latex: more than just academic papers and theses

Introduction Document Types Special Material Wrapping Up

LATEX: More Than Just Academic Papers and Theses

Lim Lian Tze

[email protected]

http://liantze.penguinattack.org/

cbna

Malaysian Open Source Conference 2011

Lim Lian Tze | mosc 2011 1 / 48

[email protected]

http://liantze.penguinattack.org/


Contents

1 What are TEX, LATEX and Friends?

2 Document Types

3 Special Material

4 Wrapping Up



Contents


2 Document Types

3 Special Material

4 Wrapping Up



What are TEX and LATEX, and Friends?

TEX ASCII TeX, /tEx/, /tEk/

A computer typesetting system created by Donald Knuthfor ‘the creation of beautiful books’

LATEX ASCII LaTeX, /"leItEx/, /"leItEk/, /"lA:tEx/, /"lA:tEk/

A document preparation system by Leslie Lamport

Binaries 𝜀-TEX: additional primitives to TEXpdfTEX: additional PDF-related primitivesX ETEX: native UTF-8 input; can access system fontsLuaTEX: includes the Lua scripting engine

Friends BIBTEX, MakeIndex, METAFONT,METAPOST, . . .http://www.ctan.org/what_is_tex.html


http://www.ctan.org/what_is_tex.html


Why?From http://www.ctan.org/what_is_tex.html

Output QualityIt has the best output.

It knows typesetting.

Superior EngineeringIt’s fast.

It’s stable.

It’s not rigid (extensible).

Plain text input.

Many output types.

FreedomIt’s free.

It runs anywhere.

PopularityIt’s the standard (inacademia and science).


http://www.ctan.org/what_is_tex.html


Where Would I Want to Use LATEX?

Documents with complex structures

Lots of mathematics (or other speciVc needs)

When publishers require them

Batch processing

Back-end of other applications



How Do I Use It?

1 Write a plain text LATEX Vle (.tex)

2 Run it through pdflatex or xelatex → PDF output(or latex + dvips + ps2pdf for DVI + PS + PDF)

3 Run bibtex and/or makeindex to process bibliographies, indices

4 Re-run pdflatex to resolve references and pointers



Example .tex File

\documentclass[a4paper,11pt]{article}\author{Lim Lian Tze}\title{An Introductory Paper}\date{\today}\usepackage[english]{babel}

\usepackage[ngerman]{babel}\usepackage[bahasam]{babel}

\begin{document}\maketitle\tableofcontents

\begin{abstract}This paper introduces\ldots\end{abstract}

\section{Introduction}We consider\ldots

\section{State of the Art}We look at\ldots

\subsection{Document Formats}There are many\ldots\end{document}

An Introductory Paper

Lim Lian Tze

June 7, 2011

Contents

1 Introduction 1

2 State of the Art 12.1 Document Formats . . . . . . . . . . . . . . . . . . . . . . . . 1

Abstract

This paper introduces. . .

1 Introduction

We consider. . .

2 State of the Art

We look at. . .

2.1 Document Formats

There are many. . .

1


Lim Lian Tze

7. Juni 2011

Inhaltsverzeichnis

1 Introduction 1


Zusammenfassung


1 Introduction

We consider. . .

2 State of the Art

We look at. . .


There are many. . .

1


Lim Lian Tze

7 Jun 2011

Kandungan

1 Introduction 1


Abstrak


1 Introduction

We consider. . .

2 State of the Art

We look at. . .


There are many. . .

1

pdflatex



Where Do I Get It?

Windows MiKTEX, TEXLive

Un*x, GNU/Linux TEXLive

Mac OS X MacTEX (based on TEXLive)

Installation Use your OS’ package manager(or download manually)

Editors vi, emacs, Texmaker, TeXworks, . . .

LATEX Packages Use MiKTEX or TEXLive’s package manager

Documentation (TEXLive) $ texdoc <package name>

(MiKTEX) $ mthelp <package name>



Easy to Learn, Hard to Master

Customising may not be straightforward (vs word processors)Intentionally so: Style guidelines should be followed strictly

Publisher/organisation provides document class or style VlesUse these to take care of formatting and styling, focus on the content

Fair enough.But where do I learn all the stuU the TEXnicians and TEXperts do?

(There is a learning curve)



Getting Help

Many free tutorials and e-books on the Web (beware of obsolete ones!)Getting to Grips with LATEX. Andy Roberts.http://www.andy-roberts.net/misc/latex/

LATEX: Beautiful Typesetting. Lim Lian Tze.http://liantze.penguinattack.org/latextypesetting.html

LATEX and Friends. M.R.C. van Dongen.http://csweb.ucc.ie/~dongen/LaTeX-and-Friends.pdf

The LATEX WikiBook. http://en.wikibooks.org/wiki/LaTeX

Questions?TEX FAQ. http://www.tex.ac.uk/cgi-bin/texfaq2html

TEX.SX. http://tex.stackexchange.com/

comp.text.tex usenet groupMalaysian LATEX User Group. http://latex-my.blogspot.com/

Arrange for training


http://www.andy-roberts.net/misc/latex/

http://liantze.penguinattack.org/latextypesetting.html

http://csweb.ucc.ie/~dongen/LaTeX-and-Friends.pdf

http://en.wikibooks.org/wiki/LaTeX

http://www.tex.ac.uk/cgi-bin/texfaq2html

http://tex.stackexchange.com/

http://latex-my.blogspot.com/


So, What Can LATEX Do?



Contents


2 Document Types

3 Special Material

4 Wrapping Up



Basic Types

Books

\documentclass{book}

\author{...}

\title{...}

\begin{document}

\maketitle

\chapter{...}

\section{...}

...

\subsection{...}

\end{document}

A Wonderful Read

A. Dummy

3rd June 2011

Chapter 1

Heading on level 0 (chapter)

Hello, here is some text without a meaning. This text should show, how aprinted text will look like at this place. If you read this text, you will get noinformation. Really? Is there no information? Is there a difference betweenthis text and some nonsense like »Huardest gefburn«. Kjift – Never mind!A blind text like this gives you information about the selected font, how theletters are written and the impression of the look. This text should containall letters of the alphabet and it should be written in of the original language.There is no need for a special contents, but the length of words should matchto the language.

1.1 Heading on level 1 (section)


1.1.1 Heading on level 2 (subsection)

Hello, here is some text without a meaning. This text should show, how aprinted text will look like at this place. If you read this text, you will get noinformation. Really? Is there no information? Is there a difference between

3

4 CHAPTER 1. HEADING ON LEVEL 0 (CHAPTER)

this text and some nonsense like »Huardest gefburn«. Kjift – Never mind!A blind text like this gives you information about the selected font, how theletters are written and the impression of the look. This text should containall letters of the alphabet and it should be written in of the original language.There is no need for a special contents, but the length of words should matchto the language.

Heading on level 3 (subsubsection)


Heading on level 4 (paragraph) Hello, here is some text without ameaning. This text should show, how a printed text will look like at thisplace. If you read this text, you will get no information. Really? Is thereno information? Is there a difference between this text and some nonsenselike »Huardest gefburn«. Kjift – Never mind! A blind text like this givesyou information about the selected font, how the letters are written and theimpression of the look. This text should contain all letters of the alphabetand it should be written in of the original language. There is no need for aspecial contents, but the length of words should match to the language.

1.2 Lists

1.2.1 Example for list (itemize)

• First item in a list

• Second item in a list

• Third item in a list

• Fourth item in a list

• Fifth item in a list

1.2. LISTS 5

Example for list (4*itemize)


– First item in a list

∗ First item in a list· First item in a list· Second item in a list

∗ Second item in a list

– Second item in a list


1.2.2 Example for list (enumerate)

1. First item in a list

2. Second item in a list

3. Third item in a list

4. Fourth item in a list

5. Fifth item in a list

Example for list (4*enumerate)


(a) First item in a list

i. First item in a listA. First item in a listB. Second item in a list

ii. Second item in a list

(b) Second item in a list




Basic Types (cont’d)

Articles

\documentclass{article}

\author{...}

\title{...}

\begin{document}

\maketitle

\section{...}

...

\subsection{...}

\end{document}

A Wonderful Read

A. Dummy

3rd June 2011

1 Heading on level 1 (section)Hello, here is some text without a meaning. This text should show, how aprinted text will look like at this place. If you read this text, you will get noinformation. Really? Is there no information? Is there a difference betweenthis text and some nonsense like »Huardest gefburn«. Kjift – Never mind!A blind text like this gives you information about the selected font, how theletters are written and the impression of the look. This text should containall letters of the alphabet and it should be written in of the original language.There is no need for a special contents, but the length of words should matchto the language.

1.1 Heading on level 2 (subsection)


1.1.1 Heading on level 3 (subsubsection)

Hello, here is some text without a meaning. This text should show, how aprinted text will look like at this place. If you read this text, you will get noinformation. Really? Is there no information? Is there a difference betweenthis text and some nonsense like »Huardest gefburn«. Kjift – Never mind!

1

A blind text like this gives you information about the selected font, how theletters are written and the impression of the look. This text should containall letters of the alphabet and it should be written in of the original language.There is no need for a special contents, but the length of words should matchto the language.

Heading on level 4 (paragraph) Hello, here is some text without ameaning. This text should show, how a printed text will look like at thisplace. If you read this text, you will get no information. Really? Is thereno information? Is there a difference between this text and some nonsenselike »Huardest gefburn«. Kjift – Never mind! A blind text like this givesyou information about the selected font, how the letters are written and theimpression of the look. This text should contain all letters of the alphabetand it should be written in of the original language. There is no need for aspecial contents, but the length of words should match to the language.

