Youth Turkey Hunt – This Kid is Good!
This kid can call in some turkeys! Doesn't hurt when your dad is the one and only original American Outdoorsman.
Complete turkey hunt in Missouri with Steve Bailey.
Join the AO Pro Staff as they look to tame the wild exotic hunting grounds in Osceola County, Florida.


This exhibition has resulted from an unprecedented series of contacts, reciprocal visits and discussions involving artists, curators and intellectuals from India and China. The title of the exhibition signals the invitation to encounter locations and histories across old and new borders. Despite market driven interest in the contemporary art of these two countries, these has been little, if any, interaction between the two art cultures in recent times. Awareness of each other's art practice is limited to occasional encounters at biennales and art fairs in different parts of the world.
Place, time and play have connotations of location, history and creativity, respectively. Reckonings with place imply an understanding of contextual difference, and an attempt to enter another site or location. Taking time is both a requirement of this process, and an opportunity to encounter a different sense of historical time, and for artists to think about and work with the formidable burdens of tradition as well as current economic, political and artistic conditions. The invitation to "play" is extended on the premise that idea of play (and the ludic instinct that underpins it) is a fundamentally life-affirming gesture, too often lost in the midst of the quest for topical issues that the art object must address. Through its invitation to indulge in a basic and universal human activity (and need), the project hopes to inaugurate a more lasting series of relationships between artist communities across the two nations.
We intend this project to be a curatorial experiment. We have encouraged the Indian artists to create works targeted at the Chinese audience, without the intention of ultimately submitting to the scrutiny of other international platforms. They have been asked to treat China as a laboratory for testing new ideas, and as an object of desire or critique.


text:
Home About Commercial Diving Underwater welding: the allure of a niche job
Underwater welding: the allure of a niche job
By Alan Berner
Seattle Times staff photographer
Underwater welding is not for everyone.
There are only about 3,000 people in the United States with the highest level of international certifications, and only two places to achieve it.
The oldest is the Divers Institute of Technology on North Lake Union, founded in 1968. The other is in Florida.
Instructor Jim Bernacki says, "It's for thrill seekers looking for a skill that's challenging."
Former carpenter and now diving teacher Jake Dow says, "We're slamming iron together, not just looking at fish all day long."
Combining the challenges of diving with construction skills comes with rewards, including high pay and the allure of a niche job.
Few others will see the work the welders do on oil rigs or bridges, pipelines or piers, and deep sea salvage.
There's hazardous-materials work in cooling ponds of nuclear- power plants. There's maintenance, cleaning and inspection of municipal water tanks.
Dow says, "You should be fit and trim." Fat stores nitrogen, the gas that usually causes the bends, the decompression sickness for divers ascending too quickly.
"We're not risk takers, we're risk managers."
Of the education, says Dow, "you will not find this in books." And it cannot be learned online.
The 7-month program costs $25,900, plus gear.
More than half of the 160 students are military veterans from around the country.
Students have roll call every weekday at 7 a.m. before a full day of class.
Brandon Hall and Andrew Skrumeda work their way into 140 pounds of diving gear.
They're suiting up for a welding test in a tank and have up to 80 minutes to display their skills.
Student Christian Holien says, "What's appealing is the freedom. It's like being in space."
As a visiting group of topside welders from Yakima passes by, the tour leader tells them, "This is the Harvard of diving schools."
Students at the Divers Institute of Technology complete an underwater welding test as a part of their studies. (Alan Berner / The Seattle Times)
professional diving
underwater welding
Previous articleDive Responsibly
Next articleIMCA Opens Registration for Annual Seminar

redpajama_set_name: RedPajamaCommonCrawl


I live in a small village in the Amatola mountains of the Eastern Cape in South Africa. The natural beauty of the area and wildlife inspires me. Art is an expression of emotion and this is evident especially in my abstract works. I have been involved with art for the last 25 years.
Various small ones in Hogsback.


A couple of our favorites collaborated, and it resulted in this super clean lookbook. Stussy, a streetwear icon, and Herschel Supply Co. teamed up to bring you the "Dot" collection, as displayed in this simplistic lookbook. The contents of this collection feature polka dots which grace the surface of a backpack, duffle bag, "waist bag", tote and a couple of smaller, catch all pouches. We all are gonna have to haul some stuff at some point, might as well do it in style. Head over and cop what suits your needs now, before it's too late and you're left schlepping an adidas bag from your high school track days on the private jet. Complete lookbook after the jump.


text:
Q: Late 70s-80s sci fi drama movie about kids in spaceship since Earth had become uninhabitable I saw this film that was shown on a projector when I was in about 2nd grade (in the 90s) that was so moving it stuck with me since. I'd love to be able to find it. I don't believe it had been made recently but I'm not certain (could have been made in the 70s-early 90s). I don't remember many details but that much of it was shot pretty darkly and it was dramatic.
It took place on some kind of space colony or spaceship since Earth had become uninhabitable. There were kids on it, one of whom remembered Earth and I believe I remember something about them having to get UV artificial un treatment.

A: I think (based on you saying 'space colony or spaceship' and 'one kid remembering Earth' and also the UV sunlamp) that you actually watched "All Summer in a Day" by Ray Bradbury.
From Wikipedia:

"All Summer in a Day" is a science fiction short story by Ray Bradbury, first published in the March 1954 issue of The Magazine of Fantasy & Science Fiction.

And the plot synopsis from there:

The story is about a class of students on Venus, which, in this story, is a world of constant rainstorms, where the Sun is visible for only one hour every seven years.
One of the children, Margot, moved to Venus from Earth five years earlier, and she is the only one in her class to remember sunshine, since the Sun shone regularly on Earth. She describes the Sun as "a penny", or "like a fire in the stove", and relays in a poem to the class that "I think the sun is a flower/That blooms for just one hour". The other children, being too young ever to have seen it themselves, do not believe her. The other students, in particular a boy named William, bully and ostracize her, and just before the sun comes out, William rallies the other children and they lock her in a closet down a tunnel.
As the Sun is about to appear, their teacher arrives to take the class outside to enjoy their two hours of sunshine and, in their astonishment and joy, they all forget about Margot. They run, play, skip, jump, and prance about, savoring every second of their newly found freedoms. "Oh, it's better than the sunlamps, isn't it?" one of them cries.
Suddenly, a girl catches a raindrop in her hands. Thunder sounds, then the lightning comes, and the children run back inside as the sun disappears and it starts to pour again. At this point, one of them remembers Margot, who is still locked in the closet. Ashamed, they let her out of the closet, standing frozen, embarrassed over what they have done and unable to "meet each other's glances."
The precious Sun has come and gone, and because of their despicable act, Margot, who loved the Sun the most, has missed it.

Wikipedia also mentions it was adapted into a 30 minute show for TV:

A 30-minute television adaptation, originally broadcast on the PBS' children's series WonderWorks in 1982. The ending is expanded to show the children atoning for their horrible act by giving Margot flowers that they picked while the Sun was out.

A: When I hear of a space movie involving kids, my brain immediately jumps towards Earth Star Voyager, a 1988 TV pilot by Disney.

In the year 2082, the Earth deep-space exploration vessel, Vanguard Explorer, has suffered heavy damage, due to the efforts of Captain Jacob 'Jake' Brown trying to keep his ship out of the hands of mutineers led by his second-in- command, Vance Arthur. Feigning compliance, Jake attacks Vance and knocks him senseless long enough for him to get to an escape pod and launch into space. However, the cause of the damage on board the Vanguard Explorer is unknown.
The German-language VHS introduces the plot: "In year 2087. The Earth is dying. They must find a new planet."
  It is now six years later, in the year 2088. Command cadet Jonathan Hays, twenty-one, and his best friend Jessie 'Beanie' Bienstock, a 14-year-old computer specialist, are among the young military cadets chosen to serve as the crew of Earth*Star Voyager, the planet's newest interstellar vessel. Due to worsening ecological conditions on Earth, there is a plan in place to evacuate the planet and colonize another world. (This mission is noted on the cover of the German VHS: see image at right). Probes have been sent out six years ago via the Vanguard Explorer, and one has sent data back on Berenson's Star; she has a life-zone planet which has been named 'Demeter'. The mission of Earth*Star Voyager is to go to Demeter, perform a full planetary survey and return that information because if conditions on Demeter prove accurate (the probe's data indicates that human life could survive on Demeter), then the human race will colonize the world. The plan to colonize Demeter has already begun; ships are already being built to transport the population but will take forty years to complete construction of all the necessary vessels, and the trip to Demeter, with 'plasma-thrust' engines and the Bauman Drive (named for Professor Bauman, the creator) will take 26 years. That was the rationale of choosing the crew from the Academy—for their youth and intelligence. Jonathan, as second-in-command, will assume command if Forbes is shown to be unable to continue his duties due to age. It is also mentioned that a modified form of cryogenic suspension will be used by the crew during the voyage in order to slow the ageing process. (Beanie mentions that he'll be forty years old upon their return to Earth, and Captain Forbes also mentions that, although the process will slow their ageing, they will still age.)

