text:
Warriors' Klay Thompson uses ocean waters…
Warriors' Klay Thompson uses ocean waters to wash away shooting funk
The Warriors' shooting star says a dip in the Pacific helped to clear his mind
Golden State Warriors guard Klay Thompson, left, and forward Kevin Durant, center, congratulate each other as Clippers guard Patrick Beverley walks by during the second half in Game 4 of a first-round NBA playoff series Sunday, April 21, 2019, in Los Angeles. The Warriors won 113-105. (AP Photo/Mark J. Terrill)
By Kyle Goon | kgoon@scng.com |
PUBLISHED: April 21, 2019 at 5:26 pm | UPDATED: April 21, 2019 at 5:48 pm
Lou Williams (23) of the Clippers moves against of the Golden State Warriors during Game 4 of of the Western Conference first-round series at Staples Center in Los Angeles on Sunday, April 21, 2019. (Photo by Leonard Ortiz, Orange County Register/SCNG)
Kevin Durant (35), right, and Klay Thompson (11) of the Golden State Warriors celebrate after scoring against the LA Clippers during the 4th game of of the Western Conference first-round series at Staples Center in Los Angeles on Sunday, April 21, 2019. (Photo by Leonard Ortiz, Orange County Register/SCNG)
Andre Iguodala (9) of the Golden State Warriors reacts after scoring agains the LA Clippers during the 4th game of of the Western Conference first-round series at Staples Center in Los Angeles on Sunday, April 21, 2019. (Photo by Leonard Ortiz, Orange County Register/SCNG)
Golden State Warriors head coach Steve Kerr signals his players in a game against the LA Clippers during the 4th game of of the Western Conference first-round series at Staples Center in Los Angeles on Sunday, April 21, 2019. (Photo by Leonard Ortiz, Orange County Register/SCNG)
LOS ANGELES — In the halls of the Beverly Hills Montage on Saturday, Steph Curry saw his teammate of eight seasons in a wet T-shirt and sunglasses. The signs were unmistakable: Klay Thompson had just plunged into the Pacific Ocean. "Typical Klay kind of vibe," he said. "I just had a smile on my face […]
Patrick Beverley of the LA Clippers (21) goes for a layup over the defense of the Golden State Warriors during the 4th game of of the Western Conference first-round series at Staples Center in Los Angeles on Sunday, April 21, 2019. (Photo by Leonard Ortiz, Orange County Register/SCNG)
Kevin Durant (35) of the of the Golden State Warriors shoots over Landry Shamet (20) of the LA Clippers during the 4th game of of the Western Conference first-round series at Staples Center in Los Angeles on Sunday, April 21, 2019. (Photo by Leonard Ortiz, Orange County Register/SCNG)
Kevin Durant (35) of the Golden State Warriors celebrates after scoring agains the LA Clippers during the 4th game of of the Western Conference first-round series at Staples Center in Los Angeles on Sunday, April 21, 2019. (Photo by Leonard Ortiz, Orange County Register/SCNG)
Stephen Curry (30), center, of the Golden State Warriors moves to the basked against Jerome Robinson (10) and Montrezl Harrell (5) of the LA Clippers during the 4th game of of the Western Conference first-round series at Staples Center in Los Angeles on Sunday, April 21, 2019. (Photo by Leonard Ortiz, Orange County Register/SCNG)
Danilo Gallinari (8) of the LA Clippers shoots over the defense of Andre Iguodala (9) of the Golden State Warriors during the 4th game of of the Western Conference first-round series at Staples Center in Los Angeles on Sunday, April 21, 2019. (Photo by Leonard Ortiz, Orange County Register/SCNG)
LA Clippers head coach Doc Rivers disagrees with the referees call in a game against the Golden State Warriors during the 4th game of of the Western Conference first-round series at Staples Center in Los Angeles on Sunday, April 21, 2019. (Photo by Leonard Ortiz, Orange County Register/SCNG)
Lou Williams (23) of the LA Clippers battles for the ball with Alfonzo McKinnie (28) of the Golden State Warriors during the 4th game of of the Western Conference first-round series at Staples Center in Los Angeles on Sunday, April 21, 2019. (Photo by Leonard Ortiz, Orange County Register/SCNG)
Lou Williams (23) of the LA Clippers, left, gets his shot deflected by the the Golden State Warriors during the 4th game of of the Western Conference first-round series at Staples Center in Los Angeles on Sunday, April 21, 2019. (Photo by Leonard Ortiz, Orange County Register/SCNG)
Danilo Gallinari (8) of the LA Clippers shoots over Stephen Curry (30) in the first perion of the Golden State Warriors during the 4th game of of the Western Conference first-round series at Staples Center in Los Angeles on Sunday, April 21, 2019. (Photo by Leonard Ortiz, Orange County Register/SCNG)
Warriors guard Klay Thompson shoots the ball over the Clippers' Lou Williams during the second half of Game 4 of their first-round playoff series on Sunday afternoon at Staples Center. (Photo by Yong Teck Lim/Getty Images)
The Clippers' Lou Williams drives to the basket against the Warriors' Kevin Durant during the second half of Game 4 of their first-round playoff series on Sunday afternoon at Staples Center. (Photo by Yong Teck Lim/Getty Images)
LOS ANGELES, CALIFORNIA – APRIL 21: Alfonzo McKinnie #28 of the Golden State Warriors drives to the basket against Montrezl Harrell #5 of the Los Angeles Clippers during the second half of Game Four of Round One of the 2019 NBA Playoffs at Staples Center on April 21, 2019 in Los Angeles, California. NOTE TO USER: User expressly acknowledges and agrees that, by downloading and or using this photograph, User is consenting to the terms and conditions of the Getty Images License Agreement. (Photo by Yong Teck Lim/Getty Images)
Los Angeles Clippers guard Shai Gilgeous-Alexander, right, is hit by Golden State Warriors center Andrew Bogut as he shoots during the first half in Game 4 of a first-round NBA basketball playoff series Sunday, April 21, 2019, in Los Angeles. (AP Photo/Mark J. Terrill)
LOS ANGELES — In the halls of the Beverly Hills Montage on Saturday, Steph Curry saw his teammate of eight seasons in a wet T-shirt and sunglasses.
The signs were unmistakable: Klay Thompson had just plunged into the Pacific Ocean.
"Typical Klay kind of vibe," he said. "I just had a smile on my face when I saw him because I knew what it meant."
It meant that Thompson, five-time All-Star and three-time champion, had decided to try to get his mind right at the beach, one of the places where he truly can recharge. It makes sense for Thompson, who spent several of his formative years growing up in Orange County minutes away from the Southern California shores, to find some clarity there.
In the first three games of the first-round series with the Clippers, the Golden State guard has been averaging 13.7 points on 44.4 percent shooting – not terrible, but not necessarily great either. He felt due for a big game. So when he went out to the beach to play volleyball with Jonas Jerebko and saw the shore, he made a decision that he thought would help.
"I was like, 'Jonas, I'm gonna go jump in the ocean. I just know that will reset my mind,' " he recounted. "And it worked."
Something worked: Thompson's 32 points, including going 6 for 9 from behind the arc, was a crucial piece of the Warriors' 113-105 Game 4 win over the Clippers at Staples Center on Sunday afternoon. His hot shooting helped the team overcome a spotty stretch by Curry, who had just 12 points, and combat the standout performance of Clippers rookie Shai Gilgeous-Alexander, who had 25 points.
It had been long enough since Thompson had been truly hot, which helps vault the Warriors from very good to nearly unbeatable. Coach Steve Kerr said nothing excites his team's bench more than when Thompson – who is famously stingy on dribbles and long touches – starts streaking. He had 27 points by halftime.
As Curry put it: "The Pacific Ocean is undefeated."
Ocean diving aside, the Warriors took other steps to prepare, including one Thompson didn't particularly like: With a 12:30 p.m. tip-off on Sunday, the team scheduled a 9 a.m. practice at Staples Center that stretched on for two hours.
Clippers say players-only film sessions give them needed boost
Kawhi Leonard, Clippers start fast, overwhelm Orlando
Clippers rooting for Markelle Fultz – but not on Thursday
Whicker: When you try to score on Maurice Harkless, it's no walk in the park
Clippers' Kawhi Leonard maintains growing pains are part of the process
A common cautionary tale of teams enjoying Los Angeles nightlife was rumored to be a motivating factor, but Thompson said he thought the Warriors wanted to practice working out in the morning. Not that it made it any more enjoyable.
"I'm not a morning person, so it was a struggle to get up and practice that early," he said. "Felt like I was back at Santa Margarita (High School)."
As the game wore on, the Clippers tried to narrow in on Thompson, particularly during a fourth-quarter stretch without Curry or Kevin Durant on the floor. But during that span, Thompson hit a 3-pointer and a mid-range shot, extending the lead to eight points with nine minutes left, and the Clippers were never again within one possession.
With a 3-1 lead in the best-of-seven series, it's increasingly likely that the Warriors have played their last game in Los Angeles this season, which will limit Thompson's access to temperate waters. But maybe one dip is enough.
"I don't know if I'm going to jump up north because it's freezing, but it's something I'll definitely contemplate if I don't shoot the ball that well the rest of the year," he said. "Hopefully that doesn't happen."
Klay Thompson (17 PTS, 3 3PM) starts 7-7 from the floor in Game 4! #StrengthInNumbers #NBAonABC pic.twitter.com/urBVN8SFVI
— NBA (@NBA) April 21, 2019
Older cars don't get a break when it comes to smog checks
Kyle Goon
Kyle Goon covers the Lakers for the Southern California News Group. Before taking his talents to Los Angeles, he worked for The Salt Lake Tribune for eight years, covering everything from high school rodeo to the Utah Jazz. Gregg Popovich once baptized him by fire in a media scrum.
Follow Kyle Goon	@kylegoon
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You've likely seen photos of the incredible Western Brook Pond fjord before. Maybe you even asked yourself how you could hike the view for yourself.
One of the beautiful valleys you hike through to get to the summit.
3 hour hike for this: Very worth it.
Okay. Don't jump around up there. Or do. But be careful.
The truth is, this view doesn't have a road to the top. No buses in, no gondolas up, no convenient photo perches or glossy pamphlets. The only way to get to this beautiful location is via a hike that requires an advanced fitness level. But, the fact that there are obstacles to getting there makes this view and hike all the more unique.
Your only option to get to this magnificent view top in a day is booking a guided hike with BonTours. After meeting your guide in the Western Brook pond parking lot (route 430) the day begins with a pretty stunning 45-minute walk to the docks of the Western Brook Pond, this tour company shuttles you across the water and drops you off with a guide at the mouth of the fjord. From there, you and your guide hike to the top of the gorge. Along the way your guide can give you a rundown of why this incredible natural attraction exists and give you a bit of info on the history of the area. At the top, you can take a breather and a quick lunch break before heading down to the dock for a return shuttle ride up the pond with the gentle hike back to the parking area.
The entire outing takes about 8 hours, with a 450 meter, 12 kilometre back-country hike over varying terrain to the top that can be very challenging depending on your hiking experience and level of fitness. Hiking through the park's river beds, beneath cliffs, beside waterfalls and within forests and meadows is a rewarding and cool task. But the view itself is what you're after, so bring your camera and keep your mind on the goal.
What: A guided hike up a mountain alongside a landlocked fjord to one of the most epic views in all of Newfoundland and Labrador.
Stats: The distance from the boat drop off to the summit and back is approximately 12 kilometres with an elevation gain of 450 meters.
When: Late June to early September.
What to bring: There's no food, shelter, or water on the trail. As such you need to bring all necessary hiking gear. Pack a waterproof windbreaker as mountain weather is drastically different than weather at lower altitudes. Bring at least one litre of water per person and even more if it's warmer. Lastly, be sure to wear comfortable and sturdy footwear.


