text:
I am writing a 4-5 pages article document, and a full-width, traditional table of contents really feels over the top and would take up too much space, but I still feel a toc is really great to show the document structure. Ideally, I would like to have my toc framed in the top left of the article (with the content starting inmmediately to its right), just like many journals or even wikipedia does.

So far, I've attempted to achieve that with columns, minipage and wrapfigure, but none of those solutions is really satisfying (I've been using latex for some time now, but I always let him do the job, so I'm not that familiar with manually doing stuff).

The main issues are getting the starting content to properly and automatically wrap around the toc (didn't achieve with colums or minipage), and getting the starting content and the toc correctly aligned and clean (didn't achieve with wrapfigure).

Here's my best attempt so far:

\documentclass[a4paper]{article}
\usepackage[frenchb]{babel}
\usepackage[utf8]{inputenc}
\usepackage{textcomp}
\usepackage[T1]{fontenc}
\usepackage{fixltx2e}

\usepackage{lipsum}

\usepackage{float}                            %floats and figures
\usepackage{wrapfig}                          %figures within text
\usepackage{subcaption}                       %multiple subfigures and subcaptions

\usepackage{amsmath}

\begin{document}
\begin{center}
{\LARGE Rapport de visite d'entreprise}\\
{\includegraphics[width=.3\textwidth]{ABB.jpg}}\\
Nathan Dwek\\
\today
\end{center}

\begin{wrapfigure}{L!}{.5\textwidth}
\tableofcontents
\end{wrapfigure}

\section{Le groupe ABB}
\lipsum[1]

\section{ABB Jumet}

\lipsum[2]

\section{La ligne de production de condensateurs}

\subsection{La métallisation}

\subsection{Le choupage\label{choupage}}

\subsection{Traitements de finition}

\section{Systèmes à condensateurs}

\subsection{Condensateurs en triphasé}

\subsection{Armoires à condensateurs}

\end{document}

and the result:

as you can see the section title is out of place (should be right above starting text) and the toc has a weird horizontal alignment (see @Keks Dose 's answer for this part)

Any hints?

You're very nearly there! The main problem you're facing is discussed in Strange space left above wrapfig figures and Wrapped figure diverges from text level after sections. As such the important part of my code is

\section{Le groupe ABB}
\begingroup
\setlength\intextsep{0pt} %<---- IMPORTANT
\begin{wrapfigure}{l}{.5\textwidth}
....
\end{wrapfigure}
\lipsum[1]
\endgroup

You have many choices to help with the framing; I like the tcolorbox package or else the mdframed package.

% arara: pdflatex
% !arara: indent: {overwrite: yes}
\documentclass[a4paper]{article}
\usepackage{lipsum}
\usepackage[demo]{graphicx}
\usepackage{wrapfig}
\usepackage{tcolorbox}
\tcbuselibrary{skins}

\begin{document}
\begin{center}
{\LARGE Rapport de visite d'entreprise}\\
{\includegraphics[width=.3\textwidth]{ABB.jpg}}\\
Nathan Dwek\\
\today
\end{center}

\section{Le groupe ABB}
\begingroup
\setlength\intextsep{0pt}
\begin{wrapfigure}{l}{.5\textwidth}
\begin{tcolorbox}[
enhanced,fonttitle=\bfseries\Large,
colback=gray!10,colframe=gray,%colframe=red!50!black,
title={Contents},]
\makeatletter
\@starttoc{toc}
\makeatother
\vspace{.5cm}
\end{tcolorbox}
\end{wrapfigure}
\lipsum[1]
\endgroup

\section{ABB Jumet}

\lipsum[2]

\section{La ligne de production de condensateurs}

\subsection{La métallisation}

\subsection{Le choupage\label{choupage}}

\subsection{Traitements de finition}

\section{Systèmes à condensateurs}

\subsection{Condensateurs en triphasé}

\subsection{Armoires à condensateurs}

\end{document}

# Update following comment

If you want the section next to the toc, you can trick wrapfig into believing there is a paragraph there using, for example

...
\end{wrapfigure}
\paragraph{}
\unskip
\section{Le groupe ABB}
\lipsum[1]

You can use the titlesec package to help with spacing surrounding the heading- a final result is:

Here's the complete code to play with at your leisure.

% arara: pdflatex
% !arara: indent: {overwrite: yes}
\documentclass[a4paper]{article}
\usepackage{lipsum}
\usepackage[demo]{graphicx}
\usepackage{wrapfig}
\usepackage{tcolorbox}
\usepackage{titlesec}
\tcbuselibrary{skins}

% From the titlesec package
% \titlespacing{command}{left spacing}{before spacing}{after spacing}[right]
% spacing: how to read {12pt plus 4pt minus 2pt}
%           12pt is what we would like the spacing to be
%           plus 4pt means that TeX can stretch it by at most 4pt
%           minus 2pt means that TeX can shrink it by at most 2pt
%       This is one example of the concept of, 'glue', in TeX
\titlespacing\section{0pt}{5pt plus 0pt minus 2pt}{5pt plus 2pt minus 2pt}

\begin{document}
\begin{center}
{\LARGE Rapport de visite d'entreprise}\\
{\includegraphics[width=.3\textwidth]{ABB.jpg}}\\
Nathan Dwek\\
\today
\end{center}

\begingroup
\titlespacing\section{0pt}{-5pt plus 0pt minus 2pt}{5pt plus 2pt minus 2pt}
\titlespacing{\paragraph}{0pt}{0pt plus .1ex minus .2ex}{0pt}
\setlength\intextsep{0pt}
\begin{wrapfigure}{l}{.5\textwidth}
\begin{tcolorbox}[
enhanced,fonttitle=\bfseries\Large,
colback=gray!10,colframe=gray,%colframe=red!50!black,
title={Contents},]
\makeatletter
\@starttoc{toc}
\makeatother
\end{tcolorbox}
\end{wrapfigure}
\paragraph{}
\unskip
%\vspace*{-2\parskip}
\section{Le groupe ABB}
\lipsum[1]
\endgroup

\section{ABB Jumet}

\lipsum[2]

\section{La ligne de production de condensateurs}

\subsection{La métallisation}

\subsection{Le choupage\label{choupage}}

\subsection{Traitements de finition}

\section{Systèmes à condensateurs}

\subsection{Condensateurs en triphasé}

\subsection{Armoires à condensateurs}

\end{document}

• Maybe I should've precised it in my mwe, but my major concern was getting the section title right above where the text starts, and not above the toc. Upvote for the horizontal alignment fix though. – nathdwek Feb 23 '14 at 21:21
• @nathdwek sure no problem- see the update – cmhughes Feb 24 '14 at 0:43

\documentclass[a4paper]{article}
\usepackage[frenchb]{babel}
\usepackage[utf8]{inputenc}
\usepackage{textcomp, multicol}
\usepackage[T1]{fontenc}
\usepackage{fixltx2e}

\usepackage{lipsum}

% \usepackage{float}                            %floats and figures
% \usepackage{wrapfig}                          %figures within text
% \usepackage{subcaption}                       %multiple subfigures and subcaptions

% \usepackage{amsmath}

\begin{document}
\begin{center}
{\LARGE Rapport de visite d'entreprise}\\
%  {\includegraphics[width=.3\textwidth]{ABB.jpg}}\\
Nathan Dwek\\
\today
\end{center}

\begin{multicols}{2}
\tableofcontents

\columnbreak

\section{Le groupe ABB}
\lipsum[1]
\end{multicols}

\section{ABB Jumet}

\lipsum[2]

\section{La ligne de production de condensateurs}

\subsection{La métallisation}

\subsection{Le choupage\label{choupage}}

\subsection{Traitements de finition}

\section{Systèmes à condensateurs}

\subsection{Condensateurs en triphasé}

\subsection{Armoires à condensateurs}

\end{document}

In your real document you'd have to play around with the placement of \columnbreak.

• As said in my question, I tried this solution, and it indeed looks good, my only issue is that I'd have to, as you say, play around with \columnbreak to adjust relative size. – nathdwek Feb 23 '14 at 21:07


text:
# 5.Co- and Contravariant Hom Functors

## 22.06.2020

### Contents/Index

1.Definition
2.Full and Faithful
3.Preserves and Reflects
4.Contra-, Co- and Bivariant
@5.Co- and Contravariant Hom Functors

For the following let $\mathcal{C}$ be a locally small category.

## The hom-functor (covariant)

Given $\mathcal{C}$, each object $A$ determines a functor $$\mathcal{C}(A,-) : \mathcal{C} \rightarrow Set$$ This functor takes each $\mathcal{C}$-object $X$ to the set $\mathcal{C}(A,X)$, that is the set of arrows from $A$ to $X$. Furthermore the functor takes each $\mathcal{C}$-arrow $f : X \rightarrow Y$ to the function $$\mathcal{C}(A,f) : \mathcal{C}(A,X) \rightarrow \mathcal{C}(A,Y)$$ given as $$\mathcal{C}(A,f)(g : A \rightarrow X) = f \circ g$$ where the right side composition, $f \circ g$, is in $\mathcal{C}$. $\mathcal{C}(A,-)$ is called a hom-functor. This is sketched in Figure 1.