2 Lists

2.1 Example for list (itemize)






2.1.1 Example for list (4*itemize)


– First item in a list∗ First item in a list

· First item in a list· Second item in a list

∗ Second item in a list– Second item in a list


2

2.2 Example for list (enumerate)



3. Third item in a list

4. Fourth item in a list

5. Fifth item in a list

2.2.1 Example for list (4*enumerate)


(a) First item in a list

i. First item in a listA. First item in a listB. Second item in a list

ii. Second item in a list

(b) Second item in a list


2.3 Example for list (description)

First item in a list

Second item in a list

Third item in a list

Fourth item in a list

Fifth item in a list

2.3.1 Example for list (4*description)



First item in a listFirst item in a list

3

Second item in a listSecond item in a list



4



Journal and Conference Proceedings Articles

IEEE

\documentclass{IEEEtran}

1

A Wonderful ReadA. Dummy

Abstract—Hello, here is some text without a meaning.This text should show, how a printed text will look likeat this place. If you read this text, you will get noinformation. Really? Is there no information? Is therea difference between this text and some nonsense like»Huardest gefburn«. Kjift – Never mind! A blind text likethis gives you information about the selected font, how theletters are written and the impression of the look. This textshould contain all letters of the alphabet and it should bewritten in of the original language. There is no need fora special contents, but the length of words should matchto the language.

I. HEADING ON LEVEL 1 (SECTION)

Hello, here is some text without a meaning. Thistext should show, how a printed text will look likeat this place. If you read this text, you will getno information. Really? Is there no information?Is there a difference between this text and somenonsense like »Huardest gefburn«. Kjift – Nevermind! A blind text like this gives you informationabout the selected font, how the letters are writtenand the impression of the look. This text shouldcontain all letters of the alphabet and it should bewritten in of the original language. There is no needfor a special contents, but the length of words shouldmatch to the language.

A. Heading on level 2 (subsection)

Hello, here is some text without a meaning. Thistext should show, how a printed text will look likeat this place. If you read this text, you will getno information. Really? Is there no information?Is there a difference between this text and somenonsense like »Huardest gefburn«. Kjift – Nevermind! A blind text like this gives you informationabout the selected font, how the letters are writtenand the impression of the look. This text shouldcontain all letters of the alphabet and it should bewritten in of the original language. There is no needfor a special contents, but the length of words shouldmatch to the language.

1) Heading on level 3 (subsubsection): Hello,here is some text without a meaning. This textshould show, how a printed text will look likeat this place. If you read this text, you will getno information. Really? Is there no information?Is there a difference between this text and somenonsense like »Huardest gefburn«. Kjift – Nevermind! A blind text like this gives you informationabout the selected font, how the letters are writtenand the impression of the look. This text shouldcontain all letters of the alphabet and it should bewritten in of the original language. There is no needfor a special contents, but the length of words shouldmatch to the language.

a) Heading on level 4 (paragraph): Hello,here is some text without a meaning. This textshould show, how a printed text will look likeat this place. If you read this text, you will getno information. Really? Is there no information?Is there a difference between this text and somenonsense like »Huardest gefburn«. Kjift – Nevermind! A blind text like this gives you informationabout the selected font, how the letters are writtenand the impression of the look. This text shouldcontain all letters of the alphabet and it should bewritten in of the original language. There is no needfor a special contents, but the length of words shouldmatch to the language.

II. LISTS

A. Example for list (itemize)

• First item in a list• Second item in a list• Third item in a list• Fourth item in a list• Fifth item in a list1) Example for list (4*itemize):• First item in a list

– First item in a list∗ First item in a list

· First item in a list

ACM

\documentclass{sig-alternate}

A Wonderful Read

A. Dummy

ABSTRACTHello, here is some text without a meaning. This text should show,how a printed text will look like at this place. If you read thistext, you will get no information. Really? Is there no informa-tion? Is there a difference between this text and some nonsenselike »Huardest gefburn«. Kjift – Never mind! A blind text like thisgives you information about the selected font, how the letters arewritten and the impression of the look. This text should containall letters of the alphabet and it should be written in of the originallanguage. There is no need for a special contents, but the length ofwords should match to the language.

1. Heading on level 1 (SECTION)Hello, here is some text without a meaning. This text should

show, how a printed text will look like at this place. If you readthis text, you will get no information. Really? Is there no infor-mation? Is there a difference between this text and some nonsenselike »Huardest gefburn«. Kjift – Never mind! A blind text like thisgives you information about the selected font, how the letters arewritten and the impression of the look. This text should containall letters of the alphabet and it should be written in of the originallanguage. There is no need for a special contents, but the length ofwords should match to the language.

1.1 Heading on level 2 (subsection)Hello, here is some text without a meaning. This text should


1.1.1 Heading on level 3 (subsubsection)Hello, here is some text without a meaning. This text should

Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. To copy otherwise, torepublish, to post on servers or to redistribute to lists, requires prior specificpermission and/or a fee.MOSC 2011, July 3–5, 2011, Penang, Malaysia.Copyright 2011 ACM 123-4-56789-012-3/11/0007 ...$10.00.


Heading on level 4 (paragraph).Hello, here is some text without a meaning. This text should


2. Lists

2.1 Example for list (itemize)






2.1.1 Example for list (4*itemize)


– First item in a list

∗ First item in a list· First item in a list· Second item in a list

∗ Second item in a list

– Second item in a list


LLNCS

\documentclass{llncs}

A Wonderful Read

A. Dummy

No Institute Given

Abstract Hello, here is some text without a meaning. This text shouldshow, how a printed text will look like at this place. If you read this text,you will get no information. Really? Is there no information? Is there adifference between this text and some nonsense like »Huardest gefburn«.Kjift – Never mind! A blind text like this gives you information aboutthe selected font, how the letters are written and the impression of thelook. This text should contain all letters of the alphabet and it shouldbe written in of the original language. There is no need for a specialcontents, but the length of words should match to the language.

1 Heading on level 1 (section)

Hello, here is some text without a meaning. This text should show,how a printed text will look like at this place. If you read this text,you will get no information. Really? Is there no information? Is therea difference between this text and some nonsense like »Huardestgefburn«. Kjift – Never mind! A blind text like this gives you infor-mation about the selected font, how the letters are written and theimpression of the look. This text should contain all letters of the al-phabet and it should be written in of the original language. There isno need for a special contents, but the length of words should matchto the language.

1.1 Heading on level 2 (subsection)

Hello, here is some text without a meaning. This text should show,how a printed text will look like at this place. If you read this text,you will get no information. Really? Is there no information? Is therea difference between this text and some nonsense like »Huardestgefburn«. Kjift – Never mind! A blind text like this gives you infor-mation about the selected font, how the letters are written and the



Some Goodies

Quick language-switching with babel

Automatic generation of cross-referencing labels:\section{Introduction}\label{sec:intro}

... We saw in section \ref{sec:intro}...

Automatic generation of lists:\tableofcontents, \listoffigures, \listoftables

Automatic generation of bibliographies and indices:\cite{Knuth:1976}...\bibliography{references.bib}

...the Linux kernel\index{Linux!kernel}... \printindex

Fully hyperlinked PDF with bookmarks: \usepackage{hyperref}

Inclusion of selected pages from other PDFs(while inserting new page headers/footers!)\usepackage{pdfpages}

\includepdf[pages={1,3-5,8},pagecommand=\thispagestyle{plain}]{file.pdf}



University Theses

Universiti Sains Malaysia \documentclass{usmthesis}

WRITING YOUR THESIS WITH LATEX

by

LIM LIAN TZE

Thesis submitted in fulfilment of the requirementsfor the degree of

Master of Science

December 2007

TABLE OF CONTENTS

Acknowledgements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii

Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi

List of Plates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

List of Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii

List of Symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

Abstrak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

CHAPTER 1 – INTRODUCTION: SAMPLES OF BASIC LATEXCOMMANDS

1.1 Some Simple Command Usages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Special Characters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 Useful Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

CHAPTER 2 – CITATIONS AND BIBLIOGRAPHY

2.1 The *.bib File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 Citations using the natbib package. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2.1 Author-Year System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2.2 Numeric System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

CHAPTER 3 – FIGURES, TABLES, EQUATIONS, ALGORITHMS, ETC

3.1 Inserting Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.2 Inserting Plates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.3 Inserting Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

iii

CHAPTER 1

INTRODUCTION: SAMPLES OF BASIC LATEXCOMMANDS

Hello and welcome, fellow Universiti Sains Malaysia (USM) research postgrad! The

usmthesis package and template files were written in the hope that they may help

you prepare your research thesis using LATEX, based on the Institut Pengajian Siswazah

(IPS) requirements (IPS, 2007). Please note that this version is based on the new

guidelines, in force 17 Dec 2007 onwards. (Song, Cai, Lyu and Cai, 2002)

LATEX is powerful and produces beautiful documents. However, there is definitely

a learning curve to it – one that is worth the effort. If you find any errors in these

templates or documents, or have any suggestions or feedback, do e-mail me about it

([email protected]). The author cannot always guarantee prompt response, how-

ever. ©

MiKTEX, my recommended LATEX distribution for Windows, is available on the

CSPC’07 CD. A step-by-step installation walkthrough is available at (Lim, 2009).

1.1 Some Simple Command Usages

There are plenty of free LATEX tutorials online, some of which are listed in the bibli-

ographies or available at http://e-office.cs.usm.my. This sample thesis includes some

examples to do some common tasks. We start with some examples for lists (both bul-

1

REFERENCES

Changsheng, X., Wang, J., Lu, L. and Zhang, Y. (2008). A novel framework forsemantic annotation and personalized retrieval of sports video, Multimedia, IEEETransactions on 10(3): 421–436.

D’Orazio, T., Leo, M., Spagnolo, P., Mazzeo, P. L., Mosca, N., Nitti, M. and Distante,A. (2009). An investigation into the feasibility of real-time soccer offside detec-tion from a multiple camera system, IEEE TRANSACTIONS ON CIRCUITS ANDSYSTEMS FOR VIDEO TECHNOLOGY 19(12): 1804–1818.

D’Orazio, T., Leo, M., Spagnolo, P., Nitti, M., Mosca, N. and Distante, A. (2009).A visual system for real time detection of goal events during soccer matches,Computer Vision and Image Understanding 113(5): 622–632. Computer VisionBased Analysis in Sport Environments.URL: http://www.sciencedirect.com/science/article/B6WCX-4S50K9H-1/2/fe82b213b3ec28e07aef15882eb37538

IPS (2007). A Guide to the Preparation, Submission and Examination of Theses, In-stitute of Graduate Studies, Universiti Sains Malaysia, Penang, Malaysia.