They are traveling through space because Earth is in bad environmental shape. Due to various mutinous events, most of the adults are eliminated and the teenage cadets run more of the ship's duties. Part of the plot takes place on a space station,  "the 2020 World's Fair". I don't yet remember any specific mention of UV lamps as treatment, and it's a bit later of a film than you remember.

redpajama_set_name: RedPajamaStackExchange


Fall in love with this serene setting! Enjoy the view on over 5 acres. This home offers over 1500 sqft, 3 large bedrooms (possibly 4), 9 year old roof, updated windows, a storage shed in back, 2 car attached garage, partial basement for storage, and a huge lot for a new pole barn to store all your toys! Call me today for a showing.


text:
Artists & Climate Change
Celebrating artists who explore the intersection of arts & climate change.
A Note on Terminology
Reader-Submitted Stories
Tiny Coronavirus Stories
Arts & Climate in Higher Education
Black Artists & Storytellers
Climate Art Interviews
Imagining Water
Indigenous Voices
Persistent Acts
Wild Authors
Thomas Peterson
Editor & Writer
Thomas Peterson is a theater director and researcher whose work focuses on the climate crisis. He is an Artistic Associate with The Arctic Cycle, co-organizing Climate Change Theatre Action. He studied American History & Literature and Theater, Dance & Media at Harvard, and was a Harvard Williams-Lodge Scholar at the Sorbonne Nouvelle in Paris, where he wrote a thesis on the aesthetic of the sublime in the theatrical representation of the Anthropocene. He recently directed and created Roy Loves America, a multi-form devised performance piece about Roy Cohn, and is developing an original adaptation of Ibsen's The Wild Duck and a production of Kat Zhou's adaptation of Boris Vian's The Empire Builders. He has worked with companies like the American Repertory Theater and Colt Coeur, and with directors like Jenny Koons and Daniel Kramer. His engagement in climate activism stretches back to high school, when he led a successful fossil fuel divestment campaign.


text:
Q: How can i enable fingerprint logon for RDP sessions from Windows 10 like in Windows 8?
Screenshot is of Windows 8 mstsc.exe client letting me logon with just the swipe of the finger.
Windows 10 does not provide this capability, how does one enable it for Windows 10. Yes, i have a PIN and Windows Hello with fingerprints is setup, but not this feature.

A: Windows 10 made some changes to the way the biometric authentication, including fingerprint, works in order to improve security. Because of this, RDP with fingerprint login is not currently supported in Windows 10. But looks like face authentication works in current version.
Source


Portarlington Golf Club is an 18 Hole Mature Parkland course. A hidden gem which prides itself on excellent course presentation. Portarlington is a popular venue for Seasoned golfers, while also providing an excellent society destination. Portarlington is surely a must for this years outing.


Basically I already wrote on the topic even before the thread on Planet Gnome started, but I would like to add a few things in that context.
Basically I also think that the way for Gnome from now on is to evolving rather than rewriting stuff for Gnome 3.0.
But in order to be able to evolve reasonable, one has to mutate first. And mutating is hard being Gnome – to catch up my former post – since Gnome is written in C.
In order to explore new concepts of usability, one need prototyping, but writing a prototype from scratch is hard and also unnecessary, since we already have applications for that task. So the straightforward thing would be to create a fork for experimenting and change the structure as necessary.
And here is where where Gnome fails; changing a python program is very easy since the language suits RAD very well, while hacking away in C is hardly possible.
That is also the reason why all the promising things are written in dynamic languages; GnomeDo is C#, Gimmie is Python, only AWN is C, but most of its Plugins are Python.
Now imagine Nautilus would have been written in Python – how easy would it be to hack away a version solely relying on Tracker for file organisation. I definatly think we would see much more innovative stuff here.


After weeks of anticipation, the new Samsung Galaxy S8 is nearly here! The official UK release date is April 28 but some lucky pre-order customers will be able to get their hands on the new handset as soon as 20th April, the South Korean manufacturer has confirmed.
With the phone having already broken pre-order records in Korea, there's a great deal of hype surrounding this latest release.
If you've already made up your mind and want to get hold of an S8, you can purchase one at Very, Argos or directly from Samsung. Simply place your order (or pre-order), use our address as your shipping address (account registration required) and we'll forward it to you as soon as we receive it.
In this blog we'll look at the phone's key features and how it's set to stack up against its predecessor and closest rivals.
The Samsung Galaxy S8 is available in Orchid Grey or Black and is being launched with an initial SIM-free retail price of £689. In addition to the standard sized model, the S8 also comes in a Plus model, complete with a 6.2 inch screen (5.8 inches on the standard S8 handset).
One of the headline features of the new Samsung Galaxy S8 is its display. Featuring an Infinity Display, with the screen seeming wrap the whole way around the phone's edges, the screen on the S8 is also HDR-enabled, giving a beautiful, vivid quality, particularly to video and gaming experiences.
Both the standard and plus S8 models come with 64Gb of internal memory, with the further opportunity to expand storage capacity to up to an additional 256Gb with a microSD card. Another nifty feature is the phone's built-in wireless charging – powering the handset up quickly and claiming the phone stays charged longer.
Samsung have also launched their own version of Siri and Alexa in the new S8, named Bixby. Billed as their own version of a contextual assistant that can work out what you need through voice, touch or the camera, Bixby will only initially be available in Samsung's native Korea and the US, so it's difficult to say how good it will be as yet.
Similar to its predecessor, the S8 is IP68-rated - so is water and dust resistant.
Design wise, the S8 is slicker in appearance than its predecessor and others on the market in no small part thanks to the loss of the fingerprint scanner from the front of the phone to the back – where it's now located next to the camera.
Additionally, every corner and edge on the S8 has been rounded, giving it a nice feel in the hand and a super-smooth appearance.
2017 is set to be a big year for phone releases. However, the Samsung Galaxy S8 appears to be well positioned to take its rivals on – and potentially come out on top. In particular its Infinity Display is clear, bright, colourful and bound to impress.


text:
Carriera

Gypsy Sun and Rainbows
Lee era un vecchio amico di Jimi Hendrix e Billy Cox; avevano tutti e tre suonato insieme in vari gruppi R&B, e nel 1969 Lee si unì alla nuova band di Hendrix, i Gypsy Sun and Rainbows, in veste di chitarrista ritmico, suonando occasionalmente anche la chitarra solista. La neonata band fu scritturata per suonare al Festival di Woodstock per il quale Hendrix era stato precedentemente ingaggiato per esibirsi con i Jimi Hendrix Experience, il suo vecchio gruppo da poco scioltosi. Lee era appena tornato reduce dalla guerra del Vietnam due settimane prima, ed era disoccupato quando Hendrix lo contattò chiedendogli di entrare nei Gypsy Sun and Rainbows una settimana prima del concerto a Woodstock.

Dopo Woodstock il gruppo si disintegrò in breve tempo, anche se continuò ad accompagnare Hendrix in studio per qualche periodo, aiutandolo a sviluppare il suo nuovo "sound" e stile compositivo che comprendeva canzoni meno orientate al pop rock come: Machine Gun, Message to Love e Izabella.

Periodo con Al Green
Nel corso degli anni settanta, Lee entrò a far parte della touring band di Al Green. Apparve nel Tonight Show di Johnny Carson e in varie trasmissioni televisive insieme a Green. Oltre che musicista, Lee era anche un cantatutore e scrisse brani per un gruppo della Stax Records, i The Astors. Judy, brano composto durante il periodo trascorso con Hendrix a Nashville, venne reinterpretato da Al Green & the Spidells. Per breve tempo, Lee accompagnò in tournée il bluesman Albert King, venendo però da lui licenziato perché, a detta dello stesso Lee, il suo modo di suonare oscurava quello di King.

Morte
Larry Lee è morto a Memphis, in Tennessee, il 29 ottobre 2007 a causa di un cancro allo stomaco, e venne sepolto il 6 novembre nel West Tennessee Veterans Cemetery. Lasciò la moglie Carrie Lee, la figlia April D. Lee e tre figli maschi: Lawrence H. Lee III, Robert A. Lee, e Thomas Lee.

Discografia parziale
con Jimi Hendrix
Nine to the Universe (1980)
Woodstock (1994)
Live at Woodstock (1999)

con Al Green
I'll Rise Again (1983)

con Elmo and the Shades
Blue Memphis (2009)

Voci correlate
Al Green
Jimi Hendrix

Collegamenti esterni

redpajama_set_name: RedPajamaWikipedia


Hand crafted, made in the USA and guaranteed for life by designer Sergio Lub. This design is a classic and you will love it as much or more a year from now as the day it arrives at your door.
Ancient Himalayan design representing the spirit (silver) hugging the copper and the golden mind. A Tibetan master told me this design often identifies people seeking deeper awareness. Order today and this pure copper bracelet will be at your door in 2 � 3 days FYI & we pay the shipping!
- No Plating, No Casting, No Machine Stamping - Handcrafted in the USA - Lead Free - 99.99% Pure Copper The process of making Nepalese Cord : All Sergio Lub Jewelry is handcrafted in the United States. The solid metals used in the bracelets include pure copper, jeweler's brass, and German silver. The copper is 99% pure Copper, Arizona copper to be exact. The Jewelers Brass is an alloy of 85% Copper and 15% Zinc. The white metal is German silver (65% Copper, 17% Zinc, 16% Nickel, 2% silver), an alloy used in making white gold. All three of these metals contain Copper and Zinc, which are essential minerals needed by our bodies to remain healthy.


text:
Erica Kundidzora Azim is a musician based in Berkeley, California, who is associated with a not-for-profit organisation.