### Proof of functor

We can prove that the above is a functor. Fix the object $A$. Let objects $X,Y$. All in $\mathcal{C}$. Let $F$ be shorthand for $\mathcal{C}(A,-)$, that is $F(X) = \mathcal{C}(A,X)$ and for $f : X \rightarrow Y$ in $\mathcal{C}$ we have that $F(f) = \mathcal{C}(A,X) \rightarrow \mathcal{C}(A,Y)$. Now let $f : X \rightarrow Y$ and $g : Y \rightarrow Z$ in $\mathcal{C}$. And we get $$F(g \circ f) = \mathcal{C}(A,X) \rightarrow \mathcal{C}(A,Z)$$ and $$F(g) F(f) = (\mathcal{C}(A,Y) \rightarrow \mathcal{C}(A,Z)) (\mathcal{C}(A,X) \rightarrow \mathcal{C}(A,Y))$$ and hence $$F(g) F(f) = \mathcal{C}(A,X) \rightarrow \mathcal{C}(A,Z) = F(g \circ f)$$ Furthermore we have that $$F(id_X) = \mathcal{C}(A,X) \rightarrow \mathcal{C}(A,X) = id_{F(X)}$$

## Contravariant hom-functor

The contravariant hom-functor is defined as $$\mathcal{C}(-,B) : \mathcal{C}^{op} \rightarrow Set$$ that takes each object $X$ to the set $\mathcal{C}(X,B)$. Each arrow $h : X \rightarrow Y$ is taken to the function $$\mathcal{C}(h,B) : \mathcal{C}(X,B) \rightarrow \mathcal{C}(Y,B)$$ given as $$\mathcal{C}(h,B)(g : X \rightarrow B) = g \circ h$$ As shown in Figure 2. Note that since $h \in \mathcal{C}^{op}$, we have the reverse version as $h : Y \rightarrow X \in \mathcal{C}$. Hence we have that $g \circ h \in \mathcal{C}(Y,B)$.

## Natural relation between covariant and contravariant

The two above are related in a natural manner. That is for every $\mathcal{C}$-arrows $f : B \rightarrow B'$ and $h : A' \rightarrow A$ the following diagram commutes

## Bivariant hom-functor

The bivariant hom-functor is for objects defined as $$\mathcal{C}(-,-) : \mathcal{C}^{op} \times \mathcal{C} \rightarrow Set$$ This is just a composite of the two above functors.

format: plain


text:
## Monday, July 2, 2012

### Is your domain migration data valid?

For many of us who spend alot of time in customer environments, fear of customer supplied data can cause the sandman to detour around our sleeping quarters more than one night in a row. It is difficult for those of us who practice so much self-reliance to depend on lists given to us by the people we are trying to help. And failure to validate the data they supply us with can lead to results worthy of that fear.

So please, for the sake of your own sleep quota, take a moment to consider checking the data.

I do lots of domain migrations, and of all the things I do in those migrations, merging users is one of the most potentially tedious simply due to the negative and far reaching effects that the mispelling of a login or samAccountName can have. If a user is migrated with the intent of merging with another account in a target domain, the mispelling has an avalanche effect. First, instead of merging the accounts, a "new" account is generated in the target domain containing the login name and sidHistory that was intended to be merged. And finding these duplicates is not as easy as simply running a "Find" on the directory, but may require running a CSVDE export to locate the offending object. Also, running a computer migration after this mistake can create a secondary profile on the workstation instead of merging the source and target domains, causing the user to lose the ability to see the original profile settings and making it appear that the user data is lost, requiring a backout process that CAN lose data if done incorrectly.

So why not make sure it is just done right in the first place? I modified a couple of simple scripts to help validate the data before using it in the ADMT tool.

The first one is a powershell .ps1 script that can verify if a user name submitted for migration exists and is spelled right. It requires one input .txt file with a single column of data, with UserName as the header followed by a login name on each subsequent line, like this:

user1
user2
user3

The body of the script looks like this:

$struser = import-Csv C:\users\res_migrator\desktop\userinput.csv foreach($User in $struser) {$dom = [System.DirectoryServices.ActiveDirectory.Domain]::GetCurrentDomain()
$root =$dom.GetDirectoryEntry()
$search = [System.DirectoryServices.DirectorySearcher]$root
$search.Filter = "(samAccountName="+$user.username+")"
$result =$search.FindOne() #start-transcript "c:\mu.txt"
if ($result -ne$null)
{
Out-File -Append c:\userexists.txt -inputObject $user.username } else { Out-File -Append c:\userdoesnotexist.txt -inputObject$user.username

}

There are two output files, verified names and unverified names. The second one, "userdoesnotexist.txt" will have the unverified user names that be corrected. This script can be run in both the source and target domains.

Once the names are validated, user migrations can be done with a high degree of certainty that a merge will occur and sidHistory copied over, if that option is picked.

But that's not all you get, folks! I have a second .bat script to verify the sidHistory existence on each of the target accounts, using the same list of target login names used in the migration. This one requires an input .txt file, users.txt which requires only a single column of names without a header, i.e.:

user1
user2
user3

and the text of the batch file is:

For /f %%i in (C:\Users.txt) Do (
dsquery * -Filter "(samaccountname=%%i)" -Attr samAccountName ObjectSID sidHistory >> C:\User_Info.txt)

The resulting output file should have a sidHistory attribute listed for each name. If the attribute is missing, search for duplicate accounts by running csvde -f c:\users.csv and searching the resulting .csv for instances of the login name to find potential duplicates.

Hopefully, this will make your migrations go a little smoother. It sure helped me alot on my last one!

Happy Migrating!

"That's no moon...it's a space station".

- Peter Trast, SQL Expert; MCITP DBA, MCITP EA, MCT LinkIn with Peter


text:
How to solve this implicit differentiation problem concerning arcsin?

My overarching question is about differentiating when you have these inverse trig functions, but listed below is the specific question I am trying to solve. If you help me with the problem, it'll help me (and others) apply it to similar questions.

Problem: y = arcsin(x) - sqrt(1 - x^2) Find dy/dx

Answer Choices: 1/2sqrt(1-x^2) or 2/sqrt(1-x^2) or (1+x)/sqrt(1-x^2) or (x^2)/sqrt(1-x^2) or 1/sqrt(1+x)

The arcsin(x) is primarily what is getting me stuck. To try to solve the problem I moved the root to the other side by adding it to both sides.

1. y + sqrt(1 - x^2) = arcsin(x)

Then I converted the equation into a sin equation...I don't feel like this is correct

1. sin(y + sqrt(1 - x^2)) = x

From here, if I take dy/dx of both sides, it seems utterly confusing and on the wrong track. (I believe I applied chain rule correctly, but I could be wrong)

1. cos(y + sqrt(1 - x^2)) * [dy/dx + 1/2sqrt(1-x^2) * -2x] = 1

I also examined the square root in the problem carefully because I noticed it had a striking resemblance to another problem I saw earlier in a book:

Differentiate y = arcsin(x) 1. sin(y) = x 2. cos(y) dy/dx = 1 3. dy/dx = 1/cos(y) = 1/sqrt(1- (sin(y))^2) = 1/sqrt(1-x^2) because of the trig identity (sin(x))^2 + (cos(x))^2 = 1 & because subtracting (sin(y))^2 is the same as subtracting x^2 because of Step One conversion

So because I saw the sqrt(1-x^2) in the tougher problem I'm doing right now, I tried to find a way to utilize the technique from the earlier one, but I couldn't. So perhaps that could be the key to solving it.

• Your work up to "Differentiate ..." is unnecessary. Trying to turn this into an implicit differentiation problem just makes it harder. Here's what you should do: $$y = \arcsin(x) - \sqrt{1 - x^2} \implies y' = (\arcsin(x))' - \left(\sqrt{1-x^2}\right)'$$ You figured out $(\arcsin(x))'$ correctly (you used implicit differentiation for this part but that's all the i.d. you need in this). For $\left(\sqrt{1-x^2}\right)'$ you use the chain rule. – user137731 Jul 20 '16 at 3:47
• Your work up to step 3 is correct, just solve for $\frac{dy}{dx}$, and then use the angle addition formula on the cosine expression if necessary. – florence Jul 20 '16 at 3:48
• @Bye_World Thanks, that makes sense. The workbook says to use implicit differentiation though, so that's the only reason I asked that – K_7 Jul 20 '16 at 3:53
• @florence So if I kept going, I would get the answer? – K_7 Jul 20 '16 at 3:54
• They must have meant for you to use implicit differentiation on the $\arcsin$ portion only because otherwise you're just causing yourself a lot of undue difficulty. – user137731 Jul 20 '16 at 3:54


text:
()

CBSE NET July 2016 Geography paper-3 detailed solutions will be made available at www.examrace.com and for full Examrace CBSE (UGC) NET-JRF Geography Series visit www.examrace.com. For complete detailed solutions for July 2016 paper you can refer www.doorsteptutor.com

1. The book Polar Geopolitics is written by whom of the following?

a) Richard Powel

b) R. D. Dixit

d) Taylor, P. J. and Jonston, R. J.

2. “Social processes explain the spatial organization”, this statement pertains to

a) Humanism

b) Marxism

c) Positivism

d) Behaviorism

3. Match List – I with List – II and select the correct answer using the codes given below:

 List – I List – II I. Haggerstrand II. William Bunge III. Gilbert IV. Walter Isard A. Theoretical geography B. Geographical dissemination of Innovation C. Methods of regional analysis D. Human responses to floods

Codes:

 I II III IV a) D B A C b) C A B D c) A C B D d) B A D C

4. Given below are two statement, one labelled as Assertion (A) and other labelled as Reason (R). Select your answer from the codes given below:

Assertion: Social indicator movement was quickly developed in 1960s in the USA.

Reason: It grew in response to rise of a wide range of social problem in US society.

Codes:

a) Both (A) and (R) are true and (R) is correct explanation of (A).

b) Both (A) and (R) are true, but (R) is not correct explanation of (A).

c) (A) is true, but (R) is false.

d) (A) is false, but (R) is true.

