I think it has been almost a year since my quest finding the right skincare regime for myself. My skin used to be very dry, my t-zone gets super oily quickly. These are the products that I have repurchase a couple of times this year (they last quite long!) and I believe it had helps improve my skin. I have a better skin now although still in the dry to normal skin type. I don't remember when was the last time I had breakouts. That's good, right?
I cleanse my face daily using Bio-Essence Tanaka White Double Whitening Cleansing Foam. I then put few drops of Bioderma on cotton pad as toner and then I use a pump of Laneige Moisture Balancing Emulsion for my whole face. Pat on Laneige Water Bank Essence serum (daily every night for extra hydration). For moisturiser, I use Laneige Water Bank Moisture cream. Every other night I also use Laneige Water Sleeping Pack. Various times in the month, I also use St Ives Moisturising Olive scrub or Good Things Deep Clean Blackhead Exfoliator depending to what I need at that time. A little bit of skin pampering.


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ENGL 3380 - Studies in the History of Literary Production
Resources to Help with Historical Research
Video: How to Develop Keywords
Provides full-text access to information and background articles about every topic from across Oxford University Press's Dictionaries, Companions and Encyclopedias.
Dates of Coverage: Various
Historical Abstracts
Indexes the worldwide literature (excluding the United States and Canada) for history from 1450 to the present. Includes journals, books and dissertations.
Subject Matter Timeframe: 1450 - Current
America: History and Life
The major index to the history literature of the United States and Canada, from prehistory to the present.
Subject Matter Timeframe: 19th Century- Current
Archive of articles from scholarly journals for arts, sciences, social sciences and humanities. Includes selected book chapters and primary sources.
Dates of Coverage: 1600s - Current
Full text of books and journals from university presses and scholarly societies in the humanities and social sciences.
Date of Foundation: 1993
Literature Criticism Online
Provides literary criticism with a range of modern and historical views on authors and their works worldwide. Covers all time periods and genres, such as poetry, Shakespeare, Medieval literature, short story, drama, and children's literature.
Dates of Coverage: Classical and Medieval Period - Current
URL: https://guides.lib.uoguelph.ca/ENGL3380
Subjects: ENGL - English

redpajama_set_name: RedPajamaCommonCrawl


Got no other gold missing after that.
Last edited by Shaanan on Mon Nov 14, 2016 5:51 pm, edited 1 time in total.
This account was banned because of gold trafficking activities.
can u unban my account, so I can say goodbye to my friends plz?
My account was freez and under investigation just before the shut down of nostalrius.
The response i received said I was banned in a wave for gold trafficking.
I never duped, bought, or sold gold or any items or did any illegal actions in the game.
I contact Hades (GM Elysium) for unban my account, and he said : It is in Nostalrius hands not ours, If they unban the account it will be unbanned here.
Thank you again for taking the time to read my post and i sincerely hope someone can help me.
I have explain my prob before the end of Nostal and got no response.
I didn t bot or try to hack ... i just been Hacked and robbed.
Last edited by Shaanan on Wed Dec 14, 2016 9:33 pm, edited 2 times in total.
Can i have help for my case?
Bring our Shaani back !
I'm having a similar problem. I know your struggle; best of luck to you.


Cody Younker is running for city council in Revelstoke in the October election.
Being a politician is a good way to give back to the community, he said.
Younker moved to Revelstoke in 2015 to be the manager at Save On Foods.
Working for Overwaitea Food Group since 2007, the business took Younker all over B.C., but he said Revelstoke is his favourite city so far.
Now working at Southside Market as an assistant manager, Younker has purchased a house and plans on raising a family in Revelstoke.
Since he was a kid he has been interested in politics. He was voted "most likely to be the Prime Minister of Canada" in Grade 6 and studied political science in law classes in High School.
Always one to follow federal and provincial politics in each of the places he lived, Younker was impressed with the amount of participation in municipal politics in Revelstoke.
He moved here when the mall on the Trans Canada Highway was being proposed and since then has kept a close eye on local political issues.
Nomination period opened Sept. 4 and closes Sept. 14. The campaign period begins Sept. 22. Advanced polls are on Oct. 10 and voting day is Oct. 20.
An All Candidates Forum hosted by the Chamber of Commerce is scheduled for Oct. 2 at the Community Centre.
The inaugural council meeting is scheduled for Nov. 6.
More election coverage to come. Are you running for council? Let us know by emailing jocelyn.doll@revelstokereview.com or calling 250-837-4667 ext. 1407.


Welcome to Joseph Scafidi Inc., custom tailors and shirtmakers.
Here in our club-like shop we assist customers in choosing the perfect wardrobe items from among the finest fabrics and furnishings available for men. Each garment from Joseph Scafidi Inc. is custom-made to individual measurements and painstakingly fitted until even the most rigorous sense of style is satisfied. Of course, since our garments are custom-made for each client, we cannot offer items for purchase on-line. But we invite you to browse this site and to visit us in our shop in downtown Cleveland. If you are unable to come to our store, one of our master tailors would be happy to attend to you at your home or office.
From time to time, we, at Joseph Scafidi, Inc. have a particular event that deserves mention here. Especially at the beginning of each season, we have particular fabrics or seasonal items that we are featuring. Please browse through this page to find out about our current offerings.


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One Song A Day Keeps The Evil Away…
Artist Guides
15/09/2018: "I Get Around" by The Beach Boys
I wasn't around in the early 60s in California, but when I listen to this, I'm right there cruising in a Chevy along the beachfront!
It's incredibly wholesome stuff about a bunch of lads driving around in their car trying to meet girls, which in comparison to today's rather more explicit and probably misogynistic rap songs, is quite adorable.
It's quintessential surf rock, but it's also an enduring classic song, with it's faltering rhythms, vocal harmonies, and tight guitar riffs. It's the Beach Boys doing what they do best. That first vocal "Round round get around, I get around" is one of those melodies that uses its simplicity to great effect.
It's first and foremost pop music, but there's more to it musically than first meets the ear. Not only are there innovative rhythms, but the song has little touches such as the handclaps on the verses which elevate the feeling and help to boost that innate catchiness.
Mick Jagger was a fan of the song and promoted it, which helped it to become the first big success for the band in Britain.
The song is pretty swell, for sure!
CategoriesPop, Rock Tags60s, Pop, Pop Rock, Rock, Surf Rock, The Beach Boys
Previous PostPrevious 14/09/2018: "Hound Dog" by Elvis Presley
Next PostNext 16/09/2018: "Strawberry Fields Forever" by The Beatles
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Hualos Christ as featured on the Rochester Arts in Mission page.
St. Mary's Church, Green Street Green, is a treasure trove of creative arts. There is the beautiful stained glass window that members of the congregation designed and made, there are icons, collages, and altar rail cushions that pick up the same colours coming through the stained glass. The Art Group, led by the vicar, Rev'd. Karl Carpani and the principal artist, Marina Price, have clearly been very busy.
Above the communion table, embedded in the wall, is a beautiful stained glass cross which, in the early morning sun, radiates colours of red, yellow and purple throughout the chancel. The empty cross is a powerful reminder of the resurrected and glorified Christ and in an iconic way, the St. Mary's stained glass cross is a visual representation of the interaction between the Glorified Christ and humanity.
Partly inspired by the work of the sculptor Antony Gormley, Karl begain to explore how the cross could be used as a background for a sculpture of the crucified Christ. He began making sketches to scale in order to visualise the piece and explored using wire and nails as his material. He quickly realised that wire offered strength and stability and yet was flexible enough to shape and bend as required.
The chancel is the darkest area of the church building and without artificial light there are only two small windows high up and the stained glass cross to illuminate the space naturally. The makes the wire sculpture almost invisible to anyone taking a quick glance. However, at certain times of the day the light from the windows and the stained glass subtly light the wire and the image of the crucified Christ seems to appear from nowhere, with the brilliance of the stained glass serving as a background. The two images of Easter, the Crucified Christ and the Empty Cross therefore combine to proclaim the suffering and the glory of Jesus.


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Uber, But for Grand Theft Auto?
By Polly Mosendz On 12/10/14 at 10:44 AM EST
An unusual trademark filing may indicate Uber wants its own version of a popular driving game. Damir Sagolj/Reuters
Tech & Science Uber Taxi Travis Kalanick Sexual assault
Uber filed a curious trademark at the beginning of December for a mobile application, UberDrive, as first reported by Buzzfeed News. Unlike the company's existing e-hailing app, this app is about the make-believe world of driving. The trademark filing describes UberDrive as a "computer game software for mobile devices," which is meant to be used as an "entertainment service." Uber's trademark attorney also filed what may be a new logo, presumably for UberDrive, the same day.
A video game would be a diversion for the company, which has been focused on expanding its core service into Southeast Asian markets. Travis Kalanick, CEO, wrote in a recent blog post that the company had "just raised a financing round of $1.2 billion, with additional capacity remaining for strategic investments. This financing will allow Uber to make substantial investments, particularly in the Asia-Pacific region. This kind of growth has also come with significant growing pains. The events of the recent weeks have shown us that we also need to invest in internal growth and change." Kalanick did not mention gaming as a potential area of expansion in the post.
Grand Theft Auto, one of the best known video games that features driving, has sold over $2 billion worth in copies for its most recent installment, and it has amassed almost 18,000 ratings on its mobile application.
This filing could also, of course, mean nothing. Many companies, including the likes of Facebook and Apple, sit on a trademark or patent for years without use. Uber replied, "No comment," to the matter.
Here's How Long It Takes to Get an Uber in U.S. Cities


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var configDB = require('./knexfile.js');
var Knex = require('knex')(configDB);
var Bookshelf = require('bookshelf')(Knex);
Bookshelf.plugin( 'registry' );
Bookshelf.plugin('pagination');

module.exports = Bookshelf;

redpajama_set_name: RedPajamaGithub


Our collection of decorator wind spinners and twists are made to use outdoors or in. This selection of Birdhouse and Apple or Eagle, Airplane & Butterfly twists and wind spinners are great.
Each comes with a dimensional character at the top and a spiral attached underneath them.


