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Melanated Moments, S1E1: The Dean – William Grant Still
March 18, 2020Melanated Moments in Classical Music
On our debut episode of Melanated Moments in Classical Music, Joshua introduces us to one of his favorite composers, William Grant Still. Known as the Dean of African American Composers, his Africa Suite transports the listener to the dawn of time and the cradle of civilization. Joshua and Angela discuss the piece's second movement, Land of Romance as well as cartoons, relationships, and defying stereotypes.
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MMCM Theme (00:15):
[inaudible]
Joshua Thompson (00:15):
Welcome to Melanated Moments in Classical Music. I'm your co-host Joshua Thompson.
Angela Brown (00:19):
I'm Angela Brown. And we're here to introduce and reintroduce you to melanated masterpieces written and performed by for and about Black people.
That is correct from light bright to midnight. There's a whole lot of people–
Did you go there?
I did, and we gon' stay there, but there's a whole lot of people, uh, pieces, performances that we would like to cover. But we are here to talk about and take you inside the classical music that most folks don't know a whole lot about, or they don't hear a whole lot about. So, you know, that's what we do.
Indeed we do. Who, and what do you have for us today, Maestro Joshua?
Well, I got a special treat for you all from one of my all time favorite composers. And I would say out of the Black composers who tends to get acknowledgement and recognition, this man seems to come at the, at the top of the list.
Well who is it?
William Grant Still.
Okay. So you've heard of him?
Oh yes, baby.
Often known as the Dean of classical music for Black folk. I just think he's a Dean of classical music period. The brother was bad, was bad. He's got well over 150 compositions to his credit. So he was writing for solo piano. He was writing for voice, for opera, for small ensembles for full orchestras, you name it–
Song cycles, I've sung some songs cycles of his. And like you mentioned, the opera Troubled Island.
Yes. So the piece I wanted to highlight of William Grant Still, even though it's not going to be Troubled Island, another one that's even lesser known, I think, but no less impactful is his Africa Suite for solo piano.
Oh. So you're going to cover an entire continent today.
Well, I'm gonna try. Okay. We're gonna break it down. We'll break it down. So I wanted to highlight the second movement. So there's three movements to the entire work, but we're going to talk about the second movement. It's called Land of Romance.
I do love me some romance. Right.
And who doesn't? Who doesn't? So I don't know. Let me, let me ask you this. What is the first thing you think of, or what do you think of when you hear the word Romance?
I think of a roaring fire, of soft, beautiful things wrapped around my body. Anything soft and beautiful and lush.
So you come with all the feels, the tactile of romance, right? I think if you would ask a lot of people, they think of romance as in boy meets girl, girl meets boy or any other combination.
So relationships, it's kind of what you think of, but I'm going to change it up just a little bit. And we're going to think about the word romance using this movement to think about our relationship between nature and with our creator.
Oh. Okay. So you pulled me all the way back from my little–
Wait a minute though–
Consensual stuff.
Your eyebrows were kind of furrowing and I was like, "Oh, I've lost her," but hey, I promise I'm gonna bring you and everyone else back.
Okay, come on.
So first things, first little bit of background on William Grant Still, lots to cover. We just go and hit the highlights born in 1895 died in 1978 and he was from Mississippi. And he's the first African-American composer to have a major work premiered by a major symphony. So lots of firsts for him, but William Grant Still was a very religious, spiritual, philosophical person. And that is really reflected in almost all of his compositions. He's going to make your brain think about it just a little bit. So Land of Romance for him was really talking about the idea of how we got here as a people, as a human race, how we wound up on the planet and our relationship as we have evolved as the species with our creator and with nature. Am I getting too deep for you?
Chile, we in the ocean.
Okay, well, you know, well I brought my waders and I brought you some boots–
Thank you, we gon' wade on through them boots.
Really love this piece. The second movement, this Land of Romance for me, gives a honest and dignified depiction of the totality of Blackness. So this piece is sort of broken up into three separate parts, within one movement. And in the beginning…
Music Plays (04:43):
[William Grant Still, "Land of Romance"]
The beginning starts with this very dream-like, almost sunrise type sentiment to it. It's like the Dawn of time.
[William Grant Still, "Land of Romance," continued]
So here we are in the cradle of civilization, which is the Africa and it's very light and ambient and mysterious, it was very beautiful too.
And then we get a little bit deeper.
There's more.
Then we get into a little bit more of that sensual coquettish–
Flirtiness with our existence, a little bit [laughter].
Music Plays (05:49)
So yeah, he's being very flirty here, and he does it brilliantly in an almost jazzy and bluesy way, but it's also still iconically classical.
So where's the pieces developing, we are developing okay.
As people, as a people, as Black people, but also as, as a species developing into our own, coming up with our own language, our own movement, our own sensibilities and sexualities. And then it finishes itself off with this very, nice stately and bombastic and dignified statement of Africa and the people who were in it. And this idea of creation.
And at this point, I just like to close my eyes and let myself sink into the music.
So by this point, we've gone from the Dawn of time to establishing a whole culture and civilization. And he does all of this in eight minutes.
A whole continent, a whole civilization.
In eight minutes.
Eight minutes.
I told you the brothers bad, the brothers bad. And we don't hear this piece all that often. I think for me, it's a big problem for me. I really get upset when pieces like this don't get played a whole lot.
Well, that's why we're here–
To get people to discover and rediscover pieces that they didn't know existed.
One of my favorite parts about the whole work in general, about the second movement, is it offers a very sincere and authentic musical representation of the Black aesthetic. Right? A lot of times I think we're used to, if everything isn't draped in kente cloth and with drums, it's not authentically Black, but don't get me wrong. There's a place for that. It's part of our culture.
Yes, I like wearing my Mud cloth.
I know, as you have it on now.
However, we're broader than that.
We're more comprehensive. There's a lot more intricate that's going on there. So those highs and lows and that natural way that he mixes classical music and romanticism with jazz and blues, because that's the era he's writing in, seamlessly fits together to kind of create this amazing musical tapestry that our people and other people in general can tap into, identify, and be proud of
He's a product of his environment. Yes.
So those are some things that I feel so saying what some of those things too, I think this was a good piece to help us shed some stereotypes of Black people and Blackness and the continent of Africa. I don't know. I was a big cartoon watcher growing up, were you?
Oh yes, definitely.
What were your cartoons?
Tom and Jerry. And oh… That, uh, hen that would go around–
Foghorn Leghorn.
Foghorn Leghorn!
"I say, I say, I say!" [Laughter] So I can distinctly remember there is a cartoon, it's from the forties or fifties, and it's got Bugs Bunny in it and he's flying over the continent of Africa. And as he is over West Africa, it says "Dark Africa," that's a little dark. And then as he flies towards central Africa, it says "Darker Africa" and is darker. And then as he's getting East and South Africa, it says "Darkest Africa" and it's pitch black. Right. And so as a kid, you're thinking no big deal, but as adults, we all know what that means. Right?
It's funny you would even mention that because it brings up, this is totally off of William Grant Still–
That's alright.
Because I don't know why we're going towards, with the cartoon. But imma go with you, you stay with the cartoons, boo boo… But I got to tell this story, one time I was doing this Aida and there was something that I wanted to do that was very African. And this director, he was like, "Well, no. She wouldn't do that because it's not that dark. The Egyptians, aren't part of the real part of Africa or the darkest part of Africa" or something like that. And that was like isn't Africa, Africa. I'm sorry.
Wow. So how did you fix your face to stay professional, keep it moving?
Let me look over at the camera, but I'm gonna look at you right now. And that was like, "I'm sorry, honey. Africa is Africa. Now you, yeah, you have different cultures and things going within Africa, but the continent of Africa–
Is what it is.
If you from there, you African, okay, or Afrikaans.
Your story does relate to my cartoon, into my whole thing is these are these stereotypes that are out there that remain out there in literature and cartoons, and also in the music or the misinterpretation of, we're not heathens. We're not savages. So this piece gives an opportunity to elevate, not just the music, but also a people and show us in a very comprehensive, respectful, dignified manner. That's what I think.
And that's why the song and the selection went on the show today.
All right now.
So there are three movements to it. I'm sure there'll be many, many more from William Grant Still that we can, and that we probably will highlight as we continue with this incredible show to highlight Black folks and everything. So with that being said, you have heard the second movement of the Africa Suite, Land of Romance, written by William Grant Still. I'm Joshua Thompson.
Angela Brown.
Unison (13:05):
And this has been your Melanated Moment in Classical Music.
Melanated Moments in Classical Music is a production of Classical Music Indy.
Our Producer is Ezra Bakker Trupiano. Our production intern is Auboni Hart. Our theme music was composed by Laura Karpman. Melanated Moments in Classical Music is proud to partner with CAAPA, the Coalition for African-Americans in the Performing Arts; supporting black classical musicians, opera singers, youth and others in the performing arts by bringing color to the classics. Learn more at four –that's, the number 4caapa.org. Classical Music Indy- The Streaming and Podcasting is made possible by the Allen Whitehill Clowes Charitable foundation. Melanated Moments in Classical Music is proud to partner with Morning Brown Incorporated; working to bridge the gap between accessible, live music programs and underserved individuals, schools, and communities where the offering of classical music is rare or the cost of experiencing classical music is prohibitive. Learn more at morningbrown.org.
[inaudible].
LOCAL CLASSICAL – HOST OKARA IMANI
Hey, Starshine! This is Okara Imani, Media Production Fellow for Classical Music Indy, and your guide to The "I" in Classical Music. I'm here to highlight the cultural and social intersections of the classical art form, beyond the Classical Period and beyond the constructs of Euro-centric high society origins.
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Previous Episode: Introducing: Melanated Moments in Classical Music Podcast Series
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Local Classical – Eliza Brown
We continue our celebration of Women's History Month this week by featuring music by composer Eliza Brown. Brown's work is often interdisciplinary, with a recent focus on musical theater and opera. She also writes music that is, what she calls, intertextual, opening dialogues with existing pieces of music, historical styles, and other cultural artifacts.


