text:
They were the first British soldiers into Basra as the allies invaded Iraq. Some still teenagers, they were pitched into fierce fighting with Saddam Hussein's army. Matthew Taylor finds out what has become of 3 Platoon 1 Company Irish Guards - and the city they held.
Fri 12 Sep 2008 19.01 EDT First published on Fri 12 Sep 2008 19.01 EDT
He looks calm, perhaps even a little stern - the image of the perfect soldier. But, slumped in a corner of the recreation room at the Mons barracks in Aldershot five years later, Adam Moran recalls something very different. The photograph of his platoon taken in Basra in April 2003 hides a "world of turmoil", he says. "I couldn't stand the army, couldn't stand it. I was misled. I was told at a careers thing in Birmingham that I would never have to wear a uniform and that I could go round the world boxing. I was told I'd be a tracksuit soldier, that it would be the time of my life."
Just a few weeks after passing out (completing basic training), Moran found himself on the frontline in Iraq. "My head was spinning," he says. Moran, now 29, was part of 3 Platoon 1 Company Irish Guards. In March 2003, as part of the Scots Dragoon Battle Group, the 34 soldiers in the photograph above (Moran is in the second row, fourth from left) were among thousands of allied forces dispatched from Kuwait across the deserts of southern Iraq as part of the US-led mission to overthrow Saddam Hussein. The men of 3 Platoon were among the first - they believe the first - into Basra, Iraq's second city and main port. They were given the task of first encircling, then by night pushing farther into Basra, taking on what remained of Saddam's army.
Five years on, the number of British troops has dwindled from 45,000 to 4,000 based at an airport outside Basra city, and the Ministry of Defence is talking about cutting back further, to just a few hundred, in the first half of next year.
But what has happened to the men of 3 Platoon? What do they remember of the "Micks" 2003 tour of Iraq? What, if anything, do they recall about the photograph, taken outside a temporary base near Basra University six weeks after they entered the country - and just days before President Bush, aboard an aircraft carrier off the coast of California, declared the war won on May 2? It shows them during a break between patrols, some of them teenagers thrust into weeks of fierce fighting, never having seen action before. More informal than many platoon photographs, there are no regimental banners or flags. The men, known to each other by their nicknames - "Robbo" Robinson, "Happy" Harris, "Picky" Pickford - are dressed in regular fatigues, guns clasped in front of them. "It's a working shot," one of them tells me, with pride.
James Moulton bounds across the station concourse at Waterloo in London and, before we have time even to introduce ourselves, offers to do whatever he can to help track down the men he used to command. With his shock of black hair, Moulton is instantly recognisable from the photograph: hands on knees, he stares out confidently from the centre of the front row, the platoon captain, 27 years old. "I can honestly say they were a wonderful bunch - honest, hard-working and decent," he says. "Just don't listen if they tell you I spent all my time sunbathing - it's only half true!"
Moulton left the army in 2005 and since then has crisscrossed the globe, acting as a private security consultant in many of the world's trouble spots, from Haiti to Algeria, Angola to Iraq. "I suppose it has been pretty eventful since I packed it in, but I fear it may be more a sign that I don't know quite what I want to do when I grow up than anything else."
When I tracked him down, Moulton, now 32, was in Nigeria offering security advice and logistical support to workers in the country's oilfields. A few weeks later he answers his mobile and lets slip that he's now in Houston, Texas, having flown in from Iraq hours earlier. It's 3am. "I'm not sure what country I'm in, let alone what time it is," he says apologetically.
It becomes clear that he often thinks about his time in Basra. He talks about the "craic" - the good times and the bond built up between the soldiers as the firefights intensified. But he is also keen to convey what he describes as the "strangeness" of the two-and-a-half-month tour. "It was very different from anything else I've done," he says. "Not just dangerous, but slightly bizarre. We were out there doing a pretty difficult job, but it was the more mundane things that I often think about these days."
One night they set up camp in what he thinks was a student accommodation block. "I was in a room and it was obviously someone's home. Their things were there - family pictures and food in the cupboards. It felt quite invasive, and brought home that human side of it - that this was about real people."
