@Preamble{
"\hyphenation{
Ath-ina
Aus-tin
Bang-ert
Gauch-er-and
Krzysz-tof
Pat-rick
Szym-kat
Tag-li-ani
}" #
"\ifx \undefined \booktitle \def \booktitle #1{{{\em #1}}} \fi" #
"\ifx \undefined \circled \def \circled #1{(#1)}\fi" #
"\ifx \undefined \cprime \def \cprime {$\mathsurround=0pt '$}\fi" #
"\ifx \undefined \mathbb \def \mathbb #1{{\bf #1}}\fi" #
"\ifx \undefined \ocirc \def \ocirc #1{{\accent'27#1}}\fi" #
"\ifx \undefined \pkg \def \pkg #1{{{\tt #1}}} \fi" #
"\ifx \undefined \reg \def \reg {\circled{R}}\fi" #
"\ifx \undefined \TM \def \TM {${}^{\sc TM}$} \fi" #
"\def \toenglish #1\endtoenglish{[{\em English\/}: #1\unskip]} "
}
@String{ack-nhfb = "Nelson H. F. Beebe,
University of Utah,
Department of Mathematics, 110 LCB,
155 S 1400 E RM 233,
Salt Lake City, UT 84112-0090, USA,
Tel: +1 801 581 5254,
e-mail: \path|beebe@math.utah.edu|,
\path|beebe@acm.org|,
\path|beebe@computer.org| (Internet),
URL: \path|https://www.math.utah.edu/~beebe/|"}
@String{ack-njh = "Nick Higham,
e-mail: \path|higham@vtx.ma.man.ac.uk|"}
@String{inst-CSC = "Center for Scientific Computing,
Department of Mathematics, University of
Utah"}
@String{inst-CSC:adr = "Salt Lake City, UT 84112, USA"}
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@String{inst-MATHWORKS:adr = "3 Apple Hill Drive, Natick, MA 01760-2098,
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@String{inst-NPL = "National Physical Laboratory"}
@String{inst-NPL:adr = "Teddington, Middlesex TW11 0LW, UK"}
@String{inst-U-MANCHESTER = "University of Manchester"}
@String{inst-U-MANCHESTER:adr = "Manchester M13 9PL, England"}
@String{inst-MCCM = "Manchester Centre for Computational Mathematics"}
@String{inst-MCCM:adr = "Manchester, England"}
@String{j-ACM-COMM-COMP-ALGEBRA = "ACM Communications in Computer Algebra"}
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@String{j-IEEE-TRANS-VLSI-SYST = "IEEE Transactions on Very Large Scale
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Computing Applications"}
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Revue Internationale de Statistique"}
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Institute of Standards and Technology"}
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Languages (PACMPL)"}
@String{j-PLOS-ONE = "PLoS One"}
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@String{j-SIAM-J-MAT-ANA-APPL = "SIAM Journal on Matrix Analysis and
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@String{pub-ORA-MEDIA:adr = "1005 Gravenstein Highway North,
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@String{pub-SV:adr = "Berlin, Germany~/ Heidelberg,
Germany~/ London, UK~/ etc."}
@String{ser-LNCS = "Lecture Notes in Computer Science"}
@Article{Bezanson:2012:JFD,
author = "Jeff Bezanson and Stefan Karpinski and Viral B. Shah
and Alan Edelman",
title = "{Julia}: a Fast Dynamic Language for Technical
Computing",
journal = "arXiv.org",
volume = "??",
number = "??",
pages = "1--27",
day = "25",
month = sep,
year = "2012",
bibdate = "Thu Apr 08 07:54:32 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://arxiv.org/abs/1209.5145;
https://arxiv.org/pdf/1209.5145.pdf",
abstract = "Dynamic languages have become popular for scientific
computing. They are generally considered highly
productive, but lacking in performance. This paper
presents Julia, a new dynamic language for technical
computing, designed for performance from the beginning
by adapting and extending modern programming language
techniques. A design based on generic functions and a
rich type system simultaneously enables an expressive
programming model and successful type inference,
leading to good performance for a wide range of
programs. This makes it possible for much of the Julia
library to be written in Julia itself, while also
incorporating best-of-breed C and Fortran libraries.",
acknowledgement = ack-nhfb,
}
@InProceedings{Shah:2013:NAA,
author = "Viral B. Shah and Alan Edelman and Stefan Karpinski
and Jeff Bezanson and Jeremy Kepner",
editor = "{IEEE}",
booktitle = "{2013 IEEE High Performance Extreme Computing
Conference (HPEC)}",
title = "Novel algebras for advanced analytics in {Julia}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--4",
year = "2013",
DOI = "https://doi.org/10.1109/HPEC.2013.6670347",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Bezanson:2014:AOU,
author = "Jeff Bezanson and Jiahao Chen and Stefan Karpinski and
Viral Shah and Alan Edelman",
booktitle = "{Proceedings of ACM SIGPLAN International Workshop on
Libraries, Languages, and Compilers for Array
Programming}",
title = "Array Operators Using Multiple Dispatch",
publisher = pub-ACM,
address = pub-ACM:adr,
month = jun,
year = "2014",
DOI = "https://doi.org/10.1145/2627373.2627383",
bibdate = "Thu Apr 8 08:02:29 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@InProceedings{Chen:2014:PPP,
author = "J. Chen and A. Edelman",
booktitle = "{2014 First Workshop for High Performance Technical
Computing in Dynamic Languages}",
title = "Parallel Prefix Polymorphism Permits Parallelization,
Presentation Proof",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "47--56",
year = "2014",
DOI = "https://doi.org/10.1109/HPTCDL.2014.9",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@InProceedings{Heitzinger:2014:JNH,
author = "Clemens Heitzinger and Gerhard Tulzer",
editor = "{IEEE}",
booktitle = "{2014 First Workshop for High Performance Technical
Computing in Dynamic Languages}",
title = "{Julia} and the Numerical Homogenization of {PDEs}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "36--40",
year = "2014",
DOI = "https://doi.org/10.1109/HPTCDL.2014.8",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Knopp:2014:EMT,
author = "Tobias Knopp",
editor = "{IEEE}",
booktitle = "{2014 First Workshop for High Performance Technical
Computing in Dynamic Languages}",
title = "Experimental Multi-threading Support for the {Julia}
Programming Language",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--5",
year = "2014",
DOI = "https://doi.org/10.1109/HPTCDL.2014.11",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@InProceedings{Olver:2014:PFI,
author = "S. Olver and A. Townsend",
booktitle = "{2014 First Workshop for High Performance Technical
Computing in Dynamic Languages}",
title = "A Practical Framework for Infinite-Dimensional Linear
Algebra",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "57--62",
year = "2014",
DOI = "https://doi.org/10.1109/HPTCDL.2014.10",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@InProceedings{Udell:2014:COJ,
author = "Madeleine Udell and Karanveer Mohan and David Zeng and
Jenny Hong and Steven Diamond and Stephen Boyd",
editor = "{IEEE}",
booktitle = "{2014 First Workshop for High Performance Technical
Computing in Dynamic Languages}",
title = "Convex Optimization in {Julia}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "18--28",
year = "2014",
DOI = "https://doi.org/10.1109/HPTCDL.2014.5",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Book{Balbaert:2015:GSJ,
editor = "Ivo Balbaert and Kevin Colaco and Neeshma Ramakrishnan
and Rashmi Sawant",
title = "Getting started with {Julia} programming: enter the
exciting world of {Julia}, a high-performance language
for technical computing",
publisher = pub-PACKT,
address = pub-PACKT:adr,
pages = "214",
year = "2015",
ISBN = "1-78328-479-X, 1-78328-480-3 (e-book)",
ISBN-13 = "978-1-78328-479-5, 978-1-78328-480-1 (e-book)",
LCCN = "QA297 .B353 2015eb",
bibdate = "Thu Apr 8 10:48:12 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
series = "Community Experience Distilled",
URL = "http://public.ebookcentral.proquest.com/choice/publicfullrecord.aspx?p=1973847;
http://site.ebrary.com/id/11025933;
http://www.vlebooks.com/vleweb/product/openreader?id=none\%26isbn=9781783284801",
abstract = "This book is for you if you are a data scientist or
working on any technical or scientific computation
projects. The book assumes you have a basic working
knowledge of high-level dynamic languages such as
MATLAB, R, Python, or Ruby.",
acknowledgement = ack-nhfb,
subject = "Numerical analysis; Computer programs; Mathematical
analysis; Reference; Questions and Answers; Computer
programs.",
tableofcontents = "Preface \\
The Rationale for Julia \\
1: Installing the Julia Platform \\
Installing Julia \\
Windows version \\
usable from Windows XP SP2 onwards \\
Ubuntu version \\
OS X \\
Building from source \\
Working with Julia's shell \\
Startup options and Julia scripts \\
Packages \\
Adding a new package \\
Installing and working with Julia Studio \\
Installing and working with IJulia \\
Installing Sublime-IJulia \\
Installing Juno \\
Other editors and IDEs \\
How Julia works \\
Summary \\
2: Variables, Types, and Operations \\
Variables, naming conventions, and comments \\
Types \\
Integers \\
Floating point numbers \\
Elementary mathematical functions and operations \\
Rational and complex numbers \\
Characters \\
Strings \\
Formatting numbers and strings \\
Regular expressions \\
Ranges and arrays \\
Other ways to create arrays \\
Some common functions for arrays \\
How to convert an array of chars to a string \\
Dates and times \\
Scope and constants \\
Summary \\
3: Functions \\
Defining functions \\
Optional and keyword arguments \\
Anonymous functions \\
First-class functions and closures \\
Recursive functions \\
Map, filter, and list comprehensions \\
Generic functions and multiple dispatch \\
Summary \\
4: Control Flow \\
Conditional evaluation \\
Repeated evaluation \\
The for loop \\
The while loop \\
The break statement \\
The continue statement \\
Exception handling \\
Scope revisited \\
Tasks \\
Summary \\
5: Collection Types \\
Matrices \\
Tuples \\
Dictionaries \\
Keys and values \\
looping \\
Sets \\
Making a set of tuples \\
Example project \\
word frequency \\
Summary \\
6: More on Types, Methods, and Modules \\
Type annotations and conversions \\
Type conversions and promotions \\
The type hierarchy \\
subtypes and supertypes \\
Concrete and abstract types \\
User-defined and composite types \\
When are two values or objects equal or identical? \\
Multiple dispatch example \\
Types and collections \\
inner constructors \\
Type unions \\
Parametric types and methods \\
Standard modules and paths \\
Summary \\
7: Metaprogramming in Julia \\
Expressions and symbols \\
Eval and interpolation \\
Defining macros \\
Built-in macros \\
Testing \\
Debugging \\
Benchmarking \\
Starting a task \\
Reflection capabilities \\
Summary \\
8: I/O, Networking, and Parallel Computing \\
Basic input and output \\
Working with files \\
Reading and writing CSV files \\
Using DataFrames \\
Other file formats \\
Working with TCP sockets and servers \\
Interacting with databases \\
Parallel operations and computing \\
Creating processes \\
Using low-level communications \\
Parallel loops and maps \\
Distributed arrays \\
Summary \\
9: Running External Programs \\
Running shell commands \\
Interpolation \\
Pipelining \\
Calling C and FORTRAN \\
Calling Python \\
Performance tips \\
Tools to use \\
Summary \\
10: The Standard Library and Packages \\
Digging deeper into the standard library \\
Julia's package manager",
}
@InProceedings{Dogaru:2015:UPJ,
author = "Ioana Dogaru and Radu Dogaru",
editor = "{IEEE}",
booktitle = "{2015 20th International Conference on Control Systems
and Computer Science}",
title = "Using {Python} and {Julia} for Efficient
Implementation of Natural Computing and Complexity
Related Algorithms",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "599--604",
year = "2015",
DOI = "https://doi.org/10.1109/CSCS.2015.37",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/python.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@InProceedings{Edelman:2015:JFA,
author = "Alan Edelman",
editor = "{IEEE}",
booktitle = "{2015 IEEE International Parallel and Distributed
Processing Symposium}",
title = "{Julia}: a fresh approach to parallel programming",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "517--517",
year = "2015",
DOI = "https://doi.org/10.1109/IPDPS.2015.122",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@InProceedings{Edelman:2015:JI,
author = "Alan Edelman",
editor = "{IEEE}",
booktitle = "{2015 IEEE International Parallel and Distributed
Processing Symposium Workshop}",
title = "{Julia} Introduction",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1271--1271",
year = "2015",
DOI = "https://doi.org/10.1109/IPDPSW.2015.181",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Lubin:2015:COR,
author = "Miles Lubin and Iain Dunning",
title = "Computing in Operations Research Using {Julia}",
journal = j-INFORMS-J-COMPUT,
volume = "27",
number = "2",
pages = "238--248",
month = "Spring",
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1287/ijoc.2014.0623",
ISSN = "1091-9856 (print), 1526-5528 (electronic)",
ISSN-L = "1091-9856",
MRclass = "68N15 (90-04)",
MRnumber = "3347876",
bibdate = "Mon Apr 9 08:21:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/informs-j-comput.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://pubsonline.informs.org/doi/abs/10.1287/ijoc.2014.0623",
acknowledgement = ack-nhfb,
ajournal = "INFORMS J. Comput.",
fjournal = "INFORMS Journal on Computing",
journal-URL = "https://pubsonline.informs.org/journal/ijoc",
keywords = "Julia programming language",
onlinedate = "March 16, 2015",
}
@InProceedings{Ovsyak:2015:AMA,
author = "V. Ovsyak and O. Ovsyak and D. Bui and J. Petruszka",
booktitle = "{2015 IEEE 13th International Scientific Conference on
Informatics}",
title = "Algebraic models of application of computer systems
and information technologies",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "189--194",
year = "2015",
DOI = "https://doi.org/10.1109/Informatics.2015.7377831",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@InProceedings{Przigoda:2015:VDD,
author = "N. Przigoda and J. Stoppe and J. Seiter and R. Wille
and R. Drechsler",
booktitle = "{2015 Euromicro Conference on Digital System Design}",
title = "Verification-Driven Design Across Abstraction Levels:
a Case Study",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "375--382",
year = "2015",
DOI = "https://doi.org/10.1109/DSD.2015.88",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Book{Sherrington:2015:MJD,
author = "Malcolm Sherrington",
title = "Mastering {Julia}: develop your analytical and
programming skills further in {Julia} to solve complex
data processing problems",
publisher = pub-PACKT,
address = pub-PACKT:adr,
pages = "xiv + 385",
year = "2015",
ISBN = "1-78355-331-6 (paperback), 1-78355-332-4 (e-book)",
ISBN-13 = "978-1-78355-331-0 (paperback), 978-1-78355-332-7
(e-book)",
LCCN = "QA76.7 .S547 2015; QA76.73.J8 S54 2015",
bibdate = "Thu Apr 8 10:58:21 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
abstract = "This hands-on guide is aimed at practitioners of data
science. The book assumes some previous skills with
Julia and skills in coding in a scripting language such
as Python or R, or a compiled language such as C or
Java.",
acknowledgement = ack-nhfb,
tableofcontents = "Preface \\
1: The Julia Environment \\
Introduction \\
Philosophy \\
Role in data science and big data \\
Comparison with other languages \\
Features \\
Getting started \\
Julia sources \\
Building from source \\
Installing on CentOS \\
Mac OS X and Windows \\
Exploring the source stack \\
Juno \\
IJulia \\
A quick look at some Julia \\
Julia via the console \\
Installing some packages \\
A bit of graphics creating more realistic graphics with
Winston \\
My benchmarks \\
Package management \\
Listing, adding, and removing \\
Choosing and exploring packages \\
Statistics and mathematics \\
Graphics \\
Web and networking \\
Database and specialist packages \\
How to uninstall Julia \\
Adding an unregistered package \\
What makes Julia special \\
Parallel processing \\
Multiple dispatch \\
Homoiconic macros \\
Interlanguage cooperation \\
Summary \\
2: Developing in Julia \\
Integers, bits, bytes, and bools \\
Integers \\
Logical and arithmetic operators \\
Booleans \\
Arrays \\
Operations on matrices \\
Elemental operations \\
A simple Markov chain \\
cat and mouse \\
Char and strings \\
Characters \\
Strings \\
Unicode support \\
Regular expressions \\
Byte array literals \\
Version literals \\
An example \\
Real, complex, and rational numbers \\
Reals \\
Operators and built-in functions \\
Special values \\
BigFloats \\
Rationals \\
Complex numbers \\
Juliasets \\
Composite types \\
More about matrices \\
Vectorized and devectorized code \\
Multidimensional arrays \\
Broadcasting \\
Sparse matrices \\
Data arrays and data frames \\
Dictionaries, sets, and others \\
Dictionaries \\
Sets \\
Other data structures \\
Summary \\
3: Types and Dispatch \\
Functions \\
First-class objects \\
Passing arguments \\
Default and optional arguments \\
Variable argument list \\
Named parameters \\
Scope \\
The Queen's problem \\
Julia's type system \\
A look at the rational type \\
A vehicle datatype \\
Typealias and unions \\
Enumerations (revisited) \\
Multiple dispatch \\
Parametric types \\
Conversion and promotion \\
Conversion \\
Promotion \\
A fixed vector module \\
Summary \\
4: Interoperability \\
Interfacing with other programming environments \\
Calling C and Fortran \\
Mapping C types \\
Calling a Fortran routine \\
Calling curl to retrieve a web page \\
Python \\
Some others to watch \\
The Julia API \\
Calling API from C \\
Metaprogramming \\
Symbols \\
Macros \\
Testing \\
Error handling \\
The enum macro \\
Tasks \\
Parallel operations \\
Distributed arrays \\
A simple MapReduce \\
Executing commands \\
Running commands \\
Working with the filesystem \\
Redirection and pipes \\
Perl one-liners \\
Summary \\
5: Working with Data \\
Basic I/O \\
Terminal I/O \\
Disk files \\
Text processing \\
Binary files \\
Structured datasets \\
CSV and DLM files \\
HDF5 \\
XML files \\
DataFrames and RDatasets \\
The DataFrames package \\
DataFrames \\
RDatasets \\
Subsetting, sorting, and joining data \\
Statistics \\
Simple statistics \\
Samples and estimations \\
Pandas \\
Selected topics \\
Time series \\
Distributions \\
Kernel density \\
Hypothesis testing \\
GLM \\
Summary",
}
@InProceedings{Wang:2015:IDF,
author = "Yi Wang and Meilin Liu and Huiping Li and Shu Liang
and Qunsheng Cao",
editor = "{IEEE}",
booktitle = "{2015 IEEE International Symposium on Antennas and
Propagation \& USNC\slash URSI National Radio Science
Meeting}",
title = "Implementation of {DG-fem} with dynamic {Julia}
language for accurate {EM} simulation",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1850--1851",
year = "2015",
DOI = "https://doi.org/10.1109/APS.2015.7305314",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
}
@Book{Balbaert:2016:JHP,
author = "Ivo Balbaert and Avik Sengupta and Malcolm
Sherrington",
title = "{Julia}: high performance programming: learning path:
leverage the power of {Julia} to design and develop
high performing programs",
publisher = pub-PACKT,
address = pub-PACKT:adr,
pages = "697",
year = "2016",
ISBN = "1-78712-570-X, 1-78712-610-2 (e-book)",
ISBN-13 = "978-1-78712-570-4, 978-1-78712-610-7 (e-book)",
LCCN = "QA76.7 .B353 2016",
bibdate = "Thu Apr 8 16:55:30 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
series = "Learning path",
abstract = "Leverage the power of Julia to design and develop high
performing programs About This Book Get to know the
best techniques to create blazingly fast programs with
Julia Stand out from the crowd by developing code that
runs faster than your peers' code Complete an extensive
data science project through the entire cycle from ETL
to analytics and data visualization Who This Book Is
For This learning path is for data scientists and for
all those who work in technical and scientific
computation projects. It will be great for Julia
developers who are interested in high-performance
technical computing. This learning path assumes that
you already have some basic working knowledge of
Julia's syntax and high-level dynamic languages such as
MATLAB, R, Python, or Ruby. What You Will Learn Set up
your Julia environment to achieve the highest
productivity Solve your tasks in a high-level dynamic
language and use types for your data only when needed
Apply Julia to tackle problems concurrently and in a
distributed environment Get a sense of the
possibilities and limitations of Julia's performance
Use Julia arrays to write high performance code Build a
data science project through the entire cycle of ETL,
analytics, and data visualization Display graphics and
visualizations to carry out modeling and simulation in
Julia Develop your own packages and contribute to the
Julia Community In Detail In this learning path, you
will learn to use an interesting and dynamic
programming language - Julia! You will get a chance to
tackle your numerical and data problems with Julia.
You'll begin the journey by setting up a running Julia
platform before exploring its various built-in types.
We'll then move on to the various functions and
constructs in Julia. We'll walk through the two
important collection types - arrays and matrices in
Julia. You will dive into how Julia uses type
information to achieve its performance goals, and how
to use multiple dispatch to help the compiler emit high
performance machine code. You will see how Julia's
design makes code fast, and you'll see its distributed
computing capabilities. By the end of this learning
path, you will see how data works using simple
statistics and analytics, and you'll discover its high
and dynamic performance - its real strength, which
makes it particularly useful in highly intensive
computing tasks. This learning path combines some of
the best that Packt has to offer in one complete,
curated package.",
acknowledgement = ack-nhfb,
keywords = "Closures and anonymous functions.",
subject = "Programming languages; Julia; Computer programming;
COMPUTERS; General.; Computer programming.",
tableofcontents = "Preface \\
Table of Contents \\
Module 1: Getting Started with Julia \\
The Rationale for Julia \\
The scope of Julia \\
Julia's place among the other programming languages \\
A comparison with other languages for the data
scientist \\
Useful links \\
Summary \\
1: Installing the Julia Platform \\
Installing Julia \\
Working with Julia's shell \\
Startup options and Julia scripts \\
Packages \\
Installing and working with Julia Studio \\
Installing and working with IJulia \\
Installing Sublime-IJulia \\
Installing Juno \\
Other editors and IDEs \\
How Julia works \\
Summary \\
2: Variables, Types, and Operations \\
Variables, naming conventions, and comments \\
Types \\
Integers \\
Floating point numbers \\
Elementary mathematical functions and operations \\
Rational and complex numbers \\
Characters \\
Strings \\
Regular expressions \\
Ranges and arrays \\
Dates and times \\
Scope and constants \\
Summary \\
3: Functions \\
Defining functions \\
Optional and keyword arguments \\
Anonymous functions \\
First-class functions and closures \\
Recursive functions \\
Map, filter, and list comprehensions \\
Generic functions and multiple dispatch \\
Summary \\
4: Control Flow \\
Conditional evaluation \\
Repeated evaluation \\
Exception handling \\
Scope revisited \\
Tasks \\
Summary \\
5: Collection Types \\
Matrices \\
Tuples \\
Dictionaries \\
Sets \\
Example project \\
word frequency \\
Summary \\
6: More on Types, Methods, and Modules \\
Type annotations and conversions \\
The type hierarchy \\
subtypes and supertypes \\
User-defined and composite types \\
Types and collections \\
inner constructors \\
Type unions \\
Parametric types and methods \\
Standard modules and paths \\
Summary \\
7: Metaprogramming in Julia \\
Expressions and symbols \\
Eval and interpolation \\
Defining macros \\
Built-in macros \\
Reflection capabilities \\
Summary \\
8: I/O, Networking, and Parallel Computing \\
Basic input and output \\
Working with files \\
Using DataFrames \\
Working with TCP sockets and servers \\
Interacting with databases \\
Parallel operations and computing \\
Summary \\
9: Running External Programs \\
Running shell commands \\
Calling C and FORTRAN \\
Calling Python \\
Performance tips \\
Summary \\
10: The Standard Library and Packages \\
Digging deeper into the standard library \\
Julia's package manager \\
Publishing a package \\
Graphics in Julia \\
Using Gadfly on data \\
Summary \\
Appendix: List of Macros and Packages \\
Macros \\
List of packages \\
Module 2: Julia High Performance \\
1: Julia is Fast \\
Julia \\
fast and dynamic \\
Designed for speed \\
How fast can Julia be? \\
Summary \\
2: Analyzing Julia Performance \\
Timing Julia code \\
The Julia profiler \\
Analyzing memory allocation \\
Statistically accurate benchmarking \\
Summary \\
3: Types in Julia \\
The Julia type system \\
Type-stability \\
Kernel methods \\
Types in storage locations \\
Summary \\
4: Functions and Macros \\
Structuring Julia Code for High Performance \\
Using globals \\
Inlining",
}
@InCollection{Bohm:2016:BPC,
author = "Janko B{\"o}hm and Wolfram Decker and Claus Fieker and
Santiago Laplagne and Gerhard Pfister",
booktitle = "{Mathematical Software --- ICMS 2016}",
title = "Bad Primes in Computational Algebraic Geometry",
publisher = "Springer International Publishing",
address = "Cham, Switzerland",
pages = "93--101",
year = "2016",
DOI = "https://doi.org/10.1007/978-3-319-42432-3_12",
bibdate = "Thu Apr 8 08:02:29 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@InProceedings{Chan:2016:PAB,
author = "Jose Juan Mijares Chan and Yuyin Mao and Ying Ying Liu
and Parimala Thulasiraman and Ruppa K. Thulasiram",
booktitle = "{Parallel Processing and Applied Mathematics}",
title = "Parallel Ant Brood Graph Partitioning in {Julia}",
publisher = pub-SV,
address = pub-SV:adr,
pages = "??--??",
year = "2016",
DOI = "https://doi.org/10.1007/978-3-319-32152-3_17",
bibdate = "Fri Apr 9 07:54:52 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://link.springer.com/chapter/10.1007/978-3-319-32152-3_17",
acknowledgement = ack-nhfb,
}
@InProceedings{Chen:2016:JID,
author = "Alexander Chen and Alan Edelman and Jeremy Kepner and
Vijay Gadepally and Dylan Hutchison",
editor = "{IEEE}",
booktitle = "{2016 IEEE High Performance Extreme Computing
Conference (HPEC)}",
title = "{Julia} implementation of the {Dynamic Distributed
Dimensional Data Model}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--7",
month = sep,
year = "2016",
DOI = "https://doi.org/10.1109/HPEC.2016.7761626",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Creel:2016:NJM,
author = "Michael Creel",
title = "A Note on {Julia} and {MPI}, with Code Examples",
journal = j-COMP-ECONOMICS,
volume = "48",
number = "3",
pages = "??--??",
month = "",
year = "2016",
CODEN = "CNOMEL",
DOI = "https://doi.org/10.1007/s10614-015-9516-5",
ISSN = "",
ISSN-L = "0927-7099",
bibdate = "Fri Apr 9 07:54:52 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/pvm.bib",
URL = "http://link.springer.com/article/10.1007/s10614-015-9516-5",
acknowledgement = ack-nhfb,
fjournal = "Computational Economics",
}
@InProceedings{Elmqvist:2016:SMP,
author = "Hilding Elmqvist and Toivo Henningsson and Martin
Otter",
booktitle = "{Leveraging Applications of Formal Methods,
Verification and Validation: Discussion, Dissemination,
Applications}",
title = "Systems Modeling and Programming in a Unified
Environment Based on {Julia}",
publisher = pub-SV,
address = pub-SV:adr,
pages = "198--217",
year = "2016",
DOI = "https://doi.org/10.1007/978-3-319-47169-3_15",
bibdate = "Fri Apr 9 07:54:52 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://link.springer.com/chapter/10.1007/978-3-319-47169-3_15",
acknowledgement = ack-nhfb,
}
@InProceedings{Fourie:2016:NBS,
author = "D. Fourie and J. Leonard and M. Kaess",
booktitle = "{2016 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS)}",
title = "A nonparametric belief solution to the {Bayes} tree",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "2189--2196",
year = "2016",
DOI = "https://doi.org/10.1109/IROS.2016.7759343",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Frost:2016:PGJ,
author = "Simon D. W. Frost",
title = "\pkg{Gillespie.jl}: Stochastic Simulation Algorithm in
{Julia}",
journal = j-J-OPEN-SOURCE-SOFT,
volume = "1",
number = "3",
pages = "42:1--42:1",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.21105/joss.00042",
ISSN = "2475-9066",
ISSN-L = "2475-9066",
bibdate = "Thu Sep 13 08:09:35 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/joss.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://joss.theoj.org/papers/10.21105/joss.00042",
acknowledgement = ack-nhfb,
fjournal = "Journal of Open Source Software",
journal-URL = "http://joss.theoj.org/;
https://github.com/openjournals/joss-papers/",
onlinedate = "30 July 2016",
ORCID-numbers = "Simon DW Frost / 0000-0002-5207-9879",
}
@Article{Gonzalez:2016:CMC,
author = "J. D. Gonzalez and E. F. Lavia and S. Blanc",
title = "A Computational Method to Calculate the Exact Solution
for Acoustic Scattering by Fluid Spheroids",
journal = "{Acta Acustica} united with {Acustica}",
volume = "102",
number = "6",
pages = "1061--1071",
month = nov,
year = "2016",
DOI = "https://doi.org/10.3813/aaa.919019",
bibdate = "Thu Apr 8 08:02:29 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Book{Joshi:2016:JDS,
author = "Anshul Joshi",
title = "{Julia} for data science: explore the world of data
science from scratch with {Julia} by your side",
publisher = pub-PACKT,
address = pub-PACKT:adr,
pages = "339",
year = "2016",
ISBN = "1-78355-386-3 (e-book), 1-78528-969-1",
ISBN-13 = "978-1-78355-386-0 (e-book), 978-1-78528-969-9",
LCCN = "QA76.73.J8; T55.4-60.8",
bibdate = "Fri Apr 9 05:20:49 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
abstract = "Explore the world of data science from scratch with
Julia by your side About This Book An in-depth
exploration of Julia's growing ecosystem of packages
Work with the most powerful open-source libraries for
deep learning, data wrangling, and data visualization
Learn about deep learning using \pkg{Mocha.jl} and give
speed and high performance to data analysis on large
data sets Who This Book Is For This book is aimed at
data analysts and aspiring data scientists who have a
basic knowledge of Julia or are completely new to it.
The book also appeals to those competent in R and
Python and wish to adopt Julia to improve their skills
set in Data Science. It would be beneficial if the
readers have a good background in statistics and
computational mathematics. What You Will Learn Apply
statistical models in Julia for data-driven decisions
Understanding the process of data munging and data
preparation using Julia Explore techniques to visualize
data using Julia and D3 based packages Using Julia to
create self-learning systems using cutting edge machine
learning algorithms Create supervised and unsupervised
machine learning systems using Julia. Also, explore
ensemble models Build a recommendation engine in Julia
Dive into Julia's deep learning framework and build a
system using Mocha.jl In Detail Julia is a fast and
high performing language that's perfectly suited to
data science with a mature package ecosystem and is now
feature complete. It is a good tool for a data science
practitioner. There was a famous post at Harvard
Business Review that Data Scientist is the sexiest job
of the 21st century.
(https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century).
This book will help you get familiarised with Julia's
rich ecosystem, which is continuously evolving,
allowing you to stay on top of your game. This book
contains the essentials of data science and gives a
high-level overview of advanced statistics and
techniques. You will dive in and will work on
generating insights by performing inferential
statistics, and will reveal hidden patterns and trends
using data mining. This has the practical coverage of
statistics and machine learning. You will develop
knowledge to build statistical models and machine
learning systems in Julia with attractive
visualizations. You will then delve into the world of
Deep learning in Julia and will understand the
framework, \pkg{Mocha.jl} with which you can create
artificial neural networks and implement deep
learning.",
acknowledgement = ack-nhfb,
subject = "Julia (Computer program language); Data structures
(Computer science); Information visualization;
COMPUTERS / Data Modeling and Design; Data structures
(Computer science); Information visualization; Julia
(Computer program language)",
tableofcontents = "Preface \\
1: The Groundwork \\
Julia's Environment \\
Julia is different \\
Setting up the environment \\
Installing Julia (Linux) \\
Installing Julia (Mac) \\
Installing Julia (Windows) \\
Exploring the source code \\
Using REPL \\
Using Jupyter Notebook \\
Package management \\
Pkg.status() \\
package status \\
Pkg.add() -{\`E}adding packages \\
Working with unregistered packages \\
Pkg.update() -{\`E}package update \\
METADATA repository \\
Developing packages \\
Creating a new package \\
Parallel computation using Julia \\
Julia's key feature \\
multiple dispatch \\
Methods in multiple dispatch \\
Ambiguities \\
method definitions \\
Facilitating language interoperability \\
Calling Python code in Julia \\
Summary \\
References \\
2: Data Munging \\
What is data munging? \\
The data munging process \\
What is a DataFrame? \\
The NA data type and its importance \\
DataArray series-like data structure \\
DataFrames tabular data structures \\
Installation and using DataFrames.jl \\
Writing the data to a file \\
Working with DataFrames \\
Understanding DataFrames joins \\
The Split-Apply-Combine strategy \\
Reshaping the data \\
Sorting a datasetFormula \\
a special data type for mathematical expressions \\
Pooling data \\
Web scraping \\
Summary \\
References \\
3: Data Exploration \\
Sampling \\
Population \\
Weight vectors \\
Inferring column types \\
Basic statistical summaries \\
Calculating the mean of the array or dataframe \\
Scalar statistics \\
Standard deviations and variances \\
Measures of variation \\
Z-scores \\
Entropy \\
Quantiles \\
Modes \\
Summary of datasets \\
Scatter matrix and covariance \\
Computing deviations \\
Rankings \\
Counting functions \\
Histograms \\
Correlation analysis \\
Summary \\
References \\
4: Deep Dive into Inferential Statistics \\
Installation \\
Understanding the sampling distribution \\
Understanding the normal distribution \\
Parameter estimation \\
Type hierarchy in \pkg{Distributions.jl} \\
Understanding Sampleable \\
Representing probabilistic distributions \\
Univariate distributions \\
Retrieving parameters \\
Statistical functions \\
Evaluation of probability \\
Sampling in Univariate distributions \\
Understanding Discrete Univariate distributions and
types \\
Bernoulli distribution \\
Binomial distribution \\
Continuous distributions \\
Cauchy distribution \\
Chi distribution \\
Chi-square distribution \\
Truncated distributions \\
Truncated normal distributions \\
Understanding multivariate distributions \\
Multinomial distribution \\
Multivariate normal distribution \\
Dirichlet distribution \\
Understanding matrixvariate distributions \\
Wishart distribution \\
Inverse-Wishart distribution \\
Distribution fitting \\
Distribution selection \\
Symmetrical distributions \\
Skew distributions to the right \\
Skew distributions to the left \\
Maximum Likelihood Estimation \\
Sufficient statistics \\
Maximum-a-Posteriori estimation \\
Confidence interval \\
Interpreting the confidence intervals \\
Usage \\
Understanding z-score",
}
@Book{Kwon:2016:JPO,
author = "Changhyun Kwon",
title = "{Julia} programming for operations research: a primer
on computing",
publisher = "CreateSpace Independent Publishing Platform",
address = "North Charleston, SC, USA",
pages = "x + 236",
year = "2016",
ISBN = "1-5333-2879-X, 1-63462-196-4",
ISBN-13 = "978-1-5333-2879-3, 978-1-63462-196-0",
LCCN = "QA76.73.J85 K86 2016",
bibdate = "Thu Apr 8 16:52:30 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
abstract = "This book is neither a textbook in numerical methods,
a comprehensive introductory book to Julia programming,
a textbook on numerical optimization, a complete manual
of optimization solvers, nor an introductory book to
computational science and engineering --- it is a
little bit of all.",
acknowledgement = ack-nhfb,
subject = "Julia (Computer program language); Mathematical
models; Computer simulation; Julia (Computer program
language); Object-oriented programming (Computer
science); Computer simulation.; Dynamic programming.",
tableofcontents = "1. Introduction and installation \\
2. Simple linear optimization \\
3. Basics of the Julia language \\
4. Selected topics in numerical methods \\
5. The simplex method \\
6. Network optimization problems \\
7. General optimization problems \\
8. Monte Carlo methods \\
9. Lagrangian relaxation \\
10. Parameters in optimization solvers \\
11. Useful and related packages",
}
@InProceedings{Maidens:2016:PDP,
author = "J. Maidens and A. Packard and M. Arcak",
booktitle = "{2016 IEEE 55th Conference on Decision and Control
(CDC)}",
title = "Parallel dynamic programming for optimal experiment
design in nonlinear systems",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "2894--2899",
year = "2016",
DOI = "https://doi.org/10.1109/CDC.2016.7798700w",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@InProceedings{OMalley:2016:TJH,
author = "Daniel O'Malley and Velimir V. Vesselinov",
editor = "{IEEE}",
booktitle = "{2016 IEEE High Performance Extreme Computing
Conference (HPEC)}",
title = "{ToQ.jl}: a high-level programming language for
{D-Wave} machines based on {Julia}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--7",
year = "2016",
DOI = "https://doi.org/10.1109/HPEC.2016.7761616",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Book{Rohit:2016:JC,
author = "Jalem Raj Rohit",
title = "{Julia} Cookbook",
publisher = pub-PACKT,
address = pub-PACKT:adr,
pages = "v + 157",
year = "2016",
ISBN = "1-78588-201-5, 1-78588-363-1 (e-book)",
ISBN-13 = "978-1-78588-201-2, 978-1-78588-363-7 (e-book)",
LCCN = "QA76.73.J8; T55.4-60.8",
bibdate = "Thu Apr 8 11:05:21 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://sbiproxy.uqac.ca/login?url=https://international.scholarvox.com/book/88843406",
abstract = "Over 40 recipes to get you up and running with
programming using Julia. About This Book Follow a
practical approach to learn Julia programming the easy
way Get an extensive coverage of Julia's packages for
statistical analysis This recipe-based approach will
help you get familiar with the key concepts in Julia.
