Lorem ipsum dolor sit amet, consectetur adipiscing elit

Mattis Vestibulum1, Fringilla Pretium2, Velit Lobortis3*

1 Université One, Country_A

2 Université Two, Country_B

3 Université Three, Country_C

*Corresponding author: Velit Lobortis vloborti@mail.a

Abstract

Sed non est ac erat varius mattis vestibulum tristique massa. Fusce leo elit, volutpat non massa nec, semper iaculis enim. Praesent commodo ante nec arcu dapibus dapibus commodo sit amet tellus. Donec auctor a ante non semper. Interdum et malesuada fames ac ante ipsum primis in faucibus. Nunc cursus dolor vitae massa fringilla semper. Ut nisl purus, porta id pretium tempus, porttitor sit amet felis. Maecenas nec leo faucibus, accumsan leo quis, rhoncus ante. Morbi a libero mollis, pellentesque tellus vel, consequat justo. Interdum et malesuada fames ac ante ipsum primis in faucibus..

keywords

magna condimentum; vel ligula; elementum

INTRODUCTION

Sed eu tempor ipsum, vel cursus arcu. Maecenas non dignissim nunc, ac ornare tortor. Aenean pretium arcu metus, id pulvinar enim tempus nec. Mauris faucibus mollis sodales. Sed porttitor sed metus vitae vestibulum. Quisque a vehicula nunc. Aenean fringilla condimentum diam, ac gravida quam. Integer ultrices feugiat enim nec tempus. Vestibulum ornare in magna ultrices dapibus. Nulla facilisi.

I TITLE

1.1 Subtitle

Pellentesque dignissim ultrices fringilla. Vivamus eu luctus ante, vel bibendum magna. Curabitur elit purus, tincidunt non dui vitae, elementum bibendum neque. Curabitur ullamcorper sit amet justo at hendrerit. Fusce ut arcu imperdiet nibh mollis tempus a aliquet tellus. Quisque pharetra cursus nisi, vel lobortis ante consectetur et. Vivamus sed congue neque. Proin pellentesque risus nec dui consequat rutrum. Vestibulum nunc diam, placerat quis auctor vel, faucibus non justo. Etiam dictum purus neque. Phasellus imperdiet mauris ligula, eu laoreet nisi elementum ut. Sed sed porta massa. Aenean faucibus risus ultrices ornare porta. Quisque faucibus ante a tincidunt vestibulum. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

1.3.1 Sub-subtitle

Suspendisse vel dui nec felis molestie tincidunt. Vestibulum rutrum ligula lacus, ac molestie nulla fermentum ornare. Nulla non nunc euismod, porta lacus vestibulum, malesuada massa. Curabitur massa eros, rutrum sed lectus sed, volutpat semper metus. Mauris hendrerit aliquam commodo. Vivamus fermentum tempus pellentesque. Maecenas a hendrerit urna. In elit ipsum, ultrices non dolor in, pulvinar porttitor lacus. Nunc euismod nibh quis odio condimentum, a feugiat massa rutrum. Nulla erat erat, adipiscing vitae lectus id, consectetur fermentum elit. Nunc eu est eu neque dapibus semper. Nam commodo urna dapibus, tincidunt turpis a, cursus sem. Vivamus venenatis adipiscing mollis. Cras fringilla sodales lobortis. Aliquam aliquet felis id est cursus auctor. Duis sodales tellus vulputate lectus egestas volutpat.

II TABLES AND FIGURES

2.1 Table

Sepal.Length	Sepal.Width	Petal.Length	Petal.Width
Setosa	5.006	3.428	1.462	0.246
Versicolor	5.936	2.770	4.260	1.326
Verginica	6.588	2.974	5.552	2.026

Table 1. Morbi malesuada diam at magna condimentum.

2.2 Figure

Figures should be like below (numbered, and finished with a dot). Try to regroup them in one slot for resembling ones. Make pictures with a good definition, as possible closed to 300 dpi (dot per inch). With readable legends in English for each axis.