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Climate Shocks Worsening Somalia's Development
The recent floods in Somalia have disrupted law and order and displaced over 370,000 people, with the UN estimating just under half a million people to have been affected. Farming communities have been wiped off the map and many remain without food, water or shelter. The crisis contrasts sharply with events earlier this year when the nation faced one of the driest seasons on record.
The Gu (rainy) season produced some of the lowest cereal harvests in decades and rainfall fell well below expectations. Somalia is experiencing continuous waves of climatic shocks; the region is described as one of the worst-affected areas subject to climate change, with the UNDP stating that "the country is highly vulnerable to the current and future impacts of climate change.
This vulnerability is further compounded by the fact that the country is coastal, low-lying, poor and disrupted by war." With the world climate expected to become more extreme, these shocks are expected to increase in both frequency and severity, while Somalia already suffers from problems of desertification, overgrazing and deforestation.
Nasra Ismail, director for the Somalian NGO Consortium which consists of over 80 NGOs, described the crisis as "a global responsibility. Climatic shocks are not a local phenomenon but a manifestation of the growing environmental emergency." According to UN estimates, 2 million people remain food-insecure with climate change wreaking havoc upon the region through a series of droughts, floods, and heatwaves, heightening the already violent situation. The country has found itself in a perpetual cycle, resulting in a steady flow of international attention; with almost three decades of disease and extreme weather, the armed conflict has worsened, disrupting the flow of resources, and displacing millions within and around Somalia. The New Humanitarian reports that the failure of seasonal rains affects the livelihoods of its farming communities, on top of clan-based violence and terrorist activities under groups like al-Shabab. Local efforts combatting climate change remain disorganised, with war, corruption and poverty hindering climate-adaptive initiatives in the country; Considering each climatic shock reduces the people's ability to recover, the situation will only continue to worsen.
In 2018 alone, The Norwegian Refugee Council (NRC) reported that 880,000 Somalis have been displaced, 300,000 from violence, and 500,000 from flooding or drought due to climatic shocks. The holding capacity of many of Somalia's rivers has fallen, considering that river levels rise as high as 8 metres in some areas according to the UN Office for Coordination of Humanitarian Affairs (OCHA). OCHA reports that over half the population remains incredibly vulnerable. Three decades of conflict continue to displace civilian populations, farming communities are getting sucked into tribal conflicts decreasing overall production, and humanitarian organisations are facing a challenging, often hostile environment. Considering the recent floods, OCHA reported that "Farmland, infrastructure and roads have been destroyed, and livelihoods disrupted in some of the worst-hit areas." Despite the at least 105,000 affected people, large swaths of territory have had no assistance. Crucial to Somalia's old government was its system of canals which ensured irrigation to farming communities, however perpetual violence has brought them into disrepair.
In the aftermath of the 2016 – 2017 drought, the new government announced a climate emergency proposing new government policies which were widely supported by the EU, UN, and the World Bank, with the central goal of reestablishing the canal system. Water management is central to the crisis, either through flooding or drought: direct action is required for rebuilding and protecting its infrastructure. Although helping to overcome the seemingly complex and difficult challenges with regard to Somalia's poverty, its reliance on international aid and the absence of long-term goals doesn't bolster any effective climate action. Somalia remains one of the poorest countries in the world with long-lasting change requiring effective international action. The UN Food and Agriculture Organisation (FAO) coordinator Andrew Lanyon argues that "If we want to talk about the longer-term solutions that are going to help Somalia better adapt to climate volatility and climate change, that is going to take multi-year investments." Currently the FAO is running projects rehabilitating irrigation systems and water catchment areas although Lanyon notes that current efforts fall short of resolving Somalia's water crisis.
Written by Jonno McPike, Student at Victoria University of Wellington, New Zealand. Focusing on education policy and its role within foreign aid.
Fears over strong storm expected to hit Somalia and Somaliland today
Amisom seeks help of locals to curb recruitment of child soldiers in Somalia


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Home › Posts tagged Energy Minister
Energy Minister
Solar in Esperance – Micro Power Systems Coming.
Posted on March 6, 2019 by Mike	— No Comments ↓
Solar in Esperance – Micro Power Systems will be installed across 14 properties this year to help stabilise their grid and offer access to renewable energy.
Solar in Esperance
Solar in Esperance, WA has been an issue for a while as the existing powerlines are easily downed – winds, trees, or lightning strikes can make for some very expensive repairs.
Esperance its a town on the south coast of Western Australia. The McGowan Government in Western Australia has proposed that 13 Micro Power Systems (MPS) be installed in the area, in order to deliver a "safer, more cost efficient and reliable power supply to remote customers in the Esperance region", according to the official media statement on the Government of Western Australia website.
Rural solar is a big issue in Australia so it's fantastic to see governments working on combating this by offering modern solutions. The MPS' will be supplied by state-owned corporation Horizon Power who are currently tasked with supplying energy to 100,000 residents and 10,000 businesses over a whopping 2.3 million square kilometres, according to Solar Quotes. The MPS devices include solar panels, battery storage and a backup diesel generator in case the battery is empty and the sun's not shining.
Energy Minister Bill Johnston provided some quotes on his website with regards to the new plan:
"The MPS project for Esperance highlights the McGowan Government's commitment to transitioning to renewable energy technologies at the lowest cost possible to taxpayers.
"These farmers are at the fringe of the power grid, east of Esperance and the Condingup area, where reliability isn't as good and power outages take longer to restore" Minister Johnston said.
"The MPS will provide the farmers with more reliable and safe power that will cost the State less to provide."
Solar in Esperance – Energy Minister Bill Johnston
CPS National, an Australian company with over 20 years of experience in critical power and remote area power solutions, will deliver and install the systems.
Construction the on micro power systems will begin in April and is expected to be completed, with the systems fully operational, by the end of this year.
P.S. If you'd like to learn more about the company installing these MPS', I have embedded a video about CPS below.
Flex PowerPlay Smart Solar Power System
Empowering Homes – Interest free solar battery loans in NSW
Broome Solar Power – Horizon Power's Plan
Posted in Government Solar, Government Solar Investment, Rural Solar, Western Australia Solar
Tagged with Bill Johnston, Condingup Solar, CPS National, Energy Minister, Esperance, Esperance Solar, Micro Power Systems, MPS, Rural Solar, Solar in Esperance, Western Australia Solar
National Energy Guarantee Approval – Next Steps
National Energy Guarantee Approval – the NEG has been approved by the states and territories of Australia 'in principle' – allowing it to move to the next step. There's still plenty of discussion to go before we see anything signed off, but it's a step in the right direction for those who believe in the NEG and its ostensible goal of cheaper, more reliable power with less carbon emissions.
National Energy Guarantee Approval
National Energy Guarantee Approval – Malcolm Turnbull (source: yourlifechoices.com.au)
As with most political decisions in this country, there is a lot of posturing and point scoring going on – depending on who you ask, it's either a 'great step forward' or the governments 'withholding support'. Regardless of the case, the Federal Government has now released a draft of the energy bill which will be taken to next week's party room meeting for approval. If you want to learn more about what happened with the NEG during the week, please click here.
The states want to see detailed legislation and some of them have 'red line' conditions which must be met before they fit in to the National Energy Guarantee – there's still a long way before any of this becomes law in Australia.
Victoria were especially strident in their remarks about the NEG. Victoria's Energy Minister, Labor's Lily D'Ambrosio, said agreeing to the plan today would be like signing "with a blindfold on". advising that they won't support it unless the following four demands are met:
The emission reduction targets can only ever increase and must not decrease.
Targets need to be set in regulation (this one's going to be a bit of a problem as Energy Minister Josh Frydenberg has already rejected it).
Emission reduction targets must be set every three years, three years in advance.
Creation of a registry which is transparent and accessible by regulators and governments.
The emissions reduction target in the NEG is to bring down emissions in the electricity sector by 26 per cent by 2030.
COAG Energy Ministers will have another discussion after the Coalition Party Room meeting on Tuesday. Watch this space! We'll keep you posted.
National Energy Guarantee approved by Coalition party room
NEG – National Energy Guarantee and its Australian Future
Solar Grid Parity in Australia – By 2020?
Posted in ACT Solar, Australian Capital Territory Solar, Australian Solar, Australian Solar Farms, Australian Solar Power, Global Solar Investment, Government Solar Investment, National Energy Guarantee, NSW Solar Tariffs, NT Solar, Queensland Solar, Renewable Energy Challenges, renewable energy in resources, Renewable Energy Storage Target, Renewable Energy Targets, Solar Forecasting, Solar Investment, Solar Investment Australia, Solar Power Future in Australia, Solar Power in Australia, Solar Power Statistics, Solar Subsidies Australia, Solar Tariffs, Uncategorized
Tagged with COAG, Energy Minister, Josh Frydenberg, Lily D'Ambrosio, National Energy Guarantee, NEG


Lou Gehrig, July 4, 1939. "I may have had a tough break, but I have an awful lot to live for." Diagnosed with ALS, he would pass away two years later.
You don't speed anywhere in an LVT, you slow.
19 I see what you did there.


