text:
Q: Need help creating Cron to download XML file daily I would like to create a cron job to download an xml file daily at midnight.
I want to overwrite any pre-existing file as well.
I tried
1 0 *** usr/bin/wget  http://ace-tv.xyz:25461/xmltv.php?username=xxxx&password=xxxx --output-file=/home/username/myxmlfile.xml

I get a 200 ok for connecting...but nothing downloads.
I think the problem has something to do with the request coming from a php server and I'm not familiar with wget or curl enough to figure it out.
If I paste the URL into a browser the xml schema shows up and I can right click and do "save as" but I need this done by cron.
Any help, greatly appreciated.

A: For this purpose there is "--output-document=.." option:
1 0 *** usr/bin/wget --output-document=/home/username/myxmlfile.xml "http://ace-tv.xyz:25461/xmltv.php?username=xxxx&password=xxxx"

Also please note that you need to use URL string in quotes if it contains characters like "&"


text:
Kris Spisak
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S2: E6 – All the Honeys Makin' Money — or is it "Moneys"? "Monies"?
"Death, taxes and childbirth! There's never any convenient time for any of them." So says Margaret Mitchell, author of Gone with the Wind, and you know, it's a true statement.
It's tax season, so let's dive in. Into the numbers? Nope, not here. Let's dive into the language of money.
When should "money" be written in the plural form "monies"? When should we talk about "the amount of" something versus "the number of" something? Yep, there's a difference. And lastly, what is the language equation that baffles math minds everywhere where the plural plus the plural of this money term equals the singular form? (You've probably heard me talk about this last one before, but it's one of my favorite math equations. I can't help but bring it to my podcast.)
This is season 2, episode 6 of the Words You Should Know podcast
S2: E5 – Et tu, Brute! (And the Latin Prefixes that Stump You)
Why do I call this the "Words You Should Know" podcast? It's not a lecture. The whole idea of grammar police annoys me. This is the podcast that reminds you that Ben Franklin and Kurt Cobain both influenced our modern English language, and spelling memes deserve to be debunked. See past episodes for those details.
And speaking of last episodes, it's kind of ironic that the episode after I talk about apples, "how do you like them apples," and old versions of the expression about how they keep the doctor away, I get sick.
But onward, as always. Thanks for bearing with my slightly scratchy voice today.
S2: E4 – How do you Like Them Apples? (No, seriously, let's talk this out)
If you're listening along as I publish each episode, you might know it's flu season in the U.S. I'm not going to go into how to spell "flu," as in influenza, versus a chimney "flue" or the past tense of "fly," but I do want to talk about how an apple a day can keep the doctor away. Or if not the medical side of this conversation, let's at least turn to the language side of things.
Apples. Are they "preventative" or "preventive"? What's the surprising linguistic twist of the Bible's mention of the apple in the Garden of Eden? And while we're on the subject, what's the explosive story behind the expression, "how do you like them apples?"
Apples, apples, everywhere. This is the "Words You Should Know" podcast, season 2, episode 4
S2: E3 – "Butts" Where They Shouldn't Be (i.e., Expressions you should really handle better)
It's time to get a grip on your communications, isn't it?
The English language can be difficult, I know. But there's a certain pet peeve of mine we're going to dive into today. It's all about the word "butt." And it's not about starting a sentence with it. I'm talking about butts—B-U-T-T-S—that shouldn't be there.
What are you walking into here? Keep listening to find out.
S2: E2 – The plural form of "you" already exists (and all these other words you've always wanted)
This podcast is on one of my favorite explorations. It's all about words you wish existed.
Sometimes the world seems annoyed that there just isn't a word to express exactly what you want. There's no English word for that intimate feeling of sitting around a fire in the winter with close friends (though it exists in Danish: hygge). There's no word for the feeling of anticipation when you're waiting for someone to show up at your house and you keep going outside to see if they're there yet (though it exists in Inuit: iktsuarpok). Or what about the word for that panicky hesitation just before you have to introduce someone whose name you can't quite remember? (The Scots call this tortle.)
What's the word for the act of annoying older brothers (vybafnout)? Or the scratching of your head in order to help you remember something you've forgotten (pana po'o)? Or that old trick where you tap someone lightly on the opposite shoulder from behind to fool them (mencolek)? Just look at Czech, Hawaiian or Indonesian respectively, and they have your answers.
Of course, sometimes the word you need seems simple. How do you differentiate a singular versus plural "you"?
But here's the secret. For this one, at least, the English language already has an answer.
S2: E1- When Grammar Goes Big: Beyoncé, Gwen Stefani & Broadway (Bringing in the New Year by Ringing in the New Year)
This is the start of season 2. How to start with that bang? Let's talk Beyoncé, Gwen Stefani, and a classic Broadway show—and, of course, the words they use. You might not have known it, but they have a major disagreement on the usage of a phrase. Specifically, should it be "if I were" or "if I was"?
Today, we're going to get down to the root of it, plus answer another big one that I know has been bothering you: Is it "ring in the new year" or "bring in the new year"?
It's time to dive in.
S1: E12 – Don't Meddle with My Metal Medal (or my Oranges)
At twelve months old, babies are already beginning to recognize "adjacent relationships" of words, how language fits together to organize itself. The learning process with listening, speaking, and writing, begins there and often lasts for a lifetime.
There's no medal awarded for correctness, but a strong command of your words is one way to show your mettle. But we'll get back to that.
S1: E11 – That "turkey" isn't from Turkey
Now that we've established whether it's "prouder" or "more proud" than a peacock in last week's episode, I want to keep talking about birds. Why? Because calling a North American turkey a "turkey" is up there with the earliest of #fakenews. But, accuracy has never been the English language's strong suit.
It's the week of Thanksgiving in the United States, and this is the time of year one question is asked time and time again:
Why on earth do we call a turkey a "turkey"?
S1: E10 – "Prouder" or "More Proud" than a Peacock? (Plus a Spelling Meme Debunked)
Is it possible to be more proud than a peacock? Or, wait, is it "prouder" than a peacock? One is the correct form, but do you know the answer?
Let's talk language tips for the curious or confused. This is Season 1, Episode 10. How we have only two episodes left in this first season, I have no idea, but here we are.
I'm presently building my list of ideas for Season 2, so contact me if you have a question or some fascinating word trivia to share. But for now, let's dive in.
S1: E9 – Cowboy Slang and Ace-High Wild West Typos
Language tips for the curious or confused. Because we really can fit into either category, depending on the day, the moment, the answer that slipped from your mind.
This is episode 9 of season 1, and I'll admit it, this podcast started out as an experiment. Who in the world is as fascinated as I am by words and language and fine-tuning our communications, spit-polishing them until them shine? It turns out, I'm not alone. I love it.
But let's dive in. Or should I say, let's giddy-up. Today, we're talking about cowboys.
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25 Business Writing Redundancies
25 Fiction Writing Redundancies
S3: E6 – Why "Soccer" instead of "Football," Americans? Seriously, why?
S3: E5 – Where Do We Get the Word "Hipster"?
S3: E4 – The Origin of "Silhouette" & How Do You Spell "Piecemeal" ("Peacemeal"? "Piecemail"?) Anyway?
S3: E3 – The origin of "compete" and "boycott" & is "lineup" / "line up" one or two words anyway?
S3: E2 – "Bragging rights" (or "rites")? "Rites (or "rights") of passage"? And what's the story about "braggadocio"?
Writing Tip 395: "Daylight Savings Time" or "Daylight Saving Time"?
Writing Tip 394: "Succession" vs. "Secession" (& "Secede" vs. "Succeed")
Writing Tip 393: "Maze" vs. "Labyrinth"
Writing Tip 392: "Sick" vs. "Sic" (or "Sick 'Em" vs. "Sic 'Em")
Writing Tip 391: "Manila" vs. "Vanilla" Folders
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I know I answered this one in my previous Christmas Tag, but I will say it again, Mince pies with brandy sauce. I say this because it is truly the one thing on Christmas day that I eat, that I don't have at any other time of year. It is delicious and as soon as I see Mince pies in the store I feel all giddy and in the Christmas spirit, and as soon as I smell the brandy sauce being warmed up, I want to jump straight to dessert.
3: Favourite Day of Christmas.
That would be Mariah Carey's 'All I want for Christmas' followed by 'Never had a dream come true' by S Club 7. But I pretty much love all the old classic British Christmas songs.
'The Santa Clause' and 'The Holiday', they both make me feel so happy inside and ready for the Christmas season.
We always have crackers on Christmas day and New years day and its so much fun sharing them with each other and trying to win. My family likes to wear our paper hats and tell the jokes and laugh about how bad they are and how goofy the toys are. The ones my Mum gets usually have toys like marbles, tweezers, a mini sewing kit, clippers, magic tricks and paperclips in them.
Christmas trees!! :) I adore my childhood Christmas tree with all its colour and sentimental ornaments, I could sit by it and stare at it all day long. I get so giddy when it's time to put it up. And this year I absolutely LOVE the new tree that my husband and I got, its the prettiest thing and it is definitely one of my new favourite things about Christmas.
10: Favourite Christmas Candle Scent.
I smiled so big when I first saw the Samsung advert with Dax Shepherd and Kristen Bell, it is the most adorable advert ever and so so cute, I could just watch it over and over again.
Also, when I came home my sisters showed me the Sainsburys advert and I do have to say that that bought a tear to my eye and I thought it was rather beautiful and is definitely up there too in being a favourite.
Going to put some on, not all.
Favourite Festive Food: Turkey, always think of Turkey when I think of Christmas dinner. And leftover for Sandwich.
Favourite Christmas Song: Christmas Singles by Spitting Image it was a song from tv show called Spitting Images, 1980-1997, it catchy and funny, never stop listening to it (https://www.youtube.com/watch?v=Xb_OT9sk-U0&index=3&list=LL7CuedHapvTefg2lIDD3FPw) . Also Christmas Time by The Darkness.
Favourite Festive Film: Home Alone 2, that all I can't think of and like. It also wanted me to go to New York. Don't know, Disney's Forzen.
Favourite Christmas Decoration: Christmas Tree, an icon of Christmas. Wanted one when I was young but my dad don't want any of it. Now I got a small Christmas Tree in my room, like it small.
Favourite Place to Spend Christmas: At home.
Sorry these are the questions I can answered.
Loved this! Hope you had a fab time here,love!