We've probably all heard the term glass ceiling, which is used to describe the invisible barriers that prevent women from advancing to senior leadership roles. The general view is that women face this barrier later on in their career. But what if this isn't the case?
Managing Director at Accenture Labs Mary Hamilton joins us on the podcast today for a deeper dive into their report, "Getting To Equal 2018: Spotlight on Young Leaders". Mary will share with us the factors that drive inequality among millennial women. She'll also reveal key actions that leaders and organizations can take to remove these barriers.


text:
Q: Symfony 2 forms - access parameters.ini from the form class I need to access parameters.ini from a form.
Of-course I can access from the controller:
$myVal = $this->container->getParameter('my_val');

Pass it to the controller:
$demoForm = $this->createForm(new demoFormType($myVal), $demoObject);

and in the form to save this value which is passed to the constructor (which, of-course, should be explicitly implemented).
However, this form is used in several controllers, so it would be much cleaner to simply access in some "magic" way from the form class.
Is it possible??? I couldn't find any answer for that.
Thanks!

A: I'm not sure doing it the way you suggest is the best idea.  Forms shouldn't be config-bound like that.
Instead, maybe you could create a new controller to inherit from.
namespace Your\Bundle\Controller;

use Symfony\Bundle\FrameworkBundle\Controller\Controller;
use Your\Bundle\Form\Type\demoFormType;

class FormAwareController extends Controller
{
  protected function createDemoForm( $demoObject )
  {
    $myVal = $this->container->getParameter( 'my_val' );
    return $this->createForm( new demoFormType( $myVal, $demoObject ) );
  }
}

And then, extend this class where you need to
namespace Your\Bundle\Controller;

class SampleController extends FormAwareController
{
  public function indexAction()
  {
    $demoObject = new stdClass; // or whatever
    $form = $this->createDemoForm( $demoObject );

    // etc...
  }
}

A: Define your form as a service then pass parameters to it using the %parameter% notation.  Your controllers can then just grab the service without needing to know anything about the initialization process.
The technique is also very useful for setting entity managers and other dependencies.

A: Here is the Process to send parameters.ini value to view in Symfony2
parameters.ini
app_name = "Application Name";

in Controller
$data = array(
app_name =>$this->container->getParameter( 'app_name' );
)
return $this->render('Bundle:Home:index.html.twig', $data);

in View
{{app_name}}

redpajama_set_name: RedPajamaStackExchange


The Win32_LoadOrderGroupServiceDependencies class represents an association between a base service and a load order group that the service depends on to start running.
The Antecedent reference represents the Win32_LoadOrderGroup containing the properties of the load order group that must start before the dependent base service of this class can start.
The Dependent reference represents the Win32_BaseService containing the properties of the base service that is dependent upon the load order group in order to start running.


As the passionate City Council continues to work for the city on the rise, The City of Riviera Beach, moves forward in hiring the City Manager process for the City.
It was another heating and passionate debate at the council meeting in the City of Riviera Beach. Especially, when it was to hire the city manager to move the city forward.
After the meeting, Wanda Moore, went up to Councilman Davis to ask about the texts, which he responded that the staff was taking care of it. He added that Wanda Moore has made several requests and that it takes time to process all of them. Especially, since the city is understaffed.
He then asked Wanda Moore, why did you lie?
Why don't you report good things in our city?
As the expression on Wanda Moore face changed and she clinched the microphone, she responded I didn't lie.
He asked if she had a special interest in this city?
Wanda became so flustered that she didn't realize she asked the same question twice. Again, she asked about the text and he responded the same that the staff is taking care of it.
Other residents in council did say they were having the same issues in getting the records. However, only Wanda Moore insinuated that the Councilman Terence Davis, Councilwoman Lynne Hubbard, and Councilwoman Dawn Pardo are trying to cover up something because the process is taking too long.
The Councilman asked the reporter, why only do bad stories in the City of Riviera Beach, at which she smiled.
He also pressed her own relationship with the Mayor of the City, of which she had no answers.
Wanda Moore for far too long you have tried to make this a "city in turmoil". As the councilman corrected you last night and I will, we are a city on the rise. We are moving forward. We are asking for fair and balanced reporting in the City of Riviera Beach.
More Lie Wanda Moore in her new reporting, as I did apply for the job. They did say I was one of the applicants they liked. Yes! I have my Doctorate in Business Administration, specializing in Finance from Walden University, an online for-profit school and I am proud of it! I accomplished my degree while being a wife, mother of three children that I homeschooled, and a ran my businesses.
Being an online student is an asset. It has helped me to achieve all the innovative skills as a self-motivated individual. I am an independent thinker, a researcher, and have acquired all the entrepreneurial skills necessary to help the City of Riviera Beach grow.
The first business started in 2009, Aljdaas Isles, Inc,. Also, in case you missed it, the government and the private sector are the same, but you failed to mention it. I would like to clear up any misinformation about Roxborough Medical, and E-Z Weld, Inc, . All these companies Wanda Moore conveniently could not find in her precious reporting, claimed was fake, and would not retract her stories.
I wonder why the effort to discredit me from working in the City of Riviera Beach? But, I realize the fear of having someone with a Doctorate, with the ability to move this city forward could hinder Wanda Moore special interest. Nothing will stop me from doing what right from helping to move this city forward, especially if that's the pleasure of the board.
I am so thankful for the support of the Councilman Terence Davis, and Councilwoman Lynne Hubbard for the recommendation and Dawn Schlitz Pardo, Tonya Davis Johnson, and KaShamba Miller-Anderson for the consideration. I know we all may not always agree, but I appreciate the support of my community and the people who believe in my stand to help the City of Riviera Beach.
As we move forward, I wanted you all to see the real interview with as a passionate Councilman Terence Davis defends the City is asking Wanda more why do you lie? He also answered the questions about the texts and asked her about her special interest. Here she started babbling.
We are a city on the rise!!! Let's move forward!!


Looking for an easy to customize and easy to use the navigations in the android launcher? Yeah, you are at the right page here we are gonna posting about Nova Launcher. It is the fastest and fully customizable launcher. if you finding the latest version then you can also get it from here. so we can change anything in this launcher like examples are given below.
Get the Nova Launcher for free and use the full features of premium as shown below.
You will get these features which written below after the getting premium APK version but for downloading the premium app you don't have to pay.
Gesture – Get the Swiping feature on your launcher. Make a shortcut of your gesture this will help you to open your app instantly.
Unread Count – Get the pending number of your unread messages or notification from the app.
Custom Drawer Group – Modify your app drawer with your choice change the icon or name of the application from the app drawer.
Hide App – Do hide your app from the launcher and make it clean with your unused app.
More Scroll Effect – New Transition Effect on changing the home screen page.
Note: This is the Moded version of Nova Launcher Prime APK. So, If you are having a problem in Installing the App. Please Uninstall the previous version of App from your phone.


text:
Q: Error setting certificate verify locations when using Amazon S3 PHP sdk Here is the code to implement Amazon S3 delete object
    require_once(APPPATH . 'libraries/aws.phar');

    $s3 = S3Client::factory(array(
                'key' => S3_KEY,
                'secret' => S3_SECRET
    ));

    $s3->deleteMatchingObjects(S3_BUCKET, "abc.mp4");

The error message is:
Fatal error: Uncaught exception 'Guzzle\Http\Exception\CurlException' with message '[curl] 77: error setting certificate verify locations: CAfile: /tmp/guzzle-cacert.pem CApath: none [url] https://testdomain.s3.amazonaws.com?prefix=abc.mp4' in phar:///home/in001/public_html/yoga/application/libraries/aws.phar/Guzzle/Http/Curl/CurlMulti.php:359 Stack trace: #0 phar:///home/in001/public_html/yoga/application/libraries/aws.phar/Guzzle/Http/Curl/CurlMulti.php(292): Guzzle\Http\Curl\CurlMulti->isCurlException(Object(Guzzle\Http\Message\Request), Object(Guzzle\Http\Curl\CurlHandle), Array) #1 phar:///home/in001/public_html/yoga/application/libraries/aws.phar/Guzzle/Http/Curl/CurlMulti.php(257): Guzzle\Http\Curl\CurlMulti->processResponse(Object(Guzzle\Http\Message\Request), Object(Guzzle\Http\Curl\CurlHandle), Array) #2 phar:///home/in001/public_html/yoga/application/libraries/aws.phar/Guzzle/Http/Curl/CurlMulti.php(240): Guzzle\Http\Curl\CurlMulti->processMessages() #3 phar:///home/in001/public_html/yo in phar:///home/in001/public_html/yoga/application/libraries/aws.phar/Aws/Common/Client/AbstractClient.php on line 258

I have followed the example here:
Delete object or bucket in Amazon S3?
and logged all parameter is correct, any idea of how to fix it?
Thanks for helping