Recordings
Mbira - Healing Music of Zimbabwe
	The Relaxation Company CD, 2000

Mbira Dreams
	The Relaxation Company CD 3261 and cassette, 1996

"Nyama musango" on Belief: A Collection of World Sacred Music
	Beliefnet CD 1001, 2000

"Mwanangu" on Mama's Lullaby and Mother Earth Lullaby
	Ellipsis Arts CDs 4291 and 4293, March 2001 and 2002

"Nyama musango" (background for Peter Matthiesen reading from "Sand Rivers")
on The Naturalists
	Audio Literature, 1997

with Forward Kwenda: Forward Kwenda - Svikiro: Meditations of an Mbira Master
	Shanachie CD 64095, 1997 (extensive liner notes by Erica Azim)

References

External links
Mbira.org This site also includes a biography page for Azim

Living people
Year of birth missing (living people)
American ethnomusicologists


text:
Nancy Pelosi Defends Democrat John Conyers Against Harassment Allegations After Praising #MeToo
By Jason Silverstein On 11/26/17 at 12:35 PM EST
Rep. John Conyers (D-Mich.). Jonathan Ernst/Reuters
House Minority Speaker Nancy Pelosi on Sunday defended a fellow Democrat accused of sexual harassment right after she applauded the national movement to oust powerful harassers.
Within the same interview on NBC News' "Meet the Press," Pelosi championed the #MeToo movement while apparently casting doubt on the allegations against Representative John Conyers of Michigan, who she praised as an "icon" for women's rights.
Pelosi dodged questions about whether Conyers should resign and if she believes his accusers.
When asked if he should step down, she said her Democratic colleague deserved a fair hearing.
"We are strengthened by due process," the California Democrat said.
"Just because someone is accused — was it one accusation? Was it two? John Conyers is an icon in our country. He's done a great deal to protect women."
WATCH: Rep. @NancyPelosi (D-Calif.): Accused Congressman Conyers is an "icon" in our country. #MTP pic.twitter.com/qko01yRqXj
— Meet the Press (@MeetThePress) November 26, 2017
She said a House ethics committee would determine next steps for Conyer, but she wouldn't weigh in on his accusers.
"I don't know who they are. Do you? They have not come forward."
Her comments came only minutes after Pelosi said she was pleased to see the "transformative" treatment of alleged harassers, in which she called a "generational change" in how accusations are treated.
"Women are saying zero tolerance, no more, and we're going to speak out on it. This is so wholesome, so refreshing, and so different," she said.
Soon after the interview aired, Conyers announced he would step down as the ranking Democrat on the House Judiciary Committee during the ethics investigation, but still remain in office.
Conyers, 88, is a Korean War veteran and civil rights leader who has served in Congress for more than 50 years and worked on the Violence Against Women Act of 1994, which ramped up federal efforts to crack down on abuse.
BuzzFeed News reported last week that Conyer's office had paid $27,000 in secret settlements after four women had accused him of inappropriate touching and sexual advances. Another woman has accused him of verbal abuse and showing up to meetings in his underwear.
Conyer has acknowledged the settlements but denied any wrongdoing and said he has no plans to resign.
The report emerged as another prominent Democrat, Senator Al Franken of Minnesota, faced accusations of inappropriate touching from two women. Franken also is not planning to resign.
Meanwhile, Democrats have been attacking Republican Alabama Senate candidate Roy Moore and President Donald Trump for the respective allegations of sexual harassment and assault against them. Moore has refused calls from the GOP to drop out of the Senate race and Trump, while withholding a formal endorsement of Moore, has said he wants to make sure Democratic candidate Doug Jones doesn't win. He called Jones a "Schumer/Pelosi puppet" and repeatedly pointed out that Jones has denied the accusations against him.
Conyers Refuses Resignation Calls From His Own Party
Black Lawmaker Group to Hold Sex Harassment Training
Trump Says He Prefers Roy Moore Over Democrat 'Puppet'


We had to organise the full house cleaning after the refurbishment for one of our clients in Reading at the last minute. Thanks to Royal Cleaners we had the whole property perfectly cleaned on time. Great service!


The new bearcat wheeled vacuum WV190 is great for residential or commercial use. It has a 190cc Briggs & Stratton recoil start engine and a five-blade radial fan that delivers more CFM than any other competitor vacuum and easy-to-maneuver 12 x 2.5 inch rubber tires with ball bearing hubs.
An ECHO Bear Cat Wheeled Vacuum is a great way for homeowners, commercial crews, or municipalities to quickly and efficiently pick up leaves, grass, or other light debris. With a variety of engines available and either push or 3-speed, self-propelled drive options, there's a Wheeled Vacuum that will fit your job.
Will this unit pick up acorns that lie in the grass? I know that acorns that are in bedded in the turf are not likely to be pick up. I tried a Billygoat to no avail, but your nozzle is close to the turf and offers a better position above the turf. Thank you.
It may pick up some acorn debris but not all. Acorns are a big problem for most vacuums. I bigger unit would do a much better job. I have posted a link below for your reference.


You don't need a short-term teaser rate when the real deal is this good.
Simmons First National Bank, has branches across Arkansas and one of the lowest credit card rates in the country.
You must have excellent credit to qualify for its Platinum Visa card, but if you do, you'll pay a fixed rate of 7.25% for purchases and 11.25% for cash advances.
You might think this isn't quite as good a deal as the Pulaski Bank Visa Card — 6.50% on purchases and cash advances.
But Pulaski charges a $35 annual fee, and we know how feeaphobic Bankaholic readers can be.
So if you want a great rate and no annual fee, the Simmons deal might be a better fit.
FYI I just opened a roth ira cd at Bank of America in Oakland Ca. The one year rate was 2.71, 2.75APY.


Both IBM and Apple shares look like great buys today, but one tech stock appears more compelling for a few important reasons.
The past year has seen something of a reversal of fortunes for shares of Apple (NASDAQ:AAPL) and International Business Machines (NYSE:IBM). While Apple has outperformed IBM shares in recent years, the opposite has held true over the past 12 months.
So, is Apple stock or IBM stock the more attractive opportunity today? Let's examine each of these tech giants across three important investment and business criteria to determine which stock is the better buy.
Both companies are well-established and hugely profitable enterprises. However, there can be little doubt that Apple outshines IBM when it comes to the strength of its financial resources. Here's a quick overview of the two companies' most pertinent balance sheet and leverage ratios.
Data source: Apple & IBM investor relations. TTM = trailing 12 months.
Importantly, as of its most recent quarter, $216 billion of Apple's cash and investments are held overseas to avoid paying repatriation taxes that could eat billions from the company's gross cash balance. As such, the number in the chart above overstates Apple's actual day-to-day liquidity, though Apple is confident its liquidity remains sufficient to fund its continued business needs.
IT giant IBM also adeptly manages its balance sheet. The company leverages its investment-grade credit rating as a source of cheap financing, and its strong cash generation allows it to do so without placing its liquidity in jeopardy. As perhaps the bluest of the blue chips, IBM's century-plus legacy of successful management makes its sensible capital structure unsurprising. In a showdown of financial fortitude, IBM would trounce most businesses. However, when compared against the most profitable company ever, IBM loses this battle through no fault of its own.
Although Apple and IBM enjoy identifiable competitive advantages, each company also has a weak point or two in their business models that investors should understand.
Regarding IBM's strengths, the company remains an absolute beast when it comes to R&D output. In fact, IBM was granted the most patents in the U.S. for the 23rd consecutive year in 2015. This commitment to research has allowed IBM to adapt and continually thrive in the constantly shifting world of technology. It's no accident that IBM has been able to leapfrog across generational shifts in technology, reorienting its business from punch card computers, to mainframes, to PCs, to servers and consulting, to its current focus on analytics, big data, and the like. It takes a truly special company to survive a single generational shift in the tech sector, so IBM's ability to continually remake itself for more than a century speaks to the company's strength in consistently developing and monetizing new technologies.
In terms of IBM's weaknesses, the shift toward cloud computing presents a new set of challenges for Big Blue. Cloud computing enables companies to more cost-effectively access computing power since companies no longer need to own their own servers and other computing hardware. While hugely beneficial for customers, this decreased cost of computing also lowers the switching costs between cloud software vendors to some extent, though major data migrations are certainly still a massive and risky undertaking.
Turning to Apple, the world's largest company clearly enjoys a few competitive advantages, the largest of which is its closed-loop operating system. Unlike Alphabet's Android OS, consumers can only access Apple's software and app ecosystem by buying the company's high-priced hardware. Using Apple services like iCloud, iTunes, and more creates something of a switching cost for consumers, though third-party applications like Spotify in music diminish this to an extent. Apple's comprehensive emphasis on design also reinforces its product's appeal against the competition. It's no accident that the overwhelming majority of iPhone users purchase another iPhone when they replace their smartphones.
Furthermore, Apple's globally recognized brand is perhaps its single most valuable asset. Virtually unanimously agreed upon as the most valuable brand in the world, the Apple brand alone is worth an estimated $170 billion. Few brands, if any, can evoke the kind of emotional response Apple's does, and its repeated ability to define itself as more than just a technology company also helps support its high-margin business model.
At the same time, Apple is, at its core, a product-driven business and thus remains exposed to the usual risk factors facing a company that is always only as good as its next product. Apple's more recent upgrades have seemed somewhat incremental. The Apple Watch 2, iPhone 7, and MacBook Pro refreshes, for instance, all offered moderate improvements over their predecessors. However, Apple's ability to attract and retain tech talent and design talent still makes it highly likely the company will continue to churn out winning products for the foreseeable future.
Apple and IBM are mature, highly profitable companies. It should come as no surprise, then, that both Apple and IBM are each valued at a substantial discount to the overall market.
Clearly, both companies are cheap. The question then becomes, are they too cheap? In each case, I think the answer is a clear yes.
Apple's top line may remain somewhat stagnant until the launch of the reportedly dramatic redesign of the iPhone 8 next year. However, the company continues to be an emerging capital return dynamo, giving Apple the ability to engineer bottom-line growth regardless of its top-line progress.
Similarly, IBM's business model transition will take years to complete. Replacing its current revenue base with sales from new businesses means its top line could stay flat at the IT giant for some time. However, like Apple, the company's outsized profitability also gives it the ability to deliver EPS and dividend growth regardless of its near-term top-line changes.
To be clear, I like both IBM and Apple stock right now. IBM shares are dirt-cheap at a time when the market as a whole looks anything but, and its business model pivot has clearly gained traction. However, Apple's moderately larger revenue growth story and greater financial resources give the Cupertino tech giant a larger degree of flexibility in terms of how it can produce sustained EPS growth in the years to come. So, while IBM looks like an attractive stock to buy, too, Apple shares appear to be the better buy today.


If you're a fan of lo-fi images produced by plastic or pinhole camera, you don't have to carry around multiple cameras or lenses. The "Subjective" lens give you four shooting modes in one lens and is compatible with Nikon and Canon.
All lenses have a 65mm focal length. The lens is available at the Photojojo store for $249, but you can get 10% off, courtesy The Digital Photography School if you use the code: photojojodps.