Lim, L. T. (2009). LATEX: Beautiful typesetting, [Online]. [Accessed January 22, 2011].Available from World Wide Web: http://liantze.googlepages.com/latextypesetting.

Mittelbach, F., Goossens, M., Braams, J., Carlisle, D. and Rowley, C. (2004). TheLATEX Companion, Addison-Wesley Series on Tools and Techniques for ComputerTypesetting, 2nd edn, Addison-Wesley, Boston, MA, USA.

Oetiker, T., Partl, H., Hyna, I. and Schlegl, E. (2006). The Not So Short Introductionto LATEX 2ε , 4.2 edn.

Roberts, A. (2005). Getting to grips with LATEX, [Online]. [Accessed January 22,2011]. Available from World Wide Web: http://www.andy-roberts.net/misc/latex/index.html.

Song, J. Q., Cai, M., Lyu, M. R. and Cai, S. J. (2002). A new approach for linerecognition in large-size images using hough transform, Proceedings of the 16thInternational Conference on Pattern Recognition., Vol. 1, pp. 33–36.

24



University Theses (cont’d)

Multimedia University \documentclass{mmuthesis}

THE MMUTHESIS LATEX DOCUMENTCLASS

BY

LIM LIAN TZEB.Sc. (Hons), University of Warwick, United Kingdom

M.Sc., Universiti Sains Malaysia, Malaysia

THESIS SUBMITTED IN FULFILMENT OF THE

REQUIREMENT FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

(by Research)

in the

Faculty of Information Technology

MULTIMEDIA UNIVERSITYMALAYSIA

April 2010

TABLE OF CONTENTS

COPYRIGHT PAGE ii

DECLARATION iii

ACKNOWLEDGEMENTS iv

DEDICATION v

ABSTRACT vi

TABLE OF CONTENTS vii

LIST OF TABLES viii

LIST OF FIGURES ix

PREFACE x

CHAPTER 1: INTRODUCTION, BACKGROUND STORY,MOTIVATIONS 1

1.1 First Test and I need a really long title, please do oblige me won’t you?Just a few more words and yes we’re there 11.1.1 Second Test 1

1.2 Yeah 2

CHAPTER 2: DUMMY CHAPTER 3

APPENDIX A: MANUALS, TECHNICAL SPECIFICATIONS,DOCUMENTATIONS, EXAMPLE SCENARIOS 4

APPENDIX B: TRY 5

REFERENCES 6

GLOSSARY 7

INDEX 8

PUBLICATION LIST 9

vii

CHAPTER 1

INTRODUCTION, BACKGROUND STORY, MOTIVATIONS

1.1 First Test and I need a really long title, please do oblige me won’t you? Justa few more words and yes we’re there

Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Ut purus elit, vestibu-lum ut, placerat ac, adipiscing vitae, felis. Curabitur dictum gravida mauris. Nam arculibero, nonummy eget, consectetuer id, vulputate a, magna. Donec vehicula augueeu neque. Pellentesque habitant morbi tristique senectus et netus et malesuada famesac turpis egestas. Mauris ut leo. Cras viverra metus rhoncus sem. Nulla et lectusvestibulum urna fringilla ultrices. Phasellus eu tellus sit amet tortor gravida placerat.Integer sapien est, iaculis in, pretium quis, viverra ac, nunc. Praesent eget sem vel leoultrices bibendum. Aenean faucibus. Morbi dolor nulla, malesuada eu, pulvinar at,mollis ac, nulla. Curabitur auctor semper nulla. Donec varius orci eget risus. Duisnibh mi, congue eu, accumsan eleifend, sagittis quis, diam. Duis eget orci sit amet orcidignissim rutrum.

Nam dui ligula, fringilla a, euismod sodales, sollicitudin vel, wisi. Morbi auctorlorem non justo. Nam lacus libero, pretium at, lobortis vitae, ultricies et, tellus. Donecaliquet, tortor sed accumsan bibendum, erat ligula aliquet magna, vitae ornare odiometus a mi. Morbi ac orci et nisl hendrerit mollis. Suspendisse ut massa. Cras nec ante.Pellentesque a nulla. Cum sociis natoque penatibus et magnis dis parturient montes,nascetur ridiculus mus. Aliquam tincidunt urna. Nulla ullamcorper vestibulum turpis.Pellentesque cursus luctus mauris.

Test 1

Figure 1.1: First figure. OK?

1.1.1 Second Test

Their (Audibert, 2004) requirements1 are really amazing2 (Budanitsky & Hirst,2006).

1See here, how weird, how to fill out an entire line. See here, how weird, how to fill out an entire line. See here, how weird,how to fill out an entire line. See here, how weird, how to fill out an entire line. See here, how weird, how to fill out an entire line.

2don’t you agree?

1

REFERENCES

[1] Audibert, L. (2004). Word sense disambiguation criteria: a systematic study. In 20th in-ternational conference on computational linguistics (coling 2004) (pp. 910–916). Geneva,Switzerland: COLING.

[2] Budanitsky, A., & Hirst, G. (2006). Evaluating WordNet-based measures of lexical semanticrelatedness. Computational Linguistics, 32(1), 13–47.

6



University Theses (cont’d)

Universiti Malaya \documentclass{umalayathesis}

THE UMALAYATHESIS LATEX DOCUMENT CLASS

LIM LIAN TZE

THESIS SUBMITTED IN FULFILMENTOF THE REQUIREMENTS

FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

INSTITUTE OF POSTGRADUATE STUDIESUNIVERSITY OF MALAYA

KUALA LUMPUR

2010

TABLE OF CONTENTS

ABSTRACT ii

DECLARATION iii

ACKNOWLEDGEMENTS iv

TABLE OF CONTENTS v

LIST OF FIGURES vi

LIST OF TABLES vii

LIST OF SYMBOLS AND ACRONYMS viii

LIST OF APPENDICES ix

PREFACE x

CHAPTER 1: INTRODUCTION, BACKGROUND STORY, MOTIVATIONS 11.1 First Test and I need a really long title, please do oblige me won’t you?

Just a few more words and yes we’re there 11.1.1 Second Test 2

1.2 Yeah 2

CHAPTER 2: DUMMY CHAPTER 5

APPENDICES 6

REFERENCES 9

v

CHAPTER 1

INTRODUCTION, BACKGROUND STORY, MOTIVATIONS

1.1 First Test and I need a really long title, please do oblige me won’t you? Just afew more words and yes we’re there

Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Ut purus elit, vestibulum

ut, placerat ac, adipiscing vitae, felis. Curabitur dictum gravida mauris. Nam arcu libero,

nonummy eget, consectetuer id, vulputate a, magna. Donec vehicula augue eu neque. Pel-

lentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas.

Mauris ut leo. Cras viverra metus rhoncus sem. Nulla et lectus vestibulum urna fringilla

ultrices. Phasellus eu tellus sit amet tortor gravida placerat. Integer sapien est, iaculis

in, pretium quis, viverra ac, nunc. Praesent eget sem vel leo ultrices bibendum. Aenean

faucibus. Morbi dolor nulla, malesuada eu, pulvinar at, mollis ac, nulla. Curabitur auctor

semper nulla. Donec varius orci eget risus. Duis nibh mi, congue eu, accumsan eleifend,

sagittis quis, diam. Duis eget orci sit amet orci dignissim rutrum.

Nam dui ligula, fringilla a, euismod sodales, sollicitudin vel, wisi. Morbi auctor

lorem non justo. Nam lacus libero, pretium at, lobortis vitae, ultricies et, tellus. Donec

aliquet, tortor sed accumsan bibendum, erat ligula aliquet magna, vitae ornare odio metus

a mi. Morbi ac orci et nisl hendrerit mollis. Suspendisse ut massa. Cras nec ante. Pellen-

tesque a nulla. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur

ridiculus mus. Aliquam tincidunt urna. Nulla ullamcorper vestibulum turpis. Pellen-

tesque cursus luctus mauris.

Figure 1.1: First figure. OK?

1

REFERENCES

Audibert, L. (2004). Word sense disambiguation criteria: a systematic study. In 20th International Confer-ence on Computational Linguistics (COLING 2004) (pp. 910–916). Geneva, Switzerland: COLING.

Budanitsky, A., & Hirst, G. (2006). Evaluating WordNet-based measures of lexical semantic relatedness.Computational Linguistics, 32(1), 13–47.

9



Highly ConVgurable Documentsmemoir and KOMA-Script Classes

Sectional headings

Running headers and footers

Good font, colour and illustration choices

http://latex-my.blogspot.com/search/label/bookdesign

Grid Computing ClusterThe Development and Integration of

Grid Services and Applications

Grid@USM2008/09 Report

Edited by Bahari Belaton and Lim Lian TzePlatform for Information & Communication Technology Research

Universiti Sains Malaysia

CONTENTSProject OverviewIntroduction 2Grid@USM: Developing & Integrating Grid Applications & Services 4

Project Milestones 7

Project Expenditures 8

Sub-ProjectsSetting up USM Campus Grid 10

AP Chan Huah Yong, Ang Sin Keat, Tan Chin Min, Kheoh Hooi Leng, Cheng Wai Khuen, M. Muzzammil bin Mohd Salahudin

Dynamic Replica Management in Data Grid Environment 14AP Chan Huah Yong, Aloysius Indrayanto and Muhammad Muzzammil bin Mohd Salahudin

Using Grid Technology to Create a Render-Farm for Blender 3D Animation 18AP Phua Kia Ken and Zafri Muhammad

Grid-enabled Blexisma2 23AP Tang Enya Kong, Lim Lian Tze, Ye Hong Hoe and Dr Didier Schwab

B2B Standards Component Modeling 27Dr Vincent Khoo Kay Teong, Ting Tin Tin, Rinki Yadav, Johnson Foong and Kor Chan Hock

An Automated Java Testing Tool on the Grid 31Dr Kamal Zuhairi Zamli, Dr Nor Ashidi Mat Isa, Mohammed Issam Younis and Saidatul Khatimah binti Said

iNet-Grid 35Prof. Sureswaran Ramadass, AP Rahmat Budiarto, AP Chan Huah Yong and Dr Ahmed M. Manasrah

Grid Application to Wave Front Propagation and Containment of Vector Borne Diseases 40Prof. Koh Hock Lye, Dr Teh Su Yean and Tan Kah Bee

Project ActivitiesOrganised Events 46

Other Events 48Publications 50

Note: The abbreviations Prof. and AP are used for Professor and Associate Professor respectively throughout this book.