Who This Book Is For: This book is for data scientists
and data analysts who are familiar with the basics of
the Julia language. Prior experience of working with
high-level languages such as MATLAB, Python, R, or Ruby
is expected. What You Will Learn Extract and handle
your data with Julia Uncover the concepts of
metaprogramming in Julia Conduct statistical analysis
with \pkg{StatsBase.jl} and \pkg{Distributions.jl}.
Build your data science models Find out how to
visualize your data with Gadfly Explore big data
concepts in Julia. In Detail Want to handle everything
that Julia can throw at you and get the most of it
every day? This practical guide to programming with
Julia for performing numerical computation will make
you more productive and able work with data more
efficiently. The book starts with the main features of
Julia to help you quickly refresh your knowledge of
functions, modules, and arrays. We'll also show you how
to utilize the Julia language to identify, retrieve,
and transform data sets so you can perform data
analysis and data manipulation. Later on, you'll see
how to optimize data science programs with parallel
computing and memory allocation. You'll get familiar
with the concepts of package development and networking
to solve numerical problems using the Julia platform.
This book includes recipes on identifying and
classifying data science problems, data modelling, data
analysis, data manipulation, meta-programming,
multidimensional arrays, and parallel computing. By the
end of the book, you will acquire the skills to work
more effectively with your data. Style and approach
This book has a recipe-based approach to help you grasp
the concepts of Julia programming.",
acknowledgement = ack-nhfb,
subject = "Julia (Computer program language); Programming
languages (Electronic computers); Julia (Computer
program language); Programming languages (Electronic
computers)",
tableofcontents = "About the Author \\
About the Reviewer \\
www.PacktPub.com \\
Table of Contents \\
Preface \\
1: Extracting and Handling Data \\
Introduction \\
Why should we use Julia for data science? \\
Handling data with CSV files \\
Getting ready \\
How to do it and Handling data with TSV files \\
Getting ready \\
How to do it and Working with databases in Julia \\
Getting ready \\
How to do it and MySQL \\
PostgreSQL \\
There's more and MySQL \\
PostgreSQL \\
SQLite \\
Interacting with the Web \\
Getting ready \\
How to do it and GET request \\
There's more and \ldots{} \\
2: Metaprogramming \\
Introduction \\
Representation of a Julia program \\
Getting ready \\
How to do it and How it works \\
There's more \\
Symbols and expressions \\
Symbols \\
Getting ready \\
How to do it and How it works \\
There's more \\
Quoting \\
How to do it and How it works \\
Interpolation \\
How to do it and How it works \\
There's more \\
The Eval function \\
Getting ready \\
How to do it and How it works \\
Macros \\
Getting ready \\
How to do it and How it works \\
Metaprogramming with DataFrames \\
Getting ready \\
How to do it and How it works \\
3: Statistics with Julia \\
Introduction \\
Basic statistics concepts \\
Getting ready \\
How to do it and How it works \\
Descriptive statistics \\
Getting ready \\
How to do it and How it works \\
Deviation metrics \\
Getting ready \\
How to do it and How it works \\
Sampling \\
Getting ready \\
How to do it and How it works \\
Correlation analysis \\
Getting ready \\
How to do it and How it works \\
4: Building Data Science Models \\
Introduction \\
Dimensionality reduction \\
Getting ready \\
How to do it and How it works \\
Linear discriminant analysis \\
Getting ready \\
How to do it and How it works \\
Data preprocessing \\
Getting ready \\
How to do it and How it works \\
Linear regression \\
Getting ready \\
How to do it and How it works \\
Classification Getting ready \\
How to do it and How it works \\
Performance evaluation and model selection \\
Getting ready \\
How to do it and How it works \\
Cross validation \\
Getting ready \\
How to do it and How it works \\
Distances \\
Getting ready \\
How to do it and How it works \\
Distributions \\
Getting ready \\
How to do it and How it works \\
Time series analysis \\
Getting ready \\
How to do it and How it works \\
5: Working with Visualizations \\
Introduction \\
Plotting basic arrays \\
Getting ready \\
How to do it and How it works \\
Plotting dataframes \\
Getting ready \\
How to do it and How it works \\
Plotting functions \\
Getting ready \\
How to do it and how it works \\
Exploratory data analytics through plots \\
Getting ready \\
How to do it and How it works \\
Line plots \\
Getting ready \\
How to do it and How it works \\
Scatter plots \\
Getting ready \\
How to do it and How it works \\
Histograms \\
Getting ready \\
How to do it and How it works \\
Aesthetic customizations \\
Getting ready \\
How to do it and How it works \\
6: Parallel Computing \\
Introduction \\
Basic concepts of parallel computing \\
Getting ready \\
How to do it and How it works \\
Data movement \\
Getting ready \\
How to do it and How it works \\
Parallel maps and loop operations \\
Getting ready",
}
@InProceedings{Rong:2016:SCD,
author = "H. Rong and J. Park and L. Xiang and T. A. Anderson
and M. Smelyanskiy",
booktitle = "{2016 International Conference on Parallel
Architecture and Compilation Techniques (PACT)}",
title = "{Sparso}: Context-driven optimizations of sparse
linear algebra",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "247--259",
year = "2016",
DOI = "https://doi.org/10.1145/2967938.2967943",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Book{Sengupta:2016:JHP,
author = "Avik Sengupta",
title = "{Julia} high performance: design and develop high
performing programs with {Julia}",
publisher = pub-PACKT,
address = pub-PACKT:adr,
pages = "115",
year = "2016",
ISBN = "1-78588-091-8, 1-78588-782-3 (e-book)",
ISBN-13 = "978-1-78588-091-9, 978-1-78588-782-6 (e-book)",
LCCN = "QA76.76.D47",
bibdate = "Fri Apr 9 05:31:56 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
series = "Community experience distilled",
URL = "http://proquest.tech.safaribooksonline.de/9781785880919",
abstract = "Design and develop high performing programs with
Julia. About This Book Learn to code high reliability
and high performance programs Stand out from the crowd
by developing code that runs faster than your peers'
codes This book is intended for developers who are
interested in high performance technical programming.
Who This Book Is For This book is for beginner and
intermediate Julia programmers who are interested in
high performance technical computing. You will have a
basic familiarity with Julia syntax, and have written
some small programs in the language. What You Will
Learn Discover the secrets behind Julia's speed Get a
sense of the possibilities and limitations of Julia's
performance Analyze the performance of Julia programs
Measure the time and memory taken by Julia programs
Create fast machine code using Julia's type information
Define and call functions without compromising Julia's
performance Understand number types in Julia Use Julia
arrays to write high performance code Get an overview
of Julia's distributed computing capabilities. In
Detail Julia is a high performance, high-level dynamic
language designed to address the requirements of
high-level numerical and scientific computing. Julia
brings solutions to the complexities faced by
developers while developing elegant and high performing
code. Julia High Performance will take you on a journey
to understand the performance characteristics of your
Julia programs, and enables you to utilize the promise
of near C levels of performance in Julia. You will
learn to analyze and measure the performance of Julia
code, understand how to avoid bottlenecks, and design
your program for the highest possible performance. In
this book, you will also see how Julia uses type
information to achieve its performance goals, and how
to use multiple dispatch to help the compiler to emit
high performance machine code. Numbers and their arrays
are obviously the key structures in scientific
computing --- you will see how Julia's design makes
them fast. The last chapter will give you a taste of
Julia's distributed computing capabilities. Style and
approach This is a hands-on manual that will give you
good explanations about the important concepts related
to Julia programming.",
acknowledgement = ack-nhfb,
subject = "Application software; Programming languages
(Electronic computers); Development; Programming
languages (Electronic computers); Application software;
Programming languages (Electronic computers)",
tableofcontents = "Preface \\
1: Julia is Fast \\
Julia \\
fast and dynamic \\
Designed for speed \\
JIT and LLVM \\
Types \\
How fast can Julia be? \\
Summary \\
2: Analyzing Julia Performance \\
Timing Julia code \\
Tic and Toc \\
The @time macro \\
The @timev macro \\
The Julia profiler \\
Using the profiler \\
ProfileView \\
Analyzing memory allocation \\
Using the memory allocation tracker \\
Statistically accurate benchmarking \\
Using Benchmarks.jl \\
Summary \\
3: Types in Julia \\
The Julia type system \\
Using types \\
Multiple dispatch \\
Abstract types \\
Julia's type hierarchy \\
Composite and immutable types \\
Type parameters \\
Type inference \\
Type-stability \\
Definitions \\
Fixing type-instability \\
Performance pitfalls \\
Identifying type-stability \\
Loop variables \\
Kernel methods \\
Types in storage locations \\
Arrays \\
Composite types \\
Parametric composite types \\
Summary \\
4: Functions and Macros \\
Structuring Julia Code for High Performance \\
Using globals \\
The trouble with globals \\
Fixing performance issues with globals \\
Inlining \\
Default inlining \\
Controlling inlining \\
Disabling inlining \\
Closures and anonymous functions \\
FastAnonymous \\
Using macros for performance \\
The Julia compilation process \\
Using macros \\
Evaluating a polynomial \\
Horner's method \\
The Horner macro \\
Generated functions \\
Using generated functions \\
Using generated functions for performance \\
Using named parameters \\
Summary \\
5: Fast Numbers \\
Numbers in Julia \\
Integers \\
Integer overflow \\
BigInt \\
The floating point \\
Unchecked conversions for unsigned integers \\
Trading performance for accuracy \\
The fastmath macro \\
The K-B-N summation \\
Subnormal numbers \\
Subnormal numbers to zero \\
Summary \\
6: Fast Arrays \\
Array internals in Julia \\
Array representation and storage \\
Column-wise storage \\
Bound checking \\
Removing the cost of bound checking \\
Configuring bound checks at startup \\
Allocations and in-place operations \\
Preallocating function output \\
Mutating versions \\
Array views \\
SIMD parallelization \\
Yeppp! \\
Writing generic library functions with arrays \\
Summary \\
7: Beyond the Single Processor \\
Parallelism in Julia \\
Starting a cluster \\
Communication between Julia processes \\
Programming parallel tasks \\
@everywhere \\
@spawn \\
Parallel for \\
Parallel map \\
Distributed arrays \\
Shared arrays \\
Threading \\
Summary \\
Index",
}
@InProceedings{Spoto:2016:JSA,
author = "Fausto Spoto",
booktitle = "{Static Analysis}",
title = "The {Julia} Static Analyzer for {Java}",
publisher = pub-SV,
address = pub-SV:adr,
pages = "39--57",
year = "2016",
DOI = "https://doi.org/10.1007/978-3-662-53413-7_3",
bibdate = "Fri Apr 9 07:54:52 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/java2010.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://link.springer.com/chapter/10.1007/978-3-662-53413-7_3",
acknowledgement = ack-nhfb,
}
@InProceedings{Vidhyaa:2016:HED,
author = "V. G. Vidhyaa and S. A. Rajalakshmi and R. Raghavan
and G. S. V. {Venu Gopal} and R. Gandhiraj",
booktitle = "{2016 International Conference on Communication and
Signal Processing (ICCSP)}",
title = "{Huffman} encoding and decoding algorithm using
{IJulia}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "0587--0591",
year = "2016",
DOI = "https://doi.org/10.1109/ICCSP.2016.7754207",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Book{Voulgaris:2016:JDS,
author = "Zacharias Voulgaris",
title = "{Julia} for data science",
publisher = "Technics Publications LLC",
address = "Basking Ridge, NJ, USA",
pages = "x + 350",
year = "2016",
ISBN = "1-63462-130-1 (print), 1-63462-131-X (Kindle),
1-63462-132-8 (ePub)",
ISBN-13 = "978-1-63462-130-4 (print), 978-1-63462-131-1 (Kindle),
978-1-63462-132-8 (ePub)",
LCCN = "QA76.73.J8 V68 2016",
bibdate = "Thu Apr 8 10:55:28 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
subject = "Julia (Computer program language); Programming
languages (Electronic computers); Application software;
Development; Development; Julia (Computer program
language); Programming languages (Electronic
computers)",
}
@Article{Zhang:2016:MDE,
author = "Weijian Zhang and Nicholas J. Higham",
title = "{Matrix Depot}: an extensible test matrix collection
for {Julia}",
journal = "PeerJ Computer Science",
volume = "2",
pages = "e58:1--e58:25",
month = apr,
year = "2016",
DOI = "https://doi.org/10.7717/peerj-cs.58",
ISSN = "2376-5992",
bibdate = "Tue Apr 23 06:36:55 2024",
bibsource = "https://www.math.utah.edu/pub/bibnet/authors/h/higham-nicholas-john.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
author-dates = "Nicholas John Higham (25 December 1961--20 January
2024)",
journal-URL = "https://peerj.com/cs/",
}
@Article{Bassen:2017:JCM,
author = "David M. Bassen and Michael Vilkhovoy and Mason Minot
and Jonathan T. Butcher and Jeffrey D. Varner",
title = "{JuPOETs}: a constrained multiobjective optimization
approach to estimate biochemical model ensembles in the
{Julia} programming language",
journal = "{BMC} Systems Biology",
volume = "11",
number = "1",
month = jan,
year = "2017",
CODEN = "BSBMCC",
DOI = "https://doi.org/10.1186/s12918-016-0380-2",
ISSN = "1752-0509",
ISSN-L = "1752-0509",
bibdate = "Thu Apr 8 11:13:46 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://link.springer.com/article/10.1186/s12918-016-0380-2",
acknowledgement = ack-nhfb,
}
@Article{Bezanson:2017:JFA,
author = "Jeff Bezanson and Alan Edelman and Stefan Karpinski
and Viral B. Shah",
title = "{Julia}: a Fresh Approach to Numerical Computing",
journal = j-SIAM-REVIEW,
volume = "59",
number = "1",
pages = "65--98",
month = "????",
year = "2017",
CODEN = "SIREAD",
DOI = "https://doi.org/10.1137/141000671",
ISSN = "0036-1445 (print), 1095-7200 (electronic)",
ISSN-L = "0036-1445",
bibdate = "Fri Mar 10 06:16:32 MST 2017",
bibsource = "http://epubs.siam.org/toc/siread/59/1;
https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/siamreview.bib",
acknowledgement = ack-nhfb,
fjournal = "SIAM Review",
journal-URL = "http://epubs.siam.org/sirev",
onlinedate = "January 2017",
}
@InProceedings{Blas:2017:STD,
author = "M. J. Blas and F. Hauque and S. Re and M. Castellaro",
booktitle = "{2017 XLIII Latin American Computer Conference
(CLEI)}",
title = "A support tool designed as didactic material for
teaching and learning programming",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--10",
year = "2017",
DOI = "https://doi.org/10.1109/CLEI.2017.8226382",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Datseris:2017:PDJ,
author = "George Datseris",
title = "\pkg{DynamicalBilliards.jl}: An easy-to-use, modular
and extendable {Julia} package for {Dynamical Billiard}
systems in two dimensions",
journal = j-J-OPEN-SOURCE-SOFT,
volume = "2",
number = "19",
pages = "458:1--458:4",
month = nov,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.21105/joss.00458",
ISSN = "2475-9066",
ISSN-L = "2475-9066",
bibdate = "Thu Sep 13 08:09:35 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/joss.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://joss.theoj.org/papers/10.21105/joss.00458",
acknowledgement = ack-nhfb,
fjournal = "Journal of Open Source Software",
journal-URL = "http://joss.theoj.org/;
https://github.com/openjournals/joss-papers/",
onlinedate = "19 November 2017",
ORCID-numbers = "George Datseris / 0000-0002-6427-2385",
}
@Article{Dunning:2017:JML,
author = "Iain Dunning and Joey Huchette and Miles Lubin",
title = "{JuMP}: a Modeling Language for Mathematical
Optimization",
journal = j-SIAM-REVIEW,
volume = "59",
number = "2",
pages = "295--320",
month = jan,
year = "2017",
CODEN = "SIREAD",
DOI = "https://doi.org/10.1137/15m1020575",
ISSN = "0036-1445 (print), 1095-7200 (electronic)",
ISSN-L = "0036-1445",
bibdate = "Thu Apr 8 08:02:29 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
fjournal = "SIAM Review",
journal-URL = "http://epubs.siam.org/sirev",
keywords = "Julia programming language",
}
@InProceedings{Edelman:2017:MOE,
author = "Alan Edelman",
editor = "{IEEE}",
booktitle = "{2017 IEEE International Conference on Big Data (Big
Data)}",
title = "A more open efficient future for {AI} development and
data science with an introduction to {Julia}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "2--2",
year = "2017",
DOI = "https://doi.org/10.1109/BigData.2017.8257901",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@InProceedings{Fieker:2017:NH,
author = "Claus Fieker and William Hart and Tommy Hofmann and
Fredrik Johansson",
booktitle = "{Proceedings of the 2017 ACM on International
Symposium on Symbolic and Algebraic Computation}",
title = "{Nemo\slash Hecke}",
publisher = pub-ACM,
address = pub-ACM:adr,
month = jul,
year = "2017",
DOI = "https://doi.org/10.1145/3087604.3087611",
bibdate = "Thu Apr 8 08:02:29 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Heirendt:2017:PDJ,
author = "Laurent Heirendt and Ines Thiele and Ronan M. T.
Fleming",
title = "\pkg{DistributedFBA.jl}: High-level, high-performance
flux balance analysis in {Julia}",
journal = j-BIOINFORMATICS,
pages = "1--3",
month = jan,
year = "2017",
DOI = "https://doi.org/10.1093/bioinformatics/btw838",
ISSN = "1367-4803 (print), 1367-4811 (electronic)",
ISSN-L = "1367-4803",
bibdate = "Fri Apr 9 06:05:36 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
fjournal = "Bioinformatics",
journal-URL = "http://bioinformatics.oxfordjournals.org/",
}
@InProceedings{Hylton:2017:PEC,
author = "A. Hylton and G. Henselman-Petrusek and J. Sang and R.
Short",
booktitle = "{2017 IEEE 36th International Performance Computing
and Communications Conference (IPCCC)}",
title = "Performance enhancement of a computational persistent
homology package",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--8",
year = "2017",
DOI = "https://doi.org/10.1109/PCCC.2017.8280468",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Jacobsen:2017:GSJ,
author = "Robert Dahl Jacobsen and Morten Nielsen and Morten
Grud Rasmussen",
title = "Generalized Sampling in {Julia}",
journal = j-J-OPEN-RES-SOFT,
volume = "5",
number = "1",
pages = "12--??",
day = "20",
month = apr,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.5334/jors.157",
ISSN = "2049-9647",
ISSN-L = "2049-9647",
bibdate = "Sat Sep 8 10:03:50 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jors.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://openresearchsoftware.metajnl.com/articles/10.5334/jors.157/",
acknowledgement = ack-nhfb,
fjournal = "Journal of Open Research Software",
journal-URL = "https://openresearchsoftware.metajnl.com/issue/archive/",
}
@Article{Lauwens:2017:PRC,
author = "Ben Lauwens",
title = "\pkg{ResumableFunctions}: {C\#} sharp style generators
for {Julia}",
journal = j-J-OPEN-SOURCE-SOFT,
volume = "2",
number = "18",
pages = "400:1--400:2",
month = oct,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.21105/joss.00400",
ISSN = "2475-9066",
ISSN-L = "2475-9066",
bibdate = "Thu Sep 13 08:09:35 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/csharp.bib;
https://www.math.utah.edu/pub/tex/bib/joss.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://joss.theoj.org/papers/10.21105/joss.00400",
acknowledgement = ack-nhfb,
fjournal = "Journal of Open Source Software",
journal-URL = "http://joss.theoj.org/;
https://github.com/openjournals/joss-papers/",
onlinedate = "31 October 2017",
ORCID-numbers = "Ben Lauwens / 0000-0003-0761-6265",
}
@InProceedings{Milechin:2017:DED,
author = "Lauren Milechin and Vijay Gadepally and Siddharth
Samsi and Jeremy Kepner and Alexander Chen and Dylan
Hutchison",
booktitle = "{2017 IEEE High Performance Extreme Computing
Conference (HPEC)}",
title = "{D4M 3.0}: Extended database and language
capabilities",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--6",
month = sep,
year = "2017",
DOI = "https://doi.org/10.1109/hpec.2017.8091083",
bibdate = "Thu Apr 8 08:02:29 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@InProceedings{Moore:2017:ITI,
author = "D. G. Moore and G. Valentini and S. I. Walker and M.
Levin",
booktitle = "{2017 IEEE Symposium Series on Computational
Intelligence (SSCI)}",
title = "{Inform}: a toolkit for information-theoretic analysis
of complex systems",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--8",
year = "2017",
DOI = "https://doi.org/10.1109/SSCI.2017.8285197",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Book{Nagar:2017:BJP,
author = "Sandeep Nagar",
title = "Beginning {Julia} Programming: For Engineers and
Scientists",
publisher = pub-APRESS,
address = pub-APRESS:adr,
pages = "xxi + 351 + 20 + 18",
year = "2017",
DOI = "https://doi.org/10.1007/978-1-4842-3171-5",
ISBN = "1-4842-3170-8, 1-4842-3171-6",
ISBN-13 = "978-1-4842-3170-8, 978-1-4842-3171-5",
LCCN = "QA76.7-76.73; QA76.76.C65",
bibdate = "Thu Apr 8 10:39:19 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://www.springerlink.com/content/978-1-4842-3171-5",
abstract = "Get started with Julia for engineering and numerical
computing, especially data science, machine learning,
and scientific computing applications. This book
explains how Julia provides the functionality,
ease-of-use and intuitive syntax of R, Python, MATLAB,
SAS, or Stata combined with the speed, capacity, and
performance of C, C++, or Java. You'll learn the OOP
principles required to get you started, then how to do
basic mathematics with Julia. Other core functionality
of Julia that you'll cover, includes working with
complex numbers, rational and irrational numbers,
rings, and fields. Beginning Julia Programming takes
you beyond these basics to harness Julia's powerful
features for mathematical functions in Julia, arrays
for matrix operations, plotting, and more. Along the
way, you also learn how to manage strings, write
functions, work with control flows, and carry out I/O
to implement and leverage the mathematics needed for
your data science and analysis projects. ``Julia walks
like Python and runs like C''. This phrase explains why
Julia is quickly growing as the most favored option for
data analytics and numerical computation. After reading
and using this book, you'll have the essential
knowledge and skills to build your first Julia-based
application. You will: Obtain core skills in Julia
Apply Julia in engineering and science applications
Work with mathematical functions in Julia Use arrays,
strings, functions, control flow, and I/O in Julia
Carry out plotting and display basic graphics.",
acknowledgement = ack-nhfb,
subject = "Computer science; Computer programming; Programming
languages (Electronic computers); Mathematical logic;
Programming Languages, Compilers, Interpreters;
Mathematical Logic and Formal Languages; Big Data;
Programming Techniques; Inform{\'a}tica; Logic,
Symbolic and mathematical; Computer programming;
Computer science.; Logic, Symbolic and mathematical;
Programming languages (Electronic computers)",
tableofcontents = "1. Introduction \\
2. Object Oriented Programming \\
3. Basic Mathematics with Julia \\
4. Complex Numbers \\
5. Rational and Irrational numbers \\
6. Mathematical Functions \\
7. Arrays \\
8. Arrays for Matrix Operations \\
9. Strings \\
10. Functions \\
11. Control Flow \\
12. Input Output \\
13. Plotting",
}
@InProceedings{Nagar:2017:BMJ,
author = "Sandeep Nagar",
booktitle = "{Beginning Julia Programming}",
title = "Basic Math with {Julia}",
publisher = pub-SV,
address = pub-SV:adr,
pages = "??--??",
year = "2017",
DOI = "https://doi.org/10.1007/978-1-4842-3171-5_3",
bibdate = "Fri Apr 9 07:54:52 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://link.springer.com/chapter/10.1007/978-1-4842-3171-5_3",
acknowledgement = ack-nhfb,
}
@Article{Pastell:2017:PWJ,
author = "Matti Pastell",
title = "\pkg{Weave.jl}: Scientific Reports Using {Julia}",
journal = j-J-OPEN-SOURCE-SOFT,
volume = "2",
number = "11",
pages = "204:1--204:1",
month = mar,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.21105/joss.00204",
ISSN = "2475-9066",
ISSN-L = "2475-9066",
bibdate = "Thu Sep 13 08:09:35 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/joss.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/litprog.bib",
URL = "http://joss.theoj.org/papers/10.21105/joss.00204",
acknowledgement = ack-nhfb,
fjournal = "Journal of Open Source Software",
journal-URL = "http://joss.theoj.org/;
https://github.com/openjournals/joss-papers/",
onlinedate = "22 March 2017",
ORCID-numbers = "Matti Pastell / 0000-0002-5810-4801",
}
@InProceedings{Poulding:2017:ART,
author = "S. Poulding and R. Feldt",
booktitle = "{2017 IEEE International Conference on Software
Testing, Verification and Validation (ICST)}",
title = "Automated Random Testing in Multiple Dispatch
Languages",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "333--344",
year = "2017",
DOI = "https://doi.org/10.1109/ICST.2017.37",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Rackauckas:2017:PDJ,
author = "Christopher Rackauckas and Qing Nie",
title = "\pkg{DifferentialEquations.jl} --- A Performant and
Feature-Rich Ecosystem for Solving Differential
Equations in {Julia}",
journal = j-J-OPEN-RES-SOFT,
volume = "5",
number = "1",
pages = "15--??",
day = "25",
month = may,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.5334/jors.151",
ISSN = "2049-9647",
ISSN-L = "2049-9647",
bibdate = "Sat Sep 8 10:03:50 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jors.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://openresearchsoftware.metajnl.com/articles/10.5334/jors.151/",
acknowledgement = ack-nhfb,
fjournal = "Journal of Open Research Software",
journal-URL = "https://openresearchsoftware.metajnl.com/issue/archive/",
}
@Article{Ruthotto:2017:JFJ,
author = "Lars Ruthotto and Eran Treister and Eldad Haber",
title = "{jInv} --- a Flexible {Julia} Package for {PDE}
Parameter Estimation",
journal = j-SIAM-J-SCI-COMP,
volume = "39",
number = "5",
pages = "S702--S722",
month = "????",
year = "2017",
CODEN = "SJOCE3",
DOI = "https://doi.org/10.1137/16M1081063",
ISSN = "1064-8275 (print), 1095-7197 (electronic)",
ISSN-L = "1064-8275",
bibdate = "Fri Jan 12 07:30:22 MST 2018",
bibsource = "http://epubs.siam.org/toc/sjoce3/39/5;
https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/siamjscicomput.bib",
acknowledgement = ack-nhfb,
fjournal = "SIAM Journal on Scientific Computing",
journal-URL = "http://epubs.siam.org/sisc",
onlinedate = "January 2017",
}
@InProceedings{Serrano:2017:MIP,
author = "E. Serrano and J. G. Blas and J. Carretero and M.
Abella and M. Desco",
booktitle = "{2017 17th IEEE/ACM International Symposium on
Cluster, Cloud and Grid Computing (CCGRID)}",
title = "Medical Imaging Processing on a Big Data Platform
Using {Python}: Experiences with Heterogeneous and
Homogeneous Architectures",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "830--837",
year = "2017",
DOI = "https://doi.org/10.1109/CCGRID.2017.56",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/python.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Sinaie:2017:PMP,
author = "Sina Sinaie and Viet Ha Nguyen and Chi Thanh Nguyen
and St{\'e}phane Bordas",
title = "Programming the material point method in {Julia}",
journal = j-ADV-ENG-SOFTWARE,
volume = "105",
number = "??",
pages = "17--29",
month = mar,
year = "2017",
CODEN = "AESODT",
DOI = "https://doi.org/10.1016/j.advengsoft.2017.01.008",
ISSN = "0965-9978 (print), 0141-1195 (electronic)",
ISSN-L = "0965-9978",
bibdate = "Fri Apr 09 05:58:14 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://www.sciencedirect.com/science/article/pii/S0965997816302769",
abstract = "This article presents the implementation of the
material point method (MPM) using Julia. Julia is an
open source, multi-platform, high-level,
high-performance dynamic programming language for
technical computing, with syntax that is familiar to
Matlab and Python programmers. MPM is a hybrid
particle-grid approach that combines the advantages of
Eulerian and Lagrangian methods and is suitable for
complex solid mechanics problems involving contact,
impact and large deformations. We will show that a
Julia based MPM code, which is short, compact and
readable and uses only Julia built in features,
performs much better (with speed up of up to 8) than a
similar Matlab based MPM code for large strain solid
mechanics simulations. We share our experiences of
implementing MPM in Julia and demonstrate that Julia is
a very interesting platform for rapid development in
the field of scientific computing.",
acknowledgement = ack-nhfb,
fjournal = "Advances in Engineering Software",
journal-URL = "https://www.sciencedirect.com/journal/advances-in-engineering-software",
keywords = "Julia, Material point method (MPM), High-performance
dynamic programming language, Technical computing",
}
@InProceedings{Thankachan:2017:IPO,
author = "R. V. Thankachan and E. R. Hein and B. P. Swenson and
J. P. Fairbanks",
booktitle = "{2017 IEEE High Performance Extreme Computing
Conference (HPEC)}",
title = "Integrating productivity-oriented programming
languages with high-performance data structures",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--8",
year = "2017",
DOI = "https://doi.org/10.1109/HPEC.2017.8091068",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@InProceedings{Ahmadi:2018:PET,
author = "Shervin Parvini Ahmadi and Anders Hansson",
editor = "{IEEE}",
booktitle = "{2018 22nd International Conference on System Theory,
Control and Computing (ICSTCC)}",
title = "Parallel Exploitation for Tree-Structured Coupled
Quadratic Programming in {Julia}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "597--602",
year = "2018",
DOI = "https://doi.org/10.1109/ICSTCC.2018.8540646",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
}
@Book{Anonymous:2018:DSA,
author = "Anonymous",
title = "Distributed {L}-shaped Algorithms in {Julia}",
publisher = "KTH, Reglerteknik",
address = "????",
year = "2018",
LCCN = "????",
bibdate = "Fri Jan 3 15:02:48 MST 2025",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-249834",
abstract = "We present LShapedSolvers.jl, a suite of scalable
stochastic programming solvers implemented in the Julia
programming language. The solvers, which are based on
the L-shaped algorithm, run efficiently in parallel,
exploit problem structure, and operate on distributed
data. The implementation introduces several flexible
high-level abstractions that result in a modular design
and simplify the development of algorithm variants. In
addition, we demonstrate how the abstractions available
in the Julia module for distributed computing are
exploited to simplify the implementation of the
parallel algorithms. The performance of the solvers is
evaluated on large-scale problems for finding optimal
orders on the Nordic day-ahead electricity market. With
16 worker cores, the fastest algorithm solves a
distributed problem with 2.5 million variables and 1.5
million linear constraints about 19 times faster than
Gurobi is able to solve the extended form directly.",
acknowledgement = ack-nhfb,
}
@Book{Balbaert:2018:JP,
author = "Ivo Balbaert",
title = "{Julia 1.0} Programming",
publisher = pub-PACKT,
address = pub-PACKT:adr,
pages = "iv + 184",
year = "2018",
ISBN = "1-78899-909-6",
ISBN-13 = "978-1-78899-909-0",
LCCN = "QA76.73.J85 2018",
bibdate = "Thu Apr 8 11:10:55 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://international.scholarvox.com/book/88863229",
abstract = "Enter the exciting world of Julia, a high-performance
language for technical computing Key Features Leverage
Julia's high speed and efficiency for your applications
Work with Julia in a multi-core, distributed, and
networked environment Apply Julia to tackle problems
concurrently and in a distributed environment Book
Description The release of Julia 1.0 is now ready to
change the technical world by combining the high
productivity and ease of use of Python and R with the
lightning-fast speed of C++. Julia 1.0 programming
gives you a head start in tackling your numerical and
data problems. You will begin by learning how to set up
a running Julia platform, before exploring its various
built-in types. With the help of practical examples,
this book walks you through two important collection
types: arrays and matrices. In addition to this, you
will be taken through how type conversions and
promotions work. In the course of the book, you will be
introduced to the homo-iconicity and metaprogramming
concepts in Julia. You will understand how Julia
provides different ways to interact with an operating
system, as well as other languages, and then you'll
discover what macros are. Once you have grasped the
basics, you'll study what makes Julia suitable for
numerical and scientific computing, and learn about the
features provided by Julia. By the end of this book,
you will also have learned how to run external
programs. This book covers all you need to know about
Julia in order to leverage its high speed and
efficiency for your applications. What you will learn
Set up your Julia environment to achieve high
productivity Create your own types to extend the
built-in type system Visualize your data in Julia with
plotting packages Explore the use of built-in macros
for testing and debugging, among other uses Apply Julia
to tackle problems concurrently Integrate Julia with
other languages such as C, Python, and MATLAB Who this
book is for Julia 1.0 Programming is for you if you are
a statistician or data scientist who wants a crash
course in the Julia programming language while building
big data applications. A basic knowledge of mathematics
is needed to understand the various methods that are
used or created during the course of the book to
exploit the capabilities that Julia is designed with.
Downloading the example code for this book You can
download the example code files for all Packt books you
have purchased from your account at
http://www.PacktPub.com.",
acknowledgement = ack-nhfb,
}
@Article{Bezanson:2018:JDP,
author = "Jeff Bezanson and Jiahao Chen and Benjamin Chung and
Stefan Karpinski and Viral B. Shah and Jan Vitek and
Lionel Zoubritzky",
title = "{Julia}: dynamism and performance reconciled by
design",
journal = j-PACMPL,
volume = "2",
number = "OOPSLA",
pages = "120:1--120:23",
month = oct,
year = "2018",
DOI = "https://doi.org/10.1145/3276490",
ISSN = "2475-1421",
bibdate = "Sat Aug 8 07:56:30 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/pacmpl.bib;
https://www.math.utah.edu/pub/tex/bib/python.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3276490",
abstract = "Julia is a programming language for the scientific
community that combines features of productivity
languages, such as Python or MATLAB, with
characteristics of performance-oriented languages, such
as C++ or Fortran. Julia's productivity features
include: dynamic typing, automatic memory management,
rich type annotations, and multiple dispatch. At the
same time, Julia allows programmers to control memory
layout and leverages a specializing just-in-time
compiler to eliminate much of the overhead of those
features. This paper details the design choices made by
the creators of Julia and reflects on the implications
of those choices for performance and usability.",
acknowledgement = ack-nhfb,
articleno = "120",
fjournal = "Proceedings of the ACM on Programming Languages",
journal-URL = "https://pacmpl.acm.org/",
}
@InProceedings{Biel:2018:DSA,
author = "Martin Biel and Mikael Johansson",
editor = "{IEEE}",
booktitle = "{2018 IEEE\slash ACM Parallel Applications Workshop,
Alternatives To MPI (PAW-ATM)}",
title = "Distributed {L}-shaped Algorithms in {Julia}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "57--69",
year = "2018",
DOI = "https://doi.org/10.1109/PAW-ATM.2018.00011",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Blankrot:2018:PPS,
author = "Boaz Blankrot and Clemens Heitzinger",
title = "\pkg{ParticleScattering}: Solving and optimizing
multiple-scattering problems in {Julia}",
journal = j-J-OPEN-SOURCE-SOFT,
volume = "3",
number = "25",
pages = "691:1--691:3",
month = may,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.21105/joss.00691",
ISSN = "2475-9066",
ISSN-L = "2475-9066",
bibdate = "Thu Sep 13 08:09:35 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/joss.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://joss.theoj.org/papers/10.21105/joss.00691",
acknowledgement = ack-nhfb,
fjournal = "Journal of Open Source Software",
journal-URL = "http://joss.theoj.org/;
https://github.com/openjournals/joss-papers/",
onlinedate = "14 May 2018",
ORCID-numbers = "Boaz Blankrot / 0000-0003-3364-9298; Clemens
Heitzinger / 0000-0003-1613-5164",
}
@Book{Bornemann:2018:NLA,
author = "Folkmar Bornemann",
title = "Numerical Linear Algebra: a Concise Introduction with
{MATLAB} and {Julia}",
publisher = "Springer International Publishing",
address = "Cham, Switzerland",
pages = "x + 153",
year = "2018",
DOI = "https://doi.org/10.1007/978-3-319-74222-9",
ISBN = "3-319-74221-3, 3-319-74222-1 (e-book)",
ISBN-13 = "978-3-319-74221-2 (print), 978-3-319-74222-9
(e-book)",
ISSN = "1615-2085",
LCCN = "QA184-205; QA297-299.4",
bibdate = "Thu Apr 8 17:01:32 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/matlab.bib",
series = "Springer Undergraduate Mathematics Series",
abstract = "This book offers an introduction to the
algorithmic-numerical thinking using basic problems of
linear algebra. By focusing on linear algebra, it
ensures a stronger thematic coherence than is otherwise
found in introductory lectures on numerics. The book
highlights the usefulness of matrix partitioning
compared to a component view, leading not only to a
clearer notation and shorter algorithms, but also to
significant runtime gains in modern computer
architectures. The algorithms and accompanying
numerical examples are given in the programming
environment MATLAB, and additionally - in an appendix -
in the future-oriented, freely accessible programming
language Julia. This book is suitable for a two-hour
lecture on numerical linear algebra from the second
semester of a bachelor's degree in mathematics.",
acknowledgement = ack-nhfb,
subject = "Mathematics; Matrix theory; Algebra; Numerical
analysis; Linear and Multilinear Algebras, Matrix
Theory; Matem{\'a}ticas; Algebra.; Mathematics.;
Numerical analysis.",
tableofcontents = "Intro \\
Preface \\
Student's Laboratory \\
Contents \\
I: Computing with Matrices \\
1 What is Numerical Analysis? \\
2 Matrix Calculus \\
3 MATLAB \\
4 Execution Times \\
5 Triangular Matrices \\
6 Unitary Matrices \\
II: Matrix Factorization \\
7 Triangular Decomposition \\
8 Cholesky Decomposition \\
9 QR Decomposition \\
III: Error Analysis \\
10 Error Measures \\
11 Conditioning of a Problem \\
12 Machine Numbers \\
13 Stability of an Algorithm \\
14 Three Exemplary Error Analyses \\
15 Error Analysis of Linear Systems of Equations \\
IV: Least Squares \\
16 Normal Equation17 Orthogonalization \\
V: Eigenvalue Problems \\
18 Basic Concepts \\
19 Perturbation Theory \\
20 Power Iteration \\
21 QR Algorithm \\
Appendix \\
A MATLAB: A Very Short Introduction \\
General Commands \\
Matrices \\
Functions \\
Control Flow \\
Logic Functions \\
Componentwise Operations \\
B Julia: A Modern Alternative to MATLAB \\
C Norms: Recap and Supplement \\
D The Householder Method for QR Decomposition \\
E For the Curious, the Connoisseur, and the Capable \\
Model Backwards Analysis of Iterative Refinement \\
Global Convergence of the QR Algorithm without Shifts
\\
Local Convergence of the QR Algorithm with Shifts \\
A Stochastic Upper Bound of the Spectral Norm \\
F More Exercises \\
Computer Matrices \\
Matrix Factorization \\
Error Analysis \\
Least Squares \\
Elgenvalue Problems \\
Notation \\
Index",
}
@InProceedings{Breiding:2018:PHJ,
author = "Paul Breiding and Sascha Timme",
booktitle = "{Mathematical Software ICMS 2018}",
title = "\pkg{HomotopyContinuation.jl}: a Package for Homotopy
Continuation in {Julia}",
publisher = pub-SV,
address = pub-SV:adr,
pages = "??--??",
year = "2018",
DOI = "https://doi.org/10.1007/978-3-319-96418-8_54",
bibdate = "Fri Apr 9 07:54:52 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://link.springer.com/chapter/10.1007/978-3-319-96418-8_54",
acknowledgement = ack-nhfb,
}
@InProceedings{Coffrin:2018:PJO,
author = "C. Coffrin and R. Bent and K. Sundar and Y. Ng and M.