Laro Foot Print Gema60 House slippers for women, slippers made with textile materials. Nice and fun design with a mouse and a cat, each drawing on one of the feet so that when joining both feet you can see the complete drawing. They are warm through the soft hair lining. We will have warm feet and walk comfortably through our house.
Laro Foot Print Gema78 House slippers for women, undercut slippers made with textile materials. Nice and fun design with a mouse and a cat, each drawing on one of the feet so that when joining both feet you can see the complete drawing. They are warm through the soft hair lining. We will have warm feet and walk comfortably through our house.
Calzados Laro is a family company with many years of experience in the manufacture of different types of footwear. They manufacture with great quality both in their materials and in their designs and that is why it is highly appreciated by professionals in the sector.
Dare with its cheerful designs and bright colors.


This cutting board was made of several different woods, Padauk, Hard Maple, and Walnut. These woods were chosen for this cutting board due to just how they complement each other. The finish consisted of linseed oil and bees wax. I chose this finish because I wanted to make sure that this cutting board was guaranteed to stay in stellar shape due to the amount of use it would get.
It is 18 inches by 10 inches. It is made from hard maple and walnut. We used a mineral oil and wax as a finish on this board.


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Auction Method
MEET OUR SELLERS
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ONLINE AUCTION - Ben Hill County, GA
Online Auction Begins Monday, March 1, 2021 and Bidding Ends Wednesday, March 3, 2021 at 4:00 P.M. *Ben Hill County Tax Parcel 5 11 56 * 9.8 Acres * For any questions, Call Sale Manager, Greg Williamson in Vidalia, at 912-293-3429 or Hudson & Marshall at 478-743-1511.
Mar 1 @ 9:00am EST (Start)
Mar 3 @ 4:00pm EST (End)
Online Auction Begins Monday, March 1, 2021 and Bidding Ends Wednesday, March 3, 2021 at 4:00 P.M.
*Ben Hill County Tax Parcel 5 11 56 * 9.8 Acres
* For any questions, Call Sale Manager, Greg Williamson in Vidalia, at 912-293-3429 or Hudson & Marshall at 478-743-1511.
Property Sells Via ONLINE BIDDING ONLY
ONLINE BIDDING INSTRUCTIONS: To begin the bidding process visit www.hudsonmarshall.com. This H&M Online Auction begins Monday, March 1, 2021 at 9:00 A.M. and the property starts to close on Wednesday, March 3, 2021 at 4:00 P.M.. This window will extend for two (2) minutes as long is there is active bidding. Bidders who cannot be available at that time can set a maximum bid on their property of interest. The system will bid on your behalf as much as necessary to maintain your position as high bidder, up to your maximum bid amount. Bidders must have a MyH&M account to bid. Internet connection required.
A 10% Buyer's Premium will be added to the High Bid amount.
Buyer must execute a cash contract.
Buyer must provide Articles of Incorporation or signing authority if purchasing in a company name (LLC, etc.).
Properties are purchased AS-IS, WHERE IS with NO INSPECTION PERIOD.
Properties are sold with NO LIENS, NO BACK TAXES, and INSURABLE TITLE
All contracts must close within 30 days.
EARNEST MONEY: Bidders will be required to pay 10% of the total purchase price down as the earnest money binder.
TERMS OF SALE: Bidders will be notified no later than 5:00 P.M. Friday, March 5, 2021 if their bid is accepted or not accepted. The Purchase and Sale Agreement will be emailed for execution. Contracts must be signed and returned to Hudson & Marshall no later than Tuesday, March 9, 2021 by 5:00 p.m. and accompanied with the 10% earnest money binder. The earnest money can be in the form of a check or wire transfer. Hudson & Marshall will provide wiring instruction upon notification.
BUYERS PREMIUM: All real estate sold at this auction will be sold with a 10% buyer's premium fee. (Example: If the purchaser bids $10,000, then we charge 10% ($1,000) for a final purchase price of $11,000.
CLOSING COSTS: The Seller shall pay its pro-rata share of the estimated 2021 real estate taxes as a credit to Purchaser at Closing. Purchaser shall be responsible for the 2021 taxes when due. Seller shall also pay the cost of preparation of the deed. Purchaser shall pay for any title exam fee, title insurance premium or attorney's title opinion, loan closing costs, appraisal fees, recording charges, the State of Georgia transfer tax and any and all other costs associated with closing this transaction.
SURVEY: The property will sell by the existing deed and/or survey.
INSPECTION: The land is available for inspection at anytime.
CLOSING: Full payment and Closing shall occur within thirty (30) days of Seller execution of the contract.
BUYER'S NOTE: Personal on-site inspection of the property is strongly recommended. THE PROPERTY WILL SELL "AS IS - WHERE IS" WITH NO WARRANTIES EXPRESSED OR IMPLIED. Information was gathered from reliable sources and is believed to be correct; however, the information set forth herein has not been independently verified by seller nor auctioneers. Its accuracy is not warranted in any way. There is no obligation on the part of seller or the auctioneer to update this information. All announcements made on wwww.hudsonmarshall.com take precedence over all other advertising. Seller has the right to establish a required minimum bid. There is no obligation on the part of the seller to accept any backup bids in the event the high bidder fails to perform. All bids are subject to Seller Confirmation.
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PURCHASE AND SALE AGREEMENT: For a copy of the P&S agreement visit www.hudsonmarshall.com or contact Hudson & Marshall at 478-743-1511 for an emailed copy.
AGENCY DISCLOSURE: Hudson & Marshall and all licensees employed by or associated with auctioneers, represent seller in the sale of this property.
SALES AGENT: Call Steve Slocumb for any questions - 478-957-4283 or 800-841-9400.
Auction: BG Hudson Jr. #103, AM Marshall IV #1605, RS Slocumb #3512, W. Marshall #4018, H&M #274
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redpajama_set_name: RedPajamaCommonCrawl


text:
Q: Connect Mainframe Using Java/Groovy? I am currently working on a web service project which is exposed..  I need to automate the below process
Step 1: Connect Mainframe and collect test data from the Mainframe and storing data in xls.
Step 2: Run the Soap Request and Verify that the response is 200
Step 3: Connect Mainframe again and Extract each parameter from Response and validate those against in MF data.

Related to Webservice Test, i have worked on Soap UI.. Step 1 can be optional may be i can run a batch jobs to get that..
Step 2 and Step 3 is it possible in Soap UI itself.  I am using Rocket Bluezone Emulator to test this..
There is a way that I can write a vb script and call that vbscript using groovy that is the least option..
Is there any other way that i can connect Rocket Bluezone using Java/Groovy to achieve this instead of running separately vbscript.

A: To answer a part of your question, you can call VBScript from Groovy.
A script:
if WScript.Arguments.Count = 0 then
    WScript.Echo "Missing parameters"
end if

function func(parm)
    Wscript.Echo("You sent " & parm )
    func = "You sent " & parm
end function

func(Wscript.Arguments(0))

You can call it, pass a parameter, and get the return value from Groovy like this:
def script = "src/myScript.vbs"
def exe = "C:\\Windows\\System32\\cscript.exe"
def parm = "Hello"
def cmd = "${exe} ${script} \"${parm}\""
def proc = cmd.execute()
def outputStream = new StringBuffer()
proc.waitForProcessOutput(outputStream, System.err)
println outputStream.toString()

Hope that helps.

redpajama_set_name: RedPajamaStackExchange


text:
JAMPRO, Minister Crawford Endorse 'Make The Cut'
Damion Crawford
Following the recent news of JAMPRO's lauding of Red Stripe's cassava project, the company has yet again impressed the government organisation. Another metaphorical gold star was handed to the beer company as JAMPRO and minister of state in the ministry of tourism and entertainment, Damion Crawford, showed their support in favour of Red Stripe's 'Make the Cut' competition, where budding young film-makers have been invited to write, produce, direct, and film the newest Red Stripe TV commercial.
Carole Beckford, film commissioner/manager at JAMPRO, is positive the competition can be a fulfilling project for all concerned.
"JAMPRO is happy to support the development of quality content as we build the value of the film/tv industry. We applaud Red Stripe's efforts to bring this section of the industry in full view. As JAMPRO continue to focus on capacity building and professional development, we are pleased to have private sector on board, which sees the value and actually become part of the process," Beckford said.
Creative Economies
"The creative economies become more viable when it attracts investment and enhance the quality of work by content producers. We are keen on helping the participants move forward and wish for them a worthwhile experience," she added.
Beckford has a background in journalism and sports marketing, and was in charge of publicity for superstar Jamaican sprinter Usain Bolt for four years. This type of recognition can only further promote Make the Cut as a very positive project, with not only recognition for the company being so close-knit with the young community, but a project that can really bring to light the talent Jamaica possesses on a whole.
Crawford also supports the movement.
Provide Opportunities
"The drink responsibly campaign and the partnership with budding film-makers, comes against the backdrop of a local industry that has seen the recent release of two features and a vignette of shorts during 2014 – Destiny, Ching Pow and Ring Di Alarm. This Red Stripe campaign will provide opportunities for creative young people – a pursuit which I heartily support. Make the Cut is a step towards us realising possibilities – possibilities that become real when corporate Jamaica becomes involved in the growth and development of the entertainment and creative industries. I therefore look forward to the outcome of Make the Cut, and the talent that will be unearthed during this process."
Kamal Powell, brand PR manager at Red Stripe, is elated that JAMPRO and the minister are impressed with the programme and hopes that this will drive more interest around the competition and more involvement in the Drink Right campaign.
Source: http://jamaica-gleaner.com/gleaner/20141203/ent/ent2.html
Published Date: December 3rd, 2014


The spirit can assert itself only through the medium of clear form.
To judge a composer's work, one must consider it as a whole.
What I wanted and what I visualized while composing has not always been realized.
It is easier to achieve a desired result in short pieces.


The article considers the influence of the transformation policy of Alexander I in the field of education and in the central administration on the rapid development of the Russian higher school in the early 19th century. The Charter of 1804.
A sailing charter; causes of death of people; extreme situations; acts of nature; the human factor; safe transportation of passengers; rules of safe behaviour on a sailing charter.


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text:
Q: Keyboard inputting extra characters I'm having a very frustrating issue with my 14.04 laptop due to water damage (lent my laptop to the wrong person). Something is inputting extra characters when certain keys are pressed. Everything works fine with an external keyboard.
o inputs a home (cursor goes to start of line) then the o, though a capital o will input 7 before the O.
l is *l (or *L)
9 is -9 (or "-(" )
and insert is p (this one i can live with).
I've been experimenting with AutoKey to try and find a workaround, but it doesn't seem possible to substitute strings that are shorter than the abbreviations. I also don't know how to deal with the o key using this method.