Grid@USM:DEVELOPING & INTEGRATINGGRID APPLICATIONS & SERVICES

Image: A simple network. Illustration by ©Svilen Mushkatov (http://www.sxc.hu/profile/svilen001).

USM’S R&D SUSTAINABILITYTo attain the level of excellence as aresearch university (RU), USM in re-cent years has seriously engaged her-self in various efforts to formulateand chart her future research anddevelopment (R&D) directions. Thepinnacle of these efforts was trans-lated into a 2 year strategic plan “USMResearch-Intensive University 2007–2009”.

One of the crucial input elements

for attaining and subsequently sus-taining RU status is the creationof highly conducive research envi-ronment and infrastructure. Gridcomputing is an example of suchinfrastructure, aiming at providingexcellent research facilities by en-suring good computational horse-power to support high impact do-mains in the bio-sciences, physicalsciences, information and communi-cation technology (ICT), Environmentsciences, Education, Arts and others.

Grid computing is by its na-ture highly distributed geographically,consists of highly specialised equip-ment (storage, grid engines and man-agement tools), as well as expensive.It is as such an ideal example of com-mon, core computational resourcesto be created and pooled among as-piring researchers who require highperformance resources.

Equally important, USM needs a fo-cused, holistic and dedicated effort

4

Grid@USM

SLB on utmkblex

LexiconDispenser

PWNAnalyser

PWNAnalyser

LexiconDispenser

PWNAnalyser

Remote Host

LexiconDispenser

PWNAnalyser

Remote Host

LexiconDispenser

PWNAnalyser

Remote Host

LexiconDispenser

Sense-Tagger

SyntacticParser

Remote Host

Figure 3: Blexisma2 agents are deployed on USM Campus Grid nodes using the Globus job submission toolkit.

(a) Diferent meanings of star in context (b) Diferent meanings of school in context

Figure 4: SLB data generated by Blexisma2 agents used to determine most probable meanings of ambiguous lexicalitem. A Web interface to the Sense-Tagger is available at http://utmkblex.usmgrid.myren.net.my:8080/Blexisma2Servlets/CVSenseTagger.

interested to improve its design toa multilingual setting, where morecomplicated cross-lingual phenomenawill have to be considered. We alsohope to improve the agent commu-nication mechanisms to reduce la-tency. Apart from creating moreagents that implement different learn-ing algorithms and heuristics, we arealso planning agents responsible forother NLP tasks such as deep parsing,detecting named entities, etc. In otherwords, we hope to have agents of var-ious responsibilities so that they canbe ‘mixed-and-matched’ to constructnew NLP applications, such as thosementioned in the introduction.

On the performance issues, wemay attempt several solutions for theSLB bottleneck problem mentionedearlier:

Ț hardware (RAM) upgrades,Ț further optimisation of PostgreSQL

server settings,Ț database connection pooling,Ț load balancing,Ț parallelising queries.

PROJECT PUBLICATIONSLim, L. T. & Schwab, D. (2008). Lim-

its of Lexical Semantic Relatedness

with Ontology-based ConceptualVectors. In Proceedings of the 5th In-ternational Workshop on Natural Lan-guage Processing and Cognitive Sci-ence (NLPCS’08). Barcelona, Spain;pp. 153–158.

Schwab, D. & Lim, L. T. (2008). Blex-isma2: a Distributed Agent Frame-work for Constructing a SemanticLexical Database based on Con-ceptual Vectors. In Proceedingsof the International Conference onDistributed Frameworks & Applica-tions 2008 (DFmA 2008). Penang,Malaysia; pp. 102–110.

@G

rid

U

S M

26 GRID-ENABLED BLEXISMA2

Table of Contents

n Conceptual Business Plan 41. Executive Summary 5

2. Company Summary 62.1. Company Background 6

2.2. Business Intent 7

2.3. Start-up Summary 7

3. Service and Product Development 93.1. Technology Overview 9

3.2. Services and Products 10

3.3. Research & Development 13

4. Market Analysis 134.1. Target Market 13

4.2. Market Segmentation 14

4.3. Competitors 17

4.4. SWOT Analysis 19

5. Marketing Plan 205.1. Product Pricing 21

5.2. Sales Strategy 22

5.3. Advertising and Promotional Plan 22

5.4. Sales Forecast 24

6. Management Plan 256.1. Management Team 26

6.2. Human Resources Plan 28

6.3. Job Responsibilities 28

7. Operational Plan 297.1. Location 30

7.2. Production Plan 30

7.3. Service Plan 31

7.4. Distribution Methods 34

7.5. ICT Plan 34

7.6. Suppliers and Vendors 34

7.7. Operational Costs 34

2

B 21 Tingkat Meranti 6, Taman Meranti, 13000 Butterworth, Penang, Malaysia.T +60 (0) 4 123 4567. k [email protected]. m http://texart.penguinattack.org.

TEXAr tTraining

Service and Product Development

3. Service and Product Development

TEXArt Training offers LATEX-related training services and document pre-

paration solutions, catering especially to researchers in Malaysian IHLs in

the company's start-up stage.

3.1. Technology Overview

LATEX is a professional-quality document preparation system. It is open

source, and is both free for use and free of charge.

LATEX is especially suitable for typesetting complex documents with

heavy mathematical material, and is the standard publishing tool in tech-

nical disciplines and many related �elds (Talbert, 2010). Documents pro-

duced with LATEX are of professional publishing quality: the TEX typesetting

engine places lines of text on the page based on complex calculations

and typography rules for that polished professional look (Mittelbach, Goos-

sens, Braams, Carlisle & Rowley, 2004). See Appendix A for sample LATEX

output.

LATEX encourages the writing of well-structured documents. An author

using LATEX therefore focuses on the logical organisation of the contents,rather than being distracted by visual formatting concerns � a common an-noyance with word processors. In other words, LATEX has a clear advantage

over What You See Is What You Get (WYSIWYG) word processors when a

documents grows in complexity and length (Brunton, 2007, Figure 1).

impossible to do

WordÆ

LATEX

document complexity and size

effortandtimeconsumption

Figure 1: Scalability of LATEX and Microsoft WordÆ against document sizeand complexity (Source: Brunton, 2007)

9


TEXAr tTraining

Management Plan

M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M120

2

4

6

8

10

12

Month

Sales(RM1,000)

Beginner Intermediate

Advanced Consultation

Figure 3: Sales forecast for Year 1

Table 11: Sales forecast for �rst 3 years

Category Y1 Y2 Y3

Sales

Training SalesNo. of training seats soldBeg. 450 400 450

Interm. 400 600 700

Adv. 30 60 150

Sales volume (RM)Beg. 22,500 20,000 22,500

Interm. 60,000 90,000 105,000

Adv. 7,500 15,000 37,500

Consultation SalesServices 600 660 720

Total sales 90,600 125,660 165,720

Direct Cost of Sales

Production 396 477 585

Travel 7,200 8,800 11,200

Total cost 7,596 9,277 11,785

6. Management Plan

TEXArt Training is a general partnership owned by Lim Lian Tze andRonald Wong Mun Choong. They own equal parts of the company, and

also share all pro�ts and losses equally.

25


TEXAr tTraining

Projected Monthly Balance Sheet for Year 1

E. Projected Monthly Profit and Loss for Year 1

M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12

Sales 0.00 2500.00 2500.00 7500.00 10000.00 5300.00 10000.00 12500.00 12500.00 10000.00 8750.00 9050.00

Cost of sales 0.00 422.50 422.50 845.00 845.00 422.50 840.50 845.00 845.00 840.50 633.75 633.75

Gross Margin 0.00 2077.50 2077.50 6655.00 9155.00 4877.50 9159.50 11655.00 11655.00 9159.50 8116.25 8416.25

Gross Margin % 0.00 83.10% 83.10% 88.73% 91.55% 92.03% 91.60% 93.24% 93.24% 91.60% 92.76% 93.00%

Operational Costs

Salaries 3865.70 3865.70 3865.70 3865.70 3865.70 3865.70 3865.70 3865.70 3865.70 3865.70 3865.70 3865.70

Rental 150.00 150.00 150.00 150.00 150.00 150.00 150.00 150.00 150.00 150.00 150.00 150.00

Utilities, bills, etc 558.50 558.50 558.50 558.50 558.50 558.50 558.50 558.50 558.50 558.50 558.50 558.50

Total operational costs 4574.20 4574.20 4574.20 4574.20 4574.20 4574.20 4574.20 4574.20 4574.20 4574.20 4574.20 4574.20

Earnings before interest -4574.20 -2496.70 -2496.70 2080.80 4580.80 303.30 4585.30 7080.80 7080.80 4585.30 3542.05 3842.05

Interest 240.00 240.00 240.00 240.00 240.00 240.00 240.00 240.00 240.00 240.00 240.00 240.00

Net Income -4814.20 -2736.70 -2736.70 1840.80 4340.80 63.30 4345.30 6840.80 6840.80 4345.30 3302.05 3602.05

% 0.00 -109.47% -109.47% 24.54% 43.41% 1.19% 43.45% 54.73% 54.73% 43.45% 37.74% 39.80%

F. Projected Monthly Cash Flow for Year 1

M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12

Cash Revenue

Cash from receivables 0.00 2500.00 2500.00 7500.00 10000.00 5000.00 10000.00 12800.00 12500.00 10000.00 8750.00 8750.00

Total Revenue 0.00 2500.00 2500.00 7500.00 10000.00 5000.00 10000.00 12800.00 12500.00 10000.00 8750.00 8750.00