Lubin",
booktitle = "{2018 Power Systems Computation Conference (PSCC)}",
title = "{PowerModels}. {JL}: An Open-Source Framework for
Exploring Power Flow Formulations",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--8",
year = "2018",
DOI = "https://doi.org/10.23919/PSCC.2018.8442948",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Book{Dan:2018:LJE,
author = "Toomey Dan",
title = "Learning {Jupyter 5}: explore interactive computing
using {Python}, {Java}, {JavaScript}, {R}, {Julia}, and
{JupyterLab}",
publisher = pub-PACKT,
address = pub-PACKT:adr,
pages = "282",
year = "2018",
ISBN = "1-78913-740-3, 1-78913-744-6",
ISBN-13 = "978-1-78913-740-8, 978-1-78913-744-6",
LCCN = "Q183.9; QA76.9.I52 .T666 2018",
bibdate = "Fri Apr 9 05:38:17 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/java2010.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/python.bib;
https://www.math.utah.edu/pub/tex/bib/s-plus.bib",
acknowledgement = ack-nhfb,
tableofcontents = "Preface \\
1: Introduction to Jupyter \\
First look at Jupyter \\
Installing Jupyter \\
Notebook structure \\
Notebook workflow \\
Basic Notebook operations \\
File operations \\
Duplicate \\
Rename \\
Delete \\
Upload \\
New text file \\
New folder \\
New Python 3 \\
Security in Jupyter \\
Security digest \\
Trust options \\
Configuration options for Jupyter \\
Summary \\
2: Jupyter Python Scripting \\
Basic Python in Jupyter \\
Python data access in Jupyter \\
Python pandas in Jupyter \\
Python graphics in Jupyter \\
Python random numbers in Jupyter \\
Summary \\
3: Jupyter R Scripting \\
Adding R scripting to your installation \\
Adding R scripts to Jupyter on macOS \\
Adding R scripts to Jupyter on Windows \\
Adding R packages to Jupyter \\
R limitations in Jupyter \\
Basic R in Jupyter \\
R dataset access \\
R visualizations in Jupyter \\
R 3D graphics in Jupyter \\
R 3D scatterplot in Jupyter \\
R cluster analysis \\
R forecasting \\
R machine learning \\
Dataset \\
Summary \\
4: Jupyter Julia Scripting \\
Adding Julia scripting to your installation \\
Adding Julia scripts to Jupyter \\
Adding Julia packages to Jupyter \\
Basic Julia in Jupyter \\
Julia limitations in Jupyter \\
Standard Julia capabilities \\
Julia visualizations in Jupyter \\
Julia Gadfly scatterplot \\
Julia Gadfly histogram \\
Julia Winston plotting \\
Julia Vega plotting \\
Julia PyPlot plotting \\
Julia parallel processing \\
Julia control flow \\
Julia regular expressions \\
Julia unit testing \\
Summary \\
5: Jupyter Java Coding \\
Adding the Java kernel to your installation \\
Installing Java 9 or later \\
A Jupyter environment is required \\
Configuring IJava \\
Downloading the IJava project from GitHub \\
Building and installing the kernel \\
Available options \\
Jupyter Java console \\
Jupyter Java output \\
Java Optional \\
Java compiler errors \\
Java lambdas \\
Java Collections \\
Java streams \\
Java summary statistics \\
Summary \\
6: Jupyter JavaScript Coding \\
Adding JavaScript scripting to your installation \\
Adding JavaScript scripts to Jupyter on macOS or
Windows \\
JavaScript Hello World Jupyter Notebook \\
Adding JavaScript packages to Jupyter \\
Basic JavaScript in Jupyter \\
JavaScript limitations in Jupyter \\
Node.js d3 package \\
Node.js stats-analysis package \\
Node.js JSON handling \\
Node.js canvas package \\
Node.js plotly package \\
Node.js asynchronous threadsNode.js decision-tree
package \\
Summary \\
7: Jupyter Scala \\
Installing the Scala kernel \\
Scala data access in Jupyter \\
Scala array operations \\
Scala random numbers in Jupyter \\
Scala closures \\
Scala higher-order functions \\
Scala pattern matching \\
Scala case classes \\
Scala immutability \\
Scala collections \\
Named arguments \\
Scala traits \\
Summary \\
8: Jupyter and Big Data \\
Apache Spark \\
Installing Spark on macOS \\
Windows install \\
First Spark script \\
Spark word count \\
Sorted word count \\
Estimate pi \\
Log file examination \\
Spark primes \\
Spark text file analysis",
}
@Article{Datseris:2018:PDJ,
author = "George Datseris",
title = "\pkg{DynamicalSystems.jl}: A {Julia} software library
for chaos and nonlinear dynamics",
journal = j-J-OPEN-SOURCE-SOFT,
volume = "3",
number = "23",
pages = "598:1--598:5",
month = mar,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.5334/jors.151",
ISSN = "2475-9066",
ISSN-L = "2475-9066",
bibdate = "Thu Sep 13 08:09:35 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/joss.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://joss.theoj.org/papers/10.21105/joss.00598",
acknowledgement = ack-nhfb,
fjournal = "Journal of Open Source Software",
journal-URL = "http://joss.theoj.org/;
https://github.com/openjournals/joss-papers/",
onlinedate = "14 March 2018",
ORCID-numbers = "George Datseris / 0000-0002-6427-2385",
}
@Article{Gawron:2018:PQJ,
author = "Piotr Gawron and Dariusz Kurzyk and {\L}ukasz Pawela",
editor = "Nicholas Chancellor",
title = "\pkg{QuantumInformation.jl} --- a {Julia} package for
numerical computation in quantum information theory",
journal = j-PLOS-ONE,
volume = "13",
number = "12",
pages = "e0209358",
month = dec,
year = "2018",
CODEN = "POLNCL",
DOI = "https://doi.org/10.1371/journal.pone.0209358",
ISSN = "1932-6203",
bibdate = "Thu Apr 8 08:02:29 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
fjournal = "PLoS One",
journal-URL = "http://www.plosone.org/",
keywords = "Julia programming language",
}
@Article{Hoffimann:2018:PGJ,
author = "J{\'u}lio Hoffimann",
title = "\pkg{GeoStats.jl} --- High-performance geostatistics
in {Julia}",
journal = j-J-OPEN-SOURCE-SOFT,
volume = "3",
number = "24",
pages = "692:1--692:4",
month = apr,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.21105/joss.00692",
ISSN = "2475-9066",
ISSN-L = "2475-9066",
bibdate = "Thu Sep 13 08:09:35 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/joss.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://joss.theoj.org/papers/10.21105/joss.00692",
acknowledgement = ack-nhfb,
fjournal = "Journal of Open Source Software",
journal-URL = "http://joss.theoj.org/;
https://github.com/openjournals/joss-papers/",
onlinedate = "25 April 2018",
ORCID-numbers = "J{\'u}lio Hoffimann / 0000-0003-2789-297X",
}
@InCollection{Humenberger:2018:AJT,
author = "Andreas Humenberger and Maximilian Jaroschek and Laura
Kov{\'a}cs",
booktitle = "{Lecture Notes in Computer Science}",
title = "\pkg{Aligator.jl} --- a {Julia} Package for Loop
Invariant Generation",
publisher = "Springer International Publishing",
address = "Cham, Switzerland",
pages = "111--117",
year = "2018",
DOI = "https://doi.org/10.1007/978-3-319-96812-4_10",
bibdate = "Thu Apr 8 08:02:29 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@InProceedings{Humenberger:2018:PAJ,
author = "Andreas Humenberger and Maximilian Jaroschek and Laura
Kov{\'a}cs",
booktitle = "{Intelligent Computer Mathematics}",
title = "\pkg{Aligator.jl} --- a {Julia} Package for Loop
Invariant Generation",
publisher = pub-SV,
address = pub-SV:adr,
pages = "??--??",
year = "2018",
DOI = "https://doi.org/10.1007/978-3-319-96812-4_10",
bibdate = "Fri Apr 9 07:54:52 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://link.springer.com/chapter/10.1007/978-3-319-96812-4_10",
acknowledgement = ack-nhfb,
}
@Article{Innes:2018:PFE,
author = "Mike Innes",
title = "\pkg{Flux}: Elegant machine learning with {Julia}",
journal = j-J-OPEN-SOURCE-SOFT,
volume = "3",
number = "25",
pages = "602:1--602:1",
month = may,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.21105/joss.00602",
ISSN = "2475-9066",
ISSN-L = "2475-9066",
bibdate = "Thu Sep 13 08:09:35 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/joss.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://joss.theoj.org/papers/10.21105/joss.00602",
acknowledgement = ack-nhfb,
fjournal = "Journal of Open Source Software",
journal-URL = "http://joss.theoj.org/;
https://github.com/openjournals/joss-papers/",
onlinedate = "03 May 2018",
ORCID-numbers = "Mike Innes / 0000-0003-0788-0242",
}
@Book{Kaminski:2018:JPC,
author = "Bogumi{\l} Kami{\'n}ski and Przemys{\l}aw Szufel",
title = "{Julia 1.0} Programming Cookbook: over 100 numerical
and distributed computing recipes for your daily data
science workflow",
publisher = pub-PACKT,
address = pub-PACKT:adr,
pages = "xiii + 439",
year = "2018",
ISBN = "1-78899-836-7 (paperback)",
ISBN-13 = "978-1-78899-836-9 (paperback)",
LCCN = "QA76.73.J85",
bibdate = "Thu Apr 8 10:52:14 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
abstract = "Discover the new features and widely used packages in
Julia to solve complex computational problems in your
statistical applications. Key Features Address the core
problems of programming in Julia with the most popular
packages for common tasks Tackle issues while working
with Databases and Parallel data processing with Julia
Explore advanced features such as metaprogramming,
functional programming, and user defined types Book
Description Julia, with its dynamic nature and
high-performance, provides comparatively minimal time
for the development of computational models with
easy-to-maintain computational code. This book will be
your solution-based guide as it will take you through
different programming aspects with Julia. Starting with
the new features of Julia 1.0, each recipe addresses a
specific problem, providing a solution and explaining
how it works. You will work with the powerful Julia
tools and data structures along with the most popular
Julia packages. You will learn to create vectors,
handle variables, and work with functions. You will be
introduced to various recipes for numerical computing,
distributed computing, and achieving high performance.
You will see how to optimize data science programs with
parallel computing and memory allocation. We will look
into more advanced concepts such as metaprogramming and
functional programming. Finally, you will learn how to
tackle issues while working with databases and data
processing, and will learn about on data science
problems, data modeling, data analysis, data
manipulation, parallel processing, and cloud computing
with Julia. By the end of the book, you will have
acquired the skills to work more effectively with your
data What you will learn Boost your code's performance
using Julia's unique features Organize data in to
fundamental types of collections: arrays and
dictionaries Organize data science processes within
Julia and solve related problems Scale Julia
computations with cloud computing Write data to IO
streams with Julia and handle web transfer Define your
own immutable and mutable types Speed up the
development process using metaprogramming Who this book
is for This book is for developers who would like to
enhance their Julia programming skills and would like
to get some quick solutions to their common programming
problems. Basic Julia programming knowledge is assumed.
Downloading the example code for this book. \ldots{}",
acknowledgement = ack-nhfb,
}
@Article{Kemmer:2018:NJE,
author = "Thomas Kemmer and Sergej Rjasanow and Andreas
Hildebrandt",
title = "\pkg{NESSie.jl} --- Efficient and intuitive finite
element and boundary element methods for nonlocal
protein electrostatics in the {Julia} language",
journal = j-J-COMPUT-SCI,
volume = "28",
pages = "193--203",
month = sep,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1016/j.jocs.2018.08.008",
ISSN = "1877-7503 (print), 1877-7511 (electronic)",
ISSN-L = "1877-7503",
bibdate = "Fri Apr 9 15:22:25 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jcomputsci.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://www.sciencedirect.com/science/article/pii/S187775031730738X",
abstract = "The development of scientific software can be
generally characterized by an initial phase of rapid
prototyping and the subsequent transition to
computationally efficient production code.
Unfortunately, most programming languages are not
well-suited for both tasks at the same time, commonly
resulting in a considerable extension of the
development time. The cross-platform and open-source
Julia language aims at closing the gap between
prototype and production code by providing a usability
comparable to Python or MATLAB alongside
high-performance capabilities known from C and C++ in a
single programming language. In this paper, we present
efficient protein electrostatics computations as a
showcase example for Julia. More specifically, we
present both finite element and boundary element
solvers for computing electrostatic potentials of
proteins in structured solvents. By modeling the latter
in an implicit but nonlocal fashion, we account for
correlation of molecular polarization due to the
solvent structure around the solute and sustain
accuracy without suffering from infeasible runtimes as
compared to the explicit case. In this context, we show
that our implementation is on par with optimized C code
and highlight the components of the implementation that
can be transferred to more general tasks.",
acknowledgement = ack-nhfb,
ajournal = "J. Comput. Sci.",
fjournal = "Journal of Computational Science",
journal-URL = "https://www.sciencedirect.com/journal/journal-of-computational-science",
keywords = "Protein electrostatics, Finite element method,
Boundary element method, Julia language",
}
@Article{Kieffer:2018:IBC,
author = "Jean Kieffer and Luca {De Feo}",
title = "Isogeny-based cryptography in {Julia\slash Nemo}: a
case study",
journal = j-ACM-COMM-COMP-ALGEBRA,
volume = "52",
number = "4",
pages = "130--132",
month = dec,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3338637.3338643",
ISSN = "1932-2232 (print), 1932-2240 (electronic)",
ISSN-L = "1932-2232",
bibdate = "Wed Oct 23 07:34:18 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/sigsam.bib",
abstract = "The Couveignes--Rostovtsev--Stolbunov key-exchange
protocol based on isogenies of elliptic curves is of
interest because it may resist quantum attacks, but its
efficient implementation remains a challenge. We
briefly present the computations involved, and
efficient algorithms to achieve the critical steps,
with timing results for our implementations in Sage and
Julia\slash Nemo.",
acknowledgement = ack-nhfb,
fjournal = "ACM Communications in Computer Algebra",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1000",
}
@Article{Kramer:2018:PQJ,
author = "Sebastian Kr{\"a}mer and David Plankensteiner and
Laurin Ostermann and Helmut Ritsch",
title = "\pkg{QuantumOptics.jl}: a {Julia} framework for
simulating open quantum systems",
journal = j-COMP-PHYS-COMM,
volume = "227",
number = "??",
pages = "109--116",
month = jun,
year = "2018",
CODEN = "CPHCBZ",
DOI = "https://doi.org/10.1016/j.cpc.2018.02.004",
ISSN = "0010-4655 (print), 1879-2944 (electronic)",
ISSN-L = "0010-4655",
bibdate = "Fri Mar 16 13:51:08 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/compphyscomm2010.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://www.sciencedirect.com/science/article/pii/S0010465518300328",
abstract = "We present an open source computational framework
geared towards the efficient numerical investigation of
open quantum systems written in the Julia programming
language. Built exclusively in Julia and based on
standard quantum optics notation, the toolbox offers
speed comparable to low-level statically typed
languages, without compromising on the accessibility
and code readability found in dynamic languages. After
introducing the framework, we highlight its features
and showcase implementations of generic quantum models.
Finally, we compare its usability and performance to
two well-established and widely used numerical quantum
libraries. Program summary Program Title:
QuantumOptics.jl Program Files doi:
https://doi.org/10.17632/3696r5jhm4.1 Licensing
provisions: MIT Programming language: Julia
Supplementary material: Full list of functions (API) as
html Nature of problem: Dynamics of open quantum
systems Solution method: Numerically solving the
Schr{\"o}dinger or master equation or a Monte Carlo
wave-function approach. Additional comments including
Restrictions and Unusual features: The framework may be
used for problems that fulfill the necessary conditions
such that they can be described by a Schr{\"o}dinger or
master equation. Furthermore, the aim is to efficiently
and easily simulate systems of moderate size rather
than pushing the limits of what is possible
numerically.",
acknowledgement = ack-nhfb,
fjournal = "Computer Physics Communications",
journal-URL = "http://www.sciencedirect.com/science/journal/00104655",
keywords = "Julia programming language; Numerics; Quantum
mechanics; Quantum optics",
}
@InCollection{Kroger:2018:JOS,
author = "Ole Kr{\"o}ger and Carleton Coffrin and Hassan Hijazi
and Harsha Nagarajan",
booktitle = "{Integration of Constraint Programming, Artificial
Intelligence, and Operations Research}",
title = "{Juniper}: an Open-Source Nonlinear Branch-and-Bound
Solver in {Julia}",
publisher = "Springer International Publishing",
address = "Cham, Switzerland",
pages = "377--386",
year = "2018",
DOI = "https://doi.org/10.1007/978-3-319-93031-2_27",
bibdate = "Thu Apr 8 08:02:29 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://link.springer.com/chapter/10.1007/978-3-319-93031-2_27",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@InProceedings{Lage-Freitas:2018:ADS,
author = "A. Lage-Freitas and R. P. Ribeiro and N. D. C.
Oliveira and A. C. Frery",
booktitle = "{IGARSS 2018 --- 2018 IEEE International Geoscience
and Remote Sensing Symposium}",
title = "An Automatic Deployment Support for Processing Remote
Sensing Data in the Cloud",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "2054--2057",
year = "2018",
DOI = "https://doi.org/10.1109/IGARSS.2018.8518964",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Infra.jl; Julia Master/Worker programming; Julia
programming language",
}
@Article{Landeros:2018:PBJ,
author = "Alfonso Landeros and Timothy Stutz and Kevin L. Keys
and Alexander Alekseyenko and Janet S. Sinsheimer and
Kenneth Lange and Mary E. Sehl",
title = "\pkg{BioSimulator.jl}: Stochastic simulation in
{Julia}",
journal = j-COMPUT-METH-PROG-BIOMED,
volume = "167",
pages = "23--35",
month = dec,
year = "2018",
CODEN = "CMPBEK",
DOI = "https://doi.org/10.1016/j.cmpb.2018.09.009",
ISSN = "0169-2607 (print), 1872-7565 (electronic)",
ISSN-L = "0169-2607",
bibdate = "Thu Apr 8 08:02:29 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://www.sciencedirect.com/science/article/pii/S0169260718301822",
abstract = "Background and Objectives: Biological systems with
intertwined feedback loops pose a challenge to
mathematical modeling efforts. Moreover, rare events,
such as mutation and extinction, complicate system
dynamics. Stochastic simulation algorithms are useful
in generating time-evolution trajectories for these
systems because they can adequately capture the
influence of random fluctuations and quantify rare
events. We present a simple and flexible package,
BioSimulator.jl, for implementing the Gillespie
algorithm, -leaping, and related stochastic simulation
algorithms. The objective of this work is to provide
scientists across domains with fast, user-friendly
simulation tools. Methods: We used the high-performance
programming language Julia because of its emphasis on
scientific computing. Our software package implements a
suite of stochastic simulation algorithms based on
Markov chain theory. We provide the ability to (a)
diagram Petri Nets describing interactions, (b) plot
average trajectories and attached standard deviations
of each participating species over time, and (c)
generate frequency distributions of each species at a
specified time. Results: BioSimulator.jl's interface
allows users to build models programmatically within
Julia. A model is then passed to the simulate routine
to generate simulation data. The built-in tools allow
one to visualize results and compute summary
statistics. Our examples highlight the broad
applicability of our software to systems of varying
complexity from ecology, systems biology, chemistry,
and genetics. Conclusion: The user-friendly nature of
BioSimulator.jl encourages the use of stochastic
simulation, minimizes tedious programming efforts, and
reduces errors during model specification.",
acknowledgement = ack-nhfb,
fjournal = "Computer Methods and Programs in Biomedicine",
keywords = "-leaping; Gillespie algorithm; Julia programming
language; Stochastic simulation; Systems biology",
}
@Book{McNicholas:2018:DSJ,
author = "Paul D. McNicholas and Peter A. Tait",
title = "Data Science with {Julia}",
publisher = "Taylor and Francis, CRC Press",
address = "Boca Raton, FL, USA",
pages = "241",
year = "2018",
DOI = "https://doi.org/10.1201/9781351013673",
ISBN = "1-138-49998-6 (paperback), 1-351-01364-5 (e-book:
Mobipocket), 1-351-01365-3 (e-book), 1-351-01366-1
(e-book: PDF), 1-351-01367-X (e-book)",
ISBN-13 = "978-1-138-49998-0 (paperback), 978-1-351-01364-2
(e-book: Mobipocket), 978-1-351-01365-9 (e-book),
978-1-351-01366-6 (e-book: PDF), 978-1-351-01367-3
(e-book)",
LCCN = "QA76.73.J85 M37 2018",
bibdate = "Sat Sep 7 07:35:13 MDT 2019",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/python.bib",
abstract = "``This book is a great way to both start learning data
science through the promising Julia language and to
become an efficient data scientist.''- Professor
Charles Bouveyron, INRIA Chair in Data Science,
Universitae Caote d'Azur, Nice, France. Julia, an
open-source programming language, was created to be as
easy to use as languages such as R and Python while
also as fast as C and Fortran. An accessible,
intuitive, and highly efficient base language with
speed that exceeds R and Python, makes Julia a
formidable language for data science. Using well known
data science methods that will motivate the reader,
Data Science with Julia will get readers up to speed on
key features of the Julia language and illustrate its
facilities for data science and machine learning work.
Features: Covers the core components of Julia as well
as packages relevant to the input, manipulation and
representation of data. Discusses several important
topics in data science including supervised and
unsupervised learning. Reviews data visualization using
the Gadfly package, which was designed to emulate the
very popular ggplot2 package in R. Readers will learn
how to make many common plots and how to visualize
model results. Presents how to optimize Julia code for
performance. Will be an ideal source for people who
already know R and want to learn how to use Julia
(though no previous knowledge of R or any other
programming language is required). The advantages of
Julia for data science cannot be understated. Besides
speed and ease of use, there are already over 1,900
packages available and Julia can interface (either
directly or through packages) with libraries written in
R, Python, Matlab, C, C++ or Fortran. The book is for
senior undergraduates, beginning graduate students, or
practicing data scientists who want to learn how to use
Julia for data science.",
acknowledgement = ack-nhfb,
subject = "Julia (Computer program language); Data structures
(Computer science); COMPUTERS / Data Modeling and
Design; BUSINESS and ECONOMICS / Statistics;
MATHEMATICS / Probability and Statistics / General;
Data structures (Computer science); Julia (Computer
program language)",
tableofcontents = "Cover \\
Half Title \\
Title Page \\
Copyright Page \\
Dedication \\
Table of Contents \\
Foreword \\
Preface \\
About the Authors \\
1: Introduction \\
1.1 DATA SCIENCE \\
1.2 BIG DATA \\
1.3 JULIA \\
1.4 JULIA AND R PACKAGES \\
1.5 DATASETS \\
1.5.1 Overview \\
1.5.2 Beer Data \\
1.5.3 Coffee Data \\
1.5.4 Leptograpsus Crabs Data \\
1.5.5 Food Preferences Data \\
1.5.6 x2 Data \\
1.5.7 Iris Data \\
1.6 OUTLINE OF THE CONTENTS OF THIS MONOGRAPH \\
2: Core Julia \\
2.1 VARIABLE NAMES \\
2.2 OPERATORS \\
2.3 TYPES \\
2.3.1 Numeric \\
2.3.2 Floats \\
2.3.3 Strings \\
2.3.4 Tuples \\
2.4 DATA STRUCTURES \\
2.4.1 Arrays \\
2.4.2 Dictionaries \\
2.5 CONTROL FLOW \\
2.5.1 Compound Expressions \\
2.5.2 Conditional Evaluation \\
2.5.3 Loops \\
2.5.3.1 Basics \\
2.5.3.2 Loop termination \\
2.5.3.3 Exception handling \\
2.6 FUNCTIONS \\
3: Working with Data \\
3.1 DATAFRAMES \\
3.2 CATEGORICAL DATA \\
3.3 INPUT/OUTPUT \\
3.4 USEFUL DATAFRAME FUNCTIONS \\
3.5 SPLIT-APPLY-COMBINE STRATEGY \\
3.6 QUERY. JL \\
4: Visualizing Data \\
4.1 GADFLY. JL \\
4.2 VISUALIZING UNIVARIATE DATA \\
4.3 DISTRIBUTIONS \\
4.4 VISUALIZING BIVARIATE DATA \\
4.5 ERROR BARS \\
4.6 FACETS \\
4.7 SAVING PLOTS \\
5: Supervised Learning \\
5.1 INTRODUCTION \\
5.2 CROSS-VALIDATION \\
5.2.1 Overview \\
5.2.2 K-Fold Cross-Validation \\
5.3 K-NEAREST NEIGHBOURS CLASSIFICATION \\
5.4 CLASSIFICATION AND REGRESSION TREES \\
5.4.1 Overview \\
5.4.2 Classification Trees \\
5.4.3 Regression Trees \\
5.4.4 Comments \\
5.5 BOOTSTRAP \\
5.6 RANDOM FORESTS \\
5.7 GRADIENT BOOSTING \\
5.7.1 Overview \\
5.7.2 Beer Data \\
5.7.3 Food Data \\
5.8 COMMENTS \\
6: Unsupervised Learning \\
6.1 INTRODUCTION \\
6.2 PRINCIPAL COMPONENTS ANALYSIS \\
6.3 PROBABILISTIC PRINCIPAL COMPONENTS ANALYSIS \\
6.4 EM ALGORITHM FOR PPCA \\
6.4.1 Background: EM Algorithm \\
6.4.2 E-step \\
6.4.3 M-step \\
6.4.4 Woodbury Identity \\
6.4.5 Initialization \\
6.4.6 Stopping Rule \\
6.4.7 Implementing the EM Algorithm for PPCA \\
6.4.8 Comments \\
6.5 K-MEANS CLUSTERING \\
6.6 MIXTURE OF PROBABILISTIC PRINCIPAL COMPONENTS
ANALYZERS \\
6.6.1 Model \\
6.6.2 Parameter Estimation \\
6.6.3 Illustrative Example: Coffee Data \\
6.7 COMMENTS \\
7: R Interoperability \\
7.1 ACCESSING R DATASETS \\
7.2 INTERACTING WITH R \\
7.3 EXAMPLE: CLUSTERING AND DATA REDUCTION FOR THE
COFFEE DATA \\
7.3.1 Coffee Data \\
7.3.2 PGMM Analysis \\
7.3.3 VSCC Analysis \\
7.4 EXAMPLE: FOOD DATA \\
7.4.1 Overview \\
7.4.2 Random Forests \\
APPENDIX A: Julia and R Packages Used Herein \\
APPENDIX B: Variables for Food Data \\
APPENDIX C: Useful Mathematical Results \\
C.1 BRIEF OVERVIEW OF EIGENVALUES \\
C.2 SELECTED LINEAR ALGEBRA RESULTS \\
C.3 MATRIX CALCULUS RESULTS \\
APPENDIX D: Performance Tips \\
D.1 FLOATING POINT NUMBERS \\
D.1.1 Do Not Test for Equality \\
D.1.2 Use Logarithms for Division \\
D.1.3 Subtracting Two Nearly Equal Numbers \\
D.2 JULIA PERFORMANCE \\
D.2.1 General Tips \\
D.2.2 Array Processing \\
D.2.3 Separate Core Computations \\
APPENDIX E: Linear Algebra Functions \\
E.1 VECTOR OPERATIONS \\
E.2 MATRIX OPERATIONS \\
E.3 MATRIX DECOMPOSITIONS \\
References \\
Index",
}
@Article{Mogensen:2018:POM,
author = "Patrick K. Mogensen and Asbj{\o}rn N. Riseth",
title = "\pkg{Optim}: A mathematical optimization package for
{Julia}",
journal = j-J-OPEN-SOURCE-SOFT,
volume = "3",
number = "24",
pages = "615:1--615:3",
month = apr,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.21105/joss.00615",
ISSN = "2475-9066",
ISSN-L = "2475-9066",
bibdate = "Thu Sep 13 08:09:35 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/joss.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://joss.theoj.org/papers/10.21105/joss.00615",
acknowledgement = ack-nhfb,
fjournal = "Journal of Open Source Software",
journal-URL = "http://joss.theoj.org/;
https://github.com/openjournals/joss-papers/",
onlinedate = "04 April 2018",
ORCID-numbers = "Patrick K. Mogensen / 0000-0002-4910-1932; Asbj{\o}rn
N. Riseth / 0000-0002-5861-7885",
}
@Article{Nardelli:2018:JSR,
author = "Francesco Zappa Nardelli and Julia Belyakova and Artem
Pelenitsyn and Benjamin Chung and Jeff Bezanson and Jan
Vitek",
title = "{Julia} subtyping: a rational reconstruction",
journal = j-PACMPL,
volume = "2",
number = "OOPSLA",
pages = "113:1--113:27",
month = oct,
year = "2018",
DOI = "https://doi.org/10.1145/3276483",
ISSN = "2475-1421",
bibdate = "Sat Aug 8 07:56:30 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/pacmpl.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3276483",
abstract = "Programming languages that support multiple dispatch
rely on an expressive notion of subtyping to specify
method applicability. In these languages, type
annotations on method declarations are used to select,
out of a potentially large set of methods, the one that
is most appropriate for a particular tuple of
arguments. Julia is a language for scientific computing
built around multiple dispatch and an expressive
subtyping relation. This paper provides the first
formal definition of Julia's subtype relation and
motivates its design. We validate our specification
empirically with an implementation of our definition
that we compare against the existing Julia
implementation on a collection of real-world programs.
Our subtype implementation differs on 122 subtype tests
out of 6,014,476. The first 120 differences are due to
a bug in Julia that was fixed once reported; the
remaining 2 are under discussion.",
acknowledgement = ack-nhfb,
articleno = "113",
fjournal = "Proceedings of the ACM on Programming Languages",
journal-URL = "https://pacmpl.acm.org/",
}
@InProceedings{Regier:2018:CVU,
author = "J. Regier and K. Pamnany and K. Fischer and A. Noack
and M. Lam and J. Revels and S. Howard and R. Giordano
and D. Schlegel and J. McAuliffe and R. Thomas and
Prabhat",
booktitle = "{2018 IEEE International Parallel and Distributed
Processing Symposium (IPDPS)}",
title = "Cataloging the Visible Universe Through {Bayesian}
Inference at Petascale",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "44--53",
year = "2018",
DOI = "https://doi.org/10.1109/IPDPS.2018.00015",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
abstract = "Astronomical catalogs derived from wide-field imaging
surveys are an important tool for understanding the
Universe. We construct an astronomical catalog from 55
TB of imaging data using Celeste, a Bayesian
variational inference code written entirely in the
high-productivity programming language Julia. Using
over 1.3 million threads on 650,000 Intel Xeon Phi
cores of the Cori Phase II supercomputer, Celeste
achieves a peak rate of 1.54 DP PFLOP/s. Celeste is
able to jointly optimize parameters for 188M stars and
galaxies, loading and processing 178 TB across 8192
nodes in 14.6 minutes. To achieve this, Celeste
exploits parallelism at multiple levels (cluster, node,
and thread) and accelerates I/O through Cori's Burst
Buffer. Julia's native performance enables Celeste to
employ high-level constructs without resorting to
hand-written or generated low-level code
(C/C++/Fortran), and yet achieve petascale
performance.",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Book{Salceanu:2018:JPP,
author = "Adrian Salceanu",
title = "{Julia} programming projects: learn {Julia 1.x} by
building apps for data analysis, visualization, machine
learning, and the {Web}",
publisher = pub-PACKT,
address = pub-PACKT:adr,
pages = "ix + 482",
year = "2018",
ISBN = "1-78829-725-3",
ISBN-13 = "978-1-78829-274-0, 978-1-78829-725-7 (e-book)",
LCCN = "QA76.73.J85",
bibdate = "Thu Apr 8 10:45:11 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://proquest.safaribooksonline.com/?fpi=9781788292740",
abstract = "A step-by-step guide that demonstrates how to build
simple-to-advanced applications through examples in
Julia Lang 1.x using modern tools Key Features Work
with powerful open-source libraries for data wrangling,
analysis, and visualization Develop full-featured,
full-stack web applications Learn to perform supervised
and unsupervised machine learning and time series
analysis with Julia Book Description Julia is a new
programming language that offers a unique combination
of performance and productivity. Its powerful features,
friendly syntax, and speed are attracting a growing
number of adopters from Python, R, and Matlab,
effectively raising the bar for modern general and
scientific computing. After six years in the making,
Julia has reached version 1.0. Now is the perfect time
to learn it, due to its large-scale adoption across a
wide range of domains, including fintech, biotech,
education, and AI. Beginning with an introduction to
the language, Julia Programming Projects goes on to
illustrate how to analyze the Iris dataset using
DataFrames. You will explore functions and the type
system, methods, and multiple dispatch while building a
web scraper and a web app. Next, you'll delve into
machine learning, where you'll build a books
recommender system. You will also see how to apply
unsupervised machine learning to perform clustering on
the San Francisco business database. After
metaprogramming, the final chapters will discuss dates
and time, time series analysis, visualization, and
forecasting. We'll close with package development,
documenting, testing and benchmarking. By the end of
the book, you will have gained the practical knowledge
to build real-world applications in Julia. What you
will learn Leverage Julia's strengths, its top
packages, and main IDE options Analyze and manipulate
datasets using Julia and DataFrames Write complex code
while building real-life Julia applications Develop and
run a web app using Julia and the HTTP package Build a
recommender system using supervised machine learning
Perform exploratory data analysis Apply unsupervised
machine learning algorithms Perform time series data
analysis, visualization, and forecasting Who this book
is for Data scientists, statisticians, business
analysts, and developers who are interested in learning
how to use Julia to crunch numbers, analyze data and
build apps will find this book useful. A basic
knowledge of programming is assumed.",
acknowledgement = ack-nhfb,
subject = "Julia (Computer program language); Application
software; Development; Computer programs; COMPUTERS /
Programming Languages / General; Computer programs;
Julia (Computer program language)",
tableofcontents = "Cover \\
Title Page \\
Copyright and Credits \\
Dedication \\
About Packt \\
Contributors \\
Table of Contents \\
Preface \\
1: Getting Started with Julia Programming \\
Technical requirements \\
Why Julia? \\
Good performance \\
Concise, readable, and intuitive syntax \\
Powerful and productive dynamic type system \\
Designed for parallelism and distributed computation
\\
Efficient intercommunication with other languages \\
Powerful REPL and shell-like capabilities \\
And more \ldots{} \\
Installing Julia \\
Windows \\
Official Windows installer \\
Using Chocolatey \\
Windows Subsystem for Linux \\
macOS \\
Official image \\
HomebrewLinux and FreeBSDDocker \\
JuliaPro \\
JuliaBox \\
Choosing an IDE \\
Juno (Atom) \\
Visual Studio Code \\
IJulia (JuliaBox) \\
Other options \\
Getting started with Julia \\
The Julia REPL \\
Interacting with the REPL \\
The ans variable \\
Prompt pasting \\
Tab completion \\
Cleaning the REPL scope \\
Additional REPL modes \\
Accessing the documentation with the help mode \\
Shell mode \\
Search modes \\
The startup.jl file \\
REPL hooks \\
Exiting the REPL \\
The package system \\
Adding a package \\
OhMyREPL \\
Custom package installation \\
Revise \\
Checking the package status \\
Using packages \\
One more step \\
Updating packages \\
Pinning packages \\
Removing packages \\
Discovering packages \\
Registered versus unregistered \\
Summary \\
2: Creating Our First Julia App \\
Technical requirements \\
Defining variables \\
Constants \\
Why are constants important? \\
Comments \\
Strings \\
Triple-quoted strings \\
Concatenating strings \\
Interpolating strings \\
Manipulating strings \\
Unicode and UTF-8 \\
Regular expressions \\
Raw string literals \\
Numbers \\
Integers \\
Overflow behavior \\
Floating-point numbers \\
Rational numbers \\
Numerical operators \\
Vectorized dot operators \\
There's more to it \\
Tuples \\
Named tuples \\
Ranges \\
Arrays \\
Iteration \\
Mutating arrays \\
Comprehensions \\
Generators \\
Exploratory data analysis with Julia \\
The Iris flower dataset \\
Using the RDatasets package \\
Using simple statistics to better understand our data
\\
Visualizing the Iris flowers data \\
Loading and saving our data \\
Saving and loading using tabular file formats \\
Working with Feather files \\
Saving and loading with MongoDB \\
Summary \\
3: Setting Up the Wiki Game \\
Technical requirements \\
Data harvesting through web scraping \\
How the web works \\
a crash course \\
Making HTTP requests \\
Learning about HTTP methods \\
Understanding HTTPS \\
Understanding HTML documents \\
HTML selectors \\
Learning about the HTML attributes \\
Learning about CSS and JavaScript selectors \\
Understanding the structure of a link \\
Accessing the internet from Julia \\
Making requests with the HTTP package \\
Handling HTTP responses \\
HTTP status codes \\
Learning about HTTP headers \\
The HTTP message body \\
Understanding HTTP responses \\
The status code \\
The headers \\
The message body \\
Learning about pairs \\
Dictionaries \\
Constructing dictionaries \\
Ordered dictionaries \\
Working with dictionaries \\
Using the HTTP response \\
Manipulating the response body",
}
@InProceedings{Srivastava:2018:PEE,
author = "P. Srivastava and M. Kang and S. K. Gonugondla and S.