Figure 1. Fusce luctus purus a augue ullamcorper.

III DEFINITIONS, ALGORITHMS AND FORMULAs

3.1 Definition

Definition 1: alpha

Curabitur ullamcorper sit amet justo at hendrerit.

Hence we set the following definition:

Definition 2: beta

Etiam sed nulla viverra, ultrices ligula ac, consectetur libero.

If you used a list, you need to write a bulleted list :

Nunc id justo scelerisque.

metus id enim iaculis tristique.

3.2 Formula

Example of formula :

(1)

where <M>l is a mean vector of a line from M. plays as a regulation factor to regulate the rate of nearest neighbours, in fact the number of nearest neighbours is not defined explicitly.

Another example of formula :

(2)

3.3 Algorithm

This is an algorithm :

Nearest-Neighbour Algorithm

Input:

M: a sparse matrix terms x documents, with dim(M)=(n,m) such that M[i,j] is the number of occurrences of a term i in the document j,

Min: minimum frequency

Max: maximum frequency

Beta: scaling factor

Binary: 0 if real data, 1 if data are binary

Output: Layout in 2-Dimensions

# layout with Fruchterman algorithm

Create a vector V, with dim(V)=n such that RowSums(M[i,])<= Max and RowSums(M[i,]) >= Min

M’ = M[V > 0]

Create is a matrix terms x terms: TD = M’ * t(M’)

Compute Vm the mean vector by line with dim(Vm) = n such that Vm[i] = mean(M[i,]) with M[i,j] =/= 0 for all j

IF Bin = 1	Make scaling operation, TD_norm = TD – Beta*Vm

TD_ norm = TD_norm>=0

ELSE	GoodVal = TD >= -1*Beta*Vm & TD <= +1*Beta*Vm

TD_norm = TD * good_value

Binary transform, TD_norm[TD_norm > 0] = 1

Keep positive values TD = TD_norm[ rowSums(TD_norm) > 0]

Compute the mean of links per node, Nb_mean_link = mean(rowSums(TD))

Generate the layout Fruchterman for display with TD as adjacency matrix.

# layout with DRL

Create a vector V, with dim(V)=n such that V[i] <= Max and V[i] >= Min

Create a binary clone M of M, M’ = M[M > 1] <- 1

Create a vector V, with dim(V)=n such that RowSums(M’[i,]) <= Max and RowSums(M’[i,]) >= Min

M’’ = M[V > 0]

Create matrix terms x terms: TD = M’’ * t(M’’)

TD’ = TD [TD > 1] <- 1

Compute Vm the mean vector by line with dim(Vm) = n such that Vm[i] = mean(TD’[i,]) with TD’[i,j] =/= 0 for all j

IF Bin = 1	Make scaling operation, TD_norm = TD’ – Beta*Vm

TD_pos = TD_norm>=0

ELSE	GoodVal = TD’ >= -1*Beta*Vm & TD’ <= +1*Beta*Vm

TD_norm = TD * good_value

Binary transform, TD_norm[TD_norm > 0] = 1

Keep positive values TD = TD_norm[rowSums(TD_norm) > 0]

Compute the mean of links per node, Nb_mean_link = mean(rowSums(TD))

Generate the layout DRL for display with TD as adjacency matrix.

IV REFERENCES AND CITATIONS

From [Sinclair, 1991] we pick up a general definition ...

[Ounis et al, 2000] explain that ...

[Wood and Napel, 1992] recommend ...

4.1 Discussion

Nam id eros massa. Fusce luctus purus a augue ullamcorper, sit amet vehicula mauris tristique. Suspendisse eget pulvinar odio, nec bibendum turpis. Nullam quis lectus porttitor, ullamcorper nisi et, condimentum leo. Quisque sed orci fermentum, rutrum velit eget, ultricies augue. Nunc porttitor consectetur tincidunt. Nulla tincidunt justo enim, vitae dignissim erat mattis ut. Nulla.

