Waching daily Feb 2 2017

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For more infomation >> (DCV For Live) - Kreeft met zee darts, interessante levenservaring Ontdek het leven - Duration: 3:09.

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(DCV For Live) - De reis naar de Gorge plezierige ervaring jagen, garnalenvisserij, vangst slangen - Duration: 6:53.

For more infomation >> (DCV For Live) - De reis naar de Gorge plezierige ervaring jagen, garnalenvisserij, vangst slangen - Duration: 6:53.

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Dramatic Celeb Transformations That Are Still Hard To Believe - Duration: 6:42.

It's virtually impossible to go through life without experiencing some sort of recognizable

change.

It's a rite of passage, and it's bound to happen sometime.

But the main difference between us and celebs is that we have the privilege of changing

in private.

Although transforming while the world is watching may be traumatizing for some celebs, their

ups and downs can often be inspirational...or a perfect example of what we should never,

ever do.

For better or worse, here are a few stars who went through intense transformations.

Khloé Kardashian

The transformation of Khloe Kardashian from curvy girl to a proud bearer of rock hard

abs is truly inspiring.

Of course, haters are gonna hate, but there's really no denying that Kardashian has totally

whipped her famous bod into shape over the last couple of years, which she's documented

in her app.

She even released a bestselling book titled, Strong Looks Better Naked, which helps to

cement her status as the most ripped Kardashian sister.

Khloe's upcoming show, Revenge Body, will get into her transformation process even deeper,

so get ready for more of less-Khloe.

Kylie Jenner

Emerging from her older sisters' shadows and becoming a coveted star in her own right,

Kylie has become the most-viewed person on Snapchat, and has a habit of selling out of

newly released products faster than you can click.

Although there was controversy surrounding whether or not she'd received lip injections,

she turned the negativity around and created her own cosmetics line.

Although the youngest of the Kardashian/Jenner clan, Kylie has completely secured her place

as one of the most influential businesswomen from the notorious family.

With a successful cosmetics line and upcoming clothing collection, Kylie is well on her

way to breaking the internet through her Kylie Cosmetics line, forging her own empire one

lip kit at a time.

Justin Bieber

Once an adorable child star under the master tutelage of Usher, the effortless flow of

his famous bowl cut was immaculate, and he seemed to have the voice of an angel.

Bieber had most teenage girls, and a few confused grown women, in the palm of his hand.

Unfortunately, Bieber eventually spun out of control, making the transition from teenage

heartthrob, to being convicted of multiple crimes over the years.

After the DUI, resisting arrest, alleged drug abuse, and assault, many people were convinced

that his career was over for good.

But he may be leaving his bad boy antics behind.

He now attends Hillsong Church, a popular place of worship among celebrities, and he's

also regained the interest and popularity of fans old and new through new hits like,

"Sorry."

Which he probably is after all of those arrests.

Raven-Symoné

Launching her career on The Cosby Show, and transitioning into the title character for

the hit Disney series, That's So Raven, Raven Symone won our hearts with her various "wholesome"

roles.

Today, Raven has embraced a much edgier look, including new piercings, daring hairstyles,

and dark makeup.

Her appearance on Oprah and her platform on ABC's The View has allowed her to express

herself freely on various topics, where she generally took a more progressive stance.

Symone has revealed that she's leaving The View to return to a revamp of That's So Raven,

so as much as she's changed, she's still returning to her roots.

Britney Spears

She began her career as pop royalty and stayed lodged in our iTunes playlists for years,

but Britney Spears ultimately reached her breaking point in 2008 after feuding with

her ex-husband, Kevin Federline, over her children.

Many of her fans feared that she'd never be the same pop princess again after she shaved

her head and started using umbrellas as weapons.

"Britney spears.

She's bald."

Luckily, she made another transition back to her rightful place as eternal pop princess,

securing a residency in Las Vegas, and continuing to kill it at virtually every award show she's

invited to perform at.

We're left wondering…will she bring back the snake?

Nicole Richie

"You ever had a real job?"

"No."

Originally gaining fame as a vapid, lazy, party girl and Lionel Richie's secret shame

on The Simple Life, Nicole Richie has successfully put away her questionable past and become

grounded in maturity.

These days, she's a wife, a mother, and businesswoman, opening House of Harlow 1960, a jewelry and

clothing line named after her first daughter.

She also created a hilarious series entitled Candidly Nicole, returning to her reality

TV roots in an entirely new way.

"What about anemones, which are my very favorite flower at the moment.

They're very chic."

"They're a little slutty."

"Well, so are you, so maybe that's the perfect flower."

"Okay.

Alright."

Richie has transitioned from the rebellious young partier to an entrepreneur and philanthropist,

giving back through such charities as The Richie Madden Children's Foundation and Baby2Baby.