Lim and J. Choi and V. Adve and N. S. Kim and N.
Shanbhag",
booktitle = "{2018 ACM/IEEE 45th Annual International Symposium on
Computer Architecture (ISCA)}",
title = "{PROMISE}: An End-to-End Design of a Programmable
Mixed-Signal Accelerator for Machine-Learning
Algorithms",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "43--56",
year = "2018",
DOI = "https://doi.org/10.1109/ISCA.2018.00015",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@InProceedings{Voulgaris:2018:J,
author = "Zacharias Voulgaris",
booktitle = "{Encyclopedia of Big Data Technologies}",
title = "{Julia}",
publisher = pub-SV,
address = pub-SV:adr,
pages = "??--??",
year = "2018",
DOI = "https://doi.org/10.1007/978-3-319-63962-8_268-2",
bibdate = "Fri Apr 9 07:54:52 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://link.springer.com/referenceworkentry/10.1007/978-3-319-63962-8_268-2",
acknowledgement = ack-nhfb,
}
@Article{Amores:2019:ACB,
author = "V{\'\i}ctor Jes{\'u}s Amores and Jos{\'e} Mar{\'\i}a
Ben{\'\i}tez and Francisco Javier Mont{\'a}ns",
title = "Average-chain behavior of isotropic incompressible
polymers obtained from macroscopic experimental data.
{A} simple structure-based {WYPiWYG} model in {Julia}
language",
journal = j-ADV-ENG-SOFTWARE,
volume = "130",
pages = "41--57",
year = "2019",
CODEN = "AESODT",
DOI = "https://doi.org/10.1016/j.advengsoft.2019.01.004",
ISSN = "0965-9978 (print), 0141-1195 (electronic)",
ISSN-L = "0965-9978",
bibdate = "Fri Apr 9 15:22:25 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://www.sciencedirect.com/science/article/pii/S0965997818310779",
abstract = "Elastomeric materials and soft biological tissues are
made up of synthetic and protein fibers, respectively.
The uncoiling of these fibers during loading produces a
non-linear elastic macroscopic behavior in the regime
of finite strains. Many hyperelastic models have been
developed to reproduce this behavior assuming the
existence of a strain energy function. In
structure-based models, the analytical energy function
is obtained from the stored energy of all the material
constituents. This stored energy is given frequently by
the entropy of the chain network obtained from Langevin
statistical treatment of the possible configurations
adopted by the chains, and a representative cell for
their spatial distribution. One of the most used models
is the eight chain model, being its salient feature
that it reproduces the overall response of isotropic
hyperelastic materials with only two material
parameters obtained from a tensile test. On the other
hand, in WYPiWYG hyperelasticity the stored energies
are numerical instead of analytical and capture, to any
precision, the experimental tests on the material.
However, due to their phenomenological nature, their
determination requires more tests. In this work, we
develop a microstructure-based WYPiWYG hyperelastic
model in which the average chain behavior is obtained
from macroscopic tests through a simple automatic
inverse procedure. We show that, without assuming a
probability distribution function nor any particular
chain arrangement, we obtain, at the same computational
cost, better predictions than the 8-chain model. Code
of the model and of the examples in the Julia
programming language are included.",
acknowledgement = ack-nhfb,
fjournal = "Advances in Engineering Software (1978)",
journal-URL = "http://www.sciencedirect.com/science/journal/01411195",
keywords = "Eight chain model, WYPiWYG hyperelasticity,
Micromechanics, Julia",
}
@Book{Anonymous:2019:PJP,
author = "Anonymous",
title = "{POLO.Jl}: Policy-based optimization algorithms in
{Julia}",
publisher = "KTH, Reglerteknik",
year = "2019",
LCCN = "????",
bibdate = "Fri Jan 3 15:02:48 MST 2025",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260996",
abstract = "We present POLO. j1- a Julia package that helps
algorithm developers and machine-learning practitioners
design and use state-of-the-art parallel optimization
algorithms in a flexible and efficient way. POLO. j1
extends our C++ library POLO, which has been designed
and implemented with the same intentions. POLO. j1 not
only wraps selected algorithms in POLO and provides an
easy mechanism to use data manipulation facilities and
loss function definitions in Julia together with the
underlying compiled C++ library, but it also uses the
policy-based design technique in a Julian way to help
users prototype optimization algorithms from their own
building blocks. In our experiments, we observe that
there is little overhead when using the compiled C++
code directly within Julia. We also notice that the
performance of algorithms implemented in pure Julia is
comparable with that of their C++ counterparts. Both
libraries are hosted on GitHub(1)under the free MIT
license, and can be used easily by pulling the
pre-built 64-bit architecture Docker images.",
acknowledgement = ack-nhfb,
}
@Book{Balbaert:2019:JPC,
author = "Ivo Balbaert and Adrian Salceanu",
title = "{Julia 1.0} programming complete reference guide:
discover {Julia}, a high-performance language for
technical computing",
publisher = pub-PACKT,
address = pub-PACKT:adr,
pages = "viii + 451",
year = "2019",
ISBN = "1-83882-467-7",
ISBN-13 = "978-1-83882-224-8, 978-1-83882-467-9",
LCCN = "QA76.73.J84",
bibdate = "Thu Apr 8 11:03:56 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
series = "Learning path",
URL = "http://proquest.safaribooksonline.com/?fpi=9781838822248",
abstract = "Learn dynamic programming with Julia to build apps for
data analysis, visualization, machine learning, and the
web Key Features Leverage Julia's high speed and
efficiency to build fast, efficient applications
Perform supervised and unsupervised machine learning
and time series analysis Tackle problems concurrently
and in a distributed environment Book Description Julia
offers the high productivity and ease of use of Python
and R with the lightning-fast speed of C++. There's
never been a better time to learn this language, thanks
to its large-scale adoption across a wide range of
domains, including fintech, biotech and artificial
intelligence (AI). You will begin by learning how to
set up a running Julia platform, before exploring its
various built-in types. This Learning Path walks you
through two important collection types: arrays and
matrices. You'll be taken through how type conversions
and promotions work, and in further chapters you'll
study how Julia interacts with operating systems and
other languages. You'll also learn about the use of
macros, what makes Julia suitable for numerical and
scientific computing, and how to run external programs.
Once you have grasped the basics, this Learning Path
goes on to how to analyze the Iris dataset using
DataFrames. While building a web scraper and a web app,
you'll explore the use of functions, methods, and
multiple dispatches. In the final chapters, you'll
delve into machine learning, where you'll build a book
recommender system. By the end of this Learning Path,
you'll be well versed with Julia and have the skills
you need to leverage its high speed and efficiency for
your applications. This Learning Path includes content
from the following Packt products: Julia 1.0
Programming - Second Edition by Ivo Balbaert Julia
Programming Projects by Adrian Salceanu What you will
learn Create your own types to extend the built-in type
system Visualize your data in Julia with plotting
packages Explore the use of built-in macros for testing
and debugging Integrate Julia with other languages such
as C, Python, and MATLAB Analyze and manipulate
datasets using Julia and DataFrames Develop and run a
web app using Julia and the HTTP package Build a
recommendation system using supervised machine learning
Who this book is for If you are a statistician or data
scientist who wants a quick course in the Julia
programming language while building big data
applications, this Learning Path is for you.",
acknowledgement = ack-nhfb,
subject = "Julia (Computer program language); Application
software; Development; Development; Julia (Computer
program language)",
}
@Article{Besard:2019:EEP,
author = "Tim Besard and Christophe Foket and Bjorn {De
Sutter}",
title = "Effective Extensible Programming: Unleashing {Julia}
on {GPUs}",
journal = j-IEEE-TRANS-PAR-DIST-SYS,
volume = "30",
number = "4",
pages = "827--841",
month = apr,
year = "2019",
CODEN = "ITDSEO",
DOI = "https://doi.org/10.1109/TPDS.2018.2872064",
ISSN = "1045-9219 (print), 1558-2183 (electronic)ITDSEO",
ISSN-L = "1045-9219",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ieeetranspardistsys.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://ieeexplore.ieee.org/document/8471188/",
acknowledgement = ack-nhfb,
fjournal = "IEEE Transactions on Parallel and Distributed
Systems",
journal-URL = "https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=71",
keywords = "Julia programming language",
}
@Article{Biel:2019:PPJ,
author = "Martin Biel and Arda Aytekin and Mikael Johansson",
title = "\pkg{POLO.jl}: Policy-based optimization algorithms in
{Julia}",
journal = j-ADV-ENG-SOFTWARE,
volume = "136",
pages = "102695",
year = "2019",
CODEN = "AESODT",
DOI = "https://doi.org/10.1016/j.advengsoft.2019.102695",
ISSN = "0965-9978 (print), 0141-1195 (electronic)",
ISSN-L = "0965-9978",
bibdate = "Fri Apr 9 15:22:25 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://www.sciencedirect.com/science/article/pii/S0965997818311049",
abstract = "We present POLO.jl a Julia package that helps
algorithm developers and machine-learning practitioners
design and use state-of-the-art parallel optimization
algorithms in a flexible and efficient way. POLO.jl
extends our C++ library POLO, which has been designed
and implemented with the same intentions. POLO.jl not
only wraps selected algorithms in POLO and provides an
easy mechanism to use data manipulation facilities and
loss function definitions in Julia together with the
underlying compiled C++ library, but it also uses the
policy-based design technique in a Julian way to help
users prototype optimization algorithms from their own
building blocks. In our experiments, we observe that
there is little overhead when using the compiled C++
code directly within Julia. We also notice that the
performance of algorithms implemented in pure Julia is
comparable with that of their C++ counterparts. Both
libraries are hosted on
GitHub11https://github.com/pologrpunder the free MIT
license, and can be used easily by pulling the
pre-built 64-bit architecture Docker
images.22https://hub.docker.com/r/pologrp/polo-julia/",
acknowledgement = ack-nhfb,
fjournal = "Advances in Engineering Software (1978)",
journal-URL = "http://www.sciencedirect.com/science/journal/01411195",
}
@InProceedings{Bogomolov:2019:J,
author = "Sergiy Bogomolov and Marcelo Forets and Goran Frehse
and Kostiantyn Potomkin and Christian Schilling",
booktitle = "{Proceedings of the 22nd ACM International Conference
on Hybrid Systems: Computation and Control}",
title = "{JuliaReach}",
publisher = pub-ACM,
address = pub-ACM:adr,
month = apr,
year = "2019",
DOI = "https://doi.org/10.1145/3302504.3311804",
bibdate = "Thu Apr 8 08:02:29 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Book{Caraiani:2019:IQM,
author = "Petre Caraiani",
title = "Introduction to Quantitative Macroeconomics Using
{Julia}: From Basic to State-of-the-Art Computational
Techniques",
publisher = pub-ACADEMIC,
address = pub-ACADEMIC:adr,
year = "2019",
DOI = "https://doi.org/10.1016/B978-0-12-812219-8.00008-2",
ISBN = "0-12-812219-6, 0-12-813512-3 (e-book)",
ISBN-13 = "978-0-12-812219-8, 978-0-12-813512-9 (e-book)",
LCCN = "HB172.5",
bibdate = "Thu Apr 8 16:44:48 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://www.sciencedirect.com/science/article/pii/B9780128122198000082",
abstract = "\booktitle{Introduction to Quantitative Macroeconomics
Using Julia: From Basic to State-of-the-Art
Computational Techniques} facilitates access to
fundamental techniques in computational and
quantitative macroeconomics. It focuses on the recent
and very promising software, Julia, which offers a
MATLAB-like language at speeds comparable to C/Fortran,
also discussing modeling challenges that make
quantitative macroeconomics dynamic, a key feature that
few books on the topic include for macroeconomists who
need the basic tools to build, solve and simulate
macroeconomic models. This book neatly fills the gap
between intermediate macroeconomic books and modern
DSGE models used in research. Combines an introduction
to Julia, with the specific needs of macroeconomic
students who are interested in DSGE models and PhD
students and researchers interested in building DSGE
models Teaches fundamental techniques in quantitative
macroeconomics by introducing theoretical elements of
key macroeconomic models and their potential
algorithmic implementations. Exposes researchers
working in macroeconomics to state-of-the-art
computational techniques for simulating and solving
DSGE models''",
acknowledgement = ack-nhfb,
keywords = "Julia Language, IDE, Type System, Multiple Dispatch,
Vectorization",
subject = "Macroeconomics; Computer simulation; Julia (Computer
program language); BUSINESS and ECONOMICS / Economics /
Macroeconomics; POLITICAL SCIENCE / Economic
Conditions; Julia (Computer program language); Computer
simulation.",
tableofcontents = "Introduction to Julia \\
Basic numerical techniques \\
Solving and simulating DSGE models \\
Dynamic programming \\
Advanced numerical techniques \\
Heterogeneous agents models",
}
@Article{Carlsson:2019:TJT,
author = "Kristoffer Carlsson and Fredrik Ekre",
title = "\pkg{Tensors.jl} --- Tensor Computations in {Julia}",
journal = j-J-OPEN-RES-SOFT,
volume = "7",
number = "1",
pages = "7--??",
day = "21",
month = mar,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.5334/jors.182",
ISSN = "2049-9647",
ISSN-L = "2049-9647",
bibdate = "Fri Dec 2 07:12:44 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jors.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://openresearchsoftware.metajnl.com/articles/10.5334/jors.182/",
acknowledgement = ack-nhfb,
fjournal = "Journal of Open Research Software",
journal-URL = "https://openresearchsoftware.metajnl.com/issue/archive/",
}
@InProceedings{Congedo:2019:JPM,
author = "M. Congedo and S. Jain",
editor = "{IEEE}",
booktitle = "{2019 IEEE International Conference on Systems, Man
and Cybernetics (SMC)}",
title = "A {Julia} Package for manipulating Brain--Computer
Interface Data in the Manifold of Positive Definite
Matrices",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "211--216",
year = "2019",
DOI = "https://doi.org/10.1109/SMC.2019.8914223",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@InProceedings{Cunningham:2019:PPJ,
author = "Nathan Cunningham and Jim E. Griffin and David L. Wild
and Anthony Lee",
booktitle = "{Bayesian Statistics and New Generations}",
title = "\pkg{particleMDI}: a {Julia} Package for the
Integrative Cluster Analysis of Multiple Datasets",
publisher = pub-SV,
address = pub-SV:adr,
pages = "??--??",
year = "2019",
DOI = "https://doi.org/10.1007/978-3-030-30611-3_7",
bibdate = "Fri Apr 9 07:54:52 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://link.springer.com/chapter/10.1007/978-3-030-30611-3_7",
acknowledgement = ack-nhfb,
}
@InProceedings{Dinari:2019:DMI,
author = "Or Dinari and Angel Yu and Oren Freifeld and John
Fisher",
editor = "{IEEE}",
booktitle = "{2019 19th IEEE\slash ACM International Symposium on
Cluster, Cloud and Grid Computing (CCGRID)}",
title = "Distributed {MCMC} Inference in {Dirichlet} Process
Mixture Models Using {Julia}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "518--525",
year = "2019",
DOI = "https://doi.org/10.1109/CCGRID.2019.00066",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
}
@Book{Downey:2019:TJ,
author = "Allen B. Downey and Ben Lauwens",
title = "Think {Julia}",
publisher = pub-ORA-MEDIA,
address = pub-ORA-MEDIA:adr,
year = "2019",
ISBN = "1-4920-4500-4 (e-book), 1-4920-4503-9",
ISBN-13 = "978-1-4920-4500-7 (e-book), 978-1-4920-4503-8",
LCCN = "QA76.73.J85 L38 2019",
bibdate = "Thu Apr 8 16:41:21 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/unicode.bib",
abstract = "If you're just learning how to program, Julia is an
excellent JIT-compiled, dynamically-typed language with
a clean syntax. This hands-on guide uses Julia (version
1.0) to walk you through programming one step at a
time, beginning with basic programming concepts before
moving on to more advanced capabilities, such as
creating new types and multiple dispatch. Designed from
the beginning for high performance, Julia is a
general-purpose language not only ideal for numerical
analysis and computational science, but also for web
programming or scripting. Through exercises in each
chapter, you'll try out programming concepts as you
learn them. Think Julia is ideal for students at the
high school or college level, as well as self-learners,
home-schooled students, and professionals who need to
learn programming basics. Start with the basics,
including language syntax and semantics Get a clear
definition of each programming concept Learn about
values, variables, statements, functions, and data
structures in a logical progression Discover how to
work with files and databases Understand types,
methods, and multiple dispatch Use debugging techniques
to fix syntax, runtime, and semantic errors Explore
interface design and data structures through case
studies.",
acknowledgement = ack-nhfb,
subject = "Julia (Computer program language); Computer
programming; Data structures (Computer science);
Object-oriented programming (Computer science);
COMPUTERS; Programming Languages; General; Computer
programming; Data structures (Computer science); Julia
(Computer program language); Object-oriented
programming (Computer science)",
tableofcontents = "The way of the program \\
Variables, expressions, and statements \\
Functions \\
Case study: interface design \\
Conditionals and recursion \\
Fruitful functions \\
Iteration \\
Strings \\
Case study: word play \\
Arrays \\
Dictionaries \\
Tuples \\
Case study: data structure selection \\
Files \\
Structs and objects \\
Structs and functions \\
Multiple dispatch \\
Subtyping \\
The goodies: syntax \\
The goodies: base and standard library \\
Debugging \\
Unicode input \\
JuliaBox",
}
@InProceedings{Farhana:2019:SPE,
author = "Effat Farhana and Nasif Imtiaz and Akond Rahman",
editor = "{IEEE}",
booktitle = "{2019 IEEE International Conference on Software
Maintenance and Evolution (ICSME)}",
title = "Synthesizing Program Execution Time Discrepancies in
{Julia} Used for Scientific Software",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "496--500",
year = "2019",
DOI = "https://doi.org/10.1109/ICSME.2019.00083",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Fischer:2019:BRD,
author = "Daniel Fischer",
title = "Book Review: {{\booktitle{Data Science with Julia}},
Paul D. McNicholas and Peter A. Tait, CRC Press, 2019,
220 pages, \pounds 37.59, paperback, ISBN:
978-1-138-49998-0}",
journal = j-INT-STAT-REV,
volume = "87",
number = "2",
pages = "445--446",
month = aug,
year = "2019",
CODEN = "ISTRDP",
DOI = "https://doi.org/10.1111/insr.12345",
ISSN = "0306-7734 (print), 1751-5823 (electronic)",
ISSN-L = "0306-7734",
bibdate = "Sat Sep 7 07:31:05 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/intstatrev.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
fjournal = "International Statistical Review",
journal-URL = "http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1751-5823;
http://www.jstor.org/journals/03067734.html",
onlinedate = "13 August 2019",
}
@InProceedings{Gevorkyan:2019:SSC,
author = "Migran N. Gevorkyan and Anna V. Korolkova and Dmitry
S. Kulyabov and Konstantin P. Lovetskiy",
booktitle = "{Numerical Methods and Applications}",
title = "Statistically Significant Comparative Performance
Testing of {Julia} and {Fortran} Languages in Case of
{Runge--Kutta} Methods",
publisher = pub-SV,
address = pub-SV:adr,
pages = "??--??",
year = "2019",
DOI = "https://doi.org/10.1007/978-3-030-10692-8_45",
bibdate = "Fri Apr 9 07:54:52 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/fortran3.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://link.springer.com/chapter/10.1007/978-3-030-10692-8_45",
acknowledgement = ack-nhfb,
}
@InProceedings{Gjersvik:2019:PSA,
author = "A. Gjersvik and R. J. Moss",
booktitle = "{2019 IEEE High Performance Extreme Computing
Conference (HPEC)}",
title = "A Parallel Simulation Approach to {ACAS X}
Development",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--6",
year = "2019",
DOI = "https://doi.org/10.1109/HPEC.2019.8916301",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
abstract = "With a rapidly growing and evolving National Airspace
System (NAS), ACAS X is intended to be the
nextgeneration airborne collision avoidance system that
can meet the demands its predecessor could not. The
ACAS X algorithms are developed in the Julia
programming language and are exercised in simulation
environments tailored to test different characteristics
of the system. Massive parallelization of these
simulation environments has been implemented on the
Lincoln Laboratory Supercomputing Center cluster in
order to expedite the design and performance
optimization of the system. This work outlines the
approach to parallelization of one of our simulation
tools and presents the resulting simulation speedups as
well as a discussion on how it will enhance system
characterization and design. Parallelization has made
our simulation environment 33 times faster, which has
greatly sped up the development process of ACAS X.",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Glos:2019:PQJ,
author = "Adam Glos and Jaros{\l}aw Adam Miszczak and Mateusz
Ostaszewski",
title = "\pkg{QSWalk.jl}: {Julia} package for quantum
stochastic walks analysis",
journal = j-COMP-PHYS-COMM,
volume = "235",
number = "??",
pages = "414--421",
month = feb,
year = "2019",
CODEN = "CPHCBZ",
DOI = "https://doi.org/10.1016/j.cpc.2018.09.001",
ISSN = "0010-4655 (print), 1879-2944 (electronic)",
ISSN-L = "0010-4655",
bibdate = "Thu Apr 8 08:02:29 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/compphyscomm2010.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://www.sciencedirect.com/science/article/pii/S0010465518303151",
abstract = "The paper describes QSWalk.jl package for Julia
programming language, developed for the purpose of
simulating the evolution of open quantum systems. The
package enables the study of quantum procedures
developed using stochastic quantum walks on arbitrary
directed graphs. We provide a detailed description of
the implemented functions, along with a number of usage
examples. The package is compared with the existing
software offering a similar functionality. Program
summary Program Title: QSWalk.jl Program Files doi:
https://doi.org/10.17632/6x37kcvvrp.1 Licensing
provisions: MIT Programming language: Julia Nature of
problem: The package implements functions for
simulating quantum stochastic walks, including local
regime, global regime, and nonmoralizing global regime
(Julia documentation, 2018). It can be used for
arbitrary quantum continuous evolution based on GKSL
master equation on arbitrary graphs. Solution method:
We utilize Expokit routines for fast sparse matrix
exponentials on vectors. For dense matrices,
exponentiation is computed separately, which is faster
for small matrices. Restrictions: Currently package
requires Julia v0.6 or higher. [1] K. Domino, A. Glos,
M. Ostaszewski, Superdiffusive quantum stochastic walk
definable of arbitrary directed graph, Quantum Inform.
Comput 17 (11-12) (2017) 973 986.",
acknowledgement = ack-nhfb,
fjournal = "Computer Physics Communications",
journal-URL = "http://www.sciencedirect.com/science/journal/00104655",
keywords = "Directed graph; Julia programming language; Moral
graph; Open quantum system; Quantum walk",
}
@InProceedings{Huang:2019:PCJ,
author = "Ruizhu Huang and Weijia Xu and Yinzhi Wang and Silvia
Liverani and Ann E. Stapleton",
editor = "{IEEE}",
booktitle = "{2019 IEEE International Conference on Big Data (Big
Data)}",
title = "Performance Comparison of {Julia} Distributed
Implementations of {Dirichlet} Process Mixture Models",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "3350--3354",
year = "2019",
DOI = "https://doi.org/10.1109/BigData47090.2019.9005453",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@InProceedings{Koolen:2019:JRS,
author = "Twan Koolen and Robin Deits",
editor = "{IEEE}",
booktitle = "{2019 International Conference on Robotics and
Automation (ICRA)}",
title = "{Julia} for robotics: simulation and real-time control
in a high-level programming language",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "604--611",
year = "2019",
DOI = "https://doi.org/10.1109/ICRA.2019.8793875",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Book{Kwon:2019:JPO,
author = "Changhyun Kwon",
title = "{Julia} programming for operations research",
publisher = "Independently published",
address = "North Charleston, SC, USA",
edition = "Second",
pages = "x + 250",
year = "2019",
ISBN = "1-79820-547-5",
ISBN-13 = "978-1-79820-547-1",
LCCN = "QA76.73.J85 K8-62-019",
bibdate = "Thu Apr 8 16:47:14 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
abstract = "This book is neither a textbook in numerical methods,
a comprehensive introductory book to Julia programming,
a textbook on numerical optimization, a complete manual
of optimization solvers, nor an introductory book to
computational science and engineering --- it is a
little bit of all.",
acknowledgement = ack-nhfb,
remark = "Now Julia v1.0 and JuMP v0.19 have been released.
There were major upgrades in both Julia and JuMP
including many breaking changes. The second edition of
this book has been updated to reflect these changes. In
addition, new chapters for ``Interior Point Methods''
and ``Complementarity Problems'' are added.",
subject = "Operations Research.; Programmierung.; Julia
(Programmiersprache)",
tableofcontents = "1. Introduction and installation \\
2. Simple linear optimization \\
3. Basics of the Julia language \\
4. Selected topics in numerical methods \\
5. The simplex method \\
6. Network optimization problems \\
7. Interior point methods \\
8. Nonlinear optimization problems \\
9. Monte Carlo methods \\
10. Lagrangian relaxation \\
11. Complementary problems \\
12. Parameters in optimization solvers",
}
@Book{Kwong:2019:HDP,
author = "Tom Kwong",
title = "Hands-on design patterns with {Julia 1.0}: a
comprehensive guide to build robust, reusable, and
easily maintainable applications",
publisher = pub-PACKT,
address = pub-PACKT:adr,
pages = "532",
year = "2019",
ISBN = "1-83864-661-2, 1-83864-661-2 (PDF)",
ISBN-13 = "978-1-83864-661-5, 978-1-83864-661-5 (PDF)",
LCCN = "QA76.73.J85",
bibdate = "Thu Apr 8 17:04:42 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
abstract = "Design and develop high-performance, reusable, and
maintainable applications using traditional and modern
Julia patterns with this comprehensive guide. Key
Features Explore useful design patterns along with
object-oriented programming in Julia 1.0. Implement
macros and metaprogramming techniques to make your code
faster, concise, and efficient Develop the skills
necessary to implement design patterns for creating
robust and maintainable applications Book Description
Design patterns are fundamental techniques for
developing reusable and maintainable code. They provide
a set of proven solutions that allow developers to
solve problems in software development quickly. This
book will demonstrate how to leverage design patterns
with real-world applications. Starting with an overview
of design patterns and best practices in application
design, you'll learn about some of the most fundamental
Julia features such as modules, data types,
functions\slash interfaces, and metaprogramming. You'll
then get to grips with the modern Julia design patterns
for building large-scale applications with a focus on
performance, reusability, robustness, and
maintainability. The book also covers anti-patterns and
how to avoid common mistakes and pitfalls in
development. You'll see how traditional object-oriented
patterns can be implemented differently and more
effectively in Julia. Finally, you'll explore various
use cases and examples, such as how expert Julia
developers use design patterns in their open source
packages. By the end of this Julia programming book,
you'll have learned methods to improve software design,
extensibility, and reusability, and be able to use
design patterns efficiently to overcome common
challenges in software development. What you will learn
Master the Julia language features that are key to
developing large-scale software applications Discover
design patterns to improve overall application
architecture and design. Develop reusable programs that
are modular, extendable, performant, and easy to
maintain. Weigh up the pros and cons of using different
design patterns for use cases. Explore methods for
transitioning from object-oriented programming to using
equivalent or more advanced Julia techniques Who this
book is for This book is for beginner to
intermediate-level Julia programmers who want to
enhance their skills in designing and developing
large-scale applications.",
acknowledgement = ack-nhfb,
subject = "Julia (Computer program language); Computer software;
Development; Development; Julia (Computer program
language)",
}
@Book{Lauwens:2019:TJH,
author = "Ben Lauwens and Allen Downey",
title = "Think {Julia}: how to think like a computer
scientist",
publisher = pub-ORA-MEDIA,
address = pub-ORA-MEDIA:adr,
pages = "xviii + 276",
year = "2019",
ISBN = "1-4920-4503-9",
ISBN-13 = "978-1-4920-4503-8",
LCCN = "QA76.73.J85",
bibdate = "Thu Apr 8 17:09:12 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://proquest.safaribooksonline.com/?fpi=9781492045021",
abstract = "If you're just learning how to program, Julia is an
excellent JIT-compiled, dynamically-typed language with
a clean syntax. This hands-on guide uses Julia (version
1.0) to walk you through programming one step at a
time, beginning with basic programming concepts before
moving on to more advanced capabilities, such as
creating new types and multiple dispatch. Designed from
the beginning for high performance, Julia is a
general-purpose language not only ideal for numerical
analysis and computational science, but also for web
programming or scripting. Through exercises in each
chapter, you'll try out programming concepts as you
learn them. Think Julia is ideal for students at the
high school or college level, as well as self-learners,
home-schooled students, and professionals who need to
learn programming basics. Start with the basics,
including language syntax and semantics Get a clear
definition of each programming concept Learn about
values, variables, statements, functions, and data
structures in a logical progression Discover how to
work with files and databases Understand types,
methods, and multiple dispatch Use debugging techniques
to fix syntax, runtime, and semantic errors Explore
interface design and data structures through case
studies.",
acknowledgement = ack-nhfb,
subject = "Julia (Computer program language); Dynamic
programming; Dynamic programming.; Julia (Computer
program language)",
}
@InProceedings{Lobianco:2019:IJO,
author = "Antonello Lobianco",
booktitle = "{Julia Quick Syntax Reference}",
title = "Interfacing {Julia} with Other Languages",
publisher = pub-SV,
address = pub-SV:adr,
pages = "??--??",
year = "2019",
DOI = "https://doi.org/10.1007/978-1-4842-5190-4_7",
bibdate = "Fri Apr 9 07:54:52 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://link.springer.com/chapter/10.1007/978-1-4842-5190-4_7",
acknowledgement = ack-nhfb,
}
@Book{Lobianco:2019:JQS,
author = "Antonello Lobianco",
title = "{Julia} quick syntax reference: a pocket guide for
data science programming",
publisher = pub-APRESS,
address = pub-APRESS:adr,
pages = "xvii + 216 + 66",
year = "2019",
DOI = "https://doi.org/10.1007/978-1-4842-5190-4",
ISBN = "1-4842-5189-X, 1-4842-5190-3 (e-book)",
ISBN-13 = "978-1-4842-5189-8, 978-1-4842-5190-4 (e-book)",
LCCN = "QA76.73.J85",
bibdate = "Thu Apr 8 11:08:50 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://link.springer.com/book/10.1007/978-1-4842-5190-4",
abstract = "This quick Julia programming language guide is a
condensed code and syntax reference to the Julia 1.x
programming language, updated with the latest features
of the Julia APIs, libraries, and packages. It presents
the essential Julia syntax in a well-organized format
that can be used as a handy reference. This book
provides an introduction that reveals basic Julia
structures and syntax; discusses data types, control
flow, functions, input/output, exceptions,
metaprogramming, performance, and more. Additionally,
you'll learn to interface Julia with other programming
languages such as R for statistics or Python. You will
learn how to use Julia packages for data analysis,
numerical optimization and symbolic computation, and
how to disseminate your results in dynamic documents or
interactive web pages. In this book, the focus is on
providing important information as quickly as possible.