SEATTLE - One day after breaking a conference record with her seventh Pac-10 Pitcher of the Week honor, Washington pitcher Danielle Lawrie made history again when she was named the USA Softball National Player of the Week for the third time this season, the first player ever to do so.
Lawrie was also named the NFCA National Player of the Week to sweep the national honors for the second time this season. She won the first USA Softball Player of the Week honor of the season for the week of Feb. 8-14 when she also claimed the NFCA Award and also earned the USA Softball honor during week nine, April 5-11.
The senior went 3-0 last week against Stanford to help the Huskies clinch their first Pac-10 title since 2000. She fired two shutouts in the three games, including her fourth no-hitter of the season, allowing just one run and six hits in 20 innings pitched, with the lone run of the weekend coming on a solo home run off the bat of USA National Team player Ashley Hansen. Lawrie struck out at least 10 in each game and walked just three combined all weekend as she moved just 11 strikeouts shy of Alicia Hollowell's Pac-10 career record.
On top of being 33-2 with a 0.92 ERA and 392 strikeouts for the season, Lawrie is 15-2 with a 0.86 ERA, 197 strikeouts and seven shutouts in 17 appearances in Pac-10 play. In fact, since her last loss at Oregon on April 17 in which she allowed a season-high tying four runs, Lawrie has gone 9-0 with a 0.23 ERA, allowing just two runs in 61 innings while striking out 109 batters.


I am passionate about many activities, one of which is helping others. I have had many opportunities to assist others. I have learned that having pride in a strong community is something that is well worth in someones life.,Throughout my summers volunteer service, I have learned how to be humble, but also how to have confidence.
By already having the background of working with special needs individuals, I feel that I am able to start a special education field of study with a passion and interest for this program.
When I think of things that inspire its not just one person, its a whole group of people.


Please use our return request located in the web store to initiate a return of products. Please note that products eligible for return must be in new, sellable condition. Personalized products are not returnable.
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text:
Q: Efficient ways to iterate over several numpy arrays and process current and previous elements? I've read a lot about different techniques for iterating over numpy arrays recently and it seems that consensus is not to iterate at all (for instance, see a comment here). There are several similar questions on SO, but my case is a bit different as I have to combine "iterating" (or not iterating) and accessing previous values.
Let's say there are N (N is small, usually 4, might be up to 7) 1-D numpy arrays of float128 in a list X, all arrays are of the same size. To give you a little insight, these are data from PDE integration, each array stands for one function, and I would like to apply a Poincare section. Unfortunately, the algorithm should be both memory- and time-efficient since these arrays are sometimes ~1Gb each, and there are only 4Gb of RAM on board (I've just learnt about memmap'ing of numpy arrays and now consider using them instead of regular ones).
One of these arrays is used for "filtering" the others, so I start with secaxis = X.pop(idx). Now I have to locate pairs of indices where (secaxis[i-1] > 0 and secaxis[i] < 0) or (secaxis[i-1] < 0 and secaxis[i] > 0) and then apply simple algebraic transformations to remaining arrays, X (and save results). Worth mentioning, data shouldn't be wasted during this operation.
There are multiple ways for doing that, but none of them seem efficient (and elegant enough) to me. One is a C-like approach, where you just iterate in a for-loop:
import array # better than lists
res = [ array.array('d') for _ in X ]
for i in xrange(1,secaxis.size):
  if condition: # see above
    co = -secaxis[i-1]/secaxis[i]
    for j in xrange(N):
      res[j].append( (X[j][i-1] + co*X[j][i])/(1+co) )

This is clearly very inefficient and besides not a Pythonic way.
Another way is to use numpy.nditer, but I haven't figured out yet how one accesses the previous value, though it allows iterating over several arrays at once:
# without secaxis = X.pop(idx)
it = numpy.nditer(X)
for vec in it:
  # vec[idx] is current value, how do you get the previous (or next) one?

Third possibility is to first find sought indices with efficient numpy slices, and then use them for bulk multiplication/addition. I prefer this one for now:
res = []
inds, = numpy.where((secaxis[:-1] < 0) * (secaxis[1:] > 0) +
                   (secaxis[:-1] > 0) * (secaxis[1:] < 0))
coefs = -secaxis[inds] / secaxis[inds+1] # array of coefficients
for f in X: # loop is done only N-1 times, that is, 3 to 6
    res.append( (f[inds] + coefs*f[inds+1]) / (1+coefs) )

But this is seemingly done in 7 + 2*(N - 1) passes, moreover, I'm not sure about secaxis[inds] type of addressing (it is not slicing and generally it has to find all elements by indices just like in the first method, doesn't it?).
Finally, I've also tried using itertools and it resulted in monstrous and obscure structures, which might stem from the fact that I'm not very familiar with functional programming:
def filt(x):
  return (x[0] < 0 and x[1] > 0) or (x[0] > 0 and x[1] < 0)
import array
from itertools import izip, tee, ifilter
res = [ array.array('d') for _ in X ]
iters = [iter(x) for x in X]   # N-1 iterators in a list
prev, curr = tee(izip(*iters)) # 2 similar iterators, each of which
                               # consists of N-1 iterators
next(curr, None) # one of them is now for current value
seciter = tee(iter(secaxis))
next(seciter[1], None)
for x in ifilter(filt, izip(seciter[0], seciter[1], prev, curr)):
  co = - x[0]/x[1]
  for r, p, c in zip(res, x[2], x[3]):
    r.append( (p+co*c) / (1+co) )

Not only this looks very ugly, it also takes an awful lot of time to complete.
So, I have following questions:

*

*Of all these methods is the third one indeed the best? If so, what can be done to impove the last one?

*Are there any other, better ones yet?

*Out of sheer curiosity, is there a way to solve the problem using nditer?

*Finally, will I be better off using memmap versions of numpy arrays, or will it probably slow things down a lot? Maybe I should only load secaxis array into RAM, keep others on disk and use third method?

*(bonus question) List of equal in length 1-D numpy arrays comes from loading N .npy files whose sizes aren't known beforehand (but N is). Would it be more efficient to read one array, then allocate memory for one 2-D numpy array (slight memory overhead here) and read remaining into that 2-D array?

A: The numpy.where() version is fast enough, you can speedup it a little by method3(). If the > condition can change to >=, you can also use method4().
import numpy as np

a = np.random.randn(100000)

def method1(a):
    idx = []
    for i in range(1, len(a)):
        if (a[i-1] > 0 and a[i] < 0) or (a[i-1] < 0 and a[i] > 0):
            idx.append(i)
    return idx

def method2(a):
    inds, = np.where((a[:-1] < 0) * (a[1:] > 0) +
                       (a[:-1] > 0) * (a[1:] < 0))
    return inds + 1

def method3(a):
    m = a < 0
    p = a > 0
    return np.where((m[:-1] & p[1:]) | (p[:-1] & m[1:]))[0] + 1

def method4(a):
    return np.where(np.diff(a >= 0))[0] + 1

assert np.allclose(method1(a), method2(a))
assert np.allclose(method2(a), method3(a))
assert np.allclose(method3(a), method4(a))

%timeit method1(a)
%timeit method2(a)
%timeit method3(a)
%timeit method4(a)

the %timeit result:
1 loop, best of 3: 294 ms per loop
1000 loops, best of 3: 1.52 ms per loop
1000 loops, best of 3: 1.38 ms per loop
1000 loops, best of 3: 1.39 ms per loop

A: I'll need to read your post in more detail, but will start with some general observations (from previous iteration questions).
There isn't an efficient way of iterating over arrays in Python, though there are things that slow things down.  I like to distinguish between the iteration mechanism (nditer, for x in A:) and the action (alist.append(...), x[i+1] += 1).  The big time consumer is usually the action, done many times, not the iteration mechanism itself.
Letting numpy do the iteration in compiled code is the fastest.
 xdiff = x[1:] - x[:-1]

is much faster than
 xdiff = np.zeros(x.shape[0]-1)
 for i in range(x.shape[0]:
     xdiff[i] = x[i+1] - x[i]

The np.nditer isn't any faster.
nditer is recommended as a general iteration tool in compiled code.  But its main value lies in handling broadcasting and coordinating the iteration over several arrays (input/output).  And you need to use buffering and c like code to get the best speed from nditer (I'll look up a recent SO question).
https://stackoverflow.com/a/39058906/901925
Don't use nditer without studying the relevant iteration tutorial page (the one that ends with a cython example).
=========================
Just judging from experience, this approach will be fastest.  Yes it's going to iterate over secaxis a number of times, but those are all done in compiled code, and will be much faster than any iteration in Python.  And the for f in X: iteration is just a few times.
res = []
inds, = numpy.where((secaxis[:-1] < 0) * (secaxis[1:] > 0) +
                   (secaxis[:-1] > 0) * (secaxis[1:] < 0))
coefs = -secaxis[inds] / secaxis[inds+1] # array of coefficients
for f in X:
    res.append( (f[inds] + coefs*f[inds+1]) / (1+coefs) )

@HYRY has explored alternatives for making the where step faster.  But as you can see the differences aren't that big.  Other possible tweaks
inds1 = inds+1
coefs = -secaxis[inds] / secaxis[inds1]
coefs1 = coefs+1
for f in X:
    res.append(( f[inds] + coefs*f[inds1]) / coefs1)

If X was an array, res could be an array as well.
res = (X[:,inds] + coefs*X[:,inds1])/coefs1

But for small N I suspect the list res is just as good.  Don't need to make the arrays any bigger than necessary.  The tweaks are minor, just trying to avoid recalculating things.
=================
This use of np.where is just np.nonzero.  That actually makes two passes of the array, once with np.count_nonzero to determine how many values it will return, and create the return structure (list of arrays of now known length).  And a second loop to fill in those indices.  So multiple iterations are fine if it keeps action simple.