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» The Days of Genesis 1, part 1
» The Satanic Baby-Killers of London
» The Matter with "All Lives Matter"
» Global Warming or End Times?
» The Election-Rigging Conspiracy
» The Morality of Capitalism, part 1: Capitalism is Good
» Rasing Successful Kids
» The Morality of Capitalism, part 3: When Government Is Good
» Partisanship Aside
Republican Leadership on Climate Change?
published by Bruce on Wed, 05/13/2015 - 23:00
In these days, when Republican presidential candidates are proud to deny the reality of anthropogenic climate change, and the Republican chairman of the Environment and Public Works Committee is leading denier in all of Washington, it might be hard to remember the time a leading Republican spoke these words:
The issue of climate change respects no border. Its effects cannot be reined in by an army nor advanced by any ideology. Climate change, with its potential to impact every corner of the world, is an issue that must be addressed by the world.
It wasn't long ago when a Republican leader was willing to listen to input from climate scientists...
My Cabinet-level working group has met regularly for the last 10 weeks to review the most recent, most accurate, and most comprehensive science. They have heard from scientists offering a wide spectrum of views. They have reviewed the facts, and they have listened to many theories and suppositions. The working group asked the highly-respected National Academy of Sciences to provide us the most up-to-date information about what is known and about what is not known on the science of climate change.
...when a Republican leader was not afraid to acknowledge the latest science...
First, we know the surface temperature of the earth is warming. It has risen by .6 degrees Celsius over the past 100 years. There was a warming trend from the 1890s to the 1940s. Cooling from the 1940s to the 1970s. And then sharply rising temperatures from the 1970s to today.
There is a natural greenhouse effect that contributes to warming. Greenhouse gases trap heat, and thus warm the earth because they prevent a significant proportion of infrared radiation from escaping into space. Concentration of greenhouse gases, especially CO2, have increased substantially since the beginning of the industrial revolution. And the National Academy of Sciences indicate that the increase is due in large part to human activity.
...to acknowledge the unintended consequences of our actions...
For example, our useful efforts to reduce sulfur emissions may have actually increased warming, because sulfate particles reflect sunlight, bouncing it back into space.
...to acknowledge the limited options for solving the problem...
There are only two ways to stabilize concentration of greenhouse gases. One is to avoid emitting them in the first place; the other is to try to capture them after they're created.
..and to acknowledge the United States' leading role in causing the problem.
Our country, the United States is the world's largest emitter of manmade greenhouse gases. We account for almost 20 percent of the world's man-made greenhouse emissions.
At the beginning of his first term in office, President George W. Bush could say, with all seriousness:
This is a challenge that requires a 100 percent effort; ours, and the rest of the world's.
Of course, he said all these things in the context of explaining why the United States would not be supporting the Kyoto Protocol. Bush argued that any solution would need to balance the environment and the economy, and that the burden placed on the U. S. economy by the Kyoto Protocol was too great compared to the possible environmental benefits.
Bush was rightly criticized by environmentalist groups for ignoring the implications of the science. For today's Republican candidates, to even acknowledge the work done by climate scientists would require more courage than any of them have yet shown.


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Trump Picks Reince Priebus As Chief Of Staff, Steve Bannon As Chief Strategist
"I am thrilled to have my very successful team continue with me in leading our country," Trump said.
By Hamza Shaban and David Mack
Hamza Shaban BuzzFeed News Reporter David Mack BuzzFeed News Reporter
Jim Watson / AFP / Getty Images
President-elect Donald Trump on Sunday picked Reince Priebus, the chairman of the Republican National Committee, to be his White House chief of staff and appointed former campaign chairman and conservative media firebrand Steve Bannon his chief strategist and a senior counselor.
In a list of potential Trump cabinet picks previously obtained by BuzzFeed News, Priebus was the only candidate listed for the position. However, Trump advisers had indicated that Breitbart News executive Bannon was also in the running for the job.
Priebus and Bannon are the first two people to officially join the Trump administration. "Steve and Reince are highly qualified leaders who worked well together on our campaign and led us to a historic victory. Now I will have them both with me in the White House as we work to make America great again," Trump said in a statement.
Both men said they were thankful for the chance to serve in Trump's White House.
"I want to thank President-elect Trump for the opportunity to work with Reince in driving the agenda of the Trump Administration," said Bannon. "We had a very successful partnership on the campaign, one that led to victory. We will have that same partnership in working to help President-elect Trump achieve his agenda."
"It is truly an honor to join President-elect Trump in the White House as his Chief of Staff," said Priebus. "I am very grateful to the President-elect for this opportunity to serve him and this nation as we work to create an economy that works for everyone, secure our borders, repeal and replace Obamacare, and destroy radical Islamic terrorism. He will be a great President for all Americans."
Bannon in Trump Tower on Friday.
Unlike other mainstream Republicans, Priebus maintained a close relationship with Trump during the chaotic 2016 presidential election campaign, urging the businessman to tone down his controversial comments while repeatedly pleading with his party members to unite behind their nominee.
During Trump's victory's speech early Wednesday morning, the president-elect singled out Priebus for his assistance during the campaign, saying, "Reince is really a star and he is the hardest-working guy."
The position of chief of staff is a hugely influential role during any administration, with the officeholder serving as a form of gatekeeper to the president and providing advice.
Despite this, Bannon's appointment was listed first in the statement released by the Trump team. Before taking the reins of the Trump campaign in August, Bannon helmed Breitbart News, a right-wing media outlet known, in part, for its critical coverage of establishment conservatives. Bannon has framed Trump's rise as part of a "sea change in American politics," connected to a global nationalist wave.
Some political observers on the left voiced their dismay to the announcement. David Axelrod, the former top adviser and chief campaign strategist to President Barack Obama said Bannon's appointment is "deeply troubling." Jon Lovett, a former Obama speechwriter said, "My stomach dropped when I saw the Bannon news, but not because I was surprised. It's just our worst fears in the grim wide open."
Democratic leader Nancy Pelosi said in a statement Monday, "Bringing Steve Bannon into the White House is an alarming signal that President-elect Trump remains committed to the hateful and divisive vision that defined his campaign."
Nancy Pelosi @NancyPelosi
Trump's naming of Steven Bannon is an alarming signal of his commitment to a hateful and divisive vision.… https://t.co/yz13vG5dL6
John Weaver, the top campaign strategist for Republican presidential contender and Ohio Govenor Jon Kasich, was even more critical. "The racist, fascist extreme right is represented footsteps from the Oval Office. Be very vigilant America," he said on Twitter, referring to Bannon's new post.
John Weaver @JWGOP
The racist, fascist extreme right is represented footsteps from the Oval Office. Be very vigilant America.
Just to be clear news media, the next president named a racist, anti-semite as the co-equal of the chief of staff. #NotNormal
In a statement released Sunday night, the head of the Anti-Defamation League, Jonathan Greenblatt, said the organization "strongly opposes the appointment of Steve Bannon."
"It is a sad day when a man who presided over the premier website of the 'alt-right' — a loose-knit group of white nationalists and unabashed anti-Semites and racists — is slated to be a senior staff member in the 'people's house,'" Greenblatt said.
In a statement, J Street condemned Trump's appointment of Bannon, calling the decision "alarming and unacceptable."
Hamza Shaban @hshaban
J Street tells @BuzzFeedNews it condemns the appointment of Steve Bannon to the Tump White House. "alarming and una… https://t.co/VucGYRhJE3
In response to a tweet saying they were "apoplectic" about Bannon's appointment, a spokesperson for the American Israel Public Affairs Committee (AIPAC) told BuzzFeed News, "AIPAC has a long-standing policy of not taking positions on presidential appointments — any suggestions or rumors to the contrary either privately or publicly are completely false."
Jonathan Franks @jonfranks
Sources: @AIPAC is privately apoplectic about Steve Bannon appointment
A spokesperson for the RNC did not initially respond to a request for comment as to who would serve as the interim RNC chair.
"The Bannon-Priebus team is a tremendous choice by President elect Trump," Newt Gingrich said on Twitter after the announcement. "Bannon for key strategies, Priebus for daily management. Great team."
Gingrich is among other names being considered for the Trump cabinet, according to a list obtained by BuzzFeed News, including Chris Christie, Ben Carson, Sarah Palin, Rudy Giuliani, and Mike Huckabee.
Secretary Of Education Ben Carson? Here's A List Of Potential Trump Cabinet Picks
Hamza Shaban is a technology policy reporter for BuzzFeed News and is based in Washington, DC.
Contact Hamza Shaban at Hamza.Shaban@buzzfeed.com.
David Mack is a deputy director of breaking news for BuzzFeed News and is based in New York.
Contact David Mack at david.mack@buzzfeed.com.


Cillian Vallely, piper of Irish supergroup Lúnasa, has released The Raven's Rock, his solo debut. It features a number of all-star players, including his brothers Niall & Caoimhin Vallely, Sean Óg Graham & Ryan McGiver on guitar and fiddler Jeremy Kittel.
Cillian Vallely, piper of the trad supergroup Lúnasa, has released The Raven's Rock, his solo debut. Featuring a number of all-star players, including his brothers Niall Vallely & Caoimhin Vallely (Buille), Sean Óg Graham (Beoga, Uláid), Paul Meehan (Lúnasa, At First Light), Ryan McGiver, Jeremy Kittel (Jeremy Kittel Trio), and others, the album offers a ten track mixture of traditional tunes and some of Cillian's original compositions played with a contemporary flair. Vallely's music is widely admired and critically acclaimed. His mastery of chanter, drones and regulators gives him a distinctive voice and a reputation as one of Irish music's top uilleann pipers, things that are very much apparent on The Raven's Rock.