5. Which one of the following five year plans of India adopted the Harrod-Domar growth model?

a) Second

b) Fourth

c) Third

d) First

6. Which one of the following contributors made refinements to W. Christaller’s central place by including concept of ‘city rich’ sectors and ‘city poor’ sectors?

a) R. P. Mishra

b) B. J. L. Berry

c) Walter Isard

d) August Losch

7. Which one of the following indicators is not concerned with Human Development Index (H. D. I. )?

a) Life expectancy at birth

c) Educational attainment

d) Standard of living

8. Given below are two statements, one labelled as Assertion (A) and other labelled as Reason (R). Select your answer from the codes given below:

Assertion: There are regional disparities in spatial organization of economy

Reason: Interaction among inherent factor in economic activities affect the texture of spatial organization.

Codes:

a) Both (A) and (R) are true and (R) is correct explanation of (A).

b) Both (A) and (R) are true, but (R) is not correct explanation of (A).

c) (A) is true, but (R) is false.

d) (A) is false, but (R) is true.

9. Match List – I with List – II and select the correct answer using the codes given below:

 List – I (Type of Regions) List – II (Definition) I. Problem Regions II. Major Regions III. Prospective Regions IV. Dynamic Regions A. Immense resource potentials but face socio-economic obstacles to technological transformations B. Those regions which show little promise of development in the near future C. Support advanced industrial areas and predominantly urban population D. Those have minimum disparities within and distinctiveness from their neighbors in respect of regional character and resource for development

Codes:

 I II III IV a) C B D A b) D A B C c) B D A C d) A C D B

10. Match List – I with List – II and select the correct answer using the codes given below:

 List – I (Type of Towns) List – II (Cities) I. Port Towns II. Industrial Towns III. Mineral research based towns IV. Historic towns A. Singhbhum, Keonjhar and Rewa B. Agra, Jaipur and Madurai C. Bombay, Calcutta and Madras D. Bhilai, Rourkela and Jamshedpur

Codes:

 I II III IV a) B A C D b) D B A C c) A C D B d) C D A B

11. Given below are two statement, one labelled as Assertion (A) and other labelled as Reason (R). Select your answer from the codes given below:

Assertion: Metropolitan cities in general are flanked by satellite towns.

Reason: The towns have an important function to release the pressure of the city.

Codes:

a) Both (A) and (R) are true and (R) is correct explanation of (A).

b) Both (A) and (R) are true, but (R) is not correct explanation of (A).

c) (A) is true, but (R) is false.

d) (A) is false, but (R) is true.

12. The famous Makrana marbles are located in which of the following Indian Status?

a) Gujarat

b) Rajasthan

d) Maharashtra

13. The Environmental Protection Act in India was passed when the government was headed by

a) Late Mrs. Indira Gandhi

b) Late Mr. V. P. Singh

c) Late Mr. Lal Bahadur Shastri

d) Late Mr. Rajiv Gandhi

14. Which one of the following sequence of physical features is correct?

a) Mountain, Tarai, Plain and Bhabhar

b) Plain, Bhabhar, Tarai and Mountain

c) Mountain, Plain, Bhabhar and Tarai

d) Plain, Tarai, Bhabhar and Mountain

15. Match List – I with List – II and select the correct answer using the codes given below:

 List – I (Indian Tribes) List – II (States) I. Baiga II. Bhotias III. Karbi IV. Rangpa A. Assam B. Sikkim C. Uttrakhand D. Madhya Pradesh

Codes:

 I II III IV a) C D B A b) D C A B c) D B A C d) C A B D

16. Match List – I with List – II and select the correct answer using the codes given below:

 List – I (Hill Name) List – II (State Name) I. Mikir Hills II. Amarkantak III. Khasi Hills IV. Baba Budan Hills A. Madhya Pradesh B. Assam C. Karnataka D. Meghalaya

Codes:

 I II III IV a) B C A D b) D A C B c) B A D C d) D C B A

17. Match List – I with List – II and select the correct answer using the codes given below:

 List – I (River Name) List – II (Urban Settlement) I. Ganga II. Krishna III. Brahmaputra IV. Mahanadi A. Vijayawada B. Tezpur C. Kanpur D. Cuttack

Codes:

 I II III IV a) A B C D b) C B A D c) C A D B d) C A B D

18. Match List – I with List – II and select the correct answer using the codes given below:

 List – I (Sat. Sensors) List – II (IFOV-Pan) I. IRS-IC LISS II II. SPOT-5-HRV III. DG-QuickBird-2 IV. ADEOS-AVNIR A. 0.61 m B. 5.80 m C. 8.00 m D. 5.00 m

Codes:

 I II III IV a) D B C A b) B D A C c) A B C D d) B A D C

19. Distribution of monthly rainfall of a particular place for the month of June 2010 is given below:

 Rainfall (cms. ) 10 – 20 20 – 30 30 – 40 No. of days 6 20 4

Which one of the following calculated mean values of the distribution is correct?

Codes:

a) 21.50

b) 22.33

c) 24.33

d) 25.50

20. Which one of the following diagrams can be used to represent non – spatial attribute data?

a) Flow line

b) Bars and Cubes

c) Choropleth

d) Isolines

21. Which one of the following correlation coefficients is matched with its value?

a) Perfect positive r = $\mp$ 1.0

b) Imperfect negative r = $-\phantom{\rule{0.2em}{0ex}}1.0\phantom{\rule{0.2em}{0ex}}$

c) No correlation r = 0.0

d) Positive significant r = 0.5$<$1.0

22. Given below are two statement, one labelled as Assertion (A) and other labelled as Reason (R). Select your answer from the codes given below:

Assertion: Partial correlation is a method of dealing with three or more variables

Reason: Basically, it is the correlation between two variables holding the third

Codes:

a) Both (A) and (R) are true and (R) is correct explanation of (A).

b) Both (A) and (R) are true, but (R) is not correct explanation of (A).

c) (A) is true, but (R) is false.

d) (A) is false, but (R) is true.

23. Given below are two statement, one labelled as Assertion (A) and other labelled as Reason (R). Select your answer from the codes given below:

Assertion: There are data restrictions in using Lorenz curve since variable must be expressed as frequencies and negative values cannot be included.

Reason: This technique is difficult to be applied to study of continuous variables.

Codes:

a) Both (A) and (R) are true and (R) is correct explanation of (A).

b) Both (A) and (R) are true, but (R) is not correct explanation of (A).

c) (A) is true, but (R) is false.

d) (A) is false, but (R) is true.

24. Match List – I with List – II and select the correct answer using the codes given below:

 List – I (Scales) List – II (Depiction) I. Statement II. Simple linear III. Representative fraction IV. Comparative scale A. 1: 25, 000 B. 1 inch to 1 mile C. What is the name of this scale Find the name of this scale D. Matching correct scale with its name Find the correct scale with its name

Codes:

 I II III IV a) B D C A b) A B C D c) D A B C d) B D A C

25. Which one of the following codes given for abbreviations correctly depicts landscape models?

A. DEM

B. DRG

C. DLG

D. DTM

Codes:

a) (A) and (B)

b) (B) and (C)

c) (A) and (D)

d) (C) and (D)


text:
# the camel and the eye of the needle [closed]

A farmer told me, without lying:

"you see this camel over there" showing me a camel eating, "it passed whole through the eye of a normal needle"

How is it possible, knowing that I too do not lie and there are no word games?

• lateral-thinking is probably the correct tag here. – Mordechai Aug 7 '19 at 21:32
• why is it not close ? – Dattier Oct 3 '19 at 10:18

He did so

as the camel was still a microscopic embryo.

Perhaps the camel passed through

Needle's Eye in South Yorkshire.

In fact, according to the Wikipedia article,

"It was constructed approximately in the mid-late 18th century and believed to have been made in order to win a wager, where the second Marquis of Rockingham claimed he was able to 'drive a coach and horses through an eye of a needle'"

It just

walked past a needle. The statement says "by", not "through"

• hidabroot.fr/article/136643/… – Dattier Aug 7 '19 at 12:10
• there are no word games – Dattier Aug 7 '19 at 12:12
• @Dattier Words have meaning. If you tell me you might drive by my house tonight, I’d expect you to drive past it, not through the middle of it. The difference there is pretty huge, and is not just playing word games. If you intend to say “through the eye”, please say that. – Rubio Aug 7 '19 at 12:57
• yes, sorry my english is bad. – Dattier Aug 7 '19 at 13:01


text:
# Conic Section: Ellipse

Ellipse

An ellipse is the locus of the point such that its distance from the focus bears the constant ratio to the distance from the line called directrix .the value of eccentricity lies between the 0 and 1

Equation of an ellipse in standard form

$\frac{{\mathrm{x}}^{2}}{{\mathrm{a}}^{2}}\phantom{\rule{mediummathspace}{0ex}}+\frac{{\mathrm{y}}^{2}}{{\mathrm{b}}^{2}}\phantom{\rule{mediummathspace}{0ex}}$= 1

When the center of an ellipse is ( h, k)

$\frac{{\left(\mathrm{x}-\mathrm{h}\right)}^{2}}{{\mathrm{a}}^{2}}\phantom{\rule{mediummathspace}{0ex}}+\frac{{\left(\mathrm{y}-\mathrm{k}\right)}^{2}}{{\mathrm{b}}^{2}}\phantom{\rule{mediummathspace}{0ex}}$= 1

Hyperbola

A hyperbola is a set of all points in a plane such that the difference of the distances from the two fixed points called foci is a constant.