A: You're trying to find a software work-around to a hardware failure and that's like steering towards the right after a car accident on the left instead of just repairing your car...
Just:

*

*download the service manual of your laptop

*order a replacement keyboard and have that wrong person pay for it

*mount the keyboard yourself (the most expensive part is the mounting, not the keyboard itself)


Stop jabbing as soon as they're dead and equip your stone pickaxe—it harvests more meat than hides and the hatchet does the opposite. Chip away at the corpse until it dissolves. Chip away at... Things get tougher during winter when animals leave and plants freeze over, making them inedible (though that doesn't stop you from harvesting berries). Arid shrublands have large numbers of agave plants growing, providing lots of food (though agave fruit doesn't taste good raw), and large animals that yield lots of meat.
Domesticated: Like many of the untameable ocean dwellers, Ammonitina still has enough utility to be a valuable hunting target. If a tribe is willing to risk the wrath of nearby would-be protectors, Ammonitina bile can be harvested from its corpse's innards. This bile can be worked over with other... Things get tougher during winter when animals leave and plants freeze over, making them inedible (though that doesn't stop you from harvesting berries). Arid shrublands have large numbers of agave plants growing, providing lots of food (though agave fruit doesn't taste good raw), and large animals that yield lots of meat.
Bible verses about birds. Scripture makes it clear that God is a bird watcher and He loves and cares about all birds. That is an awesome thing for us. how to turn on the market on facebook Chemical decomposition is the breakdown of complex molecules into simpler substances. In biology, it refers to the breakdown of organic animal and plant tissues into the elements and compounds that can be used by other lifeforms.
Use a non harvesting dino to kill then order attack target to harvest excellent for prime gathering 7 points Utility Oct 23, 2017 If you set them to wander they will harvest from any bushes or trees they come across, allowing you to passively collect huge quantities of fiber and thatch for crafting, aswell as berries for brews, beer, and keeping your animals fed.
15/04/2017 · Cant Harvest Tamed Animals In PvE? Hello . I recently joined a PvE server, having no experience with PvE. Built a base and eventually tamed 2 opposite gender sheep and bred a little baby, with plans to slaughter him when he was grown.


text:
Leonard Cohen reveals details of world tour
NME Mar 11, 2008 12:08 pm GMT
The veteran singer/songwriter will play eight UK dates
Legendary singer/songwriter Leonard Cohen has revealed details of his world tour.
The jaunt, his first in 15 years, will include eight UK dates, four in Manchester, one in London, one in Edinburgh and the already confirmed slot at Glastonbury Festival on June 29, and a slot at The Big Chill festival on August 3.
The shows announced so far are:
Toronto Sony Centre For The Performing Arts (June 6/7)
Dublin IMMA (June 14/15)
Manchester Opera House (18-21)
Montreal Place des Arts (23-26)
Glastonbury Festival (29)
Oslo, Norway (July 1)
Helsingborg Open Air (3)
Copenhagen Rosenborg Castle (5)
Arhuus Radhus Parken (6)
Montreux Jazz Festival (8)
Lyon Festival (9)
Bruges Cactus (10)
Amsterdam Westerdam (12)
Edinburgh Castle (16)
London O2 Arena (17)
Lisbon Passeio Maritimo (19)
Benicassim Festival (20)
Nice Jazz Festival (22)
Lorrach Stimmen De Welt (25)
Lucca Summer Festival (27)
Athens Lykabettus Theatre (29)
Ledbury The Big Chill (August 3)
Istanbul Arena (5/6)
Prague Castle (10)
Budapest Sziget Festival (12)
Girona Cap Roig (14/15)
Vienna Opera House (28/29)
Ticket details will be announced shortly. More worldwide dates will continue to be announced in the coming weeks.
Cohen was inducted into the US Rock And Roll Hall Of Fame last night (March 10).


Home > About Aspen Avionics, Inc.
Aspen Avionics, based in Albuquerque, New Mexico, was founded by aviation enthusiasts in 2004. Aspen Avionics specializes in bringing the most advanced technology and capability into General Aviation cockpits—and budgets. Our products increase situational awareness and reduce pilot workload, making it even easier and safer to fly in both VFR and IFR conditions. The way we look at it, getting the latest avionics technology shouldn't always mean spending a lot of money—on equipment or installation. At Aspen Avionics, we design products to be affordable, easy to install, and easy to own.
Aspen's flagship product line is the Evolution Flight Display system, the most innovative—and affordable—glass cockpit system ever for certified general aviation (GA) aircraft. The Evolution system enables aircraft owners to upgrade their primary flight instrument "six pack" to glass cockpit technology all at once, or in stages. Easy and inexpensive software upgrades add new features and capabilities, while future-proofing the system and protecting owners' investments.
Aspen's first product, certified in November 2005, is the AT300 Hazard Awareness Display, a combined terrain awareness display and vertical speed indicator. It is designed to directly replace a mechanical vertical speed indicator, and is certified under TSO C8d (Vertical Speed Indicator) and TSO C113 (Multipurpose Electronic Display).
The company raised its first round of venture capital in December 2005, and closed a Series B round in June 2007, to facilitate its continued strong growth. The Company's investors include Granite Ventures, vSpring Capital, Wasatch Venture Fund, Fort Washington Capital Partners, and Sun Mountain Capital.
Led by industry veteran John Uczekaj, Aspen Avionics' team of top talent—with deep roots in aviation product development and certification—will continue to grow the company's business through pioneering solutions for the general aviation market.


text:
Armie Hammer is in Austin, and he isn't worried about any social media spats
Armie Hammer says he thinks actor James Woods "must have blocked me on Twitter" because he hasn't "heard a word" from Woods since a dustup on social media.
It all started back in November, just before the release of "Call Me by Your Name," when Woods got riled up about the movie about a romantic gay relationship between a 24-year-old graduate student and a 17-year-old son of a professor.
In the movie, Hammer plays the grad student, while Timothee Chalamet plays the youth. (Chalamet is in his 20s.). In a tweet about the movie, Woods acted aghast over the subject matter, saying that the film was quietly chipping away "the last barriers of decency."
RELATED: SXSW celebrity sighting — Armie Hammer at Lady Bird Lake
When Hammer, 31, saw the tweet, he responded to Woods by pointing out that he has dated young women, writing, "Didn't you date a 19 year old when you were 60…….?"
Indeed, several reports have mentioned Woods' penchant for dating women either 19 or 20.
And then actress Amber Tamblyn entered the Twitter storm, saying Woods hit on her when she was a teenager – and tried to take her to Vegas.
Hammer, who was in Austin to receive an award from the Austin Film Society and to promote Friday's premiere of his new movie, "Final Portrait," seems to be enjoying himself around town – and he says he's not all bothered by being blocked by Woods. He'll be on the red carpet for "Final Portrait" – promoting another movie in which he plays a gay character, James Lord, the biographer of artist Alberto Giacometti.
Texas Film Awards Gala red carpet
Photos: Celebrities at SXSW 2018
Plan ahead: The SXSW side party guide


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redpajama_set_name: RedPajamaGithub


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About PEFC
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With more than 40 million hectares of forestlands certified to PEFC, Canada is your source for certified wood and paper products.
PEFC Canada represents organizations in Canada that have certified their forestry operations to the PEFC-endorsed Canadian Standards Association (CSA) Sustainable Forest Management (SFM) standard, as well as organizations and businesses that are certified to the PEFC International Chain of Custody Standard.
Canada has been a member of PEFC International since 2001. The CSA SFM standard (CAN/CSA-Z809), first published in 1996 and revised three times since, has been endorsed by PEFC since March 2005. The current version of the CSA SFM standard was assessed and re-endorsed by PEFC International in March 2018, with the next review of the standard to commence by 16 September 2021.
PEFC Canada offers the use of the international PEFC Chain of Custody certification standard and is authorized to issue rights to use the PEFC label to qualified organizations in Canada. PEFC Chain of Custody certification together with the PEFC on-product label provides an assurance that wood and paper products are sourced from sustainably managed forests.
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On Tuesday 20 November 2018, the Women Office of the Syria Democratic Council organized an outreach lecture for the women of Al-Kesra area in the Education center.
During submitting the lecture ,the role of women in building a proper family and the development of society. At the same time, the effects of organized violence against women throughout history and its adverse results on society have shown, Alham Omer said.
She added that statistics of violence against women globally indicate that in every ten cases of violence, seven of them are against women, and every hour there are three cases of violence against women, which negatively affects them and their psychological state.
It is worth mentioning that these lectures are made within one of the programs which are organized by the Women Office at the level of the North and East of Syria.


text:
\section{Introduction}
Much of the success of modern deep learning relies on supervised learning \citep{krizhevsky2012imagenet,he2016deep,huang2017densely,ravi2016optimization,santoro2016one,van2016wavenet}. Supervised learning describes a family of learning algorithms that, given a set of input-output pairs, can learn a model that can map a set of input data to a set of output labels. A good supervised model should not only be able to predict the correct output for the data it was trained on (i.e. the training set) but also have high generalization power (i.e. to be able to predict the correct outputs for previously unseen data points).

\begin{figure}[ht]
    \centering
    \includegraphics[width=0.99\columnwidth]{unsupervised_few_shot_learning_v51.png}
    \caption{Proposed method.  We follow the set-to-set \cite{vinyals2016matching} few-shot learning framework, where during inference time, a learner acquires task-specific information from a \textbf{support set}, and is then evaluated on a \textbf{target set} for that task. Building support and target sets for few-shot tasks ordinarily requires labelled information. However, to produce such tasks in the absence of labelled data, we randomly select some data points from a label-free dataset and assign random labels to them, in order to form the support set. Then, the support set data-points are augmented, and the label information kept the same to form the target set.}
    \label{fig:unsupervised_task_sampling}
\vspace{-7.0mm}
\end{figure}

In general, to train a deep neural network model via supervised learning, one needs to have a large number of input-output pairs available. Labels are usually acquired by human annotators, and the labelling process is laborious. There have been attempts at semi-automating the process \cite{parkhi2015deep} but even those require a strong pretrained model to be available, which needs substantial data in order to be trained in the first place. The data labeling component of data acquisition is considered by many to be the most costly and time consuming part of the machine learning pipeline. In stark contrast, for many problems, unlabeled data is freely available in large quantities in the internet.