Jennifer Hudson

Introduced to us in season 3 of American Idol, Jennifer Hudson didn't go on to win that year,

but she was able to secure a major role in the film Dreamgirls alongside none other than

Miss Beyonce Knowles.

Not only did she put on a stellar performance, but Hudson also won an Academy Award for Best

Supporting Actress.

It's no secret that her character, Effie, was typically illustrated as slightly bigger

than her counterparts, and at that time, Hudson was proud to represent the curvy skin she

was in.

Through her partnership with Weight Watchers, Hudson has lost a significant amount of weight,

and is looking happier than ever.

Mandy Moore

Even though she's traded in her blond locks and singing talent for a brunette coif and

acting chops, Mandy Moore remains beloved by her loyal fans.

Her new life arguably started with her role in A Walk to Remember, as a girl diagnosed

with leukemia who happens to fall in love with the bad boy next door.

It's clear that Moore has found a love in full-time acting, transitioning from singing

to playing Rebecca Pearson, the matriarch character in NBC's This Is Us, for which she

was nominated for a "Best Supporting Actress" Golden Globe!

That's not something you get for just singing.

Caitlyn Jenner

Transforming from a man to a woman is no easy feat, especially when you're in the public

eye, and an Olympic medalist with ten children.

Starting life as Bruce Jenner, Caitlyn Jenner officially started her new life in 2015, after

revealing herself to the world on the cover of Vanity Fair.

Her E! reality show, I Am Cait, delved deep into the first few stages of Jenner's new

experiences as a woman, as well as her family and friends' reaction to her transformation.

Jenner has since helped to pave the way for other LGBTQ people to speak their truths,

and transition proudly in the ways that best support who they are as people.

Thanks for watching!

Click the List icon to subscribe to our YouTube channel.

Plus check out all this cool stuff we know you'll love, too!

For more infomation >> Dramatic Celeb Transformations That Are Still Hard To Believe - Duration: 6:42.

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Encodage de l'information 2 : Compression - Duration: 8:34.

Hi everybody!

In the last video we have seen how to encode information in general

In this video we will see how to make the information as small as possible in the computer

We will talk about compression

We must distinguish two types of compression : lossless compression and lossy compression

lossless compression is used for example for text or zip folder

which means we can't allow to lose any information

on the other hand with lossy compression, which is used typically for image, sound, video

we can afford to lose some information

So for an image if I don't encode all information it won't be too perceptible by the human eye

so we can afford to discard that information

Let's start with lossless compression

There are several methods, we are gonna see one called Huffman encoding

The idea is intuitive : in a text you have characters appearing more often than others

So we could encode the most frequent ones with a short sequence of bits

and the rarest ones with a long sequence of bits

If you take for example a string with only 5 different characters a,b,c,d,e

We have seen last time how we can encode each letter with a sequence of bits

but the same length for each letter

But actually it is not necessary we can encode each letter with a sequence of different length

The list of letters along with their encoding sequences is called a encoding dictionary

we can easily represent these encoding with what we call an encoding tree, like this

So we have here our encoding tree

and here we have a sequence we want to decode using the tree

so we start with the first bit 0

and from the beginning of the tree we follow the symbol so here wo go to the left

we start again with the second bit

we continue to the left and find the symbol 'a'

so we know that these 2 bits of information represent the symbol 'a'

Then we start again from the top of the tree with the third bit 0

So we go again to the left but this time the 4th bit is 1 so we go right

we find the symbol 'b' and so we know these 2 bits represent the symbol 'b'

then the 5th bit is 1 so this time from the top of the tree we go to the right

then we have a 0 so we go to the left

then we have a symbol 1 so we go to the right and find the symbol 'd'

so we see that these 3 bits are the symbol 'd'

similarly we then have a 1 and again a 1 and find the symbol 'e'

and then we have 100 if you follow the tree you can see it is the symbol 'c'

So this sequence is abdec

So now imagine in this text there are 200 a, 300 b, 40 c, 60 d and 400 e

so we have a text of 1000 characters

Now if I try to encode this text with the first dictionary with a constant length of 3 bits per symbol

it will give us a length : **on screen**

Now if I try to encode the text with the second dictionary

this time we have 2 bits to encode a,b and e

So the total length will be : **on screen**

As you can see, only by changing the dictionary, with the most frequent symbols

encoded with a shorter sequence of bits, we managed to go from 3000 bits to 2100 bits for the same text

We have then saved 900 bits which is a 30% compression rate

another way to calculate the compression is with the probabilities of each symbol's appearance

so for example if you consider 'a' there are 200 'a' in the text out of 1000 characters

so the probability that a given symbol is an 'a' is 0.2

similarly for the other symbols we have these probabilities

We can now calculate the mean length of a symbol in bits

to that end we sum over all probabilities multiplied by their sequence length

So for the first dictionary it gives us a mean length of 3 bits per symbol

and for the second dictionary it gives us a mean of 2.1 bits per symbol

We could wonder if we tried a third dictionary if we could achieve an even better compression