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Weapons, Long Range, Great Three,
Original Weapons
Highest Range
Max Stat Weapon
Click-And-Release Weapon
Projectile Based Weapon
Ion/Charged Gas/Electricity Based Projectile
Existing Weapon
The Robots' Weapon
Raze 2 Demo Weapon
"Red Weapon"
Red Spark Weapon
Soft Zapping Sound
The railgun shooting. Notice that several red circles travel slowly along the path that the railgun shot.
A battery powered sniper rifle that creates a particle accelerated beam that is capable of passing through many enemies, as long as they are in a straight line.
40% Damage Close Range to 90% Damage Long Range
3 Shots x 2 Clips
The Railgun is a black gun with red lines on the whole gun, which looks remarkably like the .50 Caliber Sniper and is battery powered. The Railgun fires a red beam of energy that is capable of piercing opponents. Each of its shots deal 40-90% damage depending on how far the shot needs to travel. Due to the gun's lack of power and speed, the Railgun is essentially outclassed when compared to other guns. The Electro Bolt, the only other long range weapon capable of hitting multiple opponents, is a slightly better alternative than this weapon if in high enemy concentrations. This weapon is great for Capture the Flag, as demonstrated in the photo. Best practised in Frostbite and the Mine.
I was able to shoot right through corpses, thus giving me an advantage when corpses are in the way.
During Raze 1, this weapon was one of the "The Great Three", weapons of mass destruction (others being the Rocket Launcher and Icy Chaingun). However, the Railgun had its power greatly reduced during the revamp which also made it look completely different. If you do not know how to use this gun wisely, it will be a useless weapon to you. In small maps. It is very effective to reach low-health enemies, but rather useless in any other kind of combat. It also fires blue lightning instead of a red line. The sound it produces is a soft "ZEAW!" unlike the railgun of Raze 1, which produces a sound like a loud power generator starting up. In the Raze 2 demo, the railgun (Image) was the .50 cal sniper.
The clips of the railgun. The railgun of Raze 1 had double the power per shot, and can store way more clips.
Interesting facts Edit
The railgun now looks much bigger than the one in Raze 1 and can toss enemies further
Best used in places where corpses are common, as they can shoot through corpses, thus rendering it as a shield.
Tosses corpses very far compared to other guns.
Does not have a visible "fire-again" bar. Look closely at the gun from Raze 1. You will notice that after you shoot, a red bar appears that slowly turns blue. Barely 1 second later, you can fire the gun
Transport Bots make this weapon look like the ultimate weapon, which is...not!!!
It isn't actually that simple adding batteries to a sniper rifle. The entire interior transforms from a sniper's interior to 2 superconducting tubes carrying lots of electricity. In addition, most of the room would be batteries and only the front barrel is the rail.
Adrenaline Boost
Reality Edit
A railgun is a electrical gun that accelerates a conductive projectile along a pair of metal rails using the same principles as the homopolar motor. Railguns use two sliding or rolling contacts that permit a large electric current to pass through the projectile. This current interacts with the strong magnetic fields generated by the rails and this accelerates the projectile. Particular characteristics are the lack of propellant (only the projectile and the electrical energy to launch it are required to be expended) and the ability to launch projectiles much faster than firearms-based technology allows.
Railguns have long existed as experimental and demonstrator technology. However, in recent years, some are moving towards becoming feasible military technology. For example, in the late 2000s, the US Navy tested a railgun that accelerates a 3.2 kg (7 pound) projectile to approximately 2.4 kilometres per second (5,400 mph). They gave the project the Latin motto "Velocitas Eradico", which they translate as "Speed Destroys" but can more accurately be translated "I am Speed and I Destroy," or something similar.
The railgun was planned to be put onto US navy destroyers and cruisers and uses new technology that will both increase the lethalness of the projectile and save money at the same time. This technology is called "The Kinetic Warhead" because the projectile is travelling so fast that there is no need for an explosive. All it needs is the kinetic energy to accelerate the projectile, same with future Anti-satalite weapons missiles. This saves money on explosive projectiles and it's also "A million dollars a shot". Railguns can accelerate projectiles so fast that there is no need for "a million dollars a shot". However, the electrical current required to accelerate the projectile that fast is immense and the gun is huge, the railgun will have to await the dawn of fusion recactors capable of
How a railgun works
releasing more energy than it is put into it. Fusion technology is the key to railguns of the future. There is another technological drawback and its materials. We need lighter materials to build ships capable of carrying multiple railguns. Future railgun destroyers will probably be modified supercarriers like the USS Enterprise or the USS Nimitz.
(Note- The railgun is made for projectile speed, not to shoot through people. Its electrical current is only present to boost its speed. It can't make the projectile shoot through the enemies.
Weapons of Raze Two
M9 Barret, Plasma Pistol, Ripper, Golden Mauser
Uzi, Magnum, Burst Torch, Photon Blaster, Vamp Charger
Shotgun, Plasma Shot, Zapper, Terminator, Particle Cannon
Assault Rifle, Bubble Blaster, Precision Laser, Hail Storm, Assault Bouncer
.50 Cal Sniper, Railgun, Pulsator, Holy Grail, Electro Bolt
Laser Minigun, Shredder, Focus Beam, Destroyer, Flamethrower
Rocket Launcher, Ion Cannon, Grenade Launcher, Ice Hunter, Acid Hound, Credit Cannon
Retrieved from "https://razetwo.fandom.com/wiki/Railgun?oldid=10593"
Great Three


The Government should allow international travellers to carry controlled drugs if they are legally prescribed by an authorised person in their home countries.
This is the proposal by the Samoa Law Reform Commission in their Drug Reform report released in December last year.
"Samoa does not have a provision under Samoa's Narcotics Act exempting travellers who are authorised by their home countries to carry drugs for treatment services to another country," the report reads.
"Currently, any illegal drug discovered at the border by Customs are seized and detained.
According to the S.L.R.C. report, in the 1990s there was a case where an Australian passenger was travelling to Samoa carrying illegal drugs.
This person was allowed to carry such drugs into Samoa on the basis that it was authorised by his doctor back in Australia.
"Tonga and New Zealand both have exemptions for international travellers who carry with them illegal drugs required for treating a medical condition (or for a person under their care as in N.Z.), and the drug has been lawfully supplied in the country of origin by a treating medical practitioner and it is no more than a one month supply.
The Commission sought submissions on whether the new drugs framework should contain such a provision regarding international traveller exemption from carrying illegal drugs.
"The common view was that Samoa should allow international travellers to carry controlled drugs if legally prescribed by an authorised person in their home countries.
"One submitter expressed the view that cannabis for instance, is now known to have good uses for cancer, and that the world may be heading this way particularly with the rise of cancer cases globally.
"Therefore, more and more people are relying on these illegal drugs for treatment and take it with them while travelling.
"It was suggested that the N.Z. approach could be used as guidance because it is reasonable.
"However it was strongly emphasised that there should also be strict guidelines and conditions in place to effectively regulate such cases.
The S.L.R.C. report says that one submitter noted that further review/discussions of the issue is needed before a decision to exempt international travellers is made.
Yet another suggested that such exemption should not be included in the law at all.
"The Commission notes that the international travellers' exemptions allow travellers to carry with them drugs that are otherwise prohibited in the country of destination, provided that such drugs are accompanied by a certificate issued by a medical practitioner or a relevant authorised body in their country of origin.
The S.L.R.C. recommended the new drugs framework should include a provision exempting international travellers who have been authorised to carry illegal drugs for treatment when entering Samoa similar to Tonga and New Zealand.
Last month, Ministry of Health Director, Leausa Dr. Take Naseri said a medical card is not valid in Samoa.
"Overseas, if you are prescribed to use marijuana medically, you must have a medical card to show at border control.
He said a cancer patient from overseas requested to grow marijuana at their residence for pain relief.
"It's impossible for us to allow that.


SDLNet_GenericSocket -- a generic type for UDP and TCP sockets.
ready Non-zero when data is ready to be read, or a server socket has a connection attempt ready to be accepted.
This data type is able to be used for both UDPsocket and TCPsocket types.
After calling SDLNet_CheckSockets, if this socket is in SDLNet_SocketSet used, the ready will be set according to activity on the socket. This is the only real use for this type, as it doesn't help you know what type of socket it is.


text:
Announcements · Summer Festivals
Odesza, Illenium, Zeds Dead, Above & Beyond to pump up Electric Forest 2023
Internationally acclaimed electronic music stars once again will dominate the Rothbury, Mich., landscape come June 22-25, along with The String Cheese Incident. Tix on sale Friday.
Sherwood Court: One of Electric Forest's Biggest Stages will light up again in June. (Photo/Derek Ketchum)
SCROLL DOWN FOR ELECTRIC FOREST LINEUP POSTER
Racking up literally hundreds of millions of listens on Spotify, the electronic music stars set to play Electric Forest 2023 represent some of the most popular DJs and artists on the international festival scene.
Odesza, Illenium, Zeds Dead and Above & Beyond will headline next June's festival in Rothbury, along with "house band" The String Cheese Incident, which will play two sets over the four-day event.
Jamie XX, Sofi Tukker, Gryffin, Lane 8, Ganja White Night, Chris Lake, Chromeo, Rezz, Goose and Madeon also pump up the initial 2023 lineup announced by organizers, with festival passes going on sale Friday (Dec. 9).
Popular returnees will include Dixon's Violin and Gorgon City, who are among more than 50 acts revealed thus far for the June 22-25 festival at Double JJ Resort that hosts upwards of 50,000 attendees each year.
Wristbands start at $485 (plus fees) for general admission, with a variety of more expensive camping options ranging from $725 to $2,949, and cabins/log homes priced as high as $8,239. Get details online at electricforest.com.
Heading Back to Michigan: Odesza played Grand Rapids' Breakaway Festival in 2018. (Photo/Kendra Petersen-Kamp)
Passes usually sell out the first day that they're offered for sale by Electric Forest.
Reaction from fans to the lineup announcement included many expressing disappointment that Colorado's The String Cheese Incident would only be playing two sets for the 2023 event, as opposed to the usual three or four of past years. Some questioned the EDM-heavy lineup and urged the addition of more jam bands.
"This lineup is so recycled," fan Will Huber wrote on Facebook. "It feels like I blasted back to 2015. All of those guys have played EF multiple times and in my mind they're all old news."
Others insisted the Electric Forest festival atmosphere remains the main draw, regardless of the lineup.
"Kinda hard to not have an amazing experience at Forest regardless of the lineup," wrote Michael Ritchie. "Just go enjoy the vibes and everything the forest has to offer."
Regarded as Michigan's biggest music festival, the 2022 spectacle featured sets by the likes of GRiZ, The Disco Biscuits, Sylvan Esso, Big Gigantic, Louis The Child, Lotus and many more performing on a half-dozen different stages on the sprawling West Michigan site.
Revisit Local Spins coverage of Electric Forest 2022.
In addition to performances on several stages, the festival features a silent disco, daily yoga sessions and various curated events during the weekend.
THE INITIAL 2023 ELECTRIC FOREST LINEUP>


