This document discusses the benefits of using Beamer, a LaTeX presentation template, compared to PowerPoint. Beamer allows for neat mathematical typesetting and formula copying from written reports. It generates PDF documents viewable on any system without surprises. Basic Beamer usage involves specifying document classes and creating overlays using commands like \pause. Packages may need downloading but are now fairly standard. Settings can be adjusted to customize presentations.
Hoje em dia é fácil juntar quantidades absurdamente grandes de dados. Mas, uma vez de posse deles, como fazer para extrair informações dessas montanhas amorfas de dados? Nesse minicurso vamos apresentar o modelo de programação MapReduce: entender como ele funciona, para que serve e como construir aplicações usando-o. Vamos ver também como usar o Elastic MapReduce, o serviço da Amazon que cria clusters MapReduce sob-demanda, para que você não se preocupe em administrar e conseguir acesso a um cluster de máquinas, mas em como fazer seu código digerir de forma distribuída os dados que você possui. Veremos exemplos práticos em ação e codificaremos juntos alguns desafios.
The slides shown here have been used for talks given to scientists in informal contexts.
Python is introduced as a valuable tool for both producing and evaluating data.
The talk is essentially a guided tour of the author's favourite parts of the Python ecosystem. Besides the Python language itself, NumPy and SciPy as well as Matplotlib are mentioned.
A last part of the talk concerns itself with code execution speed. With this problem in sight, Cython and f2py are introduced as means of glueing different languages together and speeding Python up.
The source code for the slides, code snippets and further links are available in a git repository at
https://github.com/aeberspaecher/PythonForScientists
Programming Languages & Tools for Higher Performance & ProductivityLinaro
By Hitoshi Murai, RIKEN AICS
For higher performance and productivity of HPC systems, it is important to provide users with good programming environment including languages, compilers, and tools. In this talk, the programming model of the post-K supercomputer will be shown.
Hitoshi Murai Bio
Hitoshi Murai received a master's degree in information science from Kyoto University in 1996. He worked as a software developer in NEC from 1996 to 2010. He received a Ph.D degree in computer science from University of Tsukuba in 2010. He is currently a research scientist of the programming environment research team and the Flagship 2020 project in Advanced Institute for Computational Science, RIKEN. His research interests include compilers and parallel programming languages.
Email
h-murai@riken.jp
For more info on The Linaro High Performance Computing (HPC) visit https://www.linaro.org/sig/hpc/
Hoje em dia é fácil juntar quantidades absurdamente grandes de dados. Mas, uma vez de posse deles, como fazer para extrair informações dessas montanhas amorfas de dados? Nesse minicurso vamos apresentar o modelo de programação MapReduce: entender como ele funciona, para que serve e como construir aplicações usando-o. Vamos ver também como usar o Elastic MapReduce, o serviço da Amazon que cria clusters MapReduce sob-demanda, para que você não se preocupe em administrar e conseguir acesso a um cluster de máquinas, mas em como fazer seu código digerir de forma distribuída os dados que você possui. Veremos exemplos práticos em ação e codificaremos juntos alguns desafios.
The slides shown here have been used for talks given to scientists in informal contexts.
Python is introduced as a valuable tool for both producing and evaluating data.
The talk is essentially a guided tour of the author's favourite parts of the Python ecosystem. Besides the Python language itself, NumPy and SciPy as well as Matplotlib are mentioned.
A last part of the talk concerns itself with code execution speed. With this problem in sight, Cython and f2py are introduced as means of glueing different languages together and speeding Python up.
The source code for the slides, code snippets and further links are available in a git repository at
https://github.com/aeberspaecher/PythonForScientists
Programming Languages & Tools for Higher Performance & ProductivityLinaro
By Hitoshi Murai, RIKEN AICS
For higher performance and productivity of HPC systems, it is important to provide users with good programming environment including languages, compilers, and tools. In this talk, the programming model of the post-K supercomputer will be shown.
Hitoshi Murai Bio
Hitoshi Murai received a master's degree in information science from Kyoto University in 1996. He worked as a software developer in NEC from 1996 to 2010. He received a Ph.D degree in computer science from University of Tsukuba in 2010. He is currently a research scientist of the programming environment research team and the Flagship 2020 project in Advanced Institute for Computational Science, RIKEN. His research interests include compilers and parallel programming languages.
Email
h-murai@riken.jp
For more info on The Linaro High Performance Computing (HPC) visit https://www.linaro.org/sig/hpc/
During the past few years R has become an important language for data analysis, data representation and visualization. R is a very expressive language which combines functional and dynamic aspects, with laziness and object oriented programming. However the default R implementation is neither fast nor distributed, both features crucial for "big data" processing.