Cash Disbursements

Salary 3865.70 3865.70 3865.70 3865.70 3865.70 3865.70 3865.70 3865.70 3865.70 3865.70 3865.70 3865.70

Rental 150.00 150.00 150.00 150.00 150.00 150.00 150.00 150.00 150.00 150.00 150.00 150.00

Utilities, bills, etc. 0.00 558.50 558.50 558.50 558.50 558.50 558.50 558.50 558.50 558.50 558.50 558.50

Loan repayment 440.00 440.00 440.00 440.00 440.00 440.00 440.00 440.00 440.00 440.00 440.00 440.00

Sales costs 0.00 422.50 422.50 845.00 845.00 422.50 840.50 845.00 845.00 840.50 633.75 633.75

Total Disbursements 4455.70 5436.70 5436.70 5859.20 5859.20 5436.70 5854.70 5859.20 5859.20 5854.70 5647.95 5647.95

Opening Cash Bal. 21670.00 17214.30 14277.60 11340.90 12981.70 17122.50 16685.80 20831.10 27771.90 34412.70 38558.00 41660.05

Net Cash Flow -4455.70 -2936.70 -2936.70 1640.80 4140.80 -436.70 4145.30 6940.80 6640.80 4145.30 3102.05 3102.05

Closing Cash Bal. 17214.30 14277.60 11340.90 12981.70 17122.50 16685.80 20831.10 27771.90 34412.70 38558.00 41660.05 44762.10

G. Projected Monthly Balance Sheet for Year 1

Starting Balance M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12

Assets

Current assetsCash 21670.00 17214.30 14277.60 11340.90 12981.70 17122.50 16685.80 20831.10 27771.90 34412.70 38558.00 41660.05 44762.10

Accounts receivable 0.00 0.00 0.00 0.00 0.00 0.00 300.00 300.00 0.00 0.00 0.00 0.00 300.00

Total current assets 21670.00 17214.30 14277.60 11340.90 12981.70 17122.50 16985.80 21131.10 27771.90 34412.70 38558.00 41660.05 45062.10

Long term assets 8780.00 8780.00 8780.00 8780.00 8780.00 8780.00 8780.00 8780.00 8780.00 8780.00 8780.00 8780.00 8780.00

Total assets 30450.00 25994.30 23057.60 20120.90 21761.70 25902.50 25765.80 29911.10 36551.90 43192.70 47338.00 50440.05 53842.10

Liabilities

Accounts payable 0.00 558.50 558.50 558.50 558.50 558.50 558.50 558.50 558.50 558.50 558.50 558.50 558.50

Current borrowing 12000.00 11800.00 11600.00 11400.00 11200.00 11000.00 10800.00 10600.00 10400.00 10200.00 10000.00 9800.00 9600.00

Total liabilities 12000.00 12358.50 12158.50 11958.50 11758.50 11558.50 11358.50 11158.50 10958.50 10758.50 10558.50 10358.50 10158.50

Owner's Equity

Paid-in capital 20000.00 20000.00 20000.00 20000.00 20000.00 20000.00 20000.00 20000.00 20000.00 20000.00 20000.00 20000.00 20000.00

Retained Earnings -1550.00 -6364.20 -9100.90 -11837.60 -9996.80 -5656.00 -5592.70 -1247.40 5593.40 12434.20 16779.50 20081.55 23683.60

Net Equity 18450.00 13635.80 10899.10 8162.40 10003.20 14344.00 14407.30 18752.60 25593.40 32434.20 36779.50 40081.55 43683.60

Total Liab. & Eq. 30450.00 25994.30 23057.60 20120.90 21761.70 25902.50 25765.80 29911.10 36551.90 43192.70 47338.00 50440.05 53842.10

50


TEXAr tTraining


http://latex-my.blogspot.com/search/label/bookdesign


Presentation Slides

This presentation was made with LATEX!

Many possible classes: powerdot, beamer

\documentclass{beamer}\usetheme{Warsaw

SzegedBergenoxygen

}

\author ...

\begin{document}\titleframe

\section{Intro}

\begin{frame}\frametitle{Some Background}...\end{frame}\end{document}

Intro

A First Presentation

Lim Lian Tze

June 3, 2011

Lim Lian Tze A First Presentation

Intro


Lim Lian Tze

June 3, 2011



Who? Lim Lian Tze

When? June 3, 2011

Intro


Lim Lian Tze

June 3, 2011

Lim Lian Tze — A First Presentation 1/2

Intro

Some Background

Once upon a time

There were programmers

Lim Lian Tze A First Presentation

Intro

Some Background

◮ Once upon a time

◮ There were programmers


Some Background

Once upon a time


Intro

Some Background

Once upon a time


Lim Lian Tze — A First Presentation 2/2



Oversized Posters

Many possible solutions: sciposter, flowfram, beamerposter

\documentclass{beamer}\usepackage[orientation=portrait,→˓ size=a0]{beamerposter}\usetheme{...}\author ... % Meta−information

\begin{document}\begin{frame}... % Poster contents goes here\end{frame}\end{document}

Low-Cost Construction of aMultilingual Lexicon from Bilingual Lists

Introduction

◮ Bilingual MRDs are good resources for buildingmultilingual lexicons, but heterogeneous structures

◮ Lowest common denominator: list ofsource language item→ target language item(s)

◮ Proposal: Multilingual lexicon construction using onlysimple bilingual lists

One-time Inverse Consultation [1]◮ Generates a bilingual lexicon for new language pair

from existing bilingual lists◮ JP–EN, EN–MS, MS–EN lexicons ⇒ JP–MS

Japanese English Malaymark tanda

p seal anjing lautstamp teraimprintgauge

score(‘tera’) = 2 × |E1 ∩ E2||E1| + |E2|

= 2 × 2

3 + 4= 0.57

∴�p�↔ ‘tera’ is most likely valid

Merging Translation Triples into Sets

◮ (Example: Malay–English–Chinese)◮ Retain OTIC ‘middle’ language links◮ For each ‘head’ language LI, discard triples with score

< αX or score2 < βX, where X = max score of alltriples containing that LI

(garang, ferocious, ö�)(garang, �erce, ö�)

(garang, ööö��) 0.143

(garang, jazzy, ÀÈ)

(garang, ÀÀÀÈÈÈ) 0.125

(garang, bold, 'Æ)

(garang, '''ÆÆÆ) 0.111

(garang, bold, ÑS)

(garang, ÑÑÑSSS) 0.048

(garang, bold, �S)

(garang, ��SSS) 0.048

⋮

◮ Merge all triples with common bilingual pairs

garang

bengkeng�erce

ferocious ö�

Ð→(garang, ferocious,ö�)(garang, �erce,ö�)(bengkeng, �erce,ö�)

References[1]F. Bond and K. Ogura. “Combining linguistic resources to create a

machine-tractable Japanese–Malay dictionary”. In: LanguageResources and Evaluation 42 (2008), pp. 127–136.

Adding a New Language

◮ (Example: Malay–English–Chinese + French)◮ Construct also French–English–Malay triples◮ Add French members to existing M-E-C clusters with

common English & Malay members

garang

bengkeng�erce

ferocious ö�

+ (cruel, ferocious, garang)(féroce, �erce, garang)Ð→

garang

cruelféroce

bengkeng

�erce

ferocious ö�

Precision of 100 Random Translation Sets

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

0.7

0.75

0.8

0.85

β

Precision

α = 0.0α = 0.2α = 0.8

◮ Precision generally around 0.70–0.82; max 0.86

F1 and Rand Index of Selected Translation Sets

◮ Evaluating accuracy of sets with polysemous ‘middle’language members, e.g. ‘plant’, ‘target’Test Rand Index F1 Best accuracy whenword min max min max α β

‘bank’ 0.417 0.611 0.588 0.632 0.6 0.4‘plant’ 0.818 0.927 0.809 0.913 0.6 0.2‘target’ 0.821 1.000 0.902 1.000 0.4 0.2‘letter’ 0.709 0.818 0.724 0.792 0.8 0.2

Discussion and Conclusion

◮ Low thresholds (α, β): more coverage; low precision◮ High thresholds: good precision; low coverage◮ α = 0.6, β = 0.2 gives good trade-oU between

coverage, precision and recall◮ Results are encouraging for such simple input data!◮ Future plan: Integrate lexicon

into an MT systemwith WSD

Lian Tze Lim Bali Ranaivo-Malançon Enya Kong [email protected] {ranaivo,enyakong}@mmu.edu.myNLP-SIG, Faculty of Information Technology, Multimedia University, Malaysia

Limits of Lexical Semantic Relatedness withOntology-based Conceptual Vectors

Lian-Tze Lim (Universiti Sains Malaysia)Didier Schwab (Université Pierre Mendès France)

Introduction

Miller-Charles dataset (1991)◮ Human judgements of similarity between pairs of English words◮ Machine-computed scores can be compared against dataset

We aim to◮ Represent thematic aspects of text (incl. word senses) with Conceptual

Vectors◮ Define semantic relatedness based on conceptual vectors◮ Study behavior of CVs constructed based on ontology◮ . . . by comparing against Miller-Charles dataset

Complementing WordNet with Non-discrete Navigation

��A B�CCDE��A

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�� A��◮ WordNet: explicit semantic

relations between senses◮ Introduce non-discrete

navigation with idea ofneightbourhood

◮ To solve (in part) TennisProblem of WordNet

◮ CVs for WordNet sensesprojects them ontohyperspace

Conceptual Vectors (CV)

Principle and Thematic Distance◮ Inspired by componential semantics

◮ Formalism projecting semantic components into vectorial space◮ CV elements correspond to concepts indirectly◮ Overlap of shared ideas between word senses X & Y :

Thematic proximity: Sim(X ,Y ) = cos(X̂ ,Y ) = X ·Y‖X‖×‖Y‖

Angular distance: DA = arccos(Sim(A,B))

Operations on Vectors◮ Normalised Vectorial Sum (⊕): averages operand vectors◮ Vectorial Term-to-Term Product (⊙): highlights common ideas◮ Weak Contextualisation: emphasizes features shared by two terms,

accentuated by each other

γ(X ,Y ) = X ⊕ (X ⊙ Y )

◮ Synonymy: tests thematic closeness of two meanings X and Y , eachenhanced with what it has in common with a third (C)

SynR(X ,Y ,C) = DA (γ(X ,C), γ(Y ,C))

◮ Partial Synonymy: SynR where the context is the sum of contextualisation ofX and Y of their means

SynP(X ,Y ) = SynR(X ,Y , γ(X ,X ⊕ Y )⊕ γ(Y ,X ⊕ Y ))

Construction◮ Can be computed from definitions from different sources (dictionaries,

synonym lists, hand-crafted indices, . . . )◮ Fabricates new CVs from existing CVs→ requires bootstrap kernel of pre-computed CVs

◮ Emergence from randomised vectors (Lafourcade 2006)◮ dimension of the vector space can be chosen freely◮ more constant lexical density in vector space◮ no prior sense-to-concept mapping required◮ requires much more iterations, time and computing resources

◮ We try with ontology-based CVs first

Constructing Ontology-based CV for WordNet Senses

Vector Construction for Ontology Classes◮ 1992 elements (each correspond to class in SUMO/MILO (not all were used)) in CV◮ For each ontology class C, compute V (C) = (v1(C), v2(C), v3(C), . . .)