247 Drainage in Wouldham is a company built on efficiency and making sure that every client has access to top quality and affordable drain unblocking services. As professionals, we know how difficult it is to live with blocked drains in any property, which is why we always do our best to provide quick and effective service. We experienced with handling emergency drainage issues. Since 247 Drainage in Wouldham do not charge for call-outs, we are the preferred drainage/unblocking solution for thousands of Wouldham residents. For any drainage issues throughout Wouldham, you can count on 247 Drainage UK to arrive within the hour and solve the problem.
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Cream cheese, dried cherries and lemon zest give this healthy oatmeal recipe cheesecake-like flavor. Short on time in the morning? Try our overnight oatmeal variation.
Bring water and salt to a boil in a small saucepan. Stir in oats, reduce heat to medium and cook, stirring occasionally, until most of the liquid is absorbed, about 5 minutes. Remove from heat, cover and let stand 2 to 3 minutes. Top with cream cheese, cherries, walnuts, sugar and lemon zest.
This is a wonderful combination. I used fat free cream cheese and it was great. Didn't even add the sugar and didn't miss it.


text:
Bloodbound – Creatures Of The Dark Realm – Album Review
Artist: Bloodbound
Album Title: Creatures Of The Dark Realm
Label: AFM Records
Sweden's Bloodbound are another of those bands that has always been at the periphery of my knowledge. I have dipped into their back catalogue from time to time since their formation just after the Millennium, and it's a hefty body of work, with eight previous full-length records released, from the 2005 debut, 'Nosferatu', to 'Rise Of The Dragon Empire' that saw the light of day in 2019. Add to that a 2020 EP, and it's fair to say that Bloodbound are one of the most prolific bands out there in the power metal scene. But despite a love of power metal, Bloodbound have never really ignited a fire within me. With the imminent release of album number nine, 'Creatures Of The Dark Realm', I felt it was a good opportunity to see if the sextet could finally convert me.
Let's start with the positives: the production is great, the band are as tight as a duck's behind, and there's a real energy to the musical output. It could be argued that I'm damning Bloodbound with faint praise, but actually, these three things go a long way to ensuring that any album is going to be of a high quality. The fact that the only new member in the last five years is the drummer, Daniel Sjögren, means that Bloodbound are a tightly honed unit, with a clear identity and modus operandi. Added to which, the principle songwriting trio of vocalist Patrik J. Selleby, guitarist Tomas Olsson, and keyboardist Fredrik Bergh hasn't changed for a decade, with 'Creatures Of The Dark Realm' once again written by these three comrades.
When you think of power metal, it is natural to expect a ballad or two, especially when an album is comprised of twelve tracks. Not so here. 'Creatures Of The Dark Realm' has shunned the ballad in favour of creating an entire album full of driving power metal anthems. To use the cliché, it's pedal to the metal' from the point at which the brief acoustic instrumental intro piece subsides, to the very end of closer, 'The Wicked And The Weak'. This is pure, unadulterated European power metal with a decent symphonic, epic flavour.
Another positive element of this album is that it is undeniably catchy. Each song is laced with plenty of hooks, or a big chorus to wrap your ears around. I like the fact that there's a nice balance within the music, so that no single instrument seeks to hog the limelight. Each musician can be heard, and each brings forth an element that's integral to the overall sound, be it a thundering double pedal drum assault, a heavy riff, a satisfying bass rhythm, or a keyboard embellishment to add texture or atmosphere to the music. And, with Selleby, Bloodbound are blessed with a highly competent vocalist that can hit the high notes whilst injecting passion into his performances.
I know you could sense it coming, so here it is:…but….
For all of the histrionics, for all of the energy, for all of the melody, I just don't engage with the music as much as I wanted to. Yes it's catchy with hooks galore, but the music feels a little too easy, safe, and saccharine for my tastes; there's a lack of an edge to the music that I was hoping I might discover with repeated spins. The melodies don't resonate however hard I try. I normally like to lace my reviews with a more in-depth description of some or occasionally all of the individual tracks. However, on this occasion, I feel it's a little redundant as an exercise. It's unfair to say 'hear one, hear them all', because there is a slight ebb and flow, as well as subtle differences within some of the tracks. The fact remains though, that you're either going to love or hate this record after the completion of the opening duo that consists of the title track and 'When Fate Is Calling'. That's because Bloodbound have a distinct, relatively narrow style and the album follows this path pretty closely throughout.
It would be unfair of me to say that 'Creatures Of The Dark Realm' is not enjoyable, because that's not the case. At times, I do find myself nodding my head along with the music, or tapping my foot, but that's because there are some solid riffs to be heard, as well as some rousing choruses on the album. After all, you'd have to be dead or unconscious to not feel something when listening to such a catchy record. It's just that the album never stops me dead in my tracks or delivers a goosebump inducing moment. I smile, I nod appreciatively, but I don't give myself over to the music entirely. It does its thing in the background whilst I work, do the housework, or walk the dog. Headphones or speakers, it makes no difference – the music is just there. I don't want to say that it's background music, but you get the idea.
I have tried. I came to this album with as open a mind as possible. I wanted to like it. And up to a point, I do. But I've not been blown away by Bloodbound or 'Creatures Of The Dark Realm' as much as I hoped or wanted to be. There is definitely a market for this style of music though, and for those of you who enjoy up-tempo, rousing, and melodic European power metal, I'm sure this will sit very well with you. Unfortunately, it's just doesn't do enough for me.
Posted in Album Review, Albums, Entertainment, Melodic Metal, Music, Power MetalTagged AFM Records, Album Review, Bloodbound, Creatures Of The Dark Realm, manofmuchmetal, Melodic Metal, metal, music, power metal, review
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@vivlalu Hey Viv, I really do like it yes. So much to listen to & enjoy! I appreciate your kind words, but never fo… twitter.com/i/web/status/1… 40 minutes ago


text:
\section{Introduction}

The impressive performance of a deep neural network depends largely on its architecture, which is usually designed by a handful of human experts with extensive knowledge in designing neural architectures. Unfortunately, this knowledge is not possessed by most interested users.  Therefore, how to automatically design a high-performance neural architecture has always been the interest of researchers. As the mainstream method, neural architecture search (NAS)\cite{elsken2019neural} formulate the architecture design as optimization problems, via solving these problems with well-designed optimization algorithms, the promising neural architectures are obtained\cite{DARTS, sun2020automatically, xie2018snas, sun2019completely}.

Affected by the optimization algorithms adopted, different NAS algorithms vary in the execution processes. Specifically, reinforcement learning (RL)\cite{kaelbling1996reinforcement} based NAS algorithms\cite{RL_NAS, zoph2016neural} use a controller to sample child networks, these child networks are trained to obtain the accuracies as rewards for controller. By maximizing these rewards, The controller learns to design satisfying neural architectures. Gradient-based NAS algorithms\cite{DARTS} relaxes the candidate operations with softmax to make the search space continuous, in order to use gradient descent to optimize the architecture with respect to its performance. Evolutionary computation (EC)\cite{back1997handbook}-based NAS algorithms\cite{real2019regularized, sun2019evolving} start with an initial population of neural architectures and generate better architectures iteratively by exploiting EC techniques. However, these NAS algorithms all have a common feature, they are constrained by the demands of enormous computation resources.

NAS algorithms essentially consist of three parts: sampling, evaluating and updating. The time-consuming evaluation process is regarded by many researchers as the culprit for extensive computing resource requirements. Although various methods such as performance predictor\cite{sun2021novel, tang2020semi, liu2021homogeneous}, early stopping policy\cite{sun2018particle}  and  weight inheritance\cite{cai2018efficient, real2017large} have been proposed to speed up this process.  Regrettably, these methods do not solve the problem completely, because they failed to change the search-refine nature of the NAS. In addition, this search characteristic leads to another problem: the architecture design knowledge NAS learned during search is not transferable. When the search space changes, the distribution of network performance also changes. NAS algorithms need to be run from scratch again to obtain satisfactory results. This further causes the inefficiency of NAS.

To overcome the aforementioned limitations, we start from another direction to solve the problem of automatic neural architecture design. Different from those search methods, we learn from existing network structures and build a sort of (neural architecture, link guidance) knowledge repository which could provide link guidance based on the current network structure. By leveraging the feature extraction capability of graph neural networks (GNNs), we further introduce a GNN-based link prediction model to make discrete information in the knowledge repository continuous. In this way, the proposed method can design neural architecture in a computationally efficient way, and  could deal with unseen network structures even the search space changed. The contributions of this work could be summarized as follows:

\begin{itemize}

\item We propose a novel approach to automatically design deep architectures dubbed as NAL(Neural Architecture Learning). Different from NAS approach that refine architectures through expensive performance evaluation, the proposed approach can directly achieve automation through learning knowledge of constructing high-performance deep architectures.