Conclusion

Maecenas egestas metus id enim iaculis tristique. Etiam sed nulla viverra, ultrices ligula ac, consectetur libero. Nullam vitae massa ac odio pharetra condimentum. Maecenas in elementum libero, non gravida quam. Praesent adipiscing consectetur consectetur. Vivamus at orci sed augue varius hendrerit. Donec neque metus, dignissim nec erat at, ultricies consequat libero. Donec eget eleifend leo. Aliquam at nunc porta, mollis sapien eu, eleifend tortor. Nam egestas, metus ac pellentesque feugiat, lectus purus ornare est, vitae cursus felis turpis sit amet lacus. Donec consequat massa mi, ac suscipit arcu posuere et. Vivamus et semper risus. Sed ut arcu quam.

References

Hentschel J., Paton J.A., Schneider H. and Heinrichs J. Acceptance of Liochlaena Nees and Solenostoma Mitt., the systematic position of Eremonotus Pearson and notes on Jungermannia L. s.l. (Jungermanniidae) based on chloroplast DNA sequence data. Plant Systematics and Evolution. 2007;268(1-4):147-157.

http1	http://www.biodiversa.org/

http2	http://www.ncbi.nlm.nih.gov/pubmed

Jones S. Antonymy: a corpus-based perspective. Routledge (London), 2002.

Justeson J. and Katz S. Co-occurrence of antonymous adjectives and their contexts. Computational Linguistics. 1991;17(1):1-19.

Ounis A., Cerovic Z.G., Briantais J.M. and Moya I. DE-FLIDAR: a new remote sensing instrument for estimation of epidermal UV absorption in leaves and canopies. Proceedings of EARSeL-SIG-Workshop LIDAR, Dresden/FRG, June 16-17, 2000.

R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2013. http://www.R-project.org.

Sinclair J. Corpus, concordance, collocation. Oxford University Press (Oxford), 1991.

Wood S.L. and Napel S. Artifacts and illusions in surface and volume rendering. Engineering in Medicine and Biology Society, 1992 14th Annual International Conference of the IEEE. 1992;5:2091-2092.

ANNEX 1

Pellentesque dignissim ultrices fringilla. Vivamus eu luctus ante, vel bibendum magna. Curabitur elit purus, tincidunt non dui vitae, elementum bibendum neque. Curabitur ullamcorper sit amet justo at hendrerit. Fusce ut arcu imperdiet nibh mollis tempus a aliquet tellus. Quisque pharetra cursus nisi, vel lobortis ante consectetur et. Vivamus sed congue neque. Proin pellentesque risus nec dui consequat rutrum. Vestibulum nunc diam, placerat quis auctor vel, faucibus non justo. Etiam dictum purus neque. Phasellus imperdiet mauris ligula, eu laoreet nisi elementum ut. Sed sed porta massa. Aenean faucibus risus ultrices ornare porta. Quisque faucibus ante a tincidunt vestibulum. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

ANNEX 2

Cras tristique vel nisi at aliquet. Proin egestas erat sit amet velit lobortis imperdiet. Integer et arcu sapien. Etiam id blandit sapien. Nam tempus lacus ac massa semper, vel laoreet turpis rutrum. Mauris eget nibh vitae justo porta imperdiet sed vel ligula. In imperdiet, augue vel condimentum convallis, neque augue imperdiet neque, eget dapibus nunc mauris ultricies tortor. Nam eget nunc egestas, blandit lectus non, aliquam nunc. Cras sed quam vitae arcu ornare lobortis. Ut ut lacus hendrerit, convallis orci sit amet, commodo nunc. Pellentesque eget tincidunt tortor. Nunc ornare molestie mauris id vehicula. Suspendisse pharetra tortor metus, sit amet fermentum tellus vehicula ut.