The Hawai`i Convention Center welcomes the American Society for Reproductive Medicine (ASRM) for their 70th Annual Meeting. Medical professionals from around the world will gather to address clinical and research challenges in reproductive medicine.
This entry was posted in Featured and tagged medical by Editorial Team. Bookmark the permalink.


Men's Pink Dolphin Whirlpool T-Shirt in black with high quality sublimation logos on the front and back, short sleeves made of 100% cotton material. Recommend for men based on Pink Dolphin's Brand Size Chart. Lightweight and comfortable.


The venue of Bar Convent Berlin is Station Berlin. This site was originally opened on June 17th, 1875 as the Dresdener Bahnhof train station with a new route connecting trains between Berlin and Dresden, and further afield with Vienna and Prague. The station was converted into a postal railway station in 1907 and served Berlin for the next 90 years as a sorting destination for packages and goods from around the world.
As the final stop on one of the most important lifelines connecting West Berlin with the rest of former West Germany, the station served an extremly important function during Germany's division throughout the Cold War. Station Berlin still holds many reminders of its eventful past today. The huge gateways through which steam-driven locomotives once passed, the remarkable roof of Hall 7 and the markings on the walls, doors and floors all bear witness to the rich legacy of one of the most important freight hubs in Germany's history.
Station Berlin is still a hub today as a transfer site for great ideas, products and events. This coming October 07th, 08th and 09th will see Station Berlin transformed into Europe's recreational beverages headquarters when it welcomes beverage experts and entrepreneurs from all over Europe to the Bar Convent Berlin trade fair.
You can reach Tegel Airport using public regional transit by connecting with the S-Bahn light rail (S41/42 to Jungfernheide Station), the U-Bahn subway (U7 to Jakob-Kaiser-Platz) or the bus (bus lines X9 and 109).
Travel time from either airport to City Center/Potsdamer Platz – depending on local traffic – is between 25 to 45 minutes.
U-Bahn (subway): Take the U1 or U2 to the Gleisdreieck U-Bahn Station; take the U1 or U2 to the Möckernbrücke U-Bahn Station; from there, it's about a 6-minute walk.
S-Bahn (municipal railway): Take the S1/S2/S25/S26 lines to the Anhalter Bahnhof S-Bahn Station; from there, it's about an 8-minute walk.
Bus connections: Bus line 248 to Möckernstrasse; the Anhalter Bahnhof S-Bahn Station is located within walking distance (about 8 minutes). To/from there, you can take the M41 bus line, for example.
Take the A100 autobahn (toward Dresden) to Exit B96/Tempelhofer Damm. Left exit onto Tempelhofer Damm. Drive straight ahead until Mehringdamm. Turn left onto Hallesches Ufer. Turn left onto Schöneberger Strasse, and then drive into Luckenwalder Strasse.
You'll find STATION Berlin on the right side.
Entry: Bellevuestraße and Ben Gurion Str.
Information are supplied without liabilty.


Solar Innovations® Architectural Glazing Systems family of Universal® Clamps provides customers with simple, dependable, tested clamping solutions for attaching loads to any standing seam, metal roof.
Solar Innovations® installed a photovoltaic panel system on the roof their corporate office and manufacturing facility in 2010. In true Solar fashion, and because there was a need within the market, Solar Innovations® developed the new Universal® Clamps, a standing seam roof clamp for the safe attachment of racking and panel systems, using it's own 3,000+ panel installation as the testing grounds.
Most knowledgeable installers are aware that some clamps used to attach photovoltaic panels to standing seam roofs require pressure points, which may cause possible holes. Points of wear and exposure to corrosion are never desirable on a roof, no matter how small they appear or how well they are sealed.
The Universal® Clamp is designed to attach loads to flanged materials and standing seam roofing by using an innovative design which uses continuous pressure contact against the attachment surface. This prevents permanent damage to the roof while balancing the load over the span of the clamp not on an isolated point of contact. Installation is simple, and the roof is never compromised. An extruded part called a hammer was designed to fit into the Universal® Clamp to reduce pressure placed on the seam. The hammers maintain a continuous point of contact with the mounting surface,so there is no threat of damage to the roof. This is accomplished by spreading the load of the clamp over the total hammer rather than the points of two fasteners.
The hammers can also be included with an isolator, to reduce concerns related to vibration and metal edges. Custom hammers and custom clip lengths and depths can be designed with Universal® Clamps. The Universal® Clamp is compatible with the majority of racking systems, not only those installed by Solar Innovations®. Custom designs are available on project specific applications as needed.
Various hammer designs solve the mounting problems from custom flanges and seams. Currently, Solar has five varieties: Standard Standing Seam, Standard Standing Seam with Insulators, "L" Type Standing Seam, Round Standing Seam, and Thin Standard Standing Seam.
The Universal® Clamp is supplied with the required Stainless Steel hardware for installation of the clamp, as well as mounting an object to the clamp. This innovative design utilizes an extremely efficient manufacturing process, resulting in quick installation, high performance, and an innovative solution.
If you have questions about Universal® Clamps, renewable energy systems, or if you would like some help deciding which system is best for you, please give us a call at 800-618-0669, and ask the operator to refer you to the next available clamp representative.


text:
\section{Introduction}
\label{sec:intro}
Graph Convolutional Neural Networks (GCNNs) have been receiving increasing attention as a powerful tool for irregularly structured data on graphs, such as citation networks, social networks and 3D point clouds.
The construction of an appropriate graph topology plays a critical role in GCNNs for efficient feature learning.
In settings where the graph is inaccurate or even not readily available, it is necessary to infer or learn a graph topology from data before it is used for guiding graph convolution in GCNNs.

Most of the previous studies construct underlying graphs from data empirically, such as $k$-Nearest-Neighbor ($k$-NN) graphs \cite{wang2019dgcnn,te2018rgcnn}, which may lead to sub-optimal solutions.
Few methods exploit \textit{graph learning} for optimized representation \cite{Jiang2018GraphLN,li2018adaptive,shi2018non,li2019spatio}, which learns a fixed and shared graph for all instances, or an individual graph for each instance, or a combination of shared and individual graphs.
However, only \textit{one fixed} graph is learned and applied to all layers of the entire network, which may not well capture the underlying structure of node features in different layers dynamically.

Extending on these previous studies, we propose a Joint Learning Graph Convolutional Network (JLGCN), which exploits \textit{dynamic} learning of graphs and node features \textit{jointly} in GCNNs.
In particular, we optimize an underlying graph kernel from data features via distance metric learning that characterizes pairwise similarities of data.
We deploy the \textit{Mahalanobis distance} \cite{mahalanobis1936}, which takes into account data correlations for intrinsic representation.
Given a K-dimensional feature vector $\mathbf{f}_i$ per node $i$, $\mathbf{f}_i \in \mathbb{R}^K$, the Mahalanobis distance between the features $\mathbf{f}_i$ and $\mathbf{f}_j$ of nodes $i$ and $j$ is defined as:
$d_{\mathbf{M}}(\mathbf{f}_i,\mathbf{f}_j)=\sqrt{(\mathbf{f}_i - \mathbf{f}_j)^{\top} \mathbf{M} (\mathbf{f}_i - \mathbf{f}_j)}$, where $\mathbf{M} \in \mathbb{R}^{K \times K}$ is the Mahalanobis distance metric matrix which reflects feature correlations.
Hence, we convert the problem of graph learning to the optimization of $\mathbf{M}$.
As $\mathbf{M}$ is positive semi-definite (PSD), it is often nontrivial to solve efficiently.
Instead, we decompose it as $\mathbf{M} = \mathbf{R} \mathbf{R}^{\top}$ and learn $\mathbf{R}$ for ease of optimization, where $\mathbf{R} \in \mathbb{R}^{K \times S}$ has a lower dimension $S<<K$ to reduce number of parameters for efficient implementation.
Given features $\mathbf{f}$, we seek to minimize the Graph Laplacian Regularizer (GLR) \cite{shuman2013emerging} $\mathbf{f}^{\top}\mathbf{L}(\mathbf{R})\mathbf{f}$ by optimizing $\mathbf{R}$, which measures the smoothness of features $\mathbf{f}$ with respect to the graph Laplacian $\mathbf{L}$\footnote{In spectral graph theory, a graph Laplacian matrix is an algebraic representation of connectivities and node degrees of the corresponding graph, which will be defined formally later.}.
This essentially enforces the graph encoded in $\mathbf{L}(\mathbf{R})$ to capture pairwise similarities of $\mathbf{f}$.

Hence, we formulate the joint learning of graphs $\mathbf{L}(\mathbf{R})$ and node features $\mathbf{f}$ as an optimization problem, which minimizes a weighted sum of the GLR and cross-entropy.
We set this objective as the loss function of the proposed JLGCN to guide network model optimization, which employs a localized first-order approximation of spectral graph convolution as in \cite{Kipf2016SemiSupervisedCW}.
Further, the learned graph at the previous layer is added to that of the current layer for multi-level feature learning.
The proposed JLGCN can be integrated into any GCNN architecture for applications such as node classification and graph classification.
To validate the effectiveness of the proposed JLGCN, we apply it to semi-supervised learning for citation networks and 3D point cloud learning problems.
Extensive experimental results demonstrate the superiority and robustness of JLGCN compared with state-of-the-art methods on four datasets even with a small model size.

Our contributions can be summarized as follows:
\begin{itemize}
\item We propose joint learning of underlying graphs and node features at each layer of a GCNN, which captures pairwise similarities of node features dynamically.
\item We cast the graph learning problem as distance metric learning with the Mahalanobis distance deployed. We further decompose the distance metric into a low-dimensional matrix for efficient implementation, which is optimized from both the GLR and cross entropy along with node features.
\item Extensive experiments on semi-supervised learning and point cloud classification demonstrate the superiority and robustness of our method.
\end{itemize}

\section{Related Work}
\label{sec:related}
\subsection{Graph Convolution Neural Networks}
As a generalization of CNNs to irregular graph domain, GCNNs can be categorized into two main classes: spectral methods and spatial methods.