A: Instead of using the .phar , download the zip file
https://github.com/aws/aws-sdk-php/releases
and require like this:
require_once(APPPATH . 'libraries/aws/aws-autoloader.php');

fixed the problem


text:
Q: In proving that $N_{G}(H) \subseteq C_G(H),$ what is the difference between the def. of $N_{G}(H)$ and $C_{G}(H)$? Here is the problem I want to solve:
Let $H$ be a subgroup of order $2$ in $G.$ Show that $N_{G}(H) = C_G(H).$
And here is my solution to it:
Remember that, the normalizer of $H$ in $G$ is defined as $N_G(H) = \{g \in G| gHg^{-1} = H\}$ where  $gHg^{-1} = \{ghg^{-1} | h \in H\}.$ Also, the centralizer of $H$ in $G$ is defined as $C_G(H) = \{g \in G| ghg^{-1} = h \text{ for all } h \in H\}$\
Let $H$ be a subgroup of order $2$ in the group $G.$ We want to show that $N_{G}(H) = C_G(H).$
But clearly, $C_G(H) \subseteq N_{G}(H)$ by the definition of both. Now, it remains to show that $N_{G}(H) \subseteq C_G(H).$
Suppose $g \in N_G(H).$ Since $H$ is a subgroup of order $2$ in $G,$ then $H$ contains the identity element $e$ and only one other element $h \neq e$ i.e., $H = \{e, h\}.$ So, by the definition of the $N_{G}(H)$ and since $g \in N_G(H),$ we have $\{g e g^{-1} = e, g h g^{-1} = h\}$ i.e., $\{g e g^{-1}, g h g^{-1} \} = \{e, h\} = H$ which means that $g h g^{-1} = h$ for every $h \in H.$ Therefore, we get, by the definition of $C_G(H)$ that $g \in C_G(H)$. Hence $N_{G}(H) \subseteq C_G(H)$ as required.
I am not sure if the my justification in bold above is correct or no, could anyone check this for me please? as my justification made me feel that the definition of $C_G(H)$ and $N_G(H)$ are the same.

A: You are right. To see why they differ, assume there are three elements in $H$. Then $N_G(H)$ is all of the elements of $g$ that stabilize the set $H$ under conjugation. If $H=\{e,x,x^2\}$, then certainly $e$ is always centralized, not just normalized. But it could be that some element of $G$ conjugates $x$ to $x^2$ (and vice versa). That would normalize the subgroup $H$ but not centralize it.
This goes on explicitly in the symmetric group $S_3$. Here $H=\{1,(1,2,3),(1,3,2)\}$ is normalized by the whole group, but $(1,2)$ swaps by conjugation the two $3$-cycles.
But in the case $|H|=2$, $H=\{e,x\}$. Again, $e$ is always centralized by anything normalizing $H$, but now $x$ doesn't have anywhere to go in $H$ other than itself. So anything normalizing $H$ must centralize both elements of it, i.e., centralize $H$ as a whole.


Showing all items, newest first.
	NEDERLAND WEST INDIES - USA …NORWEGEN SHIPPING & TRADE MISSION..	1944 (Mar)The Norwegian Shipping and Trade Mission, 22 Kerkstraat, Curacao printed envelope by airmail to office's at 80 Broad Street, New York., N.Y bearing 35c tied Willemstad cds & with open circular GEZIEN / CURACAO / CENSUUR & transparent EXAMINED BY /5955 tape applied New York & with on reverse violet broken bar with black dot (Mayo M 1. 1). ................................................................................................. These devices were to detirmine whether this was privileged mail or not, the bar & dot = Not privliged ...Dot only = priviledgd mail ...80 Broad Street was the New York office of The Norwegian Shipping and Trade Mission (Nortraship) that was established in London in April 1940 to administer the Norwegian merchant fleet outside German-controlled areas. It made a major contribution to the Allied war effort.


text:
\section{Introduction}

The human language represents a major factor responsible for the success of our species; and its written form is one of the main expression used to convey and share information. Owing to the ubiquity of language in several contexts, many linguistic aspects have been studied via the application of methods and tools borrowed from diverse scientific fields. As a consequence, several findings related to the origins, organization and structure of the language have been unveiled. One of the most fundamental patterns arising from statistical analysis of huge amounts of text is the Zipf's law, which states that the frequency of the words decreases inversely to their rank~\cite{zipf0,zipf,zipf1}. Other fundamental recurrent pattern is the Heap's law, which states that the vocabulary size grows slowly with the number of tokens of the document~\cite{heap1,heap2,heap3}. More recently, concepts from Physics have been applied to model several language features. For example, with regard to long-scale properties, concepts from dynamical systems have successfully been employed to compare the burstiness of the spatial disbribution of words in documents~\cite{katz,authorship}. In a similar fashion, the spatial distribution of words in documents and analogous systems have also been studied in terms of level statistics~\cite{level}, entropy~\cite{statistical} and intermittency measurements~\cite{imeas}

A well-known approach to study written texts is the word adjacency network model~\cite{ref1,wan,masucci}, which considers short-scale textual properties to form the networks. In this representation, relevant words conveying meaning are modeled as nodes and adjacency relationships are used to establish links. Using this model, several characteristics of texts and languages have been inferred from statistical analyses performed in the structure of networks~\cite{hliu}. Language networks have been increasingly employed to understand theoretical linguistic aspects, such as the origins of fundamental properties~\cite{origins} and the underlying mechanisms behind language acquisition in early years~\cite{acquisition}. In practical terms, networks have been applied in the context of machine translations~\cite{cnammt}, autommatic summarization~\cite{extractive}, sense disambiguation~\cite{navigli,unveiling}, complexity/quality analysis~\cite{complexity,forte} and document classification~\cite{mihalcea}. Despite the relative success of applying networks concepts to better understand language phenomena, in many real-world applications attributes extracted from networked models have not contributed to the advancement of the state of art.
For example, when one considers the document classification task, a strong dependency of network features on textual characteristics have been observed. However, the best performance are still achieved with traditional statistical natural language processing features.
In this context, the present study address this problem by devising methods that effectively take advantage of network properties to boost the performance of the textual classification task. Here I focus on the text classification based on stylistic features, where a text is classified according to stylistic marks left by specific authors~\cite{surveyaut} or literary genres~\cite{liter}. Upon introducing a hybrid classifier relying on the fuzzy definition of supervised pattern recognition methods~\cite{fuzzyknn}, I show that the performance of style-based classifications can be significantly improved when topological information is included in traditional models. \Red{Given the generality of the proposed method, the framework devised here could be applied to improve the characterization and description of many related textual applications.}

\section{Representing texts as networks} \label{represent}

There are several ways to map texts into networks~\cite{ref1,hliu,baroncheli,lantiq,extractive,patterns}. The most suitable form depends on the context of the application. In occasions where the semantics is relevant, the words sharing some semantical relationship are linked~\cite{unveiling,arenas,adv,highorder,complexity}. In a similar fashion, other semantic-based models link the words appearing in a given context~\cite{veronis,windows} (e.g. in the same sentence or paragraph). \Red{A general model for establishing significant ``semantical'' links between co-occurring elements was devised in~\cite{disen}. More specifically, the model considers the existence of a set of elements $V = \{v_1,\ldots,v_n\}$, which may occur in one or more sets of a given collection $\xi = \{S_1,\ldots,S_N\}$.
Given two elements $\alpha \in V$ and $\beta \in V$, the model computes the probability $p$ that more than $r$ sets in $\xi$ contain both elements $\alpha$ and $\beta$ as
\begin{equation}
    p_t = \sum_{j \geq r} p(j),
\end{equation}
where $p(j)$ is the probability that $\alpha$ and $\beta$ co-occur exactly $j$ times in the same set. Given a confidence level $p_0$, the strength of the link between $\alpha$ and $\beta$ is $s = \log(p_0/p_t)$.
}

\Red{In applications where the style (or structure) plays an important role, the links among words are established according to \emph{syntactical relationships}~\cite{extractive,patterns}}.
A well-known approach for grasping stylistic features of texts is the word adjacency model~\cite{wan,identification,machine}, which  basically connect adjacent words in the text. \Red{Differently from the model devised in~\cite{disen}, the word adjacency model captures the stylistic features of texts~\cite{voynich}}.
It has been shown that the adjacency model is able to capture most of the syntactical links with the benefit of being language independent. Despite being a simplification of the syntactical analysis, the adjacency model has been employed in several contexts because the topological properties of word adjacency and syntactical networks are similar. Such high degree of similarity can be explained by the fact that most of the syntactical links occurs among neighbouring words~\cite{patterns}. In the current paper, the traditional word adjacency representation was adopted.

Before mapping the text into a word adjacency network, some pre-processsing steps are
usually applied. First, words conveying low semantic content, such as articles and prepositions, are removed from the text. These words, referred to as \emph{stopwords}, are disregarded because they just serve to link content words. As a consequence, they can be straightforwardly replaced by network edges. The remaining words are then lemmatized, i.e. they are transformed to their canonical forms. To assist the lemmatization process, all words are labeled with their part-of-speech tags. Particularly, in this study, I used a model based on maximum entropy~\cite{verref}.
To exemplify the construction of a word adjacency network, I show in table \ref{tab.pre} the pre-processing steps performed in a short text. The corresponding network is shown in figure \ref{fig.pre}.

\begin{table}[h]
\caption{\label{tab.pre}Pre-processing steps applied to the poem ``In the Middle of the Road'', by Carlos Drummond de Andrade. First, \emph{stopwords} are removed. Then, the remaining words are mapped to their canonical form via lemmatization. Note that transformations fatigued $\mapsto$ fatigue and retinas $\mapsto$ retina occur during the lemmatization step. The network obtained from the poem is illustrated in figure \ref{fig.pre}.}
\begin{tabular}{|c|l|}
\hline
{\bf Processing step} & {\bf Outcome} \\
\hline
\multirow{7}{*}{Original text}  & In the middle of the road there was a stone / there was a stone \\
              & in the middle of the road there was a stone in the middle of the\\
              & road there was a stone. Never should I forget this event / in the\\
              & lifetime of my fatigued retinas / Never should I forget that in \\
              & the middle of the road / there was a stone / there was a stone  \\
              & in the middle of the road / in the middle of the road there was \\
              & a stone. \\
\hline
                  & middle road stone stone middle road stone middle road stone never \\
Stopwords removal & forget event lifetime fatigued retinas never forget middle road stone \\
                  & stone middle road middle road stone \\
\hline
              & middle road stone stone middle road stone middle road stone never \\
Lemmatization & forget event lifetime fatigue retina never forget middle road stone \\
              & stone middle road middle road stone \\
\hline
\end{tabular}

