v5, ml protect(50) seed(349285) Principal-factor analysis based on a correlation matrix cmat with a sample size of 800 factormat cmat, n(800) As above, retain only factors with eigenvalues greater than or equal to 1 factormat cmat, n(800) mineigen(1) Menu factor Statistics > Multivariate analysis > Factor and principal component analysis > Factor analysis factormat Statistics > Multivariate analysis > Factor and principal component analysis > Factor analysis of a correlation matrix 1 2 factor — Factor analysis Syntax Factor analysis of data factor varlist if in weight , method options Factor analysis of a correlation matrix factormat matname, n(#) method options factormat options matname is a square Stata matrix or a vector containing the rowwise upper or lower triangle of the correlation or covariance matrix. -
Functional and Logic Programming - Wolfgang Schreiner
COMPUTER SCIENCE AND ENGINEERING - Functional and Logic Programming - Wolfgang Schreiner FUNCTIONAL AND LOGIC PROGRAMMING Wolfgang Schreiner Research Institute for Symbolic Computation (RISC-Linz), Johannes Kepler University, A-4040 Linz, Austria, [email protected]. Keywords: declarative programming, mathematical functions, Haskell, ML, referential transparency, term reduction, strict evaluation, lazy evaluation, higher-order functions, program skeletons, program transformation, reasoning, polymorphism, functors, generic programming, parallel execution, logic formulas, Horn clauses, automated theorem proving, Prolog, SLD-resolution, unification, AND/OR tree, constraint solving, constraint logic programming, functional logic programming, natural language processing, databases, expert systems, computer algebra. Contents 1. Introduction 2. Functional Programming 2.1 Mathematical Foundations 2.2 Programming Model 2.3 Evaluation Strategies 2.4 Higher Order Functions 2.5 Parallel Execution 2.6 Type Systems 2.7 Implementation Issues 3. Logic Programming 3.1 Logical Foundations 3.2. Programming Model 3.3 Inference Strategy 3.4 Extra-Logical Features 3.5 Parallel Execution 4. Refinement and Convergence 4.1 Constraint Logic Programming 4.2 Functional Logic Programming 5. Impacts on Computer Science Glossary BibliographyUNESCO – EOLSS Summary SAMPLE CHAPTERS Most programming languages are models of the underlying machine, which has the advantage of a rather direct translation of a program statement to a sequence of machine instructions. Some languages, however, are based on models that are derived from mathematical theories, which has the advantages of a more concise description of a program and of a more natural form of reasoning and transformation. In functional languages, this basis is the concept of a mathematical function which maps a given argument values to some result value. -
SQL Processing with SAS® Tip Sheet
SQL Processing with SAS® Tip Sheet This tip sheet is associated with the SAS® Certified Professional Prep Guide Advanced Programming Using SAS® 9.4. For more information, visit www.sas.com/certify Basic Queries ModifyingBasic Queries Columns PROC SQL <options>; SELECT col-name SELECT column-1 <, ...column-n> LABEL= LABEL=’column label’ FROM input-table <WHERE expression> SELECT col-name <GROUP BY col-name> FORMAT= FORMAT=format. <HAVING expression> <ORDER BY col-name> <DESC> <,...col-name>; Creating a SELECT col-name AS SQL Query Order of Execution: new column new-col-name Filtering Clause Description WHERE CALCULATED new columns new-col-name SELECT Retrieve data from a table FROM Choose and join tables Modifying Rows WHERE Filter the data GROUP BY Aggregate the data INSERT INTO table SET column-name=value HAVING Filter the aggregate data <, ...column-name=value>; ORDER BY Sort the final data Inserting rows INSERT INTO table <(column-list)> into tables VALUES (value<,...value>); INSERT INTO table <(column-list)> Managing Tables SELECT column-1<,...column-n> FROM input-table; CREATE TABLE table-name Eliminating SELECT DISTINCT CREATE TABLE (column-specification-1<, duplicate rows col-name<,...col-name> ...column-specification-n>); WHERE col-name IN DESCRIBE TABLE table-name-1 DESCRIBE TABLE (value1, value2, ...) <,...table-name-n>; WHERE col-name LIKE “_string%” DROP TABLE table-name-1 DROP TABLE WHERE col-name BETWEEN <,...table-name-n>; Filtering value AND value rows WHERE col-name IS NULL WHERE date-value Managing Views “<01JAN2019>”d WHERE time-value “<14:45:35>”t CREATE VIEW CREATE VIEW table-name AS query; WHERE datetime-value “<01JAN201914:45:35>”dt DESCRIBE VIEW view-name-1 DESCRIBE VIEW <,...view-name-n>; Remerging Summary Statistics DROP VIEW DROP VIEW view-name-1 <,...view-name-n>; SELECT col-name, summary function(argument) FROM input table; Copyright © 2019 SAS Institute Inc. -