\subsubsection{Spectral methods} This class of methods define graph convolution based on the spectral representation of graphs.
\cite{bruna2013spectral} defines the convolution in graph Fourier transform domain by eigen-decomposition of the graph Laplacian, but requires intense computations.
\cite{defferrard2016convolutional} addresses this problem by leveraging Chebyshev polynomials to approximate spectral filters and achieves localized filtering.
GCN \cite{Kipf2016SemiSupervisedCW} further simplifies the previous work by employing only first-order approximation of the filters.
Our proposed joint learning of graphs and node features is based on GCN, which has spectral interpretation and scales linearly with the number of edges.

\subsubsection{Spatial methods} Instead of spectral representation, spatial methods define graph convolution directly on each node and its neighbors for feature propagation.
Mixture model network \cite{monti2017geometric} provides a unified generalization of CNN architectures on graphs. Graph attention network (GAT) in \cite{velivckovic2017graph} employs self-attention mechanism to solve the node classification problem on citation networks. DGCNN \cite{wang2019dgcnn} proposes edge convolution to aggregate local features, which is applied to point cloud learning problem.

\subsection{Graph Learning in GCNNs}
Graph Learning in GCNNs can be categorized into three classes based on the graph domain to address:
1) fixed-domain graph learning, in which the underlying graph is fixed;
2) varying-domain graph learning, where graphs vary across data instances;
and 3) hybrid graph learning, which combines the former two classes.

\subsubsection{Fixed-domain Graph Learning}
This class of graph learning assumes there is a shared graph structure underlying all instances with fixed vertices.
In applications such as semi-supervised node classification of citation networks, there is only \textit{one fixed} graph to be learned and shared by all the instances to classify.
GLCN \cite{Jiang2018GraphLN} learns a non-negative function that represents the pairwise relationship between two vertices using attention mechanism via a single-layer neural network, and optimizes it by minimizing GLR in the loss function. Instead, our method models pairwise similarity more explicitly by adopting Mahalanobis distance, and we learn different graphs at different layers.

\subsubsection{Varying-domain Graph Learning}
Instances corresponding to varying domains may require different graphs with possibly arbitrary number of vertices.
Graph learning for such data applications learns adaptive graphs tailored for different individuals.
Spatio-temporal graph routing (STGR) is proposed \cite{li2019spatio} to learn one spatial graph and one temporal graph for each skeleton instance.
Adaptive graph convolution network (AGCN) is proposed \cite{li2018adaptive} to construct a unique residual Laplacian matrix by learning Mahalanobis distance metric for each instance.
Different from the task-driven AGCN where the Mahalanobis distance metric $\mathbf{M}$ is learned by minimizing the cross-entropy only, we optimize $\mathbf{M}$ via both the GLR and cross-entropy.
Also, we assume $\mathbf{M}$ is low rank and set $S << K$ to reduce number of parameters while AGCN adopts $S = K$;

\subsubsection{Hybrid Graph Learning}
For special applications like skeleton-based action recognition, both fixed-domain and varying-domain graph learning can be applied according to the way of graph construction.
NLGCN \cite{shi2018non} builds three graphs for each instance: the pre-defined physical skeleton graph, a shared graph for all instances, and an individual graph learned from each instance's own feature in a non-local \cite{wang2018non} manner.

\vspace{-0.05in}
\section{Background in Spectral Graph Theory}
\label{sec:graph}
\subsection{Graph and Graph Laplacian}
An undirected graph $\mathcal{G}=\{\mathcal{V},\mathcal{E},\mathbf{A}\}$ is composed of a vertex set $\mathcal{V}$ of cardinality $\left|\mathcal{V}\right|=N$, an edge set $\mathcal{E}$ connecting vertices, and a weighted adjacency matrix $\mathbf{A}$. $\mathbf{A} \in \mathbb{R}^{N \times N}$ is a real and symmetric matrix, where $a_{i,j} \geq 0$ is the weight assigned to the edge $(i,j)$ connecting vertices $i$ and $j$.
Edge weights often measure the similarity between connected vertices.

The graph Laplacian matrix is defined from the adjacency matrix.
Among different variants of Laplacian matrices, the \textit{combinatorial graph Laplacian} is defined as $\mathbf{L}:=\mathbf{D}-\mathbf{A}$, where $\mathbf{D}$ is the \textit{degree matrix}---a diagonal matrix where $d_{i,i}=\sum_{j=1}^N a_{i,j}$.

\subsection{Graph Laplacian Regularizer}
Graph signal refers to data that resides on the nodes of a graph, such as temperatures on a sensor network. A graph signal $\mathbf{x}\in\mathbb{R}^N$ defined on a graph $\mathcal{G}$ is \textit{smooth} with respect to $\mathcal{G}$ \cite{spielman2007spectral} if
\begin{equation}
	\mathbf{x}^{\top} \mathbf{L} \mathbf{x} =\sum_{i=1}^{N} \sum_{j=1}^{N} w_{i,j}(x_i - x_j)^2 < \epsilon,
	\label{eq:prior}
\end{equation}
where $\epsilon$ is a small positive scalar.
To satisfy Eq. \eqref{eq:prior}, connected node pair $x_i$ and $x_j$ must be similar for a large edge weight $w_{i,j}$; for a small $w_{i,j}$, $x_i$ and $x_j$ can differ significantly. Hence, Eq. \eqref{eq:prior} forces $\mathbf{x}$ to adapt to the topology of $\mathcal{G}$, and is commonly called the \textit{graph Laplacian Regularizer} (GLR) \cite{shuman2013emerging,pang2017graph}.

\vspace{-0.05in}
\section{Joint Learning of Graphs and Features}
\label{sec:method}
In this section, we first propose the problem formulations of graph learning and node feature learning respectively assuming one of the other is known, and then combine them to the formulation of jointly learning.

\subsection{Problem Formulation of Graph Learning}
As a graph essentially captures \textit{pairwise similarities} within data $\mathbf{x} \in \mathbb{R}^N$, we cast the problem of graph learning as \textit{distance metric learning}, \textit{i.e.}, learning a distance function for each data pair $\{x_i,x_j\}$ for similarity calculation.
Specifically, given K-dimensional feature vectors $\mathbf{f}^i_l \in \mathbb{R}^K$ and $\mathbf{f}^j_l \in \mathbb{R}^K$ of node $i$ and $j$ at the $l$-th layer respectively, we employ the commonly used Gaussian kernel to define an edge weight $a_{i,j}$ as
\begin{equation}
    a_{i,j}=\exp\left\{-d^2(\mathbf{f}^i_l,\mathbf{f}^j_l)\right\},
\end{equation}
where $d(\mathbf{f}^i_l,\mathbf{f}^j_l)$ is a distance metric between $\mathbf{f}^i_l$ and $\mathbf{f}^j_l$. The Gaussian kernel enforces edge weights to be in range $[0,1]$, thus ensuring the resulting combinatorial graph Laplacian to be PSD \cite{cheung2018graph}.

While there exist various definitions of distance metrics, such as Euclidean distance and bilateral filtering distance \cite{tomasi1998bilateral}, we deploy the \textit{Mahalanobis distance}, which is defined as
\begin{equation}
d_{\mathbf{M}_l}(\mathbf{f}^i_l,\mathbf{f}^j_l) = \sqrt{(\mathbf{f}^i_l - \mathbf{f}^j_l)^{\top} \mathbf{M}_l (\mathbf{f}^i_l - \mathbf{f}^j_l)},
\label{eq:m_distance}
\end{equation}
where $\mathbf{M}_l \in \mathbb{R}^{K \times K}$ is a PSD matrix, and $l$ is the index of layers.
The Mahalanobis distance captures correlations among features via $\mathbf{M}_l$, which is widely adopted in the machine learning literature \cite{li2018adaptive,luo2019robust}.

Each edge weight is then computed as
\begin{equation}
a_{i,j} = \exp\left\{-(\mathbf{f}^i_l - \mathbf{f}^j_l)^{\top} \mathbf{M}_l (\mathbf{f}^i_l - \mathbf{f}^j_l)\right\},
\label{eq:gaussian_kernel}
\end{equation}
from which we can calculate the corresponding adjacency matrix and graph Laplacian.

As the graph Laplacian can be computed from edge weights by definition,
we convert the graph learning problem to an optimization problem over $\mathbf{M}_l$.
We optimize $\mathbf{M}_l$ by minimizing Graph Laplacian Regularizer in Eq. \eqref{eq:prior}, which enforces the graph to capture the underlying structure of features.
Let $s_{i,j} = (x_i-x_j)^2$, we have the following problem formulation for graph learning over all the layers of the network, with the objective as the sum of GLR over all the layers:
\begin{equation}
\begin{split}
&\begin{split}
	\min_{\mathbf{M}_l}
     & \sum_l \sum_{\{i,j\}}\exp\left\{-(\mathbf{f}^i_l-\mathbf{f}^j_l)^{\top} \mathbf{M}_l (\mathbf{f}^i_l-\mathbf{f}^j_l) \right\} \, s_{i,j}
     \end{split} \\
& \text{s.t.} \quad \,\mathbf{M}_l \succeq 0, \, \forall l.
\label{eq:optimization}
\end{split}
\end{equation}

Since $\mathbf{M}_l$ is PSD and symmetric, Eq. \eqref{eq:optimization} is computationally expensive to solve in general.
Instead, we decompose $\mathbf{M}_l$ into
\begin{equation}
    \mathbf{M}_l = \mathbf{R}_l \mathbf{R}^{\top}_l,
    \label{eq:learnable_r}
\end{equation}
where $\mathbf{R}_l \in \mathbb{R}^{K \times S}$, and $S \le K$ is a hyper-parameter to control the complexity of computing distance between high-dimensional features.
When $K$ is large and $S << K$, the number of parameters for $\mathbf{M}_l$ is significantly reduced while assuming $\mathbf{M}_l$ is low rank, which is a common assumption in distance metric learning \cite{liu15aaai,luo2019robust}.