\item We implement the proposed approach through a GNN-based link prediction model. We further present an GNN upon the model to inversely aggregate node information for the node perception. This design could smooth the automation process.

\item We show that NAL can efficiently and effectively automate neural architecture for CIFAR-10 when the knowledge is obtained from NAS-Bench-101, and also be able to construct well-performing architecture for ImageNet when this knowledge is transferred to DARTS search space.

\end{itemize}

\section{Related Works}
This work is close to link prediction, cell-based neural architecture design, and representation learning on neural networks, which are briefly introduced in this section.
\subsection{Link Prediction}

Link prediction is to predict whether an edge exists between two entities in a network. It is closely related  to various real-world application such as knowledge graph completion\cite{knowledgegraph1, knowledgegraph2} , recommend system\cite{recommender1, recommender2, recommender3}, and biological network reconstruction\cite{oyetunde2017boostgapfill}. As effective approaches for link prediction, Heuristic methods use various score functions to estimate the likelihood of links\cite{lu2011link}. Depending on the number of hop when calculating node's neighborhood, existing heuristics can be classified into first-order\cite{barabasi1999emergence}, second-order\cite{zhou2009predicting} and high-order\cite{jeh2002simrank}. \citet{lu2011link} has shown high-order heuristics performing better than first and second-order ones, and it has been proved by \cite{zhang2018link} that a large h is not necessarily needed to learn high-order graph structure features. As a matter of fact, these link prediction heuristics belong to graph structure features, which are located within the node and edge structures of the network. A number of algorithms have been proposed to extract the graph structure features, \citet{zhang2017weisfeiler} proposed Weisfeiler-Lehman neural machine to automatically learn suitable features from a network. They extract subgraph in nodes' neighborhood and train a neural network on these subgraph. For better graph feature extracting ability,  researchers began to focus on using GNN to handle this problem and achieving promising result\cite{zhang2018end, defferrard2016convolutional}. In this work we use Graph Attention Networks (GATs)\cite{velickovic2017graph} as our GNN model. With the help of attention mechanisms, GAT could highlight important information while reduce negative impact of noise in a automatic manner,which is exactly what we want when predicting the links in neural networks.

\subsection{Cell-based Neural Architecture Design}
Motivated by those well-performing neural networks\cite{he2016deep, huang2017densely} that have been artificially designed by stacking repeated motifs, \citet{zhong2018practical, RL_NAS} propose to form the final architecture by stacking the computation cells in a predefined way. Compared with layer-based and block-based neural architectures, this construction strategy makes the design of neural networks more dexterous and simple. Besides, it has been experimentally demonstrated by \cite{RL_NAS, szegedy2016rethinking, DARTS} that cell-based architectures could achieve superior performance and could be easily transferred to other datasets. Based on these advantages, we exploit the knowledge from existing computation cells and utilize it to design high-performance cell-based architectures.

\subsection{Representation Learning on neural networks}
With the field of GNNs\cite{wu2020comprehensive, gori2005new, scarselli2008graph} which has seen a steep rise in interest, some inspiring researches\cite{dudziak2020brp} apply GNN to learn representation of neural networks. Specifically,
\citet{li2020neural} combined GNN with variational autoencoder to map neural architecture to a continuous representation, then training a regression model to fit the performance of the continuous representation. \citet{knyazev2021parameter} leverages GNN to learn the representation of neural architectures and proposes a model that could directly predict the parameters of the input architectures. \citet{wen2020neural} uses Graph Convolutional Networks to generate feature embedding of the neural network architectures and pair these embeddings with their accuracies to train a neural predictor. They predict a large number of random architectures in the search space and fully train the top-K best predicted models, after which the best model is chosen as the final output. In contrast, we employ GAT to generate node representation in neural architectures, and predict beneficial link existence based on these node representations to design promising neural network.

\section{The proposed method}

The proposed NAL is a new perspective for automating neural architecture, the methodology is uniform and can be generalized to various Spaces. NAL constructs an experience database by exploiting the knowledge of existing network structure, based on these experiences, it could automatically design neural networks by predicting appropriate links. In the following section, we describe the details of NAL, which is composed of three parts: knowledge representation, experience storing, and structure prediction.

\subsection{knowledge representation}
\label{3.1}
\begin{figure}[htp]
    \centering
    \includegraphics[width=1\textwidth]{3.1.pdf}
    \caption{An illustration to the knowledge representation part of NAL. }
    \label{fig1}
\end{figure}

The ultimate goal is to automat neural architecture design under the guidance of NAL, to achieve this goal, we need an appropriate way to express the knowledge of designing. Neural architecture is made up of operations and the links between them, In a given search space, the optional operations are fixed but the links between operations are uncertain. These links have a significant impact on the performance of the network, therefore, we decided to learn the design knowledge by studying links in existing networks. As shown in Figure \ref{fig1}, We select some high-performance and low-performance neural architectures to construct positive and negative training samples. Then randomly take out a link in the network architecture, and mark the source node and target node corresponding to this link. This marked incomplete network will be combined with its label to form a training sample.  These training samples will be used to train a GNN-based link prediction model, the model takes these incomplete network structures as inputs and predicts the links between the marked source nodes and target nodes. In order to make this link prediction model learn more rich design knowledge, we carefully designed the division of positive and negative samples for training data. For building positive samples,  we choose $K$ neural architectures in NasBench-101\cite{NASBench-101} with the highest test accuracy, each link in these architectures is taken out to form a  positive sample. In addition to this, we take apart the network structure step by step to mimic the network construction process, each time we remove an edge from the current network structure and form a  new sample, we randomly take a portion of them to join in the positive samples. For building negative samples, we take the same number $K$ of low accuracy network structures and form the negative samples in the same way we build positive samples. Furthermore, edges that do not exist in those highest test accuracy neural are also taken as negative samples. The final training data has consisted of the positive samples and the negative samples from the above process.

\subsection{Experience storing}
\begin{figure}[htp]
    \centering
    \includegraphics[width=1\textwidth]{3.2.pdf}
    \caption{An illustration to the Experience storing part of NAL.}
    \label{fig2}
\end{figure}
To learn and store experience from these training samples, we modify a GNN link prediction model to adapt to the task of link prediction in neural networks. As shown in Figure \ref{fig2}, a neural network architecture is represented by a DAG that uses nodes to represent operations and edges to represent the connections, the first step is to transform the representation of neural network architecture in order to facilitate the calculation of GNN model. We use the one-hot adjacency matrix encoding strategy which is commonly used in NAS researches, the $N$ nodes are encoded based on their corresponding operation by a one-hot vector $V =  \left\{V_1, V_2, ..., V_N \right\},  V\in \mathbb{R}^{N\times D} $, where $D$ is the number of possible operation types, and the connections of these nodes are contained in an adjacency matrix $A \in \mathbb{R}^{N\times N}$. Then the GNN model $\phi$ will compute representation $h$ for each node $i$ based on $V$ and $A$:
\begin{equation}
h_{i} = \phi (A, V)
\end{equation}
In neural networks, different operations have different meanings, their importance can not simply be equated. With this consideration, we choose the graph attention network model with three GAT layers as our GNN model. The input to GAT layer is a set of node embeddings $ h^{(l)} = \left\{ h_1^{(l)}, h_2^{(l)}, ..., h_N^{(l)}\right\}, h^{(l)}\in \mathbb{R}^F$ , where $l$ means current layer, $N$ is the number of nodes and $F$ is the number of feature in each node.  We initialize node features $h^{(0)}$ with their one-hot vector $V$ , each GAT layer $l$ computes the node embedding $h_{i}^{l}$  from the embedding of layer $l-1$ by leveraging the attention mechanism:
\begin{equation}
h_{i}^{(l)} = \sigma \Big(\sum_{j \in N_{(i)}} \alpha_{ij}^{(l-1)} {W}h_{j}^{(l-1)}  \Big)
\end{equation}
Where  $\sigma$ is an activation function,  $N{(i)}$ denotes the  one-hop neighbors of node $i$ in adjacency matrix $A$, $\alpha_{ij}^{(l-1)}$ indicates the attention coefficient of node $j$ to node $i$ in layer $l-1$, $W$ is a learnable matrix for node-wise feature transformation. Owing the links in neural architectures are unidirectional, which means the information can only flow in one direction when aggregating. This will lead to a problem that the source node is not aware of the information about those nodes linked by it, for example, the node embedding $h^{(l)}$of the input node will never change no matter how many nodes it has linked to. To solve this problem,  we add a feedback adjacency matrix $A^{\prime}$ by reversing all edges in the original graph. To avoid interference with the real links in the neural network, we introduce a separate GAT model  $\phi^{\prime}$ to compute embedding $h^{\prime}$ on the feedback adjacency matrix $A^{\prime}$ in the manner described above. By doing so, the embedding calculation of Equation 1 is redefined as:
\begin{equation}
\label{formulation 3}
h_{i} = \phi (A, V ) \parallel\phi^{\prime}(A^{\prime}, V)
\end{equation}
We concatenate the node embeddings calculated by these two GAT models as the final embedding $h_{i}$. The Basic idea of GNN based link prediction model is to calculate the score $y_{u, v}$ of link possibility between node u and v by their embedding. In practice, we use a MLP with two fully-connected layers to perform this calculation, the MLP takes node embedding $h_u$ and $h_v$ as input and outputs a one-dimensional vector that indicates the link possibility $y_{u, v}$. By iteratively minimizing  the cross-entropy loss between $y_{u, v}$ and label$t_{u, v}$ in Equation \ref{equation 4}:
\begin{equation}
\label{equation 4}
Loss = -(t_{u, v}\cdot \log(y_{u, v}) + (1 - t_{u, v})\cdot\log(1 - y_{u, v}))
\end{equation}
The link prediction model stores the experience of designing neural networks, and could clearly distinguish what links are beneficial and what links are detrimental to the network performance in terms of link possibility score $y_{u, v}$.