Equation of the hyperbola in a standard form is given by$\frac{{\mathrm{x}}^{2}}{{\mathrm{a}}^{2}}\phantom{\rule{mediummathspace}{0ex}}-\phantom{\rule{mediummathspace}{0ex}}\frac{{\mathrm{y}}^{2}}{{\mathrm{b}}^{2}}\phantom{\rule{mediummathspace}{0ex}}$= 1 centre at origin C (0, 0) foci = (± ae, 0) directrix ,x =± $\frac{\mathrm{a}}{\mathrm{e}}$

Length of latus rectum =


text:
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# NGC 4981

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 Spiral galaxies observed in the near-infrared K band. I. Data analysis and structural parametersDeep surface photometry in the K band was obtained for 54 normal spiralgalaxies, with the aim of quantifying the percentage of faint bars andstudying the morphology of spiral arms. The sample was chosen to cover awider range of morphological types while inclination angles anddistances were limited to allow a detailed investigation of the internalstructure of their disks and future observations and studies of the diskkinematics. An additional constraint for a well defined subsample wasthat no bar structure was seen on images in the visual bands. Accuratesky projection parameters were determined from the K maps comparingseveral different methods. The surface brightness distribution wasdecomposed into axisymmetric components while bars and spiral structureswere analyzed using Fourier techniques.Bulges were best represented by a Sérsic r1/n law withan index in the typical range of 1-2. The central surface brightness ofthe exponential disk and bulge-to-disk ratio only showed weakcorrelation with Hubble type. Indications of a central point source werefound in many of the galaxies. An additional central, steep, exponentialdisk improved the fit for more than 80% of the galaxies suggesting thatmany of the bulges are oblate.Bars down to the detection level at a relative amplitude of 3% weredetected in 26 of 30 galaxies in a subsample classified as ordinary SAspirals. This would correspond to only 5% of all spiral galaxies beingnon-barred at this level. In several cases, bars are significantlyoffset compared to the starting points of the main spiral pattern whichindicates that bar and spiral have different pattern speeds. A smallfraction (10%) of the sample has complex central structuresconsisting of several sets of bars, arcs or spirals.A majority of the galaxies (60%) displays a two-armed, grand-designspiral pattern in their inner parts which often breaks up into multiplearms in their outer regions. Phase shifts between the inner and outerpatterns suggest in some cases that they belong to different spiralmodes. The pitch angles of the main two-armed symmetric spiral patternin the galaxies have a typical range of 5-30 °. The sample shows alack of strong, tight spirals which could indicate that such patternsare damped by non-linear, dynamical effects due to their high radialforce perturbations.Based on observations collected at the European Southern Observatory, LaSilla, Chile; programs: ESO 63.N-0343, 65.N-0287, 66.N-0257.Table 2 is only available in electronic form at the CDS via anonymousftp to cdsarc.u-strasbg.fr (130.79.128.5) or viahttp://cdsweb.u-strasbg.fr/cgi-bin/qcat?J/A+A/423/849Appendix A is only available in electronic form athttp://www.edpsciences.org The ISOPHOT 170 μm Serendipity Survey II. The catalog of optically identified galaxies%The ISOPHOT Serendipity Sky Survey strip-scanning measurements covering≈15% of the far-infrared (FIR) sky at 170 μm were searched forcompact sources associated with optically identified galaxies. CompactSerendipity Survey sources with a high signal-to-noise ratio in at leasttwo ISOPHOT C200 detector pixels were selected that have a positionalassociation with a galaxy identification in the NED and/or Simbaddatabases and a galaxy counterpart visible on the Digitized Sky Surveyplates. A catalog with 170 μm fluxes for more than 1900 galaxies hasbeen established, 200 of which were measured several times. The faintest170 μm fluxes reach values just below 0.5 Jy, while the brightest,already somewhat extended galaxies have fluxes up to ≈600 Jy. For thevast majority of listed galaxies, the 170 μm fluxes were measured forthe first time. While most of the galaxies are spirals, about 70 of thesources are classified as ellipticals or lenticulars. This is the onlycurrently available large-scale galaxy catalog containing a sufficientnumber of sources with 170 μm fluxes to allow further statisticalstudies of various FIR properties.Based on observations with ISO, an ESA project with instruments fundedby ESA Member States (especially the PI countries: France, Germany, TheNetherlands and the UK) and with the participation of ISAS and NASA.Members of the Consortium on the ISOPHOT Serendipity Survey (CISS) areMPIA Heidelberg, ESA ISO SOC Villafranca, AIP Potsdam, IPAC Pasadena,Imperial College London.Full Table 4 and Table 6 are only available in electronic form at theCDS via anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or viahttp://cdsweb.u-strasbg.fr/cgi-bin/qcat?J/A+A/422/39 Companions of Bright Barred Shapley-Ames GalaxiesCompanion galaxy environment for a subset of 78 bright and nearby barredgalaxies from the Shapley-Ames Catalog is presented. Among the spiralbarred galaxies, there are Seyfert galaxies, galaxies with circumnuclearstructures, galaxies not associated with any large-scale galaxy cloudstructure, galaxies with peculiar disk morphology (crooked arms), andgalaxies with normal disk morphology; the list includes all Hubbletypes. The companion galaxy list includes the number of companiongalaxies within 20 diameters, their Hubble type, and projectedseparation distance. In addition, the companion environment was searchedfor four known active spiral galaxies, three of them are Seyfertgalaxies, namely, NGC 1068, NGC 1097, and NGC 5548, and one is astarburst galaxy, M82. Among the results obtained, it is noted that theonly spiral barred galaxy classified as Seyfert 1 in our list has nocompanions within a projected distance of 20 diameters; six out of 10Seyfert 2 bar galaxies have no companions within 10 diameters, six outof 10 Seyfert 2 galaxies have one or more companions at projectedseparation distances between 10 and 20 diameters; six out of 12 galaxieswith circumnuclear structures have two or more companions within 20diameters. The far-infrared/radio correlation in the ISO era. The warm and cold far-infrared/radio correlationsWe present the correlation between the far-infrared (FIR) and radioemissions from a composite sample of 72 nearby normal galaxies observedwith the ISOPHOT instrument on board the Infrared Space Observatory. Thegalaxies in the sample have measurements at three FIR wavelengths (60,100 and 170 mu m), which allowed a direct determination of the warm andcold FIR emission components. This is the first time that thecorrelation has been established for the total FIR luminosity, of whichmost is carried by the cold dust component predominantly emittinglongwards of the spectral coverage of IRAS. The slope of thiscorrelation is slightly non-linear (1.10+/- 0.03). Separate correlationsbetween the warm and cold FIR emission components and the radio emissionhave also been derived. The slope of the warm FIR/radio correlation wasfound to be linear (1.03 +/- 0.03). For the cold FIR/radio correlationwe found a slightly non-linear (1.13 +/- 0.04) slope. We qualitativelyinterpret the correlations in terms of star formation rate and find thatboth the FIR and radio emissions may be consistent with a non-lineardependence on star formation rate for galaxies not undergoing starburstactivity.Based on observations with the Infrared Space Observatory (ISO), an ESAproject with instruments funded by ESA member States (especially the PIcountries: France, Germany, The Netherlands, and the UK) and with theparticipation of ISAS and NASA.Table \ref{Tab2} and Appendices A and B are only available in electronicform at http://www.edpsciences.org Bar Galaxies and Their EnvironmentsThe prints of the Palomar Sky Survey, luminosity classifications, andradial velocities were used to assign all northern Shapley-Ames galaxiesto either (1) field, (2) group, or (3) cluster environments. Thisinformation for 930 galaxies shows no evidence for a dependence of barfrequency on galaxy environment. This suggests that the formation of abar in a disk galaxy is mainly determined by the properties of theparent galaxy, rather than by the characteristics of its environment. Local velocity field from sosie galaxies. I. The Peebles' modelPratton et al. (1997) showed that the velocity field around clusterscould generate an apparent distortion that appears as tangentialstructures or radial filaments. In the present paper we determine theparameters of the Peebles' model (1976) describing infall of galaxiesonto clusters with the aim of testing quantitatively the amplitude ofthis distortion. The distances are determined from the concept of sosiegalaxies (Paturel 1984) using 21 calibrators for which the distanceswere recently calculated from two independent Cepheid calibrations. Weuse both B and I-band magnitudes. The Spaenhauer diagram method is usedto correct for the Malmquist bias. We give the equations for theconstruction of this diagram. We analyze the apparent Hubble constant indifferent regions around Virgo and obtain simultaneously the Local Groupinfall and the unperturbed Hubble constant. We found:[VLG-infall = 208 ± 9 km s-1] [\log H =1.82 ± 0.04 (H ≈ 66 ± 6 km s-1Mpc-1).] The front side and backside infalls can be seenaround Virgo and Fornax. In the direction of Virgo the comparison ismade with the Peebles' model. We obtain: [vinfall} =CVirgo/r0.9 ± 0.2] withCVirgo=2800 for Virgo and CFornax=1350 for Fornax,with the adopted units (km s-1 and Mpc). We obtain thefollowing mean distance moduli: [μVirgo=31.3 ± 0.2(r=18 Mpc )] [μFornax=31.7 ± 0.3 (r=22 Mpc). ] Allthese quantities form an accurate and coherent system. Full Table 2 isonly available in electronic form at the CDS via anonymous ftp tocdsarc.u-strasbg.fr (130.79.128.5) or viahttp://cdsweb.u-strasbg.fr/cgi-bin/qcat?J/A+A/393/57 Supernovae in isolated galaxies, in pairs and in groups of galaxiesIn order to investigate the influence of environment on supernova (SN)production, we have performed a statistical investigation of the SNediscovered in isolated galaxies, in pairs and in groups of galaxies. 