Here, FastR-Flink compiler is presented, a compiler based on Oracle's R implementation FastR with support for some operations of Apache Flink, a Java/Scala framework for distributed data processing. The Apache Flink constructs such as map, reduce or filter are integrated at the compiler level to allow the execution of distributed stream and batch data processing applications directly from the R programming language.
Try to imagine the amount of time and effort it would take you to write a bug-free script or application that will accept a URL, port scan it, and for each HTTP service that it finds, it will create a new thread and perform a black box penetration testing while impersonating a Blackberry 9900 smartphone. While you’re thinking, Here’s how you would have done it in Hackersh:
“http://localhost” \
-> url \
-> nmap \
-> browse(ua=”Mozilla/5.0 (BlackBerry; U; BlackBerry 9900; en) AppleWebKit/534.11+ (KHTML, like Gecko) Version/7.1.0.346 Mobile Safari/534.11+”) \
-> w3af
Meet Hackersh (“Hacker Shell”) – A new, free and open source cross-platform shell (command interpreter) with built-in security commands and Pythonect-like syntax.
Aside from being interactive, Hackersh is also scriptable with Pythonect. Pythonect is a new, free, and open source general-purpose dataflow programming language based on Python, written in Python. Hackersh is inspired by Unix pipeline, but takes it a step forward by including built-in features like remote invocation and threads. This 120 minute lab session will introduce Hackersh, the automation gap it fills, and its features. Lots of demonstrations and scripts are included to showcase concepts and ideas.
JXL is the library of JExcel API, which is an open source Java API that performs the task to dynamically read, write, and modify Excel spreadsheets.
We can use its powerful features to build an automated testing framework using Selenium Web Drivers. The JXL works as a data provider where multiple sets of data is required as input. Moreover, users can read and write information using external excel files. The JXL also helps create custom reports where users have all authority to design reports as per their need.
Listen to this webinar to explore JXL with examples.
Slides from Phil Pennington\'s talk on Using Parallel Computing with Visual Studio 2010 and .NET 4.0, originally presented at the North Houston .NET Users Group (facebook.com/nhdnug).
Compilers have been improving programmer productivity ever since IBM produced the first FORTRAN compiler in 1957. Today, we mostly take them for granted but even after more than 60 years, compiler researchers and practitioners continue to push the boundaries for what compilers can achieve as well as how easy it is to leverage the sophisticated code bases that encapsulate those six decades of learning in this field. In this talk, I want to highlight how industry trends like the migration to cloud infrastructures and data centers as well as the rise of flexibly licensed open source projects like LLVM and Eclipse OMR are paving the way towards even more effective and powerful compilation infrastructures than have ever existed: compilers with the opportunity to contribute to programmer productivity in even more ways than simply better hardware instruction sequences, and with simpler APIs so they can be readily used in scenarios where even today's most amazing Just In Time compilers are not really practical.
Programming in Java: Getting Started. Last delivered in 2016. All educational material listed or linked to on these pages in relation to King's College London may be provided for reference only, and therefore does not necessarily reflect the current course content.
During the past few years R has become an important language for data analysis, data representation and visualization. R is a very expressive language which combines functional and dynamic aspects, with laziness and object oriented programming. However the default R implementation is neither fast nor distributed, both features crucial for "big data" processing.
Here, FastR-Flink compiler is presented, a compiler based on Oracle's R implementation FastR with support for some operations of Apache Flink, a Java/Scala framework for distributed data processing. The Apache Flink constructs such as map, reduce or filter are integrated at the compiler level to allow the execution of distributed stream and batch data processing applications directly from the R programming language.
Try to imagine the amount of time and effort it would take you to write a bug-free script or application that will accept a URL, port scan it, and for each HTTP service that it finds, it will create a new thread and perform a black box penetration testing while impersonating a Blackberry 9900 smartphone. While you’re thinking, Here’s how you would have done it in Hackersh:
“http://localhost” \
-> url \
-> nmap \
-> browse(ua=”Mozilla/5.0 (BlackBerry; U; BlackBerry 9900; en) AppleWebKit/534.11+ (KHTML, like Gecko) Version/7.1.0.346 Mobile Safari/534.11+”) \
-> w3af
Meet Hackersh (“Hacker Shell”) – A new, free and open source cross-platform shell (command interpreter) with built-in security commands and Pythonect-like syntax.