It is packed with useful information and is a must-have
for any Julia programmer. What You Will Learn Set up
the software needed to run Julia and your first Hello
World example Work with types and the different
containers that Julia makes available for rapid
application development Use vectorized, classical
loop-based code, logical operators, and blocks Explore
Julia functions by looking at arguments, return values,
polymorphism, parameters, anonymous functions, and
broadcasts Build custom structures in Julia Interface
Julia with other languages such as C/C++, Python, and R
Program a richer API, modifying the code before it is
executed using expressions, symbols, macros, quote
blocks, and more Maximize your code's performance Who
This Book Is For Experienced programmers new to Julia,
as well as existing Julia coders new to the now stable
Julia version 1.0 release.",
acknowledgement = ack-nhfb,
subject = "Julia (Computer program language); Computer
programming; Handbooks, manuals, etc; Computer
programming; Julia (Computer program language)",
tableofcontents = "Part 1. Language Core \\
1. Getting Started \\
2. Data Types and Structures \\
3. Control Flow and Functions \\
4. Custom Types \\
5. Input? Output \\
6. Metaprogramming and Macros \\
7. Interfacing Julia with Other Languages \\
8. Efficiently Write Efficient Code \\
Part 2. Packages Ecosystem \\
9. Working with Data \\
10. Mathematical Libraries \\
11. Utilities",
}
@Book{McNicholas:2019:DSJ,
author = "Paul D. McNicholas and Peter A. Tait",
title = "Data science with {Julia}",
publisher = "Chapman and Hall\slash CRC",
address = "Boca Raton, FL, USA",
pages = "xix + 220",
year = "2019",
DOI = "https://doi.org/10.1201/9781351013673",
ISBN = "1-138-49999-4, 1-351-01364-5, 1-351-01365-3,
1-351-01366-1, 1-351-01367-X",
ISBN-13 = "978-1-138-49998-0 (paperback), 978-1-138-49999-7,
978-1-351-01364-2, 978-1-351-01365-9,
978-1-351-01366-6, 978-1-351-01367-3",
LCCN = "QA76.73.J85 M37 2019eb",
bibdate = "Fri May 21 17:39:58 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
abstract = "Julia, an open-source programming language, was
created to be as easy to use as languages such as R and
Python while also as fast as C and Fortran. An
accessible, intuitive, and highly efficient base
language with speed that exceeds R and Python, makes
Julia a formidable language for data science. Using
well known data science methods that will motivate the
reader, Data Science with Julia will get readers up to
speed on key features of the Julia language and
illustrate its facilities for data science and machine
learning work.",
acknowledgement = ack-nhfb,
subject = "Julia (Computer program language); Data structures
(Computer science); Data structures (Computer science);
Julia (Computer program language)",
tableofcontents = "Cover \\
Half Title \\
Title Page \\
Copyright Page \\
Dedication \\
Table Of Contents \\
Foreword \\
Preface \\
About The Authors \\
1: Introduction \\
1.1 Data Science \\
1.2 Big Data \\
1.3 Julia \\
1.4 Julia And R Packages \\
1.5 Datasets \\
1.5.1 Overview \\
1.5.2 Beer Data \\
1.5.3 Coffee Data \\
1.5.4 Leptograpsus Crabs Data \\
1.5.5 Food Preferences Data \\
1.5.6 X2 Data \\
1.5.7 Iris Data \\
1.6 Outline Of The Contents Of This Monograph \\
2: Core Julia \\
2.1 Variable Names \\
2.2 Operators \\
2.3 Types \\
2.3.1 Numeric \\
2.3.2 Floats \\
2.3.3 Strings \\
2.3.4 Tuples \\
2.4 Data Structures \\
2.4.1 Arrays \\
2.4.2 Dictionaries \\
2.5 Control Flow \\
2.5.1 Compound Expressions \\
2.5.2 Conditional Evaluation \\
2.5.3 Loops \\
2.5.3.1 Basics \\
2.5.3.2 Loop Termination \\
2.5.3.3 Exception Handling \\
2.6 Functions \\
3: Working With Data \\
3.1 Dataframes \\
3.2 Categorical Data \\
3.3 Input/Output \\
3.4 Useful Dataframe Functions \\
3.5 Split-Apply-Combine Strategy \\
3.6 Query. Jl \\
4: Visualizing Data \\
4.1 Gadfly. Jl \\
4.2 Visualizing Univariate Data \\
4.3 Distributions \\
4.4 Visualizing Bivariate Data \\
4.5 Error Bars \\
4.6 Facets \\
4.7 Saving Plots \\
5: Supervised Learning \\
5.1 Introduction \\
5.2 Cross-Validation \\
5.2.1 Overview \\
5.2.2 K-Fold Cross-Validation \\
5.3 $K$-Nearest Neighbours Classification \\
5.4 Classification And Regression Trees \\
5.4.1 Overview \\
5.4.2 Classification Trees \\
5.4.3 Regression Trees \\
5.4.4 Comments \\
5.5 Bootstrap \\
5.6 Random Forests \\
5.7 Gradient Boosting \\
5.7.1 Overview \\
5.7.2 Beer Data \\
5.7.3 Food Data \\
5.8 Comments \\
6: Unsupervised Learning \\
6.1 Introduction \\
6.2 Principal Components Analysis \\
6.3 Probabilistic Principal Components Analysis \\
6.4 Em Algorithm for Ppca \\
6.4.1 Background: EM Algorithm \\
6.4.2 E-step \\
6.4.3 M-step \\
6.4.4 Woodbury Identity \\
6.4.5 Initialization \\
6.4.6 Stopping Rule \\
6.4.7 Implementing the EM Algorithm for PPCA \\
6.4.8 Comments \\
6.5 $K$-Means Clustering \\
6.6 Mixture Of Probabilistic Principal Components
Analyzers \\
6.6.1 Model \\
6.6.2 Parameter Estimation \\
6.6.3 Illustrative Example: Coffee Data \\
6.7 Comments \\
7: R Interoperability \\
7.1 Accessing R Datasets \\
7.2 Interacting With R \\
7.3 Example: Clustering And Data Reduction For The
Coffee Data \\
7.3.1 Coffee Data \\
7.3.2 PGMM Analysis \\
7.3.3 VSCC Analysis \\
7.4 Example: Food Data \\
7.4.1 Overview \\
7.4.2 Random Forests \\
Appendix A: Julia and R Packages Used Herein \\
Appendix B: Variables for Food Data \\
Appendix C: Useful Mathematical Results \\
C.1 Brief Overview of Eigenvalues \\
C.2 Selected Linear Algebra Results \\
C.3 Matrix Calculus Results \\
Appendix D: Performance Tips \\
D.1 Floating Point Numbers \\
D.1.1 Do Not Test for Equality \\
D.1.2 Use Logarithms for Division \\
D.1.3 Subtracting Two Nearly Equal Numbers \\
D.2 Julia Performance \\
D.2.1 General Tips \\
D.2.2 Array Processing \\
D.2.3 Separate Core Computations \\
Appendix E: Linear Algebra Functions \\
E.1 Vector Operations \\
E.2 Matrix Operations \\
E.3 Matrix Decompositions \\
References \\
Index",
}
@InProceedings{Medeiros:2019:USP,
author = "Johannes D. {Medeiros, Jr.} and Eduardo T. Costa",
booktitle = "{XXVI Brazilian Congress on Biomedical Engineering}",
title = "Ultrasound Signal Processing Using the {Julia}
Programming Language",
publisher = pub-SV,
address = pub-SV:adr,
pages = "??--??",
year = "2019",
DOI = "https://doi.org/10.1007/978-981-13-2517-5_77",
bibdate = "Fri Apr 9 07:54:52 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://link.springer.com/chapter/10.1007/978-981-13-2517-5_77",
acknowledgement = ack-nhfb,
}
@Article{Moille:2019:PFU,
author = "Gregory Moille and Qing Li and Lu Xiyuan and Kartik
Srinivasan",
title = "{pyLLE}: a Fast and User Friendly {Lugiato--Lefever}
Equation Solver",
journal = j-J-RES-NATL-INST-STAND-TECHNOL,
volume = "124",
month = may,
year = "2019",
CODEN = "JRITEF",
DOI = "https://doi.org/10.6028/jres.124.012",
ISSN = "1044-677X (print), 2165-7254 (electronic)",
bibdate = "Thu Apr 8 08:02:29 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
fjournal = "Journal of research of the National Institute of
Standards and Technology",
journal-URL = "http://www.nist.gov/nvl/jres.cfm",
keywords = "Julia programming language",
}
@InProceedings{Moura:2019:UJP,
author = "Rodolfo A. R. Moura and Marco A. O. Schroeder and
Samuel J. S. Silva and Erivelton G. Nepomuceno and
Pedro H. N. Vieira and Antonio C. S. Lima",
editor = "{IEEE}",
booktitle = "{2019 International Symposium on Lightning Protection
(XV SIPDA)}",
title = "The Usage of {Julia} Programming in Grounding Grids
Simulations: An alternative to {MATLAB} and {Python}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--4",
year = "2019",
DOI = "https://doi.org/10.1109/SIPDA47030.2019.8951702",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/matlab.bib;
https://www.math.utah.edu/pub/tex/bib/python.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@MastersThesis{Plankensteiner:2019:CDS,
author = "David Plankensteiner",
title = "Collective dynamics and spectroscopy of coupled
quantum emitters",
type = "{M.Sc.} thesis",
school = "Universit{\"a}t Innsbruck",
address = "Innsbruck, Austria",
pages = "ix + 193",
month = may,
year = "2019",
bibdate = "Thu Apr 08 16:33:38 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://data.onb.ac.at/rec/AC15419096",
acknowledgement = ack-nhfb,
keywords = "\pkg{QuantumOptics.jl}: a Julia framework for
simulating open quantum systems",
}
@Article{Regier:2019:CVU,
author = "Jeffrey Regier and Keno Fischer and Kiran Pamnany and
Andreas Noack and Jarrett Revels and Maximilian Lama
and Steve Howard and Ryan Giordano and David Schlegel
and Jon McAuliffe and Rollin Thomas and Prabhat",
title = "Cataloging the visible universe through {Bayesian}
inference in {Julia} at petascale",
journal = j-J-PAR-DIST-COMP,
volume = "127",
number = "??",
pages = "89--104",
month = may,
year = "2019",
CODEN = "JPDCER",
DOI = "https://doi.org/10.1016/j.jpdc.2018.12.008",
ISSN = "0743-7315 (print), 1096-0848 (electronic)",
ISSN-L = "0743-7315",
bibdate = "Thu Mar 14 15:55:59 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jpardistcomp.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://www.sciencedirect.com/science/article/pii/S0743731518304672",
abstract = "A key task in astronomy is to locate astronomical
objects in images and to characterize them according to
physical parameters such as brightness, color, and
morphology. This task, known as cataloging, is
challenging for several reasons: many astronomical
objects are much dimmer than the sky background,
labeled data is generally unavailable, overlapping
astronomical objects must be resolved collectively, and
the datasets are enormous terabytes now, petabytes
soon. In this work, we infer an astronomical catalog
from 55 TB of imaging data using Celeste, a Bayesian
variational inference code written entirely in the
high-productivity programming language Julia. Using
over 1.3 million threads on 650,000 Intel Xeon Phi
cores of the Cori Phase II supercomputer, Celeste
achieves a peak rate of 1.54 DP PFLOP/s. Celeste is
able to jointly optimize parameters for 188 M stars and
galaxies, loading and processing 178 TB across 8192
nodes in 14.6 min. To achieve this, Celeste exploits
parallelism at multiple levels (cluster, node, and
thread) and accelerates I/O through Cori's burst
buffer. Julia's native performance enables Celeste to
employ high-level constructs without resorting to
hand-written or generated low-level code
(C/C++/Fortran) while still achieving petascale
performance.",
acknowledgement = ack-nhfb,
fjournal = "Journal of Parallel and Distributed Computing",
journal-URL = "http://www.sciencedirect.com/science/journal/07437315",
keywords = "Astronomy, Bayesian, Distributed optimization,
Variational inference, Julia, High-performance
computing",
}
@InProceedings{Reinhardt:2019:DAB,
author = "O. Reinhardt and A. M. Uhrmacher and M. Hinsch and J.
Bijak",
booktitle = "{2019 Winter Simulation Conference (WSC)}",
title = "Developing Agent-Based Migration Models in Pairs",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "2713--2724",
year = "2019",
DOI = "https://doi.org/10.1109/WSC40007.2019.9004946",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Roughan:2019:PSS,
author = "Matthew Roughan",
title = "Practically surreal: {Surreal} arithmetic in {Julia}",
journal = j-SOFTWAREX,
volume = "9",
number = "??",
pages = "293--298",
month = jan # "\slash " # jun,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1016/j.softx.2019.03.005",
ISSN = "2352-7110",
ISSN-L = "2352-7110",
bibdate = "Mon Oct 14 09:45:43 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/fparith.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/softwarex.bib",
URL = "https://www.sciencedirect.com/science/article/pii/S2352711018302152",
abstract = "This paper presents an implementation of arithmetic on
Conway's surreal numbers. It also provides tools for
visualising complicated surreals in the form of graph
visualisations, and illustrates their use through
several examples, and a small contribution to the
theory of surreals.",
acknowledgement = ack-nhfb,
fjournal = "SoftwareX",
journal-URL = "https://www.sciencedirect.com/journal/softwarex/issues",
keywords = "Conway's surreal numbers",
}
@Book{Sengupta:2019:JHP,
author = "Avik Sengupta",
title = "{Julia} high performance optimizations, distributed
computing, multithreading, and {GPU} programming with
{Julia 1.0} and beyond",
publisher = pub-PACKT,
address = pub-PACKT:adr,
edition = "Second",
pages = "218",
year = "2019",
ISBN = "1-78829-230-8, 1-78829-811-X",
ISBN-13 = "978-1-78829-230-6, 978-1-78829-811-7",
LCCN = "????",
bibdate = "Thu Apr 8 16:49:31 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://portal.igpublish.com/iglibrary/search/PACKT0005341.html",
abstract = "Julia is a high-level, high-performance dynamic
programming language for numerical computing. This book
will help you understand the performance
characteristics of your Julia programs and achieve
near-C levels of performance in Julia.",
acknowledgement = ack-nhfb,
subject = "Julia (Computer program language); Application
software; Development; Development.; Julia (Computer
program language)",
tableofcontents = "Foreword \\
Contributors \\
Table of Contents \\
Preface \\
1: Julia is Fast \\
Julia \\
fast and dynamic \\
Designed for speed \\
JIT and LLVM \\
Types, type inference, and code specialization \\
How fast can Julia be? \\
Summary \\
2: Analyzing Performance \\
Timing Julia functions \\
The @time macro \\
Other time macros \\
The Julia profiler \\
Using the profiler \\
ProfileView \\
Using Juno for profiling \\
Using TimerOutputs \\
Analyzing memory allocation \\
Using the memory allocation tracker \\
Statistically accurate benchmarking \\
Using \pkg{BenchmarkTools.jl} \\
Summary \\
3: Types, Type Inference, and Stability \\
The Julia type system \\
Using types \\
Multiple dispatch \\
Abstract types \\
Julia's type hierarchy \\
Composite and immutable types \\
Type parameters \\
Type inference \\
Type-stability \\
Definitions \\
Fixing type instability \\
The performance pitfalls \\
Identifying type stability \\
Loop variables \\
Kernel methods and function barriers \\
Types in storage locations \\
Arrays \\
Composite types \\
Parametric composite types \\
Summary \\
4: Making Fast Function Calls \\
Using globals \\
The trouble with globals \\
Fixing performance issues with globals \\
Inlining \\
Default inlining \\
Controlling inlining \\
Disabling inlining \\
Constant propagation \\
Using macros for performance \\
The Julia compilation process \\
Using macros \\
Evaluating a polynomial \\
Horner's method \\
The Horner macro \\
Generated functions \\
Using generated functions \\
Using generated functions for performance \\
Using keyword arguments \\
Summary \\
5: Fast Numbers \\
Numbers in Julia, their layout, and storage \\
Integers \\
Integer overflow \\
BigInt \\
The floating point \\
Floating point accuracy \\
Unsigned integers \\
Trading performance for accuracy \\
The @fastmath macro \\
The K-B-N summation \\
Subnormal numbers \\
Subnormal numbers to zero \\
Summary \\
6: Using Arrays \\
Array internals in Julia \\
Array representation and storage \\
Column-wise storage \\
Adjoints \\
Array initialization \\
Bounds checking \\
Removing the cost of bounds checking \\
Configuring bound checks at startup \\
Allocations and in-place operations \\
Preallocating function output \\
sizehint! \\
Mutating functions \\
Broadcasting \\
Array views \\
SIMD parallelization (AVX2, AVX512) \\
SIMD.jl \\
Specialized array types \\
Static arrays \\
Structs of arrays \\
Yeppp!Writing generic library functions with arrays \\
Summary \\
7: Accelerating Code with the GPU \\
Technical requirements \\
Getting started with GPUs \\
CUDA and Julia \\
CuArrays \\
Monte Carlo simulation on the GPU \\
Writing your own kernels \\
Measuring GPU performance \\
Performance tips \\
Scalar iteration \\
Combining kernels \\
Processing more data \\
Deep learning on the GPU \\
ArrayFire \\
Summary \\
8: Concurrent Programming with Tasks \\
Tasks \\
Using tasks \\
The task life cycle \\
task\_local\_storage \\
Communicating between tasks \\
Task iteration \\
High-performance I/O",
}
@InProceedings{Sliwak:2019:JMP,
author = "Julie Sliwak and Manuel Ruiz and Miguel F. Anjos and
Lucas L{\'e}tocart and Emiliano Traversi",
editor = "{IEEE}",
booktitle = "{2019 IEEE Milan PowerTech}",
title = "A {Julia} Module for Polynomial Optimization with
Complex Variables applied to Optimal Power Flow",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--6",
year = "2019",
DOI = "https://doi.org/10.1109/PTC.2019.8810960",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Thomas:2019:BDP,
author = "Ebby Thomas",
editor = "{IEEE}",
booktitle = "{2019 IEEE Innovative Smart Grid Technologies --- Asia
(ISGT Asia)}",
title = "Big Data in Power Systems: an Introduction to {Julia}
Linear Models using {Tensor Flow}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "731--735",
year = "2019",
DOI = "https://doi.org/10.1109/ISGT-Asia.2019.8881136",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Voulgaris:2019:J,
author = "Zacharias Voulgaris",
booktitle = "{Encyclopedia of Big Data Technologies}",
title = "{Julia}",
publisher = pub-SV,
address = pub-SV:adr,
pages = "??--??",
year = "2019",
DOI = "https://doi.org/10.1007/978-3-319-77525-8_268",
bibdate = "Fri Apr 9 07:54:52 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://link.springer.com/referenceworkentry/10.1007/978-3-319-77525-8_268",
acknowledgement = ack-nhfb,
}
@Article{Zhang:2019:SSE,
author = "Zhiping Zhang and Jeffrey D. Varner",
title = "{SEML}: a Simplified {English} Modeling Language for
Constructing Biological Models in {Julia}",
journal = "IFAC-PapersOnLine",
volume = "52",
number = "26",
pages = "121--128",
year = "2019",
DOI = "https://doi.org/10.1016/j.ifacol.2019.12.246",
ISSN = "2405-8963",
bibdate = "Fri Apr 9 15:22:25 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
note = "8th Conference on Foundations of Systems Biology in
Engineering FOSBE 2019",
URL = "https://www.sciencedirect.com/science/article/pii/S2405896319321299",
abstract = "Many markup languages can be used to encode biological
networks, each with strengths and weaknesses. Model
specifications written in these languages can then
used, in conjunction with proprietary software packages
e.g., MATLAB, or open community alternatives, to
simulate the behavior of biological systems. In this
study, we present the Simplified English Modeling
Language (SEML) and associated compiler, as an
alternative to existing approaches. SEML supports the
specification of biological reaction systems in a
simple natural language like syntax. Models encoded in
SEML are transformed into executable code using a
compiler written in the open-source Julia programming
language. The compiler performs a sequence of
operations, including tokenization, syntactic and
semantic error checking, to convert SEML into an
intermediate representation (IR). From the intermediate
representation, the compiler then generates executable
code in one of three programming languages: Julia,
Python or MATLAB. Currently, SEML supports both kinetic
and constraint based model generation for signal
transduction and metabolic modeling. In this study, we
demonstrate SEML by modeling two proof-of-concept
prototypical networks: a constraint-based model solved
using flux balance analysis (FBA) and a kinetic model
encoded as Ordinary Differential Equations (ODEs). SEML
is a promising tool for encoding and sharing
human-readable biological models, however it is still
in its infancy. With further development, SEML has the
potential to handle more unstructured natural language
inputs, generate more complex models types and convert
its natural language markup to currently used model
interchange formats such systems biology markup
language.",
acknowledgement = ack-nhfb,
keywords = "simplified English modeling language, markup language,
biological modeling, compiler, Julia",
}
@Article{Amores:2020:DDS,
author = "V{\'{\i}}ctor Jes{\'u}s Amores and Jos{\'e}
Mar{\'{\i}}a Ben{\'{\i}}tez and Francisco Javier
Mont{\'a}ns",
title = "Data-driven, structure-based hyperelastic manifolds: a
macro--micro--macro approach to reverse-engineer the
chain behavior and perform efficient simulations of
polymers",
journal = "Computers {\&} Structures",
volume = "231",
pages = "106209",
month = apr,
year = "2020",
DOI = "https://doi.org/10.1016/j.compstruc.2020.106209",
bibdate = "Thu Apr 8 08:02:29 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Apreutesey:2020:LMR,
author = "Anna Maria Yu Apreutesey and Anna V. Korolkova and
Dmitry S. Kulyabov",
title = "Languages for modeling the {RED} active queue
management algorithms: {Modelica} vs. {Julia}",
journal = "arXiv.org",
day = "18",
month = jul,
year = "2020",
LCCN = "????",
bibdate = "Fri Jan 3 15:02:48 MST 2025",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://arxiv.org/abs/2007.09488",
abstract = "This work is devoted to the study of the capabilities
of the Modelica and Julia programming languages for the
implementation of a continuously discrete paradigm in
modeling hybrid systems that contain both continuous
and discrete aspects of behavior. A system consisting
of an incoming stream that is processed according to
the Transmission Control Protocol (TCP) and a router
that processes traffic using the Random Early Detection
(RED) algorithm acts as a simulated threshold system.
Comment: in English; in Russian.",
acknowledgement = ack-nhfb,
}
@InProceedings{Asaeikheybari:2020:PHH,
author = "G. Asaeikheybari and C. Hughart and D. Gupta and A.
Avery and M. M. Step and J. M. Smith and J. Kratz and
J. Briggs and M.-C. Huang",
booktitle = "{2020 Second International Conference on
Transdisciplinary AI (TransAI)}",
title = "Precision {HIV} Health App, Positive Peers, Powered by
Data Harnessing, {AI}, and Learning",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "108--112",
year = "2020",
DOI = "https://doi.org/10.1109/TransAI49837.2020.00024",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@InProceedings{Barros:2020:ALS,
author = "Diana A. Barros and Cristiana Bentes",
editor = "{IEEE}",
booktitle = "{2020 IEEE 32nd International Symposium on Computer
Architecture and High Performance Computing
(SBAC-PAD)}",
title = "Analyzing the Loop Scheduling Mechanisms on {Julia}
Multithreading",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "257--264",
year = "2020",
DOI = "https://doi.org/10.1109/SBAC-PAD49847.2020.00043",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/multithreading.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Book{Battig:2020:AMM,
author = "Daniel B{\"a}ttig",
title = "{Angewandte Mathematik 1 mit MATLAB und Julia: Ein
anwendungs- und beispielorientierter Einstieg f{\"u}r
technische Studieng{\"a}nge}. ({German}) [{Applied
Mathematics 1} with {MATLAB} and {Julia}: an
application and example-oriented introduction to
technical courses]",
publisher = "Springer Vieweg",
address = "Berlin and Heidelberg, Germany",
pages = "xiii + 254",
year = "2020",
ISBN = "3-662-60951-7 (print), 3-662-60952-5 (ePub)",
ISBN-13 = "978-3-662-60951-4 (print), 978-3-662-60952-1 (ePub)",
LCCN = ">>>>",
bibdate = "Thu Apr 8 11:26:54 MDT 2021",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/matlab.bib",
acknowledgement = ack-nhfb,
language = "German",
subject = "Engineering; Applied mathematics; Engineering
mathematics; Computer science; Mathematics;
Mathematical analysis; Analysis (Mathematics);
Algebras, Linear; Maths for scientists; Calculus and
mathematical analysis; Algebra; Maths for engineers;
Computers; Computer Science; Mathematical Analysis;
Algebra; Linear; Technology and Engineering;
Engineering (General); Algebras, Linear; Mathematics;
Engineering; Engineering mathematics; Mathematical
analysis.",
tableofcontents = "Zahlensysteme: Mathematik und Computer, \\
Vektoren und Programmieren von Schleifen \\
Vektoren, Geometrie und Mechanik, \\
Lineare Gleichungssysteme und Matrizes \\
Input-Output: Funktionen \\
Spezielle mathematische Funktionen \\
{\"U}berbestimmte Systeme, affine Funktionen und die
Methode der kleinsten Quadrate \\
Die Ableitung einer Funktion \\
Anwendungen der Ableitung \\
Literaturverzeichnis \\
Sachverzeichnis",
}
@Article{Bauer:2020:FSD,
author = "Carsten Bauer",
title = "Fast and stable determinant quantum {Monte Carlo}",
journal = "{SciPost} Physics Core",
volume = "2",
number = "2",
month = jun,
year = "2020",
DOI = "https://doi.org/10.21468/scipostphyscore.2.2.011",
bibdate = "Thu Apr 8 08:02:29 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Belyakova:2020:WAJ,
author = "Julia Belyakova and Benjamin Chung and Jack Gelinas
and Jameson Nash and Ross Tate and Jan Vitek",
title = "World age in {Julia}: optimizing method dispatch in
the presence of eval",
journal = j-PACMPL,
volume = "4",
number = "OOPSLA",
pages = "207:1--207:26",
month = nov,
year = "2020",
DOI = "https://doi.org/10.1145/3428275",
ISSN = "2475-1421",
bibdate = "Tue Mar 30 08:10:50 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/pacmpl.bib",
URL = "https://dl.acm.org/doi/10.1145/3428275",
abstract = "Dynamic programming languages face semantic and
performance challenges in the presence of features,
such as eval, that can inject new code into a running
program. The Julia programming language introduces the
novel concept of world age to insulate optimized code
from one of the most disruptive side-effects of eval:
changes to the definition of an existing function. This
paper provides the first formal semantics of world age
in a core calculus named juliette, and shows how world
age enables compiler optimizations, such as inlining,
in the presence of eval. While Julia also provides
programmers with the means to bypass world age, we
found that this mechanism is not used extensively: a
static analysis of over 4,000 registered Julia packages
shows that only 4--9\% of packages bypass world age.
This suggests that Julia's semantics aligns with
programmer expectations.",
acknowledgement = ack-nhfb,
articleno = "207",
fjournal = "Proceedings of the ACM on Programming Languages",
journal-URL = "https://pacmpl.acm.org/",
}
@InProceedings{Burbach:2020:NVJ,
author = "Laura Burbach and Poornima Belavadi and Patrick
Halbach and Lilian Kojan and Nils Plettenberg and
Johannes Nakayama and Martina Ziefle and Andr{\'e}
Calero Valdez",
booktitle = "{Digital Human Modeling and Applications in Health,
Safety, Ergonomics and Risk Management. Human
Communication, Organization and Work}",
title = "{Netlogo} vs. {Julia}: Evaluating Different Options
for the Simulation of Opinion Dynamics",
publisher = pub-SV,
address = pub-SV:adr,
pages = "??--??",
year = "2020",
DOI = "https://doi.org/10.1007/978-3-030-49907-5_1",
bibdate = "Fri Apr 9 07:54:52 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://link.springer.com/chapter/10.1007/978-3-030-49907-5_1",
acknowledgement = ack-nhfb,
}
@InProceedings{Chaber:2020:PCC,
author = "B. Chaber",
booktitle = "{2020 IEEE 21st International Conference on
Computational Problems of Electrical Engineering
(CPEE)}",
title = "Particle-in-Cell code for gas discharge simulations",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--4",
year = "2020",
DOI = "https://doi.org/10.1109/CPEE50798.2020.9238682",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Coleman:2020:MPJ,
author = "Chase Coleman and Spencer Lyon and Lilia Maliar and
Serguei Maliar",
title = "{Matlab}, {Python}, {Julia}: What to Choose in
Economics?",
journal = j-COMP-ECONOMICS,
volume = "",
number = "",
pages = "??--??",
month = "",
year = "2020",
CODEN = "CNOMEL",
DOI = "https://doi.org/10.1007/s10614-020-09983-3",
ISSN = "",
ISSN-L = "0927-7099",
bibdate = "Fri Apr 9 07:54:52 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/matlab.bib;
https://www.math.utah.edu/pub/tex/bib/python.bib",
URL = "http://link.springer.com/article/10.1007/s10614-020-09983-3",
acknowledgement = ack-nhfb,
fjournal = "Computational Economics",
}
@InProceedings{Drakopoulos:2020:ODC,
author = "Georgios Drakopoulos and Eleanna Kafeza",
editor = "{IEEE}",
booktitle = "{2020 5th South-East Europe Design Automation,
Computer Engineering, Computer Networks and Social
Media Conference (SEEDA-CECNSM)}",
title = "One Dimensional Cross-Correlation Methods for
Deterministic and Stochastic Graph Signals with a
{Twitter} Application in {Julia}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--8",
year = "2020",
DOI = "https://doi.org/10.1109/SEEDA-CECNSM49515.2020.9221815",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Fathurrahman:2020:PJJ,
author = "Fadjar Fathurrahman and Mohammad Kemal Agusta and
Adhitya Gandaryus Saputro and Hermawan Kresno
Dipojono",
title = "{PWDFT.jl}: a {Julia} package for electronic structure
calculation using density functional theory and plane
wave basis",
journal = j-COMP-PHYS-COMM,
volume = "256",
number = "??",
pages = "Article 107372",
month = nov,
year = "2020",
CODEN = "CPHCBZ",
DOI = "https://doi.org/10.1016/j.cpc.2020.107372",
ISSN = "0010-4655 (print), 1879-2944 (electronic)",
ISSN-L = "0010-4655",
bibdate = "Sat Mar 13 08:21:39 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/compphyscomm2020.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0010465520301600",
acknowledgement = ack-nhfb,
fjournal = "Computer Physics Communications",
journal-URL = "http://www.sciencedirect.com/science/journal/00104655",
}
@Article{Fathurrahman:2020:PPJ,
author = "Fadjar Fathurrahman and Mohammad Kemal Agusta and
Adhitya Gandaryus Saputro and Hermawan Kresno
Dipojono",
title = "\pkg{PWDFT.jl}: a {Julia} package for electronic
structure calculation using density functional theory
and plane wave basis",
journal = j-COMP-PHYS-COMM,
volume = "256",
pages = "107372",
year = "2020",
CODEN = "CPHCBZ",
DOI = "https://doi.org/10.1016/j.cpc.2020.107372",
ISSN = "0010-4655 (print), 1879-2944 (electronic)",
ISSN-L = "0010-4655",
bibdate = "Fri Apr 9 15:22:25 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://www.sciencedirect.com/science/article/pii/S0010465520301600",
abstract = "We describe the implementation of PWDFT.jl, a package
for electronic structure calculations written in Julia
programming language using plane wave basis set and
pseudopotentials. In this package, a typical Kohn Sham
density functional theory (KSDFT) is divided into three
steps: initializing the molecular or crystalline
structure, constructing the Kohn Sham Hamiltonian, and
solving the Kohn Sham problem using self-consistent
field (SCF) calculation. To facilitate various tasks
involved in these steps, we provide several custom data
types which are transparent and easy to be modified.
Basic operations such as wave function
orthogonalization, action of kinetic and potential
operators to wave functions and iterative
diagonalization of Hamiltonian have been implemented in
pure Julia. Several algorithms to solve the Kohn Sham
problems such as self-consistent field and direct
energy minimization have also been implemented in
PWDFT.jl. To assess the validity of our implementation,
we present the results of total energy calculations
against the well-established ABINIT package. We also
show how one can use PWDFT.jl to write a simple
self-consistent field implementation. Program summary
Program Title: PWDFT.jl CPC Library link to program
files: https://doi.org/10.17632/b87xzmzm2z.1 Licensing
provisions: GPL-v2 Programming language: Julia Nature
of problem: Electronic structure of interacting
electrons in material Solution method: Kohn Sham
density functional theory, using plane wave basis set
and pseudopotentials Additional comments including
restrictions and unusual features: Due to the
precompilation step, the program may appear to be slow
at the first call. Parallelization is not yet
considered.",
acknowledgement = ack-nhfb,
fjournal = "Computer Physics Communications",
journal-URL = "http://www.sciencedirect.com/science/journal/00104655",
keywords = "Density functional theory, Pseudopotential plane wave
method, Julia programming language",
}
@Article{Frison:2020:BAB,
author = "Gianluca Frison and Tommaso Sartor and Andrea Zanelli
and Moritz Diehl",
title = "The {BLAS API} of {BLASFEO}",
journal = j-TOMS,
volume = "46",
number = "2",
pages = "1--36",
month = jun,
year = "2020",
CODEN = "ACMSCU",
DOI = "https://doi.org/10.1145/3378671",
ISSN = "0098-3500 (print), 1557-7295 (electronic)",
ISSN-L = "0098-3500",
bibdate = "Thu Apr 8 08:02:29 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
fjournal = "ACM Transactions on Mathematical Software",
journal-URL = "http://dl.acm.org/pub.cfm?id=J782",
keywords = "Julia programming language",
}
@Article{Gao:2020:JLMa,
author = "Kaifeng Gao and Gang Mei and Francesco Piccialli and
Salvatore Cuomo and Jingzhi Tu and Zenan Huo",
title = "{Julia} Language in Machine Learning: Algorithms,
Applications, and Open Issues",
journal = "arXiv.org",
day = "23",
month = mar,
year = "2020",
LCCN = "????",
bibdate = "Fri Jan 3 15:02:48 MST 2025",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://arxiv.org/abs/2003.10146",
abstract = "Machine learning is driving development across many
fields in science and engineering. A simple and
efficient programming language could accelerate
applications of machine learning in various fields.
Currently, the programming languages most commonly used
to develop machine learning algorithms include Python,
MATLAB, and C/C++. However, none of these languages
well balance both efficiency and simplicity. The Julia
language is a fast, easy-to-use, and open-source
programming language that was originally designed for
high-performance computing, which can well balance the
efficiency and simplicity. This paper summarizes the
related research work and developments in the
application of the Julia language in machine learning.
It first surveys the popular machine learning
algorithms that are developed in the Julia language.
Then, it investigates applications of the machine
learning algorithms implemented with the Julia
language. Finally, it discusses the open issues and the
potential future directions that arise in the use of
the Julia language in machine learning. Comment:
Published in Computer Science Review.",
acknowledgement = ack-nhfb,
}
@Article{Gao:2020:JLMb,
author = "Kaifeng Gao and Gang Mei and Francesco Piccialli and
Salvatore Cuomo and Jingzhi Tu and Zenan Huo",
title = "{Julia} language in machine learning: Algorithms,
applications, and open issues",
journal = j-COMP-SCI-REV,
volume = "37",
pages = "100254",
month = aug,
year = "2020",
DOI = "https://doi.org/10.1016/j.cosrev.2020.100254",
ISSN = "1574-0137 (print), 1876-7745 (electronic)",
bibdate = "Thu Apr 8 08:02:29 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://www.sciencedirect.com/science/article/pii/S157401372030071X",
abstract = "Machine learning is driving development across many
fields in science and engineering. A simple and
efficient programming language could accelerate
applications of machine learning in various fields.
Currently, the programming languages most commonly used
to develop machine learning algorithms include Python,
MATLAB, and C/C ++. However, none of these languages
well balance both efficiency and simplicity. The Julia
language is a fast, easy-to-use, and open-source
programming language that was originally designed for
high-performance computing, which can well balance the
efficiency and simplicity. This paper summarizes the
related research work and developments in the
applications of the Julia language in machine learning.
It first surveys the popular machine learning
algorithms that are developed in the Julia language.
Then, it investigates applications of the machine
learning algorithms implemented with the Julia
language. Finally, it discusses the open issues and the
potential future directions that arise in the use of
the Julia language in machine learning.",
acknowledgement = ack-nhfb,
fjournal = "Computer Science Review",
journal-URL = "http://www.sciencedirect.com/science/journal/15740137",
keywords = "Artificial neural networks; Deep learning; Julia
programming language; Machine learning; Supervised
learning; Unsupervised learning",
}
@InProceedings{Geth:2020:CVF,
author = "F. Geth and S. Claeys and G. Deconinck",
booktitle = "{2020 8th Workshop on Modeling and Simulation of
Cyber-Physical Energy Systems}",
title = "Current-Voltage Formulation of the Unbalanced Optimal
Power Flow Problem",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--6",
year = "2020",
DOI = "https://doi.org/10.1109/MSCPES49613.2020.9133699",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language; PowerModels.jl;
PowerModelsDistribution.jl",
}
@InProceedings{Goualard:2020:GRF,
author = "Fr{\'e}d{\'e}ric Goualard",
title = "Generating Random Floating-Point Numbers by Dividing
Integers: a Case Study",
crossref = "Krzhizhanovskaya:2020:CSI",
pages = "15--28",
year = "2020",
DOI = "https://doi.org/10.1007/978-3-030-50417-5_2",
bibdate = "Thu Jun 25 07:31:47 2020",
bibsource = "https://www.math.utah.edu/pub/bibnet/authors/d/dongarra-jack-j.bib;
https://www.math.utah.edu/pub/tex/bib/fparith.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/matlab.bib;
https://www.math.utah.edu/pub/tex/bib/prng.bib",
acknowledgement = ack-nhfb,
keywords = "error analysis; floating-point number; GMP; Julia;
Matlab; Mersenne Twister; PRNG; pseudo-random numbers;
random number",
}
@Article{Helmreich:2020:BRD,
author = "James E. Helmreich",
title = "Book Review: {{\booktitle{Data Science with Julia}}}",
journal = j-J-STAT-SOFT,
volume = "94",
number = "??",
pages = "??--??",
month = "????",
year = "2020",
CODEN = "JSSOBK",
DOI = "https://doi.org/10.18637/jss.v94.b01",
ISSN = "1548-7660",
ISSN-L = "1548-7660",
bibdate = "Wed May 19 07:43:41 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jstatsoft.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://www.jstatsoft.org/index.php/jss/article/view/v094b01;
https://www.jstatsoft.org/index.php/jss/article/view/v094b01/v94b01.pdf",
acknowledgement = ack-nhfb,
journal-URL = "http://www.jstatsoft.org/",
}
@InProceedings{Hernandez:2020:IJS,
author = "Miguel Hernandez and Damian Valles and David C.