Another night, he was lying in a cramped and stuffy warehouse, head to toe with the other soldiers of his platoon. "It got to the point where you weren't sure whether it was your feet or someone else's that you were smelling, but you knew you had to get out. So a few of us took our mats up on to the roof and I was lying there with one of the other guys late one night and tracer bullets were flying over our heads - perhaps five metres above us. We were just chatting and smoking, and it suddenly hit me how normalised the whole thing had become."
Initially, the platoon was given the task of providing security for a British army base at "Bridge Four" on one of the main roads south out of the city. But soon they left the relative safety of the base, pushing farther and farther into the city, sleeping in their Warrior armoured vehicles and taking on well-armed Iraqi fighters along the way. They fought their way building by building, room by room, through Basra's university complex. At one stage, as the fighting became more ferocious, Moulton drove his Warrior down what the platoon had dubbed "mortar mile". He stuck his head out of the turret in an attempt to draw enemy fire and locate Iraqi artillery. "I remember one RPG [rocket-propelled grenade] went right across in front of us and another from the other side bounced off. There were a lot whizzing around our heads and into the vehicles, and we took a few direct hits. I was too busy trying to identify their positions to worry about being hit, but a few were quite close. It was pretty exciting."
A week later, Moulton calls from Jordan to say he is helping set up a medical evacuation company that will get injured civilians out of Iraq. He says he has been up in Kurdistan for a while, "playing around with planes". We talk about his new business venture before the conversation turns back to the 2003 tour. Does he have any regrets?
He pauses. "Of course there are problems now - the main one seems to be a lack of planning about what we were going to do after the first phase. But I suppose I think I am pleased I was there, if anyone was, because - and I suppose this sounds pretty arrogant - I trust my judgment, I trust that I will try to do the right thing at the right time."
Some of the others in the photograph are harder to locate. Neither the MoD nor the regiment keeps a record of what happens to soldiers who leave the army, and some simply cut their ties and move on.
All that was known of the man with the moustache on the far left of the photograph was that he was probably called Jones and may have moved north, perhaps to Liverpool. His first name could be David. The soldier directly below him (third row from front, far left), slightly smiling, eyes narrowed in the sun, is known as "JR" Hartley, after a character in an 80s TV advert. JR's friends say he left the army a couple of years ago and now lives somewhere in Northern Ireland. A ring-round of Hartleys in the province gets nowhere; then someone phones back to say that Darren "JR" Hartley - or Big Daz - is working as a security guard, and passes on a mobile number.
"I met a woman and I wanted to start a family," Hartley says when he returns my call. He left the Irish Guards in 2005 and now works for Group 4. "I didn't want them to be moving all the time and me being away, so I called it a day. I wanted somewhere they could call home."
He was not only the first member of his family, but the first person in his village, to join the British army. "I wanted to get away and we had got a sort of peace over here, so I thought why not join up and try to help other people who are in trouble." Now that he is back home in Northern Ireland, he is wary of talking freely about his previous career. "Some people know - my mates and family - and they are fine with it, but it's better not to go on about it more widely. You still have to be careful." Many of the Irish Guards come from Northern Ireland and a few from the Republic; others, from the mainland and farther afield, tend to have Irish connections of one kind or another.
Hartley's time with 3 Platoon seems a bit crazy now, he says. "It takes me right back when I see stuff on television about Iraq, and I definitely miss the craic of being with the lads... We were from all over the place, so being thrown together with all these different people was not what I had been used to back home. In the UK or on training exercises, we would go out at the weekends and got to know each other pretty well - we always had a laugh and a bit of piss-taking." But in Iraq, when the platoon was under pressure, often involved in 20 or 30 "contacts" a day, he says the mood in the platoon changed.
"It was a pretty serious time, so there weren't many jokes really. We were just supporting each other - when we got letters or food parcels from home, we would share stuff out or talk about what people were up to at home. Some of the boys didn't get letters, so it helped them." Hartley remembers his mother and sister sending him a message on forces radio. "My mum said hi and my sister sang me a song - Take Me Home, Country Roads, I think it was. I missed it because I was out on patrol, but the lads told me all about it and we had a laugh. It was that sort of thing that kept you going when stuff got a bit tricky."