But we won't test all possible encodings to find the one that gives us an optimal compression

For that we have a method called the Huffman algorithm that allows us to find the perfect encoding that maximizes the compression

We will in another module exactly what is an algorithm in details

but for now you only need to know that it is like a recipe, a method to solve a problem

In our case we have list of symbols with their probabilities and we want a method to find

the optimal encoding maximizing the compression

So with the Huffman algorithm we will construct an optimal tree

I will write the different symbols along with their probabilities

**on screen**

Then we will take the 2 symbols with the lowest probabilities

and put them together to build the tree from these 2 symbols

Here with c and d I can build the tree from these 2

Here we indicate a bit 0 and a bit 1

and then i add these 2 probabilities which gives us 0.1

then from this probability and the 3 remaining, i start over the same process

so i again take the 2 lowest probabilities, in this case 'a' with 0.2 and 0.1

and i link them

I again write 0 and 1, I add them and it gives me 0.3

and we start over : we take the 0.3 with the 'b' (also 0.3) because these are the 2 lowest probabilities

I link them and i gives me 0.6

Then i only have the 'e' left. Of course the sum of the last 2 probabilities should always be 1

With the Huffman algorithm we could compress even more

We can prove, but we won't do it here, that the Huffman algorithm is always optimal

which means there is no other better encoding to compress

Now if we have a text of 200 pages with 1000 characters per page so 200000 characters

we will see how many bits it takes with our different encodings

If we take our first encoding it gives us **on screen**

With our second modified encoding : **on screen**

and if we take our third encoding with the Huffman algorithm it gives us : **on screen**

Let's move on to lossy compression

There are several methods, I will present here the main method

In essence, information like images, sound or videos can be seen as a signal with frequencies

It is possible to filter these frequencies to keep only the most important

meaning those that are the most perceptible for the human senses

This way we can compress information a lot

In particular, if you consider a JPEG image, we can reduce its size up to 20 times without the loss being perceptible to the human eye

The presentation I gave you is superficial because to really understand this method

You must have some knowledge about signal processing, integral calculus and Fourier transforms

and so i will not go into details here but for those interested here is a link towards a more advanced video

I hope you understood this introduction to data compression and see you soon for a new video!

For more infomation >> Encodage de l'information 2 : Compression - Duration: 8:34.

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POWERFUL # war film "gray NIGHT" # 2017 Russian New! - Duration: 1:41:54.

POWERFUL # war film "gray NIGHT" # 2017 Russian New!

For more infomation >> POWERFUL # war film "gray NIGHT" # 2017 Russian New! - Duration: 1:41:54.

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Prayer of the day Thursday, february 02, 2017 - Duration: 11:28.

For more infomation >> Prayer of the day Thursday, february 02, 2017 - Duration: 11:28.

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Relaxation music & Inspirational Quotes - Duration: 3:16.

For more infomation >> Relaxation music & Inspirational Quotes - Duration: 3:16.

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(DCV For Live) - Ervaar realistische Hoe te overleven in de jungle Ontdek het leven - Duration: 8:42.

For more infomation >> (DCV For Live) - Ervaar realistische Hoe te overleven in de jungle Ontdek het leven - Duration: 8:42.

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5 Powerful Health Benefits of Kefir (Backed by Science) - Duration: 4:37.

For more infomation >> 5 Powerful Health Benefits of Kefir (Backed by Science) - Duration: 4:37.

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คลิบผีของจริง กับรวมคลิบสิ่งลี้ลับที่โดนจับภาพเอาไว้ได้ - Duration: 15:06.

For more infomation >> คลิบผีของจริง กับรวมคลิบสิ่งลี้ลับที่โดนจับภาพเอาไว้ได้ - Duration: 15:06.

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皇室戰爭教學-如何對抗飛斧屠夫|劊子手完剋飛桶流?實戰解說|CLASH ROYALE how to counter Executioner - Duration: 14:13.

For more infomation >> 皇室戰爭教學-如何對抗飛斧屠夫|劊子手完剋飛桶流?實戰解說|CLASH ROYALE how to counter Executioner - Duration: 14:13.

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(DCV For Live) - Het vissen op haaien, haai koken in het bos, Experience Life - Duration: 7:46.

(DCV For Live)

For more infomation >> (DCV For Live) - Het vissen op haaien, haai koken in het bos, Experience Life - Duration: 7:46.

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Новая Полиция Одессы - Оспариваем двойную сплошную Ч.2 - Duration: 17:52.

For more infomation >> Новая Полиция Одессы - Оспариваем двойную сплошную Ч.2 - Duration: 17:52.

