Symbols above the letters (for scientific publications)

When writing papers, we often come up with author(s) names with symbols above some characters of author(s) names.

How do you use those symbols in LaTeX?

Here is a Wikipedia link where the details are provided.

https://en.wikipedia.org/wiki/Diacritic

In order to use in LaTeX, a number of answers in the following SE site is given.

https://tex.stackexchange.com/tags/accents/info

Some of the frequently used symbols are (from SE site):

Text mode

Plain TeX makes it possible to typeset the most commonly used accents:

\` (grave accent): à
\' (acute accent): á
\^ (circumflex or “hat”): â
\" (umlaut or dieresis): ä
\~ (tilde or “squiggle”): ã
\= (macron or “bar”): ā
\. (dot accent): ȧ
\u (breve accent): ă
\v (háček or “check”): ǎ
\H (long Hungarian umlaut): ő
\t (tie-after accent): a͡
\c (cedilla): ş
\d (dot-under accent): ạ
\b (bar-under accent): ο̩
\k (ogonek): ą

The Unicode character encoding UTF8 includes several special characters and characters with accents. The following code specifies that the encoding of the LaTeX document source file is UTF8. As font encoding is specified T1, because it supports the encoding of extended character sets in fonts:

\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}

Of course, the encoding in the text editor needs to be set to utf8, as well.

Math mode

The following commands may be used only in math mode to produce accents;

\hat{o} (circumflex):
\widehat{oo} (wide version of \hat over several letters):
\check{o} (vee or check):
\tilde{o} (tilde)
\widetilde{oo} (wide tilde)
\acute{o} (acute accent):
\grave{o} (grave accent):
\dot{o} (dot over the letter):
\ddot{o} (two dots over the letter):
\breve{o} (breve):
\bar{o} (macron):
\vec{o} (vector (arrow) over the letter):

Fortran Resources (recipes, libraries, learning materials, etc)

Fortran95 features
from Wikipedia

List of Fortran Numerical Libraries
from Wikipedia

Numerical Methods for Fortran Programmars
from people.sc.fsu.edu
contain links to fortran libraries and subroutines

Scientific Programming and Numerical Computation
from Wu-ting Tsai, National Taiwan University.

Fortran95 Interface to Matlab
from Matlab

Fortran Resources List
from BCS Fortran Specialist Group

Fortran Wiki
contains tutorials and FORTRAN code collections
contains list of links that contain FORTRAN codes

Fortran Tutorials ( a list of tutorials)
a list form The Fortran Company

Computational Physics using Fortran (official link)
an NPTEL course with a brief introduction to Fortran

Fortran90 Tutorial
From MTU

How to read a technical book (Physics, Chemistry, Maths, CS, engineering, etc)

If you want to read a novel or biography, go ahead.

But for a technical book which is full of concepts, mathematical equations, etc, you need to find out a suitable way to read those books. Actually, in schools and colleges, the first course should be how to read books.

Here are some of my notes on how to read technical books.

In LinkedIn I read "not all books are for everyone".

First answer these questions before starting to read the book.

As yourself "Why do I want to read this book?"
Go through the table of content. After that, make sure that you want to read this book.
Does the content of this book meet my requirement?
If that book contain mathematical equations, do I need paper and pencil to practice as I read; or if your book is related to computer science programming, then, do I need to practice in a computer while reading the book? Answer this question.
Have I read similar book earlier or this is completely new?

Why reading a technical book is hard?

book contains many technical terms
book contain rigorous mathematics beyond our comprehension
the programming book contain complicated syntax and difficult algorithms
the book assumes that you have pre-requisites such as prior programming knowledge or math knowledge

You may think that mere patience is enough to complete and comprehend and understand any technical book. Now also I am thinking so (which is going to change soon). But, this is not working.

Unless it is a must read book, check following.

You must read the perquisite first. In my opinion any technical book should mention what the author assumes to have known by the readers. But, most of the authors fail to do so. Similarly, any course description should start with perquisites. See the courses in MIT OCW. For all the courses, the prerequisites have been given which is a very good practice that everyone should adapt. A preface should contain this information.