(LS-Factor) for Modeling Soil Erosion by Water
Geosciences 2015, 5, 117-126; doi:10.3390/geosciences5020117 OPEN ACCESS geosciences ISSN 2076-3263 www.mdpi.com/journal/geosciences Short Communication A New European Slope Length and Steepness Factor (LS-Factor) for Modeling Soil Erosion by Water Panos Panagos 1,*, Pasquale Borrelli 1,† and Katrin Meusburger 2,† 1 European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via Enrico Fermi 2749, I-21027 Ispra (VA), Italy; E-Mail: [email protected] 2 Environmental Geosciences, University of Basel, Bernoullistrasse 30, 4056 Basel, Switzerland; E-Mail: [email protected] † These authors contributed equally to this work. * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +39-0332-785574; Fax: +39-0332-786394. Academic Editor: Jesus Martinez-Frias Received: 24 December 2014 / Accepted: 23 March 2015 / Published: 3 April 2015 Abstract: The Universal Soil Loss Equation (USLE) model is the most frequently used model for soil erosion risk estimation. Among the six input layers, the combined slope length and slope angle (LS-factor) has the greatest influence on soil loss at the European scale. The S-factor measures the effect of slope steepness, and the L-factor defines the impact of slope length. The combined LS-factor describes the effect of topography on soil erosion. The European Soil Data Centre (ESDAC) developed a new pan-European high-resolution soil erosion assessment to achieve a better understanding of the spatial and temporal patterns of soil erosion in Europe. The LS-calculation was performed using the original equation proposed by Desmet and Govers (1996) and implemented using the System for Automated Geoscientific Analyses (SAGA), which incorporates a multiple flow algorithm and contributes to a precise estimation of flow accumulation. -
5 Command Line Functions by Barbara C
ADAPS: Chapter 5. Command Line Functions 5 Command Line Functions by Barbara C. Hoopes and James F. Cornwall This chapter describes ADAPS command line functions. These are functions that are executed from the UNIX command line instead of from ADAPS menus, and that may be run manually or by automated means such as “cron” jobs. Most of these functions are NOT accessible from the ADAPS menus. These command line functions are described in detail below. 5.1 Hydra Although Hydra is available from ADAPS at the PR sub-menu, Edit Time Series Data using Hydra (TS_EDIT), it can also be started from the command line. However, to start Hydra outside of ADAPS, a DV or UV RDB file needs to be available to edit. The command is “hydra rdb_file_name.” For a complete description of using Hydra, refer to Section 4.5.2 Edit Time-Series Data using Hydra (TS_EDIT). 5.2 nwrt2rdb This command is used to output rating information in RDB format. It writes RDB files with a table containing the rating equation parameters or the rating point pairs, with all other information contained in the RDB comments. The following arguments can be used with this command: nwrt2rdb -ooutfile -zdbnum -aagency -nstation -dddid -trating_type -irating_id -e (indicates to output ratings in expanded form; it is ignored for equation ratings.) -l loctzcd (time zone code or local time code "LOC") -m (indicates multiple output files.) -r (rounding suppression) Rules • If -o is omitted, nwrt2rdb writes to stdout; AND arguments -n, -d, -t, and -i must be present. • If -o is present, no other arguments are required, and the program will use ADAPS routines to prompt for them. -
Rexx to the Rescue!