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Bacon Pineapple Bites Recipe - Amy Lynn's Kitchen - Duration: 2:48.

[Amy: Creator] Hey everyone!

I'm Amy.

And it's almost time for the Super Bowl!

So today I'm making bacon pineapple bites.

[background music] These pineapple chunks are wrapped in bacon

and then covered with a sauce.

They're baked in the oven to make a delicious

sweet and salty treat.

They're great for the Super Bowl!

So let's get started!

Preheat your oven to 350 degrees

and take a small baking sheet

and line it with parchment paper.

And then set it aside.

For this recipe, you will need 30 pineapple chunks.

So take a 20 ounce can of pineapple chunks

and remove 30 of them.

And then drain them and dry them between paper towels.

Take some bacon slices and cut them into about 3 inch pieces.

Now wrap a cut piece of bacon around a pineapple chunk

and secure it with a toothpick.

And then place it onto your prepared baking sheet.

Now repeat until all of your pineapple chunks are wrapped in bacon.

And then set them aside.

Now we're going to make the sauce.

So take a saucepan

and add 3 tablespoons of brown sugar,

1/2 cup of ketchup,

1/4 cup of water,

and 1/4 cup of grape jelly.

Now cook this over medium high heat stirring constantly.

Bring it to a boil and then reduce the heat

and let it simmer for about 5 to 10 minutes

or until it thickens.

Then spoon the sauce over each pineapple chunk.

Now just bake this in the oven for about 30 minutes

or until the bacon is fully cooked.

And here they are... bacon pineapple bites.

These appetizers are really easy to prepare and

they're sure to be a big hit at your Super Bowl parties!

For this recipe and many more, check out my website at

amylynnskitchen.com.

You can also find me on Instagram, Twitter and Facebook!

[bloopers] Hey everyone, I'm Amy!

Woah.. [laughing] gee-whiz..

do that again!

Whew! [laughing] Noooo! [laughing]

Nooooooooo! [laughing]

For more infomation >> Bacon Pineapple Bites Recipe - Amy Lynn's Kitchen - Duration: 2:48.

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Complément : Signal Processing - Duration: 3:00.

Hi everybody!

Welcome to this complement video about signal processing and lossy compression

It is a complement to my main video about compression that you can watch here

In this video i will present the basic mathematics of signal processing

and how we can do lossy compression with it

I assume here you already have some basics mathematics knowledge, in particular about Fourier transforms

The idea of signal processing is that analog information can be seen as a function we call 'signal'

In the case of sound, it is a function from R to R

I remind you here the definition of a Fourier transform

which shows we can express a function as a sum of sines and cosines

We use here the Euler's formula to convert an exponential to a sine and cosine

We will then represent a signal as a discrete sum of sines

Here for each sine 'ai' denotes the amplitude of the signal, 'fi' the frequency and 'di' the phase

An important notion we must define for a signal is what we call the bandwidth

which is nothing else but the maximum frequency of the signal

When we have an analog signal we want to encode

We will choose different points of the function and encode each of them in the computer

This process is called 'sampling'

So now we don't have a signal defined at every point t anymore

But a signal only defined at points nTe

Te is the sampling period

and the inverse of the sampling frequency

So you can imagine that the more points we have the better we will be able to reconstruct the original signal

Which means the sampling frequency must be high

How high must it be?

Well we have the sampling theorem that tells us the sampling frequency must be greater than twice the bandwidth

So that we can reconstruct the original analog signal

So if this condition holds, it is possible to reconstruct perfectly the signal

with the interpolation formula

As you can see it is only a theoretical formula since we sum over all numbers in Z

So this reconstructed signal is characterized by frequencies

and we will be able to apply filters on these frequencies

one of the simplest filter is the low-pass filter

which is defined with a cutoff frequency fc

we will simply remove all frequencies greater than the cutoff frequency

We then have what we call the moving-average filter

which consists in integrating the signal over a predefined period

So actually it is a mean over a predefined interval

So with these filters we can delete the unnecessary information

and keep only the most important data

and compress the information

I hope you understood the basics of signal processing and lossy compression

I also hope you enjoyed this more advanced and technical format

feel free to comment if you want something even more advanced or on the contrary something more accessible

And see you soon for a new video!

For more infomation >> Complément : Signal Processing - Duration: 3:00.

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รวมคลิปเทพๆ คลิปเจ๋งๆ สนุกๆดูเพลิน - Duration: 15:31.

For more infomation >> รวมคลิปเทพๆ คลิปเจ๋งๆ สนุกๆดูเพลิน - Duration: 15:31.

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(DCV For Live) - Ontdek de geheimen van de goudmijn in het bos, ontdekken opwindend leven - Duration: 10:04.

(DCV For Live)