Now steps to read the book.

read the preface
gently go through the entire book like bird eye view. In this stage, you can read the head title. But, don't even go through the subtitle. Just hover around the book once.
Then, read the contents
Go through each chapter, subtitles, figures, tables, diagrams, etc. Just look at the equations but don't try to comprehend the equation.
Then, again read the contents
Now, go through each chapter and read the title, subtitle, first and last sentence of each paragraph. Reading the first and last sentence of any paragraph should summarize the information in that entire paragraph. A well written piece of anything (article or book) should obey this rule
points 1-6 should be done in a single sitting. Points 7 8 and 9 may take many days or even weeks or months to complete based on the difficulty, depth, subject, your expertise in the field, etc.
In the next step, go through the entire book without doing anything practical such as working out the equation in a math equation intense book or doing coding in a computer while reading. Just go through the text and everything and try to comprehend the book. While reading, take important points, write unknown things, write what you need to learn a specific part in the specific unit, etc.
Then, learn what you need to learn before understanding this book. Otherwise, you are wasting your time. If a particular unit uses a specific mathematical equation and you don't know its physical meaning, then what is the use? If that portion is out of your interest or you want to neglect at present, write what you omit (for later reference)
After that, if you read a math book, have a notebook and pen. If it is programming book, have a computer to check the code. Practice the math/programming while reading through each chapter. While doing this you can revise your notes by adding or minimizing the notes you wrote in the previous step. Once you complete this procedure, you will be a master in that subject.
Make sure to go through the entire book and you understand each and everything there.
Before completing this book, don't ready any other book either on the same topic/subject. For example, if you read Quantum Mechanics by R Shankar, you should follow the procedure till you finish the book. While you complete the first chapter of the book, don't take another book on Quantum Mechanics with some other author. No matter how much another books is better or best, once decided a book, go through it entirely. You have a way. Some concepts or portions in your selected book may be hard to understand. Note down those parts and you can check those topics in other books after completing this book. But never ever take the second book while reading the first book. This is the biggest mistake I am doing for the entire life so far. I am changing now. That is why, choosing a book for reading is the very book step. Once chosen, stick with that book. Don't go to other books. In contrast, if you are reading novel or biography books, or fiction/non-fiction you can pick one book on each category and you can switch between each of these books.

What do you think about this article?

Write your thoughts and comments here.

Journals for Machine Learning and Deep Learning applications

Here are the list of journals that publish papers in machine learning applications.

https://www.tandfonline.com/loi/uaai20?gclid=CjwKCAiAsIDxBRAsEiwAV76N8-hqz0nV_zroA4jazsDqBaakJPmrUgi_X_hYzjkLNKe6i5SqFkZvnhoC0vkQAvD_BwE
https://www.journals.elsevier.com/pattern-recognition-letters
https://www.journals.elsevier.com/artificial-intelligence
https://www.nature.com/natmachintell/
https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5962385
https://www.journals.elsevier.com/neurocomputing
https://www.worldscientific.com/worldscinet/ijait
https://www.worldscientific.com/worldscinet/ijprai?campaignid=336010695&adgroupid=23075846775&creative=81823290255&keyword=&gclid=CjwKCAiAsIDxBRAsEiwAV76N8z5zITmxaVXQqeB-kOkTUcYw6uPEL754iVotNoSgYCXHYrNd7PUE8hoCkx0QAvD_BwE
https://www.tandfonline.com/toc/teta20/current
https://www.journals.elsevier.com/information-sciences
https://www.springer.com/journal/10044
https://www.journals.elsevier.com/pattern-recognition
https://www.journals.elsevier.com/neural-networks
https://www.springer.com/journal/10994
http://www.jmlr.org/
https://link.springer.com/journal/10618

Tools for reviewers

If you review research articles, or aspiring researcher, following links may be useful to you.

PubPeer
Publons
COPE
Retraction Watch

Machine Learning

Question: Which of them is a supervised classification problem?

Answer: Using labeled financial data to predict whether the value of a stock will go up or go down next week.

"Exactly! In this example, there are two discrete, qualitative outcomes: the stock market going up, and the stock market going down. This can be represented using a binary variable, and is an application perfectly suited for classification."