Days in Iraq melted into each other. "We were often going for 20, sometimes 24, hours at a time. Then we would try to grab a bit of sleep in the back of the wagon - the Warrior - maybe an hour or two before we were off again. I'm 6ft 4in, so it was a bit rough." Hartley often catered for the crew - boil-in-the-bag ration packs of corned beef hash or lancashire hotpot - also in the back of the Warrior.
One of the chief discomforts was the heat. "The highest temperature I remember was 56C, but mainly it was in the high 40s. It took a long time to get used to it, and we all spent a lot of time drinking water - and then peeing it out. I used to dream of finding a cold bottle of water on the wagon but because of the heat and the engines they were always warm." But what has stayed with him above all "is the people and the shit they have had to put up with. I just think how lucky we are even to have stuff like running water." He recalls how the attitude of ordinary Iraqis changed as the tour went on. "When we first went there, it felt like we were doing something worthwhile - the people would run up to us and cheer - but after a couple of weeks that changed. We had stuff thrown at us by Iraqis in the street."
Initially the British forces prided themselves on their hearts-and-minds, helmets-off approach in Basra, which was so much less aggressive than their US counterparts in Baghdad. But over time, as welcome turned to hostility and militiamen loyal to the Shia cleric Moqtada al-Sadr began ambushing patrols and firebombing, there was a rethink. By May 2006 - three years after Bush declared victory - the British were calling a month-long state of emergency in Basra when more than 100 people were killed in sectarian clashes as a result of the occupation.
Of the 34 soldiers in the picture, 14 have left the army. According to the Royal British Legion, this level of "churn" - the number of soldiers who leave - is not unusual for operational units that have seen regular frontline action. Most of the others are still with the Irish Guards and based in Aldershot.
The taxi driver who takes me from the station to the barracks says the arrival of the "Micks" (the Irish Guards' nickname) has changed the atmosphere in town. "The Paras could be a real pain, although they did police the town - they were the only ones allowed to cause trouble. The Irish Guards are different, good to have around, less bother."
The 3 Platoon photograph is kept in the regimental album. A group who are still part of the platoon are waiting for me to talk over their experiences.
Stevie King (second row, second from left) tells me how he got his first taste of war as night fell on April 6 2003. Holed up in the basement of a partially built housing complex near the centre of Basra, he was stuffing down the contents of his ration pack while others grabbed a couple of minutes' rest among the sand and rubble.
"I was only a pup and can say I was pretty scared - I can't see how anybody wasn't," says King, now 23. The unit came under attack from Iraqi snipers. "There was a bit of a commotion and we realised they were hitting us. We scrambled to cover and it went quiet again." What King didn't realise was that the short "contact" had killed two soldiers from another Irish Guards platoon: 28-year-old Ian Malone and 20-year-old Christopher Muzvuru (Malone was from Dublin, Muzvuru Zimbabwean). They were among the first British deaths of the war. Five years later, King haltingly recalls the moment he found out what had happened. "It was devastating. It was a couple of hours later and we were taken to a classroom in Basra University and told that the two lads had died - I think that was when we all understood this was for real."
King was one of the youngest soldiers involved in the invasion. On March 12, he celebrated his 18th birthday practising room clearances in Kuwait, where UK forces were stationed before crossing into Iraq. A few days later, he joined the rest of the platoon on the road to Basra.
"Once I had my 18th birthday, I was eligible to go in. If we had moved before March 12, I would have had to stay behind because I was too young."
When they reached Basra, King worked alongside platoon sergeant Ian "Picky" Pickford. "I was just his bitch really," King says. "The sergeant always comes up from the rear and picks up the enemy or the wounded, and I was helping him."
He has no regrets. "I wouldn't swap what I learned in Iraq for anything. I still love the army, and that tour meant I had to grow up very fast. I went out there an older boy but came back a man."