◮ Initialise V 0(C) with v0i (C) = 1 if vi corresponds to C, 0 otherwise

◮ Then V (C) with vi(C) = v0i (C) +

∑dim(V )j=1

v0j (C)

2dist(C,Cj)

Constructing Ontology-based CV for WordNet Senses (cont.)

Kernel Vectors for WordNet Synsets◮ For each WN sense s, initialise V 0(s) = V (C); C is the SUMO/MILO class

assigned to s (Niles & Pease, 2003)

Learning CVs for WN Senses◮ Iteratively compute V (s) as in (Schwab et al, 2007) but using V 0(s) based

on eXtended WordNet

Comparison with Miller–Charles (M&C) Set

◮ We define two proximity measures based on CV:proxcv(A,B) = 1 −

(DA(A,B)÷ π

2

)

proxsyn(A,B) = 1 −(SynP(A,B)÷ π

2

)

◮ Take highest proxcv and proxsyn values of WN noun senses

Word Pair M&C proxcv proxsynCorr. with M&Cproxcv proxsyn

automobile car 0.98 1.00 1.00 1.000 1.000cord smile 0.03 0.48 0.57 1.000 1.000glass magician 0.03 0.47 0.57 1.000 1.000gem jewel 0.96 1.00 1.00 1.000 1.000rooster voyage 0.02 0.44 0.53 0.999 0.998magician wizard 0.88 0.90 0.92 0.997 0.997bird crane 0.74 0.82 0.86 0.996 0.996crane implement 0.42 0.60 0.68 0.989 0.991noon string 0.02 0.38 0.47 0.988 0.988bird cock 0.76 0.78 0.83 0.983 0.985coast shore 0.93 0.86 0.89 0.980 0.982journey voyage 0.96 0.85 0.88 0.974 0.976midday noon 0.86 1.00 1.00 0.965 0.968furnace stove 0.78 0.70 0.77 0.950 0.956implement tool 0.74 0.67 0.74 0.936 0.944brother lad 0.42 0.47 0.55 0.925 0.930food rooster 0.22 0.34 0.43 0.920 0.921lad wizard 0.11 0.20 0.30 0.915 0.911asylum madhouse 0.90 0.70 0.76 0.901 0.897food fruit 0.77 0.60 0.69 0.885 0.885boy lad 0.94 0.62 0.70 0.856 0.860monk slave 0.14 0.62 0.69 0.837 0.839car journey 0.29 0.71 0.77 0.818 0.818monk oracle 0.28 0.71 0.77 0.800 0.798coast hill 0.22 0.71 0.77 0.777 0.774forest graveyard 0.21 0.81 0.85 0.735 0.731coast forest 0.11 0.70 0.77 0.711 0.704brother monk 0.71 0.34 0.43 0.644 0.634

Corr. with M&C 0.644 0.634

Discussion

Results are not too impressive. . .

Suitability of WN–SUMO/MILO mappings◮ ֒brother֓–֒monk֓ scored too low because mapped to very different classes

(HUMAN and RELIGIOUSORGANISATION)◮ ֒coast֓–֒hill֓, ֒forest֓–֒graveyard֓ and ֒coast֓–֒forest֓ considered dissimilar

by humans, but all LANDAREA in mapping◮ (Take these away and correlations with M&C rise to 0.800 and 0.798)

Suitability of the M&C Set◮ ֒cars֓ and ֒gasoline֓ are semantically related;֒cars֓ and ֒bicycles֓ are semantically similar (Resnik, 1995)

◮ ֒car֓–֒journey֓: low M&C score; high proxcv and proxsyn scores◮ M&C assesses semantic similarity◮ CVs (via proxcv and proxsyn) assesses semantic relatedness

Conclusion

◮ CVs can model the ideas conveyed by lexical meanings◮ Can be constructed based on ontological sources◮ Can be used to measure lexical semantic relatedness◮ Disadvantage of ontology-based CVs:

◮ non-standard density of the hierarchy◮ different philosophies in mapping lexical senses to ontology classes

Future Work◮ Effects of hierarchy-free CVs i.e. construction by emergence◮ Collect human ratings on lexical semantic relatedness as benchmark

[email protected] Computer-aided Translation Unit, School of Computer Sciences, University Sains Malaysia http://[email protected] GETALP-LIG, Laboratoire d’Informatique de Grenoble, Université Pierre Mendès France http://getalp.imag.fr



LeaWets

leaflet: arrange contents into 6 pages on a foldable double-sided sheet

\documentclass[foldmark,a4paper]{leaflet}\author ... % Meta−information

\begin{document}\maketitle\section ...... % LeaWet contents\end{document}

Discussion

▸ Low thresholds (α, β): more coverage; low precision▸ High thresholds: good precision; low coverage▸ α ≈ 0.6, β ≈ 0.2 gives good trade-o� between coverage,precision and recall▸ Results are encouraging for such simple input data!Especially suitable for under-resourced language pairs▸ Future plan: Integrate multilingual lexicon into anMT

system withWSD and user interaction features

Related Work

▸ Many multilingual lexicon projects [2, 3]) aligned withPrinceton WordNet [4]⊳ Overly �ne sense distinctions in Princeton WordNet▸ Pan Lexicon [5]: compute context vectors of words frommonolingual corpora of di�erent languages, thengrouping into translation sets by matching contextvectors via bilingual lexicons⊳ Sense distinctions derived from corpus evidence⊳ Produces many translation sets that contain

semantically related but not synonymous words,e.g. ‘shoot’ and ‘bullet’ (lower precision)⊳ 44% precision based on evaluators’ opinions (75% ifinter-evaluator agreement is not required)⊳ Does not handle multi-word expressions▸ Markó, Schulz and Hahn [6] use cognate mappings to

derive new translation pairs, validate by processingparallel corpora (medical domain)⊳ Complex terms indexed on the level of sub-words

e.g. ‘pseudo⊕hypo⊕para⊕thyroid⊕ism’⊳ 46% accuracy for each language pair⊳ Requires large aligned thesaurus corpora (easier toacquire for specialised domains?)⊳ Cognate-based approach not applicable for languagepairs that are not closely related▸ Lafourcade [7]: compute contextual vectors for

translation pairs based on gloss text and associated classlabels from semantic hierarchy; compare vectors fromdi�erent bilingual lexicons to detect synonymy⊳ Resource requirements not available for all language

pairs, costly task of assigning class labels

References[1] F. Bond and K. Ogura. “Combining linguistic resources to

create a machine-tractable Japanese–Malay dictionary”. In:Language Resources and Evaluation 42 (2008), pp. 127–136.

[2] P. Vossen. “EuroWordNet: A Multilingual Database ofAutonomous and Language-speci�c Wordnets Connected viaan Inter-Lingual-Index”. In: Special Issue on Multilingual

Databases, International Journal of Linguistics 17.2 (2004).

[3] D. Tu�ş, D. Cristeau, and S. Stamou. “BalkaNet: Aims,Methods, Results and Perspectives – A General Overview”.In: Romanian Journal of Information Science and Technology

Special Issue 7.1 (2004), pp. 9–43.

[4] C. Fellbaum, ed.WordNet: An Electronic Lexical Database.Language, Speech, and Communication. Cambridge,Massachusetts: MIT Press, 1998.

[5] M. Sammer and S. Soderland. “Building asense-distinguished multilingual lexicon from monolingualcorpora and bilingual lexicons”. In: Proceedings of Machine

Translation Summit XI. Copenhagen, Denmark, 2007,pp. 399–406.

[6] K. Markó, S. Schulz, and U. Hahn. “Multilingual LexicalAcquisition by Bootstrapping Cognate Seed Lexicons”. In:Proceedings of the International Conference on Recent

Advances in Natural Language Processing (RANLP) 2005.Borovets, Bulgaria, 2005.

[7] M. Lafourcade. “Automatically Populating Acception LexicalDatabase through Bilingual Dictionaries and ConceptualVectors”. In: Proceedings of PAPILLON-2002. Tokyo, Japan,Aug. 2002.

[8] C. Boitet, M. Mangeot, and G. Sérasset. “�e PAPILLONproject: Cooperatively Building a Multilingual LexicalDatabase to Derive Open Source Dictionaries & Lexicons”.In: Proceedings of the 2nd Workshop on NLP and XML

(NLPXML’02). Taipei, Taiwan, 2002, pp. 1–3.