22SNe in 18 isolated galaxies, 48 SNe in 40 galaxy members of 37 pairs and211 SNe in 170 galaxy members of 116 groups have been selected andstudied. We found that the radial distributions of core-collapse SNe ingalaxies located in different environments are similar, and consistentwith those reported by Bartunov, Makarova & Tsvetkov. SNe discoveredin pairs do not favour a particular direction with respect to thecompanion galaxy. Also, the azimuthal distributions inside the hostmembers of galaxy groups are consistent with being isotropics. The factthat SNe are more frequent in the brighter components of the pairs andgroups is expected from the dependence of the SN rates on the galaxyluminosity. There is an indication that the SN rate is higher in galaxypairs compared with that in groups. This can be related to the enhancedstar formation rate in strongly interacting systems. It is concludedthat, with the possible exception of strongly interacting systems, theparent galaxy environment has no direct influence on SN production. Lick Observatory Photographic Supernova SpectraSeventy-eight photographic spectra of 18 bright supernovae that wereobtained by various observers at the Lick Observatory between 1937 and1971 are presented and briefly discussed. Microphotometer transmissiontracings of the photographic plates have been digitized and plotted on acommon scale with a linear wavelength axis. The spectra were prismatic,with a nonlinear dispersion, in their original form. These spectra areuseful for classification purposes and for comparative studies of theblueshifts of absorption features. Nearby Optical Galaxies: Selection of the Sample and Identification of GroupsIn this paper we describe the Nearby Optical Galaxy (NOG) sample, whichis a complete, distance-limited (cz<=6000 km s-1) andmagnitude-limited (B<=14) sample of ~7000 optical galaxies. Thesample covers 2/3 (8.27 sr) of the sky (|b|>20deg) andappears to have a good completeness in redshift (97%). We select thesample on the basis of homogenized corrected total blue magnitudes inorder to minimize systematic effects in galaxy sampling. We identify thegroups in this sample by means of both the hierarchical and thepercolation friends-of-friends'' methods. The resulting catalogs ofloose groups appear to be similar and are among the largest catalogs ofgroups currently available. Most of the NOG galaxies (~60%) are found tobe members of galaxy pairs (~580 pairs for a total of ~15% of objects)or groups with at least three members (~500 groups for a total of ~45%of objects). About 40% of galaxies are left ungrouped (field galaxies).We illustrate the main features of the NOG galaxy distribution. Comparedto previous optical and IRAS galaxy samples, the NOG provides a densersampling of the galaxy distribution in the nearby universe. Given itslarge sky coverage, the identification of groups, and its high-densitysampling, the NOG is suited to the analysis of the galaxy density fieldof the nearby universe, especially on small scales. The ISOPHOT 170 μ m serendipity survey. I. Compact sources with galaxy associationsThe first set of compact sources observed in the ISOPHOT 170 μmSerendipity Survey is presented. From the slew data with low(I100 μm <= 15 MJy/sr) cirrus background, 115well-observed sources with a high signal-to-noise ratio in all detectorpixels having a galaxy association were extracted. Of the galaxies withknown optical morphologies, the vast majority are classified as spirals,barred spirals, or irregulars. The 170 μm fluxes measured from theSerendipity slews have been put on an absolute flux level by usingcalibration sources observed additionally with the photometric mappingmode of ISOPHOT. For all but a few galaxies, the 170 μm fluxes aredetermined for the first time, which represents a significant increasein the number of galaxies with measured Far-Infrared (FIR) fluxes beyondthe IRAS 100 μm limit. The 170 μm fluxes cover the range 2 <~F170 μm la 100 Jy. Formulae for the integrated FIR fluxesF40-220μm and the total infrared fluxesF1-1000μm incorporating the new 170 μm fluxes areprovided. The large fraction of sources with a high F170μm / F100 μm flux ratio indicates that a cold(TDust la 20 K) dust component is present in many galaxies.The detection of such a cold dust component is crucial for thedetermination of the total dust mass in galaxies, and, in cases with alarge F170 μm / F100 μm flux ratio,increases the dust mass by a significant factor. The typical mass of thecoldest dust component is MDust = 107.5 +/- 0.5Msun , a factor 2-10 larger than that derived from IRASfluxes alone. As a consequence, the majority of the derived gas-to-dustratios are much closer to the canonical value of ~ 160 for the MilkyWay. By relaxing the selection criteria, it is expected that theSerendipity Survey will eventually lead to a catalog of 170 μm fluxesfor ~ 1000 galaxies. Based on observations with ISO, an ESA project withinstruments funded by ESA Member States (especially the PI countries:France, Germany, the Netherlands and the United Kingdom) and with theparticipation of ISAS and NASA. Members of the Consortium on the ISOPHOTSerendipity Survey (CISS) are MPIA Heidelberg, ESA ISO SOC Villafranca,AIP Potsdam, IPAC Pasadena, Imperial College London. The I-Band Tully-Fisher Relation for SC Galaxies: 21 Centimeter H I Line DataA compilation of 21 cm line spectral parameters specifically designedfor application of the Tully-Fisher (TF) distance method is presentedfor 1201 spiral galaxies, primarily field Sc galaxies, for which opticalI-band photometric imaging is also available. New H I line spectra havebeen obtained for 881 galaxies. For an additional 320 galaxies, spectraavailable in a digital archive have been reexamined to allow applicationof a single algorithm for the derivation of the TF velocity widthparameter. A velocity width algorithm is used that provides a robustmeasurement of rotational velocity and permits an estimate of the erroron that width taking into account the effects of instrumental broadeningand signal-to-noise. The digital data are used to establish regressionrelations between measurements of velocity widths using other commonprescriptions so that comparable widths can be derived throughconversion of values published in the literature. The uniform H I linewidths presented here provide the rotational velocity measurement to beused in deriving peculiar velocities via the TF method. The I-Band Tully-Fisher Relation for SC Galaxies: Optical Imaging DataProperties derived from the analysis of photometric I-band imagingobservations are presented for 1727 inclined spiral galaxies, mostly oftypes Sbc and Sc. The reduction, parameter extraction, and errorestimation procedures are discussed in detail. The asymptotic behaviorof the magnitude curve of growth and the radial variation in ellipticityand position angle are used in combination with the linearity of thesurface brightness falloff to fit the disk portion of the profile. TotalI-band magnitudes are calculated by extrapolating the detected surfacebrightness profile to a radius of eight disk scale lengths. Errors inthe magnitudes, typically ~0.04 mag, are dominated by uncertainties inthe sky subtraction and disk-fitting procedures. Comparison is made withthe similar imaging database of Mathewson, Ford, & Buchhorn, both aspresented originally by those authors and after reanalyzing theirdigital reduction files using identical disk-fitting procedures. Directcomparison is made of profile details for 292 galaxies observed incommon. Although some differences occur, good agreement is found,proving that the two data sets can be used in combination with onlyminor accommodation of those differences. The compilation of opticalproperties presented here is optimized for use in applications of theTully-Fisher relation as a secondary distance indicator in studies ofthe local peculiar velocity field. Bulge-Disk Decomposition of 659 Spiral and Lenticular Galaxy Brightness ProfilesWe present one of the largest homogeneous sets of spiral and lenticulargalaxy brightness profile decompositions completed to date. The 659galaxies in our sample have been fitted with a de Vaucouleurs law forthe bulge component and an inner-truncated exponential for the diskcomponent. Of the 659 galaxies in the sample, 620 were successfullyfitted with the chosen fitting functions. The fits are generally welldefined, with more than 90% having rms deviations from the observedprofile of less than 0.35 mag. We find no correlations of fittingquality, as measured by these rms residuals, with either morphologicaltype or inclination. Similarly, the estimated errors of the fittedcoefficients show no significant trends with type or inclination. Thesedecompositions form a useful basis for the study of the lightdistributions of spiral and lenticular galaxies. The object base issufficiently large that well-defined samples of galaxies can be selectedfrom it. The Southern Sky Redshift SurveyWe report redshifts, magnitudes, and morphological classifications for5369 galaxies with m_B <= 15.5 and for 57 galaxies fainter than thislimit, in two regions covering a total of 1.70 sr in the southerncelestial hemisphere. The galaxy catalog is drawn primarily from thelist of nonstellar objects identified in the Hubble Space TelescopeGuide Star Catalog (GSC). The galaxies have positions accurate to ~1"and magnitudes with an rms scatter of ~0.3 mag. We compute magnitudes(m_SSRS2) from the relation between instrumental GSC magnitudes and thephotometry by Lauberts & Valentijn. From a comparison with CCDphotometry, we find that our system is homogeneous across the sky andcorresponds to magnitudes measured at the isophotal level ~26 magarcsec^-2. The precision of the radial velocities is ~40 km s^-1, andthe redshift survey is more than 99% complete to the m_SSRS2 = 15.5 maglimit. This sample is in the direction opposite that of the CfA2; incombination the two surveys provide an important database for studies ofthe properties of galaxies and their large-scale distribution in thenearby universe. Based on observations obtained at Cerro TololoInter-American Observatory, National Optical Astronomy Observatories,operated by the Association of Universities for Research in Astronomy,Inc., under cooperative agreement with the National Science Foundation;Complejo Astronomico El Leoncito, operated under agreement between theConsejo Nacional de Investigaciones Científicas de laRepública Argentina and the National Universities of La Plata,Córdoba, and San Juan; the European Southern Observatory, LaSilla, Chile, partially under the bilateral ESO-ObservatórioNacional agreement; Fred Lawrence Whipple Observatory;Laboratório Nacional de Astrofísica, Brazil; and the SouthAfrican Astronomical Observatory. Global Relationships Among the Physical Properties of Stellar Systems.Abstract image available at:http://adsabs.harvard.edu/cgi-bin/nph-bib_query?1997AJ....114.1365B&db_key=AST X-ray emission from supernovae: a review of the observationsSupernovae have been expected to be X-ray sources for many years. Thispaper reviews the observations that have detected or placed upper limitson the emission of X-rays from supernovae. A firm upper limit exists onX-ray emission from Type Ia supernovae based upon an observation of SN1992A (parent galaxy: NGC 1380) made with ROSAT. The type II supernovaedetected include SN 1978K (NGC 1313), SN 1980K (NGC 6946), SN 1986J (NGC891), SN 1987A (Large Magellanic Cloud), and, most recently, SN 1993J(NGC 3031). These supernovae emit X-rays by at least two differentprocesses. The author briefly reviews the proposed emission mechanisms. A comparative study of morphological classifications of APM galaxiesWe investigate the consistency of visual morphological classificationsof galaxies by comparing classifications for 831 galaxies from sixindependent observers. The galaxies were classified on laser print copyimages or on computer screen using scans made with the Automated PlateMeasuring (APM) machine. Classifications are compared using the RevisedHubble numerical type index T. We find that individual observers agreewith one another with rms combined dispersions of between 1.3 and 2.3type units, typically about 1.8 units. The dispersions tend to decreaseslightly with increasing angular diameter and, in some cases, withincreasing axial ratio (b/a). The agreement between independentobservers is reasonably good but the scatter is non-negligible. In spiteof the scatter, the Revised Hubble T system can be used to train anautomated galaxy classifier, e.g. an artificial neural network, tohandle the large number of galaxy images that are being compiled in theAPM and other surveys. A Preliminary Classification Scheme for the Central Regions of Late-Type GalaxiesThe large-scale prints in The Carnegie Atlas of Galaxies