In the context of few-shot learning, the fact that one can train a robust few-shot learner using just 24K handwritten digit samples (i.e. the Omniglot training dataset) that can achieve over 95\%+ on a variety of tasks is impressive \cite{finn2017model,snell2017prototypical,ravi2016optimization,antoniou2018train}. However, a well designed machine learning algorithm should be able to utilize other larger unlabeled datasets, related to \textit{but not identical to} the test scenario, by using them to meta-learn robust few-shot learning models. A robust unsupervised few-shot learner, once trained, should be able to take new and different data from a task with labels, acquire task-specific knowledge from the test-time support set and generalize well on the test-time target set.

In this paper we propose such a method for leveraging unsupervised data to generate tasks for few-shot learners. In summary, we create support sets by giving random labels to subsets of the unsupervised data and generate corresponding target sets through data-augmentation transformations of the support set data points. Our unsupervised meta-learning must then learn networks that transfer consistent to data augmented examples. This can then be applied to real labelled data in the test setting (see Figure~\ref{fig:unsupervised_task_sampling}).

The approach is evaluated on two separate types of few-shot learning models, the Model Agnostic Meta Learning framework \cite{finn2017model} and the Prototypical Networks framework \cite{snell2017prototypical}. Once a few-shot learner is trained using the tasks generated with the proposed method, it can then be used with real-labeled (few-shot) data directly, without any fine tuning, and generalize very well on the target task. More specifically, the technique involves building a support set and target set using only unlabeled data. To build a support set, we sample a number of samples from our training dataset and then, since no labels are available, we \emph{assume} labels by randomly generating labels for our support set images. In the supervised setting the target set contains new (i.e. that do not exist in the support set) instances of the same classes found in the support set. To generate such a target set, we apply data augmentation methods to our support set, hence creating a target set with semantically the same classes as our support set, but with a different enough perspective on those samples to be a good generalization evaluation set.

The motivation for our method for unsupervised few-shot learning comes from the human ability of finding patterns among clusters of data items, even when the data items are being repeatedly re-arranged into new clusters. Humans are very good at finding patterns everywhere thus no matter how one chooses to cluster data items, an average healthy human being should be able to find features that can effectively cluster those data items correctly as shown in figure \ref{fig:random_clusters}. Following that idea we attempt to train models by assuming a clustering for a given support set, then generating a target set by augmenting the support set, and training our model using the MAML framework such that the model can acquire fast knowledge on the support set and generalize well on the target set.

In the following sections, we first describe our setting, then the MAML framework on which we built our method, before proceeding to the description of our method and the evaluation experiment types used. Finally we present our empirical results and draw our conclusions.

\section{Related Work}

The reformulation of few-shot learning as a meta-learning problem using the \emph{set-to-set} \cite{vinyals2016matching} few-shot learning setting was arguably one of the main enablers of the substantial progress that has been observed in the field of few-shot learning in the past few years.

To cast few-shot learning as a meta-learning problem, a batch of \emph{samples} is replaced with a batch of \emph{tasks}. Each task contains a dataset, which is further divided into two separate sets, a \emph{support set} used by the model for acquiring task-specific information and a \emph{target-set} used to evaluate the generalization performance of the model on that particular task. More specifically, a support set is composed of a pre-specified number of samples ($K$) from a number of classes ($N$), and a target set is composed from different (i.e. not found in the support set) instances of the same classes as found in the support set. Both sets contain label information for their respective data-points. The tasks used at test time are composed of (sample) classes that were not used during training. This is done to evaluate the model's generalization performance on previously unseen tasks. Meta-learning is about learning \emph{how} to learn such that a system can transfer well to previously unseen settings, thus testing on previously unseen tasks is of prime importance if one is to acquire a good measure of a meta-learning model's performance. The authors also proposed a short notation for describing set-to-set tasks. Each task set has a number of classes indicated by $N$, and a number of samples per class indicated by $K$, so one can instead refer to this setting as the \emph{N-way, K-shot} setting.

Once the set-to-set few-shot learning setting was introduced, a variety of meta-learning systems utilizing it were introduced. The first of which was Matching Networks \cite{vinyals2016matching} where both the support and target sets are embedded down to a low-dimensional space using two learnable \emph{embedding functions} $g$ (used on the support set) and $f$ (used on the target set) parameterized as neural networks. Once the low-dimensional embeddings are computed, the target set items are compared with the support set items using a non-parametric distance metric such as cosine distance. Once the distance vectors are acquired, one can apply a softmax function over them, to acquire a probability distribution that expresses the class of a particular target set item over the support set classes.

After embedding-based meta-learning models came the resurgence of gradient-based meta-learning models. Such models utilize inference-time model-state updates to acquire task-specific knowledge from a support set, such that they can generalize strongly on a particular task's target set. The first of such approaches was the \emph{meta-learner} LSTM \cite{ravi2016optimization} which jointly learns a gradient conditional weight update function and a parameter initialization for a \emph{base} model. At inference time, the learned weight update function is used to apply a single update step on the base-model using gradients with respect to a support set loss. Then, the updated base-model is applied on a target set to compute a target loss, which is then used as the system's optimization loss.

Subsequently to Meta-Learner LSTM the \emph{Model Agnostic Meta Learning} \cite{finn2017model} (MAML) framework was introduced. In MAML the authors propose to use standard stochastic gradient descent instead of a learnable update function and, in addition, to increase the number of update steps the model is allowed to take on a task's support set. By doing so, MAML achieved state-of-the-art performance in the supervised few-shot learning setting across all established few-shot tasks in both Omniglot and Mini-Imagenet. However, MAML also exhibited a significant amount of problems, including training instability problems, significant sensitivity to architecture selection and requirement for extensive hyperparameter tuning for it to achieve state-of-the-art results. Furthermore, many details in the design of MAML were chosen without much consideration for the multi-step nature of the model, which constrained the model's optimization process unnecessarily and in a way that reduced it's potential generalization performance and convergence speed. The authors in \citet{antoniou2018train} propose various modifications for MAML that resolve its instability problems, enable automatic learning of it's hyperparameters for a given task and unshackle MAML from its design-derived contraints. The resulting model called MAML++ achieves a significant improvement in the generalization performance across all established few-shot tasks whilst decreasing the model's computational overheads.

Unsupervised deep learning has been extensively investigated in the context of generative models \cite{doersch2016tutorial, vincent2008extracting,goodfellow2014generative}, yet, has only been briefly attempted in classification models    \cite{caron2018deep,bojanowski2017unsupervised}.

Concurrent work in unsupervised few-shot learning was introduced in the form of CACTUs \cite{hsu2018unsupervised}, where the authors propose the generation of support and target sets using k-means clustering in combination with semantically meaningful representations. Furthermore, work has also been carried out in the semi-supervised setting, which attempts to learn an unsupervised loss function \cite{semifewmaml2018}, such that a MAML-based model can learn from an unlabelled support task and generalize well on a target task. Furthermore, days prior to this paper's submission we became aware of concurrent work carried out in UMTRA \cite{unfewimagevideo}, where the authors use an approach very similar to ours to meta-learn few-shot models that generalize well to real-labelled datasets. Our work and theirs differs in that we conducted extensive ablation studies on data-augmentation strategies, ranging from very simple crops all the way to warping \cite{wong2016understanding} and cutout \cite{devries2017improved}. Furthermore, we evaluate our method on the MAML++\ model instead of the original MAML model. In terms of results, our results outperform UMTRA on the Omniglot tasks, whereas UMTRA outperforms our method by utilizing the data-augmentations learned in the AutoAugment \cite{cubuk2018autoaugment} work. However, the augmentations in AutoAugment were learned over a large subset of ImageNet, hence, utilizing AutoAugment might be considered as transfering knowledge from a model trained on a vastly larger labelled dataset. Which, in a way, defeats the purpose of unsupervised learning.

\section{Setting}\label{unmaml-setting}
The setting used is one where, given an unsupervised dataset we want to learn a few-shot learning model that can generalize very well on small few-shot labeled dataset-tasks.
Thus we make use of three sets. The first set is the \emph{meta-training} set, which is label-free and used to train a few-shot learner using the proposed unsupervised method. The second and third sets, are called the \emph{meta-validation} and \emph{meta-test} sets respectively, and contain labeled data, to be used to create evaluation few-shot tasks. Using a validation set to pick the best train model and a test set to produce the final test errors removes any potential unintended (human-derived/implicit) over-fitting. Intuitively, the meta-validation and meta-test sets are generated in the exact same manner that the validation and test tasks are generated in any other few-shot learning methodologies such as MAML \cite{finn2017model} and Prototypical Networks \cite{snell2017prototypical}.

Training meta-learning models requires using a large number of \emph{tasks}. To generate a few-shot task we use the \emph{set-to-set} few-shot learning scheme proposed in \citep{vinyals2016matching}. In the set-to-set few-shot learning setting, a task is composed by a training or \emph{support} set and a validation or \emph{target} set. The support set consists of a number of classes ($N$), and a small number of data-samples per class ($K$). The target set consists from samples from the same classes as the support set, but using different instances of those classes than the ones in the support set. A setting of $N$ classes per set and $K$ samples per class can alternatively be refereed using the shortened notation of \emph{N-way, K-shot learning} setting.

\section{Motivation}
Initial motivation for the work came from the human ability of being able to find features that can accurately describe a set of randomly clustered data-points, even when the clusters are continuously randomly reset. Transferring this setting to a machine learning setting could produce strong representations for classification.

However, using random clusters in a standard (base) deep-learning setting would not work very well since the model is trying to learn a mapping from $x$ to $y$, that holds for all seen $x$ to $y$ pairs. So if one were to randomly reset the labels ($y$) as the training progresses to create new tasks, the model would not be able to arrive to a solution that will generalize across all $x$ to $y$ pairs since many of them will be contradictory. However, intuitively, it should work in in a meta-learning setting. In a meta-learning setting the goal is to be able to learn a model that given a \emph{task}, can at inference time, acquire task-specific knowledge from a support set such that it can do well in that one task's target set, before throwing away the fast-knowledge acquired. So, intuitively, one could think that learning a meta-model that captures across-task similarities but can throw away task-specific information (such as the specific class clusters for a given task) would work very well. This is what this paper is about. Exploiting semantic similarities between data points to learn robust across-task representations that can then be used in a setting where supervised labels are available. Figure \ref{fig:random_clusters} illustrates the concept of how random clusters can enable the learning of sensible features.