\subsection{Architecture prediction}
\begin{figure}[htp]
    \centering
    \includegraphics[width=1\textwidth]{3.3.pdf}
    \caption{An illustration to the structure prediction part of NAL.}
    \label{fig3}
\end{figure}

As shown in figure \ref{fig3}, the trained link prediction model is utilized to predict the score $y_{u, v}$ for all potential links in the current network structure. After that,  we add a new link based on their score to form a new network structure for the next round of prediction. This way of predicting new links based on the network structure allows the knowledge learned by NAL to be utilized in any search space. Algorithm \ref{algorithm1} shows the process of generating architectures using the link prediction model in the specified search space.

\begin{algorithm}[H]
\label{algorithm1}
\caption{Generate neural architecture with link prediction model}
\KwIn{The limitation of maximum edges $E$, the limitation of maximum vertexes $V$, the set of candidate operations $OP$.}
\KwOut{Architecture $\mathcal{A}$ generate by link prediction model.}
Initialize neural architecture $\mathcal{A}$ with a input node, initialize score pool $P$ as $\emptyset$. Current number of vertex $v$ and number of edge $e$ are initialized as 1 and 0. Source node set $Src$ and destiny node set $Dst$ are initialized as $\left\{input\right\}$ and $\emptyset$.

\While{$e < E$}{
\For{$i$ in $Src$}{
\For{$j$ in $Dst\parallel OP$}{
Calculate link possibility score $y_{i, j}$ between u and v by link prediction model\;
Add $y_{i, j}$ to $P$\;
}}
P = Softmax(P)\;
Sample a link $l_{u,v}$ from $P$ based on its value\ and add it to $\mathcal{A}$\;
Add v to $Src$ and $Dst$\;
$e = e + 1$\;
$v = v + 1$\;
\If{$v\geq V$}{
Remove $OP$\;
}
}\end{algorithm}

In the begining, the architecture is initialized with only a input node, the source node set $Src$ and destiny node set $Dst$ with candidate operations $OP$ are used to indicate the possible link in the network.  While current number of edge is not up to limit, we use link prediction model to give every possible edge a possibility score $y_{i,j}$ and add it to a score pool $P$. The node i is taken from $Src$ represents the starting point of link, the node j taken from $OP$ indicate the link between node i and a new node that not in current network while the node j taken from $Dst$ indicate the link between two nodes in current network. After regularized $P$ by the $Softmax$ function, we sample a link $l_{u, v}$ based on the score distribution from $P$, the link will be added to current architecture. Besides, the endpoint $v$ of the link is add to $Src$ and $Dst$ for the next round of predictions. In this way, we could get a generated network in the target search space.

\section{Experiments}
In this section, we carry out experiments on two search spaces ($i.e$, NASBench-101 and DARTS.) to demonstrate the effectiveness of the proposed NAL.

\textbf{NASBench-101}. NAS-Bench-101 is the first public architecture dataset for NAS research, which contain 423K unique cell-based convolutional architectures trained and evaluated multipule times on CIFAR-10. It allows researchers to directly query the performance of a large number of networks. Each cell in NAS-Bench-101 is consist of an input node, an output node and up tp five intermediate nodes, the limitation of maximum number of edges is 9. The nodes represent operations in neural network while the edge represent connection between operations. All intermediate nodes are selected from $3\times 3$ convolution, $1\times 1$ convolution or $3\times 3$ max-pool.

\textbf{DARTS}. As a popular search space for gradient-based NAS methods. The search space of DARTS is a computation cell represented by a DAG, different from NAS-Bench-101, each node is a latent representation while each edge is stand for operation. Every cell in DARTS is composed of 7 nodes, the first two nodes as input to receive the outputs from previous two cells respectively, the next four nodes as intermediate nodes with two input edge, the last
node will concatenate all latent representation in the intermediate nodes as the output of current cell. The cell could be stacked to form a final architecture.

In the following, we first train  NAL on the neural architectures selected from NASBench-101, then introduce the experiment to demonstrate the capability of NAL in designing NASBench-101 networks. To further verify that NAL learned universal neural network design knowledge, we propose a search space mapping method that enables the NAL trained on NAS-Bench-101 to design neural architecture in DARTS search space.  We evaluate the performance of neural architecture designed by NAL with two classical datasets. In the end, we present the ablation experiment to demonstrate the impact of each part of the algorithm.

\subsection{Implementation Details}
To prepare the training data, we first sort all neural architectures in NAS-Bench-101 according to their test accuracy, then we choose 30 high-performance architectures and 30 low-performance architectures to build the training samples as mentioned in section \ref{3.1}. Due to the difference in the construction of positive and negative samples, the number of positive and negative samples is different. To avoid this difference affecting the network training, we take same number of samples randomly from the negative samples as the positive samples do. After one-hot encoding, the node representation in NAS-Bench-101 cell is a vector of dimension 5 because there are only five optional operations ($i.e.$ input, output, $3\times 3$ convolution, $1\times 1$ convolution and $3\times 3$ max-pool). The Graph Attention Network we used in NAL has three GAT layers, the input dimension is set to 5, the dimension of hidden layer and the final output are both 16, $Relu$ is used as the activation function. As mentioned in equation \ref{formulation 3}, we concatenate the embedding computed by two different GAT model as the final embedding of node, so the final embedding is a vector of dimension 32. The MLP we used in NAL consists of a fully-connected layer with hidden size 32 with $Relu$ and an output layer. For training details, we use the Adam optimizer\cite{kingma2014adam} with an initial learning rate 0.0005 and train  NAL for 200 epochs.

\subsection{Design Neural Architecture in NAS-Bench-101 search space}
\label{4.2}

\begin{table}[htp]
\caption{Performance comparison between NAL and prior work on Nas-Bench-101 dataset.}
\label{table1}
\centering
\begin{tabular}{cccc}

\hline
Methods       & Queries & Top-1 Accuracy(\%) & Strategy         \\ \hline
Random Search\cite{NASBench-101} & 1000    & 93.54             & Random                \\
RE\cite{NASBench-101}            & 1000    & 93.72             & Evolution             \\
RL\cite{NASBench-101}            & 1000    & 93.58             & REINFORCE             \\
BO\cite{NASBench-101}            & 1000    & 93.72             & Bayesian Optimization \\
NAO\cite{luo2018neural}           & 1000    & 93.81             & Gradient Decent       \\
E2EPP\cite{sun2019surrogate}         & 1000    & 93.77$\pm$0.13    & Evolution             \\
SSANA\cite{tang2020semi}         & 1000    & 94.01$\pm$0.12    & Evolution             \\ \hline
NAL           & \textbf{160}     & \textbf{94.03}$\pm$0.05    & Link Prediction       \\ \hline
\end{tabular}
\end{table}

We follow the design process mentioned in algorithm \ref{algorithm1} to design neural architectures in the NAS-Bench-101 search space.  The limitation of maximum edges E and maximum vertexes V in NAS-Bench-101 search space are set to 9 and 7 respectively, the set of candidate operations OP is \{input, $3\times 3$ convolution, $1\times 1$ convolution and $3\times 3$ max-pool, output\}. To assess the efficiency and quality of NAL, we referred to the NAS-Bench-101 experiments in \cite{tang2020semi, yan2020does}. To reduce the influence of random factors in the algorithm, we train 10 NAL models, each model designs 10 cell structures and we query their test accuracy in the NAS-Bench-101 dataset, the best one is selected as the final output of the proposed approach. Specifically, the experiment is repeated 30 times, Table \ref{table1} shows the comparison result, two evaluation indicators are used to evaluate the efficiency and quality of the algorithm respectively: the number of architecture quires and final test accuracy on the CIFAR-10 dataset. Appending the 60 training architectures mentioned before, the total queries number is 160, which means that NAL could design a more accurate architecture while its efficiency is significantly better than other methods.

\begin{figure}[htp]
    \centering

    \includegraphics[width=1\textwidth]{4_3.png}
    \caption{The cells designed by NAL in NAS-Bench-101 and DARTS search space.}
    \label{fig4}
\end{figure}

Figure \ref{fig4} shows some of the cell structures that frequently appear in NAL design results, when designing neural architecture in NAS-Bench-101, NAL hardly selects edges associated with $3\times 3$ max-pool; on the contrary, it is more inclined to select $3\times 3$ convolution as the operation of intermediate nodes or to combine $1\times 1$ convolution with $3\times 3$ convolution. Furthermore, NAL tends to link a large number of intermediate nodes for input node to increase the width of the network at the front.

\subsection{Design Neural Architecture in DARTS search space}

As mentioned above, the representation of cell and the operations in DARTS are quite different from those operations in NAS-Bench-101. In order to apply the trained NAL to DARTS search space, we need additional transformations to get a adjacency matrix $A$ and a node feature list $V$ and map the operations in DARTS to the operations in NAS-Bench-101. We replace every intermediate node that has two input edges in DARTS with two nodes that have the same index,  the two new nodes represent the operation of two original input edges respectively. For those operations in DARTS that do not appear in NAS-Bench-101, We combine the operations in NAS-Bench-101 to form local structures that have the same receptive field as alternatives. The detail of the transformation process and operation replacement strategy could be seen in supplementary materials.