\end{table}

\begin{figure}
  \centering
  \includegraphics[width=0.55\textwidth]{stone
  \caption{\label{fig.pre}Example of word adjacency network created from the poem ``In the Middle of the Road'', by Carlos Drummond de Andrade. The pre-processing steps performed to generate this word adjacency network are shown in table \ref{tab.pre}.}
\end{figure}

\section{Topological characterization of networks} \label{asmedidas}

A network can be defined as $G=\{\mathbb{V},\mathbb{E}\}$, where $\mathbb{V}$ denotes the set of nodes and $\mathbb{E}$ denotes the set of edges, which serve to link nodes. An unweighted network can be represented by an adjacency matrix $\matr{A}=\{a_{ij}\}$, where each element $a_{ij}$ stores the information concerning the connectivity of nodes $i$ and $j$. If $i$ and $j$ are connected, then $a_{ij} = 1$. Otherwise, $a_{ij} = 0$. Note that, in a undirected network devoid of self loops, $\matr{A}^T = \matr{A}$.
Currently, there are several network measurements available to characterize the topology of complex networks~\cite{surveymeas}. Here, I describe the main measurements applied for text analysis:

\begin{itemize}

  \item {\bf Degrees}: the degree ($k$)  is the number of edges connected to the node, i.e. $k_i = \sum_j a_{ij}$.
      %

      A relevant feature related to the degree that has been useful for text analysis are the are the average node degree and the standard deviation of the neighbors, which are given by
      %
      \begin{equation}
      \Red{
        k^{(n)}_{i} = \frac{1}{k_i} \sum_j a_{ij}  k_j,
      }
      \end{equation}
      %
      \begin{equation}
      \Red{
        \Delta k^{(n)}_i = \Bigg{[} \frac{1}{k_i} \sum_j a_{ij} \Bigg{(} k_j - \frac{1}{k_i} \sum_m a_{im}  k_m  \Bigg{)}^ 2  \Bigg{]}^{1/2}.
      }
      \end{equation}
      %
      In text networks, both $k^{(n)}$ and $\Delta k^{(n)}$ have been useful to quantify the structural organization of texts~\cite{58}.

  \item {\bf Accessibility}: the accessibility measurement is a extension of the node degree centrality~\cite{travencolo}. It is defined as a normalization of the diversity measurement~\cite{travencolo}, which quantifies the irregularity of a accessing neighbors through self-avoiding random walks~\cite{travencolo}. To define the accessibility, let $P_h(i,j)$ be the probability of a random walker starting at node $i$ to reach node $j$ in exactly $h$ steps. The heterogeneity of access to neighbors can be quantified with the diversity measurement:
      %
      \begin{equation} \label{eq.div}
      \Red{
        {\delta}^{(h)}_i = -\sum_j P_h(i,j) \log P_h(i,j),
      }
      \end{equation}
      %
      Given eq. \ref{eq.div}, the accessibility ($\alpha$) is computed as
      %
      \begin{equation}
        \alpha^{(h)}_i = \exp ( \delta^{(h)}_i ).
      \end{equation}
      %
      It can be shown that the accessibility is bounded according to the relation $0 \leq \alpha^{(h)} \leq n_h$, where $n_h$ is the number of nodes at the $h$-th concentric level~\cite{concentrico}. An example of the computation of the accessibility in a small network is shown in figure \ref{fig.acc}. In the example, nodes 2, 3, 4 and 5 belong to the first concentric level and nodes 6, 7, 8, 9 and 10 belong to the second concentric level. When one considers a regular access to the second level (red configuration), the accessibility corresponds to the total number of nodes located at the $h$-th concentric level. When some nodes are more accessed than others, the accessibility decreases because less nodes are effectively accessed.

      \begin{figure}
  \centering
  \includegraphics[width=0.7\textwidth]{accessibility_figure}
  \caption{\label{fig.acc}Probabilities of transition from node 1 considering $h=2$ steps for two distinct configuration of links. The first configuration considers only the red edges and the second one considers both red and blue edges. Note that, in the first configuration, the probability to reach any node at the second level is the same. In this case, $\alpha_1^{(h=2)} = 5$. When blue edges are included, nodes 7 and 9 tend to receive more visits than the other nodes, according to the considered probabilities. For this reason, the effective number of accessed nodes drops to $\alpha_1^{(h=2)} = 4.71$.}
\end{figure}

      In textual networks, it has been shown that the accessibility is more advantageous than other traditional centrality measurements as it is able to capture more information at further hierarchical levels~\cite{concentrico}. This measurement has been successfully applied to detect core concepts in texts~\cite{extractive}. Furthermore, it has also been employed to generate informative summaries~\cite{extractive}. A dependency of the distribution of this measurement with stylistic features of text was observed in~\cite{probing}.

  \item {\bf Betweenness}: the betweenness is a centrality measurement that considers that  a node is relevant if it is highly accessed via shortest paths. If $n_{sit}$ is the number of geodesic paths between $s$ and $t$ passing through node $i$; and  $n_{st}$ is the total number of shortest paths linking $s$ and $t$, then the betweenness is defined as:
      %
      \begin{equation}
        B_i = \frac{1}{N^2} \sum_s \sum_t \frac{n_{sit}}{n_{st}}
      \end{equation}
      %
      In word adjacency networks, high frequent words usually take high values of betweenness. However, some words may act as articulation points whenever they link two semantical contexts or communities~\cite{commrev}. It has been shown that the betweenness is able to identify the generality of contexts in which a word appears~\cite{comparing}. More specifically, domain-specific words tend to assume lower values of betweenness when compared with more generic words.

  \item {\bf Assortativity}: several real-world networks are formed of nodes with a specific type of classification. For example, in social networks, individuals may be classified by considering their age, sex or race. When analyzing the connectivity patterns of networks, it might be relevant to study how distinct classes connect to each other. This type of analysis is usually performed with the so-called assortativity measurement~\cite{mixing}.
      %

      %

      %
      \Red{The assortativity can be computed as the Pearson correlation coefficient} ($r$):
      %
      \begin{equation}
            r = \frac{e^{-1}\sum_{j>i}^{ } k_{i}k_{j}a_{ij} - \big[ e^{-1}\sum_{j>i}^{ } \frac{1}{2}(k_{i}+ k_{j})a_{ij}\big]^{2} }{e^{-1} \sum_{j>i}^{ } \frac{1}{2}(k_{i}^{2}+ k_{j}^{2})a_{ij}- \big[ e^{-1}\sum_{j>i}^{ } \frac{1}{2}(k_{i}+ k_{j})a_{ij}\big]^{2}},
      \end{equation}
      %
      where $e$ is the total number of edges. In word adjacency networks, the assortativity quantifies how words with distinct frequency appear as neighbors.

  \item {\bf Clustering coefficient}: the clustering coefficient ($C$) quantifies the local density of neighbors of a given node. The local definition of the clustering coefficient is given by the fraction of the number of triangles among all possible connected sets of three nodes:
      %
      \begin{equation}
        C = 3 \sum_{k>j>i} a_{ij} a_{ik} a_{jk} \Bigg{[} \sum_{k>j>i} a_{ij} a_{ik} + a_{ji} a_{jk} + a_{ki} a_{kj} \Bigg{]} ^{-1}.
      \end{equation}
      %
      Similarly to the betweenness, the clustering coefficient is useful to detect words appearing in generic contexts~\cite{comparing}. However, differently from the betweenness, the clustering coefficient analyzes only the local neighborhood of nodes.

  \item {\bf Average shortest path length}: the average shortest path length ($l$) is the typical distance between any two nodes in the network. \Red{This measurement was used in this paper because it has been useful in stylistic-based applications}~\cite{comparing}. In texts, the average shortest path length quantifies words relevance. More specifically, according to this measurement, the most relevant words are those that are close to the hubs.

\end{itemize}

Most of the measurements described in this section are local measurements, i.e. each node $i$ possesses a value $\tilde{X}_i$, where $\tilde{X}=\{k,k^{(n)},\Delta k^{(n)},\alpha,B,C,l\}$. For the purposes of this paper, \Red{it is necessary to sum up the local measurements}. The most natural choice is to characterize the documents by computing the average $\langle \tilde{X} \rangle$, where $\langle \ldots \rangle = N^{-1} \sum_{i=1}^N \ldots$ stands for the average computed over the $N$ distinct words of the text. A disadvantage associated with this type of \Red{summing procedure} is that all words receive the same weight, regardless of their number of occurrences in the text. To avoid this potential problem, I also computed the average value $\langle \ldots \rangle^* = \eta^{-1} \sum_{i=1}^\eta \ldots$ obtained when only the $\eta=50$ most frequent words are considered. The standard deviations $\Delta \tilde{X}$ and the skewness $\gamma(\tilde{X})$ were also used to characterize the documents.

A drawback associated to the computation of local topological measurements in word adjacency networks is the high correlation found between these measurements and the node degree (i.e. the word frequency). To minimize this correlation, the following procedure was adopted. Each of the measurements was normalized by the average obtained over $30$ texts produced using a word shuffling technique, where the frequencies of words are preserved. If $\mu(\tilde{X}^{(R)})$ and $\sigma(\tilde{X}^{(R)})$ are the average and deviation obtained over the random realizations, the normalized measurement $X$ and the error $\epsilon(X)$ related to $X$ are
\begin{equation}
    X = \frac{\tilde{X}}{\mu(\tilde{X}^{(R)})}
\end{equation}
\begin{equation}
    \Red{
    \epsilon(X) = \frac{\sigma(\tilde{X}^{(R)})}{\mu(\tilde{X}^{(R)})^2} \tilde{X} =  \frac{\sigma(\tilde{X}^{(R)})}{\mu(\tilde{X}^{(R)})} X.
    }
\end{equation}

\section{Traditional stylistic features} \label{tradicao}

\subsection*{Frequency of words and characters}

Traditional methods usually perform statistical analysis using specific textual features~\cite{manning}. An important contribution to the stylometry was introduced by Mosteller and Wallace~\cite{mosteller}, which showed that the frequency of function words (such as \emph{any}, \emph{of}, \emph{a} and \emph{on}) is useful to characterize the style in texts. Frequent words have also been used in strategies devised by physicists where the distance of frequency ranks was used to compute the similarity between texts~\cite{28,29,30}. Another relevant feature for characterizing styles in texts is the frequency of character bigrams \Red{(i.e. a sequence of two adjacent characters)}. These attributes have proven useful e.g. to detect the stylistic marks of specific authors~\cite{52}.