Wierschem and Rachel M. Koldenhoven and George Koutitas
and Francis A. Mendez and Semih Aslan and Jesus
Jimenez",
editor = "{IEEE}",
booktitle = "{2020 11th IEEE Annual Information Technology,
Electronics and Mobile Communication Conference
(IEMCON)}",
title = "An Initial {Julia} Simulation Approach to Material
Handling Operations from Motion Captured Data",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "0718--0722",
year = "2020",
DOI = "https://doi.org/10.1109/IEMCON51383.2020.9284829",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@InProceedings{Hodson:2020:MQT,
author = "Douglas D. Hodson",
title = "Modeling Quantum Teleportation with {Julia}",
crossref = "Anonymous:2020:SC",
pages = "??--??",
year = "2020",
bibdate = "Fri Jan 03 15:15:39 2025",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Hunold:2020:BJC,
author = "Sascha Hunold and Sebastian Steiner",
editor = "{IEEE}",
booktitle = "{2020 IEEE\slash ACM Performance Modeling,
Benchmarking and Simulation of High Performance
Computer Systems (PMBS)}",
title = "Benchmarking {Julia}'s Communication Performance: Is
{Julia} {HPC} ready or Full {HPC}?",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "20--25",
year = "2020",
DOI = "https://doi.org/10.1109/PMBS51919.2020.00008",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Huo:2020:DEP,
author = "Zenan Huo and Gang Mei and Giampaolo Casolla and Fabio
Giampaolo",
title = "Designing an efficient parallel spectral clustering
algorithm on multi-core processors in {Julia}",
journal = j-J-PAR-DIST-COMP,
volume = "138",
number = "??",
pages = "211--221",
month = apr,
year = "2020",
CODEN = "JPDCER",
DOI = "https://doi.org/10.1016/j.jpdc.2020.01.003",
ISSN = "0743-7315 (print), 1096-0848 (electronic)",
ISSN-L = "0743-7315",
bibdate = "Wed Mar 18 09:26:11 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jpardistcomp.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://www.sciencedirect.com/science/article/pii/S0743731519308743",
abstract = "Spectral clustering is widely used in data mining,
machine learning and other fields. It can identify the
arbitrary shape of a sample space and converge to the
global optimal solution. Compared with the traditional
k-means algorithm, the spectral clustering algorithm
has stronger adaptability to data and better clustering
results. However, the computation of the algorithm is
quite expensive. In this paper, an efficient parallel
spectral clustering algorithm on multi-core processors
in the Julia language is proposed, and we refer to it
as juPSC. The Julia language is a high-performance,
open-source programming language. The juPSC is composed
of three procedures: (1) calculating the affinity
matrix, (2) calculating the eigenvectors, and (3)
conducting k-means clustering. Procedures (1) and (3)
are computed by the efficient parallel algorithm, and
the COO format is used to compress the affinity matrix.
Two groups of experiments are conducted to verify the
accuracy and efficiency of the juPSC. Experimental
results indicate that (1) the juPSC achieves speedups
of approximately 14 $ \times $--18 $ \times $ on a
24-core CPU and that (2) the serial version of the
juPSC is faster than the Python version of
scikit-learn. Moreover, the structure and functions of
the juPSC are designed considering modularity, which is
convenient for combination and further optimization
with other parallel computing platforms.",
acknowledgement = ack-nhfb,
fjournal = "Journal of Parallel and Distributed Computing",
journal-URL = "http://www.sciencedirect.com/science/journal/07437315",
keywords = "Clustering algorithm, Spectral clustering, Parallel
algorithm, Multi-core processors, Julia language",
}
@InProceedings{Kaluba:2020:PPJ,
author = "Marek Kaluba and Benjamin Lorenz and Sascha Timme",
title = "\pkg{Polymake.jl}: a New Interface to \pkg{polymake}",
crossref = "Bigatti:2020:MSI",
pages = "377--385",
year = "2020",
DOI = "https://doi.org/10.1007/978-3-030-52200-1_37",
bibdate = "Wed Sep 27 13:00:10 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
}
@Article{Kannan:2020:EAM,
author = "G. Padmasudha Kannan and K. V. Nagaraja",
title = "An Efficient Automatic Mesh Generator With Parabolic
Arcs in {Julia} for Computation of {TE} and {TM} Modes
for Waveguides",
journal = j-IEEE-ACCESS,
volume = "8",
number = "",
pages = "109508--109521",
year = "2020",
DOI = "https://doi.org/10.1109/ACCESS.2020.3002091",
ISSN = "2169-3536",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE Access",
journal-URL = "https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639",
}
@InProceedings{Khan:2020:COF,
author = "H. Khan and H. Issa and J. K. Tar",
booktitle = "{2020 IEEE 20th International Symposium on
Computational Intelligence and Informatics (CINTI)}",
title = "Comparison of the Operation of Fixed Point
Iteration-based Adaptive and Robust {VS\slash SM}-type
Solutions for Controlling Two Coupled Fluid Tanks",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "29--34",
year = "2020",
DOI = "https://doi.org/10.1109/CINTI51262.2020.9305827",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@InProceedings{Kyesswa:2020:NJB,
author = "Michael Kyesswa and Philipp Schmurr and H{\"u}seyin K.
{\c{C}}akmak and Uwe K{\"u}hnapfel and Veit
Hagenmeyer",
editor = "{IEEE}",
booktitle = "{2020 IEEE\slash ACM 24th International Symposium on
Distributed Simulation and Real Time Applications
(DS-RT)}",
title = "A New {Julia}-Based Parallel Time-Domain Simulation
Algorithm for Analysis of Power System Dynamics",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--9",
year = "2020",
DOI = "https://doi.org/10.1109/DS-RT50469.2020.9213602",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Langer:2020:OHE,
author = "Lissy Langer and Thomas Volling",
title = "An optimal home energy management system for
modulating heat pumps and photovoltaic systems",
journal = "Applied Energy",
volume = "278",
pages = "115661",
month = nov,
year = "2020",
DOI = "https://doi.org/10.1016/j.apenergy.2020.115661",
bibdate = "Thu Apr 8 08:02:29 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@InProceedings{Lau:2020:DSC,
author = "S. Lau and I. Drosos and J. M. Markel and P. J. Guo",
booktitle = "{2020 IEEE Symposium on Visual Languages and
Human-Centric Computing (VL/HCC)}",
title = "The Design Space of Computational Notebooks: An
Analysis of 60 Systems in Academia and Industry",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--11",
year = "2020",
DOI = "https://doi.org/10.1109/VL/HCC50065.2020.9127201",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Merrell:2020:ISP,
author = "David Merrell and Anthony Gitter",
title = "Inferring signaling pathways with probabilistic
programming",
journal = j-BIOINFORMATICS,
volume = "36",
number = "Supplement\_2",
pages = "i822--i830",
month = dec,
year = "2020",
DOI = "https://doi.org/10.1093/bioinformatics/btaa861",
ISSN = "1367-4803 (print), 1367-4811 (electronic)",
ISSN-L = "1367-4803",
bibdate = "Thu Apr 8 08:02:29 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
fjournal = "Bioinformatics",
journal-URL = "http://bioinformatics.oxfordjournals.org/",
keywords = "Julia programming language",
}
@Article{PadmasudhaKannan:2020:AHO,
author = "G. {Padmasudha Kannan} and T. V. Smitha and K. V.
Nagaraja",
title = "Automated high-order curved mesh generator with
high-level dynamic programming language {Julia} for
photonic applications",
journal = "Materials Today: Proceedings",
year = "2020",
DOI = "https://doi.org/10.1016/j.matpr.2020.09.706",
ISSN = "2214-7853",
bibdate = "Fri Apr 9 15:22:25 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://www.sciencedirect.com/science/article/pii/S2214785320374319",
abstract = "A powerful automated high-order unstructured curved
mesh generator is proposed in this work with a
high-level dynamic programming language Julia. This
generator uses higher-order one-sided curved triangular
finite elements for the domains having curved borders
with parabolic arcs. The proposed approach of the mesh
generator can be successfully applied for solving
several industrial problems inclusive of photonics with
the finite element method. The use of the parabolic
arcs method to obtain node relations for the curved
geometry enhances the performance of the technique with
the subparametric mappings. The mesh generator
suggested is based on the prominent Gmsh mesh
generator. For all geometry, the presented technique
can be implemented. The methodology is applied in this
paper to illustrate a few photonic crystal domains.",
acknowledgement = ack-nhfb,
keywords = "Higher-order triangular finite element, Julia, Mesh
generation, Subparametric mapping, Curved elements,
Parabolic arc",
}
@InProceedings{Sells:2020:JPL,
author = "Ray Sells",
editor = "{IEEE}",
booktitle = "{2020 IEEE Aerospace Conference}",
title = "{Julia} Programming Language Benchmark Using a Flight
Simulation",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--8",
year = "2020",
DOI = "https://doi.org/10.1109/AERO47225.2020.9172277",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Book{Sengupta:2020:LBJ,
author = "Ahan. Sengupta and William Lau",
title = "The little book of {Julia} algorithms",
publisher = "Sav Publishing",
address = "Wroclaw, Poland",
pages = "117",
year = "2020",
ISBN = "1-383-17360-5, 1-83817-360-9",
ISBN-13 = "978-1-383-17360-4, 978-1-83817-360-9",
LCCN = "????",
bibdate = "Fri Jan 3 15:02:48 MST 2025",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
abstract = "This book will make readers comfortable with using
computers to solve any problems, and leave them well
prepared for more significant programming in their
maths, science or computer science courses at college.
After finishing the exercises in this book, the reader
should feel more familiar with: Loops and conditionals;
Structuring code with functions; Reading and writing
files; Installing and using packages. Sorting and
searching. Simple Statistics and Plotting",
acknowledgement = ack-nhfb,
}
@InProceedings{Smith:2020:DPJ,
author = "Einar Smith",
booktitle = "{Introduction to the Tools of Scientific Computing}",
title = "Distributed Processing in {Julia}",
publisher = pub-SV,
address = pub-SV:adr,
pages = "??--??",
year = "2020",
DOI = "https://doi.org/10.1007/978-3-030-60808-8_13",
bibdate = "Fri Apr 9 07:54:52 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://link.springer.com/chapter/10.1007/978-3-030-60808-8_13",
acknowledgement = ack-nhfb,
}
@InProceedings{Smith:2020:J,
author = "Einar Smith",
booktitle = "{Introduction to the Tools of Scientific Computing}",
title = "{Julia}",
publisher = pub-SV,
address = pub-SV:adr,
pages = "??--??",
year = "2020",
DOI = "https://doi.org/10.1007/978-3-030-60808-8_8",
bibdate = "Fri Apr 9 07:54:52 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://link.springer.com/chapter/10.1007/978-3-030-60808-8_8",
acknowledgement = ack-nhfb,
}
@InProceedings{Suslov:2020:SHM,
author = "Sergey Suslov and Michael Schiek and Markus Robens and
Christian Grewing and Stefan van Waasen",
editor = "{IEEE}",
booktitle = "{2020 IEEE\slash ACM 24th International Symposium on
Distributed Simulation and Real Time Applications
(DS-RT)}",
title = "Simulating Heterogeneous Models on Multi-Core
Platforms using {Julia}'s Computing Language Parallel
Potential",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--4",
year = "2020",
DOI = "https://doi.org/10.1109/DS-RT50469.2020.9213527",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Xiong:2020:PMM,
author = "Hao Xiong and Zhen-Yu Yin and Fran{\c{c}}ois Nicot",
title = "Programming a micro-mechanical model of granular
materials in {Julia}",
journal = j-ADV-ENG-SOFTWARE,
volume = "145",
pages = "102816",
year = "2020",
CODEN = "AESODT",
DOI = "https://doi.org/10.1016/j.advengsoft.2020.102816",
ISSN = "0965-9978 (print), 0141-1195 (electronic)",
ISSN-L = "0965-9978",
bibdate = "Fri Apr 9 15:22:25 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://www.sciencedirect.com/science/article/pii/S0965997820301071",
abstract = "Modelling the mechanical behaviour of granular
materials using the insight of physics, such as
discrete element method (DEM), usually costs a lot of
computing resources as a result of the storing and
transferring of a large amount of particle and contact
information. Unlike DEM, the micro-mechanical (MM)
model, based on statistics of directional
inter-particle contacts of a representative volume of
an element, imposes a much lower computational demand
while retaining granular physics. This paper presents
such a kinematic hypothesis-based MM modelling
framework, programmed by a dynamic coding language,
Julia. The directional local law of a recently
developed model is selected as an example of the
implementation. The entire code of the MM model
programmed by Julia is structured into several
functions by which multilevel loops are called in an
order. Moreover, a global mixed-loading control method
is proposed in this study by which the stress control
and strain control can be achieved simultaneously.
Using this method, conventional triaxial tests and
proportional strain tests are simulated to calibrate
the model according to experimental data. The same
experiments are also simulated by DEM for comparison
with the MM model to estimate the computational
efficiency and accuracy, which demonstrates a
significant advantage of the MM model. This study can
be directly used for modelling other materials by
changing the directional local law and provides helpful
guidance for programming of similar multiscale
approaches.",
acknowledgement = ack-nhfb,
fjournal = "Advances in Engineering Software (1978)",
journal-URL = "http://www.sciencedirect.com/science/journal/01411195",
keywords = "Julia language, High-performance dynamic programming,
Micromechanics, Granular materials, Multiscale,
Microstructure",
}
@InProceedings{Ziyatdinova:2020:EES,
author = "J. Ziyatdinova and O. Oleynikova and E. Valeeva",
booktitle = "{2020 IEEE Global Engineering Education Conference
(EDUCON)}",
title = "Engaging Engineering Students in Cultural Diversity
and Unity Studies",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1164--1167",
year = "2020",
DOI = "https://doi.org/10.1109/EDUCON45650.2020.9125305",
bibdate = "Thu Apr 8 07:17:08 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Borges:2021:AIA,
author = "Carlos F. Borges",
title = "{Algorithm 1014}: an Improved Algorithm for {\tt
hypot(x,y)}",
journal = j-TOMS,
volume = "47",
number = "1",
pages = "9:1--9:12",
month = jan,
year = "2021",
CODEN = "ACMSCU",
DOI = "https://doi.org/10.1145/3428446",
ISSN = "0098-3500 (print), 1557-7295 (electronic)",
ISSN-L = "0098-3500",
bibdate = "Thu Jan 7 10:31:04 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/elefunt.bib;
https://www.math.utah.edu/pub/tex/bib/fparith.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/toms.bib",
URL = "https://dl.acm.org/doi/10.1145/3428446",
abstract = "We develop fast and accurate algorithms for evaluating
$ \sqrt {x^2 + y^2} $ for two floating-point numbers
$x$ and $y$. Library functions that perform this
computation are generally named {\tt hypot(x,y)}. We
compare five approaches that we will develop in this
article to the current resident library function that
is delivered with Julia 1.1 and to the code that has
been distributed with the C math library for decades.
We will investigate the accuracy of our algorithms by
simulation.",
acknowledgement = ack-nhfb,
articleno = "9",
fjournal = "ACM Transactions on Mathematical Software (TOMS)",
journal-URL = "https://dl.acm.org/loi/toms",
}
@InProceedings{Carlson:2021:CJC,
author = "Fredrik Bagge Carlson and Mattias Falt and Albin
Heimerson and Olof Troeng",
editor = "{IEEE}",
booktitle = "{2021 60th IEEE Conference on Decision and Control
(CDC)}",
title = "{ControlSystems.jl}: a Control Toolbox in {Julia}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "4847--4853",
year = "2021",
DOI = "https://doi.org/10.1109/CDC45484.2021.9683403",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
}
@Article{Cheli:2021:PMJ,
author = "Alessandro Cheli",
title = "\pkg{Metatheory.jl}: Fast and Elegant Algebraic
Computation in {Julia} with Extensible Equality
Saturation",
journal = "Journal of Open Source Software",
volume = "6",
number = "59",
pages = "3078",
month = mar,
year = "2021",
DOI = "https://doi.org/10.21105/joss.03078",
bibdate = "Thu Apr 8 08:02:29 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Dowson:2021:PSJ,
author = "Oscar Dowson and Lea Kapelevich",
title = "\pkg{SDDP.jl}: A {Julia} Package for Stochastic Dual
Dynamic Programming",
journal = j-INFORMS-J-COMPUT,
volume = "33",
number = "1",
pages = "27--33",
month = "Winter",
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1287/ijoc.2020.0987",
ISSN = "1091-9856 (print), 1526-5528 (electronic)",
ISSN-L = "1091-9856",
bibdate = "Sat Feb 6 14:48:57 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/informs-j-comput.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://pubsonline.informs.org/doi/pdf/10.1287/ijoc.2020.0987",
acknowledgement = ack-nhfb,
ajournal = "INFORMS J. Comput.",
fjournal = "INFORMS Journal on Computing",
journal-URL = "https://pubsonline.informs.org/journal/ijoc",
onlinedate = "31 August 2020",
}
@Article{Goke:2021:AJJ,
author = "Leonard G{\"o}ke",
title = "\pkg{AnyMOD.jl}: a {Julia} package for creating energy
system models",
journal = j-SOFTWAREX,
volume = "16",
number = "??",
pages = "??--??",
month = dec,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1016/j.softx.2021.100871",
ISSN = "2352-7110",
ISSN-L = "2352-7110",
bibdate = "Thu Feb 10 10:19:26 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/softwarex.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S2352711021001382",
acknowledgement = ack-nhfb,
articleno = "100871",
fjournal = "SoftwareX",
journal-URL = "https://www.sciencedirect.com/journal/softwarex/issues",
}
@Article{Huo:2021:JJB,
author = "Zenan Huo and Gang Mei and Nengxiong Xu",
title = "{juSFEM}: a {Julia}-based open-source package of
parallel {Smoothed Finite Element Method (S-FEM)} for
elastic problems",
journal = j-COMPUT-MATH-APPL,
volume = "81",
number = "??",
pages = "459--477",
day = "1",
month = jan,
year = "2021",
CODEN = "CMAPDK",
DOI = "https://doi.org/10.1016/j.camwa.2020.01.027",
ISSN = "0898-1221 (print), 1873-7668 (electronic)",
ISSN-L = "0898-1221",
bibdate = "Thu Apr 8 08:02:29 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/computmathappl2020.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0898122120300523",
acknowledgement = ack-nhfb,
fjournal = "Computers and Mathematics with Applications",
journal-URL = "http://www.sciencedirect.com/science/journal/08981221",
keywords = "Julia programming language",
}
@Article{Huo:2021:PJJ,
author = "Zenan Huo and Gang Mei and Nengxiong Xu",
title = "\pkg{juSFEM}: a {Julia}-based open-source package of
parallel {Smoothed Finite Element Method (S-FEM)} for
elastic problems",
journal = j-COMPUT-MATH-APPL,
volume = "81",
pages = "459--477",
year = "2021",
CODEN = "CMAPDK",
DOI = "https://doi.org/10.1016/j.camwa.2020.01.027",
ISSN = "0898-1221 (print), 1873-7668 (electronic)",
ISSN-L = "0898-1221",
bibdate = "Fri Apr 9 15:22:25 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
note = "Development and Application of Open-source Software
for Problems with Numerical PDEs",
URL = "https://www.sciencedirect.com/science/article/pii/S0898122120300523",
abstract = "The Smoothed Finite Element Method (S-FEM) proposed by
Liu G. R. can achieve more accurate results than the
conventional FEM. Currently, much commercial software
and many open-source packages have been developed to
analyze various science and engineering problems using
the FEM. However, there is little work focusing on
designing and developing software or packages for the
S-FEM. In this paper, we design and implement an
open-source package of the parallel S-FEM for elastic
problems by utilizing the Julia language on multi-core
CPU. The Julia language is a fast, easy-to-use, and
open-source programming language that was originally
designed for high-performance computing. We term our
package as juSFEM. To the best of the authors
knowledge, juSFEM is the first package of parallel
S-FEM developed with the Julia language. To verify the
correctness and evaluate the efficiency of juSFEM, two
groups of benchmark tests are conducted. The benchmark
results show that (1) juSFEM can achieve accurate
results when compared to commercial FEM software
ABAQUS, and (2) juSFEM only requires 543 s to calculate
the displacements of a 3D elastic cantilever beam model
which is composed of approximately 2 million
tetrahedral elements, while in contrast the commercial
FEM software needs 930 s for the same calculation
model; (3) the parallel juSFEM executed on the 24-core
CPU is approximately 20$ \times $ faster than the
corresponding serial version. Moreover, the structure
and function of juSFEM are easily modularized, and the
code in juSFEM is clear and readable, which is
convenient for further development.",
acknowledgement = ack-nhfb,
fjournal = "Computers and Mathematics with Applications",
journal-URL = "http://www.sciencedirect.com/science/journal/08981221",
keywords = "Smoothed Finite Element Method (S-FEM), Parallel
algorithm, Julia language, Computational efficiency,
Computational accuracy",
}
@Article{Lara:2021:PJP,
author = "Jos{\'e} Daniel Lara and Clayton Barrows and Daniel
Thom and Dheepak Krishnamurthy and Duncan Callaway",
title = "\pkg{PowerSystems.jl} --- a power system data
management package for large scale modeling",
journal = j-SOFTWAREX,
volume = "15",
number = "??",
pages = "??--??",
month = jul,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1016/j.softx.2021.100747",
ISSN = "2352-7110",
ISSN-L = "2352-7110",
bibdate = "Thu Feb 10 10:19:25 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/softwarex.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S2352711021000765",
acknowledgement = ack-nhfb,
articleno = "100747",
fjournal = "SoftwareX",
journal-URL = "https://www.sciencedirect.com/journal/softwarex/issues",
}
@Article{Lavaud:2021:ABT,
author = "Corentin Lavaud and Robin Gerzaguet and Matthieu
Gautier and Olivier Berder",
title = "{AbstractSDRs}: Bring Down the Two-Language Barrier
With {Julia} Language for Efficient {SDR} Prototyping",
journal = "IEEE Embedded Systems Letters",
volume = "13",
number = "4",
pages = "166--169",
year = "2021",
DOI = "https://doi.org/10.1109/LES.2021.3054174",
ISSN = "1943-0663 (print), 1943-0671 (electronic)",
ISSN-L = "1943-0663",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Lin:2021:CJP,
author = "Wei-Chen Lin and Simon McIntosh-Smith",
editor = "{IEEE}",
booktitle = "{2021 International Workshop on Performance Modeling,
Benchmarking and Simulation of High Performance
Computer Systems (PMBS)}",
title = "Comparing {Julia} to Performance Portable Parallel
Programming Models for {HPC}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "94--105",
year = "2021",
DOI = "https://doi.org/10.1109/PMBS54543.2021.00016",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
}
@Article{Liu:2021:SDC,
author = "Lun Liu and Todd Millstein and Madanlal Musuvathi",
title = "Safe-by-default Concurrency for Modern Programming
Languages",
journal = j-TOPLAS,
volume = "43",
number = "3",
pages = "10:1--10:50",
month = sep,
year = "2021",
CODEN = "ATPSDT",
DOI = "https://doi.org/10.1145/3462206",
ISSN = "0164-0925 (print), 1558-4593 (electronic)",
ISSN-L = "0164-0925",
bibdate = "Tue Sep 14 07:20:02 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/java2020.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/toplas.bib",
URL = "https://dl.acm.org/doi/10.1145/3462206",
abstract = "Modern ``safe'' programming languages follow a design
principle that we call safety by default and
performance by choice. By default, these languages
enforce important programming abstractions, such as
memory and type safety, but they also provide
mechanisms that allow expert programmers to explicitly
trade some safety guarantees for increased performance.
However, these same languages have adopted the inverse
design principle in their support for multithreading.
By default, multithreaded programs violate important
abstractions, such as program order and atomic access
to individual memory locations to admit compiler and
hardware optimizations that would otherwise need to be
restricted. Not only does this approach conflict with
the design philosophy of safe languages, but very
little is known about the practical performance cost of
providing a stronger default semantics.
In this article, we propose a safe-by-default and
performance-by-choice multithreading semantics for safe
languages, which we call volatile-by-default. Under
this semantics, programs have sequential consistency
(SC) by default, which is the natural ``interleaving''
semantics of threads. However, the volatile-by-default
design also includes annotations that allow expert
programmers to avoid the associated overheads in
performance-critical code. We describe the design,
implementation, optimization, and evaluation of the
volatile-by-default semantics for two different safe
languages: Java and Julia. First, we present
VBD-HotSpot and VBDA-HotSpot, modifications of Oracle's
HotSpot JVM that enforce the volatile-by-default
semantics on Intel x86-64 hardware and ARM-v8 hardware.
Second, we present SC-Julia, a modification to the
just-in-time compiler within the standard Julia
implementation that provides best-effort enforcement of
the volatile-by-default semantics on x86-64 hardware
for the purpose of performance evaluation. We also
detail two different implementation techniques: a
baseline approach that simply reuses existing
mechanisms in the compilers for handling atomic
accesses, and a speculative approach that avoids the
overhead of enforcing the volatile-by-default semantics
until there is the possibility of an SC violation. Our
results show that the cost of enforcing SC is
significant but arguably still acceptable for some use
cases today. Further, we demonstrate that compiler
optimizations as well as programmer annotations can
reduce the overhead considerably.",
acknowledgement = ack-nhfb,
articleno = "10",
fjournal = "ACM Transactions on Programming Languages and
Systems",
journal-URL = "https://dl.acm.org/loi/toplas",
}
@Book{Nazarathy:2021:SJF,
author = "Yoni Nazarathy and Hayden Klok",
title = "Statistics with {Julia}: Fundamentals for Data
Science, Machine Learning and Artificial Intelligence",
publisher = pub-SV,
address = pub-SV:adr,
pages = "xvi + 532",
year = "2021",
DOI = "https://doi.org/10.1007/978-3-030-70901-3",
ISBN = "3-030-70901-9",
ISBN-13 = "978-3-030-70901-3",
ISSN = "2365-5674",
LCCN = "????",
bibdate = "Thu Apr 22 06:17:57 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://www.springer.com/us/book/9783030709006",
abstract = "This monograph uses the Julia language to guide the
reader through an exploration of the fundamental
concepts of probability and statistics, all with a view
of mastering machine learning, data science, and
artificial intelligence. The text does not require any
prior statistical knowledge and only assumes a basic
understanding of programming and mathematical notation.
It is accessible to practitioners and researchers in
data science, machine learning, bio-statistics,
finance, or engineering who may wish to solidify their
knowledge of probability and statistics. The book
progresses through ten independent chapters starting
with an introduction of Julia, and moving through basic
probability, distributions, statistical inference,
regression analysis, machine learning methods, and the
use of Monte Carlo simulation for dynamic stochastic
models. Ultimately this text introduces the Julia
programming language as a computational tool, uniquely
addressing end-users rather than developers. It makes
heavy use of over 200 code examples to illustrate
dozens of key statistical concepts. The Julia code,
written in a simple format with parameters that can be
easily modified, is also available for download from
the book's associated GitHub repository online",
acknowledgement = ack-nhfb,
remark = "Publication expected in July 2021.",
}
@InProceedings{odyga:2021:PIP,
author = "Wiktor odyga and Bartosz Chaber",
editor = "{IEEE}",
booktitle = "{2021 22nd International Conference on Computational
Problems of Electrical Engineering (CPEE)}",
title = "Parallel implementation of a {Particle-in-Cell} code
in {Julia} programming language",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--4",
year = "2021",
DOI = "https://doi.org/10.1109/CPEE54040.2021.9585274",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
}
@Article{Pelenitsyn:2021:TSJ,
author = "Artem Pelenitsyn and Julia Belyakova and Benjamin
Chung and Ross Tate and Jan Vitek",
title = "Type stability in {Julia}: avoiding performance
pathologies in {JIT} compilation",
journal = j-PACMPL,
volume = "5",
number = "OOPSLA",
pages = "150:1--150:26",
month = oct,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3485527",
ISSN = "2475-1421 (electronic)",
ISSN-L = "2475-1421",
bibdate = "Wed Mar 2 07:00:43 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/pacmpl.bib",
URL = "https://dl.acm.org/doi/10.1145/3485527",
abstract = "As a scientific programming language, Julia strives
for performance but also provides high-level
productivity features. To avoid performance
pathologies, Julia users are expected to adhere to a
coding discipline that enables so-called type
stability. \ldots{}",
acknowledgement = ack-nhfb,
articleno = "150",
fjournal = "Proceedings of the ACM on Programming Languages
(PACMPL)",
journal-URL = "https://dl.acm.org/loi/pacmpl",
}
@InProceedings{Pelletier:2021:GJP,
author = "Michel Pelletier and Will Kimmerer and Timothy A.
Davis and Timothy G. Mattson",
editor = "{IEEE}",
booktitle = "{2021 IEEE High Performance Extreme Computing
Conference (HPEC)}",
title = "The {GraphBLAS} in {Julia} and {Python}: the
{PageRank} and Triangle Centralities",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--7",
year = "2021",
DOI = "https://doi.org/10.1109/HPEC49654.2021.9622789",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/python.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Samayoa:2021:WDS,
author = "Jorge Samayoa and Preng Biba",
editor = "{IEEE}",
booktitle = "{2021 IEEE World Conference on Engineering Education
(EDUNINE)}",
title = "Workshop: Data Science with {Julia}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--2",
year = "2021",
DOI = "https://doi.org/10.1109/EDUNINE51952.2021.9429122",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
}
@Article{Stanitzki:2021:PJH,
author = "Marcel Stanitzki and Jan Strube",
title = "Performance of {Julia} for High Energy Physics
Analyses",
journal = j-COMPUT-SOFTW-BIG-SCI,
volume = "5",
number = "1",
pages = "??--??",
month = dec,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1007/s41781-021-00053-3",
ISSN = "2510-2036 (print), 2510-2044 (electronic)",
ISSN-L = "2510-2036",
bibdate = "Fri Apr 9 06:38:19 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/computsoftwbigsci.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://link.springer.com/article/10.1007/s41781-021-00053-3",
acknowledgement = ack-nhfb,
articleno = "10",
fjournal = "Computing and Software for Big Science",
journal-URL = "https://www.springer.com/journal/41781",
online-date = "Published: 09 April 2021 Article: 10",
}
@InProceedings{Yan:2021:JBH,
author = "Xiaowei Yan and Qiguo Wang and Zhun Zhong and Tianhong
Ren and Keyou Wang",
editor = "{IEEE}",
booktitle = "{2021 6th Asia Conference on Power and Electrical
Engineering (ACPEE)}",
title = "{Julia}-based high-performance electromagnetic
transient simulation method and platform for large
power grid",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "252--257",
year = "2021",
DOI = "https://doi.org/10.1109/ACPEE51499.2021.9437034",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
}
@Book{Anonymous:2022:AJO,
author = "Clemens Heitzinger",
title = "Algorithms with {JULIA}: Optimization, Machine
Learning, and Differential Equations Using the {JULIA}
Language",
publisher = "Springer International Publishing",
address = "Cham, Switzerland",
year = "2022",
DOI = "https://doi.org/10.1007/978-3-031-16560-3",
ISBN = "3-031-16560-8",
ISBN-13 = "978-3-031-16560-3",
LCCN = "????",
bibdate = "Fri Jan 3 15:02:48 MST 2025",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://www.springerlink.com/content/978-3-031-16560-3",
acknowledgement = ack-nhfb,
}
@Article{Bagci:2022:JJP,
author = "Ali Bagci",
title = "\pkg{JRAF}: a {Julia} package for computation of
relativistic molecular auxiliary functions",
journal = j-COMP-PHYS-COMM,
volume = "273",
number = "??",
pages = "Article 108276",
month = apr,
year = "2022",
CODEN = "CPHCBZ",
DOI = "https://doi.org/10.1016/j.cpc.2021.108276",
ISSN = "0010-4655 (print), 1879-2944 (electronic)",
ISSN-L = "0010-4655",
bibdate = "Tue Jan 25 06:27:42 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/compphyscomm2020.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S001046552100388X",
acknowledgement = ack-nhfb,
fjournal = "Computer Physics Communications",
journal-URL = "http://www.sciencedirect.com/science/journal/00104655",
}
@Book{Balbaert:2022:MOE,
author = "Ivo Balbaert and Adrian Salceanu and Logan
Kilpatrick",
title = "{Web} Development with {Julia} and {Genie}: a hands-on
guide to high-performance server-side web development
with the {Julia} programming language",
publisher = pub-PACKT,
address = pub-PACKT:adr,
pages = "xvii + 235",
year = "2022",
ISBN = "1-80181-095-8, 1-80181-113-X",
ISBN-13 = "978-1-80181-095-1, 978-1-80181-113-2",
LCCN = "????",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
tableofcontents = "Julia Programming Overview \\
Using Julia Standard Web Packages \\
Applying Julia in Various Use Cases on the Web \\
Building an MVC ToDo App \\
Adding a REST API \\
Deploying Genie Apps in Production \\
Adding Authentication to Our App \\
Developing Interactive Data Dashboards with Genie",
}
@Article{Biel:2022:ESP,
author = "Martin Biel and Mikael Johansson",
title = "Efficient Stochastic Programming in {Julia}",
journal = j-INFORMS-J-COMPUT,
volume = "34",
number = "4",
pages = "1885--1902",
month = "Fall",
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1287/ijoc.2022.1158",
ISSN = "1091-9856 (print), 1526-5528 (electronic)",
ISSN-L = "1091-9856",
bibdate = "Fri Oct 28 07:43:08 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/informs-j-comput.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://pubsonline.informs.org/doi/fpi/10.1287/ijoc.2022.1158",
acknowledgement = ack-nhfb,
ajournal = "INFORMS J. Comput.",
fjournal = "INFORMS Journal on Computing",
journal-URL = "https://pubsonline.informs.org/journal/ijoc",
onlinedate = "1 March 2022",
}
@InProceedings{Giordano:2022:PMP,
author = "Mos{\`e} Giordano and Milan Kl{\"o}wer and Valentin
Churavy",
editor = "{IEEE}",
booktitle = "{2022 IEEE International Conference on Cluster
Computing (CLUSTER)}",
title = "Productivity meets Performance: {Julia} on {A64FX}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "549--555",
year = "2022",
DOI = "https://doi.org/10.1109/CLUSTER51413.2022.00072",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
}
@Article{Kastner:2022:AAA,
author = "Felix Kastner and Andreas R{\"o}{\ss}ler",
title = "An Analysis of Approximation Algorithms for Iterated
Stochastic Integrals and a {Julia} and {MATLAB}
Simulation Toolbox",
journal = "arXiv.org",
day = "20",
month = jan,
year = "2022",
LCCN = "????",
bibdate = "Fri Jan 3 15:02:48 MST 2025",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/matlab.bib",
URL = "http://arxiv.org/abs/2201.08424",
abstract = "For the approximation and simulation of twofold
iterated stochastic integrals and the corresponding
L{\'e}vy areas w.r.t. a multi-dimensional Wiener
process, we review four algorithms based on a Fourier
series approach. Especially, the very efficient
algorithm due to Wiktorsson and a newly proposed
algorithm due to Mrongowius and R{\"o}ssler are
considered. To put recent advances into context, we
analyse the four Fourier-based algorithms in a unified
framework to highlight differences and similarities in
their derivation. A comparison of theoretical
properties is complemented by a numerical simulation
that reveals the order of convergence for each
algorithm. Further, concrete instructions for the
choice of the optimal algorithm and parameters for the
simulation of solutions for stochastic (partial)
differential equations are given. Additionally, we
provide advice for an efficient implementation of the
considered algorithms and incorporated these insights
into an open source toolbox that is freely available
for both Julia and MATLAB programming languages. The
performance of this toolbox is analysed by comparing it
to some existing implementations, where we observe a
significant speed-up.",
acknowledgement = ack-nhfb,
}
@Book{Kelley:2022:SNE,
author = "C. T. Kelley",
title = "Solving nonlinear equations with iterative methods
solvers and examples in {Julia}",
publisher = pub-SIAM,
address = pub-SIAM:adr,
pages = "xx + 188",
year = "2022",
DOI = "https://doi.org/10.1137/1.9781611977271",
ISBN = "1-61197-726-6, 1-61197-727-4",
ISBN-13 = "978-1-61197-726-4, 978-1-61197-727-1",
LCCN = "????",
MRclass = "65-04, 65H10",
bibdate = "Fri Jan 3 15:02:48 MST 2025",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
series = "Fundamentals of Algorithms",
abstract = "This book on solvers for nonlinear equations is a
user-oriented guide to algorithms and implementation.
It's a sequel to the author's book Solving Nonlinear
Equations Using Newton's Method. This book uses Julia
and adds new material on pseudo-transient continuation,
mixed-precision solvers, and Anderson acceleration. A
Julia package and a suite of Jupyter notebooks support
the book. The purpose of the book is to show, via
algorithms in pseudocode and Julia with several
examples, how one can choose an appropriate iterative
method for a given problem and write an efficient
solver or apply one written by others. A sequel to the
author's Solving Nonlinear Equations with Newton's
Methods (SIAM, 2003).",
acknowledgement = ack-nhfb,
xxpages = "xx + 180",
}
@Article{Luo:2022:JJB,
author = "Mimi Luo and Jiayu Qin and Gang Mei",
title = "\pkg{juSPH}: a {Julia}-based open-source package of
parallel {Smoothed Particle Hydrodynamics (SPH)} for
dam break problems",
journal = j-SOFTWAREX,
volume = "19",
number = "??",
pages = "??--??",
month = jul,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1016/j.softx.2022.101151",
ISSN = "2352-7110",
ISSN-L = "2352-7110",
bibdate = "Fri Dec 9 06:06:57 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/softwarex.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S2352711022000954",
acknowledgement = ack-nhfb,
articleno = "101151",
fjournal = "SoftwareX",
journal-URL = "https://www.sciencedirect.com/journal/softwarex/issues",
}
@Article{Maincon:2022:EJE,
author = "Philippe Main{\c{c}}on",
title = "\pkg{EspyInsideFunction.jl} --- extracting
intermediate results from numerical functions",
journal = j-SOFTWAREX,
volume = "19",
number = "??",
pages = "??--??",
month = jul,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1016/j.softx.2022.101200",
ISSN = "2352-7110",
ISSN-L = "2352-7110",
bibdate = "Fri Dec 9 06:06:57 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/softwarex.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S2352711022001194",
acknowledgement = ack-nhfb,
articleno = "101200",
fjournal = "SoftwareX",
journal-URL = "https://www.sciencedirect.com/journal/softwarex/issues",
}
@InProceedings{Mamidi:2022:PAG,
author = "Nischay Ram Mamidi and Dhruv Saxena and Kumar Prasun
and Anil Nemili and Bharatkumar Sharma and S. M.