Contact

Lian Tze Lim [email protected] Ranaivo-Malançon [email protected] Kong Tang [email protected]

NLP-SIG, Faculty of Information TechnologyMultimedia University, Cyberjaya, Malaysia.http://fit.mmu.edu.my/sig/nlp/

Low-Cost Construction of aMultilingual Lexicon from

Bilingual Lists

Lian Tze LimBali Ranaivo-Malançon

Enya Kong Tang

NLP-SIG, Faculty of Information TechnologyMultimedia University, Malaysia

Introduction

▸ BilingualMRDs are good resources for buildingmultilingual lexicons▸ ButMRDs have heterogeneous contents and structures⊳ Not all contain rich information (gloss, domain)

(Especially so for under-resourced languages)⊳ Di�erent structures (sense granularity, distinctions)▸ Lowest common denominator: list of source languageitem→ target language item(s)▸ Construct multilingual lexicon using only bilingual lists

One-time Inverse Consultation [1]

▸ Generates a bilingual lexicon for a new language pairfrom existing bilingual lists▸ Given bilingual lexicons L1–L2, L2–L3, L3–L2, generatebilingual lexicon L1–L3▸ Example: JP–EN, EN–MS, MS–EN lexicons⇒ JP–MS

Japanese English Malay

mark tandap seal anjing laut

stamp teraimprintgauge

score(‘tera’) = 2 × ∣E1 ∩E2∣∣E1∣ + ∣E2∣ = 2 × 2

3 + 4 = 0.57∴�p�↔ ‘tera’ is more likely to be valid

Merging Translation Triples into Sets

▸ Retain OTIC ‘middle’ language links▸ For each ‘head’ language LI, �lter only triples whosescore exceed thresholds (See Algorithm 1)▸ Merge all triples with common bilingual pairs▸ Malay–English–Chinese example:ms–en Kamus Inggeris–Melayu untuk Penterjemahen–zh XDict zh–en CC-CEDICT

(garang, ferocious, ö�)(garang, �erce, ö�)

(garang, ööö��) 0.143

(garang, jazzy, ÀÈ)

(garang, ÀÀÀÈÈÈ) 0.125

(garang, bold, 'Æ)

(garang, '''ÆÆÆ) 0.111

(garang, bold, ÑS)

(garang, ÑÑÑSSS) 0.048

(garang, bold, �S)

(garang, ��SSS) 0.048

⋮

garang

bengkeng

�erce

ferocious ö�

Ð→(garang, ferocious,ö�)(garang, �erce,ö�)(bengkeng, �erce,ö�)

Adding More Languages

▸ Construct L1–L2–L4 triples▸ Add L4 members to existing L1–L2–L3 clusters withcommon L1 & L2 members▸ Example: Malay–English–Chinese + French, using‘ready-made’ triples from FeM

garang

bengkeng

�erce

ferocious ö�

+(cruel, ferocious, garang)(féroce, �erce, garang)Ð→

garang

cruelféroce

bengkeng

�erce

ferocious ö�

Algorithm 1: Generating trilingual translation chains

forall the lexical items wh ∈ L1 do

Wm ← translations of wh in L2

forall the wm ∈Wm do

Wt ← translations of wm in L3

forall the wt ∈Wt do

Output a translation triple (wh ,wm ,wt)Wmr

← translations of wt in L2

score(wh ,wm ,wt) ←∑

w∈Wm

∣common words in wmr∈Wmr

and w|∣words in wmr∈Wmr

∣end

score(wh ,wt) ← 2 × ∑w∈Wmscore(wh ,w ,wt)∣Wm ∣ + ∣Wmr

∣end

X ←maxw t∈Wtscore(wh ,wt)

forall the distinct translation pairs (wh ,wt) doif score(wh ,wt) ≥ αX or (score(wh ,wt))2 ≥ βXthen

Place wh ∈ L1, wm ∈ L2, wt ∈ L3 from all triples(wh ,w . . . ,wt) into same translation setRecord score(wh ,wt) and score(wh ,wm ,wt)

else

Discard all triples (wh ,w . . . ,wt)// The sets are now grouped by(wh ,wt)

end

end

end

Merge all sets containing triples with same (wh ,wm)Merge all sets containing triples with same (wm ,wt)

Algorithm 2: Adding Lk+1 to multilingual lexicon L of{L1 , L2 , . . . , Lk}T ← translation triples of Lk+1 , Lm , Ln generated byAlgorithm 1 where Lm , Ln ∈ {L1 , L2 , . . . , Lk}forall the (wLm

,wLn,wLk+1) ∈ T do

Add wLk+1 to all entries in L that contains both wLmand

wLn

end

Precision of 100 Random Translation Sets

0 0.2 0.4 0.6 0.8

0.7

0.75

0.8

0.85

β

Precision

α = 0.0α = 0.2α = 0.8

▸ Precision increases with threshold parameters α and β▸ Precision generally around 0.70–0.82; max 0.86▸ Most false positives are not ranked at top of the list▸ Many errors caused by incorrect POS assignments

F1 and Rand Index of Selected Translation Sets

▸ False positives will frequently arise when ‘middle’language members are polysemous, e.g. ‘plant’, ‘target’▸ Evaluate accuracy of selected sets with polysemous‘middle’ language members

Precision = TP

TP + FPRecall = TP

TP + FNF1 = 2 × Precision × Recall

Precision + RecallRI = TP + TN

TP + FP + FN + TNTest Rand Index F1 Best accuracy whenword min max min max α β

‘bank’ 0.417 0.611 0.588 0.632 0.6 0.4‘plant’ 0.818 0.927 0.809 0.913 0.6 0.2‘target’ 0.821 1.000 0.902 1.000 0.4 0.2‘letter’ 0.709 0.818 0.724 0.792 0.8 0.2

▸ F1 and RI increases with α and β▸ But may decrease when they are too high and rejectvalid members (false negatives)



Fillable PDF Forms

\usepackage{hyperref}

... % various settings skipped

\TextField{Name:}\\

\TextField{Affiliation:}\\

\ChoiceMenu[radio=true]

{Are you a:}{Student, Academic}\\

Interest:

\CheckBox{Security}

\CheckBox{Systems}

\CheckBox{User space}\\

\TextField[multiline=true]

{Comments:}\\



Fillable PDF Forms (cont’d)

Use with caution!poppler-based viewers (evince, xpdf, okular)

Problem displaying and saving radio/check boxes correctlySaved forms can’t be opened by other viewers

Adobe ReaderCannot save Vlled form as PDF unless Acrobat is installedOnly as Veld-and-value text VleCan provide “Submit” button for submission to a URL

Or print hard copy of Vlled form!

PDF XChange ViewerBest freeware for Vlling and saving LATEX-created formsWindows onlyNot OSS



Flash Cards

\documentclass[avery5388,frame]

{flashcards}

\cardfrontstyle{headings}

\cardfrontfoot{Linux}

\begin{document}

\begin{flashcard}[Security]

{Certificate}

...

\end{flashcard}

\begin{flashcard}[Security]

{MAC ...}

...

\end{flashcard}

\end{document}

Security

CertiVcate

Linux

Security

MAC (Mandatory AccessControl)

Linux

A digital representation of information thatidentiVes you and is issued by Cas, which are

often a trusted third party (TTP).

Access to an object is restricted based on thesensitivity of the object (deVned by the label

that is assigned), and granted throughauthorization (Clearance) to access that level of

data.



Examination Questions

\documentclass{exam}...\begin{questions}\printanswers\question[5]What is Paul McCartney's middle name?\begin{oneparchoices}\choice John \CorrectChoice Paul\choice Ringo \choice James\end{oneparchoices}

\question[10] What was the Beatles' first˓→single in 1962?\begin{solution}Love Me Do\end{solution}

\question\begin{parts}\part[5] What was George's inspiration for˓→`While My Guitar Gently Weeps'?\begin{solution}He opened a random book and saw the words˓→``gently weep''.\end{solution}...\end{questions}

1. (5)What is Paul McCartney’s middle name?

A. John B. Paul C. Ringo D. James

2. (10)What was the Beatles’ Vrst single in 1962?

3. (a) (5)What was George’s inspiration for ‘WhileMy Guitar Gently Weeps’?

(b) (5)Who guest-performed for the song and why?

1. (5)What is Paul McCartney’s middle name?

A. John B. Paul C. Ringo D. James

2. (10)What was the Beatles’ Vrst single in 1962?

Solution: Love Me Do

3. (a) (5)What was George’s inspiration for ‘WhileMy Guitar Gently Weeps’?

Solution: He opened a random bookand saw the words “gently weep”.

(b) (5)Who guest-performed for the song and why?

Solution: Eric Clapton; he wanted aspiUy guitar solo.



Contents


2 Document Types

3 Special Material

4 Wrapping Up



Mathematics

(1) relates the golden ratio and the Fibonacci series.Recall that the golden ratio, 𝜑 = 1

2(1+√5).

𝜑 = 1+∞∑︁

n=1

(−1)n+1

FnFn+1(1)

\eqref{eq:gratio} relates the golden ratio and the Fibonacci series.

Recall that the golden ratio, $\phi = \frac{1}{2} (1 + \sqrt{5})$.

\begin{equation}\label{eq:gratio}

\phi = 1 + \sum^{\infty} _{n=1}

\frac{ (-1)^{n+1} }{ F_n F_{n+1} }

\end{equation}



Chemical Equations and Molecules

Zn2++2OH–

−−−−⇀↽−−−−+2H+

Zn(OH)2 ↓amphoteres Hydroxid

+2OH–

−−−−⇀↽−−−−+2H+

[Zn(OH)4]2–

Hydroxozikat

H C

H

H

C

H

O

\usepackage[version=3]{mhchem} % suXcient for chemical equations

\usepackage{chemfig} % for 2−D molecule drawings

...

\ce{Zn^2+ <=>[\ce{+ 2OH-}][\ce{+ 2H+}]

$\underset{\text{amphoteres Hydroxid}}{\ce{Zn(OH)2 v}}$

<=> C[+2OH-][{+ 2H+}]

$\underset{\text{Hydroxozikat}}{\cf{[Zn(OH)4]^2-}}$ }

\chemfig{H-C(-[2]H)(-[6]H)-C(-[7]H)=[1]O}



Linguistics

(1) %*WenWhom

liebtloves

seinehis

Mutter?mother

‘Who does his mother love?’

(2) [[NP He ] [VP kicked [NP the ball ]]]S

S

NP

Pron

He

VP

V

kicked

NP

Det

the

N

ball

\usepackage{linguex,qtree}...\exg. \%*Wen liebt seine Mutter?\\Whom loves his mother\\`Who does his mother love?'