have been usedto formulate a classification scheme for the central regions oflate-type galaxies. Systems that exhibit small bright central bulges ordisks (type CB) are found to be of earlier Hubble type and of higherluminosity than galaxies that do not contain nuclei (type NN). Galaxiescontaining nuclear bars, or exhibiting central regions that are resolvedinto individual stars and knots, and galaxies with semistellar nuclei,are seen to have characteristics that are intermediate between those oftypes CB and NN. The presence or absence of a nucleus appears to be auseful criterion for distinguishing between spiral galaxies andmagellanic irregulars. Quantitative Morphology of Bars in Spiral GalaxiesAs suggested by numerical simulations, the axis ratio of the bar is afundamental parameter to describe the dynamical evolution of a barredgalaxy. In a first-order approximation considering bars as ellipticalfeatures, visual measurements of bar axis ratios and lengths of 136spiral galaxies were performed on photographs of good linear scale.Despite the limitations affecting such measurements, morphologicalproperties of bars in spirals along the Hubble sequence as well as therelationship between the bar axis ratio and nuclear star formationactivity are studied. It is found that the relative length of bars inearly-type galaxies is, on average, about a factor of 3 larger than thelength observed in late-type spirals. Also, a relation between barlengths and bulge diameters is observed for both early-type andlate-type spirals, confirming results from previous works. Furthermore,although the number of objects is small, there is an apparentcorrelation between the presence of nuclear star formation activity andthe bar axis ratio: about 71% of the starburst galaxies included in thesample have a strong bar (b/a < 0.6). The introduction of thesequantitative parameters in galaxy classification schemes is discussed. A radio continuum survey of Shapley-Ames Galaxies at λ2.8 cm. I. Atlas of radio data.We present measurements of the radio continuum emission at λ2.8cm of a nearly complete sample of spiral galaxies. The sample consistsof the Shapley-Ames galaxies north of δ=-25deg and brighter thanB_T_=+12. The large, nearby galaxies were not observed during thesurvey, but measured with high sensitivity in individual projects. Theradioweak galaxies were also excluded. The observational results and thederived flux densities are given and compared with that of otherobservations. Pecularities of the radio emission of individual galaxiesare discussed. The nuclear 10 micron emission of spiral galaxiesWe examine the 10 micrometer(s) emission of the central regions of 281spiral galaxies, after having compiled all ground-based, small-aperture(approximately 5 sec) broad-band photometric observations at lambdaapproximately 10 micrometer(s) (N magnitudes) published in theliterature. We evaluate the compactness of the approximately 10micrometer(s) emission of galaxy nuclei by comparing these small-beammeasures with the large-beam IRAS 12 micrometer(s) fluxes. In theanalysis of different subsets of objects, we apply survival analysistechniques in order to exploit the information contained in 'censored'data (i.e., upper limits on the fluxes). Seyfert galaxies are found tocontain the most powerful nuclear sources of mid-infrared emission,which in approximately one-third of the cases provide the bulk of theemission of the entire galaxy; thus, mid-infrared emission in the outerdisk regions is not uncommon in Seyfert galaxies. The 10 micrometer(s)emission of Seyfert galaxies appears to be unrelated to their X-rayemission. H II region-like nuclei are stronger mid-infrared sources thannormal nuclei and LINER nuclei (whose level of emission is notdistinguishable form that of normal nuclei). Interacting objects have,on average, greater 10 micrometer(s) luminosities than noninteractingones and exhibit more compact emission. Early-type spirals have strongerand more compact 10 micrometer(s) emisison than late-type ones. Barredspirals are brighter at approximately 10 micrometer(s) than unbarredsystems, essentially because they more frequently contain H IIretion-like nuclei. The results of our detailed comparison between thebehavior of various categories of objects stress that the 10micrometer(s) emission of spiral nuclei is closely linked to the(predominantly nonthermal synchrotron) radio emission. On the size and formation mechanism of the largest star-forming complexes in spiral and irregular galaxiesThe average diameters of the largest star complexes in most of thespiral and irregular galaxies in the Sandage and Bedke Atlas of Galaxieswere measured from the Atlas photographs. The complex diametersDc correlate with galaxy magnitude as Dc = 0.18 -0.14MB, which has about the same slope as the correlation forthe largest H II regions studied by Kennicutt. There is no obviouscorrelation between Dc and either Hubble type or spiral armclass at a given magnitude. The variation of Dc withMB closely matches the expected variation in thecharacteristic length of the gaseous gravitational instabilityconsidering that the rotation curve varies with MB and thatthe stability parameter Q is about 1 in the outer regions of the disk.This match corresponds to an effective velocity dispersion of 6.1 km/sthat is about the same for all spiral and irregular galaxies. High frequency radio continuum observations of barred spiral galaxies.Abstract image available at:http://adsabs.harvard.edu/cgi-bin/nph-bib_query?1993RMxAA..25...31G&db_key=AST The extended 12 micron galaxy sampleWe have selected an all-sky (absolute value of b greater than or equalto 25 deg) 12 micron flux-limited sample of 893 galaxies from the IRASFaint Source Catalog, Version 2 (FSC-2). We have obtained accurate totalfluxes in the IRAS wavebands by using the ADDSCAN procedure for allobjects with FSC-2 12 micron fluxes greater than 0.15 Jy and increasingflux densities from 12 to 60 microns, and defined the sample by imposinga survey limit of 0.22 Jy on the total 12 micron flux. Its completenessis verified, by means of the classical log N - log S andV/Vmax tests, down to 0.30 Jy, below which we have measuredthe incompleteness down to the survey limit, using the log N - log Splot, for our statistical analysis. We have obtained redshifts (mostlyfrom catalogs) for virtually all (98.4%) the galaxies in the sample.Using existing catalogs of active galaxies, we defined a subsample of118 objects consisting of 53 Seyfert 1s and quasars, 63 Seyfert 2s, andtwo blazars (approximately 13% of the full sample), which is the largestunbiased sample of Seyfert galaxies ever assembled. Since the 12 micronflux has been shown to be about one-fifth of the bolometric flux forSeyfert galaxies and quasars, the subsample of Seyferts (includingquasars and blazars) is complete not only to 0.30 Jy at 12 microns butalso with respect to a bolometric flux limit of approximately 2.0 x10-10 ergs/s/sq cm. The average value of V/Vmaxfor the full sample, corrected for incompleteness at low fluxes, is 0.51+/- 0.04, expected for a complete sample of uniformly distributedgalaxies, while the value for the Seyfert galaxy subsample is 0.46 +/-0.10. We have derived 12 microns and far-infrared luminosity functionsfor the AGNs, as well as for the entire sample. We extracted from oursample a complete subsample of 235 galaxies flux-limited (8.3 Jy) at 60microns. The 60 micron luminosity function computed for this subsampleis in satisfactory agreement with the ones derived from the brightgalaxy sample (BGS) and the deep high-galactic latitude sample, bothselected at 60 microns. General study of group membership. II - Determination of nearby groupsWe present a whole sky catalog of nearby groups of galaxies taken fromthe Lyon-Meudon Extragalactic Database. From the 78,000 objects in thedatabase, we extracted a sample of 6392 galaxies, complete up to thelimiting apparent magnitude B0 = 14.0. Moreover, in order to considersolely the galaxies of the local universe, all the selected galaxieshave a known recession velocity smaller than 5500 km/s. Two methods wereused in group construction: a Huchra-Geller (1982) derived percolationmethod and a Tully (1980) derived hierarchical method. Each method gaveus one catalog. These were then compared and synthesized to obtain asingle catalog containing the most reliable groups. There are 485 groupsof a least three members in the final catalog. Groups of galaxies within 80 Mpc. II - The catalogue of groups and group membersThis paper gives a catalog of the groups and associations obtained bymeans of a revised hierarchical algorithm applied to a sample of 4143galaxies with diameters larger than 100 arcsec and redshifts smallerthan 6000 km/s. The 264 groups of galaxies obtained in this way (andwhich contain at least three sample galaxies) are listed, with the looseassociations surrounding them and the individual members of eachaggregate as well; moreover, the location of every entity among 13regions corresponding roughly to superclusters is specified. Finally,1729 galaxies belong to the groups, and 466 to the associations, i.e.,the total fraction of galaxies within the various aggregates amounts to53 percent. Radio absorption by the intergalactic mediumThe IR-radio correlation for spiral galaxies has been used to measurethe absorption of 1.49-GHz radiation by the intergalactic medium.Contrary to conventional expectation, a sample of 237 galaxies shows astrong inverse correlation of radio luminosity (at a given IRluminosity) with distance, Lr = about D exp -0.4, over a range ofdistances from 0.7-55 Mpc. Strong absorption by the intergalactic mediumis the only reasonable explanation of this correlation, which isstatistically significant at the 2 x 10 to the -12th level. Lunar occultations of IRAS point sources, 1991-2000Abstract image available at:http://adsabs.harvard.edu/cgi-bin/nph-bib_query?1989ApJS...69..651C&db_key=AST Nearby starburst galaxiesNew multiaperture 1.65 and 2.2 micron photometry has been obtained forthe central region of 132 spirals exhibiting 2 orders of magnitude rangein central 10-micron luminosity. This multiaperture photometry alongwith optical spectroscopy indicated that the high central 10-micronluminosity (about 1 billion solar luminosities) is powered by massivestar formation for the majority of the sample objects. The large database has led to the definition of a complete sample of nearby M82-typestarburst galaxies, the majority of which have not previously beenrecognized as such. Observation results and comparison with starburstmodels support the interpretation that, as in the case of M82, redsupergiants dominate the central 2.2-micron luminosity of the starburstsin this sample. The starburst galaxy M82 is anomalous in comparison withthe starburst galaxies identified in this study, by virtue of a rarehigh central 2.2-micron luminosity. Revised supernova rates in Shapley-Ames galaxiesObservations of 855 Shapley Ames galaxies made from November 1, 1980 toOctober 31, 1988, together with improved supernova luminosities, havebeen used to derive the frequency of supernovae of different types, andthe results are presented in tables. From a uniform database of 24supernovae discovered, the following SN rates are found, expressed in SNper century per 10 to the 10th L(B)(solar): SN Ia, 0.3; SN Ib, 0.3; andSN II, 1.0. The present data confirm the relatively high frequency of SNII in late-type galaxies that has been found by many previousinvestigators.
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# American Institute of Mathematical Sciences