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text:
Bill Ward on Black Sabbath Absence: Ozzy Osbourne 'Needs to Look to Those Who Stopped Me From Coming Through'
Chad Childers Published: September 4, 2016
Black Sabbath are hitting the home stretch of their touring career, with an early 2017 date set for their final show. Currently still estranged from band is drummer Bill Ward, who exited reunion plans a few years back over issues with the contract presented. In the time since, there has been plenty stated by Ward and members of Black Sabbath about his absence, but some wondered if he might return at some point, even if it was for the final show. During a recent interview, Ozzy Osbourne revealed he was sad that Ward never "came through," but added that any time he tried to reach out, he got yelled at. He concluded, "If something can be worked out, great," but stated that Tommy Clufetos, who has been the band's touring drummer in Ward's absence, has been "doing a great job."
Ward, who earlier this summer revealed that he was not playing on any of the remaining Black Sabbath shows, responded to Osbourne's latest comments, wanting to share his side of the story. In his posting, he reveals that Ozzy hasn't spoken to him directly since 2012 and added that the rocker needed to take a closer look at those around him if he wants to know why Ward hasn't "come through." He also revealed his own overture to see if there was a possibility to play with Sabbath and being told "no" by a band representative. You can read Ward's full statement below:
Ozzy has never reached out since January 26/27 2012 when he called to see when I would be arriving in the UK to join rehearsals for the 13 CD. Since that last phone call, he has never reached out to me, and I have not reached out to him.
Ozzy I'm sad that Bill never came through. Ozzy needs to look to those who stopped me from coming through. The contract was impossible to sign; the group rhetoric of 2012, 2013 and throughout, was fault-finding, condemning and full of dishonest evaluations of me as a person, including my so-called health issues. Had I signed the contract, I suspect I wouldn't have been labeled as having poor health or having all the other judgmental character references.
As a reminder, in September 2015, we asked a Sabbath representative if there could be an opening to play with the band again. Their representative answered "no." It's hard to come through when you receive a "no."
I love all our Sabbath fans throughout the world, and to toss around ideas that I might play the Birmingham shows is both emotionally heartbreaking for me and spiteful. I think the Sabbath fans who have loved the original band have gone through enough in terms of disappointment and sadness. For me to play Birmingham only, would, I feel, be discriminating and elitist towards our other fans all over the world. I could never be discriminating or elitist to our fans. It's been heartbreaking enough not to record and tour and it'll be heartbreaking not to play to the Birmingham fans. Please know the prevailing circumstances are not by my design. This is not what I would have wanted or what I have chosen. Had any of you had to choose to sign a contract which undermined and devalued you, I think most of you with some dignity and self-respect would have walked away too. My absence at the current shows and the future Birmingham shows reflects an undeniable and unseen misadventure that my accusers won't bear. It's called selfishness and self-centeredness; dishonesty and disloyalty. These are the culprits of Sabbath's original line-up's demise.
--Bill
Earlier this year, Ward revealed that he had founded a new band called Day of Errors. That has been the drummer's primary focus for his musical pursuits.
See Where Bill Ward Ranks Among the Top 66 Hard Rock + Metal Drummers of All Time
Source: Bill Ward on Black Sabbath Absence: Ozzy Osbourne 'Needs to Look to Those Who Stopped Me From Coming Through'
Filed Under: black sabbath, ozzy osbourne
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Ozzy Osbourne Presents Rap Award at the Grammys
Ozzy Osbourne Diagnosed With Parkinson's Disease
Elton John Plays Piano, Sings on Ozzy Osbourne's 'Ordinary Man'
Sharon Osbourne Once Asked 'Friends' Star for a Threesome
Tony Iommi and Rob Halford Recall Their First U.S. Shows
Black Sabbath's Bill Ward Hospitalized With Heart Problems


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            		 camping

            		 black fox

          </hgroup>

Note: The subject of this poem is place in the Black Hills of South Dakota I used to camp every year as a child.   Black Fox Campground.

My father is packing;

it's part of his ritual.

A way, perhaps, of mentally washing

the rigors of life's prisons away.

Prisons on high hills, made pink from Dells quartz

hold purses, not dreams nor ambitions.

Near is the time for Black Fox once more.

Time for crystal streams which go by

the name Rapid.

Time for fireflies to stage their ballet

in coolness of the impending dusk

as a doe bows her head in hunger, in prayer.

Time for trout of one year,

to pause, frozen beneath their hidden waterfall.

They are strangers here too.

Borrowing time, far from the tank of their birth.

We trespass together, invaders and protectors alike.

He lifts an old tent, one of faded green canvas.

Which long ago gave way to nylon expansion.

It still rides along,  a friend of many years,

filled once with memories, even marriages

it has long since grown out of:

Peeking out in pajamas, frozen dew fresh

from the cold before dawn.

It's scent speaks of moments ago;

coffee forged on a fire,

cakes assembled on iron,

packing still wet as time stood expired.

I think that this year I may climb a small mountain

and sleep alone without sleeping a wink.

I'll listen for bears, wolves, things which may frighten.

Prone at the summit, with ears to the valley

as my creek sings it's lullaby to adolescence, adventure.