\section{Generating Tasks for Unsupervised Few-Shot Learning}\label{unmaml}
To enable unsupervised learning in a few-shot classification setting, one must find a way to generate tasks. As defined in \ref{unmaml-setting}, a task is composed of a support and target set. A support set consists of samples from a number of distinct classes and their associated labels. Since no label information is available, we instead \emph{assume} labels by randomly assigning labels for a randomly sampled set of data-points. This will effectively cluster the data items into groups. Since the model takes update steps on the support set, it should be able to acquire fast-knowledge in its parameters that classify that support set very well. Generating a semantically related (to the support set) target set is where much of the difficulty lies. A target set should have \emph{different} instances of the \emph{same} classes appearing in the support set. Since we randomly assigned classes to the data-points we have no information on the semantics connecting the samples together in a class. To overcome this problem we generate a target set by \emph{augmenting} the support set data-points using data augmentation. Doing so sets the classes in the support set to perfectly match to the classes in the target set, but the samples in the two sets are different enough to allow the target set to serve as a good evaluation set. In algorithms \ref{alg:supervised-sampling} and \ref{alg:unsupervised-sampling} one can see how a supervised and unsupervised sampling procedure works.

\begin{algorithm}[]
	\caption{Supervised MAML Sampling Strategy}\label{alg:supervised-sampling}
	\begin{algorithmic}[1]
    	\STATE \textbf{Require:} Dataset $\mathcal{D}$ with $\mathcal{C}$ number of classes and $\mathcal{M}$ samples per class
		\STATE Sample $N$ class indexes from $\mathcal{D}$, where $N \leqslant \mathcal{C}$
        \STATE Sample the support set $S$ by sampling $K$ samples from each selected class where $K \leqslant \mathcal{M}$
        \STATE Sample the target (evaluation) set $E$ by sampling $J$ samples from each selected class, making sure that these samples do not contain any support set images  where $E \leqslant \mathcal{M}$ and ${E} \cap {K} = \emptyset$
        \STATE \textbf{Return} $S$, $E$
	\end{algorithmic}
\end{algorithm}

\begin{algorithm}[]
	\caption{Unsupervised MAML Sampling Strategy}\label{alg:unsupervised-sampling}
	\begin{algorithmic}[1]
    	\STATE \textbf{Require:} Dataset $\mathcal{D}$ with $I$ number of data-points
		\STATE Sample $N \times K$ data-points from $\mathcal{D}$, where $N$ is the number of classes per set\footnotemark and $K$ is the number of samples per class $(N \times K) \leqslant I$
        \STATE Build the support set $S$ by assigning random labels to the previously $N \times K$ sampled data-points
        \STATE Build the target (evaluation) set $E$ by augmenting the support set $S$ samples and keeping the labels identical
        \STATE \textbf{Return} $S$, $E$
	\end{algorithmic}
\end{algorithm}

 \footnotetext{One should choose the same number of classes and number of samples per class as for the supervised setting the model will be tested on at inference time.}
\begin{figure}[ht]
	\centering
	\includegraphics[width=0.73\columnwidth]{Label_free_clustering_images.pdf}
	\caption{Random Clusterings and Features: In this figure, one can see the concept of random labels and how useful features can be learned by random labels alone. For example, the items contained within the blue "Tee" shape can be assigned the labeling of white containers, thus learning features for white containers would allow a classifier to learn sensible parameters.}
	\label{fig:random_clusters}
\vspace{-8mm}
\end{figure}

\section{Meta-Learning Frameworks used for Evaluation}
AAL\ can be applied on any existing few-shot meta-learning technique, as long as the method can be trained using the set-to-set few-shot learning framework. To demonstrate this, we apply our method on two very popular and effective few-shot meta-learning frameworks, the Prototypical Networks framework and the MAML framework.

\emph{Prototypical Networks}:
Prototypical Networks \cite{snell2017prototypical} were introduced in an attempt to improve performance and reduce computational and storage requirements of Matching Networks. In Prototypical Networks, the authors propose using per-class or \emph{prototype} embeddings instead of per-sample embeddings. More specifically they propose computing the mean of per-class embeddings, hence obtaining a single embedding vector that summarizes a particular class. Once the prototypical embeddings are computed, they are compared with a target sample's embedding, using a suitable distance metric, such as euclidean or cosine distance, to obtain a probability distribution over the prototypical classes predicting the target sample's class membership. Not only does the usage of prototypical embeddings improve the generalization performance of the system, but it also reduces both the computational and space complexity of the system. In particular, using prototypical embeddings renders the computational complexity of computing the similarity vectors linearly proportional to number of classes ($N$) instead of number of samples ($N \times K$), hence reducing the computational complexity in all cases, except the case when $K = 1$. Furthermore, the storage complexity is also reduced from $N \times K$ to $N$.

\emph{Model Agnostic Meta-Learning} \cite{finn2017model} or (MAML) is a meta-learning framework for few-shot learning. It approaches few-shot learning by learning an initialization for the parameters of a base-model. Given a task, MAML computes the loss and gradients of a base-model with respect to the support set and takes a step in the direction of the gradients, thus acquiring task-specific knowledge usually refered to as the \emph{inner-loop} optimization process. This process is repeated a number of times. Once the inner loop update steps have been completed, the base model is evaluated on the task's target set to obtain the target set loss. Once computed, the target loss, is used to update the base-model's initialization, by taking a step in the direction that minimizes the target loss. Effectively, this trains a base-model parameter initialization, that after a number of SGD updates wrt a support set, the model can generalize very well on a target set.

\subsection{Selecting Target Set Data Augmentations}\label{label-free-data-augmentation}

Selecting the right combination of data augmentation techniques is crucial for our proposed approach. Selecting a data augmentation method which makes minimal changes to the data-points could produce samples that are too similar to the originals (thus being "too easy") for the model and thus causing reduced generalization performance. On the other hand using a data augmentation that changes the data-points too much could change semantics within the image that are important for the class of the image. Removing semantically important features from the image can make learning very hard, and in addition, guide the network to learn a function that does not generalize well to real-labeled tasks.

\begin{table*}[!htp]
\begin{tabular}{lll}
\hline
\multicolumn{1}{|l|}{Name}                        & \multicolumn{1}{l|}{Omniglot Hyperparameters}                 & \multicolumn{1}{l|}{Mini-Imagenet Hyperparameters}                   \\ \hline
\multicolumn{1}{|l|}{Random Crops (C)}            & \multicolumn{1}{l|}{Padding: 7 pixels}                        & \multicolumn{1}{l|}{Padding: 21 pixels}                         \\ \hline
\multicolumn{1}{|l|}{Random Horizontal Flips (H)} & \multicolumn{1}{l|}{Flip probability 50\%}                     & \multicolumn{1}{l|}{Flip probability 50\%}                       \\ \hline
\multicolumn{1}{|l|}{Random Vertical Flips (V)}   & \multicolumn{1}{l|}{Flip probability 50\%}                     & \multicolumn{1}{l|}{Flip probability 50\%}                       \\ \hline
\multicolumn{1}{|l|}{Random Rotations (R)}        & \multicolumn{1}{l|}{1 - 30 degrees}                           & \multicolumn{1}{l|}{1 - 270 degrees}                            \\ \hline
\multicolumn{1}{|l|}{Image-Pixel Dropout (DROP)}  & \multicolumn{1}{l|}{Drop probability: 30\%}                    & \multicolumn{1}{l|}{Drop probability: 70\%}                      \\ \hline
\multicolumn{1}{|l|}{Image-Pixel Cutout (CUT)}    & \multicolumn{1}{l|}{Holes: 5, Side Length: 4 - 14 pixels}     & \multicolumn{1}{l|}{Holes: 5, Side Length: 11 - 42 pixels}      \\ \hline
\multicolumn{1}{|l|}{Image-Warping (W)}           & \multicolumn{1}{l|}{Warping area: 14 + U(0, 6) x 14 + U(0,6)} & \multicolumn{1}{l|}{Warping area: 42 + U(0, 41) x 42 + U(0,41)} \\ \hline
\multicolumn{1}{|l|}{Random Grayscale (G)}        & \multicolumn{1}{l|}{Not used}                                 & \multicolumn{1}{l|}{Grayscale probability: 50\%}                 \\ \hline

\end{tabular}
\caption{This table presents the types and settings of data augmentation used to generate target sets in our experiments.}
\label{table:data-augmentations}
\end{table*}

Table \ref{table:data-augmentations}, presents the list of data augmentations that we used to generate our target sets in our experiments. In more detail we employed random crops, random rotations, random horizontal and vertical flips, randomly zeroing out a fraction of the image pixels or \emph{Image-Pixel Dropout} \cite{krizhevsky2012imagenet}, cutout, warping and randomly gray-scaling our image. Image-pixel dropout proved to be potentially the most important augmentation, since it produced the most generalizable and transferable models.