March  2006, 6(2): i-ii. doi: 10.3934/dcdsb.2006.6.2i

## Introduction

 1 Mathematics Department, South Hall, Room 6707, University of California, Santa Barbara, CA 93106, United States

Published  December 2005

The field of Materials Science is probably the most multidisciplinary among the scientific and engineering fields. It relates to the study of matter, and the interactions between the atomic, nano-, micro-, and meso- scales. The focus of this special issue of DCDS-B is the modeling, analysis, and simulations of complex systems in Materials Science. In this special issue we present a collection of articles covering a wide range of topics, including ferromagnetism, liquid crystals, electro-kinetic hydrodynamics, and mass transport, among others. The articles have undergone a rigorous peer review process.
Understanding processes such as crack propagation and fracture, or solid state phase transformations is fundamental in the manufacture of new metallic alloys, designed for specific applications. In many cases, this involves relating the desired properties of a material, to the structure of the atoms and phases in that material. Advances in this area are presented in the papers of Alikakos, Bates, Cahn, Fife, Fusco, and Tanoglu, and in the paper of Yu and Du.

Citation: Carlos J. Garcia-Cervera. Introduction. Discrete & Continuous Dynamical Systems - B, 2006, 6 (2) : i-ii. doi: 10.3934/dcdsb.2006.6.2i
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## Bounds for a spectral exponential sum

Journal of London Mathematical Society. 2019. Vol. 99. No. 2. P. 249-272.
Balkanova O., Frolenkov D.

We prove new upper bounds for a spectral exponential sum by refining the process by which one evaluates mean values of L-functions multiplied by an oscillating function. In particular, we introduce a method which is capable of taking into consideration the oscillatory behaviour of the function. This gives an improvement of the result of Luo and Sarnak when $T\geq X^{1/6+2\theta/3+\epsilon}$. Furthermore, this proves the conjecture of Petridis and Risager in some ranges. Finally, this allows obtaining a new proof of the Soundararajan-Young error estimate in the prime geodesic theorem.


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# restricting range of motion with complex constraints

I am looking for a way to restrict a robot's range of motion, using complex constraints such as not tearing of a cable attached to the robot.

Take an articulated 6-axis robot arm as shown below, with attached cable (red), fixed at points X (before axis A4) and Y (after axis A6).

The cable will limit the range of movement for the robot. It can stretch and bend only to some extend, but something like a full 360° turn of axis A4, with all other axes remaining as they are in the picture, will tie the cable around the arm and rip it off.
If joint A5 is at 0°, then A4 and A6 can still move the full 360°, but they cannot diverge too much from each other, as that would twist the cable. If A5 is tilted, the relationship becomes even more complicated.

How can you express such a constraint?

It is not a simple joint constraint, where you can independently limit the range of the joints, and it is also not a positional constraint, where you can define a region the robot must not enter. Checking a start and a goal posture is not sufficient, since along the path from start to goal posture there may still be a posture that puts too much strain on the cable.

Without limiting the robot to a small set of pre-tested paths, how can you limit the robot to movements that will not rip off the cable?

What are the standard techniques used for this sort of problem?

• Rather than trying to model this, you may be better off routing your cable such that it passes through each axis of rotation, that way as long as you have enough slack for each axis individually, you will have enough slack for any combination of axes. Alternatively, check that you can't get higher grade cable which is more flexible, or even replace a monolithic inflexible cable with a set of more flexible (possibly even coiled) cables. – Mark Booth Nov 12 '12 at 13:13
• I did a similar simulation using SolidWorks: youtube.com/watch?v=crJXUlzJ918 – LCarvalho Feb 19 '18 at 20:04

I'm not sure what the "standard techniques" are but here are a few a ideas.

You could combine a standard jacobian based forward kinematics as your base and then apply a gradient search algorithm where you bias against things such as running out of slack on your wires or other constraints.

Let me go into a bit more detail. Generally you would just give the robotic arm an end point or perhaps a series of way points you expect the end effector to follow. You can then use the forward kinematics to move the end effector between those waypoints. In order to avoid your difficult to describe constraint you can define a value function that increases as the arm moves and reduces the slack on the wire.

If you guess a path most likely your initial guess will result in the wire being snapped. You're value function will give you a score representing this fact. You can then take your in between waypoints and tweak them a bit which may result in the function represetnging your constraint to decrease. You then can tweak it a bit more reducing it further.

This is called optimization and it is a huge field but the most basic method is Gradient Descent. In theory if you use an optimization method to adjust your waypoints eventually you will find a set of waypoints that keeps the tension along the wire to a minimum. The main issue with optimization is that you can run into local minimums that don't adequately solve your problem, and methods that get around local minimums can be slow.

If you are looking to get academic about it you may want to look into the algorithm presented here. Keep in mind there might not be a simple answer to your question or a simple algorithm to solve it.

The cable has a certain amount of slack, and each joint it goes over takes up some of that slack as a function of the joint angle. The cable will break if the total amount of slack taken up by all joints exceeds the available slack.

Firstly, it would help if you could thread the cable along the robot in such a way that that each joint has a calculable effect on the slack, and each joint affects the cable independently. This will really help the calculations. Let's define the amount of slack taken up by joint 1 at an angle th1 by:

f1(th1)

Now, your robot arm can move anywhere such that

f1(th1) + f2(th2) + ... + f6(th6) <= total_slack

This assumes that the cable is fixed in such a way that it can slide up and down the robot arm, distributing the slack where it's needed. Generally, this isn't the case in most robots, and isn't easy to do.

A more reliable way to solve this problem is to rigidly fix the cable before and after each joint, and make sure there's enough slack for every joint.

Don't fix it in software. Fix it in your mechanical cable harness design. If you try to do it in software, your robot will inevitably break itself. Even if it never exceeds its limits, using a cable this way will wear it out quickly.

Trying to share the slack between joints will require sliding movement of the cable, which will damage the cable over time by abrasion and by repeatedly bending the cable around tight radii when you get close to overall limits. Sharing the slack means you'll have to attach the cable loosely, which will make it tend to flop around, encouraging you to leave even less slack.

Instead, fix the cable along each segment of the robot and treat each joint individually. Ensure that the cable bend radius is greater than 10X its diameter (more is better) over the entire range of motion of each joint. If this requires a large loop, then there are chain cable guides (looks a bit like wide plastic bicycle chain) to help keep it from getting in the way of other parts.

• Sorry Theran, but this answer doesn't actually answer the question, which is why I kept my similar suggestion as a comment. – Mark Booth Nov 15 '12 at 0:33


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Mobile QR Code

1. (Department of Computer Engineering, Kwangwoon University / Seoul, Korea {zhsjzhsj, jw03070, hyunsoo940324, cndtjq97}@gmail.com, parkcheolsoo@kw.ac.kr )

Sleep stage, Attention, Explainable artificial intelligence, Electroencephalogram, Signal decompose

## 1. Introduction

Recent studies have shown that sleep disorder patients continue to increase [1], and people with sleep disorders are more vulnerable to various motor diseases [1] or disorders such as Alzheimer's disease [3,4]. The most important part for the treatment of sleep disorders is estimation of the sleep stages. Based on the sleep stages, a doctor can determine the overall sleep quality and amount and then conduct a sleep diagnosis based on the analysis. However, the estimation of sleep stage can only proceed with the approach of manual sleep estimation by sleep specialists at a medical site [5], which results in high costs of economic resources and time. In addition, recent research reports that sleep experts’ sleep stage estimation results vary wildly from one to another [6] since the sleep stage is determined by their subjective judgment.