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#Coincidence – Government Reports suggest Children are dying of Strep A due to immense damage done to Immune System by COVID Vaccination
By The Exposé on December 14, 2022 • ( 12 Comments )
In the spring of 2022, the World Health Organization (W.H.O.) issued a 'global alert' about a new form of severe hepatitis that was killing children.
Then just days after the W.H.O. announcement, a new scientific study was "coincidentally" published that concluded Covid-19 vaccination has the ability to cause severe autoimmune hepatitis.
Now, just a few months later, we have authorities, with the help of the mainstream media, striking the fear of god into parents across the UK in regard to the safety of their children because of an alleged spate of sudden deaths among kids due to what is normally a mild bacterial infection known as Strep A (Streptococcus A).
But with –
Official Government reports proving COVID Vaccination causes immense damage to the immune system reminiscent of acquired immunodeficiency syndrome,
Official European mortality data proving excess deaths among children have increased by 552% ever since the European Medicines Agency approved the Covid-19 vaccine for children,
Secret data published by the Centers for Disease Control confirming at least 118k children & young adults have died suddenly in the USA since the Covid-19 vaccine roll-out, and
UK Government data confirming Covid-19 vaccinated children are up to 137x more likely to die than unvaccinated children,
Is it time authorities finally admitted we have now witnessed more than enough "coincidences" to confirm that Covid-19 vaccination is the most likely cause of children suddenly dying of Strep A infection due to the immense damage it does to the immune system?
These are the sort of headlines being published by the mainstream media in the UK at the moment –
And according to the UK Health Security Agency (UKHSA, there were "851 cases of scarlet fever and invasive Goup A Strep reported in week 46, compared to an average of 186 for the preceding years".
The UKHSA has also confirmed that scarlet fever, caused by a bacteria called group A streptococci, which also causes other respiratory and skin infections such as strep throat and impetigo, 'is usually a mild illness'.
The agency has also confirmed that 'there is no evidence that a new strain is circulating.'
So why are we suddenly seeing so many children dying due to the bacteria? With an alleged 16 deaths as of December 9th.
The most likely argument you'll hear from authorities is that is due to the lockdowns that were introduced in 2020 in response to the alleged Covid-19 pandemic. They will claim that the lack of human interaction will have reduced immunity among children to simple infections.
But the last full lockdown ended in March 2021, and all schools reopened at the same time. So surely we should have seen an increase in mild diseases turning into serious diseases among children in the winter of 2021 if the lack of human interaction had reduced immunity among children to simple infections?
As we all know, that didn't happen. So what other common denominators could possibly be causing children to suffer serious disease or death due to what would have otherwise been mild infections prior to 2020?
All evidence points to that common denominator being the Covid-19 injection, which was first offered to all children aged 12 to 15 in September 2021, and then offered to children aged 5 to 11 in February of 2022.
And now the UK Medicine Regulator has "authorised" the Pfizer vaccine for use in infants and children aged 6 months to 4 years as of December 6th.
All of this has been and is being done despite the fact we know that children do not suffer serious disease due to Covid-19, and the Covid-19 injections do not prevent infection or transmission. In fact, the official evidence suggests they actually increase the likelihood of infection.
So let's take a look at the evidence and then you can decide for yourself whether to believe the official narrative that 'a lack of exposure to strep A during the height of the pandemic' is to blame for a sudden rise in children dying, or whether the Covid-19 injections may in fact be to blame.
A trip down Memory Lane – Hepatitis
On April 15 2022, the World Health Organization issued a global alert about a new form of severe acute Hepatitis with an unknown aetiology (cause) affecting previously healthy children in the UK over the last month. Cases have also been notified in Spain and Ireland. Tests have excluded all previously known Hepatitis viruses.
The announcement came after the UK Health Security Agency (UKHSA) had detected higher than usual rates of liver inflammation (hepatitis) in children.
Then, just a few days later, a new scientific study was published on 21st April 2022, that concluded Covid-19 vaccination can elicit a distinct T cell-dominant immune-mediated hepatitis (liver inflammation) with a unique pathomechanism associated with vaccination-induced antigen-specific tissue-resident immunity requiring systemic immunosuppression.
The NHS began rolling out the Pfizer Covid-19 injection to five million 5 to 11-year-old children in the UK at the beginning of April 2022. It had previously been administering it to young children deemed vulnerable since the end of 2021.
Is it just a coincidence that a mere few weeks later an extremely concerning number of children were suffering hepatitis of unknown cause? A form of hepatitis so severe that children required liver transplants or lost their lives?
That's for you to decide, but you can read more about it here.
COVID Vaccination destroys the Immune System & can cause Acquired Immune Deficiency Syndrome
The UK Health Security Agency (UKHSA) used to publish a weekly Vaccine Surveillance Report, with each report containing four weeks worth of data on Covid-19 cases, hospitalisations, and deaths by vaccination status.
We analysed 5 of these published Vaccine Surveillance Reports containing data from August 16th 2021 to January 2nd 2022, in order to get a clear picture of the effect the Covid-19 vaccines were having on the immune systems of the vaccinated population, and this is what we found…
The UKHSA Vaccine Surveillance Reports used for our investigation can all be found here –
'Covid-19 Vaccine Surveillance Report – Week 37' (Published by PHE)
'Covid-19 Vaccine Surveillance Report – Week 41' (Published by UKHSA)
'Covid-19 Vaccine Surveillance Report – Week 1 – 2022' (Published by UKHSA)
You were told that the Pfizer Covid-19 mRNA injection had a vaccine effectiveness of 95%.
The following graph illustrates the increase/decrease in vaccine effectiveness by the month among each age group over a period of 5 months from 16th Aug 21 to 2nd Jan 22.
The first booster shots were administered in week 37 of 2021, and this graph illustrates clearly how they provided a boost in vaccine effectiveness in the following two months. But unfortunately, it also shows how short-lived this boost was with the effectiveness of the Covid-19 vaccines falling to frightening levels between weeks 49 and 52.
Real-world vaccine effectiveness dropped to the lowest levels yet across all age groups except for the over 70's between December 6th and January 2nd, but the over 70's still dropped into negative effectiveness.
The expected further boost to 40 to 69-year-olds did not materialise and instead, a huge tumble in vaccine effectiveness was recorded, dropping to -151% in 40-49-year-olds.
Vaccine effectiveness also tumbled in the 30-39-year-old age group to minus-123%, despite the booster jab being administered to millions in week 49.
That's a far cry from the alleged 95% effectiveness you were told about.
But what does a positive/negative vaccine effectiveness actually mean?
Vaccines work by simulating a viral attack and provoking the immune system into responding as if you have had the virus. They are supposed to train the immune system to the point where you develop natural immunity to the virus.
Therefore, vaccine effectiveness is really a measure of the immune system performance of the vaccinated compared to the immune system performance of the unvaccinated.
The data published by the UKHSA confirms that the real-world effectiveness of the Covid-19 injections (really a measure of immune system performance) wains significantly in a short amount of time.
But, unfortunately for the vaccinated population, rather than the immune system returning to the same state it was prior to vaccination, the immune system performance begins to rapidly decline, making it inferior to that of the unvaccinated.
However, the UK Government data does prove that a booster dose of the vaccine can give a short-term boost to the immune system.
But, unfortunately, this same data shows that the immune system performance then begins to decline even faster than it has been prior to the booster dose being given.
This data, therefore, suggests that the vaccinated population will now require an endless cycle of booster shots to boost their immune systems to a point where it does not fail but is inferior to that of the unvaccinated population.
Acquired immunodeficiency syndrome is a condition that leads to the loss of immune cells and leaves individuals susceptible to other infections and the development of certain types of cancers. In other words, it completely decimates the immune system.
Therefore, could we be seeing some new form of Covid-19 vaccine-induced acquired immunodeficiency syndrome?
Facebook certainly seems to think so.
Our original investigation on the above was shared by a reader on Facebook on the same day that we published the article, and Facebook took it upon themselves to immediately remove the post and label it as "misinformation".
However, the reader who shared our article disputed Facebook's censorship and on the 27th of October they finally responded to confirm that they were in fact wrong, the investigation was entirely correct, and that the reader's Facebook post is now back on the highly censored social media platform.
And if we are seeing some new form of Covid-19 vaccine-induced acquired immunodeficiency syndrome, it would certainly explain why we're suddenly seeing so many children suffer serious diseases and death this year due to what would have otherwise have been mild infections prior to the roll-out of the Covid-19 injections.
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Excess Deaths among European Children aged 0-14 have increased by 552% ever since the EMA approved the COVID Vaccine for Kids
On the 28th of May 2021, the European Medicines Agency extended the emergency use authorisation of the Pfizer Covid-19 mRNA injection to children aged 12 to 15.
Six months later they extended the emergency use authorisation to 5 to 11-year-olds.
Before they first did this, deaths among children were well below expected levels, according to EuroMOMO, a European Mortality Monitoring Project with official authorisation to collate and publish statistics given to them by the Governments of countries across Europe.
But ever since they have increased substantially.
So much so, that by the 6th of November 2022, excess deaths among children across Europe had increased by a shocking 552%.
With an 848% increase recorded up to the end of 2021 –
And a 444% increase recorded as of November 6th in 2022 –
Is this just another coincidence? That's for you to decide. But you can read the full investigation on the above here.
CDC quietly confirms at least 118k Children & Young Adults have 'Died Suddenly' in the USA since the roll-out of the COVID Vaccines
Compared to other countries, the U.S. Government has been terrible at publishing relevant and up-to-date data allowing us to analyse the consequences of rolling out the Covid-19 injections. However, we have finally managed to stumble upon it thanks to an institution known as the Organisation for Economic Co-operation and Development (OEC).
The OEC is an intergovernmental organisation with 38 member countries founded in 1961 to stimulate economic progress and world trade. And for some reason, they host a wealth of data on excess deaths. You can find that data for yourself here.
The following chart has been created using the figures found in the OEC database. Figures that have been provided to the OEC by the U.S. Centers for Disease Control (CDC). And it shows excess deaths among children and young adults aged 0-44 across the USA by week in 2020 and 2021.
The official figures reveal that there was a slight increase in excess deaths among children and young adults when the alleged Covid-19 pandemic hit the US in early 2020.
However, with the introduction of a Covid-19 injection, one would have expected deaths to have fallen significantly among the age group in 2021. But instead, the opposite happened.
Excess deaths among children and young adults were significantly higher every single week in 2021 than they were in 2020 except for weeks 29 and 30. But then in week 31, something drastic happened to cause excess deaths to rise significantly among children and young adults.
And official figures provided by the CDC, unfortunately, show that trend has continued in 2022.
The following chart shows the official CDC figures for all deaths and excess deaths among children and young adults across the USA prior to the roll-out of the Covid-19 vaccine, and after the roll-out of the Covid-19 vaccines on the 14th Dec. 2020.
The above figures reveal that the year 2022 so far has only seen 1,352 fewer excess deaths among 0-44-year-olds by week 40 than what occurred by week 51 in 2022, despite the year 2020 being the alleged height of the COVID pandemic and also including an extra 11 weeks worth of deaths.
But the most concerning figures revealed in the above chart are the overall number of deaths and excess deaths among children and young adults since the roll-out of the Covid-19 injections.
Nearly half a million people aged 0 to 44 have sadly died since week 51 of 2020, and this has resulted in an astounding 117,719 excess deaths against the 2015-2019 five-year average.
UK Government quietly confirms COVID Vaccinated Children are up to 137x more likely to die than Unvaccinated Children
A UK Government agency, known as the Office for National Statistics (ONS), recently published new data on deaths by vaccination status in England.
The latest dataset from the ONS is titled 'Deaths by Vaccination Status, England, 1 January 2021 to 31 May 2022', and it can be accessed on the ONS site here, and downloaded here.
The following chart shows the mortality rates by vaccination status per 100,000 person-years among children aged 10 to 14 in England for the period 1st January 2021 to 31st May 2022, according to the figures provided by the ONS –
In regard to Covid-19 deaths, the ONS reveals that the mortality rate among unvaccinated children aged 10 to 14 equates to 0.31. But in regards to one-dose vaccinated children the mortality rate equates to 3.24 per 100,000 person-years, and in regards to triple vaccinated children the mortality rate equates to a shocking 41.29 per 100,000 person-years.
These figures reveal that partly vaccinated children are 11x/966.67% more likely to die of Covid-19 than unvaccinated children, and triple vaccinated children are 137.3x/13,633.33% more likely to die of Covid-19 than unvaccinated children.
And unfortunately, there is little improvement when it comes to non-Covid-19 deaths. Here's the chart again showing the mortality rates by vaccination status among children in England.
The all-cause death mortality rate equates to 6.39 per 100,000 person-years among unvaccinated children and is ever so slightly higher at 6.48 among partly vaccinated children.
However, the rate goes from bad to worse following the administration of each injection. The all-cause death mortality rate equates to 97.28 among double-vaccinated children, and a shocking 289.02 per 100,000 person-years among triple-vaccinated children.
This means, according to the UK Governments own official data, double vaccinated children are 1422% / 15.22x more likely to die of any cause than unvaccinated children. Whilst triple vaccinated children are 4423% / 45.23x more likely to die of any cause than unvaccinated children.
That concludes our evidence, but it is by no means the only available evidence to support the fact that the Covid-19 injections are most likely to blame for a sudden rise in children dying due to Strep A infection.
Official Government reports from around the world prove that hundreds of thousands more children have died since the roll-out of the Covid-19 injections than would have otherwise died prior to the roll-out of the injections.
They also prove that Covid-19 vaccinated children are significantly more likely to die than unvaccinated children.
But the most important piece of evidence to support the fact Covid-19 vaccination is to blame for a sudden rise in children dying due to Strep A infection, is perhaps the official Government reports that prove Covid-19 vaccination damages the immune system and has the potential to cause some new form of acquired immunodeficiency syndrome.
It's a common misconception that Acquired Immunodeficiency Syndrome (AIDS) is only caused by the HIV virus. This simply isn't true.
Acquired (or secondary) immunodeficiency is one of the major causes of infections. These immunodeficiency disorders affect your immune system partially or as a whole, making your body an easy target for several diseases and infections. (Source)
When immunodeficiency disorders affect your immune system, your body can no longer fight bacteria and diseases. (Source)
This would explain why children are suddenly suffering serious disease or death due to a bacteria that previously caused mild infection.
Several factors in the environment can cause secondary immunodeficiency disorders. (Source)
Some common ones are:
Radiation or chemotherapy, which can lead to a secondary immunodeficiency disorder known as neutropenia
Infections due to human immunodeficiency virus (HIV) can result in acquired immune deficiency syndrome (AIDS)
Leukaemia, a cancer that begins in the cells of the bone marrow that can lead to hypogammaglobulinemia—a type of secondary immunodeficiency
Malnutrition, which affects up to 50% of populations in underdeveloped countries and leaves people vulnerable to respiratory infections and diarrhoea
But some of the less common causes include Drugs or medications. (Source)
So it's perfectly possible for a medication or drug to cause acquired immunodeficiency syndrome, and official Government reports prove Covid-19 vaccines should be added to the list.
How many lives are being lost due to the senseless obsession with net zero?
Britain's energy crisis is the result of decades of failing renewables-centric energy policy
VoicefromEurope
Please stay factual: Just check the vaccination status of these children ?
GundelP
And again, we are back to graphene oxide and that the vaccines contain it including Prevenar13 (La Quinta Columna found it), flu vaccines, etc. As it affects (Strep A) their throat I would suggest that they INHALED that graphene oxide. When it gets boosted it ruins everything around it.
As it was found in another drugs, SALINE solutions, food, rain-water, there are many possible sources however the fact it affects now mainly children points toward something what children got.
FOIA'd Contracts Show CDC Expected up to 1,000 VAERS Reports per Day for COVID Vaccineshttps://jackanapes.substack.com/p/foiad-contracts-show-cdc-expected
What they're doing is gaslighting us, the evidence is in plain sight but they outrightly lie about it to cause cognitive dissonance.
The filth have their behavioural management manual, which they don't have the creativity to deviate from/modify. Just in this millennium alone there have been several previous attempts to install their NWO, in earlier fake pandemics. Many are waking up to the evil done but this time they've burned the lifeboats, no going back so they just have to go for it.
Big Crow
At the risk of dating myself: "It's not nice to fool Mother Nature."
#Coincidence – Government Reports suggest Children are dying of Strep A due to immense damage done to Immune System by COVID Vaccination – David Icke
[…] Read More: #Coincidence – Government Reports suggest Children are dying of Strep A due to immense… […]
Bambini che muoiono di streptococco A a causa dei danni causati dalla vaccinazione contro il Covid - La rosa bianca ⋆ Green Pass News
[…] (Leggi l'articolo completo qui: https://expose-news.com/2022/12/14/strepa-covid-vaccination-killing-children/) […]
Náhoda? - Vládní zprávy naznačují, že děti umírají na streptokoka A v důsledku obrovského poškození imunitního systému očkováním COVID. - volnyblog
[…] zdroj […]
Barb Stevsy
If excess deaths increased over 500% in kids 0-14 years old in the UK and we assume not all kids that age are jabbed than the excess deaths in jabbed kids that age is much higher maybe even a 1000% meaning thats is ober 10 times higher than unjabbed kids. So the jabbed kids are dying at more than 10x than the unjabbed in the UK.
Australia: Sudden deaths at incredibly high rate +Germany: Cost to human lives due to experimental cov-vax program + Personality suddenly changes after mRNA jab + Australian doctor: Microcircuits in cov-vax + US gov.: Cov-vax caused 1433x increase in canc
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The McAlvany Daily Update - December 15, 2022 - McAlvany Financial Group
[…] Coincidence – Government Reports suggest Children are dying of Strep A due to immense damage done … […]