Due to limitations in compute, we decided that we would only search through combinations of rotations, cutout, dropout, warping and grayscaling of images, whilst keeping random crops, horizontal and vertical flips shared across all experiments. Doing so exponentially reduced the number of required experiments. Preliminary experiments with crops, horizontal and vertical flips demonstrated that their combination improved the generalization performance of models across all few-shot tasks across both Omniglot and Mini-Imagenet, hence their usage as shared (and baseline) augmentations.

\begin{table*}[!htb]
\begin{center}
\begin{tabular}{|l|l|l|l|l|}
\hline
                              & \multicolumn{2}{l|}{5-way Accuracy (val/test)}        & \multicolumn{2}{l|}{20-way Accuracy(val/test) } \\ \hline
Omniglot                                  & 1-shot             & 5-shot            & 1-shot             & 5-shot                   \\ \hline
Randomly initialized                      & $70.3\mypm0.36\%$  &$86.9\mypm0.27\%$  & $44.7\mypm0.45\%$  &$62.1\mypm0.85$\\\hline
AAL-MAML++ + CHV                              & $75.8\mypm0.63\%$  &$96.6\mypm0.32\%$  & $42.0\mypm1.16\%$  &$76.6\mypm1.14$\\\hline
AAL-MAML++ + CHVW                             & $88.4\mypm0.75\%$  &$98.0\mypm0.32\%$  & $70.2\mypm0.86\%$  &$88.3\mypm1.22$\\\hline
AAL-MAML++ + CHVR                             & $78.0\mypm2.39\%$  &$96.6\mypm0.04\%$  & $58.9\mypm1.16\%$  &$81.6\mypm0.82$\\\hline
AAL-MAML++ + CHV + DROP                       & $85.7\mypm1.09\%$  &$97.1\mypm0.12\%$  & $65.1\mypm0.62\%$  &$83.8\mypm0.62$\\\hline
AAL-MAML++ + CHV + CUT                        & $74.0\mypm1.60\%$  &$95.9\mypm0.44\%$  & $54.0\mypm1.59\%$  &$75.2\mypm0.62$\\\hline
AAL-MAML++ + CHV + DROP + CUT                 & $85.7\mypm1.09\%$  &$95.7\mypm0.38\%$  & $68.3\mypm0.63\%$  &$85.8\mypm0.62$\\\hline
AAL-MAML++ + CHVR + DROP                      & $86.16\mypm1.09\%$ &$96.5\mypm0.87\%$  & $64.6\mypm0.39\%$  &$82.0\mypm0.19$\\\hline
AAL-MAML++ + CHVR + CUT                       & $77.8\mypm1.00\%$  &$95.1\mypm1.05\%$  & $60.2\mypm0.66\%$  &$80.6\mypm0.53$\\\hline
AAL-MAML++ + CHVR + CUT + DROP                & $85.6\mypm1.05\%$  &$96.7\mypm0.45\%$  & $66.5\mypm0.27\%$  &$83.1\mypm0.64$\\\hline
MAML++ (supervised upper bound)            & $99.4\mypm1.05\%$  &$99.9\mypm0.45\%$  & $97.7\mypm0.27\%$  &$99.1\mypm0.64$\\\hline

\end{tabular}
\end{center}
\vspace{-4mm}
\caption{AAL-MAML++\ Omniglot Ablation studies}
\label{table:umaml-ablation-omniglot}
\end{table*}

\begin{table*}[!ht]
\begin{center}
\begin{tabular}{|l|l|l|l|l|}
\hline
                              & \multicolumn{2}{l|}{5-way Accuracy (val/test)}        & \multicolumn{2}{l|}{20-way Accuracy(val/test) } \\ \hline
Omniglot                                         & 1-shot             & 5-shot            & 1-shot              & 5-shot            \\ \hline
Randomly initialized                             & $25.72\mypm0.22\%$& $28.03\mypm0.28\%$ & $07.31\mypm0.41\%$& $08.04\mypm0.36\%$  \\ \hline
AAL-ProtoNets + CHV                              & $84.23\mypm0.93\%$ & $89.14\mypm0.36\%$& $64.34\mypm1.07\%$& $74.28\mypm1.76\%$  \\ \hline
AAL-ProtoNets + CHV  + CUT                        & $82.78\mypm2.25\%$ & $89.04\mypm0.09\%$& $64.23\mypm1.06\%$& $71.37\mypm1.18\%$  \\ \hline
AAL-ProtoNets + CHV  + CUT + DROP                 & $83.38\mypm0.60\%$ & $88.98\mypm0.88\%$& $66.26\mypm0.96\%$& $73.94\mypm1.18\%$  \\ \hline
AAL-ProtoNets + CHVR + CUT                    & $82.06\mypm0.53\%$ & $87.78\mypm0.21\%$& $64.77\mypm1.46\%$& $70.66\mypm0.53\%$  \\ \hline
AAL-ProtoNets + CHV  + DROP                       & $84.66\mypm0.70\%$ & $88.41\mypm0.27\%$& $68.79\mypm1.03\%$& $74.05\mypm0.46\%$  \\ \hline
AAL-ProtoNets + CHVR + DROP + CUT             & $81.24\mypm0.62\%$ & $87.15\mypm0.86\%$& $65.31\mypm0.76\%$& $72.04\mypm0.33\%$  \\ \hline
AAL-ProtoNets + CHVR + DROP                   & $82.65\mypm0.36\%$ & $88.13\mypm0.75\%$& $67.10\mypm1.13\%$& $71.92\mypm0.41\%$  \\ \hline
AAL-ProtoNets + CHVR                          & $82.12\mypm0.97\%$ & $86.04\mypm1.09\%$& $64.39\mypm1.27\%$& $70.39\mypm0.99\%$  \\ \hline
ProtoNets (supervised upper bound)         & $98.92\mypm0.07\%$ & $99.56\mypm0.07\%$& $97.87\mypm0.11\%$& $98.90\mypm0.10\%$  \\ \hline

\end{tabular}
\caption{AAL-ProtoNets\ Omniglot ablation studies.}
\label{table:uproto-ablation-omniglot}
\end{center}
\vspace{-7mm}
\end{table*}

\begin{table*}[!ht]
\begin{center}
\begin{tabular}{|l|l|l|l|l|}
\hline
                        & \multicolumn{2}{l|}{5-way Accuracy (test)}  \\ \hline
Mini-Imagenet                                     & 1-shot             &5-shot           \\ \hline
Randomly initialized                              &$28.55\mypm0.22\%$  &$36.68\mypm0.23\%$ \\ \hline
AAL-MAML++ + CHV                                &$33.06\mypm0.49\%$  &$40.75\mypm1.03\%$\\\hline
AAL-MAML++ + CHVR                     &$33.21\mypm0.90\%$  &$40.34\mypm1.64\%$\\\hline
AAL-MAML++ + CHV  + CUT                       &$33.34\mypm0.54\%$  &$39.44\mypm0.94\%$\\\hline
AAL-MAML++ + CHV  + DROP                      &$30.86\mypm0.64\%$  &$40.41\mypm0.68\%$\\\hline
AAL-MAML++ + CHVW                       &$33.30\mypm0.31\%$  &$46.98\mypm0.16\%$\\\hline
AAL-MAML++ + CHVWG                      &$34.57\mypm0.74\%$  &$49.18\mypm0.47\%$\\\hline
AAL-MAML++ + CHVR + CUT            &$33.09\mypm0.51\%$  &$40.11\mypm0.76\%$\\\hline
AAL-MAML++ + CHVR + DROP           &$31.70\mypm0.54\%$  &$39.38\mypm0.57\%$\\\hline
AAL-MAML++ + CHV  + DROP + CUT             &$31.55\mypm0.85\%$  &$38.76\mypm0.83\%$\\\hline
AAL-MAML++ + CHVR + DROP + CUT &$31.44\mypm0.78\%$  &$39.87\mypm0.89\%$\\\hline
MAML++ (supervised upper bound)                    &$52.15\mypm0.26\%$  &$67.87\mypm0.42\%$\\\hline

\end{tabular}
\caption{AAL-MAML++ ablation studies.}
\label{table:umaml-ablation-mini-imagenet}
\end{center}
\vspace{-4mm}
\end{table*}

\begin{table*}[]
\begin{center}
\begin{tabular}{|l|l|l|l|l|}
\hline
                        & \multicolumn{2}{l|}{5-way Accuracy (test)}  \\ \hline
Mini-Imagenet                & 1-shot           & 5-shot           \\ \hline
Randomly initialized                     & $22.96\mypm0.05\%$ & $24.73\mypm0.10\%$      \\\hline
AAL-ProtoNets + CHV                      & $37.67\mypm0.39\%$ & $40.29\mypm0.68\% $\\\hline
AAL-ProtoNets + CHV + CUT                & $36.38\mypm0.91\%$ & $40.89\mypm1.30\%$ \\\hline
AAL-ProtoNets + CHV + CUT + DROP         & $33.13\mypm0.95\%$ & $36.64\mypm0.79\%$ \\\hline
AAL-ProtoNets + CHVR + CUT + DROP     & $31.93\mypm0.28\%$ & $36.45\mypm1.33\%$ \\\hline
AAL-ProtoNets + CHVR + CUT            & $33.92\mypm0.60\%$ & $39.87\mypm0.53\%$ \\\hline
AAL-ProtoNets + CHV + DROP               & $32.12\mypm0.34\%$ & $36.12\mypm1.11\%$ \\\hline
AAL-ProtoNets + CHVR + DROP           & $31.13\mypm0.48\%$ & $36.83\mypm0.57\%$ \\\hline
AAL-ProtoNets + CHVR                  & $34.28\mypm0.21\%$ & $39.83\mypm0.85\%$ \\\hline
ProtoNets Protonet (supervised upper bound) & $50.16\mypm0.15\%$ & $65.56\mypm0.13\%$ \\\hline