Then the Mahalanobis distance in Eq. \eqref{eq:m_distance} becomes
\begin{equation}
\begin{split}
d_{\mathbf{M}_l}(\mathbf{f}^i_l,\mathbf{f}^j_l)
&= \sqrt{(\mathbf{f}^i_l - \mathbf{f}^j_l)^{\top} \mathbf{R}_l \mathbf{R}^{\top}_l (\mathbf{f}^i_l - \mathbf{f}^j_l)} \\
&= \sqrt{(\mathbf{R}^{\top}_l (\mathbf{f}^i_l - \mathbf{f}^j_l))^{\top}\mathbf{R}^{\top}_l (\mathbf{f}^i_l - \mathbf{f}^j_l)}\\
&= \|\mathbf{R}^{\top}_l (\mathbf{f}^i_l - \mathbf{f}^j_l)\|_2,
\end{split}
\label{eq:m_distance_decompose}
\end{equation}
which is essentially a linear transformation of the Euclidean distance by $\mathbf{R}_l$.
When $\mathbf{R}_l$ is an identity matrix, $d_{\mathbf{M}_l}(\mathbf{f}^i_l,\mathbf{f}^j_l)$ defaults to the Euclidean distance.

Hence, each edge weight can be computed as
\begin{equation}
a_{i,j} = \exp\left\{-\|\mathbf{R}^{\top}_l (\mathbf{f}^i_l - \mathbf{f}^j_l)\|_2^2 \right\}.
\label{eq:weight_R}
\end{equation}
Accordingly, the optimization problem in Eq. \eqref{eq:optimization} converts to
\begin{equation}
    \min_{\{\mathbf{R}_l\}}
     \sum_l \sum_{\{i,j\}}\exp\left\{-\|\mathbf{R}^{\top}_l (\mathbf{f}^i_l - \mathbf{f}^j_l)\|_2^2 \right\} \, s_{i,j},
     \label{eq:opt_graph_R}
\end{equation}
which allows to remove the PSD constraint of $\mathbf{M}_l$ and is thus more efficient to solve.
The optimal adjacency matrix $\mathbf{A}_l^*$ at the $l$-th layer is then computed from the optimized $\mathbf{R}_l$ via Eq. \eqref{eq:weight_R}.

\subsection{Problem Formulation of Node Feature Learning}
Assuming the availability of the learned optimal graph encoded in $\mathbf{A}_l^*$ and the optimal graph $\mathbf{A}_{l-1}$ in the previous layer, we learn node features based on a modified graph spectral convolution in GCN \cite{Kipf2016SemiSupervisedCW} for its simplicity and effectiveness.
Given features of all nodes $\mathbf{F}_{l-1}$ from the $(l-1)$-th layer, the graph convolution at the $l$-th layer computes the output feature $\mathbf{F}_{l}$ as:
\begin{equation}
\mathbf{F}_{l} = \mathbf{A}_l\mathbf{F}_{l-1} \mathbf{W}_l
\label{eq:gcn}
\end{equation}
where $\mathbf{W}_l$ is a trainable parameter, and $\mathbf{A}_l$ is the re-normalized adjacency matrix calculated as
\begin{equation}
    \mathbf{A}_l=\mathbf{\Lambda}^{-\frac{1}{2}} (\mathbf{A}_{l-1}+\mathbf{A}_l^*) \mathbf{\Lambda}^{-\frac{1}{2}}
\end{equation}
$\mathbf{\Lambda}$ is the normalization diagonal matrix.
We essentially replace the re-normalization matrix $\mathbf{I}_N$ in the original GCN layer with our learned optimal graph $\mathbf{A}_l^*$.
When $l=1$, $\mathbf{A}_0=\mathbf{0}$ is a zero matrix.
In special cases where a ground truth graph is provided such as in semi-supervised node classification, $\mathbf{A}_0$ is the ground truth graph.
The ground truth graph may be inaccurate or could be improved further.
For instance, in a citation network, an unweighted graph is often available to describe the citation relationship among papers.
We can further learn a weighted graph to learn hidden connections and characterize how well correlated connected papers are, which enhances the model capacity.

The objective of node feature learning is to minimize the cross entropy between predicted labels and ground truth labels, which is task-driven.
We predict the label of each node by applying the softmax function to the output feature at the final $L$-the layer:
\begin{equation}
    \mathbf{\hat Y} = \text{softmax}(\mathbf{F}_L).
\end{equation}
As $\mathbf{F}_L$ is learned from Eq. \eqref{eq:gcn}, $\mathbf{\hat Y}$ is a function of the set of parameters $\{\mathbf{W}_l, \mathbf{R}_l\}$ at each layer. By optimizing the cross-entropy loss, we have the following problem formulation of node feature learning:
\begin{equation}
\min_{\{\mathbf{W}_l,\mathbf{R}_l\}}
-\sum_{i=1}^N \sum_{c=1}^C \mathbf{Y}_{ic} \log(\hat{\mathbf{Y}}_{ic}(\mathbf{W}_l, \mathbf{R}_l)),
\label{eq:opt_node}
\end{equation}
where $N$ is the number of input instances, $C$ is the number of classes, and $\mathbf{Y}$ denotes the ground truth label matrix.

\subsection{Problem Formulation of Joint Learning}

Integrating Eq. \eqref{eq:opt_graph_R} and Eq. \eqref{eq:opt_node}, we pose the joint learning of underlying graphs and node features as minimizing both GLR and the cross entropy.
The final problem formulation is
\begin{equation}
\begin{split}
\min_{\{\mathbf{W}_l, \mathbf{R}_l\}}
& -\sum_{i=1}^N \sum_{c=1}^C \mathbf{Y}_{ic} \log(\hat{\mathbf{Y}}_{ic}(\mathbf{W}_l, \mathbf{R}_l)) \\
& + \lambda \sum_l \sum_{\{i,j\}}\exp\left\{-\|\mathbf{R}^{\top}_l (\mathbf{f}^i_l - \mathbf{f}^j_l)\|_2^2 \right\} \, s_{i,j},
\end{split}
\label{eq:opt_R}
\end{equation}
where $\lambda > 0$ is a hyper-parameter for the trade-off between the cross entropy and the GLR term.

\vspace{-0.05in}
\section{Proposed Network Structure}
\label{sec:network}
Having discussed the problem formulation of joint learning, we elaborate on the proposed network architecture to realize dynamic learning of graphs and node features, with focus on the JLGCN Layer.

\subsection{JLGCN Layer}

\begin{figure}[t]
\centering
\includegraphics[width=2.6in]{figures/layer.pdf}
\caption{Demonstration of a JLGCN layer. A graph is first learned from node features, and then employed to propagate node features via graph convolution.}
\label{fig:layer}
\end{figure}

As illustrated in Fig.~\ref{fig:layer}, the JLGCN layer is composed of two modules: the graph learning module and node feature learning module.
The graph learning module learns a graph encoded by an $N \times N$ dense adjacency matrix $\mathbf{A}$ from input node features.
$\mathbf{A}$ is then employed by the node feature learning module to propagate node features via our proposed graph convolution in Eq. \eqref{eq:gcn}.
Both modules are jointly optimized according to the objective in Eq. \eqref{eq:opt_R}, \textit{i.e.}, the loss function is defined as
\begin{equation}
    \begin{split}
E=& -\sum_{i=1}^N \sum_{c=1}^C \mathbf{Y}_{ic} \log(\hat{\mathbf{Y}}_{ic}(\{\mathbf{W}_l, \mathbf{R}_l\})) \\
& + \lambda \sum_l \sum_{\{i,j\}}\exp\left\{-\|\mathbf{R}^{\top}_l (\mathbf{f}^i_l - \mathbf{f}^j_l)\|_2^2 \right\} \, s_{i,j},
\end{split}
\label{eq:loss}
\end{equation}
The algorithmic details are provided in Alg. \ref{alg:layer}.

\begin{algorithm}[ht]
\caption{JLGCN layer}
\label{alg:layer}
\hspace*{\algorithmicindent} \textbf{Input:} features $\mathbf{F}_{l-1}$ and adjacency matrix $\mathbf{A}_{l-1}$ from the previous layer \\
\hspace*{\algorithmicindent} \textbf{Output:} features $\mathbf{F}_l$ and learned adjacency matrix $\mathbf{A}_l$ \\
\hspace*{\algorithmicindent} \textbf{Initialize:} trainable parameters $\mathbf{R}_l, \mathbf{W}_l$
\begin{algorithmic}[1]
\State \textbf{Forward Pass:}
\State Compute distance metric $d_\mathbf{M}$ by Eq. \eqref{eq:m_distance_decompose}
\State Update $\mathbf{A}_l $ by Eq. \eqref{eq:gaussian_kernel}
\State Update $\mathbf{F}_l $ by Eq. \eqref{eq:gcn}
\State \textbf{Backward Pass:}
\State Update $\mathbf{R}_l$ and $\mathbf{W}_l$ via backward propagation with the loss function defined in Eq. \eqref{eq:loss}.
\end{algorithmic}
\end{algorithm}

\begin{figure*}[t]
\centering
\includegraphics[width=6in]{figures/network.pdf}
\caption{The proposed JLGCN network architecture for node classification and graph classification tasks. The graph and node features at each layer are jointly updated.}
\label{fig:network}
\end{figure*}

\subsection{JLGCN Network}
In principle, our JLGCN layer can be integrated into any GCNN architecture for both node classification and graph classification tasks.
Fig.~\ref{fig:network} demonstrates the architecture of the JLGCN network.
Node features are extracted by alternatively learning an optimal graph based on features and propagating features based on the learned graph, which are then fed into the classification module for label prediction.