The Winning Way has ratings and 91 reviews. Two IIMA alumni, Sports commentator and writer Harsha Bhogle and advertising and communication. Two IIMA alumni, Sports commentator and writer Harsha Bhogle and advertising The Winning Way Learnings From Sport for Managers by Anita Bhogle. 14 Nov Introduction. The winning way by Harsha Bhogle and Anita Bhogle brings two perceptually different fields of sports and business together and.
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Passable, hsrsha in depth, better to hear Harsha in person First what i liked: Anyone who reads management books, must pick up The Winning Way. New insights into cricket and other sports.
The compilation is a stand out for the simplistic approach it adopts in proposing innovative ideas to tackle various forms of adversities we come across in professional life. This book was written from the trainings they conducted in the corporates.
What mattersis how well you the winning way harsha bhogle when you're playing badly. Post was not sent – check your email addresses! Mar 10, Winnin Parakh rated it liked it. While many of the sports examples will be well known to the readers, the way it is weaved into an overall narrative for business world is really commendable.
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A whiff of new air — cool, filtered and even! Also, Sachin's example is taken more than required. The book touches on many such simplistically beautiful the winning way harsha bhogle between sports winnning management with each chapter dedicated to one central idea. Harsha Bhogale very nicely explains how to WIN and how to sustain winning, with his cricket references and quotes from business world.
Apr 24, Dipesh Mehta rated it it was amazing. I admit, this book goes beyond that. This book really benefits from the background of its authors.


In accordance with French law, Compagnie de Saint-Gobain refers to and complies with the principles of corporate governance set out in the AFEP-MEDEF corporate governance code for French listed companies.
the Strategy and Corporate Social Responsibility Committee.
Directors are elected for a four-year term (article 9 of the bylaws), in accordance with the recommendations of the AFEP-MEDEF corporate governance code for French listed companies.
At its meeting on November 24, 2016, Compagnie de Saint-Gobain's Board of Directors decided to create the position of Lead Independent Director (Administrateur Référent), which is taken on by Jean-Dominique Senard, independent Director since 2012, since the Annual General Meeting held on June 8, 2017.
Pierre-André de Chalendar is a graduate of ESSEC Business School and École Nationale d'Administration. A former civil servant (Inspecteur des Finances), he served as deputy to the Director General for Energy and Raw Materials within the French Ministry of Industry.
Mr. Chalendar joined Compagnie de Saint-Gobain in 1989 as Vice-President, Corporate Planning. He was subsequently Vice-President of the Abrasives business in Europe (1992-1996), President of the Abrasives business worldwide (1996-2000) and General Delegate for the United Kingdom and the Republic of Ireland (2000-2002) before being appointed Senior Vice-President of Compagnie de Saint-Gobain in charge of the Building Distribution Sector in 2003.
Appointed Chief Operating Officer of Compagnie de Saint-Gobain in 2005 and elected to the Board in 2006, he was appointed Chief Executive Officer in 2007 and Chairman and Chief Executive Officer in 2010.
Pierre-André de Chalendar is also a director of BNP Paribas, and has chaired Entreprises pour l'Environnement from 2012 to 2015.
He directly supervise the Innovative Materials Sector from Saint-Gobain since October 29, 2015.
Lydie Cortes was titular member of the Works Council, Personnel Representative, member of the Hygiene, Health and Safety Committee of Weber and Central Union Delegate for the CFDT.
She was titular member of the Group Committee and Secretary of the European Convention and of the Select Committee of Saint-Gobain.
Brazilian and British national, Iêda Gomes Yell is a Director and a member of the Strategic Committee and of the Audit and Risk Committee of Bureau Veritas (a French listed company). She is also a Director of Exterran Corporation (United States) and a Director of InterEnergy Holdings.
Anne-Marie Idrac is also a Director of Total and Bouygues, Chairman of the Advisory Board of the School of Public Affairs of Sciences Po Paris and Vice-Chairman of the Robert Schuman Foundation.
German national, Pamela Knapp is also a member of the Supervisory Board, Nomination, Compensation and Governance Committee and the Finance and Audit Committee of Peugeot S.A., a Director of the consulting firm HKP Group AG (Switzerland), a Director and member of the Audit committee of Panalpina World Transport (Holding) Ltd (Switzerland) and a Director and member of the Audit Committee of NV Bekaert (Belgium).
Agnès Lemarchand is also a Director of CGG and BioMérieux and Chairman of Orchad SAS.
Frédéric Lemoine is also Chairman of the Board of Directors of Bureau Veritas and holds various positions and functions within companies registered outside France in which Wendel holds an equity interest.
Dominique Leroy is a Belgian citizen. She is CEO of Proximus (ex-Belgacom), listed on the first market of Euronext Brussels.
Dominique Leroy is also an independent member of Ahold Delhaize's Supervisory Board and a member of the Sustainability and Innovation Committee.
Dominique Leroy is Chairwoman of the International Advisory Board of the Solvay Brussels School of Economics and Management.
Jacques Pestre is also Chairman of the Supervisory Board of the Employee Savings Fund "Saint-Gobain PEG France" and holds various positions and functions within several companies of Saint-Gobain's Building Distribution Sector.
Denis Ranque is also a Director of CMA-CMG and Scilab Enterprises.
He is Chairman of the "Haut Comité du Gouvernement d'Entreprise" (the High Committee in charge of monitoring implementation of the Corporate Governance Code of Listed Corporations), co-Chairman of La Fabrique de l'industrie and Chairman of the Board of Directors of the Ecole Polytechnique Foundation.
Gilles Schnepp holds various positions and functions within subsidiaries of the Legrand Group.
Jean-Dominique Senard does not hold any other directorships.
Philippe Thibaudet was Secretary of the CHSCT, Vice-Secretary of the Works Council and Union Delegate for the CGT.
In addition, he was titular member and Vice-Secretary of the Central Enterprise Committee, Central Union Delegate of SG Isover, and titular member of the Group Committee.
He has also held coordination and animation missions for the Glass Division at the Glass Federation. Lastly, he is also alternate representative of the Central Enterprise Committee at the ISOVER Board of Directors.
Philippe Varin is also special representative of the French Minister of Foreign Affairs and International Development for the ASEAN countries and Chairman of the "Cercle de l'Industrie".
The following information was last updated on January 1, 2019.
The Board of Directors is assisted by three committees, which it consults prior to making certain decisions. The committees submit their recommendations to the Board for deliberation.
The Committee is exclusively composed of independent directors. No executive directors sit on the Committee. By virtue of their current or past positions, the Committee members each have considerable experience and high-level expertise in financial and accounting matters.
The processes used to prepare financial information.
The effectiveness of internal control and risk management systems.
The work performed by the Statutory Auditors on the financial statements of the Company and the Group.
As of January 1, 2019, two Committee members, including its Chairman, were independent Directors.
The Nomination and Remuneration Committee fulfills the duties of both a nomination committee and a remuneration committee provided for in the AFEP-MEDEF code.
Board of Directors as of June 7, 2018.