Session: G9 Rexx to the Rescue! Damon Anderson Anixter May 21, 2008 • 9:45 a.m. – 10:45 a.m. Platform: DB2 for z/OS Rexx is a powerful tool that can be used in your daily life as an z/OS DB2 Professional. This presentation will cover setup and execution for the novice. It will include Edit macro coding examples used by DBA staff to solve everyday tasks. DB2 data access techniques will be covered as will an example of calling a stored procedure. Examples of several homemade DBA tools built with Rexx will be provided. Damon Anderson is a Senior Technical DBA and Technical Architect at Anixter Inc. He has extensive experience in DB2 and IMS, data replication, ebusiness, Disaster Recovery, REXX, ISPF Dialog Manager, and related third-party tools and technologies. He is a certified DB2 Database Administrator. He has written articles for the IDUG Solutions Journal and presented at IDUG and regional user groups. Damon can be reached at [email protected] 1 Rexx to the Rescue! 5 Key Bullet Points: • Setting up and executing your first Rexx exec • Rexx execs versus edit macros • Edit macro capabilities and examples • Using Rexx with DB2 data • Examples to clone data, compare databases, generate utilities and more. 2 Agenda: • Overview of Anixter’s business, systems environment • The Rexx “Setup Problem” Knowing about Rexx but not knowing how to start using it. • Rexx execs versus Edit macros, including coding your first “script” macro. • The setup and syntax for accessing DB2 • Discuss examples for the purpose of providing tips for your Rexx execs • A few random tips along the way. -
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. -
Belgium a Logic Meta-Programming Framework for Supporting The
Vrije Universiteit Brussel - Belgium Faculty of Sciences In Collaboration with Ecole des Mines de Nantes - France 2003 ERSITEIT IV B N R U U S E S J I E R L V S C S I E A N R B T E IA N VIN TE CERE ECOLE DES MINES DE NANTES A Logic Meta-Programming Framework for Supporting the Refactoring Process A Thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science (Thesis research conducted in the EMOOSE exchange) By: Francisca Mu˜nozBravo Promotor: Prof. Dr. Theo D’Hondt (Vrije Universiteit Brussel) Co-Promotors: Dr. Tom Tourw´eand Dr. Tom Mens (Vrije Universiteit Brussel) Abstract The objective of this thesis is to provide automated support for recognizing design flaws in object oriented code, suggesting proper refactorings and performing automatically the ones selected by the user. Software suffers from inevitable changes throughout the development and the maintenance phase, and this usually affects its internal structure negatively. This makes new changes diffi- cult to implement and the code drifts away from the original design. The introduced structural disorder can be countered by applying refactorings, a kind of code transformation that improves the internal structure without affecting the behavior of the application. There are several tools that support applying refactorings in an automated way, but little help for deciding where to apply a refactoring and which refactoring could be applied. This thesis presents an advanced refactoring tool that provides support for the earlier phases of the refactoring process, by detecting and analyzing bad code smells in a software application, proposing appropriate refactorings that solve these smells, and letting the user decide which one to apply. -
Computing in the ACI Cluster
Computing in the ACI Cluster Ben Seiyon Lee Pennsylvania State University Department of Statistics October 5, 2017 Graduate Workshop 10/5/2017 1 Outline 1 High Performance Computing 2 Accessing ACI 3 Basic Unix Commands 4 Navigation and File Creation 5 SCP clients 6 PBS scripts 7 Running PBS scripts 8 Parallelization 9 Best Practices Graduate Workshop 10/5/2017 2 High Performance Computing High Performance Computing Large number of processors Large memory requirements Large storage requirements Long runtimes ACI-B: Batch Log in to a head node and submit jobs to compute nodes Groups can purchase allocations or use open queue Intel Xeon E5-2680 v2 2.8 GHz, 256 Gb RAM, 20 cores per node Statistics Department has 5 nodes (20 processors per node) Graduate Workshop 10/5/2017 3 Accessing ACI Sign up for an account: ICS-ACI Account Sign-up 2-Factor Authentication Mac Open Terminal ssh into ACI: ssh <username>@aci-b.aci.ics.psu.edu Complete 2 Factor Authentication Windows Open Putty Enter aci-b.aci.ics.psu.edu in the Host Name field Select SSH then X11 and Enable X11 forwarding Select Connection then Data and enter your username in the Auto-login username field Graduate Workshop 10/5/2017 4 Unix Commands Change directories: cd Home Directory: cd Here: cd . Up one directory: cd .. All files in the directory: ls * Wildcards: Test* . *.png Send output to another command: | Write command output to a file: ls > log.txt Create Directory: mkdir cd ~/ work mkdir Workshop mkdir WorkshopB l s Remove Directory: rmdir rmdir WorkshopB l s mkdir WorkshopB Graduate Workshop 10/5/2017 5 Unix Commands Move Files: mv mv file1 .txt ./WorkshopB/ mv ../WorkshopB/file1 .txt ./WorkshopB/file2 .txt Copy Files: cp cp ../WorkshopB/file1 .txt ../WorkshopB/file2 .txt Remove Files: rm rm file1.txt rm −r WorkshopB Access Manual for commands: man man rm q List files: ls l s ls ~/work/Workshop Graduate Workshop 10/5/2017 6 Unix Commands Print the current directoy: pwd pwd Past commands: history h i s t o r y Manage permissions for a file: chmod chmod u=rwx,g=rwx,o=rwx file1 .