\subsubsection{Node classification configuration}
We stack multiple JLGCN layers and deploy leaky ReLU activation \cite{xu2015empirical}.
Dropout is applied to the last layer's input to reduce over-fitting.
Features from the last layer is then employed for prediction of labels at each node.

\subsubsection{Graph classification configuration}
We stack multiple JLGCN layers followed by batch normalization \cite{ioffe2015batch} and leaky ReLU activation. Graph max pooling is applied to features from the last layer, which are then fed into a fully connected network for the classification of the entire graph.

Further, we perform feature concatenation at each layer $l$ with $\mathbf{f}_{l-1}$ from the previous layer for multi-level feature learning to enhance the model capacity. This leads to
\begin{equation}
\mathbf{f}_{l} = (\mathbf{f}_{l-1} || \mathbf{A}_l \mathbf{f}_{l-1}) \mathbf{W}_l,
\label{eq:gcn_concat}
\end{equation}
where $||$ is the concatenation operator in feature dimension.
This essentially assigns more weighting to each node's own feature when no ground truth graph is available and accelerates the convergence.

\vspace{-0.05in}
\section{Experimental Results}
\label{sec:results}
In order to evaluate the performance of JLGCN, we apply it to the problems of semi-supervised node classification and point cloud classification, which are discussed in order as follows.

\subsection{Semi-supervised node classification}

\subsubsection{Datasets}
We test our method on three citation network datasets, \textit{i.e.}, Citeseer, Cora and Pubmed \cite{sen2008collective}. The statistics of the datasets are summarized in Tab. \ref{tab:citation_datasets}.

\begin{table}[ht]
\small
\centering
\begin{tabular}{p{0.4in}p{0.4in}p{0.3in}p{0.3in}p{0.4in}p{0.4in}}
\hline
\textbf{Dataset} & \textbf{Nodes} & \textbf{Edges} & \textbf{Classes} & \textbf{Features} & \textbf{Label Ratio} \\ \hline
Citeseer         & 3,327          & 4,732          & 6                & 3,703             & 0.036               \\
Cora             & 2,708          & 5,429          & 7                & 1,433             & 0.052               \\
Pubmed           & 19,717         & 44,338         & 3                & 500               & 0.003               \\ \hline
\end{tabular}
\caption{Summary of citation network datasets.} \label{tab:citation_datasets}
\end{table}

\subsubsection{Experimental settings}
We follow the experimental setup of previous work \cite{yang2016revisiting,Kipf2016SemiSupervisedCW,velivckovic2017graph}, and use the same data partition as in \cite{yang2016revisiting}.
The features are $L2$-normalized before fed into the network.
Since our method builds a dense adjacency matrix among all nodes, we sub-sample 10,000 nodes for Pubmed dataset to evaluate our model due to memory limitation.
We set the number of JLGCN layers in our network to 2, and the number of hidden units in each layer to 16. For the dimension of the Mahalanobis distance metric matrix $\mathbf{R}\in \mathbb{R}^{K\times S}$, we set $S$ to 16 for both layers and initialize it randomly if not specified. We apply dropout with $p=0.5$ for both layer's input, and use leaky ReLU activation with negative slope $\alpha=0.2$ between two layers. We train our JLGCN for a maximum of 500 epochs using ADAM algorithm \cite{kingma2014adam} with a learning rate of 0.1 and weight decay of 0.0005. The learning rate is decayed by 0.5 every 100 epochs. The hyper parameter $\lambda$ for GLR is set to 0.0001. We report the average classification accuracy of 10 runs with different random seeds.

\subsubsection{Baselines}
We compare our JLGCN against the baseline of GCN \cite{Kipf2016SemiSupervisedCW} and Planetoid \cite{yang2016revisiting}, and also against some other graph neural network based semi-supervised learning methods, including Graph Attention Network (GAT) \cite{velivckovic2017graph} and Graph Markov Neural Network (GMNN) \cite{qu2019gmnn}. These methods use the same dataset partition as in our method for fair comparison with available codes.

\subsubsection{Results}

Tab.~\ref{tab:results_citation} shows the comparison results on three citation network datasets. The best results are marked in bold.
Overall, we note that
1) JLGCN outperforms the GCN baseline on all datasets significantly by only adding a graph learning module to each layer. This clearly demonstrates the effectiveness of jointly learning graphs and node features. Compared to the unweighted ground truth graph used in GCN, our learned graphs (as shown in Fig. \ref{fig:visual_cora}) enhance the semi-supervised learning results.
2) JLGCN also outperforms all the other recent work on the original Citeseer and Cora datasets and our sub-sampled Pubmed dataset. These results demonstrate the effectiveness of JLGCN in conducting semi-supervised classification tasks on graph data.

\begin{table}[ht]
\centering
\small
\begin{tabular}{p{1.4in}p{0.4in}p{0.4in}p{0.4in}}
\hline
\textbf{Algorithm} & \textbf{Citeseer} & \textbf{Cora} & \textbf{Pubmed*} \\ \hline
\begin{tabular}[c]{@{}l@{}}Planetoid\\\scriptsize\cite{yang2016revisiting}\end{tabular}     & 64.7  & 75.7  & 77.2   \\
\begin{tabular}[c]{@{}l@{}}GCN \\\scriptsize\cite{Kipf2016SemiSupervisedCW}\end{tabular}   & 70.3  & 81.5  & 78.4   \\
\begin{tabular}[c]{@{}l@{}}GAT\\\scriptsize\cite{velivckovic2017graph}\end{tabular}        & 72.5  & 83.0  & 78.8   \\
\begin{tabular}[c]{@{}l@{}}GMNN\\\scriptsize\cite{qu2019gmnn}\end{tabular}                  & 73.1  & 83.7  & 79.4   \\
\hline
JLGCN              & \textbf{73.7}            & \textbf{83.9}        & \textbf{79.7}             \\
\hline
\end{tabular}
\caption{Results of semi-supervised node classification. [*] means we sub-sample 10,000 nodes to perform the experiments.} \label{tab:results_citation}
\end{table}

\subsubsection{Robustness test}

\begin{figure*}[t]
\centering
\subfloat[]{{\includegraphics[width=3in]{figures/robust_edge.pdf} }}
\qquad
\subfloat[]{{\includegraphics[width=3in]{figures/robust_node.pdf} }}
\caption{Robustness test with different missing ratios of labeled nodes and edges on Cora dataset. (a) Accuracy with different edge missing ratios. (b) Accuracy with different node missing ratios.}
\label{fig:robust_citation}
\end{figure*}

We design two experiments to test the robustness of our model:
1) we lower the label ratio by employing less training data. We adopt five missing ratios \{0, 0.25, 0.5, 0.75, 0.9\} of training nodes to train and evaluate;
2) we test the robustness of our model to incomplete ground truth graph, \textit{i.e.}, we randomly drop out edges in the ground truth graph, with edge missing ratios set as \{0, 0.25, 0.5, 0.75, 0.9, 1.0\}.
When the edge missing ratio is 1.0, an empty ground truth graph is fed into the network.
Since randomly initialized graph is hard to converge, we set $S=K$ for the transformation matrix $\mathbf{R}$ and initialize it as an identity matrix in this case.
Fig. \ref{fig:robust_citation} shows our model significantly outperforms GCN and GAT, especially when the edge missing ratio is high.
This validates the superiority of our learned graph structure in terms of robustness.

\subsubsection{Ablation study}
We first examine the effectiveness of the graph learning module on Cora dataset.
Specifically, we compare with
1) GCN, where the ground truth graph is employed;
2) GCN + Euclidean, where we replace the proposed graph learning with manual graph construction using Euclidean distance metric to compute edge weights;
3) JLGCN without dynamic updating of graphs, \textit{i.e.}, we only learn one graph from the input data feature.
As reported in Tab. \ref{tab:ablation}, our method leads to the best result when we adopt the Mahalanobis distance metric and learn different graphs at different layers jointly with node features from data.

Secondly, we evaluate the performance of JLGCN with different $\lambda$---the weighting parameter of GLR in the loss function---with respect to different edge missing ratios.
As listed in Tab. \ref{tab:ablation}, we see that JLGCN achieves the highest accuracy when $\lambda$ is set to 0.0001.
Also, GLR helps improve the performance especially when the ground truth graph is incomplete or even unavailable.

\begin{table}[ht]
\centering
\subfloat[]{
\begin{tabular}{ll}
\hline
\textbf{Algorithm} & \textbf{Accuracy}      \\
\hline
GCN (baseline)     & 81.5                   \\
GCN + Euclidean    & 83.1                   \\
JLGCN (single graph)  & 83.4                \\
\hline
JLGCN (multiple graphs) & \textbf{83.9}          \\
\hline
\end{tabular}}
\qquad
\subfloat[]{
\begin{tabular}{llll}
\hline
\multirow{2}{*}{$\mathbf{\lambda}$} & \multicolumn{3}{l}{\textbf{Edge Missing Ratios}} \\ \cline{2-4}
                                 & 0              & 0.5            & 1.0            \\ \hline
0                                & 83.50           & 77.68          & 59.32          \\
0.0001                           & \textbf{83.88} & \textbf{77.90} & \textbf{60.52} \\
0.001                            & 82.76           & 76.96          & 59.84          \\
0.01                             & 82.45           & 76.46          & 59.40           \\ \hline
\end{tabular}}

\caption{Ablation study.(a) shows the results with different GLR weighting parameter $\lambda$ with respect to different edge missing ratios on Cora dataset. (b) shows the results of different graph learning methods on Cora dataset.}
\label{tab:ablation}
\end{table}

\subsubsection{Visualization of the learned graph}
We also visualize the learned graph at each layer in Fig. \ref{fig:visual_cora}, and compare them with the ground truth graph. The value of the matrix is log-transformed to make it more interpretable.
Note that the learned graph is both dense and weighted, which not only adds self-connection to the unweighted ground truth graph as the original GCN does, but also extracts additional hidden connections between similar nodes.
Also, the learned graph at the second layer puts higher weighting to self-connection compared to the first layer, showing that different layers correspond to varying graphs as we assumed.

\begin{figure}[t]
\centering
\subfloat[]{{\includegraphics[width=1.5in]{figures/vis_A0.pdf} }}
\qquad
\subfloat[]{{\includegraphics[width=1.5in]{figures/vis_A1.pdf} }}
\qquad
\subfloat[]{{\includegraphics[width=1.5in]{figures/vis_A2.pdf} }}
\caption{Visualization of the learned graphs at different layers for the first 50 nodes on Cora dataset. (a) shows the ground truth graph, (b) is the learned graph for the first layer, and (c) for the second layer.}
\label{fig:visual_cora}
\end{figure}

\subsection{Point cloud classification}
Furthermore, we test our model on point cloud classification to validate the effectiveness of our model when no ground truth graph is available.