text:
Seton Hall Honors N.J. Nurses CEO
By NJSNA
Seton Hall Honors N.J. Nurses CEO2019-10-152019-10-15https://njsna.org/wp-content/uploads/2018/10/njsna-logo.pngNew Jersey State Nurses Associationhttps://njsna.org/wp-content/uploads/2019/10/njsna-seton-hall-leadership-award.jpg200px200px
College of Nursing Recognizes Judith Schmidt for Leadership
Judith Schmidt, MSN, DHA (c), RN, CCRN, New Jersey State Nurses Association chief executive officer (left) pauses for a photo with Marie Foley, Ph.D., R.N., C.N.L., dean and professor at Seton Hall University College of Nursing (right) after being presented with the George J. Hebert Leadership Award for her career of nursing leadership at the 48th Annual College of Nursing Margaret C. Haley Awards held October 14, 2019 at the Highlawn Pavilion in West Orange.
TRENTON, N.J.—Oct. 15, 2019— The New Jersey State Nurses Association (NJSNA) Chief Executive Officer Judith Schmidt, MSN, DHA (c), RN, CCRN was honored for her leadership skills and passion for nursing last night by Seton Hall University College of Nursing.
Schmidt was presented with the George J. Hebert Leadership Award for her career of nursing leadership at the 48th Annual College of Nursing Margaret C. Haley Awards, advocating for the profession of nursing in New Jersey and nationally. For many years, she has been one of the most visible proponents of the advancement of the nursing profession as CEO of NJSNA where she represents the interests of more than 110,000 registered and advanced practice nurses in New Jersey.
Several weeks ago, Schmidt and NJSNA were recognized for best practices in achieving and measuring peak performance by the Commerce and Industry Association of New Jersey and its flagship publication, COMMERCE Magazine, at the 6th Annual Best Practices Conference on September 24, 2019.
"Success is all about increasing membership, fiscal viability and legislative successes," explained Schmidt. "If we can increase the numbers of our engaged and active nurse members, it only strengthens our voice. The stronger the number of engaged active members, the stronger we are financially so we can continue our mission of advocacy for excellence in patient care and the practice of nursing in New Jersey."
Judith Schmidt, MSN, DHA (c), RN, CCRN, New Jersey State Nurses Association chief executive officer
Most recently, Schmidt led New Jersey's entry into the multi-state nursing compact so nurses can practice in states that are members without obtaining a separate license, which will help alleviate a pending nursing shortage. She is also advocating for nurse title protection and to increase consumer access to care with the removal of joint protocol.
"Through legislative success, we can truly make a difference in the health care system for healthier patients who have more access to care," she added. "The secret to all of this is increasing our visibility and voice through active participation in the political arena then communicating those activities to members and non-members to showcase the value of membership. This has worked for us because NJSNA has more doubled membership in the past four years."
She was also honored by the American Association of Nurse Practitioners (AANP) with the 2019 Advocate State Award for Excellence for her exceptional nurse leadership and service to nurses.
Schmidt received her Master's degree in the Science of Nursing from Seton Hall University in 1993. She has been CEO of NJNSA since 2015 and prior to that, she served as president of the organization with a long record of board service and volunteerism for more than 30 years.
To learn more about NJSNA please visit njsna.org.
NJSNA, which was established in 1901, is a constituent member of the American Nurses Association. The New Jersey State Nurses Association (NJSNA) represents the interests of 110,000 registered nurses and advanced practice nurses as an advocate for the nursing profession. NJSNA's lobbying arm continues to protect the nursing profession through legislative victories. Its nonprofit foundation, Institute for Nursing, helps nurses further their careers by providing continuing education, scholarships and research grants in addition to invaluable networking opportunities. For more information, nurses can visit njsna.org or contact NJSNA at njsna@njsna.org or call (609) 883-5335.
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text:
\section{Introduction}

Well-designed avatar creation tools like Bitmoji ~\cite{bitmoji}, Google Cartoonset ~\cite{Google_cartoon}, and Metahuman ~\cite{metahuman} provide expressive tools for users to create digital figures based on themselves. However, customizing the ideal avatar  involves laborious selection and adjustment of parameters. Such a process consumes a significant amount of time from an average user without necessarily  resulting in their ideal design. Training a learning-based algorithm for avatar auto-creation is needed.

 Supervised learning requires the collection of pairwise training data. Given human face images, annotators manually create the corresponding avatars by selecting the best assets. Unfortunately, there are inherent issues with this \textit{direct} annotation method. During the creation process, some parameters such as hairstyle include hundreds of options with only minor differences. It is almost impossible for the annotators to consistently select a single optimal choice, resulting in low agreement with other annotators. When collected in this way, the dataset has high label noise, and majority vote aggregation does little to help.

\input{Fig_tex/Fig_teaser}

Instead, we propose a \textit{Tag-based} annotation method for avatar creation. A semantically meaningful set of tags is designed as an intermediate representation which applies to both photographs and avatar renderings. Annotators label both training photographs and the set of stylized assets using these tags. For example, annotating \textit{hair length}, \textit{hair curly level}, and \textit{hair direction} instead of simply \textit{find the best option out of two hundred hairstyles}. This design provides better instructions and encourages annotators to search for more detailed features when labeling. The proposed Tag-based annotation results in higher annotator agreements.

Given a photograph, Tag annotations do not directly provide an answer for which asset is a best match. We use a search algorithm to evaluate the similarity of image tags and the tags of each possible asset. The asset with maximum similarity is selected as the best match. In addition to providing a best match, this search provides a score for each asset, so additional plausible options are available. In contrast, direct annotation produces a single best label with no information about the quality of any other asset.

In order to understand how this change in annotation style affects the final system, we compared supervised learning models trained on Tag-based labels with models trained on Direct labels. Experimental results show that models trained with the Tag-based system produces better and more consistent predictions. Example hairstyles predicted by the tag-based system are shown in Fig.~\ref{fig:teaser}.

Finally, we demonstrate that the Tag-based system helps with generalizability by showing results on several different avatar rendering systems. When shifting to a new rendering system, direct annotation requires a new large training set of human-avatar pairs. In contrast, the tag-based labels of human images can be reused, and the only new labels required are for the relatively small set of new assets. In a typical system, assets number in the hundreds while training photographs number in the tens of thousands, so the advantage is significant.

This paper contributes a new method for avatar creation using \textit{tag-based} annotation. The advantages of this method include:
\begin{itemize}
    \item Cleaner labels with higher annotator agreement
    \item Better predictions from the ML model
    \item Lower cost generalization to new rendering systems
\end{itemize}

\section{Related work}

\noindent \textbf{Image Stylization:}
Creating a virtual character from an input human portrait image needs to overcome the domain gaps between the real world and the target styles. Gatys et al.\cite{gatys2016image} matched feature information from CNN models to achieve style transfer. Cycle consistency loss was used for image-to-image transfer with paired-wise data supervision \cite{isola2017image} and with self-supervision \cite{zhu2017unpaired, park2020contrastive}. Recently, the development of GAN inversion methods \cite{richardson2021encoding, tov2021designing} results in excellent image decomposition and high-quality reconstructions, which has been applied to image stylization \cite{song2021agilegan, cao2018cari, zhu2021mind}. However, all of these methods focused on creating high-quality images in pixel space, as opposed to selecting assets in an avatar rendering system.

\vspace{0.4em}\noindent\textbf{Avatar creation using non-semantic parameters:}
Creating avatars in parameter spaces without semantic meaning has been well-studied for many years. Extremely high quality methods for photorealistic avatars using stereo vision ~\cite{beeler2010high, Yang_2020_CVPR}, and single input images  exist ~\cite{blanz1999morphable,peng2017parametric, deng2019accurate, xu2020deep, chen2021learning}, with multiple good survey papers ~\cite{egger20203d, zollhofer2018state}.

Stylized avatar systems also exist. Some methods utilize sketches as the prior condition for generation ~\cite{han2017deepsketch2face,han2018caricatureshop}.  Other methods are guided by position and landmarks, extracting human facial features used to deform textures and meshes ~\cite{wu2018alive,cai2021landmark,lewiner2011interactive,vieira2013three}. Recently a conditional GAN has been applied in the generation process \cite{li2021deep,ye20213d}. However, these methods all utilized parameters without semantic meaning, making them inapplicable to avatar systems designed to provide user level customization of asset choice.

\noindent \textbf{Avatar creation using semantic parameters:}
To provide tools for customization of avatar creation, excellent rendering tools like Bitmoji~\cite{bitmoji}, Metahuman~\cite{metahuman}, and Google Cartoon Set~\cite{Google_cartoon} were created. These rendering systems provide explicit semantic meanings to each parameter and focus primarily on manual user creation.