\end{tabular}
\caption{AAL-ProtoNets\ ablation studies.}
\label{table:uproto-ablation-mini-imagenet}
\end{center}
\vspace{-5mm}
\end{table*}

\begin{table*}[!ht]
\begin{center}
\begin{tabular}{@{}lcccc@{}}
\toprule
Algorithm \hfill (way, shot) & (5, 1) & (5, 5) & (20, 1) & (20, 5) \\
\midrule
Training from scratch & 52.50\% & 74.78\% & 24.91\% & 47.62\% \\
ACAI CACTUs-MAML       & 68.84\% & 87.78\% & 48.09\% & 73.36\% \\
ACAI CACTUs-ProtoNets  & 68.12\% & 83.58\% & 47.75\% & 66.27\% \\
BiGAN CACTUs-MAML       & 58.18\% & 78.66\% & 35.56\% & 58.62\% \\
BiGAN CACTUs-ProtoNets  & 54.74\% & 71.69\% & 33.40\% & 50.62\% \\
UMTRA                   & 77.80\% & 92.74\% & 62.20\% & 77.50\% \\
AAL-ProtoNets\ (ours)              & 84.66\% & 89.14\% & 68.79\% & 74.28\% \\
AAL-MAML++\ (ours)              & \textbf{88.40\%} & \textbf{97.96\%} & \textbf{70.21\%} & \textbf{88.32\%} \\
\midrule
MAML (supervised upper bound) & 94.46\% & 98.83\% & 84.60\% & 96.29\% \\
MAML++ (supervised upper bound)\ & 99.42\% & 99.93\% & 97.76\% & 99.12\% \\
ProtoNets\ (supervised upper bound) & 98.92\% & 99.56\% & 97.87\% & 98.90\% \\
\bottomrule
\end{tabular}
\caption{Comparative Omniglot results.}
\label{table:compare-omniglot}
\end{center}
\vspace{-8mm}
\end{table*}

\begin{table}
\begin{center}
\begin{tabular}{@{}lcc@{}}
\toprule
Algorithm \hfill (way, shot) & (5, 1) & (5, 5) \\
\midrule
Training from scratch & 27.59\% & 38.48\% \\
DeepCluster CACTUs-MAML & \textbf{39.90\%} & \textbf{53.97\%} \\
DeepCluster CACTUs-ProtoNets & 39.18\% & 53.36\% \\
UMTRA + AutoAugment          & \textbf{39.93\%} & 50.73\% \\
AAL-ProtoNets                      & 37.67\% & 40.29\% \\
AAL-MAML++                       & 33.30\% & 49.18\% \\
\midrule
MAML (supervised upper bound) & 46.81\% & 64.13\% \\
MAML++ (supervised upper bound) & 52.15\% & 67.87\% \\
ProtoNets (supervised upper bound) & 50.16\% & 65.56\% \\
\bottomrule
\end{tabular}
\caption{Comparative Mini-Imagenet results.}
\label{table:compare-mini-imagenet}
\end{center}
\vspace{-10mm}
\end{table}

\section{Datasets}\label{dataset-umaml}
To evaluate our methods we used the Omniglot and Mini-Imagenet datasets. As discussed in Section \ref{unmaml-setting} we split our dataset in a background (or meta-training) set and an evaluation set (which is then further split into meta-validation and meta-test sets). The Omniglot dataset is composed of 1623 character classes from various alphabets. There exist 20 instances of each class in the dataset. For meta-training, meta-validation and meta-test sets we use classes 1-1150, 1150-1200 and 1200-1623 respectively.
The Mini-Imagenet dataset was proposed in \citet{ravi2016optimization}, it consists of 600 instances of 100 classes from the ImageNet dataset, scaled down to 84x84. We use the split proposed by the authors, which consists of 64 classes for training, 12 classes for validation and 24 classes for testing. We use the training, validation and testing sets as the meta-training, meta-validation and meta-test respectively for our experiments.

\section{Experiments}

To evaluate the generalization performance of the unsupervised trained models on supervised few-shot tasks we ran experiments where we trained our model using the AAL\ method and after each unsupervised meta-training epoch we applied the resulting model without any additional fine tuning on the supervised meta-validation set. Once all epochs were completed, the best validation model was then applied on the test set which produced the final test performance of the model.

We applied AAL\ on two separate model types. The first, was the MAML++ \cite{antoniou2018train} model (an improved variant of MAML), more specifically, we used the LSLR (Per-Layer Per-Step Learning Rates), along with the per-step batch norm modifications (BNWB/BNRS) and the multi-step loss optimization (MSL) to both stabilize training and improve the generalization performance.
The second, was the prototypical networks \cite{snell2017prototypical} model.

To apply AAL\ on a given model, we simply sampled training tasks using the AAL\ sampling scheme, and then evaluated our model on validation and test tasks using the supervised meta-learning formulation as done in \citet{finn2017model}. As a result, the evaluation metrics would effectively be quantifying the transferability of the representations learned using unsupervised data, on a real-labeled supervised meta-learning task, rendering our evaluation results directly comparable to supervised meta-learning models.

\subsection{Neural Network Architectures}
The architecture used for the base classifier was the one proposed in \citet{finn2017model}. A convolutional neural network with 4 layers. Each layer is composed of a convolutional layer followed by batch normalization followed by a ReLU non-linearity followed by max-pooling. Every convolutional layer, had 64 filters, a padding of 1, a stride of 1 and a filter size of 3.

In the case of MAML, following the convolutional network, we applied a linear layer and a softmax activation function to produce the network's predictions. The size of the last layer is equal to the number of classes per set.

In the case of Prototypical Networks, the networks predictions are the flattened outputs of the fourth convolutional layer, exactly as done in the original paper.

\section{Results}
Tables \ref{table:compare-mini-imagenet} and \ref{table:compare-omniglot} present comparative results between a variety of baseline and SOTA methods for unsupervised few shot learning. Our proposed method outperforms all other methods across all Omniglot tasks, and it remains competitive in the Mini-Imagenet 5-way 1-shot task. In the 5-way 5-shot Mini-Imagenet our method is outperformed by the CACTUs methods. Furthermore, to investigate the effect of the data augmentation strategies on the generalization performance of our method we conducted ablation studies. Tables \ref{table:umaml-ablation-omniglot}, \ref{table:uproto-ablation-omniglot}, \ref{table:umaml-ablation-mini-imagenet} and \ref{table:uproto-ablation-mini-imagenet} showcase the results of the ablation studies. Our ablation studies revealed that for the Omniglot task, the most useful data augmentation methods are pixel-dropout and warping. Warping produces state of the art results in Omniglot, we hypothesize that this is due to the augmented samples matching very closely natural variations of the source images. In the Mini-Imagenet tasks we observed that, surprisingly, crops and flips outperformed all other methods. Much of the success of our Omniglot results, came from using augmentations that actually generate a target set that is meaningful yet initially difficult for the underlying meta-learning method to perform on. In Mini-Imagenet, we observed that all the data augmentation strategies attempted were overfitting after just 5 epochs. This indicates that more work is required to find a good augmentation strategy for the Mini-Imagenet tasks.

\section{Conclusion}
Learning robust representations using label-free data is crucial task for any truly intelligent agent. In this paper, we propose a methodology that allows a machine to create its own tasks using unlabelled datasets by leveraging randomly clustered data-points and augmentation. Models trained with our method, are able to generalize very well to real-labeled inference tasks.
We compared our method against a variety of recent unsupervised few-shot methods. We evaluated our method on Omniglot and Mini-Imagenet. Our model outperformed all other competitors in Omniglot, whilst remaining competitive in the 5-way 1-shot Mini-ImageNet task. However, in the 5-way 5-shot task, our method was outperformed by CACTUs. Finally, a very noteworthy observation is that the performance of our method is heavily dependant on the data augmentation strategies employed. Hence, finding the most optimal one is key. Automating the search for such a data augmentation function could produce fully unsupervised systems with stronger performance.

\section{Acknowledgements}
We would like to thank Harri Edwards for supporting this project through useful discussions and suggestions. Furthermore, special thanks to Elliot Crowley and Joseph Mellor for their writing-related suggestions and corrections. Finally, we'd like to thank Luke Nicholas Darlow for the very useful discussions on random labels and their intriguing properties. This work was supported in by the EPSRC Centre for Doctoral Training in Data Science, funded by the UK Engineering and Physical
Sciences Research Council and the University of Edinburgh as well as by the European Unions Horizon 2020 research and innovation programme under grant agreement No 732204 (Bonseyes) and by the Swiss State Secretariat for Education Research and Innovation (SERI) under contract number 16.0159. The opinions expressed and arguments employed herein do not necessarily reflect the official views of these funding bodies.

redpajama_set_name: RedPajamaArXiv


text:
// Copyright 2009 the Sputnik authors.  All rights reserved.
// This code is governed by the BSD license found in the LICENSE file.

/**
* @name: S15.4.4.5_A6.6;
* @section: 15.4.4.5;
* @assertion: The join property of Array has not prototype property;
* @description: Checking Array.prototype.join.prototype;
*/

//CHECK#1
if (Array.prototype.join.prototype !== undefined) {
  $ERROR('#1: Array.prototype.join.prototype === undefined. Actual: ' + (Array.prototype.join.prototype));
}


text:
Q: Variable sleep bash alias So, I'm trying to create an alias which will run a command after sleeping x seconds.
alias ls_sleep="sleep $1 && ls"

The problem is, $1 is being passed as string and things like $(($1)) or $(($1+0)) doesn't work.
Would it be possible to make it work in a single line without using a function?

A: You can't do this with an alias, but you can do it in a single line with a function:
ls_sleep() { sleep "$1" && ls; }


text:
Making Moves in 2021
Dec 14, 2021 | Camp Rainbow Gold
By Elizabeth Lizberg, CEO/Executive Director —
The excitement of this past summer and hosting our first camps at Hidden Paradise is still providing us with so much joy. We accomplished something huge! We constantly reminisce about volunteer days, the cleaning, the demo, the construction, the challenges, (the tears shed), the victories. And that moment the first bus pulled into our new home. Wow. The stories about the man-made slip 'n' slide, the epic prank on the teen boys' cabin, and the laughter around the campfire continue. You'd think after one major move to Hidden Paradise, we'd be done for the year, but after a lot of thought, discussion, and number crunching — we made one more big move.
A New Space in Boise
We have officially cleaned out, packed up, and moved our Boise office to a smaller location! The circumstances of the past two years gave us the opportunity to step back. We started to notice when our reasoning for some decisions was, "But we have always done it this way!" Instead, we should be making sure we, as a business, are adjusting to the trends and circumstances of the day. We needed to make sure we were taking the best care of our staff and were an attractive employer at a time when our community and nation is facing a staffing shortage. All this, combined with the fact that we no longer needed major storage in Boise because we have moved most camp items up to Hidden Paradise.
For us, all this added up to shifting the Camp Rainbow Gold staff to work remotely while still having a brick and mortar spot (albeit a much smaller spot) — but a space where we can come together as a staff, or committees, or leadership teams and also meet with new families, volunteers, and donors. The Camp Rainbow Gold Boise office is now located in the Alaska Center in downtown Boise, 1020 W. Main Street, Suite 312.
Change and Opportunity
We still have lots of boxes to unpack and pictures to hang in the new space, but we're already reaping the benefits. The cost savings of this change has afforded us some incredible opportunities to support our programs. We brought on a new staff member who will help take care of one of our greatest assets — our volunteers. Recently, we announced Stacie Tornga would join us as our Volunteer Manager. Stacie is a former camper, college scholarship recipient, committee member, and volunteer as our teen camp assistant director. This position has been vacant for two years and we are excited to have the resources to focus on the recruitment, training, and care of our volunteers.
What does this change mean for you — those we serve and those who support us? We are working hard to make sure you don't feel any impact at all. We will still be available to meet in person, talk on the phone, and of course, there is always email too. Outside of the change in address, you may not have even known.
Camp Rainbow Gold has never been a place. It is a family, a community, a feeling. It is hope, love, and fun in action. Camp Rainbow Gold is a celebration of life.
2021 has certainly been a year of change and full of blessings. And we have big plans for 2022! We're planning to host all 5 camps again this year as well as continuing our teen support groups, introducing new family events, and awarding more college scholarships. We promise to continue bringing your families together and providing the opportunity to have some fun, laugh a lot, and connect with others who find comfort in knowing they are surrounded by a community that understands without saying a word.
Change is exciting, refreshing, and invigorating — and a little stressful at times. But we are thrilled to be in our new space, excited at the opportunities this change has afforded us, and extremely glad that this should be our last move for quite a while!
Thank you for your ongoing support. YOU drive our passion and our mission. And we would love to show you the new space, so please feel free to give us a call or send us an email and set up a time to stop by and say hello.
← Welcoming the First Lease Group at Hidden Paradise A Scholarship to Help Reach Your Goals →