\subsection*{Intermittence}

The uneven spatial distribution of words along texts is a feature useful for characterizing the style of texts~\cite{statistical}. The quantification of the homogeneity of the distribution of words along texts can be performed by using recurrence times, a standard measure employed to study time series~\cite{rtimes}.
In texts, the concept of time is represented in terms of the number of words occuring in a given interval. For each word $i$, the recurrence time  $T_j$ is defined as the number of words appearing between two successive occurrences of $i$ plus one. For example, the recurrence times of the word ``stone'' in the lemmatized text shown in table \ref{tab.pre} are $T_1 = 1$, $T_2 = 3$, $T_3 = 3$, $T_4 = 11$, $T_5 = 1$ and $T_6 = 5$.
If a word $i$ occurs $N_i$ times in a text comprising
$N_T$ words, it generates a sequence of $N_i-1$ recurrence times $\{T_1, T_2,\ldots,T_{N_i-1} \}$. In order to consider the time $T_f$ until the first occurrence of $i$ and the time $T_l$, the number of words between the last occurrence of $i$ and the last word of the text, the recurrence time $T_N = T_f + T_l$ is added to the set of recurrence times of word $i$. As such, $\langle T \rangle = N_T / N_i$, where $\langle \cdot \rangle$ is the average over distinct $T_j$'s. Note that the average recurrence time $\langle T \rangle$ does not provide additional information, since it only depends on the frequency. The intermittence (or burstiness) of the word $i$ is obtained from the coefficient of the variation of the recurrence times:
\begin{equation}
    I = \sigma_T / \langle T \rangle = \Bigg{[} \frac{\langle T^2 \rangle}{\langle T \rangle^2} - 1 \Bigg{]}^{1/2}.
\end{equation}
It has been shown that the intermittence has been useful to identify core concepts in texts, even when a large corpus is not available. Relevant words usually take high values of intermittency, i.e. $I \gg 1$. \emph{Stopwords}, on the other hand, are evenly distributed along the text~\cite{probing}. To illustrate the properties of the intermittency measurement, figure \ref{figintt} shows the spatial distribution of the words \emph{long} and \emph{Hobson} in the book ``Adventures of Sally'', by P.G. Wodehouse. The frequency of these words are similar, yet their values of intermittency are quite different. Note that, while the distribution of \emph{long} is relatively homogeneous, \emph{Hobson} is unevenly distributed along the text. Because bursty concepts are the most relevant words~\cite{statistical}, in the example of figure~\ref{figintt}, \emph{Hobson} would be considered a relevant character in the plot.

\begin{figure}
  \centering
  \includegraphics[width=0.9\textwidth]{data
  \caption{\label{figintt}Profile of the spatial distribution of \emph{long} ($N_i = 44$ and $I_i = 1.02$) and \emph{Hobson} ($N_i = 45$ and $I_i = 3.40$) in the book ``Adventures of Sally'', by P. G. Wodehouse. Because \emph{Hobson} is unevenly distributed along the book, this word take high values of intermittency.
  }
\end{figure}

\section{Supervised classification}

In this section, I describe the two methods employed to combine distinct strategies of classification. The objective here is to combine evidences from both traditional and CN-based methods. Before introducing the methods for combining classifiers, the main concepts concerning the supervised learning task are presented.

In a typical supervised learning task, two datasets are employed: the training and the test dataset. The training dataset $X_{\textrm{tr}} = \{(x_1,y_1),(x_2,y_2),\ldots,(x_l,y_l)\}$ is the set of instances whose classes are known beforehand. The first component of the tuples $x_i = (f_1 = a_1, f_2 = a_2,\ldots)$ represents the values of attributes used to describe the $i$-th instance.
The second element $y_i \in \mathcal{Y} = \{y_1,y_2,\ldots\}$ represents the class label of the $i$-th training instance. In the supervised learning task, the objective is to obtain the map $x \mapsto y$. The quality of the map obtained is evaluated with the test dataset $X_{\textrm{ts}} = \{(x_l,y_l),(x_{l+1},y_{l+1})\ldots,(x_{l+u},y_{l+u})\}$. The technique used to evaluate the performance of the classifiers used in this study is the well-known 10-fold cross-validation~\cite{wekabook}.

\Red{
An usual procedure in classification tasks is the quantification of the relevance of each attribute for the task. To quantify the relevance of attributes to discriminate the data, several indices have been proposed~\cite{wekabook}. A well-known index, the information gain, quantifies the homogeneity of the set of instances in $X_{\textrm{tr}}$ when the value of the attribute is specified~\cite{infogain}. Whenever a single class prevails when the value of an attribute $f_k$ is specified, the information gain associated to $f_k$ takes high values.
Mathematically, the information gain $\Omega$ is defined as
\begin{equation}
    \Omega(X_{\textrm{tr}}, f_k) = \mathcal{H}(X_{\textrm{tr}}) - \mathcal{H}(X_{\textrm{tr}}|f_k)
\end{equation}
where $\mathcal{H}(X_{\textrm{tr}})$ is the entropy of the training dataset $X_{\textrm{tr}}$ and $\mathcal{H}(X_{\textrm{tr}}|f_k)$ is the entropy of the training dataset when the $k$-th attribute is specified. The quantity $\mathcal{H}(X_{\textrm{tr}}|f_k)$ is computed as
\begin{equation}
    \mathcal{H}(X_{\textrm{tr}}|f_k) = \frac{1}{{|X_{\textrm{tr}}|}} \sum_{v \in V(f_k)} {|\{ x \in X_{\textrm{tr}} | f_k = v \} |~ }  \mathcal{H}(\{ x \in X_{\textrm{tr}} | f_k = v \})
\end{equation}
where $V(F_k)$ is the set comprising all values taken by $f_k$ in $X_{\textrm{tr}}$.
}

\subsection*{Hybrid classifier}

To define this classifier, consider the following definitions. \Red{Let $m_{ij}$ be the strength associating the $i$-th instance to the $j$-th class, where $0 \leq m_{ij} \leq 1$. The term $m_{ij}$, henceforth referred to as \emph{membership strength}, can be interpret as the likelihood of instance $i$ to belong to class $j$. Note that the quantification of $m_{ij}$ depends on the classifier being used. A detailed description of methods for computing the membership strength can be found in~\cite{fuzzyknn,fsvm,ftree,fforest,fmlp}.}
Let $m^{(R)}_{ij}$ be the membership strength obtained when topological features of complex networks are used and $m^{(T)}_{ij}$ the membership strength obtained when traditional statistical features are analyzed. According to the hybrid strategy, the combination of both topological and traditional methods is achieved according to the following convex combination
\begin{equation} \label{eq.combine}
    m^{(H)}_{ij} = \lambda m^{(R)}_{ij} + ( 1 - \lambda ) m^{(T)}_{ij},
\end{equation}
where $\lambda \in [0,1]$ accounts for the weight associated to the topological strategy. Note that the combination of evidences performed in equation \ref{eq.combine} yields a membership strength $m^{(H)}_{ij}$ ranging in the interval $[0,1]$. The final decision is then made according to the rule
\begin{equation}
    \Red{
    \tilde{y}_i = \max_j m^{(H)}_{ij},
    }
\end{equation}
where $\tilde{y}_i \in \mathcal{Y}$  denotes the correct class label associated to the $i$-th test instance.
\Red{
In practical applications, a screening on the $\lambda$ can be useful to find its best value. However, this process might be computationally unaffordable for very large datasets. In this case, the process of finding the adequate value of the parameter can be made via application of optimization heuristics}~\cite{grid1,grid2}.

To illustrate how the decision is performed with the hybrid algorithm, figure \ref{fig.clhyb} shows the classification of a text modeled as a network.
In the top panel, the central network is the text whose class is to be inferred. The left and right networks represent two networks in the training dataset. \Red{The node color} denotes the node label, i.e. the word associated to the node. In this example, two texts are semantically similar if they share the same words.
In the central panel, the scenario where the hybrid classified is set with $\lambda = 0.15$ is considered. Because $\lambda$ is close to zero, the classification is mostly based on the number of shared words. In fact, the decision boundary in this case is created so that the test instance is classified in the same class as the right training instance.
The bottom panel illustrates the decision made with $\lambda=0.85$. Now, the topological features of the text is the most prevalent feature used for the classification. As a consequence, the boundary decision moves to the right side so that the test instance is classified as the same class as the left training instance. Note that, the test instance as belonging to class $c_1$ because the $r_1$ and $r_?$ are topologically similar.

\begin{figure}
  \centering
  \includegraphics[width=1\textwidth]{lambda
  \caption{\label{fig.clhyb}Example of classification based on the hybrid classifier. In the top panel, the network $r_?$ might assume two possible classes: $c_1$ and $c_2$. An example for each of these classes is provided (see networks $r_1$ and $r_2$). In the central panel, the decision boundary obtained for $\lambda = 0.15$ is shown. Because $\lambda$ takes a low value in this case, the decision is mainly based on the number of shared nodes (words). As a consequence, $r_?$ is classified as belonging to class $c_2$. For higher values of $\lambda$, the topological features of texts takes over. In the bottom panel, $r_?$ is classified as belonging to class $c_1$ because $r_?$ and $r_1$ are topologically similar.
  }
\end{figure}

\subsection*{Tiebreaker classifier}

Similarly to the hybrid classifier, this classification scheme uses as attributes both traditional and topological features of texts modelled as networks. The objective of this approach is to use the topological attributes only when the classification performed with traditional features is not reliable, as revealed by the values of membership strength. Consider that the two most likely classes to which the unknown instance belongs are $j$ and $k$, according to traditional features. As a consequence, $m^{(T)}_{ij} \geq m^{(T)}_{ik} \geq m^{(T)}_{il}$, for each class $l \in \mathcal{Y} - \{i,j\}$. Let $\Delta$ be the difference between the probabilities associated to the two most likely classes, i.e. $\Delta = m^{(T)}_{ij} - m^{(T)}_{ik}$. If such difference surpasses a given threshold $\theta$, the tiebreaker performs the classification by using only traditional attributes. Conversely, topological attributes are employed to infer the class of the unknown instance. Equivalently,
\begin{equation} \label{lab.tiebreaker}
    \tilde{y}_i =\left\{
    \begin{array}{l l}
      \max_j m^{(T)}_{ij}, & \textrm{if $\Delta = m^{(T)}_{ij} - m^{(T)}_{ik} \geq \theta$} \\
      \max \{ m^{(R)}_{ij}, m^{(R)}_{ik} \},  & \textrm{otherwise.}
\end{array}\right.
\end{equation}
Note that the threshold $\theta$ ultimately decides which attributes (traditional or topological) are used to perform the classification. An example of classification using this approach is shown in figure \ref{theta}.

\begin{figure}
  \centering
  \includegraphics[width=0.75\textwidth]{theta
  \caption{\label{theta}Example of classification based on the tiebreaker classifier. In the left panel, the gray instances are the test instances that should be classified with the class labels red circle or blue asterisk. In this case, traditional attributes were used. Note that the square is significantly far from the decision boundary (dashed line). Therefore, the classification of this instance \Red{does not demand the use of topological features} because $\Delta \geq \theta$ in equation \ref{lab.tiebreaker}. Differently, the pentagon is located on the decision boundary. Because $\Delta < \theta$ in this case, topological attributes are used to perform the classification. According to the topological attributes (see right panel), the test instance represent by the pentagon is classified as belonging to the blue class.
  }
\end{figure}

\section{Results and discussion} \label{osresultados}

The effectiveness of the combination of traditional textual features and topological measurements of networks was evaluated in the context of two natural language processing tasks. Both tasks rely upon the characterization of stylistic marks of texts. The studied tasks were the authorship attribution and genre detection problems. The classifiers chosen to compound the combining techniques were: kNN, Support Vector Machines (SVM), Random Forest (RFO) and Multilayer Perceptron (MLP). These classifiers were chosen because they usually yield good accuracy rates with default parameters~\cite{systematic}.