Avatar prediction has been explored using self-supervised methods to avoid the difficulty of manual labeling. When the avatar is semi-photorealistic, F2P \cite{F2P} utilizes neural imitators to mimic the behaviors of the rendering system, improving efficiency and applying textures for more photorealistic visual quality ~\cite{shi2020fast,lin2021meingame}. In the stylized domain, AgileAvatar ~\cite{AgileAvatar} introduced a domain transfer module to the avatar creation pipeline. However, these self-supervised methods rely heavily on carefully tuning each style. We provide a comparison to these methods in our results section.

\noindent \textbf{Human face datasets:}
Training neural engines require the collection of human face datasets. FFHQ ~\cite{ffhq} provides a collection of high-quality human face images without annotation. CelebA ~\cite{CelebA} and MAAD\cite{maad1,maad2} datasets include some basic tags of facial attributes. FairFace ~\cite{FairFace} includes ethnic tags and provides a racially balanced set.
Hairstyle specific datasets also exist. Figaro-1k ~\cite{svanera2016figaro} provides a limited set of samples, Hairstyle-30k ~\cite{yin2017learning} treats the task as an end-to-end classification task, while K-hairstyle ~\cite{kim2021k} focus on Korean hairstyles.
None of these datasets has labels matching the specific avatar rendering systems we use in our work. We make use of the FairFace dataset for photographs of human faces.

\section{Method}
Direct annotation for avatar creation and the challenges it introduces are discussed in Sec. \ref{sec: challenge}. Our tag-based annotation system is introduced in Sec. \ref{sec: data collection}. The search algorithm which relates tags to specific assets is discussed in
Sec. \ref{sec:search algo}. The vision backbone and training loss we selected is provided in Sec.~\ref{sec: architectures}.

\input{Fig_tex/Fig_challenge}

\subsection{Direct annotation }
\label{sec: challenge}
Customizing a stylized avatar using rendering systems like Bitmoji requires users to tune more than twenty parameters. For example, hairstyle, face shape, and nose type. Some parameters include hundreds of options to choose from and we notice that some parameters dominate the perception of avatar quality. To enable detailed comparison between avatar annotation and prediction methods, we select a single parameter to demonstrate results in this paper. We choose hairstyle, because it is the attribute with the most variations, and assets often contain high ambiguity between options. Hairstyle also has high visual impact on an avatar, since it affects a large spatial region. We keep other parameters fixed to the extent possible in each rendering system we use.

Fig.~\ref{fig:challenge} shows some sample hairstyles from the Bitmoji rendering system with the grey-scaled, gender-neutral, default face. There are many hairstyles to choose from and direct annotation simply asks the annotator to pick the ground-truth asset class which best matches the input photograph. However decision making is complicated. Some options have very subtle differences between them. Often none are an ideal match and more than one hairstyle is a plausible option. A few examples of this ``no single best answer" phenomena are shown on the left side of the figure. Simply asking the annotators to directly label the best one out of two hundred options results in high label noise, and since annotators have low agreement majority vote aggregation does not often succeed.

\subsection{Tag-based annotation }
\label{sec: data collection}
In this paper, we propose Tag-based annotation. The goal is to map both human face images and avatar hairstyles to a tag space with semantic meaning. We defined our tags as in Tab.~\ref{tab:attrbutes}. Instead of providing the annotators with a massive number of options, we specifically ask them to annotate tag attributes from each region, for example \textit{Hair direction on the top of the head}, or \textit{Hair curliness level on the side of the head}. Detailed descriptions of each tag are included in the supplemental material.

\input{Fig_tex/tab_attrbutes}

Designing the appropriate tags to describe the hairstyle requires domain knowledge. Each tag requires a clear definition. For example,  \textit{short hair} and \textit{medium-short hair} is insufficient description for consistent labeling. We use an iterative design process to arrive at our final tag definitions. The researchers first designed tags to describe the hairstyles by simply looking at a set of human images and avatars. An annotator tagged all the avatar hairstyles using this tag design. A different annotator tagged a set of human images. Using the tags, the best matched avatar to each photo is retrieved. The researchers then evaluate the agreement between annotators, and the expressibility of the tag design, to make modifications to the set of tags. The process was repeated until tag design was considered sufficient.

After arriving at a tag designing, we perform the complete run of data annotation. Note that our tag design pipeline allows researchers to focus on iteratively improving their tag designs while not requiring them to work as annotators. By going through such a design process, researchers verify their designs, so that higher agreement between researchers and annotators is achieved.

\subsection{Search algorithm }
\label{sec:search algo}
Our designed tag system has 460,800 permutations, making it impossible to design a hairstyle for each permutation. This implies that for many human images there are no perfect hairstyle match.  To address this issue, we search through all existing hairstyles, computing tag similarity. The overall distance of a particular asset is computed as a weighted sum of individual tag distances. The weight of each attribute is listed in Tab.~\ref{tab:attrbutes}. To measure the tag distance for each attribute, we used L1 loss for continuous variables, and zero-one loss for discrete variables.

The distance score from the search provides ranking information for all the hairstyles, while direct label annotation only provides the Top-1 result. Fig. \ref{fig:low_high_dist} shows visual samples of low and high-distance pairs. Note that the low-distance hairstyles have better visual similarity with the inputs, while the high-distance samples are visually dissimilar.

\input{Fig_tex/Fig_low_high_dist}

\subsection{ML training}
\label{sec: architectures}
We trained our learning-based models in a supervised manner. To extract feature information from the image, we used the open-source pre-trained Resnet-50~\cite{he2016deep} from the Pytorch~\cite{paszke2017automatic} library as our vision backbone and the initial training checkpoint. During training, we used L2 loss for continuous variables and cross-entropy loss for discrete classifications.

\section{Results and Experiments}
In this section, we demonstrate the advantage of Tag-based annotation with experimental results. Sec.~\ref{sec: annotation better} shows the advantage at the annotation level, Sec.~\ref{sec: model better} shows the advantage on model convergence and consistent predictions, and Sec.~\ref{sec: Generalizable} shows that a Tag-based system can easily be compatible with new rendering systems.

\subsection{Annotation Quality }
\label{sec: annotation better}
\input{Fig_tex/tab_annotation_compar}

\input{Fig_tex/Fig_annotation_compare}

\vspace{0.5em}\noindent
\textbf{Annotator Agreement:}
Label noise is a common problem in supervised learning models~\cite{cifar-n}. Collecting multiple copies of annotation for aggregation is often required to create a high-quality dataset. Using a majority vote is the most common way to aggregate labels and reduce label noise. However, given a large number of hairstyle options, there might exist multiple plausible answers, or alternatively, no ideal match and only partially correct answers. These situations cause low agreements between annotators.   Tab.~\ref{tab:annotation_compare} provides evidence of the severity of the problem when 3 annotators provide independent labels for each target. Agreement exists between annotators in only 31.0\% of cases when using direct annotation. In the majority of cases all three annotators provide different answers. On the other hand, annotator agreement on tags exists in 96.7\% of cases when using tag annotations. In order to fairly compare against direct annotation, we compute agreement on the Top-1 hairstyle chosen by each set of tag labels. Because multiple similar hairstyles exist, agreement is lower than on raw tag prediction at only 52.0\%, but this is substantially better (+21.0\%) than the agreement when labelers provide direct annotations. Unlike direct annotation, Tag-based annotation provides additional plausible answers which can be used to improve agreement and enable aggregation. Aggregating over the Top-4 matches from each of 3 labelers, reaches an agreement level of 80.39\% (+49.37\%).  We conclude that Tag-based labels are substantially more valuable in creating a ``clean" set of labels.

\vspace{0.5em}\noindent
\textbf{Annotation cost:}
The total cost of annotation is a function of labeling time for a individual image, and the total number of labels required. The cost of individual annotation is measured in time as shown in Tab.~\ref{tab:annotation_compare}. We asked annotators from two different skill levels to annotate images and recorded annotation time. Our experiment shows well-trained professional annotators need marginally less time to annotate a face when using the Tag-based system. In contrast, untrained workers obtained from Amazon Mechanical Turk need more time when using tag-based labels. In either case, the differences in individual labeling time are bounded and not the most significant factor.

Direct annotation requires a completely new set of labels each time a change is made to the set of available hairstyles, with associated retraining of the prediction model given the new labels. In contrast, tag-based systems only need to label the new hairstyles, with no new tag labels on the much larger set of training images, and no retraining of the tag predictor. Since most avatar rendering systems will be updated with new artistic assets occasionally, we conclude that there is a substantial savings on label cost using tag-based annotation.

\vspace{0.5em}\noindent
\textbf{Visual Quality comparison:}
To compare the visual quality between direct and Tag-based annotation, we conduct two user studies through Amazon Mechanical Turk~\cite{mturk}: \textit{Matching} and \textit{Preference}. In the \textit{Matching} task, we evaluate whether visual similarity is sufficiently close that evaluators can tell which avatar goes with which human. A human image is shown with the corresponding avatar hairstyle and three random distractor hairstyles. The evaluator is required to match the human image and avatar image and is scored as a correct match if the evaluator correctly picks the original annotation. A high matching score indicates the avatar represents the human well. A total of 1,224 judgments were collected. As the result shows in Tab.~\ref{tab:annotation_compare}, our proposed Tag-based annotation preserves user identity marginally better compared to direct annotation. In the \textit{Preference} task, avatar results from both methods are presented for comparison with the human image. The evaluators were asked to provide their preferences by choosing one of the results or indifferent, a total of 612 judgments were collected. The results showed a precise split of 306 judgements for each method. The combined results of both studies indicate that our tags are sufficiently expressive to act as a replacement for direct annotation in terms of visual quality.

A visual comparisons of the two annotations methods is provided in Fig.~\ref{fig:annotation_compare}. Both annotation methods result in plausible answers. In addition to the Top-1 match, a Tag-based system can provide ranking information for all other hairstyles. The figure also shows Top-5 results from the Tag-based annotations. The visual quality of these results is reduced, but they remain plausible.