Deshpande",
editor = "{IEEE}",
booktitle = "{2022 IEEE 29th International Conference on High
Performance Computing, Data, and Analytics (HiPC)}",
title = "Performance analysis of {GPU} accelerated meshfree
{$q$-LSKUM} solvers in {Fortran}, {C}, {Python}, and
{Julia}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "156--165",
year = "2022",
DOI = "https://doi.org/10.1109/HiPC56025.2022.00031",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/fortran3.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/python.bib",
acknowledgement = ack-nhfb,
}
@Article{Plietzsch:2022:PJE,
author = "Anton Plietzsch and Raphael Kogler and Sabine Auer and
Julia Merino and Asier Gil-de-Muro and Jan Li{\ss}e and
Christina Vogel and Frank Hellmann",
title = "\pkg{PowerDynamics.jl} --- an experimentally validated
open-source package for the dynamical analysis of power
grids",
journal = j-SOFTWAREX,
volume = "17",
number = "??",
pages = "??--??",
month = jan,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1016/j.softx.2021.100861",
ISSN = "2352-7110",
ISSN-L = "2352-7110",
bibdate = "Mon Feb 28 10:41:25 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/softwarex.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S2352711021001345",
acknowledgement = ack-nhfb,
articleno = "100861",
fjournal = "SoftwareX",
journal-URL = "https://www.sciencedirect.com/journal/softwarex/issues",
}
@Article{Psarras:2022:LAM,
author = "Christos Psarras and Henrik Barthels and Paolo
Bientinesi",
title = "The Linear Algebra Mapping Problem. {Current} State of
Linear Algebra Languages and Libraries",
journal = j-TOMS,
volume = "48",
number = "3",
pages = "26:1--26:??",
month = sep,
year = "2022",
CODEN = "ACMSCU",
DOI = "https://doi.org/10.1145/3549935",
ISSN = "0098-3500 (print), 1557-7295 (electronic)",
ISSN-L = "0098-3500",
bibdate = "Sat Oct 29 08:26:38 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/matlab.bib;
https://www.math.utah.edu/pub/tex/bib/python.bib;
https://www.math.utah.edu/pub/tex/bib/s-plus.bib;
https://www.math.utah.edu/pub/tex/bib/toms.bib",
URL = "https://dl.acm.org/doi/10.1145/3549935",
abstract = "We observe a disconnect between developers and
end-users of linear algebra libraries. On the one hand,
developers invest significant effort in creating
sophisticated numerical kernels. On the other hand,
end-users are progressively less likely to go through
the time consuming process of directly using said
kernels; instead, languages and libraries, which offer
a higher level of abstraction, are becoming
increasingly popular. These languages offer mechanisms
that internally map the input program to lower level
kernels. Unfortunately, our experience suggests that,
in terms of performance, this translation is typically
suboptimal.\par
In this paper, we define the problem of mapping a
linear algebra expression to a set of available
building blocks as the ``Linear Algebra Mapping
Problem'' (LAMP); we discuss its NP-complete nature,
and investigate how effectively a benchmark of test
problems is solved by popular high-level programming
languages and libraries. Specifically, we consider
Matlab, Octave, Julia, R, Armadillo (C++), Eigen (C++),
and NumPy (Python); the benchmark is meant to test both
compiler optimizations, as well as linear algebra
specific optimizations, such as the optimal
parenthesization of matrix products. The aim of this
study is to facilitate the development of languages and
libraries that support linear algebra computations.",
acknowledgement = ack-nhfb,
articleno = "26",
fjournal = "ACM Transactions on Mathematical Software (TOMS)",
journal-URL = "https://dl.acm.org/loi/toms",
}
@InProceedings{Rackauckas:2022:CMS,
author = "Chris Rackauckas and Maja Gwozdz and Anand Jain and
Yingbo Ma and Francesco Martinuzzi and Utkarsh Rajput
and Elliot Saba and Viral B. Shah and Ranjan
Anantharaman and Alan Edelman and Shashi Gowda and Avik
Pal and Chris Laughman",
editor = "{IEEE}",
booktitle = "{2022 Annual Modeling and Simulation Conference
(ANNSIM)}",
title = "Composing Modeling and Simulation with Machine
Learning in {Julia}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--17",
year = "2022",
DOI = "https://doi.org/10.23919/ANNSIM55834.2022.9859453",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
}
@Article{Sandoval:2022:IJI,
author = "Steven Sandoval and Hasan Alshammari and Mamta Dalal",
title = "\pkg{ISA.jl}: {Instantaneous} spectral analysis in
{Julia}",
journal = j-SOFTWAREX,
volume = "20",
number = "??",
pages = "??--??",
month = dec,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1016/j.softx.2022.101239",
ISSN = "2352-7110",
ISSN-L = "2352-7110",
bibdate = "Fri Dec 9 06:06:58 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/softwarex.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S2352711022001571",
acknowledgement = ack-nhfb,
articleno = "101239",
fjournal = "SoftwareX",
journal-URL = "https://www.sciencedirect.com/journal/softwarex/issues",
}
@Article{VanGendt:2022:PAP,
author = "Michiel {Van Gendt} and Tim Besard and Stefaan
Vandenberghe and Bjorn {De Sutter}",
title = "Productively accelerating positron emission tomography
image reconstruction on graphics processing units with
{Julia}",
journal = j-IJHPCA,
volume = "36",
number = "3",
pages = "320--336",
day = "1",
month = may,
year = "2022",
CODEN = "IHPCFL",
DOI = "https://doi.org/10.1177/10943420211067520",
ISSN = "1094-3420 (print), 1741-2846 (electronic)",
ISSN-L = "1094-3420",
bibdate = "Thu May 30 07:31:45 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijsa.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://journals.sagepub.com/doi/abs/10.1177/10943420211067520",
acknowledgement = ack-nhfb,
ajournal = "????",
fjournal = "International Journal of High Performance Computing
Applications",
journal-URL = "https://journals.sagepub.com/home/hpc",
ORCID-numbers = "https://orcid.org/0000-0003-0317-2089",
}
@Article{Verdugo:2022:SDG,
author = "Francesc Verdugo and Santiago Badia",
title = "The software design of \pkg{Gridap}: a {Finite
Element} package based on the {Julia JIT} compiler",
journal = j-COMP-PHYS-COMM,
volume = "276",
number = "??",
pages = "Article 108341",
month = jul,
year = "2022",
CODEN = "CPHCBZ",
DOI = "https://doi.org/10.1016/j.cpc.2022.108341",
ISSN = "0010-4655 (print), 1879-2944 (electronic)",
ISSN-L = "0010-4655",
bibdate = "Wed May 4 06:12:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/compphyscomm2020.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0010465522000595",
acknowledgement = ack-nhfb,
fjournal = "Computer Physics Communications",
journal-URL = "http://www.sciencedirect.com/science/journal/00104655",
}
@Article{Verschelde:2022:EAS,
author = "Jan Verschelde",
title = "Exporting {Ada} Software to {Python} and {Julia}",
journal = j-SIGADA-LETTERS,
volume = "42",
number = "1",
pages = "76--78",
month = jun,
year = "2022",
CODEN = "AALEE5",
DOI = "https://doi.org/10.1145/3577949.3577961",
ISSN = "1094-3641 (print), 1557-9476 (electronic)",
ISSN-L = "0736-721X",
bibdate = "Tue Apr 11 11:59:12 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/python.bib;
https://www.math.utah.edu/pub/tex/bib/sigada.bib",
URL = "https://dl.acm.org/doi/10.1145/3577949.3577961",
abstract = "The objective is to demonstrate the making of Ada
software available to Python and Julia programmers
using GPRbuild. GPRbuild is the project manager of the
GNAT \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "ACM SIGADA Ada Letters",
journal-URL = "https://dl.acm.org/loi/sigada",
}
@Book{Zea:2022:JSS,
author = "Diego Javier Zea",
title = "Interactive Visualization and Plotting with {Julia}:
Create impressive data visualizations through {Julia}
packages such as {Plots}, {Makie}, {Gadfly}, and more",
publisher = pub-PACKT,
address = pub-PACKT:adr,
pages = "xix + 370",
year = "2022",
ISBN = "1-80181-051-6 (paperback), 1-80181-921-1 (e-book)",
ISBN-13 = "978-1-80181-051-7 (paperback), 978-1-80181-921-3
(e-book)",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Allred:2023:FNT,
author = "Taylor Allred and Xinyi Li and Ashton Wiersdorf and
Ben Greenman and Ganesh Gopalakrishnan",
editor = "????",
booktitle = "Julia Conference 2023",
title = "{FlowFPX}: Nimble Tools for Debugging Floating-Point
Exceptions",
publisher = "????",
address = "????",
pages = "8",
year = "2023",
bibdate = "Mon Sep 11 06:29:11 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/fparith.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
abstract = "Reliable numerical computations are central to
scientific computing, but the floating-point arithmetic
that enables large-scale models is error-prone. Numeric
exceptions are a common occurrence and can propagate
through code, leading to flawed results. This paper
presents FlowFPX, a toolkit for systematically
debugging floating-point exceptions by recording their
flow, coalescing exception contexts, and fuzzing in
select locations. These tools help scientists discover
when exceptions happen and track down their origin,
smoothing the way to a reliable codebase.",
acknowledgement = ack-nhfb,
keywords = "Julia programming language",
}
@Article{Axen:2023:MJE,
author = "Seth D. Axen and Mateusz Baran and Ronny Bergmann and
Krzysztof Rzecki",
title = "\pkg{Manifolds.jl}: an Extensible {Julia} Framework
for Data Analysis on Manifolds",
journal = j-TOMS,
volume = "49",
number = "4",
pages = "33:1--33:??",
month = dec,
year = "2023",
CODEN = "ACMSCU",
DOI = "https://doi.org/10.1145/3618296",
ISSN = "0098-3500 (print), 1557-7295 (electronic)",
ISSN-L = "0098-3500",
bibdate = "Sat Dec 23 05:40:24 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/toms.bib",
URL = "https://dl.acm.org/doi/10.1145/3618296",
abstract = "We present the Julia package Manifolds.jl, providing a
fast and easy-to-use library of Riemannian manifolds
and Lie groups. This package enables working with data
defined on a Riemannian manifold, such as the circle,
the sphere, symmetric positive definite matrices, or
one of the models for hyperbolic spaces. We introduce a
common interface, available in \pkg{ManifoldsBase.jl},
with which new manifolds, applications, and algorithms
can be implemented. We demonstrate the utility of
\pkg{Manifolds.jl} using B{\'e}zier splines, an
optimization task on manifolds, and principal component
analysis on nonlinear data. In a benchmark,
\pkg{Manifolds.jl} outperforms all comparable packages
for low-dimensional manifolds in speed; over Python and
Matlab packages, the improvement is often several
orders of magnitude, while over C/C++ packages, the
improvement is two-fold. For high-dimensional
manifolds, it outperforms all packages except for
Tensorflow-Riemopt, which is specifically tailored for
high-dimensional manifolds.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Math. Softw.",
articleno = "33",
fjournal = "ACM Transactions on Mathematical Software (TOMS)",
journal-URL = "https://dl.acm.org/loi/toms",
}
@Article{Christ:2023:PJU,
author = "Simon Christ and Daniel Schwabeneder and Christopher
Rackauckas and Michael Krabbe Borregaard and Thomas
Breloff",
title = "\pkg{Plots.jl} --- a User Extendable Plotting {API}
for the {Julia} Programming Language",
journal = j-J-OPEN-RES-SOFT,
volume = "11",
number = "1",
pages = "??--??",
month = "????",
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.5334/jors.431",
ISSN = "2049-9647",
ISSN-L = "2049-9647",
bibdate = "Tue Jun 13 08:02:36 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/jors.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://openresearchsoftware.metajnl.com/articles/10.5334/jors.431",
acknowledgement = ack-nhfb,
articleno = "5",
fjournal = "Journal of Open Research Software",
journal-URL = "https://openresearchsoftware.metajnl.com/issue/archive/",
onlinedate = "14 Feb 2023",
}
@Book{Engheim:2023:HDJ,
author = "Erik Engheim",
title = "{Julia} as a Second Language: General Purpose
Programming with a Taste of Data Science",
publisher = "Manning Publications",
address = "Shelter Island, NY, USA",
pages = "xxvi + 372",
year = "2023",
ISBN = "1-61729-971-5 (paperback)",
ISBN-13 = "978-1-61729-971-1 (paperback)",
LCCN = "QA76.73.J85 .E544 2023",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
xxpages = "xxvi + 414",
}
@Article{Eschle:2023:PJP,
author = "Jonas Eschle and Tam{\'a}s G{\'a}l and Mos{\`e}
Giordano and Philippe Gras and Benedikt Hegner and
Lukas Heinrich and Uwe {Hernandez Acosta} and Stefan
Kluth and Jerry Ling and Pere Mato and Mikhail
Mikhasenko and Alexander {Moreno Brice{\~n}o} and Jim
Pivarski and Konstantinos Samaras-Tsakiris and Oliver
Schulz and Graeme Andrew Stewart and Jan Strube and
Vassil Vassilev",
title = "Potential of the {Julia} Programming Language for High
Energy Physics Computing",
journal = j-COMPUT-SOFTW-BIG-SCI,
volume = "7",
number = "1",
pages = "??--??",
month = dec,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1007/s41781-023-00104-x",
ISSN = "2510-2036 (print), 2510-2044 (electronic)",
ISSN-L = "2510-2036",
bibdate = "Fri Oct 6 06:00:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/computsoftwbigsci.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://link.springer.com/article/10.1007/s41781-023-00104-x",
acknowledgement = ack-nhfb,
ajournal = "Comput. Softw. Big Sci.",
articleno = "10",
fjournal = "Computing and Software for Big Science",
journal-URL = "https://www.springer.com/journal/41781",
}
@InProceedings{Godoy:2023:EPP,
author = "William F. Godoy and Pedro Valero-Lara and T. Elise
Dettling and Christian Trefftz and Ian Jorquera and
Thomas Sheehy and Ross G. Miller and Marc
Gonzalez-Tallada and Jeffrey S. Vetter and Valentin
Churavy",
editor = "{IEEE}",
booktitle = "{2023 IEEE International Parallel and Distributed
Processing Symposium Workshops (IPDPSW)}",
title = "Evaluating performance and portability of high-level
programming models: {Julia}, {Python\slash Numba}, and
{Kokkos} on exascale nodes",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "373--382",
year = "2023",
DOI = "https://doi.org/10.1109/IPDPSW59300.2023.00068",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/python.bib",
acknowledgement = ack-nhfb,
}
@Book{Kaminski:2023:JDA,
author = "Bogumi{\l} Kami{\'n}ski",
title = "{Julia} for Data Analysis",
publisher = "Manning Publications",
address = "Shelter Island, NY, USA",
pages = "xxv + 443",
year = "2023",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
shorttableofcontents = "1. Introduction \\
Part 1: Essential Julia skills \\
2. Getting started with Julia \\
3. Julia's support for scaling projects \\
4. Working with collections in Julia \\
5. Advanced topics on handling collections \\
6. Working with strings \\
7. Handling time-series data and missing values \\
Part 2: Toolbox for data analysis \\
8. First steps with data frames \\
9. Getting data from a data frame \\
10. Creating data frame objects \\
11. Converting and grouping data frames \\
12. Mutating and transforming data frames \\
13. Advanced transformations of data frames \\
14. Creating web services for sharing data analysis
results",
tableofcontents = "1: Introduction \\
1.1: What is Julia and why is it useful? \\
1.2: Key features of Julia from a data scientist's
perspective \\
Julia is fast because it is a compiled language \\
Julia provides full support for interactive workflows
\\
Julia programs are highly reusable and easy to compose
together \\
Julia has a built-in state-of-the-art package manager
\\
It is easy to integrate existing code with Julia \\
1.3: Usage scenarios of tools presented in the book \\
1.4: Julia's drawbacks \\
1.5: What data analysis skills will you learn? \\
1.6: How can Julia be used for data analysis? \\
Part 1: Getting started with Julia \\
2.1: Representing values \\
2.2: Defining variables \\
2.3: Using the most important control-flow constructs
\\
Computations depending on a Boolean condition \\
Loops \\
Compound expressions \\
first approach to calculating the winsorized mean \\
2.4: Defining functions \\
Defining functions using the function keyword \\
Positional and key word arguments of functions \\
Rules for passing arguments to functions \\
Short syntax for defining simple functions \\
Anonymous functions \\
Do blocks \\
Function-naming convention in Julia \\
simplified definition of a function computing the
winsorized mean \\
2.5: Understanding variable scoping rules \\
3: Julia's support for scaling projects \\
3.1: Understandingjulia's type system \\
single function in Julia may have multiple methods \\
Types in Julia are arranged in a hierarchy \\
Finding all supertypes of a type \\
Finding all subtypes of a type \\
Union of types \\
Deciding what type restrictions to put in method
signature \\
3.2: Using multiple dispatch in Julia \\
Rules for defining methods of a function \\
Method ambiguity problem \\
Improved implementation of winsorized mean \\
3.3: Working with packages and modules \\
What is a module in Julia? \\
How can packages be used in Julia? \\
Using Stats Base.jl to compute the winsorized mean \\
3.4: Using macros \\
4: Working with collections in Julia \\
4.1: Working with arrays \\
Getting the data into a matrix \\
Computing basic statistics of the data stored in a
matrix \\
Indexing into arrays \\
Performance considerations of copying vs. making a view
\\
Calculating correlations between variables \\
Fitting a linear regression \\
Plotting the Anscombe's quartet data \\
4.2: Mapping key-value pairs with dictionaries \\
4.3: Structuring your data by using named tuples \\
Defining named tuples and accessing their contents \\
Analyzing Anscombe's quartet data stored in a named
tuple \\
Understanding composite types and mutability of values
in Julia \\
Advanced topics on handling collections \\
5.1: Vectorizing your code using broadcasting \\
Understanding syntax and meaning of broadcasting in
Julia \\
Expanding length-1 dimensions in broadcasting \\
Protecting collections from being broadcasted over \\
Analyzing Anscombe's quartet data using broadcasting
\\
5.2: Defining methods with parametric types \\
Most collection types in Julia are parametric \\
Rules for sub typing of parametric types \\
Using sub typing rules to define the covariance
function \\
5.3: Integrating with Python \\
Preparing data for dimensionality reduction using t-SNE
\\
Calling Python from Julia \\
Visualizing the results of the t-SNE algorithm \\
6: Working with strings \\
6.1: Getting and inspecting the data \\
Downloading files from the web \\
Using common techniques of string construction \\
Reading the contents of a file \\
6.2: Splitting strings \\
6.3: Using regular expressions to work with strings \\
Working with regular expressions \\
Writing a parser of a single line of movies.dat file
\\
6.4: Extracting a subset from a string with indexing
\\
UTF-8 encoding of strings in Julia \\
Character vs. byte indexing of strings \\
ASCII strings \\
Char type \\
6.5: Analyzinggenrefrequencyinmovies.dat \\
Finding common movie genres \\
Understanding genre popularity evolution over the years
\\
6.6: Introducing symbols \\
Creating symbols \\
Using symbols \\
6.7: Using fixed-width string types to improve
performance \\
Available fixed-width strings \\
Performance of fixed-width strings \\
6.8: Compressing vectors of strings with
PooledArrays.jl \\
Creating a file containing flower names \\
Reading in the data to a vector and compressing it \\
Understanding the internal design of PooledArray \\
6.9: Choosing appropriate storage for collections of
strings \\
7: Handling time-series data and missing values \\
7.1: Understanding the NBP Web API \\
Getting the data via a web browser \\
Getting the data by using Julia \\
Handling cases when an NBP Web API query fails \\
7.2: Working with missing data in Julia \\
Definition of the missing value \\
Working with missing values \\
7.3: Getting time-series data from the NBP Web API \\
Working with dates \\
Fetching data from the NBP Web API for a range of dates
\\
7.4: Analyzing data fetched from the NBP Web API \\
Computing summary statistics \\
Finding which days of the week have the most missing
values \\
Plotting the PLN/USD exchange rate \\
Part 2: Toolbox for data analysis \\
8: First steps with data frames \\
8.1: Fetching, unpacking, and inspecting the data \\
Downloading the file from the web \\
Working with bzip2 archives \\
Inspecting the CSV file \\
8.2: Loading the data to a data frame \\
Reading a CSV file into a data frame \\
Inspecting the contents of a data frame \\
Saving a data frame to a CSV file \\
8.3: Getting a column out of a data frame \\
Understanding the data frame's storage model \\
Treating a data frame column as a property \\
Getting a column by using data frame indexing \\
Visualizing data stored in columns of a data frame \\
8.4: Reading and writing data frames using different
formats \\
Apache Arrow \\
SQLite \\
9: Getting data from a data frame \\
9.1: Advanced data frame indexing \\
Getting a reduced puzzles data frame \\
Overview of allowed column selectors \\
Overview of allowed row-subsetting values \\
Making views of data frame objects \\
9.2: Analyzing the relationship between puzzle
difficulty and popularity \\
Calculating mean puzzle popularity by its rating \\
Fitting LOESS regression \\
10: Creating data frame objects \\
10.1: Reviewing the most important ways to create a
data frame \\
Creating a data frame from a matrix \\
Creating a data frame from vectors \\
Creating a data frame using a Tables.jl interface \\
Plotting a correlation matrix of data stored in a data
frame \\
10.2: Creating data frames incrementally \\
Vertically concatenating data frames \\
Appending a table to a data frame \\
Adding a new row to an existing data frame \\
Storing simulation results in a data frame \\
11: Converting and grouping data frames \\
11.1: Converting a data frame to other value types \\
Conversion to a matrix \\
Conversion to a named tuple of vectors \\
Other common conversions \\
11.2: Grouping data frame objects \\
Preparing the source data frame \\
Grouping a data frame \\
Getting group keys of a grouped data frame \\
Indexing a grouped data frame with a single value \\
Comparing performance of indexing methods \\
Indexing a grouped data frame with multiple values \\
Iterating a grouped data frame \\
12: Mutating and transforming data frames \\
12.1: Getting and loading the GitHub developers data
set \\
Understanding graphs \\
Fetching GitHub developer data from the web \\
Implementing a function that extracts data from a ZIP
file \\
Reading the GitHub developer data into a data frame \\
12.2: Computing additional node features \\
Creating a SimpleGraph object \\
Computing features of nodes by using the Graphs.jl
package \\
Counting a node's web and machine learning neighbors
\\
12.3: Using the split-apply-combine approach to predict
the developer's type \\
Computing summary statistics of web and machine
learning developer features \\
Visualizing the relationship between the number of web
and machine learning neighbors of a node \\
Fitting a logistic regression model predicting
developer type \\
12.4: Reviewing data frame mutation operations \\
Performing low-level API operations \\
Using the insertcols! function to mutate a data frame
\\
13: Advanced transformations of data frames-- 13.1:
Getting and preprocessing the police stop data set \\
Loading all required packages \\
Introducing the and chain macro \\
Getting the police stop data set \\
Comparing functions that perform operations on columns
\\
Using short forms of operation specification syntax \\
13.2: Investigating the violation column \\
Finding the most frequent violations \\
Vectorizing functions by using the ByRow wrapper \\
Flattening data frames \\
Using convenience syntax to get the number of rows of a
data frame \\
Sorting data frames \\
Using advanced functionalities of Data Frames Meta.jl
\\
13.3: Preparing data for making predictions \\
Performing initial transformation of the data \\
Working with categorical data \\
Joining data frames \\
Reshaping data frames \\
Dropping rows of a data frame that hold missing values
\\
13.4: Building a predictive model of arrest probability
\\
Splitting the data into train and test data sets \\
Fitting a logistic regression model \\
Evaluating the quality of a model's predictions \\
13.5: Reviewing functionalities provided by
DataFrames.jl \\
Creating web services for sharing data analysis results
\\
14.1: Pricing financial options by using a Monte Carlo
simulation \\
Calculating the payoff of an Asian option definition:
Computing the value of an Asian option \\
Understanding GBM \\
Using a numerical approach to computing the Asian
option value \\
14.2: Implementing the option pricing simulator \\
Starting Julia with multiple-thread support \\
Computing the option payoff for a single sample of
stock prices \\
Computing the option value \\
14.3: Creating a web service serving the Asian option
valuation \\
general approach to building a web service \\
Creating a web service using Genie.jl \\
Running the web service \\
14.4: Using the Asian option pricing web service \\
Sending a single request to the web service \\
Collecting responses to multiple requests from a web
service in a data frame \\
Unnesting a column of a data frame \\
Plotting the results of Asian option pricing",
}
@Article{Kastner:2023:AAA,
author = "Felix Kastner and Andreas R{\"o}{\ss}ler",
title = "An analysis of approximation algorithms for iterated
stochastic integrals and a {Julia} and {Matlab}
simulation toolbox",
journal = j-NUMER-ALGORITHMS,
volume = "93",
number = "1",
pages = "27--66",
month = may,
year = "2023",
CODEN = "NUALEG",
DOI = "https://doi.org/10.1007/s11075-022-01401-z",
ISSN = "1017-1398 (print), 1572-9265 (electronic)",
ISSN-L = "1017-1398",
bibdate = "Fri Apr 7 11:34:40 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/matlab.bib;
https://www.math.utah.edu/pub/tex/bib/numeralgorithms.bib",
URL = "https://link.springer.com/article/10.1007/s11075-022-01401-z",
acknowledgement = ack-nhfb,
ajournal = "Numer. Algorithms",
fjournal = "Numerical Algorithms",
journal-URL = "http://link.springer.com/journal/11075",
}
@Article{Ketcheson:2023:CBS,
author = "David I. Ketcheson and Hendrik Ranocha",
title = "Computing with {B}-series",
journal = j-TOMS,
volume = "49",
number = "2",
pages = "13:1--13:??",
month = jun,
year = "2023",
CODEN = "ACMSCU",
DOI = "https://doi.org/10.1145/3573384",
ISSN = "0098-3500 (print), 1557-7295 (electronic)",
ISSN-L = "0098-3500",
bibdate = "Thu Jun 29 07:01:00 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/toms.bib",
URL = "https://dl.acm.org/doi/10.1145/3573384",
abstract = "We present BSeries.jl, a Julia package for the
computation and manipulation of B-series, which are a
versatile theoretical tool for understanding and
designing discretizations of differential equations. We
give a short introduction to the theory of B-series and
associated concepts and provide examples of their use,
including method composition and backward error
analysis. The associated software is highly performant
and makes it possible to work with B-series of high
order.",
acknowledgement = ack-nhfb,
articleno = "13",
fjournal = "ACM Transactions on Mathematical Software (TOMS)",
journal-URL = "https://dl.acm.org/loi/toms",
}
@Article{Knopp:2023:NJG,
author = "Tobias Knopp and Marija Boberg and Mirco Grosser",
title = "{NFFT.jl}: Generic and Fast {Julia} Implementation of
the {Nonequidistant Fast Fourier Transform}",
journal = j-SIAM-J-SCI-COMP,
volume = "45",
number = "3",
pages = "??--??",
month = "????",
year = "2023",
CODEN = "SJOCE3",
DOI = "https://doi.org/10.1137/22M1510935",
ISSN = "1064-8275 (print), 1095-7197 (electronic)",
ISSN-L = "1064-8275",
bibdate = "Fri Sep 29 09:53:26 MDT 2023",
bibsource = "http://epubs.siam.org/toc/sjoce3/45,/3;
https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/siamjscicomput.bib",
URL = "https://epubs.siam.org/doi//doi/10.1137/22M1510935",
acknowledgement = ack-nhfb,
fjournal = "SIAM Journal on Scientific Computing",
journal-URL = "http://epubs.siam.org/sisc",
}
@Article{Rahman:2023:ECS,
author = "Akond Rahman and Dibyendu Brinto Bose and Raunak
Shakya and Rahul Pandita",
title = "{{\em Come for syntax, stay for speed, understand
defects\/}}: an empirical study of defects in {Julia}
programs",
journal = j-EMPIR-SOFTWARE-ENG,
volume = "28",
number = "4",
pages = "??--??",
month = jul,
year = "2023",
CODEN = "ESENFW",
DOI = "https://doi.org/10.1007/s10664-023-10328-5",
ISSN = "1382-3256 (print), 1573-7616 (electronic)",
ISSN-L = "1382-3256",
bibdate = "Thu Aug 10 15:49:41 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/empir-software-eng.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://link.springer.com/article/10.1007/s10664-023-10328-5",
acknowledgement = ack-nhfb,
ajournal = "Empir. Software. Eng.",
articleno = "93",
fjournal = "Empirical Software Engineering",
journal-URL = "http://link.springer.com/journal/10664",
}
@InProceedings{Rossini:2023:FJO,
author = "Matteo Rossini and Hakan Ergun and Marco Rossi",
editor = "{IEEE}",
booktitle = "{2023 Open Source Modelling and Simulation of Energy
Systems (OSMSES)}",
title = "{FlexPlan.jl} --- an open-source {Julia} tool for
holistic transmission and distribution grid planning",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--8",
year = "2023",
DOI = "https://doi.org/10.1109/OSMSES58477.2023.10089624",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
}
@Article{Steinebach:2023:CRW,
author = "Gerd Steinebach",
title = "Construction of {Rosenbrock--Wanner} method {Rodas5P}
and numerical benchmarks within the {Julia Differential
Equations} package",
journal = j-BIT-NUM-MATH,
volume = "63",
number = "2",
pages = "??--??",
month = jun,
year = "2023",
CODEN = "BITTEL, NBITAB",
DOI = "https://doi.org/10.1007/s10543-023-00967-x",
ISSN = "0006-3835 (print), 1572-9125 (electronic)",
ISSN-L = "0006-3835",
bibdate = "Thu Aug 10 14:23:15 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/bit.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://link.springer.com/article/10.1007/s10543-023-00967-x",
acknowledgement = ack-nhfb,
ajournal = "Bit Num. Math.",
articleno = "27",
fjournal = "BIT Numerical Mathematics",
journal-URL = "http://link.springer.com/journal/10543",
}
@InProceedings{Varga:2023:PPO,
author = "Matija Varga and Nikola Turk and Dominik {Cika } and
Neven {Buli }",
editor = "{IEEE}",
booktitle = "{2023 4th International Conference on Smart Grid
Metrology (SMAGRIMET)}",
title = "Pulse Pattern Optimization for Medium Voltage 3-Level
{NPC} Converter Using Open Source Optimization Tools in
{Julia}",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "1--4",
year = "2023",
DOI = "https://doi.org/10.1109/SMAGRIMET58412.2023.10128661",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
}
@InProceedings{Zhu:2023:SEI,
author = "Hao Zhu and Baojian Hua",
editor = "{IEEE}",
booktitle = "{2023 4th International Conference on Big Data \&
Artificial Intelligence \& Software Engineering
(ICBASE)}",
title = "{SAFEJ}: an Efficient Infrastructure for Securing
{Julia} Programs",
publisher = pub-IEEE,
address = pub-IEEE:adr,
pages = "221--224",
year = "2023",
DOI = "https://doi.org/10.1109/ICBASE59196.2023.10303098",
bibdate = "Mon Dec 18 08:06:55 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
}
@Book{Alabdullateef:2024:ESI,
author = "Muhannad Omar Alabdullateef and John T. {Foster,
Ph.D.}",
title = "Enhancing spontaneous imbibition analysis through
advanced optimization algorithms in {Julia} programming
language",
publisher = "University of Texas",
address = "Austin, TX, USA",
pages = "78",
year = "2024",
LCCN = "????",
bibdate = "Fri Jan 3 15:02:48 MST 2025",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
abstract = "A computational tool was developed, validated and
designed to optimize spontaneous imbibition tests in
porous media. The module aims to predict contact angle
measurements and fluid flow transport parameters using
advanced optimization algorithms in Julia programming
language. The SIsolver module leverages the
foundational analytical models for spontaneous
imbibition experiments developed by (Kazemi et al.,
1992) and further developed by (Gupta, 1998). The
module transitions the use of Excel sheet into the
Julia programming language to increase the
computational efficiency, scalability, and future
developmental ability. To choose the best approach, the
module tests two optimization algorithms, Nelder-Mead
and LBFGS and compares the results. The methodology
involves importing experimental data from the
literature, fitting it in a template, running the
optimization and producing visuals that help validate
the accuracy of the results. The results demonstrate
the robustness of the SIsolver module in estimating the
correct contact angle and other transport parameters.
The module was validated using experimental data by
producing visualization plots and synthetic data by
comparing them to the actual known parameters. The
comparison between the two optimization algorithms
showed that Nelder-Mead was a more suitable algorithm
for this module given its higher execution time and
accuracy. The findings indicate that the SIsolver
module when given highly accurate data, can
significantly enhance the analysis of spontaneous
imbibition, offering valuable insights for optimizing
oil recovery and reservoir management. The novelty of
this thesis lies in the application of advanced
optimization algorithms within the Julia programming
environment to model spontaneous imbibition. This
approach offers an improvement in computational
efficiency and scalable analysis of larger data sets.