\subsection{Authorship recognition} \label{aut.rec}

In the authorship attribution task, the objective is to identify the authors of texts whose identity is lacking~\cite{t5}. The dataset employed here comprises books written by eight authors: Arthur Conan Doyle (ACD), Bram Stoker (BRS), Charles Dickens (CHD), Thomas Hardy (THH), Pelham Grenville Wodehouse (PGW), Hector Hugh Munro (HHM) and Herman Melville (HME). To show how the topological properties of the networks modelling books can be useful to improve the characterization of authors' styles in texts, the following combination of attributes were considered:
\begin{itemize}

  \item {\bf INT+CN}: the intermittence of stopwords were considered along with the topological measurements of the network modeling the text. As stopwords, I considered all the words that
      appeared at least once in all books of the dataset. \Red{According to the dataset, there is a total of $340$ stopwords.}

  \item {\bf FR+CN}: the frequency of stopwords were considered along with the topological attributes of the networks.

  \item {\bf BG+CN}: the frequency of character bigrams were considered along with the topological attributes of the networks. All the possibilities of character bigrams ($634$) were considered in this analysis.

\end{itemize}
For each combination of attributes, the gain in performance when the topology is considered as an additional feature is represented as:
\begin{equation}
    \Delta\Gamma(x) = \frac{\Gamma_H(x) - \Gamma_T}{\Gamma_T},
\end{equation}
where $\Gamma_H(x)$ denotes the accuracy rate obtained with either the hybrid or tiebreaker classifier and $x$ is the corresponding parameter employed, i.e. $\lambda$ for the hybrid classifier and $\theta$ for the tiebreaker classifier. Figure \ref{fig.lambda.aut} shows the values of  $\Delta\Gamma(\lambda)$ when the kNN is used to compose the hybrid classifier. The results obtained for additional values of the parameter $k$ are shown in table \ref{tab.lambda.aut}. Figure \ref{fig.lambda.aut} also shows the maximum accuracy rate obtained with the variation of $\lambda$:
\begin{equation}
    \Delta\Gamma_{max} = \max_{\lambda \in [0,1]} \Delta\Gamma(\lambda).
\end{equation}
With regard to the INT+CN combination, the addition of topological features of texts yielded accuracy rates much higher than the ones obtained with intermittency features alone, since $\Delta\Gamma_{max} > 2$ for $k=\{3,4,5\}$. Note that, in this case, the network approach alone performed better than the method based solely on intermittency features, because $\Delta\Gamma(\lambda=1) > 1$. This observation might explain the high gain obtained when networks are included as an additional feature.
As for the FR+CN combination, the highest gain in performance was $\Delta\Gamma_{max} = 1.197$, which was obtained for $k=5$. The lowest gain in accuracy occurred for the BIG+CN combination. In this case, the maximum gain was $\Delta\Gamma_{max} = 1.070$ for $k=5$.
When other classifiers were used to compose the hybrid classifier, similar results have been found (see table \ref{tab.lambda.aut.o}): the highest and lowest improvement in performance occurred for the INT+CN and BIG+CN combinations, respectively. In general, the hybrid classification that combined traditional statistical features and topological measurements of networks improved the classification performance in the authorship recognition task. Interestingly, in several cases, the combination outperformed the accuracy rates obtained when the compounding strategies were analyzes separately, i.e.
$\Gamma_H(\lambda \in ]0,1[) > \Gamma_H(\lambda = 0)$ and $\Gamma_H(\lambda \in ]0,1[) > \Gamma_H(\lambda = 1)$.

\begin{figure}
  \centering
  \includegraphics[width=1\textwidth]{knn_lambda
  \caption{\label{fig.lambda.aut}Relative accuracy rate as a function of the topological weight ($\lambda$) obtained with the k nearest neighbors. The attributes employed were: (a)-(c): intermittence; (d)-(f): stopwords; (g)-(i): characters. The parameters $k$ of the $k$-nearest neighbors were: $k=3$ in (a), (d) and (g); $k=4$ in (b), (e) and (h); and $k=5$ in (g), (f) and (i). As it turns, there is an improvement of the accuracy rates when traditional methods are combined with the technique based on the topological analysis of complex networks.}
\end{figure}

\begin{table}[h]
\centering
\caption{\label{tab.lambda.aut}Best relative accuracy rate $\Delta\Gamma_{max}$ obtained with the hybrid classifier based on the k-nearest neighbors method. The threshold employed $\lambda^*$ to obtain the best accuracy rate is also shown. Note that the improvement of accuracy occurred in several occasions.}
\begin{tabular}{|l|cc|cc|cc|cc|cc|}
\hline
 & \multicolumn{2}{|c|}{$k=1$} & \multicolumn{2}{|c|}{$k=2$} & \multicolumn{2}{|c|}{$k=3$} &\multicolumn{2}{|c|}{$k=4$} & \multicolumn{2}{|c|}{$k=5$} \\
 \cline{2-11}
 & $\Delta\Gamma_{max}$ & $\Delta\lambda^*$ & $\Delta\Gamma_{max}$ & $\lambda^*$ & $\Delta\Gamma_{max}$ & $\lambda^*$ & $\Delta\Gamma_{max}$ & $\lambda^*$ & $\Delta\Gamma_{max}$ & $\lambda^*$ \\
\hline
{\bf Intermittence} & 1.252 & 0.55 & 1.524 & 0.57 & 2.012 & 0.53 & 2.026 & 0.50 & 2.242 & 0.48 \\
{\bf Stopwords}     & 1.000 & 0.00 & 1.097 & 0.41 & 1.059 & 0.51 & 1.159 & 0.45 & 1.197 & 0.56 \\
{\bf Characters}    & 1.000 & 0.00 & 1.000 & 0.00 & 1.024 & 0.38 & 1.034 & 0.44 & 1.070 & 0.37 \\
\hline
\end{tabular}
{}
\end{table}

\begin{table}[h]
\centering
\caption{\label{tab.lambda.aut.o}Best relative accuracy rate $\Delta\Gamma_{max}$ obtained with the hybrid classifier based on the Support Vector Machine (SVM), Random Forest (RFO) and Multi Layer Perceptron (MLP) methods. The threshold employed $\lambda^*$ to obtain the best accuracy rate is also shown. Note that the improvement of accuracy occurred in several occasions.}
\begin{tabular}{|l|cc|cc|cc|}
\hline
 & \multicolumn{2}{|c|}{{\bf SVM}} & \multicolumn{2}{|c|}{\bf RFO} & \multicolumn{2}{|c|}{\bf MLP} \\
 \cline{2-7}
 & $\Delta\Gamma_{max}$ & $\lambda^*$ & $\Delta\Gamma_{max}$ & $\lambda^*$ & $\Delta\Gamma_{max}$ & $\lambda^*$ \\
\hline
{\bf Intermittence} & 1.125 & 0.25 & 1.800 & 0.59 & 1.227 & 0.53 \\
{\bf Stopwords}     & 1.176 & 0.49 & 1.529 & 0.36 & 1.045 & 0.50 \\
{\bf Characters}    & 1.032 & 0.20 & 1.000 & 0.00 & 1.000 & 0.00 \\
\hline
\end{tabular}
{
}
\end{table}

Traditional and topological attributes of texts were also combined using the tiebreaker classifier. The results obtained with the kNN classifier for $k=\{3,4,5\}$ are shown in figure \ref{fig.asw}. The results obtained for other values of the parameter $k$ are displayed in table \ref{tab.tie.knn.aut}. The tiebreaker classifier built using the INT+CN combination provided an increase in performance of up to 31\%. The maximum gain, obtained with the combinations FR+CN and BG+CN, were respectively 34.2\% and 4.8\%. With regard to the other classifiers compounding the tiebreaker technique, the results obtained are shown in table \ref{tab.tie.out}. Interestingly, there was no improvement when the SVM was used ($\Delta\Gamma_{max}=1$). Differently, when the RFO classifier was used, an improvement of performance was observed in all three combinations of attributes. Finally, the tiebreaker classifier built with the MLP only provided a gain in accuracy for the combination INT+CN. While providing a gain in performance in several scenarios, the tiebreaker technique usually performed worst than the hybrid classifications performed with the same compound classifiers and parameters.
\begin{figure}
  \centering
  \includegraphics[width=1\textwidth]{knn_threshold
  \caption{\label{fig.asw}\Red{Relative accuracy rate as a function of the threshold ($\theta$) obtained with the tiebreaker algorithm applied to the the k nearest neighbors.} The attributes employed were: (a)-(c): intermittence; (d)-(f): stopwords; (g)-(i): characters. The parameters $k$ of the $k$-nearest neighbors were: $k=3$ in (a), (d) and (g); $k=4$ in (b), (e) and (h); and $k=5$ in (g), (f) and (i). As it turns, there is an improvement of the accuracy rates when traditional methods are combined with the technique based on the topological analysis of complex networks.}
\end{figure}