After transformation, we apply the NAL trained on NAS-Bench-101 to design architecture in the DARTS search space, the neural architectures designed by NAL are shown in Figure \ref{fig4}. When designing neural architectures in DARTS search space, NAL is inclined to choose  $3 \times 3$ dilated separable convolutions and $3 \times 3$ separable convolutions. Besides, NAL focuses more on expanding the network's width than its depth, this is similar to the design characteristic of NAL in the NAS-Bench-101 search space. In the following section, we evaluate the architecture designed by NAL with CIFAR-10 and ImageNet\cite{deng2009imagenet} dataset.

\subsection{Results on CIFAR-10 Image Classification}

\begin{table}[htp]
\caption{Performance comparison on CIFAR-10 dataset with state-of-the-art methods.}
\label{table2}
\centering
\begin{tabular}{ccccc}
\toprule
Methods     & Cost (GPU-day) & Test Error(\%) & Params(M)  & Strategy \\ \midrule
NASNet-A\cite{RL_NAS}    & 1,800      & 2.65       & 3.3               & RL            \\
ENAS\cite{pham2018efficient}        & 0.5       & 2.89       & 4.6                & RL            \\
AmoebaNet-B\cite{real2019regularized} & 3,150     & 2.55$\pm$0.05  & 2.8              & evolution     \\
DARTS\cite{DARTS}       & 4         & 2.76$\pm$0.09  & 3.3                  & gradient      \\
SNAS\cite{xie2018snas}        & 1.5        & 2.85$\pm$0.02  & 2.8               & gradient      \\
P-DARTS\cite{chen2019progressive}     & 0.3       & 2.50       & 3.4                & gradient      \\
PC-DARTS\cite{xu2019pc}    & 0.1       & 2.57$\pm$0.07  & 3.6                & gradient      \\
PR-DARTS\cite{zhou2020theory}    & 0.17      & 2.32       & 3.4               & gradient      \\
Fair DARTS\cite{chu2020fair}  & 0.4       & 2.54       & 2.8                & gradient      \\
ISTA-NAS\cite{yang2020ista}\  & 2.3       & 2.36$\pm$0.06     & 3.37                & gradient      \\
\midrule
NAL        & \textbf{2e-4}      & \textbf{2.18}       & 3.2               & Link Prediciton\\\bottomrule

\end{tabular}
\end{table}
Following the setting of \cite{DARTS}, our network is formed by stacking 20 cells with 36 initial channels. We use SGD to optimize parameters with the initial learning rate of 0.025 scheduled by Cosine Annealing LR. The CIFAR-10 results are present in Tabel \ref{table2}. Notably, the proposed NAL achieves a state-of-the-art error rate of 2.18\% with the size of 3.2M, the training of NAL only takes 2e-4 GPU-day (around 20 seconds).
\subsection{Results on ImageNet Image Classification}

\begin{table}[htp]
\caption{Performance comparison on ImageNet dataset with state-of-the-art methods.}
\label{table3}
\centering
\begin{tabular}{cccccc}
\toprule
\multirow{2}{*}{Methods} & Cost & \multicolumn{2}{l}{Test Error(\%)} & Params          & \multirow{2}{*}{\begin{tabular}[c]{@{}l@{}}Strategy \\\end{tabular}} \\
                         & (GPU-Day) & Top-1              & Top-5         & (M)        &                                                                           \\
\midrule
Inception-V1\cite{szegedy2016rethinking}             & -         & 30.2               & 10.1          & 6.6     & manual\\
MobileNet\cite{howard2017mobilenets}                & -         & 29.4               & 10.5          & 4.2     & manual\\
ShuffleNet v2 2x\cite{ma2018shufflenet}         & 0.5 & 25.1               & -             & $\sim$4.6        & RL\\                                                \midrule
NASNet-A\cite{RL_NAS}                 & 1,800     & 26.0               & 8.4           & 5.3     & RL\\
AmoebaNet-C\cite{real2019regularized}              & 3,150     & 24.3               & 7.6           & 6.4     & evolution\\
MnasNet-92\cite{tan2019mnasnet}               & -         & 25.2               & 8.0           & 4.4     & RL\\
ISTA-NAS\cite{yang2020ista}                & 33.6      & 24.0               & 7.1           & 5.65    & gradient\\
DARTS\cite{DARTS}    & 4         & 26.7               & 8.7           & 4.7     & gradient\\
SNAS\cite{xie2018snas}              & 1.5       & 27.3               & 9.2           & 4.3     & gradient\\
P-DARTS\cite{chen2019progressive}                  & 0.3       & 24.4               & 7.4           & 4.9     & gradient\\
BayesNAS\cite{zhou2019bayesnas}                 & 0.18      & 26.5               & 8.9           & 3.9     & gradient\\
PC-DARTS\cite{xu2019pc}                 & 0.13      & 25.1               & 7.8           & 5.3     & gradient\\
GDAS\cite{dong2019searching}                     & 0.21      & 26.0               & 8.5           & 5.3     & gradient\\
Fair DARTS\cite{chu2020fair}               & 0.4       & 24.9               & 7.5           & 4.8     & gradient\\
PR-DARTS\cite{zhou2020theory}                 & 0.17      & 24.1               & 7.3           & 4.98    & gradient\\
\midrule
NAL                     & \textbf{2e-4}      &\textbf{23.5}                & \textbf{7.0}           & 5.0       & link prediction\\
\bottomrule
\end{tabular}
\end{table}
For ImageNet image classification, the final network is formed by stacking 14 cells designed by NAL and 48 initial channels. The network is trained from scratch for 350 epochs with batch size 512 on 4 RTX 2080Ti GPUs. We use SGD to optimize parameters with the initial learning rate of 0.2 scheduled by Cosine Annealing LR, the momentum and weight decay are set to 0.9 and $4\times 10^{-4}$. Experiment results on ImageNet are present in Table \ref{table3}. Architecture designed by NAL outperforms current cell-based NAS methods with comparable model parameters while having minimal search cost.
\subsection{Ablation Study}
\begin{table}[htp]
\caption{Comparison on NAS-Bench-101. S, I and G are short for "Sample construction strategy", "Inversely information aggregating" and "GAT" seprately. The italic and bold denote the best values of each evaluation indicators}
\label{table4}
\centering
\begin{tabular}{ccc|cc}
\hline
S & I & G & Mean Accuracy(\%) & Top-1 Accuracy(\%) \\ \hline
\XSolidBrush & \XSolidBrush & \Checkmark & 92.58$\pm$0.05               & 93.87$\pm$0.09                 \\
\XSolidBrush & \Checkmark & \Checkmark & 92.77$\pm$0.06               & 93.93$\pm$0.09                \\
\Checkmark & \XSolidBrush & \Checkmark & 93.36$\pm$0.08    & 93.91$\pm$0.04      \\
\Checkmark & \Checkmark & \XSolidBrush & 93.44$\pm$0.05    & 93.98$\pm$0.08      \\ \hline
\Checkmark & \Checkmark & \Checkmark & \textbf{93.60}$\pm$0.04    & \textbf{94.03}$\pm$0.05        \\ \hline
\end{tabular}
\end{table}
In this subsection, we carry out the ablation study in the NAS-Bench-101 search space for convenience. There are two evaluation indicators, the average accuracy of the designed 100 architectures mentioned in section \ref{4.2} that indicate the stability of the algorithm. And the top-1 accuracy as the final output of NAL indicates the quality of the algorithms.

\textbf{The impact of training sample construction strategy.} As mentioned in subsection \ref{3.1}, we carefully designed the positive and negative samples to make the GNN-based link prediction model adapt to the task of link prediction in neural networks. To verify the effect of this construction strategy on NAL, we compare it with the common practice of training sample construction in the link prediction task. The common practice only takes out links from unabridged high-performance architectures to form positive sample and only take the edges that do not exist in these architectures as negative samples. As can be seen from Table \ref{table4}, there are significant differences in the performance of NAL between these two sample construction strategies. With the help of network disassembly process and the Negative sample provided by low-performance networks, NAL can learn the design knowledge of neural network more stably.

\textbf{The impact of inversely information aggregating.} To solve the node perception problem, we reverse the original links and use an extra GAT to aggregate node information. For comparison, we disabled this additional GAT with other Settings unchanged, Table 4 shows the difference between these two sample construction strategies, when the inversely information aggregating is banned, nodes in the network cannot perceive their outbound links. This will affect the sensitivity of NAL to the change of network structure, thus, the performance of the algorithm is impaired.

\textbf{The impact of GAT.} Compare with GCN, which is the common practice of neural predictors, GAT gives different weights to different neighbors when aggregating neighbor information, whereas GCN treats each neighbor as equally important. We tested the effect of these two GNN models on NAL under the same setting. As shown in Table 4, when the GNN model in NAL changes from GAT to GCN, the stability and quality of NAL are slightly reduced. This difference validates the viewpoint that different nodes in neural networks have different contributions to the overall performance, and their importance cannot be equalized easily.

\section{Conclusion}
In this paper, we provide a new approach for automatic design of neural networks, by exploiting the knowledge of existing network structure, NAL could design high performance neural architecture by predicting new links for current network. The proposed approach could relieve the enormous requirements of extensive computing resources for automatic design neural network, and make the design process of neural architecture transparent to users. We have demonstrate its effectiveness in design neural architectures with promising performance in different search space, and further show that the knowledge NAL learned from one search space could be transfer to other search space. In the future, we plan to explore the potential of NAL to exploit the knowledge of existing network structure in a more efficient and stable way.

\bibliographystyle{plainnat}

redpajama_set_name: RedPajamaArXiv


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