\subsection{ML Prediction Quality}
\label{sec: model better}
Tag-based labels result in better ML models when used for selecting avatar assets, in terms of both visual quality and consistency.

\subsubsection{Baseline methods and dataset}
\label{sec: baseline}
We compare several methods to understand the effects of tag-based annotation.
 We choose two state of the art baseline methods using self-supervised models, a model supervised by direct labels, and a model supervised by Tags.

\vspace{0.5em}\noindent
\textbf{Self-supervised baselines}
\begin{itemize}
    \item \textit{F2P}~\cite{F2P}: an optimization-based method designed for realistic game character creation.
    \item \textit{AgileAvatar}~\cite{AgileAvatar}: a state-of-the-art learning-based method for stylized avatar creation.
\end{itemize}

\vspace{0.5em}\noindent
\textbf{Supervised baselines:}
Both supervised baselines use an identical vision back bone with similar loss functions.
\begin{itemize}
    \item \textit{Direct pred}: Treat the task as a classification task, predict the best hairstyle, and calculate training loss against direct annotation.
    \item \textit{Tag pred}: Treat the task as a classification task, predict the tags, and calculate training loss against the Tag-based annotations. The final hairstyle prediction is determined using the search described in Sec.~\ref{sec:search algo}.
\end{itemize}

\vspace{0.5em}\noindent
\textbf{Dataset and annotation:}
We used human face images from the FairFace ~\cite{FairFace} dataset. Compared to the commonly used CelebA ~\cite{CelebA} dataset, FairFace is racially balanced, which ensures our trained model works well on diverse faces. FairFace also includes blurry images, which requires the trained model to be robust on lower-quality images.

\vspace{0.5em}\noindent
\textbf{Annotation:}
We collected Tag-based annotation for 17k images from FairFace using professional annotators. We randomly choose 14.5k for training, 2.8k for testing, and 204 images as a hold out set for human evaluation studies.

Inter-annotator agreement is higher for tag-based annotation than direct annotation. To avoid bias due to the quality of annotations collected, we create a set of direct labels using the tag-based labels. We use the tags for each image to find the single best hairstyle. We treat these hairstyles as the training targets for \textit{Direct Pred}. Thus both supervised learning methods share the same Top-1 training targets.

\subsubsection{Better prediction quality}
\input{Fig_tex/Fig_model_compare}
\input{Fig_tex/tab_model_matching_scores.tex}

\textbf{Visual comparisons for models:}
We visually compare all four learning-based methods in Fig.~\ref{fig:model_compare}. F2P~\cite{F2P} was designed for realistic human-style avatars and fails frequently on stylized avatars. AgileAvatar~\cite{AgileAvatar} has a human stylization module to overcome the style domain gap and has significant improvements over F2P;  however it is still not as good as the supervised learning methods. Comparing the \textit{Direct pred} and \textit{Tag pred} models, training a model using the Tag-based annotation helps the model to capture the detailed features of the hairstyles. For example, capturing the double pony-tail for the girl in the third row, and the curliness of the hair for the man in the sixth row.

\vspace{0.5em}\noindent
\textbf{Numerical comparison for models:}
 We conducted user studies to support the visual comparison with results shown in Tab.~\ref{tab:model matching scores}. The Matching test requires evaluators to match a randomly selected human against 4 avatar results. Such a test measures how well the model captures the human identity. If the avatar looks very similar to the photo, then identifying it is easy. If the avatar does not look similar it is hard to distinguish from randomly chosen distractors. Higher matching scores are better. F2P is designed for photorealistic avatars and performs poorly. The carefully tuned self-supervised AgileAvatar reaches a similar matching score to supervised \textit{Direct pred}, only 3.57\% lower. However, Tag-based annotation performs best, preserving more user identity than all other baselines.

\subsubsection{Better convergence, More consistent predictions }
\label{sec:converge consistent }
\input{Fig_tex/Fig_consistent_compare}
\input{Fig_tex/tab_consistent_compare}

\textbf{Convergence:}
During training, both supervised learning models have the same best-matching hairstyle for each human image. In the case of direct prediction this hairstyle is explicitly used in the target loss, while in the case of tag prediction it is implicitly defined by the target tags. Higher accuracy on the test set indicates converging to a more optimal point. Tab.~\ref{tab:model consistent} provides prediction accuracies. Naive comparison shows that direct prediction finds correct answer 10.29\% of the time while tag prediction is correct 95.72\% of the time. However this is not an entirely fair comparison because direct prediction must choose among hundreds of classes, while tag prediction has many fewer classes available. To create a fair comparison we require both methods to choose a final hairstyle among the hundreds available and compare this to the ``ground-truth" target. We compare model convergence toward the target loss using Top-K accuracy.  Training the model on the Tag-based systems, results in a better Top-1 {\small{\tt{\textcolor{red}{(+6.87\%)}}}} and Top-5 {\small{\tt{\textcolor{red}{(+9.32\%)}}}} accuracy compared to model training on direct labeling.

The low Top-1~(17.16\%) accuracy seems to contradict our claim of good quality predictions in Tab~\ref{tab:model matching scores}; however, in reality, Top-1 accuracy only tells a partial story. There are hundreds of hairstyles, and some options have very minor difference from other options. Given the potential existence of multiple correct answers, it's not always wrong to predict something other than the ``ground truth" option. Thus Top-1 and Top-5 are good indicators of only the relative quality of two prediction models, not the absolute quality.

As an alternative metric for quality, we used our search algorithm described in Sec.~\ref{sec:search algo} to calculate the distance between the human face and the final avatar predictions, using the tags originally provided by annotators. A low distance score indicates greater similarity between the human face and avatar at the feature level. The result in Tab.~\ref{tab:model consistent} shows \textit{Tag pred} has a lower average distance for the Top\nobreakdash-1(2.51) and Top-5(3.16) predictions. Note that most human face images don't have a perfectly matching avatar so a distance score of 0.0 is impossible. We include the distance for ``ground truth" hairstyles as chosen by annotators as an indication of a lower bound on prediction quality.

\vspace{0.5em}\noindent
\textbf{Consistency:}
We visual the Top-5 predictions of both supervised learning methods in Fig.~\ref{fig:consistent_compare}. While both methods produce plausible Top-1 answers, the Top-5 predictions from \textit{Tag Pred} have better consistency compared to \textit{Direct Pred}. The \textit{Direct Pred} model treats each hairstyle as an independent class without considering their visual similarities to the human image. \textit{Tag pred}, on the other hand, is trained to predict the visual features defined by human researchers. Thus the model was encouraged to focus on the features that are important to human observers, thus resulting in consistent Top-K predictions. Notice for example that even when direct prediction correctly predicts a short hairstyle as the Top-1 result, the next best prediction might be long hair.

\subsection{Generalizability}
\label{sec: Generalizable}

\input{Fig_tex/Fig_other_sys}
Annotating a dataset costs time and effort from both researchers and annotators. In our case, about 17k sets of tags for human images and avatar hairstyles. To make our trained model compatible with any other rendering system, the model requires an entirely new set of 17k labels if using direct annotation.

Using a Tag-based system, on the other hand, can significantly reduce this cost. Since the tags for the human images are independent of rendering systems, human tags can be reused. We are only required to collect new tags for the avatar assets in the new rendering systems. Most rendering systems provide ``only" 200 hairstyle variations, costing less than 2\% of relabeling the human images. In addition the original tag prediction ML model remains valid, and does not need to be retrained.

To demonstrate the generalizability of the Tag-based system, we collected tags for avatar samples from Bitmoji~\cite{bitmoji}, Google Cartoonset \cite{Google_cartoon}, MetaHuman ~\cite{metahuman}, and NovelAI ~\cite{novelai}. For all these systems, we only controlled the hairstyles:
\begin{itemize}
    \item \textbf{Bitmoji:} A cartoon avatar rendering system. We used the gender-neutral, gray-scaled default faces.
    \vspace{-0.5em}
    \item \textbf{Google Cartoon set:} A dataset of the results from a cartoon avatar rendering system. We controlled the hairstyles and left other attributes set random.
    \vspace{-0.5em}
    \item \textbf{Metahuman:} A realistic human avatar rendering system. Hairstyle is varied while leave other parameters constant. We choose between males and female default avatars using the gender tags in the FairFace dataset.
    \vspace{-0.5em}
    \item \textbf{NovelAI:} A diffusion-based text-to-image generation model specialized for cartoon-style figures. We asked artists to select text prompts to generate cartoon figures with different hairstyles and treat the resulting images as a set of avatars.
\end{itemize}

Fig.~\ref{fig:other_sys} shows the model-predicted visual results. Given the tag predictions from our supervised learning model, the search algorithm finds the closest matching hairstyle in each system based on the avatar tags. Not every system contains every hairstyle so some matches are closer than others, but overall the avatars selected are good approximations of the input photograph.

\section{Limitations and conclusion}

\noindent\textbf{Limitations:}
To benefit from the Tag-based system, a set of carefully designed tags with clear definitions is required, along with the cost in time and effort to produce it.

The search algorithm is sensitive to tag prediction errors. While tag prediction is substantially more accurate than direct prediction, a wrong prediction will mislead the search.

As with all avatar prediction methods, our work lacks a universally accepted benchmark or evaluation metric. Commonly used L2 loss, perceptual loss\cite{johnson2016perceptual}, or Top-K accuracy do not well represent actual user preference. This paper conducted user studies for result evaluations, but this also has shortcomings since evaluators are not the same as actual users.

\vspace{0.5em}
\noindent\textbf{Conclusion:}
In summary, we present a Tag-based annotation method for avatar creation. Our approach results in higher annotation quality with higher annotator agreements. Supervised learning models guided by the proposed annotation converge to a more optimal point, and output more consistent predictions. Our proposed method can easily be applied to new rendering systems with marginal cost. Experimental results including numerical, visual, and user studies support our claims.

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