\begin{table}[h]
\centering
\caption{\label{tab.tie.knn.aut}Best relative accuracy rate $\Delta\Gamma_{max}$ obtained with the tiebreaker method based on the k-nearest neighbors algorithm. \Red{The threshold employed $\theta^*$ to obtain the best accuracy rate is also shown.} Note that the improvement of accuracy occurs in several occasions.}
\begin{tabular}{|l|cc|cc|cc|cc|cc|}
\hline
 & \multicolumn{2}{|c|}{$k=1$} & \multicolumn{2}{|c|}{$k=2$} & \multicolumn{2}{|c|}{$k=3$} &\multicolumn{2}{|c|}{$k=4$} & \multicolumn{2}{|c|}{$k=5$} \\
 \cline{2-11}
 & $\Delta\Gamma_{max}$ & $\theta^*$ & $\Delta\Gamma_{max}$ & $\theta^*$ & $\Delta\Gamma_{max}$ & $\theta^*$ & $\Delta\Gamma_{max}$ & $\theta^*$ & $\Delta\Gamma_{max}$ & $\theta^*$ \\
\hline
{\bf Intermittence} & 1.000 & -- & 1.112 & 0.01 & 1.241 & 0.31 & 1.311 & 0.24 & 1.474 & 0.38 \\
{\bf Stopwords}     & 1.000 & -- & 1.056 & 0.31 & 1.131 & 0.46 & 1.181 & 0.34 & 1.341 & 0.47 \\
{\bf Characters}    & 1.000 & -- & 1.027 & 0.02 & 1.026 & 0.01 & 1.016 & 0.25 & 1.048 & 0.02 \\
\hline
\end{tabular}
{}
\end{table}

\begin{table}[h]
\centering
\caption{\label{tab.tie.out}Best relative accuracy rate $\Delta\Gamma_{max}$ obtained with the tiebreaker algorithm applied to the Support Vector Machine (SVM), Random Forest (RFO) and Multi Layer Perceptron (MLP) methods. \Red{The threshold employed $\theta^*$ to obtain the best accuracy rate is also shown.} Note that the improvement of accuracy depends upon the pattern recognition method and attributes employed.. }
\begin{tabular}{|l|cc|cc|cc|}
\hline
  & \multicolumn{2}{|c|}{{\bf SVM}} & \multicolumn{2}{|c|}{\bf RFO} & \multicolumn{2}{|c|}{\bf MLP} \\
  \cline{2-7}
  & $\Delta\Gamma_{max}$ & $\theta^*$ & $\Delta\Gamma_{max}$ & $\theta^*$ & $\Delta\Gamma_{max}$ & $\theta^*$ \\
\hline
{\bf Intermittence} & 1.000 & -- & 1.375 & 0.02 & 1.055 & 0.02 \\
{\bf Stopwords}     & 1.000 & -- & 1.294 & 0.23 & 1.000 & -- \\
{\bf Characters}    & 1.000 & -- & 1.030 & 0.06 & 1.000 & -- \\
\hline
\end{tabular}
{}
\end{table}

The results in tables \ref{tab.lambda.aut}-\ref{tab.tie.out} confirms that the topology of networks modeling texts is able to improve the characterization of texts because such textual description grasps relevant patterns that are mostly disregarded by traditional statistical approaches. \Red{This can be confirmed by optimized results obtained for $\lambda^*>0$ and $\theta^*>0$}.
To better understand the factors \Red{behind} the discriminability power of the network model, the relevance of each topological for discriminating authors was computed with the information gain criterion. The best topological features \Red{were the standard deviation of the accessibility at the third level} ($\Omega(\Delta \alpha^{(h=3)}) = 1.07$), the standard deviation of the \Red{average neighboorhood degree ($\Omega(\Delta k^{(n)}) = 1.05$)} and the \Red{average clustering coefficient} ($\Omega(\langle C \rangle) = 0.60$). An example of discriminability provided by the topological strategy is shown in figure \ref{fig.sep}.
\begin{figure}
  \centering
  \includegraphics[width=0.75\textwidth]{separation
  \caption{\label{fig.sep}Discriminability of authors obtained with two topological features of complex networks modelling texts. Note that, using only two features, it was possible to separate e.g. Alger from Melville. According to the information gain criterion, the most relevant network features for the authorship identication task were the standard deviation of the accessibility computed at the third level ($\Omega(\Delta \alpha^{(h=3)}) = 1.07$) and the \Red{standard deviation of the average neighboorhood degree ($\Omega(\Delta k^{(n)}) = 1.05$)}.  }
\end{figure}

\subsection{Style identification} \label{style.id}

In this section, I investigate if the topological features of complex  networks are useful to complement the traditional textual characterization for the style identification task~\cite{itask}. The dataset used was the Brown corpus~\cite{bcorpus}, which comprises documents that are classified either as informative prose (e.g. press reportage, popular folklore and scientific manuscripts) or imaginative prose (e.g general fiction, romance and love stories). Differently from the authorship attribution task, the objective here is to cluster together documents written using the same style, regardless of their authorship.

The results obtained for the style identification in the Brown corpus using the hybrid technique are shown in tables \ref{abd.tab} and \ref{abd2.tab}. Considering the INT+CN combination, \Red{the highest gain in performance were 29.0\% and 25.8\%} for the kNN ($k=4$) and SVM methods. Minor improvements in accuracy were observed for other combinations, which might be explained by the high discriminability rates observed for the traditional techniques based on the frequency of stopwords ($\Gamma=95.6\%$) and character bigrams ($\Gamma = 94.05\%$). The results obtained with the tiebreaker technique were inferior to those obtained with the hybrid technique (results not shown). All in all, the results confirm that the inclusion of topological attributes might also improve the characterization of documents in the context of stylistic-based classification tasks. In this task, the most relevant topological features were the skewness and deviation of the clustering coefficient ($\Omega(\gamma(C)) = 0.172$ and $\Omega(\Delta C) = 0.157$). A visualization of the discriminability provided by topological features is shown in figure~\ref{fig.fig}. %

\begin{table}[h]
\centering
\caption{\label{abd.tab}Best relative accuracy rate $\Gamma_{max}$ obtained with the hybrid classifier based on the k-nearest neighbors method. The threshold employed $\lambda^*$ to obtain the best accuracy rate is also shown.}
\begin{tabular}{|l|cc|cc|cc|cc|cc|}
\hline
 & \multicolumn{2}{|c|}{$k=1$} & \multicolumn{2}{|c|}{$k=2$} & \multicolumn{2}{|c|}{$k=3$} &\multicolumn{2}{|c|}{$k=4$} & \multicolumn{2}{|c|}{$k=5$} \\
 \cline{2-11}
 & $\Gamma_{max}$ & $\lambda^*$ & $\Gamma_{max}$ & $\lambda^*$ & $\Gamma_{max}$ & $\lambda^*$ & $\Gamma_{max}$ & $\lambda^*$ & $\Gamma_{max}$ & $\lambda^*$ \\
\hline
{\bf Intermittence} & 1.127 & 0.50 & 1.232 & 0.55 & 1.05 & 0.48 & 1.290 & 0.76 & 1.000 & -- \\
{\bf Stopwords}     & 1.000 & -- & 1.010 & 0.01 & 1.013 & 0.25 & 1.017 & 0.34 & 1.021 & 0.38 \\
{\bf Characters}    & 1.000 & -- & 1.000 & -- & 1.000 & -- & 1.019 & 0.39 & 1.025 & 0.32 \\
\hline
\end{tabular}
{}
\end{table}

\begin{table}[h]
\centering
\caption{\label{abd2.tab}Best relative accuracy rate $\Gamma_{max}$ obtained with the tiebreaker algorithm applied to the Support Vector Machine (SVM), Random Forest (RFO) and Multi Layer Perceptron (MLP) methods.}
\begin{tabular}{|l|cc|cc|cc|}
\hline
 & \multicolumn{2}{|c|}{{\bf SVM}} & \multicolumn{2}{|c|}{\bf RFO} & \multicolumn{2}{|c|}{\bf MLP} \\
 \cline{2-7}
 & $\Gamma_{max}$ & $\theta^*$ & $\Gamma_{max}$ & $\theta^*$ & $\Gamma_{max}$ & $\theta^*$ \\
\hline
{\bf Intermittence} & 1.258 & 0.51 & 1.111 & 0.63 & 1.015  & 0.29 \\
{\bf Stopwords}     & 1.000 & -- & 1.000 & --   & 1.000  & -- \\
{\bf Characters}    & 1.000 & -- & 1.000 & --   & 1.000  & -- \\
\hline
\end{tabular}
{
}
\end{table}

\begin{figure}
  \centering
  \includegraphics[width=0.45\textwidth]{figura
  \caption{\label{fig.fig}Projection of the Brown dataset using topological features of networks modeling texts. The linear discriminant analysis~\cite{ldaref} was employed to generate the visualization. Note that the variability of the documents classified as imaginative prose is lower than the variability of style observed for informative documents.}
\end{figure}

\section{Conclusions} \label{aconclusao}

In this study, I have shown that the styles of texts can be captured by measuring the topological properties of the corresponding network representation. More important than just to note a dependency between the structure of networks and stylistic features of texts, this study devised techniques to combine traditional and topological features, which were found to be of paramount importance to enhance the quality of current classification strategies. Two traditional stylometry tasks were studied: the authorship attribution and genre identification problems. In both tasks, the addition of topological features provided an improvement in classification performance. The highest improvement occurred with the hybrid classifier, which uses a linear convex combination of features from distinct textual evidences. The different nature of the quantities used to characterize texts suggests a complementary role in capturing distinct aspects of written texts.
\Red{Because the adequate choice of parameters for the proposed technique is far from being a trivial task, I intend, as a future work, to devise a method that automatically assigns a value of $\lambda$ and $\Theta$ for each test instance.}

\section*{Acknowledgments}
I acknowledge financial support from S\~ao Paulo Research Foundation (FAPESP-Brazil)
(grant number 14/20830-0).