\subsubsection{Datasets}
We test on ModelNet40 dataset for point cloud classification.
ModelNet40 includes 12,311 CAD models from 40 man-made categories, which are split into 9,843 for training and 2,468 for testing.
As in previous work \cite{qi2017pointnet,qi2017pointnet++,wang2019dgcnn}, we uniformly sample 1024 points on mesh faces according to face area and normalize them into a unit sphere, where only coordinates of points are employed as input features.

\subsubsection{Experimental settings}
We stack 3 JLGCN layers to extract point cloud features as well as learn the underlying graph without any ground truth graph.
The hidden units for each layer is \{64, 128, 1024\}.
After extracting point cloud features, we deploys a permutation-invariant graph max-pooling to generate a global context feature for the entire point cloud, and employs a fully connected network with hidden units of \{512, 256, 40\} to classify the point cloud.
The negative slope of leaky ReLU activation is set to 0.2 and dropout with $p=0.5$ is used in the fully connected classification network.
We train the network with a learning rate of 0.001 for 400 epochs and decay it by 0.5 every 40 epochs using ADAM.
The batch size is set to 32, weight decay to 0.0001 and $\lambda$ to 0.01.

\subsubsection{Baselines}
We also provide a GCN based model as the baseline, which empirically constructs a $k$-NN graph as the underlying graph.
$k$ is set to 20 in the classification task based on the density of point clouds.
We mainly compare our JLGCN model against the baseline GCN, and also compare against other state-of-the-art methods, including methods tailored for point cloud learning such as PointNet \cite{qi2017pointnet}, PointNet++ \cite{qi2017pointnet++}, PointGCN \cite{zhang2018graph}, RGCNN \cite{te2018rgcnn}, and DGCNN \cite{wang2019dgcnn}.

\subsubsection{Results}

Tab. \ref{tab:results_modelnet} presents results for ModelNet40 classification.
Our method improves the baseline by about 2\% with only 3MB of parameters added, demonstrating the learned graph is superior to the empirical $k$-NN graph.
Also, our method achieves comparable results compared to state-of-the-art methods for point loud learning, while our model is simpler and contains much smaller amount of parameters.
Note that, our method achieves the best performance among all spectral graph convolution based methods.

\begin{table}[ht]
\centering
\scriptsize
\begin{tabular}{p{0.3in}p{0.9in}p{0.4in}p{0.4in}p{0.4in}}
\hline
\textbf{Category} &
\textbf{Algorithm} &
\textbf{\begin{tabular}[c]{@{}l@{}}Model Size\\(MB)\end{tabular}} &
\textbf{\begin{tabular}[c]{@{}l@{}}Mean Class\\Accuracy\end{tabular}} &
\textbf{\begin{tabular}[c]{@{}l@{}}Overall\\Accuracy\end{tabular}} \\
\hline
\begin{tabular}[c]{@{}l@{}}Pointwise\\MLP\end{tabular} & \begin{tabular}[c]{@{}l@{}}PointNet\\\cite{qi2017pointnet}\end{tabular} & 40   & 86.0                        & 89.2                      \\
\begin{tabular}[c]{@{}l@{}}Pointwise\\MLP\end{tabular} & \begin{tabular}[c]{@{}l@{}}PointNet++\\\cite{qi2017pointnet++}\end{tabular}    & 12   & -                           & 90.7                      \\
\begin{tabular}[c]{@{}l@{}}Spectral\\GCNN\end{tabular} & \begin{tabular}[c]{@{}l@{}}PointGCN\\\cite{zhang2018graph}\end{tabular}        & 41    & 86.1                        & 89.5                      \\
\begin{tabular}[c]{@{}l@{}}Spectral\\GCNN\end{tabular} & \begin{tabular}[c]{@{}l@{}}RGCNN\\\cite{te2018rgcnn}\end{tabular}             & 22   & 87.3                        & 90.5                      \\
\begin{tabular}[c]{@{}l@{}}Spatial\\GCNN\end{tabular} & \begin{tabular}[c]{@{}l@{}}DGCNN\\\cite{wang2019dgcnn}\end{tabular}        & 21   & \textbf{90.2}               & \textbf{92.9}             \\
\hline
\begin{tabular}[c]{@{}l@{}}Spectral\\GCNN\end{tabular} & GCN (baseline)                      & \textbf{10}   & 84.2                        & 88.7                      \\
\begin{tabular}[c]{@{}l@{}}Spectral\\GCNN\end{tabular} & JLGCN                                & 13   & 87.2                        & 90.8                      \\
\hline
\end{tabular}
\caption{Results of point cloud classification on ModelNet40.} \label{tab:results_modelnet}
\end{table}

\subsubsection{Robustness test}
Further, we test the robustness of our model when the point cloud is of low density.
We randomly drop out points with missing ratios \{0, 0.25, 0.5, 0.75, 0.9\}.
As shown in Fig. \ref{fig:robust_pc}, our model outperforms the GCN baseline significantly, and keeps high accuracy even when the point cloud density is quite low.

\begin{figure}[ht]
\centering
\includegraphics[width=3in]{figures/robust_pc.pdf}
\caption{Robustness test with different missing ratios of point clouds.}
\label{fig:robust_pc}
\end{figure}

\vspace{-0.05in}
\section{Conclusion}
\label{sec:conclude}
We propose joint learning of graphs and node features in Graph Convolutional Neural Networks, which dynamically learns graphs adaptive to the structure of node features in different layers.
In particular, we optimize an underlying graph kernel via distance metric learning with the Mahalanobis distance employed.
The metric matrix is decomposed into a low-dimensional matrix and optimized based on Graph Laplacian Regularizer.
Extensive experiments demonstrate the superiority and robustness of our method in semi-supervised learning and point cloud learning problems.

\bibliographystyle{unsrt}

\nocite{*}

redpajama_set_name: RedPajamaArXiv


With all-wheel drive and electronic stability control, this vehicle easily supports spirited driving maneuvers. A turbocharger further enhances performance, while also preserving fuel economy. Volvo prioritized comfort and style by including: heated front and rear seats, a power liftgate, and the power moon roof opens up the cabin to the natural environment.


text:
Hispasat 36W-1, ранее известный как Hispasat AG1 (Hispasat Advanced Generation 1) — геостационарный спутник связи, который пополнит группировку спутников, управляемых испанской компанией Hispasat. Будет обеспечивать предоставление широкого спектра телекоммуникационных услуг (цифровое телевидение, высокоскоростной интернет, мобильную и фиксированную связь) для Европы, Канарских островов и Южной Америки.

Первый спутник, построенный на базе новой многоцелевой космической платформы для небольших геостационарных спутников SmallGEO, созданной немецкой компанией  в рамках программы Европейского космического агентства  ( — «перспективные исследования в системах телекоммуникаций»).

Основным подрядчиком для создания Hispasat 36W-1 выступила компания OHB System AG, полезной нагрузкой аппарата являются 20 транспондеров Ku-диапазона и 3 транспондера повышенной пропускной способности Ka-диапазона, созданных компанией . Также на спутник установлено инновационное оборудование RedSAT, разработанное компанией Thales Alenia Space, включающее бортовой процессор и настраиваемую антенну, которые позволяет одновременно управлять четырьмя лучами в Ku-диапазоне, для повышения качества сигнала. Кроме того, аппарат оборудован ещё 3 антеннами, двумя подвижными и одной фиксированной. Размеры спутника в сложенном состоянии составляют 3,7 × 1,9 × 2 м, стартовая масса — около 3,2 т. Потребляемая мощность оборудования — 3,4 кВт. Использует химическое топливо для достижения точки стояния на геостационарной орбите и электрические двигатели для удержания и коррекции позиции. Ожидаемый срок службы спутника — не менее 15 лет.

Изначально, запуск спутника был запланирован на 2012 год, но многократно откладывался. Оператором запуска является компания Arianespace. Ожидалось, что спутник будет запущен в конце 2016 года одним из со-пассажиров на ракете-носителе «Ариан-5», но затем было принято решение использовать для запуска ракету-носитель «Союз-СТ-Б».

Запуск состоялся 28 января 2017 года, в 01:03 UTC со стартовой площадки ELS космодрома Куру во Французской Гвиане. После завершения участка работы ракеты-носителя «Союз-СТ-Б», разгонный блок Фрегат МТ произвёл одно включение маршевого двигателя длительностью 18 минут и, спустя 32 минуты после запуска, успешно вывел спутник на геопереходную орбиту. Это первый запуск ракеты «Союз» с космодрома Куру на геопереходную орбиту.

Примечания

Спутники связи на геостационарной орбите
2017 год в космонавтике
2017 год в Испании
Испанские искусственные спутники Земли