The module provides a significant advancement and a
powerful tool for researchers and engineers.",
acknowledgement = ack-nhfb,
}
@Article{Allen:2024:AJI,
author = "Courtney Allen and Alexandra Mazanko and Niloofar
Abdehagh and Hermann Eberl",
title = "\pkg{ADM1jl}: a {Julia} implementation of the
Anaerobic Digestion Model 1",
journal = j-SOFTWAREX,
volume = "26",
number = "??",
pages = "??--??",
month = may,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1016/j.softx.2024.101682",
ISSN = "2352-7110",
ISSN-L = "2352-7110",
bibdate = "Wed May 29 07:44:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/softwarex.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S2352711024000530",
acknowledgement = ack-nhfb,
articleno = "101682",
fjournal = "SoftwareX",
journal-URL = "https://www.sciencedirect.com/journal/softwarex/issues",
}
@Article{Bagci:2024:BDM,
author = "A. Bagci and Gustavo A. Aucar",
title = "A bi-directional method for evaluating integrals
involving higher transcendental functions. {HyperRAF}:
a {Julia} package for new hyper-radial functions",
journal = j-COMP-PHYS-COMM,
volume = "295",
number = "??",
pages = "Article 108990",
month = feb,
year = "2024",
CODEN = "CPHCBZ",
DOI = "https://doi.org/10.1016/j.cpc.2023.108990",
ISSN = "0010-4655 (print), 1879-2944 (electronic)",
ISSN-L = "0010-4655",
bibdate = "Thu Dec 21 14:15:33 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/compphyscomm2020.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0010465523003351",
acknowledgement = ack-nhfb,
fjournal = "Computer Physics Communications",
journal-URL = "http://www.sciencedirect.com/science/journal/00104655",
}
@Article{Bent:2024:ICM,
author = "Russell Bent and Byron Tasseff and Carleton Coffrin",
title = "{InfrastructureModels}: Composable
Multi-infrastructure Optimization in {Julia}",
journal = j-INFORMS-J-COMPUT,
volume = "36",
number = "2",
pages = "600--615",
month = mar # "\slash " # apr,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1287/ijoc.2022.0118",
ISSN = "1091-9856 (print), 1526-5528 (electronic)",
ISSN-L = "1091-9856",
bibdate = "Thu Apr 11 07:12:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/informs-j-comput.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://pubsonline.informs.org/doi/full/10.1287/ijoc.2022.0118",
acknowledgement = ack-nhfb,
ajournal = "INFORMS J. Comput.",
fjournal = "INFORMS Journal on Computing",
journal-URL = "https://pubsonline.informs.org/journal/ijoc",
onlinedate = "7 December 2023",
}
@Article{Bitar:2024:RJS,
author = "Mohamad Bitar",
title = "{Rust} and {Julia} for Scientific Computing",
journal = j-COMPUT-SCI-ENG,
volume = "26",
number = "1",
pages = "72--76",
month = jan # "\slash " # mar,
year = "2024",
CODEN = "CSENFA",
DOI = "https://doi.org/10.1109/MCSE.2024.3369988",
ISSN = "1521-9615 (print), 1558-366X (electronic)",
ISSN-L = "1521-9615",
bibdate = "Sat Aug 24 09:25:48 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/computscieng.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/rust.bib",
acknowledgement = ack-nhfb,
fjournal = "Computing in Science and Engineering",
journal-URL = "https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5992",
keywords = "Computer languages; Performance evaluation; Scientific
computing",
}
@Article{Edelman:2024:BTB,
author = "Alan Edelman and Ekin Aky{\"u}rek and Yuyang Wang",
title = "Backpropagation through Back Substitution with a
Backslash",
journal = j-SIAM-J-MAT-ANA-APPL,
volume = "45",
number = "1",
pages = "429--449",
month = feb,
year = "2024",
CODEN = "SJMAEL",
DOI = "https://doi.org/10.1137/22m1532871",
ISSN = "0895-4798 (print), 1095-7162 (electronic)",
ISSN-L = "0895-4798",
bibdate = "Fri May 31 08:23:04 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/siamjmatanaappl.bib",
acknowledgement = ack-nhfb,
ajournal = "SIAM J. Matrix Anal. Appl.",
fjournal = "SIAM Journal on Matrix Analysis and Applications",
journal-URL = "http://epubs.siam.org/simax",
keywords = "Julia programming language",
}
@Article{Garcia:2024:BJM,
author = "Joaquim Dias Garcia and Guilherme Bodin and Alexandre
Street",
title = "{BilevelJuMP.jl}: Modeling and Solving Bilevel
Optimization Problems in {Julia}",
journal = j-INFORMS-J-COMPUT,
volume = "36",
number = "2",
pages = "327--335",
month = mar # "\slash " # apr,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1287/ijoc.2022.0135",
ISSN = "1091-9856 (print), 1526-5528 (electronic)",
ISSN-L = "1091-9856",
bibdate = "Thu Apr 11 07:12:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/informs-j-comput.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "https://pubsonline.informs.org/doi/full/10.1287/ijoc.2022.0135",
acknowledgement = ack-nhfb,
ajournal = "INFORMS J. Comput.",
fjournal = "INFORMS Journal on Computing",
journal-URL = "https://pubsonline.informs.org/journal/ijoc",
onlinedate = "13 December 2023",
}
@Book{Hofmann:2024:IMJ,
author = "Ulrich Hofmann",
title = "{Internet} Modeling with {Julia}: Models, Algorithms
and Programs",
publisher = "Springer Fachmedien Wiesbaden",
address = "Wiesbaden, Germany",
pages = "xi + 154 + 104",
year = "2024",
DOI = "https://doi.org/10.1007/978-3-658-44692-5",
ISBN = "3-658-44691-9, 3-658-44692-7 (e-book), 3-658-44693-5",
ISBN-13 = "978-3-658-44691-8, 978-3-658-44692-5 (e-book),
978-3-658-44693-2",
LCCN = "TK7885-7895",
bibdate = "Fri Jan 3 15:02:48 MST 2025",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
abstract = "The book combines internet modeling with the new
programming language Julia. It demonstrates Julia's
suitability for implementing established internet
models and research-oriented topics such as
car-to-infrastructure communication and black-box
models for load predictions with neural networks. After
studying the book and gaining inspiration for further
independent analyses, the reader will be able to tackle
even more complex modeling tasks in research and
development using Julia. The contents Introduction
Performance parameters, optimization, use of resources
Model modules Load generators Queuing modules
Simulation of queuing systems Simulation-based
optimisation with AI The target groups Teachers and
students (Master, PhD) Employees of research
institutions Network managers The Author Ulrich Hofmann
has over 30 years of experience in business, education
and research, focusing on Internet technologies with an
emphasis on Quality of Service (QoS). The translation
was done with the help of artificial intelligence. A
subsequent human revision was done primarily in terms
of content. This book is a translation of an original
German edition. The translation was done with the help
of artificial intelligence (machine translation by the
service DeepL.com). A subsequent human revision was
done primarily in terms of content, so that the book
will read stylistically differently from a conventional
translation.",
acknowledgement = ack-nhfb,
tableofcontents = "Introduction \\
Performance parameters, optimisation of resource use
\\
Model modules \\
Load generators \\
Operating modules \\
Simulation of operating systems \\
Simulation-based optimisation with AI",
}
@Article{Khalighi:2024:AFJ,
author = "Moein Khalighi and Giulio Benedetti and Leo Lahti",
title = "{Algorithm 1047}: {FdeSolver}, a {Julia} Package for
Solving Fractional Differential Equations",
journal = j-TOMS,
volume = "50",
number = "3",
pages = "22:1--22:??",
month = sep,
year = "2024",
CODEN = "ACMSCU",
DOI = "https://doi.org/10.1145/3680280",
ISSN = "0098-3500 (print), 1557-7295 (electronic)",
ISSN-L = "0098-3500",
bibdate = "Mon Oct 28 09:16:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/toms.bib",
URL = "https://dl.acm.org/doi/10.1145/3680280",
abstract = "We introduce FdeSolver, an open-source Julia package
designed to solve fractional-order differential
equations efficiently. The available solutions are
based on product-integration rules,
predictor--corrector algorithms, and the Newton-Raphson
method. The package covers solutions for
one-dimensional equations with orders of positive real
numbers. For higher-dimensional systems, it supports
orders up to one. Incommensurate derivatives are
allowed and defined in the Caputo sense. Here, we
summarize the implementation for a representative class
of problems and compare it with available alternatives
in Julia and MATLAB. Moreover, FdeSolver leverages the
power and flexibility of the Julia environment to offer
enhanced computational performance, and our development
emphasizes adherence to the best practices of open
research software. To highlight its practical utility,
we demonstrate its capability in simulating microbial
community dynamics and modeling the spread of COVID-19.
This latter application involves fitting the order of
derivatives grounded on real-world epidemiological
data. Overall, these results highlight the efficiency,
reliability, and practicality of the FdeSolver Julia
package.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Math. Softw.",
articleno = "22",
fjournal = "ACM Transactions on Mathematical Software (TOMS)",
journal-URL = "https://dl.acm.org/loi/toms",
}
@Article{Postnicov:2024:ECC,
author = "Vasily Postnicov and Aleksei Samarin and Marina V.
Karsanina and Mathieu Gravey and Aleksey Khlyupin and
Kirill M. Gerke",
title = "Evaluation of classical correlation functions from
{2/3D} images on {CPU} and {GPU} architectures:
Introducing \pkg{CorrelationFunctions.jl}",
journal = j-COMP-PHYS-COMM,
volume = "299",
number = "??",
pages = "??--??",
month = jun,
year = "2024",
CODEN = "CPHCBZ",
DOI = "https://doi.org/10.1016/j.cpc.2024.109134",
ISSN = "0010-4655 (print), 1879-2944 (electronic)",
ISSN-L = "0010-4655",
bibdate = "Mon May 6 07:51:16 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/compphyscomm2020.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0010465524000572",
acknowledgement = ack-nhfb,
articleno = "109134",
fjournal = "Computer Physics Communications",
journal-URL = "http://www.sciencedirect.com/science/journal/00104655",
}
@Article{Wu:2024:FJJ,
author = "Quan-feng Wu and Zhao Li",
title = "{FeAmGen.jl}: a {Julia} program for {Feynman}
Amplitude Generation",
journal = j-COMP-PHYS-COMM,
volume = "301",
number = "??",
pages = "??--??",
month = aug,
year = "2024",
CODEN = "CPHCBZ",
DOI = "https://doi.org/10.1016/j.cpc.2024.109230",
ISSN = "0010-4655 (print), 1879-2944 (electronic)",
ISSN-L = "0010-4655",
bibdate = "Tue May 28 07:01:14 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/compphyscomm2020.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S001046552400153X",
acknowledgement = ack-nhfb,
articleno = "109230",
fjournal = "Computer Physics Communications",
journal-URL = "http://www.sciencedirect.com/science/journal/00104655",
}
@Article{Allred:2025:FNT,
author = "Taylor Allred and Xinyi Li and Ashton Wiersdorf and
Ben Greenman and Ganesh Gopalakrishnan",
title = "{FlowFPX}: Nimble Tools for Debugging Floating-Point
Exceptions",
journal = "Proceedings of the {JuliaCon} Conferences",
volume = "7",
number = "67",
pages = "148:1--148:8",
year = "2025",
DOI = "https://doi.org/10.21105/jcon.00148",
bibdate = "Wed Jan 22 13:56:37 2025",
bibsource = "https://www.math.utah.edu/pub/tex/bib/fparith.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
abstract = "Reliable numerical computations are central to
scientific computing, but the floating-point arithmetic
that enables large-scale models is error-prone. Numeric
exceptions are a common occurrence and can propagate
through code, leading to flawed results. This paper
presents FlowFPX, a toolkit for systematically
debugging floating-point exceptions by recording their
flow, coalescing exception contexts, and fuzzing in
select locations. These tools help scientists discover
when exceptions happen and track down their origin,
smoothing the way to a reliable codebase.",
acknowledgement = ack-nhfb,
keywords = "debugging; floating-point; Julia",
ORCID-numbers = "Allred, Taylor/0009-0000-7238-1816; Li,
Xinyi/0009-0005-7276-7715; Wiersdorf,
Ashton/0000-0001-5524-7930; Greenman,
Ben/0000-0001-7078-9287; Gopalakrishnan,
Ganesh/0000-0002-4161-9278",
}
@Article{Martorell:2025:SJS,
author = "Pere A. Martorell and Santiago Badia",
title = "{STLCutters.jl}: a scalable geometrical framework
library for unfitted finite element discretisations",
journal = j-COMP-PHYS-COMM,
volume = "309",
number = "??",
pages = "??--??",
month = apr,
year = "2025",
CODEN = "CPHCBZ",
DOI = "https://doi.org/10.1016/j.cpc.2024.109479",
ISSN = "0010-4655 (print), 1879-2944 (electronic)",
ISSN-L = "0010-4655",
bibdate = "Fri Feb 7 15:45:43 MST 2025",
bibsource = "https://www.math.utah.edu/pub/tex/bib/compphyscomm2020.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0010465524004028",
acknowledgement = ack-nhfb,
ajournal = "Comput. Phys. Commun.",
articleno = "109479",
fjournal = "Computer Physics Communications",
journal-URL = "http://www.sciencedirect.com/science/journal/00104655",
}
@Article{Muster:2025:CJJ,
author = "Augustin Muster and Diego R. Abujetas and Frank
Scheffold and Luis S. Froufe-P{\'e}rez",
title = "{CoupledElectricMagneticDipoles.jl} --- {Julia}
modules for coupled electric and magnetic dipoles
method for light scattering, and optical forces in
three dimensions",
journal = j-COMP-PHYS-COMM,
volume = "306",
number = "??",
pages = "??--??",
month = jan,
year = "2025",
CODEN = "CPHCBZ",
DOI = "https://doi.org/10.1016/j.cpc.2024.109361",
ISSN = "0010-4655 (print), 1879-2944 (electronic)",
ISSN-L = "0010-4655",
bibdate = "Thu Nov 7 15:34:59 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/compphyscomm2020.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S0010465524002844",
acknowledgement = ack-nhfb,
articleno = "109361",
fjournal = "Computer Physics Communications",
journal-URL = "http://www.sciencedirect.com/science/journal/00104655",
}
@Article{Wan:2025:JJP,
author = "Jinyu Wan and Helena Alamprese and Christian Ratcliff
and Ji Qiang and Yue Hao",
title = "{JuTrack}: a {Julia} package for auto-differentiable
accelerator modeling and particle tracking",
journal = j-COMP-PHYS-COMM,
volume = "309",
number = "??",
pages = "??--??",
month = apr,
year = "2025",
CODEN = "CPHCBZ",
DOI = "https://doi.org/10.1016/j.cpc.2024.109497",
ISSN = "0010-4655 (print), 1879-2944 (electronic)",
ISSN-L = "0010-4655",
bibdate = "Fri Feb 7 15:45:43 MST 2025",
bibsource = "https://www.math.utah.edu/pub/tex/bib/compphyscomm2020.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
URL = "http://www.sciencedirect.com/science/article/pii/S001046552400420X",
acknowledgement = ack-nhfb,
ajournal = "Comput. Phys. Commun.",
articleno = "109497",
fjournal = "Computer Physics Communications",
journal-URL = "http://www.sciencedirect.com/science/journal/00104655",
}
@Proceedings{Anonymous:2020:SC,
editor = "Anonymous",
booktitle = "{Scientific Computing}",
title = "{Scientific Computing}",
publisher = "CSREA Press",
address = "????",
year = "2020",
ISBN = "1-60132-494-4 (paperback), 1-68392-564-5 (e-book)",
ISBN-13 = "978-1-60132-494-8 (paperback), 978-1-68392-564-4
(e-book)",
LCCN = "QA76.758 .S39 2019",
bibdate = "Fri Jan 3 15:02:48 MST 2025",
bibsource = "fsz3950.oclc.org:210/WorldCat;
https://www.math.utah.edu/pub/tex/bib/julia.bib",
acknowledgement = ack-nhfb,
tableofcontents = "Session: Computational Science, Optimization,
Performance Analysis, Algorithms and Applications \\
On Operator-splitting Approach for Plasma 3-T Radiation
Diffusion in Two and Three Dimensions / Anthony J.
Scannapieco \\
Optimizing Inverted Index Blocking for the Matrix
Comparator in Linking Unstandardized References / John
R. Talburt \\
Jobmon: So you can go Home on the Weekend / Liming Xu
\\
Augmented Reality for High-throughput Phenotyping /
Mitchell Neilsen \\
Optimization Algorithm Parameter Correlation with
Objective Function Features / Jeremy Mange \\
Framework for Determination of Ocean Wave Properties
using Unmanned Aerial Systems / Dulal Kar \\
Blockchain: Enhance the Authentication and Verification
of the Identity of a User to Prevent Data Breaches and
Security Intrusions / Lethia Jackson \\
Combat Security and Privacy Challenges of the Internet
of Things, as an Intelligent System / Daryl Stone \\
How May Location Analytics Be Used to Enhance the
Reliability of the Smart Grid? / Brian Hilton \\
Method of Implementing the Sensation of Operating
Analog Tool on Smartphone / Takayuki Fujimoto \\
GPU Comparison of a Diffuse Interface Model with Finite
Difference and Fourier Methods / Juan J. Tapia \\
Measuring Performance of Shared Applications in a Web
Environment / Joseph K. Balikuddembe \\
Survey on Energy Consumption Oriented Program Test and
Analysis Technology and Tools / Zhongzhi Luan \\
Construction of Remote Class Environment by `Low-Cost
Computing' and Introduction of Class Support
Application / Takayuki Fujimoto \\
Measures for the Cold Chain Industry in the Era of the
Fourth Industrial Revolution / Sang Ha Sung \\
Log-hypercube's Properties and Routing Algorithm /
Hyeong-Ok Lee \\
Proposal of Smartphone Usage Control Application Linked
with Camera Function / Takayuki Fujimoto \\
Insight into Security Approach in Data Stream Mining /
Dhvani Amin \\
Session: Military and Defense Modeling and Simulation
\\
Introduction to Declarative Programming in CLIPS and
PROLOG / Saverio Perugini \\
Effect of Modeling Simultaneous Events on Simulation
Results / Jeremy R. Millar \\
ECS Architecture for Modern Military Simulators /
Jeremy R. Millar \\
Session: Simulation and Modeling + Novel Applications
\\
Materials Genome Software Framework: Scalable Parallel
Simulation, Virtual Reality Visualization and Machine
Learning / Priya Vashishta \\
Linear Approach to Network Performance Modeling and a
Consolidation of Linear Performance Models of the LEAP
Cluster / Damian Valles \\
Modeling, Simulation and Verification of a New Swaging
Process using DEFORM / Sang Suk Sul \\
Design and Implementation of Disaster Information
Announcing Emulator Based on UHD Broadcasting /
Byungjun Bae \\
Simulation on Optimal Wrist Design of Wrist-hollow type
6-axis Articulated Robot using Genetic Algorithm /
Seong Ju Kim \\
Local Linear Regression Model Implemented in GIS /
Marcelo Romero Huertas \\
Session: Scientific Computing and Engineering \\
Development of a Rapid First-Order Differential
Equation Solver for Stiff Systems / Brandon Moulding
\\
Phase Lengths in the Cyclic Cellular Automaton / Kiran
Tomlinson \\
Impact Assessment of Profit and Emission Objectives on
the Operational Scheduling of a Virtual Power Plant /
Shahrzad Hadayeghparast \\
Efficient High Order Schemes for the Steady-State
Navier--Stokes Equations / Paul Bouthellier \\
Emerging Computing Techniques / Sarhan M. Musa \\
Effect of Electromagnetic Interference on Smart Grid /
Sarhan M. Musa \\
Session: Poster Papers and Extended Abstracts \\
Modified Bi-Cubic and Bi-Quintic B-Spline Basis
Functions for Simulating a Thin Plate Structure / Lisa
M. Kuhn \\
Session: Late Breaking Papers: Scientific Computing \\
Effective Data Deduplication Method by Using Similar
Image Files Clustering / Toshiyuki Kinoshita \\
Factors to Provide `Real Feel' that Accompanies the use
of Analog Devices / Takayuki Fujimoto \\
Definition of `Internet Flaming' and Prototype Case
Database / Takayuki Fujimoto \\
Modeling Quantum Teleportation with Julia / Douglas D.
Hodson \\
Use of Levenberg--Marquardt Method to Train Cellular
Simultaneous Recurrent Network to Perform Maze
Traversal Task / Steele A. Russell \\
SNS Mechanism to Highlight Low-profile Information /
Takayuki Fujimoto",
}
@Proceedings{Bigatti:2020:MSI,
editor = "Anna Maria Bigatti and Jacques Carette and James H.
Davenport and Michael Joswig and Timo de Wolff",
booktitle = "Mathematical Software --- {ICMS 2020: 7th
International Conference, Braunschweig, Germany, July
13--16, 2020, Proceedings}",
title = "Mathematical Software --- {ICMS 2020: 7th
International Conference, Braunschweig, Germany, July
13--16, 2020, Proceedings}",
publisher = pub-SV,
address = pub-SV:adr,
pages = "xxiii + 494",
year = "2020",
DOI = "https://doi.org/10.1007/978-3-030-52200-1",
bibdate = "Sat Sep 23 06:50:01 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/elefunt.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/macaulay2.bib;
https://www.math.utah.edu/pub/tex/bib/matlab.bib;
https://www.math.utah.edu/pub/tex/bib/python.bib;
https://www.math.utah.edu/pub/tex/bib/texbook3.bib",
acknowledgement = ack-nhfb,
tableofcontents = "Front Matter / / i--xxiii \\
Gr{\"o}bner Bases in Theory and Practice \\
Front Matter / / 1--1 A Design and an Implementation of
an Inverse Kinematics Computation in Robotics Using
Gr{\"o}bner Bases / Noriyuki Horigome, Akira Terui,
Masahiko Mikawa / 3--13 \\
Real Algebraic Geometry \\
Front Matter / / 15--15 \\
Curtains in CAD: Why Are They a Problem and How Do We
Fix Them? / Akshar Nair, James Davenport, Gregory
Sankaran / 17--26 \\
Chordality Preserving Incremental Triangular
Decomposition and Its Implementation / Changbo Chen /
27--36 \\
Algebraic Geometry via Numerical Computation \\
Front Matter / / 37--37 \\
$\mathbb{Q}(\sqrt{-3})$-Integral Points on a Mordell
Curve / Francesca Bianchi / 39--50 \\
A Numerical Approach for Computing Euler
Characteristics of Affine Varieties / Xiaxin Li, Jose
Israel Rodriguez, Botong Wang / 51--60 \\
Evaluating and Differentiating a Polynomial Using a
Pseudo-witness Set / Jonathan D. Hauenstein, Margaret
H. Regan / 61--69 \\
Computational Algebraic Analysis \\
Front Matter / / 71--71 \\
Algorithms for Pfaffian Systems and Cohomology
Intersection Numbers of Hypergeometric Integrals /
Saiei-Jaeyeong Matsubara-Heo, Nobuki Takayama / 73--84
\\
Software for Number Theory and Arithmetic Geometry \\
Front Matter / / 85--85 \\
Computations with Algebraic Surfaces / Andreas-Stephan
Elsenhans, J{\"o}rg Jahnel / 87--93 \\
Evaluating Fractional Derivatives of the Riemann Zeta
Function / Ricky E. Farr, Sebastian Pauli, Filip Saidak
/ 94--101 \\
Groups and Group Actions \\
Front Matter / / 103--103 \\
Towards Efficient Normalizers of Primitive Groups /
Sergio Siccha / 105--114 \\
Homomorphic Encryption and Some Black Box Attacks /
Alexandre Borovik, {\c{S}}{\"u}kr{\"u}
Yal{\c{c}}{\i}nkaya / 115--124 \\
Nilpotent Quotients of Associative
$\mathbb{Z}$-Algebras and Augmentation Quotients of
Baumslag--Solitar Groups / Tobias Moede / 125--130 \\
The GAP Package LiePRing / Bettina Eick, Michael
Vaughan-Lee / 131--140 \\
The Classification Problem in Geometry \\
Front Matter / / 141--141 \\
Classifying Simplicial Dissections of Convex Polyhedra
with Symmetry / Anton Betten, Tarun Mukthineni /
143--152 \\
Classification Results for Hyperovals of Generalized
Quadrangles / Bart De Bruyn / 153--161 \\
Isomorphism and Invariants of Parallelisms of
Projective Spaces / Svetlana Topalova, Stela Zhelezova
/ 162--172 \\
Classification of Linear Codes by Extending Their
Residuals / Stefka Bouyuklieva, Iliya Bouyukliev /
173--180 \\
The Program Generation in the Software Package
QextNewEdition / Iliya Bouyukliev / 181--189 \\
Polyhedral Methods in Geometry and Optimization \\
Front Matter / / 191--191 \\
Algebraic Polytopes in Normaliz / Winfried Bruns /
193--201 \\
Real Tropical Hyperfaces by Patchworking in polymake /
Michael Joswig, Paul Vater / 202--211 \\
Practical Volume Estimation of Zonotopes by a New
Annealing Schedule for Cooling Convex Bodies /
Apostolos Chalkis, Ioannis Z. Emiris, Vissarion
Fisikopoulos / 212--221 \\
Slack Ideals in Macaulay2 / Antonio Macchia, Amy Wiebe
/ 222--231 \\
Hyperplane Arrangements in polymake / Lars Kastner,
Marta Panizzut / 232--240 \\
A Convex Programming Approach to Solve Posynomial
Systems / Marianne Akian, Xavier Allamigeon, Marin
Boyet, St{\'e}phane Gaubert / 241--250 \\
Univalent Mathematics: Theory and Implementation \\
Front Matter / / 251--251 \\
Equality Checking for General Type Theories in
Andromeda 2 / Andrej Bauer, Philipp G. Haselwarter,
Anja Petkovi / 253--259 \\
Artificial Intelligence and Mathematical Software \\
Front Matter / / 261--261 \\
GeoLogic --- Graphical Interactive Theorem Prover for
Euclidean Geometry / Miroslav Ol{\v{s}}{\'a}k /
263--271 \\
A Formalization of Properties of Continuous Functions
on Closed Intervals / Yaoshun Fu, Wensheng Yu /
272--280 \\
Variable Ordering Selection for Cylindrical Algebraic
Decomposition with Artificial Neural Networks / Changbo
Chen, Zhangpeng Zhu, Haoyu Chi / 281--291 \\
Applying Machine Learning to Heuristics for Real
Polynomial Constraint Solving / Christopher W. Brown,
Glenn Christopher Daves / 292--301 \\
A Machine Learning Based Software Pipeline to Pick the
Variable Ordering for Algorithms with Polynomial Inputs
/ Dorian Florescu, Matthew England / 302--311 \\
Databases in Mathematics \\
Front Matter / / 313--313 \\
FunGrim: A Symbolic Library for Special Functions /
Fredrik Johansson / 315--323 \\
Accelerating Innovation Speed in Mathematics by Trading
Mathematical Research Data \\
Front Matter / / 325--325 \\
Operational Research Literature as a Use Case for the
Open Research Knowledge Graph / Mila Runnwerth, Markus
Stocker, S{\"o}ren Auer / 327--334 \\
Making Presentation Math Computable: Proposing a
Context Sensitive Approach for Translating {\LaTeX} to
Computer Algebra Systems / Andr{\'e} Greiner-Petter,
Moritz Schubotz, Akiko Aizawa, Bela Gipp / 335--341 \\
Employing C++ Templates in the Design of a Computer
Algebra Library / Alexander Brandt, Robert H. C. Moir,
Marc Moreno Maza / 342--352 \\
Mathematical World Knowledge Contained in the
Multilingual Wikipedia Project / Dennis Tobias Halbach
/ 353--361 \\
Archiving and Referencing Source Code with Software
Heritage / Roberto Di Cosmo / 362--373 \\
The Jupyter Environment for Computational Mathematics
\\
Front Matter / / 375--375 \\
Polymake.jl: A New Interface to polymake / Marek
Kaluba, Benjamin Lorenz, Sascha Timme / 377--385 \\
Web Based Notebooks for Teaching, an Experience at
Universidad de Zaragoza / Miguel Angel Marco Buzunariz
/ 386--392 \\
Phase Portraits of Bi-dimensional Zeta Values / Olivier
Bouillot / 393--405 \\
Prototyping Controlled Mathematical Languages in
Jupyter Notebooks / Jan Frederik Schaefer, Kai Amann,
Michael Kohlhase / 406--415 \\
General Session \\
Front Matter / / 417--417 \\
Method to Create Multiple Choice Exercises for Computer
Algebra System / Tatsuyoshi Hamada, Yoshiyuki Nakagawa,
Makoto Tamura / 419--425 \\
A Flow-Based Programming Environment for Geometrical
Construction / Kento Nakamura, Kazushi Ahara / 426--431
\\
MORLAB --- A Model Order Reduction Framework in MATLAB
and Octave / Peter Benner, Steffen W. R. Werner /
432--441 \\
FlexRiLoG --- A SageMath Package for Motions of Graphs
/ Georg Grasegger, Jan Legersk{\'y} / 442--450 \\
Markov Transition Matrix Analysis of Mathematical
Expression Input Models / Francis Quinby, Seyeon Kim,
Sohee Kang, Marco Pollanen, Michael G. Reynolds, Wesley
S. Burr / 451--461 \\
Certifying Irreducibility in $\mathbb{Z}[ ]$ / John
Abbott / 462--472 \\
A Content Dictionary for In-Object Comments / Lars
Hellstr{\"o}m / 473--481 \\
Implementing the Tangent Graeffe Root Finding Method /
Joris van der Hoeven, Michael Monagan / 482--492 \\
Back Matter / / 493--494",
}
@Proceedings{Krzhizhanovskaya:2020:CSI,
editor = "Valeria V. Krzhizhanovskaya and G{\'a}bor
Z{\'a}vodszky and Michael H. Lees and Jack J. Dongarra
and Peter M. A. Sloot and S{\'e}rgio Brissos and
Jo{\~a}o Teixeira",
booktitle = "{Computational Science --- ICCS 2020 20th
International Conference, Amsterdam, The Netherlands,
June 3--5, 2020, Proceedings, Part II}",
title = "{Computational Science --- ICCS 2020 20th
International Conference, Amsterdam, The Netherlands,
June 3--5, 2020, Proceedings, Part II}",
volume = "12138",
publisher = pub-SV,
address = pub-SV:adr,
pages = "xix + 697",
year = "2020",
DOI = "https://doi.org/10.1007/978-3-030-50417-5",
ISBN = "3-030-50416-6, 3-030-50417-4 (e-book)",
ISBN-13 = "978-3-030-50416-8, 978-3-030-50417-5 (e-book)",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
bibdate = "Thu Jun 25 08:21:10 2020",
bibsource = "https://www.math.utah.edu/pub/bibnet/authors/d/dongarra-jack-j.bib;
https://www.math.utah.edu/pub/tex/bib/fparith.bib;
https://www.math.utah.edu/pub/tex/bib/julia.bib;
https://www.math.utah.edu/pub/tex/bib/matlab.bib;
https://www.math.utah.edu/pub/tex/bib/prng.bib",
series = ser-LNCS,
URL = "https://link.springer.com/book/10.1007/978-3-030-50417-5",
acknowledgement = ack-nhfb,
tableofcontents = "Front Matter / / i--xix \\
Modified Binary Tree in the Fast PIES for 2D Problems
with Complex Shapes / Andrzej Ku{\.z}elewski, Eugeniusz
Zieniuk, Agnieszka Bo{\l}tu{\'c}, Krzystof Szersze{\'n}
/ 1--14 \\
Generating Random Floating--Point Numbers by Dividing
Integers: A Case Study / Fr{\'e}d{\'e}ric Goualard /
15--28 \\
An Effective Stable Numerical Method for Integrating
Highly Oscillating Functions with a Linear Phase /
Leonid A. Sevastianov, Konstantin P. Lovetskiy, Dmitry
S. Kulyabov / 29--43 \\
Fitting Penalized Logistic Regression Models Using QR
Factorization / Jacek Klimaszewski, Marcin Korze{\'n} /
44--57 \\
Uncertainty Quantification in Fractional Stochastic
Integro--Differential Equations Using Legendre Wavelet
Collocation Method / Abhishek Kumar Singh, Mani Mehra /
58--71 \\
A Direct High--Order Curvilinear Triangular Mesh
Generation Method Using an Advancing Front Technique /
Fariba Mohammadi, Shusil Dangi, Suzanne M. Shontz,
Cristian A. Linte / 72--85 \\
Data--Driven Partial Differential Equations Discovery
Approach for the Noised Multi--dimensional Data /
Mikhail Maslyaev, Alexander Hvatov, Anna Kalyuzhnaya /
86--100 \\
Preconditioning Jacobian Systems by Superimposing
Diagonal Blocks / M. Ali Rostami, H. Martin B{\"u}cker
/ 101--115 \\
NURBS Curves in Parametric Integral Equations System
for Modeling and Solving Boundary Value Problems in
Elasticity / Marta Kapturczak, Eugeniusz Zieniuk,
Andrzej Ku{\.z}elewski / 116--123 \\
Parameterizations and Lagrange Cubics for Fitting
Multidimensional Data / Ryszard Kozera, Lyle Noakes,
Magdalena Wilko{\l}azka / 124--140 \\
Loop Aggregation for Approximate Scientific Computing /
June Sallou, Alexandre Gauvain, Johann Bourcier, Benoit
Combemale, Jean--Raynald de Dreuzy / 141--155 \\
Numerical Computation for a Flow Caused by a
High--Speed Traveling Train and a Stationary Overpass /
Shotaro Hamato, Masashi Yamakawa, Yongmann M. Chung,
Shinichi Asao / 156--169 \\
B{\'e}zier Surfaces for Modeling Inclusions in PIES /
Agnieszka Bo{\l}tu{\'c}, Eugeniusz Zieniuk, Krzysztof
Szersze{\'n}, Andrzej Ku{\.z}elewski / 170--183 \\
Impact of Water on Methane Adsorption in Nanopores: A
Hybrid GCMC--MD Simulation Study / Ji Zhou, Wenbin
Jiang, Mian Lin, Lili Ji, Gaohui Cao / 184--196 \\
A Stable Discontinuous Galerkin Based Isogeometric
Residual Minimization for the Stokes Problem / Marcin
{\L}o{\'s}, Sergio Rojas, Maciej Paszy{\'n}ski, Ignacio
Muga, Victor M. Calo / 197--211 \\
Numerical Modeling of the Two--Phase Flow of Water with
Ice in the Tom River / Vladislava Churuksaeva,
Alexander Starchenko / 212--224 \\
Remarks on Kaczmarz Algorithm for Solving Consistent
and Inconsistent System of Linear Equations / Xinyin
Huang, Gang Liu, Qiang Niu / 225--236 \\
Investigating the Benefit of FP16--Enabled
Mixed--Precision Solvers for Symmetric Positive
Definite Matrices Using GPUs / Ahmad Abdelfattah, Stan
Tomov, Jack Dongarra / 237--250 \\
Simulation Versus an Ordered Fuzzy--Numbers--Driven
Approach to the Multi--depot Vehicle Cyclic Routing and
Scheduling Problem / Grzegorz Bocewicz, Zbigniew
Banaszak, Czeslaw Smutnicki, Katarzyna Rudnik, Marcin
Witczak, Robert W{\'o}jcik / 251--266 \\
Epigenetic Modification of Genetic Algorithm / Kornel
Chrominski, Magdalena Tkacz, Mariusz Boryczka /
267--278 \\
ITP--KNN: Encrypted Video Flow Identification Based on
the Intermittent Traffic Pattern of Video and
$K$-Nearest Neighbors Classification / Youting Liu, Shu
Li, Chengwei Zhang, Chao Zheng, Yong Sun, Qingyun Liu /
279--293 \\
DeepAD: A Joint Embedding Approach for Anomaly
Detection on Attributed Networks / Dali Zhu, Yuchen Ma,
Yinlong Liu / 294--307 \\
SciNER: Extracting Named Entities from Scientific
Literature / Zhi Hong, Roselyne Tchoua, Kyle Chard, Ian
Foster / 308--321 \\
GPU--Embedding of kNN--Graph Representing Large and
High--Dimensional Data / Bartosz Minch, Mateusz Nowak,
Rafa{\l} Wcis{\l}o, Witold Dzwinel / 322--336 \\
Evolving Long Short--Term Memory Networks / Vicente
Coelho Lobo Neto, Leandro Aparecido Passos, Jo{\~a}o
Paulo Papa / 337--350 \\
Personality Recognition from Source Code Based on
Lexical, Syntactic and Semantic Features / Miko{\l}aj
Biel, Marcin Kuta, Jacek Kitowski / 351--363 \\
Data Fitting by Exponential Sums with Equal Weights /
Petr Chunaev, Ildar Safiullin / 364--371 \\
A Combination of Moment Descriptors, Fourier Transform
and Matching Measures for Action Recognition Based on
Shape / Katarzyna Go{\'s}ciewska, Dariusz Frejlichowski
/ 372--386 \\
Improving Accuracy and Speeding Up Document Image
Classification Through Parallel Systems / Javier
Ferrando, Juan Luis Dom{\'\i}nguez, Jordi Torres,
Ra{\'u}l Garc{\'\i}a, David Garc{\'\i}a, Daniel Garrido
et al. / 387--400 \\
Computation of the Airborne Contaminant Transport in
Urban Area by the Artificial Neural Network / Anna
Wawrzynczak, Monika Berendt--Marchel / 401--413 \\
Exploring Musical Structure Using Tonnetz Lattice
Geometry and LSTMs / Manuchehr Aminian, Eric Kehoe,
Xiaofeng Ma, Amy Peterson, Michael Kirby / 414--424 \\
Modeling of Anti--tracking Network Based on
Convex--Polytope Topology / Changbo Tian, Yongzheng
Zhang, Tao Yin / 425--438 \\
A Workload Division Differential Privacy Algorithm to
Improve the Accuracy for Linear Computations / Jun Li,
Huan Ma, Guangjun Wu, Yanqin Zhang, Bingnan Ma, Zhen
Hui et al. / 439--452 \\
On the Automated Assessment of Open--Source Cyber
Threat Intelligence Sources / Andrea Tundis, Samuel
Ruppert, Max M{\"u}hlh{\"a}user / 453--467 \\
Malicious Domain Detection Based on K--means and SMOTE
/ Qing Wang, Linyu Li, Bo Jiang, Zhigang Lu, Junrong
Liu, Shijie Jian / 468--481 \\
Microservice Disaster Crash Recovery: A Weak Global
Referential Integrity Management / Maude Manouvrier,
Cesare Pautasso, Marta Rukoz / 482--495 \\
Hashing Based Prediction for Large--Scale Kernel
Machine / Lijing Lu, Rong Yin, Yong Liu, Weiping Wang /
496--509 \\
Picking Peaches or Squeezing Lemons: Selecting
Crowdsourcing Workers for Reducing Cost of Redundancy /
Paulina Adamska, Marta Ju{\'z}win, Adam Wierzbicki /
510--523 \\
Are $n$-gram Categories Helpful in Text Classification?
/ Jakub Kruczek, Paulina Kruczek, Marcin Kuta /
524--537 \\
Calculating Reactive Power Compensation for
Large--Scale Street Lighting / Sebastian Ernst, Leszek
Kotulski, Tomasz Lerch, Micha{\l} Rad, Adam
S{\k{e}}dziwy, Igor Wojnicki / 538--550 \\
Developing a Decision Support App for Computational
Agriculture / Andrew Lewis, Marcus Randall, Ben
Stewart--Koster / 551--561 \\
Optimal Location of Sensors for Early Detection of
Tsunami Waves / Angelie R. Ferrolino, Jose Ernie C.
Lope, Renier G. Mendoza / 562--575 \\
A Novel Formulation for Inverse Distance Weighting from
Weighted Linear Regression / Leonardo Ramos
Emmendorfer, Gra{\c{c}}aliz Pereira Dimuro / 576--589
\\
Addressing the Robustness of Resource Allocation in the
Presence of Application and System Irregularities via
PEPA Based Modeling / Srishti Srivastava, Ioana
Banicescu, William S. Sanders / 590--603 \\
An Adaptive Computational Network Model for Strange
Loops in Political Evolution in Society / Julia Anten,
Jordan Earle, Jan Treur / 604--617 \\
Joint Entity Linking for Web Tables with Hybrid
Semantic Matching / Jie Xie, Yuhai Lu, Cong Cao,
Zhenzhen Li, Yangyang Guan, Yanbing Liu / 618--631 \\
A New Coefficient of Rankings Similarity in
Decision--Making Problems / Wojciech Sa abun, Karol
Urbaniak / 632--645 \\
Innovativeness Analysis of Scholarly Publications by
Age Prediction Using Ordinal Regression / Pavel Savov,
Adam Jatowt, Radoslaw Nielek / 646--660 \\
Advantage of Using Spherical over Cartesian Coordinates
in the Chromosome Territories 3D Modeling / Magdalena
A. Tkacz, Kornel Chromi{\'n}ski / 661--673 \\
Adaptive and Efficient Transfer for Online Remote
Visualization of Critical Weather Applications / Preeti
Malakar, Vijay Natarajan, Sathish S. Vadhiyar /
674--693 \\
Back Matter / / 695--697",
}