\section*{References}

redpajama_set_name: RedPajamaArXiv


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text:
Five Imperatives to Financial Stability in the Post-Pandemic Era
As healthcare organizations seek stability after an unprecedented year, they must find a path forward to a financially sustainable future. Many healthcare organizations face similar problems and opportunities as they continue to address the monumental challenges of the last year.
The Chartis Group has conducted in-depth interviews with Chief Financial Officers (CFOs) and Chief Operating Officers (COOs) at 18 leading health systems about the current state of their organizations and identified five imperatives that organizations must address and related tactics to consider.
By applying these learnings and taking a critical lens to their own operations, executives can start to move forward on a stronger footing as they develop their own financial performance improvement strategies and tactics, tailored to their unique composition, community and organizational needs, and goals.
"There's only one way to go from here. Healthcare organizations cannot succeed by standing still."
Pam Damsky
Director and Performance Practice Co-Leader
Rob Gamble
Chartis Top Reads
As Provider Organizations Plan for the Future, Industry Forces Require Creative Solutions and Heightened Collaboration
In 2023, healthcare providers will need to address considerable recent challenges while positioning for future market changes. Partnerships across healthcare segments will be increasingly important.
From Red to Black: How Chartis Partnered with a Community Health System to Drive Financial Stability
Chartis worked quickly to address a small health system's financial challenges, ultimately setting them up for sustainability and growth.
5 Forces Leading Change in Healthcare
The healthcare ecosystem is changing faster than ever. In our Forces for Change series, Chartis leaders look to the future, outlining the critical questions organizations will face in 2023.

redpajama_set_name: RedPajamaCommonCrawl


text:
Bayer to buy Merck unit
City Index May 6, 2014 9:45 PM

Shares of both firms fell following the news.

The share price of German pharmaceutical company Bayer has slipped today (May 6th) after the company confirmed it has agreed a deal to buy Merck's consumer care business.
Stocks in the US company were down on the back of the news as well, with a statement from Bayer confirming that a deal worth a total of $14.2 billion (£8.4 billion) has been agreed.
Bayer revealed that its US rival is set to separately pay a $1 billion sum over a co-development deal regarding heart failure drugs.
The deal between the two companies coincides with a massive takeover bid by Pfizer, which wants to move forward with proposals for a merger with UK firm AstraZeneca.
"With this transaction, we are acquiring leading product brands," said Bayer HealthCare boss Olivier Brandicourt.
Bayer added in a statement that it expects significant cost savings of around $200 million per year by 2017 to be achieved due to the deal it has been able to strike with Merck over the unit.
The German company also claims increasing its commercial presence, as well as the rolling out of Merck's brands globally, is going to amount to a further $400 million in revenues per year over the course of the next three years.
However, investors did not seem to be impressed, at least initially, by the deal that has been done between the two pharmaceutical companies, with the share price of both firms down today.
By 15:30 BST, stocks in Bayer were down by 0.37 per cent compared to the start of the day, although the value of the shares was still close to the company's 52-week high.
An even larger loss has been sustained by Merck after news of the sale was released, with the share price of the business slipping back by almost two per cent on the New York Stock Exchange by 15:48 BST. Investors will be keeping a close eye on both companies in the coming days to see how markets respond to the deal for Merck's consumer care unit.
Find up to date information on the FTSE 100 and spread betting strategies at City Index


text:
Briana Barbara-Jane Evigan, née le  à Los Angeles, est une actrice et danseuse américaine. Elle est connue pour son rôle d' dans  2 au côté de Robert Hoffman et son interprétation de  dans Sœurs de sang ().

Biographie
Briana Evigan, née le  à Los Angeles, est la fille de l'acteur Greg Evigan, qui a joué dans  et Les Dessous de Veronica, et de la mannequin-actrice Pamela Serpe. Briana est la cadette de la famille. Son frère Jason Evigan est le chanteur du groupe . Sa sœur Vanessa Lee Evigan est, elle aussi, actrice dans des séries américaines comme .

Briana a commencé à étudier la danse à l'âge de neuf ans. Elle est aussi chanteuse et pianiste du groupe . Elle a fait ses premiers pas devant la caméra en 1996 dans  où elle joue la fille de son père, Greg Evigan. En 2003, elle a le premier rôle dans le clip de , Numb, où elle joue une jeune fille perturbée par sa mère très autoritaire. Par la suite, Briana est apparue dans plusieurs films.

La jeune femme joue le rôle d'une fille qui veut devenir actrice dans  en 2004. Puis, en 2006, elle interprète une lycéenne dans . Côté clip, en 2008, Briana tient le rôle principal pour la chanson  d'Enrique Iglesias. L'année 2008 est celle de son premier grand rôle dans  2. Briana Evigan y joue , une nouvelle étudiante un peu rebelle qui arrive dans une école de danse. En 2009, son interprétation de  dans Sœurs de sang () lui permet d'obtenir, avec les autres actrices de ce film, un  dans la catégorie «  ».

Dans le film Burning Bright, elle interprète le rôle principal de . Malgré des critiques positives, le film est sorti directement en DVD, le .

Briana Evigan joue le rôle d' dans le film Mother's Day, remake d'un film du même nom de 1980, prévu pour 2011. Le réalisateur est Darren Lynn Bousman qui est connu pour trois films de la saga Saw. Ils se sont déjà rencontrés en 2008 dans Le Réveillon de la fin du monde (), épisode 6 de la série télévisée . En 2012, elle joue le rôle de Ms. Merrywood dans le film The Devil's Carnival de Darren Lynn Bousman. En 2014, elle rejoue le rôle d'Andie West dans Sexy Dance 5.

En 2022, elle est enceinte de son premier enfant. Elle annonce le 13 août sur Instagram avoir donné naissance à son fils. Le petit garçon est né quinze jours plus tôt, fin juillet.

Filmographie

À la télévision
 2008 : , épisode 6, Le Réveillon de la fin du monde (). Rôle : Helen.
 2012 :  Une star pour Noël  () : Cassie
 2015 : Une nuit en enfer, la série
 2015 :  Escapade princière    : La princesse Katherine
2018 : La femme secrète de mon mari (My Husband's Secret Wife) de Tamar Halpern : Mélanie Jones

Au cinéma
 1996 : Héritage diabolique () : Aubrey South
 2008 :  2 () : Andie West
 2009 : Donnie Darko 2 : L'Héritage du sang (S. Darko) : Corey Richardson
 2009 : Sœurs de sang (Sorority Row) : Cassidy Tappan
 2010 : Dans l'œil du tigre (Burning Bright) : Kelly Taylor
 2010 :  : Butterfly
 2010 :  : Svector Orlaff
 2011 : Mother's Day de Darren Lynn Bousman : Annette Langston
 2012 : Mine Games : Lyla
 2012 : Rites of Passage : Penelope
 2012 : The Devil's Carnival : Ms. Merrywood
 2012 : Piégés : Amy Nash
 2013 :   : Charlotte
 2013 : Caretaker de Marty Murray : Ivy
 2014 : Monica : Tasha
 2014 : Sexy Dance 5: All in Vegas : Andie West
 2015 : ToY : Chloe
 2015:  Once Upon a holiday : Katie
 2018 : Cops Incrimination (River Runs Red) de Wes Miller : Marilyn

Clips vidéos
 2003 : Numb de .
 2007 :  de Flo Rida avec .
 2008 :  d'Enrique Iglesias.
 2008 :  de .
 2008 :  de Sammy Smith, chorégraphié par Briana Evigan.
 2009 :  de Shwayze.

Courts-métrages
 2019 : Angel of anywhere de James Kicklighter : Michelle

Distinctions

Récompenses
 2008 : MTV Movie Awards du meilleur baiser partagée avec Robert Hoffman dans un drame musical pour  2 () (2008).
  2009 : Lauréate du Prix de la star féminine de demain partagée avec Leah Pipes, Rumer Willis, Jamie Chung, Audrina Patridge et Margo Harshman.
 2022 : Shockfest Film Festival du meilleur thriller pour Four Hour Layover in Juarez (2022) partagée avec Wesley Alley (Réalisateur), Eric Arnold Cortez (Producteur), Amanda Markowitz (Producteur), Lou Pizarro (Producteur), Ryan McCoy (Directeur de la photographie), Erin Sanders (Actrice), Mike Manning (Acteur), Rene Aranda (Actrice), Maitlyn Pezzo (Actrice), Brian C. Chenworth (Coordinateur des cascades), Derek Larsen (Monteur), Kung Fu Vampire (Acteur), Alba Villaronga Cases (Actrice), Mary Elizabeth Boylan (Actrice) et Steven Ciceron (Acteur).

Nominations
  des Teen Choice Awards 2008 : Révélation Féminine de l'année dans un drame musical pour  2 () (2008).

Références

Liens externes

  Briana Evigan OnLine - #1 Source for Briana Evigan
  Briana Evigan biographie sur cinemotions.com

Actrice américaine
Chanteuse américaine du XXIe siècle
Danseuse américaine du XXIe siècle
Naissance en octobre 1986
Naissance à Los Angeles

redpajama_set_name: RedPajamaWikipedia


The World Exhibition opened on Saturday in Kazachstan's capital attended by more than 10,000 visitors. It addresses humankind's greatest challenge of our times – energy supplying in the future. Scenarios are developed in five theme areas illustrating what life might look like for humankind in the future. The extent to which artificial intelligence can influence, enhance or replace human creativity in the future is addressed in the EXPO art pavilion, where the world's first international group exhibition on Robotic Art is being presented, accompanied by thrilling live acts and film presentations in the adjoining Creative Energy Area. National company Astana EXPO-2017 commissioned the international creative agency Avantgarde with execution and overall coordination.
The art pavilion nestles at the heart of the 113 hectare EXPO grounds. There, 17 artists from 13 countries show their works, which were created with the help of algorithms and computer-animated programs.
The exhibition is curated by participating artist Miguel Chevalier in cooperation with Jérôme Neutres from the RMN - Grand Palais in Paris, which is responsible for the artistic components of the EXPO 2017 in Astana. The Garage Museum, Moscow developed an extensive supporting programme with creative people from all over the world for different target and age groups, including children's workshops and an inclusion programme. Adjoining the art pavilion, the Creative Energy Area is not only a place to chill and reflect, but also to provide new creative impulses with theatre, music, literature and film performances. Quayola's robot is another crowd-puller there.
Memo Akten, Jacopo Baboni Schilingi, Michel Bret & Edmond Couchot, Miguel Chevalier, Demian Conrad (Automatico), Elias Crespin, Michael Hansmeyer, Raquel Kogan, Peter Kogler, LAb[au] (Laboratory of Architecture and Urbanism), Sonia Laugier & François Brument (Inflexions), Rafael Lozano-Hemmer, Leonel Moura, Nervous System, Quayola, Stelarc, Patrick Tresset.
The live programme mainly features young Kazachstan artists like singers and authors Galymzhan Moldanazar and Akmaral Zykaeva, who combine traditional regional music elements with contemporary western influences from funk, fold, electro and pop music. Other acts include City & Shivers, an Indie band from Kazachstan, who are now based in England to bring their style of music to audiences in the western world.