So this supervised learning can be used to predict whether something will go up or down (stock prices, temperature, number of sales, etc. To do this you need labelled data because, this is a supervised learning. This can be used whether spin is up or down kind of problem.

Where this type of model can be used in physics, chemistry, mathematics, etc? Is there such studies?

A common data sets used is Iris dataset.

This data set contain

petal length
petal width
Setal length
Setal width

Here, in this blog, you can see how these four quantities are measured.

Target variable?

In machine learning, a target variable is one that should be the output (after the analysis)

Three different flower species.

0. Setosa
1. Versicolor
2. Virginica
Here, we are going to give the four data of an unknown flower (to the trained model), and we are going to find out which flower it is (the model will give 0 or 1 or 2 based on the input).

iris.data.shape gives (150, 4) which means that there are 154 rows (data) with four different information (here petal length, petal width, Setal length, Setal width).

iris.data.shape

(150,4)

iris.target_names

(names of the target in an array)

(to be continued)

RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility in jupyter notebook python

/usr/lib/python3.5/importlib/_bootstrap.py:222:

RuntimeWarning: numpy.dtype size changed,

may indicate binary incompatibility.

Expected 96, got 88 return f(*args, **kwds)

This warning is harmless.

This is due to the Numpy version 1.15.0 or 1.15.1

You can ignore this.

Or you can go back to Numpy version 1.14.5

You can do this by

conda install numpy1.14.5

Or, you can wait until Numpy1.15.2 is released.

What you need to know about job talk (MIT OCW)?

I recently watched "How To Speak by Patrick Winston" from MIT OCW. Thanks to Patrick Wilson who summarized everything you need to know when you are giving a talk which otherwise would take a complete course or at least 10 hours of lectures.

This 60 minutes talk provide you

Rules of Engagement @03:17
How to Start @04:20
Four Sample Heuristics @05:44
The Tools: Time and Place @10:23
The Tools: Boards, Props, and Slides @13:30
Informing: Promise, Inspiration, How to Think @36:35
Persuading: Oral Exams, Job Talks, Getting Famous @41:45
How to Stop: Final Slide, Final Words @53:15
Final Words: Joke, Thank You, Examples @56:40

In a job talk, Patrick Winston says (based on discussion with his colleagues) , the candidate should show

1. vision
2. done something
..
...
......
Conclusions by enumerating what you have done.

How to show that you have a vision?
You have to define the big problem and your approach to solve that big problem. He uses his field of interest "AI" to show the vision. How to understand human intelligence? What are the differences between Chimpanzee, Neanderthals and Humans? Well. Human has the ability to grasp symbols and tell story based on a number of symbols by connecting them. How can I do to machines so that I could achieve the intelligence that human have in machines? That is an example for vision.

How to show that you have done something important?
List the number of steps you need to solve in order to solve the big problem (which you defined in your vision).
You can say "Here is what needs to be done". Specify some behavior, etc.

How to conclude your job talk?
You highlight what you have done so far and emphasize on where you are in achieving the big goal which you defined in your vision.

The transcript of the full talk (a 22 page pdf document) and the video can be accessed here.

English phrases for writing papers

Following list may be useful for writing papers, articles, etc.

"In recent years” vs “in the recent years”

Native speakers would generally prefer the second. The article is unnecessary and awkward. Both are correct. Personally, I find them all awkward and am much more likely to say things like "over the last few years" or just "recently".

Ref: https://english.stackexchange.com/a/59603/161843

Why you should learn Physics?

Why you should study Physics? Here is the answer from eminent people.

Elon Musk (SpaceX, Tesla, Boring Founder).

Physics gives you the way to think. There is something called "First principles" thinking where you don't assume anything and deconstruct very big problem (no matter how big it is) in to a smallest and simplest one. Elon Musk says in an interview that he use this first principles thinking to design rocket. He goes on, "What a rocket is made of?" A bunch of metals. What is the cost of those metals? About 2% of the total cost of the rocket. So, I can design a rocket at a much lower cost".

Paul Graham (Y-Combinator)

Dr. Graham, a computer scientist (did PhD in computer science) gave a talk to aspiring entrepreneurs. What would you study in college (if a chance is given)? His answer is

Research Flow

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