To solve these problems, various methods of estimating the sleep stages using neural networks have recently been proposed. Supratak et al. [7] proposed a sleep stage automation architecture using convolution network layers [8] and long short-term memory (LSTM) cells [8]. However, there is a black-box problem of not being able to explain the resulting process due to the use of a deep neural network [9]. Results reported for 21 limited subjects have led to insufficient generalization performance and validity of the model.

In 2019, Mousavi et al. [10] proposed a sleep stage estimation model using a convolutional neural network (CNN) and bidirectional recurrent neural network (Bi-RNN) [8] for seq2seq learning [11,16]. The results showed a slight improvement in accuracy compared to a previously proposed method, but the problem of using only a small number of subjects was not solved. Also, the black-box problem was further complicated because they used multiple Bi-RNNs.

The medical community is increasingly demanding explainable artificial intelligence (XAI) [12,13]. Thus, an explainable artificial neural network model should be proposed for medical decision support systems. However, no previously proposed neural networks [7,10] at an explainable level have been proposed for sleep automation models through artificial neural network algorithms.

In this study, we propose a deep neural network-based explainable model that estimates five sleep stages based on an EEG band component. The attention layer in the model updates its attention weights to learn which EEG band components are useful for an estimate of a specific sleep stage. Through these methods, the model learns from multiple -decomposed EEG band components, which sleep specialists suggest may help to estimate more accurate sleep stages [27,30]. In addition to these proposals, we demonstrate the generalization performance and increase the robustness of the model by using at least five times more subject data (80 subjects) than previous studies [7,10].

## 2. Methodology

### 2.1 Training Data

A previously proposed sleep stage estimation model has been taught primarily using EEG signals recorded in Fpz-Cz or Pz-Oz configurations [7,10]. However, the corresponding EEG channel is not easy to measure and is reported to be noisy [17]. In comparison, brain waves of F3-M2 channels measured by attaching electrodes directly to the epidermis have the advantage of relatively low noise and robustness to external signals. In this study, we used 5-level (Wake, REM, N1, N2, and N3) sleep stage labels that were estimated every 30 seconds. This was based on data from a single EEG channel for 80 participants’ data with the F3-M2 channel configuration among the EEG data measured in polysomnography (PSG) in the Wisconsin Sleep Cohort sleep dataset [18,19]. To eliminate unnecessary ambient noise in the F3-M2-channel EEG signal, a bandpass filter with a 500 orders in a frequency range between 0.5 Hz and 55 Hz was applied [20]. Additionally, notch filters were applied at 60 Hz to reduce the multiple powerline noise [21].

### 2.2 Signal Decomposition

In order to decompose an EEG signal, many decomposition methods have been proposed, such as fast Fourier transform (FFT) [22] and wavelet transform (WT) methods [23,24]. In this study, to obtain the weight for a particular EEG band component, a signal decomposition through an FIR band-pass filter was applied to a single-channel EEG to train a model based on conventional knowledge of sleep staging reported by sleep experts. This band-pass filter is a zero-phase band-pass filter with 500 orders. A different band-pass filter was designed for each EEG frequency component.

The beta frequency component was extracted from a single-channel EEG at 13-38 Hz, the alpha component was extracted at 8-12 Hz, the theta component was extracted at 4-7 Hz, and the delta component was extracted at 1-3 Hz [14]. We used these extracted brainwave components to estimate sleep stages, which have been used by sleep experts as well [27-30]. Model parameters were then updated through these decomposed signal data.

### 2.2 Model Specifications

The attention layer [16] is located before the overall input of the model architecture, as shown in Fig. 1. How much each signal component is learned is reflected in the final label, which ranges from -1.0 to +1.0. The model can analyze the attention weight for each post-learning estimation stage by the attention layer structure. After learning the input data, analysis of the channel weights of the relevant attention layer allows us to analyze which EEG frequency component the model relies on more for estimating specific sleep stages.

To predict the sleep stage through the EEG data, the model performs representation learning and temporal learning. In the representation learning parts, 30 seconds of decomposed EEG signals were given as input to the 1D convolutional neural network (CNN). Small neural networks for each EEG frequency component were designed and constructed by a 1D CNN [8] while pooling layers eight times. Based on this structure, features for each EEG component can be learned appropriately for the input data for each CNN structure.

To learn about the time series elements of EEG data, temporal learning is proposed through Bi-GRU. Bi-GRU enables the learning of an epoch time unit with every 10 time steps for single-channel EEG information for 30 seconds of a sleep EEG signal. A model with this structure can learn sequential information in the time domain and extract features from the input EEG frequency components. The model hyper parameter is optimized based on previous sleep stage estimation algorithms [7,10], and parameters are updated by an adaptive moment estimation (ADAM) optimizer [8].

## 3. Results

To evaluate the proposed model, performance measurement of the model was performed using the F1-score [26], the most used indicator in sleep stage analysis [7,10]. The results of 5-fold cross-validation [25] of the sleep stage estimation model for 80 subjects’ single-channel EEG sleep data with the proposed method are shown in Table 2. Cross-validation of the model showed that the average F1-score was 70.61% (${\pm}$26.34).

To analyze the major EEG components from the EEG decomposition using the model in estimating sleep stages, attention weights were used to calculate which EEG frequency components have more weights to track their update rate for each sleep stage. The attention-weight analysis demonstrates the channel-specific attention weights used to estimate each sleep stage among the five sleep-stage estimation labels, as shown in Fig. 2. The results in Fig. 2 confirm the previous findings that different valid EEG signal components are used to estimate the sleep stages [27-30]. In light sleep, such as N1 and N2, and REM, high-frequency components have more attention rates, as shown in Fig. 2. This shows that the model estimates the sleep stage mainly based on the high-frequency components, whereas deep sleep (N3) shows more attention weight in the low-frequency component, showing that deep sleep is estimated mainly based on the low-frequency component.

## 4. Discussion

Analysis of the attention weights of the proposed model confirms that the model learns through different channel weights corresponding to different EEG components. This aligns with previous knowledge for sleep stage estimation that different brainwave components are used for estimating different sleep stages [28-30]. The proposed model has more clearly described which component of the EEG is more useful to detect specific sleep stages based on the selective focus learned from different frequency components of the input data to estimate a particular sleep stage. In addition, the results for the attention weights of each EEG component show similar results to those of useful EEG components reported in previous papers analyzing sleep stage through EEG channels. This shows the model might learn how to evaluate the sleep stage around the low-frequency component for deep sleep and the high-frequency component for light sleep, similar to what sleep specialists do for their estimation of the sleep stages.

Furthermore, considering the characteristics of sleep-stage estimation models, which show worse performance as the number of subjects increases, the model still shows similar F1-scores, even when subject numbers increase by more than 5 times compared to previously reported sleep-stage estimation algorithms. This implies that attention mechanisms in sleep stage analysis could be effective in estimating the sleep stages and designing an explainable model.

## 5. Conclusion

In this paper, we proposed an explainable artificial intelligence model centered on brainwave components through attention algorithms. Based on that model, we were able to show which brainwave components were useful in predicting specific sleep stages, and the accuracy was also similar to that of previously proposed sleep stage estimation models. However, the problem of chronic sleep stage estimation bias caused by an imbalance of the sleep stage can be still observed. This is particularly shown in the N1 sleep stage.

To solve this problem, we propose increasing the learning volume through the synthetic minority oversampling technique (SMOTE) [31] and data augmentation [32] to improve the performance of estimating the N1 sleep stage. In addition, there are still some black-box problems because the level of the explainable model cannot show the learning progress by time frame. To solve this problem, we propose a study of a deeper-level explainable model through time-unit attention [33].

### ACKNOWLEDGMENTS

This research was supported by the MIST(Ministry of Science and ICT), under the National Program for Excellence in SW (2017-0-00096), supervised by the IITP(Institute for Information & communications Technology Promotion )

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## Author

##### Suwhan Baek

Suwhan Baek received his B.S. degree in Computer Engineering from Kwangwoon University in Seoul, South Korea. His research interests include overall Medical AI and Auto ML(ENAS). He is also attracted by reinforcement learning, generative models.

##### Jaewoo Baek

Jaewoo Baek is in the MSc Program at the Bio Computing & Machine Learning Laboratory (BCML) in the Department of Computer Engineering at Kwangwoon University, Seoul, Republic of Korea. His research interests include biological signal processing, machine learning, deep learning, and reinforcement learning

##### Hyunsoo Yu

Hyunsoo Yu received his BS degree in Robotics Engineering from Kwangwoon University in Seoul, South Korea. His reasearch interests include in experimental setting, signal processing, machine learning and artificial Intelligence.

##### Choongseop Lee

Choongseop Lee received his BS degree in Computer Software and Engineering from Kwangwoon University in Seoul, South Korea. His reasearch interests include machine learning and computational neuroscience.

##### Cheolsoo Park

Cheolsoo Park is an associate professor in the Computer Engineering Department at Kwangwoon University, Seoul, South Korea. He received a B.Eng. in Electrical Engineering from Sogang University, Seoul, and an MSc from the Biomedical Engineering Department at Seoul National University, South Korea. In 2012, he received his PhD in Adaptive Nonlinear Signal Processing from Imperial College London, London, U.K., and worked as a postdoctoral researcher in the Bioengineering Department at the University of California, San Diego, U.S.A. His research interests are mainly in the areas of machine learning and adaptive and statistical signal processing, with applications in brain computer interfaces, computational neuroscience, and wearable technology.