Stephen "Sammi" Slater (middle row, third from right) had a grandfather, a great-grandfather and a host of uncles who had served in the armed forces, and his father had a career in the police. "Since I was very little, I knew I wanted to be a soldier," he says. Iraq was his first tour and "a proper eye-opener. I got shot at a few times. I hit the ground and hoped," he says, laughing. "Before we went into Basra, we were based outside the city, making raids and coming out again. When we were coming back, I had got out of the wagon and an Iraqi tank aimed at it but missed - the shell landed about 10ft from where I was standing. I wasn't injured, but I was covered in lots of dust. The rest of the lads were behind a wall and all came running round asking, 'What's happened? What's happened? Are you all right?' but I was fine - maybe the odd scratch, but that was it."
Slater laughs easily, especially at the recollection of his mates thinking he had been hit by a shell. All the same, it's the kind of thing you never forget, he says. When he came back from Iraq, his family and friends began to treat him differently. "I used to be pretty quiet, but when I came back I started being more open. I've been to Northern Ireland and then back to Iraq last year, but it was never like 2003. The excitement of being on the frontline and being exposed like that is something that will stay with me."
Craig McDougall (front row, far right) recalls the day the portrait was taken. "We just sat down, got the photo took and then cracked on with our next patrol," he says. "You can see by the boys' faces that everybody is properly knackered, everyone looks like they have been working, but I like it for that - it's real."
"The platoon commander was a bit crazy, a bit gung-ho, and he was always trying to push the wagons closer and closer to Basra," McDougall says. "It was a good laugh sitting in the back of the wagon hearing the rounds bouncing off the side and RPGs hitting the back doors. It's the best tour I've done because there were very few rules or regulations out there. Now it is governed by rules and all that, and the situation has changed with suicide bombings and different tactics. Back then, there was a standing enemy who would take us on and we would take them on. There were no IEDs [improvised explosive devices] or suicide bombs - it was like good old-fashioned fighting, basically."
When 3 Platoon returned from Iraq in summer 2003, they were given a couple of weeks' rest before setting off on a four-month tour in Northern Ireland. Then it was back to barracks in Aldershot for training, courses and preparation for the next trip. Three and a half years later, in 2007, the platoon served its second tour in Iraq. It was later that year that British troops withdrew from central Basra and made their base at the airport: they handed over security to the Iraqis and their role now is to train Iraqi troops.
Noel Kerr (middle row, fourth from right), 30, served on both trips. "The second time we went, people didn't want to know you and were very hostile - the first time the kids would be coming over and talking to you, but this time they would stand back and sometimes throw things at you."
Ryan "Robbo" Robinson (second row from back, far right), 31, now works as a recruiting sergeant at Belfast's Palace Barracks. "Being soldiers, particularly young soldiers who had never seen action, going to war was what we wanted to do more than anything. It is what we had joined up for." But as they gained first-hand experience of combat, the soldiers' attitude changed. "When the two soldiers were killed, I think most of us realised that war is different from what you see in Hollywood - it came home just how real and dangerous it is. That you need to take it seriously, be properly trained and respect what you are doing."
Nevertheless, tough as the 2003 tour was, it convinced many of the soldiers, some of them teenagers, that the army was for them. In his new role as recruiting officer, does Robinson spell out both the reality of war as well as the attractions of army life? "I don't tell the kids any lies about what I've seen. They'll ask, 'Were you scared?' and of course I tell them I was. And I say that I have lost mates in action, but I don't go on about it - my job is to get people to sign up to the army, so I don't want to scare the crap out of them."
Not everyone who signs up adapts well to army life. Sebert Henry (back row, far right) went awol when the platoon returned to the UK in 2003. No one seems to have been that close to him. "He was a good bloke, maybe a bit quiet, but a reliable soldier," says one of his fellow Micks. And no one seems to have heard from him since he left; when I run his name past the MOD, I draw a blank.