\exi. [[NP He ] [VP kicked [NP the ball ]]]S

\Tree [ .S [.NP [.Pron He ] ] [.VP [.V kicked ] [.NP [.Det the ] [.N ball ]˓→ ] ] ]



Program Listings

\usepackage{listings,xcolor}

...

\begin{lstlisting}

[language=C,columns=fullflexible,

basicstyle=\ttfamily,

keywordstyle=\bfseries\color{red},

commentstyle=\sffamily\color{green},

stringstyle=\rmfamily\color{orange}]

#include <stdio.h>

/*| Prints "hello world"

*/

int main(void)

{

printf("hello, world\n");

return 0;

}

\end{lstlisting}

#include <stdio.h>

/∗| Prints "hello world"∗/int main(void){

printf("hello, world\n");return 0;

}



Network Protocols

\usepackage{bytefield}

...

\begin{bytefield}{16}

\bitheader{0,7,8,15} \\

\wordgroupr{Header}

\bitbox{4}{Tag} & \bitbox{12}{Mask} \\

\bitbox{8}{Source} &

\bitbox{8}{Destination}

\endwordgroupr \\

\wordbox{3}{Data}

\end{bytefield}

0 7 8 15

Tag Mask

Source Destination

}︃Header

Data



Life Sciences

Vrst case (see text)↓

AQP1.PRO TLGLLLSCQISILRAVMYIIAQCVGAIVASAIL 112AQP2.PRO TVACLVGCHVSFLRAAFYVAAQLLGAVAGAAIL 104AQP3.PRO TFAMCFLAREPWIKLPIYTLAQTLGAFLGAGIV 112AQP4.PRO TVAMVCTRKISIAKSVFYITAQCLGAIIGAGIL 133AQP5.PRO TLALLIGNQISLLRAVFYVAAQLVGAIAGAGIL 105

↑second case (see text)

\usepackage{texshade} % for nucleotide and peptide alignments...\begin{texshade}{AQPpro.MSF}\shadingmode{similar}\threshold[80]{50}\setends{1}{80..112}\hideconsensus\feature{top}{1}{93..93}{fill:$\downarrow$}{first case (see text)}\feature{bottom}{1}{98..98}{fill:$\uparrow$}{second case (see text)}\end{texshade}



Circuits and SI Units

B

20Ω

10Ω

vxS5vx5Ω

A

3.45× 104 V2 lm3 F−1

40 km/h, 85 km/h and 103 km/h

\usepackage{siunitx}\usepackage[siunitx]{circuitikz}...\begin{circuitikz}\draw (0,0) node[anchor=east] {B}to[short, o-*] (1,0) to[R=20<\ohm>, *-*] (1,2)to[R=10<\ohm>, v=$v_x$] (3,2) -- (4,2)to[ cI=$\frac{\si{\siemens}}{5} v_x$, *-*] (4,0) -- (3,0)to[R=5<\ohm>, *-*] (3,2)(3,0) -- (1,0) (1,2) to[short, -o] (0,2) node[anchor=east]{A}

;\end{circuitikz}

\SI{3.45d4}{\square\volt\cubic\lumen\per\farad}\SIlist[per-mode=symbol]{40;85;103}{\kilo\metre\per\hour}



Meh, What Good is That? Can’t Use it Anywhere Else.

Actually, you can.

\usepackage[active,tightpage]{preview}

\PreviewEnvironment{texshade}

...

\begin{texshade}

...

\end{texshade}

Run pdflatex → cropped PDF containing only contents of texshade

gs -otexshade.png -sDEVICE=png16m -r200 -dTextAlphaBits=4

-dGraphicAlphaBits=4 texshade.pdf

Multiple environments → multi-page PDF

Use -otexshade%02d.png to get texshade01.png, texshade02.png, . . .



Bar Codes

9 781860 742712 9 783865 411143

ISBN 978-3-86541-114-3

L E 2 8 H S 9 Z

\usepackage{auto-pst-pdf} % Needed if running pdWatex; must use option−shell−escape

\usepackage{pstricks,pst-barcode}

...

\begin{pspicture}

\psbarcode{MECARD:N:Malaysia Open Source Conference...}{eclevel=L}{qrcode}

\psbarcode{9781860742712}{includetext guardwhitespace}{ean13}

\psbarcode{978-3-86541-114}{includetext guardwhitespace}{isbn}

\psbarcode{LE28HS9Z}{includetext}{royalmail}

\psbarcode{^453^178^121^239}{columns=2 rows=10}{pdf417}

\end{pspicture}



Graph Plots

101 102 103 104

10−5

10−4

10−3

10−2

10−1

Dof

L2Lmax

\usepackage{pgfplots}...\begin{tikzpicture}\begin{loglogaxis}[xlabel=Dof]\addplot table[x=dof,y=L2]{datafile.dat}; \addlegendentry{$L_2$};\addplot table[x=dof,y=Lmax]{datafile.dat}; \addlegendentry{$L_\text{max}$};\end{loglogaxis}\end{tikzpicture}



Spreadsheets(Seriously, use a proper spreadsheet application for complex stuU.)

Year ending Mar 31 2009 2008 2007

Revenue 14580.20 11900.40 8290.30Cost of sales 6740.20 5650.10 4524.20

Gross proVt 7840.00 6250.30 3766.10

\STautoround*{2}

\begin{spreadtab}{{tabular}{l rrr}}

@Year ending Mar 31 & @2009 & @2008 & @2007\\ \hline

@Revenue & 14580.2 & 11900.4 & 8290.3\\

@Cost of sales & 6740.2 & 5650.1 & 4524.2\\ \cline{2-4}

@\emph{Gross profit} & \STcopy{>}{b2-b3} & &\\ \cline{2-4}

\end{spreadtab}



Gantt Charts2010 2011

Preliminary Project 100% complete

Objective 1Task ATask B

Objective 2Task ATask B

\usepackage{pgfgantt}...\begin{tikzpicture}\begin{ganttchart}[...settings...]{16}\gantttitle{2010}{4} \gantttitle{2011}{12} \\\ganttbar[progress=100]{Preliminary Project}{1}{4} \\\ganttlink[link mid=.4]{4}{2}{5}{4} \ganttlink[link mid=.159]{4}{2}{5}{7}\ganttgroup{Objective 1}{5}{16} \\\ganttbar[progress=4]{Task A}{5}{10} \\\ganttlinkedbar[progress=0]{Task B}{11}{16} \\...\end{ganttchart}\end{tikzpicture}



Chess games

\usepackage[skaknew]%

{skak,chessboard}

...

\newgame

\mainline{1. e4 e5 2. Nf3 Nc6 3.

→˓Bb5 a6}

\chessboard[smallboard]

1 e4 e5 2 Nf3 Nc6 3 Bb5 a6

8 rZblkans7 ZpopZpop6 pZnZ0Z0Z5 ZBZ0o0Z04 0Z0ZPZ0Z3 Z0Z0ZNZ02 POPO0OPO1 SNAQJ0ZR

a b c d e f g h�



Crossword Puzzles

1

2 3 4

5

Across: 1 unit of measure2 * 5 sectioning unit

Down: 1 𝜂 3 unit ofmeasure 4 nonproportionalfont

\usepackage{cwpuzzle}

...

\begin{Puzzle}{5}{3}

|* |* |[1]E|X |* |.

|[2]A|[3]S|T |* |[4]T|.

|* |[5]P|A |R |T |.

\end{Puzzle}

\begin{PuzzleClues}{

\textbf{Across:} }

\Clue{1}{EX}{unit of measure}

\Clue{2}{AST}{$\ast$}

\Clue{5}{PART}{sectioning unit}

\end{PuzzleClues}

\begin{PuzzleClues}{

\textbf{Down:} }

\Clue{1}{ETA}{$\eta$}

\Clue{3}{SP}{unit of measure}

\Clue{4}{TT}{nonproportional font}

\end{PuzzleClues}



Song Books with Guitar Tabs

CountryC

road, take meG

home, to theAmplace I be

Flong.

West VirC

ginia, mountainG

momma, take meF

home, countryC

road.

\usepackage{gchords,guitar}

...

\begin{guitar}

\newcommand{\CMaj}{\chord{t}{n,p3,p2,n,p1,n}{C}}

\newcommand{\Amin}...

Country [\CMaj]road, take me [\GMaj]home, ...

\end{guitar}



Contents


2 Document Types

3 Special Material

4 Wrapping Up



Summary

LATEXa document preparation systemprofessional quality typesetting output

Output artefactsAcademic: papers, theses, booksDedicated document typesDomain-speciVc material

Usage scenarioDirect authoringAutomatic generation (via scripts etc)As back-end of other applications



Getting Help

Many free tutorials and e-books on the Web (beware of obsolete ones!)Getting to Grips with LATEX. Andy Roberts.http://www.andy-roberts.net/misc/latex/

LATEX: Beautiful Typesetting. Lim Lian Tze.http://liantze.penguinattack.org/latextypesetting.html

LATEX and Friends. M.R.C. van Dongen.http://csweb.ucc.ie/~dongen/LaTeX-and-Friends.pdf

The LATEX WikiBook. http://en.wikibooks.org/wiki/LaTeX

Questions?TEX FAQ. http://www.tex.ac.uk/cgi-bin/texfaq2html

TEX.SX. http://tex.stackexchange.com/

comp.text.tex usenet groupMalaysian LATEX User Group. http://latex-my.blogspot.com/

Arrange for training


http://www.andy-roberts.net/misc/latex/

http://liantze.penguinattack.org/latextypesetting.html

http://csweb.ucc.ie/~dongen/LaTeX-and-Friends.pdf

http://en.wikibooks.org/wiki/LaTeX

http://www.tex.ac.uk/cgi-bin/texfaq2html

http://tex.stackexchange.com/

http://latex-my.blogspot.com/


Thank You

[email protected]

http://latex-my.blogspot.com


[email protected]

http://latex-my.blogspot.com

latex: more than just academic papers and theses

Self Improvement