Never thought I'd miss my nans van as much as I do now that I'm in Japan.
Every morning I wake up at 5 in the morning to get ready for school. I catch the 7:20 train and walk around 30 minutes to get to school. The sun is scorching and I an feel the sweat and fabric make contact.
I miss being able to sleep in until 8 in the morning, put on my uniform and just jump in the van.
You can never go hungry while in Japan.
I've come to gain a huge appreciation for my host parents as they wake up in the early hours of the morning to do the washing, cook breakfast and make lunches. Back in New Zealand I was too lazy to get out of bed to pee, so I just held it in. Here they're very productive. Everyone here has something to do.
A lot of what my Japanese family does reminds me of my Nan back home. I have an immense amount of appreciation for her as she wakes up at 5 every morning to get us ready for the day. She will drive to the shops to buy food for breakfast and lunch, will never let us go to school without breakfast and will always offer to drive us there in her state of the art Nan van. Toyota's are cool.
In Japan, my family always has food. They'll always have ice-cream for us when we come back home so we don't die of heatstroke.
Coming from New Zealand and entering Japan made my appreciation jump to a whole other level. I've realized that there are a lot of similarities within my family from New Zealand and my family in Japan. I've started to appreciate things more. Both my families, my teachers in Aorere and Jishukan and the air conditioner in the Japanese trains.


I recently came across a very contemporary sermon preached by the late D. Martyn Lloyd-Jones, pastor of London's Westminster Chapel until 1968. Lloyd-Jones died 35 years ago; the sermon I read is from a series on Isaiah 40 entitled The All-Sufficient God that he preached in 1954.
While all the sermons in the series are needful today, a section in "The Answer to Unbelief," based on Isaiah 40:18-24, struck me as particularly applicable in this election year. The text confronts Israel with the rampant idolatry that has marginalized the Lord, actually displacing Him from everyday life. Lloyd-Jones applies that to the idols of England in his day, and I found his applications very fitting for our American idols, especially in one area. Allow me an extended quotation.
They say they cannot believe in God—but they can believe in idols!
And the world is full of this kind of thing. People tell us that they are too able and too intelligent to believe in God. But look at what they do believe in. Look at the modern idols. People say they are far too intelligent to believe in this gospel and to accept it and submit to it. But look at the things in which men and women do place their trust. Look at the gods that the world makes for itself, sometimes just sheer wealth, sometimes position and status, sometimes dress, mere clothing. In their desire to be highly thought of by the so-called great in the land, people idolize and copy them. There is no limit to the variety of idols that men and women make and are prepared to give themselves to. They impoverish themselves, as Isaiah here describes. They will give their time, their enthusiasm, their energy, and their money. There are people who are in constant financial trouble, and even become bankrupt, because they are trying to live up to a social level that is beyond them. That is an exact repetition of what these people did in ancient times with their idols of gold. It is idolatry; it is the worship of a way of life….
Then there are others who worship thought and learning. There are people who undoubtedly worship science. They talk about it as if it were some kind of deity. They have turned something that is abstract into something concrete. "This is what science says," they say. Of course, there is no such thing. They mean that certain scientists say this or that. But they have made science into a god, and bow before this tremendous thing that is there over us and over the whole life of humanity in the world…. Modern men and women have turned their backs on God. They cannot believe the gospel message, and they believe instead that the advance of knowledge and learning will save the world and put everything right and straight. Political action, too! All these are modern idols, the modern gods….
Then there is the modern attitude to "the princes" and "the judges of the earth" (v. 23). Increasingly, it seems to me, there is a belief in human leaders—it is the idea of the superman, the dictator…. Perhaps one of the greatest dangers still confronting the modern world is this tendency of people to listen in a sheep-like manner to any leader and to give unquestioning support.
Indeed, isn't it? Our American idols come in quite a variety—only one of which had a TV show to exalt him. What struck me most in this section of his sermon was Lloyd-Jones's recognition of the tendency to idolize political leaders (or would-be leaders!). Am I the only one who's observed the exalting of Presidential Candidate _____ as the man/woman who can "save America"? Have you noticed Who is missing from the discussion?
That's American idolatry, and Lloyd-Jones pegged us 62 years ago.


text:
Atherosclerosis Home
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How Atherosclerosis Causes Half of All Strokes
Written by Matthew Hoffman, MD
Medically Reviewed by David Zelman, MD on June 15, 2021
Facts About Atherosclerosis and Stroke
How Atherosclerosis Happens
Risk Factors for Atherosclerosis and Stroke
Atherosclerosis, Strokes, and 'Clot-Busters'
Having a stroke is one of the most frightening prospects of aging. Strokes can come on suddenly, stealing the use of an arm or the ability to speak. A stroke can be fatal or leave us permanently disabled.
About half of all strokes are caused by atherosclerosis -- the same process of narrowing and hardening of the arteries that causes heart attacks. Atherosclerosis progresses silently, without symptoms, putting our brains and our independence at risk.
Reducing the risk factors for atherosclerosis lowers the risk of stroke. Making a few lifestyle changes can protect your brain from this common cause of strokes.
About 700,000 strokes occur each year in the U.S.
One in five people will have a stroke in their lifetime.
One-quarter of strokes are fatal.
Stroke is the third-leading cause of death in the U.S., behind heart disease and cancer.
Although most strokes are survivable, most people never recover completely after a stroke. Around a quarter of those who survive are permanently disabled.
There are two main types of strokes:
Ischemic: An artery inside or leading to the brain becomes completely blocked. Usually this is caused by a blood clot that forms in a clogged artery. It can also be due to a blood clot traveling to the brain from the heart.
Most strokes (about 87%) are ischemic, and most of those are caused by atherosclerosis.
Hemorrhagic: These strokes are caused by bleeding into the brain. Most commonly, high blood pressure causes a small artery to burst open. Abnormal blood vessels (such as aneurysms and arteriovenous malformations) are particularly likely to rupture. The bleeding disrupts healthy blood flow to brain tissue.
Hemorrhagic strokes are less common, making up about 13% of all strokes.
Regardless of whether a stroke is caused by atherosclerosis or bleeding, the symptoms are the same:
Sudden weakness on one side (in the face, arm, or leg)
Slurred speech or inability to remember words
Sudden blurry or double vision
Within hours of a stroke's onset, brain tissue dies from lack of oxygen and nutrients, leaving permanent damage.
If stroke-like symptoms strike, the time to call for help is now. Only prompt medical attention can help prevent permanent damage from a stroke.
With these sobering facts in mind, it's worthwhile to understand the process by which atherosclerosis causes strokes.
Atherosclerosis occurs in the arteries of the brain the same way it does elsewhere in the body:
The inner layer of arteries (the endothelium) is damaged by high cholesterol, smoking, or high blood pressure.
Damaged endothelium allows LDL ("bad") cholesterol to enter the artery wall, where it accumulates.
The body sends a "clean-up crew" of white blood cells and other cells to the artery, to digest the LDL.
Over years, ongoing cholesterol buildup -- and the response to it -- creates a bump on the artery wall called a plaque.
Most commonly, the plaque grows slowly, never causing a problem. In fact, most are never discovered. The arteries in the brain adjust to the slow narrowing, and no symptoms occur.
However, for unclear reasons, plaques can also become inflamed and unstable. If a plaque ruptures, the dangerous material in its center is exposed to blood flowing by. As a result, a blood clot forms, which can rapidly block the artery. The brain tissue downstream is starved for blood and nutrients, and dies within hours.
As a cause of strokes, heart attacks, or other diseases, atherosclerosis has the same risk factors. Get to know them:
High blood pressure (the most important risk factor for stroke)
Abnormal cholesterol levels
Diet high in saturated or trans fats, low in fruits and vegetables
The best way to prevent a stroke is to control these risk factors. If you've already had a stroke or other form of atherosclerosis, reducing your risk is even more important.
Most strokes are caused by a sudden blood clot -- which itself is caused by atherosclerosis. If given quickly, "clot-busting" drugs can actually reverse some strokes.
The clot-buster (called tissue plasminogen activator, or tPA) must be given within three hours of the first symptoms of stroke to be most effective. It can be given up to six hours after the stroke if administered directly into the blockage area. This procedure is done primarily at bigger hospitals. Unfortunately, most people don't make it to the hospital quickly enough after experiencing symptoms, and tPA is underused.
If you have any symptoms of stroke, don't hesitate: call 911 immediately. It's your best chance at a good outcome from stroke.
Start reducing your risk factors for stroke today, and you'll be protecting yourself from all complications of atherosclerosis.
Medline Plus web site: "Stroke secondary to atherosclerosis."
American Stroke Association publication: "What is a Stroke?"
American Stroke Association web site: "Atherosclerosis and Stroke;" "Reducing Risk;" and ''Types of Stroke.''
American Heart Association publication: "Heart and Stroke Facts."
Kleindorfer, D. Stroke, 2004; vol 35: pp e27-29.
Mark Silverman, MD, emeritus professor of medicine, Emory University School of Medicine, chief of cardiology, Piedmont Hospital, Atlanta.
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