Aside from being interactive, Hackersh is also scriptable with Pythonect. Pythonect is a new, free, and open source general-purpose dataflow programming language based on Python, written in Python. Hackersh is inspired by Unix pipeline, but takes it a step forward by including built-in features like remote invocation and threads. This 120 minute lab session will introduce Hackersh, the automation gap it fills, and its features. Lots of demonstrations and scripts are included to showcase concepts and ideas.
JXL is the library of JExcel API, which is an open source Java API that performs the task to dynamically read, write, and modify Excel spreadsheets.
We can use its powerful features to build an automated testing framework using Selenium Web Drivers. The JXL works as a data provider where multiple sets of data is required as input. Moreover, users can read and write information using external excel files. The JXL also helps create custom reports where users have all authority to design reports as per their need.
Listen to this webinar to explore JXL with examples.
Slides from Phil Pennington\'s talk on Using Parallel Computing with Visual Studio 2010 and .NET 4.0, originally presented at the North Houston .NET Users Group (facebook.com/nhdnug).
Compilers have been improving programmer productivity ever since IBM produced the first FORTRAN compiler in 1957. Today, we mostly take them for granted but even after more than 60 years, compiler researchers and practitioners continue to push the boundaries for what compilers can achieve as well as how easy it is to leverage the sophisticated code bases that encapsulate those six decades of learning in this field. In this talk, I want to highlight how industry trends like the migration to cloud infrastructures and data centers as well as the rise of flexibly licensed open source projects like LLVM and Eclipse OMR are paving the way towards even more effective and powerful compilation infrastructures than have ever existed: compilers with the opportunity to contribute to programmer productivity in even more ways than simply better hardware instruction sequences, and with simpler APIs so they can be readily used in scenarios where even today's most amazing Just In Time compilers are not really practical.
Programming in Java: Getting Started. Last delivered in 2016. All educational material listed or linked to on these pages in relation to King's College London may be provided for reference only, and therefore does not necessarily reflect the current course content.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
1. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Beamer
The A
LT EXalternative to PowerPoint
Anonymous
March 18, 2011
2. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Outline
1 Why Beamer?
Advantages
Disadvantages
2 Basic Programming
The basics
3 Practical Matters
Compiling
Downloading
4 Playing with Settings
5 Summary
3. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Outline
1 Why Beamer?
Advantages
Disadvantages
2 Basic Programming
The basics
3 Practical Matters
Compiling
Downloading
4 Playing with Settings
5 Summary
4. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Why Beamer?
Advantages
A
Enjoy all the benefits of LTEX
Mathematical typesetting is neater, e.g.
n
1 if k = j,
Pn (x) = Lj (x)yj , with Lj (xk ) =
0 otherwise.
j=0
Formulae can be copied directly from a written report.
Generates a PDF-document, which can be viewed under any
operating system.
No surprises next time you open the file on another computer.
5. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Why Beamer?
Advantages
A
Enjoy all the benefits of LTEX
Mathematical typesetting is neater, e.g.
n
1 if k = j,
Pn (x) = Lj (x)yj , with Lj (xk ) =
0 otherwise.
j=0
Formulae can be copied directly from a written report.
Generates a PDF-document, which can be viewed under any
operating system.
No surprises next time you open the file on another computer.
6. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Why Beamer?
Advantages
A
Enjoy all the benefits of LTEX
Mathematical typesetting is neater, e.g.
n
1 if k = j,
Pn (x) = Lj (x)yj , with Lj (xk ) =
0 otherwise.
j=0
Formulae can be copied directly from a written report.
Generates a PDF-document, which can be viewed under any
operating system.
No surprises next time you open the file on another computer.
7. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Why Beamer?
Advantages
A
Enjoy all the benefits of LTEX
Mathematical typesetting is neater, e.g.
n
1 if k = j,
Pn (x) = Lj (x)yj , with Lj (xk ) =
0 otherwise.
j=0
Formulae can be copied directly from a written report.
Generates a PDF-document, which can be viewed under any
operating system.
No surprises next time you open the file on another computer.
8. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Why Beamer?
Advantages
A
Enjoy all the benefits of LTEX
Mathematical typesetting is neater, e.g.
n
1 if k = j,
Pn (x) = Lj (x)yj , with Lj (xk ) =
0 otherwise.
j=0
Formulae can be copied directly from a written report.
Generates a PDF-document, which can be viewed under any
operating system.
No surprises next time you open the file on another computer.
9. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Why Beamer?
Disadvantages
Not as “point-and-click” as PowerPoint.
A
Need to know the basics of LTEX.
Downloading of packages needs a little effort and patience
(but Beamer’s packages are fairly standard nowadays).
10. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Outline
1 Why Beamer?
Advantages
Disadvantages
2 Basic Programming
The basics
3 Practical Matters
Compiling
Downloading
4 Playing with Settings
5 Summary
11. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Basic Programming
A
Normal LTEXdocument: A
Beamer-LTEXdocument:
documentclass{book} documentclass{beamer}
mode<presentation>
title{...} usepackage{graphicx}
author{...} title{...}
date{...} author{...}
institute{...}
begin{document} date{...}
maketitle begin{document}
begin{frame} titlepage end{frame}
tableofcontents
begin{frame}
frametitle {Outline}
tableofcontents
section{Introduction} end{frame}
section{Introduction}
begin{frame}
frametitle{...}
...end{frame}
12. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Methods of revealing
You can create overlays . . .
Using the pause command:
First item.
Second item.
. . . just like that!
You can create overlays ldots
begin{itemize}
item Using the emph{pause} command: pause
begin{itemize}
item First item. pause
item Second item.
end{itemize}
pause
item ldots just like that!
end{itemize}
13. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Methods of revealing
You can create overlays . . .
Using the pause command:
First item.
Second item.
. . . just like that!
You can create overlays ldots
begin{itemize}
item Using the emph{pause} command: pause
begin{itemize}
item First item. pause
item Second item.
end{itemize}
pause
item ldots just like that!
end{itemize}
14. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Methods of revealing
You can create overlays . . .
Using the pause command:
First item.
Second item.
. . . just like that!
You can create overlays ldots
begin{itemize}
item Using the emph{pause} command: pause
begin{itemize}
item First item. pause
item Second item.
end{itemize}
pause
item ldots just like that!
end{itemize}
15. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Methods of revealing
You can create overlays . . .
Using the pause command:
First item.
Second item.
. . . just like that!
You can create overlays ldots
begin{itemize}
item Using the emph{pause} command: pause
begin{itemize}
item First item. pause
item Second item.
end{itemize}
pause
item ldots just like that!
end{itemize}
16. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Methods of revealing
You can create overlays . . .
Using the pause command:
First item.
Second item.
. . . just like that!
You can create overlays ldots
begin{itemize}
item Using the emph{pause} command: pause
begin{itemize}
item First item. pause
item Second item.
end{itemize}
pause
item ldots just like that!
end{itemize}
17. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Adding pictures
Pictures are included in the normal ways:
begin{center}
includegraphics[height=45mm]{fractal.jpg}
end{center}
18. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Outline
1 Why Beamer?
Advantages
Disadvantages
2 Basic Programming
The basics
3 Practical Matters
Compiling
Downloading
4 Playing with Settings
5 Summary
19. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
You need to compile your slides.tex file to get a slides.pdf file.
Graphics type Compiling method
png; jpg; pdf slides.tex −→ slides.pdf
eps; ps slides.tex −→ slides.ps −→ slides.pdf
20. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Practical matters: downloading
You will need to download three packages:
1 beamer ftp://ftp.sun.ac.za/CTAN/macros/latex/contrib/
2 xcolor ftp://ftp.sun.ac.za/CTAN/macros/latex/contrib/
3 pgf ftp://ftp.sun.ac.a/CTAN/graphics/
Or all of them (an many more!) at http://www.ctan.org
[ CTAN = Comprehensive TEXArchive Network ]
21. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Practical matters: downloading
You will need to download three packages:
1 beamer ftp://ftp.sun.ac.za/CTAN/macros/latex/contrib/
2 xcolor ftp://ftp.sun.ac.za/CTAN/macros/latex/contrib/
3 pgf ftp://ftp.sun.ac.a/CTAN/graphics/
Or all of them (an many more!) at http://www.ctan.org
[ CTAN = Comprehensive TEXArchive Network ]
22. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Outline
1 Why Beamer?
Advantages
Disadvantages
2 Basic Programming
The basics
3 Practical Matters
Compiling
Downloading
4 Playing with Settings
5 Summary
23. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Changing the settings
Bergen, Madrid, AanAr-
Presentation themes named af- bor, CambridgeUS, Pitts-
ter cities (except 2) burgh, Copenhagen, Han-
nover, . . .
Color themes named after flying albatross, crane, fly, dove,
animals seagull, . . .
Inner Themes, Outer Themes, . . .
24. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
Outline
1 Why Beamer?
Advantages
Disadvantages
2 Basic Programming
The basics
3 Practical Matters
Compiling
Downloading
4 Playing with Settings
5 Summary
25. Why Beamer? Basic Programming Practical Matters Playing with Settings Summary
The beamer-package allows you to have all the interactivity
you need, and
displays mathematical formulae neatly and legibily.