There are a number of S Henrys listed in his home town of Birmingham, but an afternoon of phone calls confirms none of them answers to the name Sebert. Then a breakthrough: someone passes on an old address where they say Henry used to live, in Oldbury, in the West Midlands. But as I turn into the street, I am confronted not by a row of terraced houses but by a McDonald's drive-through and a Sainsbury's supermarket.
Later, I ask Moulton what he remembers of Henry and why he'd go awol. "You've got to understand," he says, "that if you are a young man, perhaps you don't want always to be defined by something that happened in your early 20s, so you take a deep breath, move on and create a new life for yourself."
Most left the platoon in more mundane circumstances. Ivor Hill (second from back row, third from left) moved back to Northern Ireland, where he works as a tyre-maker. Twins Mike and Mark Blakeway (respectively, middle row, third from left, and second row, fourth from right) have returned to the Black Country, where they have both found jobs and partners. Andy McGill (second row, second from right) left the army to become an electrician in Ballymena, but has decided civvy street isn't for him and will rejoin the Irish Guards later this year: "I miss the excitement, the tours and my mates," he says.
When he does so, he'll bump into some of the old hands from his platoon, including Sergeant Richie "Stricky" Strickland (front row, second from left). "I remember sitting by a tank and writing a 'bluey' [a letter] to my girlfriend, and we were engaged by an enemy tank and it didn't seem that big a deal - I just got up and carried on, which seems crazy looking back," he says of his time in Iraq.
Sitting in his office overlooking the parade ground, platoon sergeant Ian "Picky" Pickford (front row, third from left) says he has nothing but respect for his men. "On night patrols, sometimes you got lucky and got a quiet one. Other times you would be engaged and it could get rough. We had some really young lads out there with us, and I was a bit worried about how some of them would shape up, but I can put my hand on my heart and say they did fantastic... It felt like I had been revising for exams and had finally got to test myself. Looking back, it was every schoolboy's dream."
But not for Adam Moran, the would-be army boxer. When 3 Platoon returned to the UK in 2003, like Sebert Henry he decided he had had enough of the army and went awol. "I was out for four and a half years, and was doing well," he says. "I was a foreman for a groundworking company and was getting myself together. But then, three months ago, I was caught on a spot check on a motorway."
Moran, 29, who now faces a court martial, makes clear that it was everyday army life that "drove him mad", rather than his experiences during the 2003 tour. "I could deal with Iraq, because although it was hectic and we were under fire, at least the day was mapped out, you didn't have time to think. What I couldn't hack was all the ceremonial duties and general army life when we got back. I simply couldn't stand it, so I left."
If the court rules against him, Moran could be sent to military prison. "I have pleaded guilty to desertion," he says, "which is a pretty serious offence... I don't know what's going to happen - they may even try to keep me in, but I hope not. Army life just isn't for me."
And what of Basra, five years on from 3 Platoon's incursion? There were a torrid few months earlier this year, when Shia militias ran riot after the British pulled out. However, the Iraqi authorities regained control thanks to massive support from US forces, and by the end of March Moqtada al-Sadr had ordered his followers to lay down their arms. According to the commander of British forces in southern Iraq, Major General Barney White-Spunner, the city is transformed - the port and the property market are booming. Iraqis take a gloomier view, but most concede that there are no longer obviously armed militiamen walking the streets.
As for 3 Platoon, they are still at Aldershot barracks, preparing for a tour of Afghanistan in early 2010.
This article was amended on 23 November 2018 to remove an inaccuracy


text:
The Challenge: Battle of the Exes II Recap Episode 4
So far on "The Challenge" it has been a whirlwind of emotions. With Diem and CT now out the remaining contestants aren't really sure what to expect. Anything can happen.
Tonight on the show Jay and Zach swapped exes and each shared a kiss with one another's former lover. Jonna and Jenny played a game of back and forth with their respective exes.
There doesn't seem to be that much bad blood between the two which is a bit surprising.
Meanwhile Adam and Brittany, although a team can't seem to see eye to eye. Adam was quick to remind everyone that he had put the aggressive moves on Brittany at the bar.
She didn't find this too endearing and the two butted heads.
There was also Nany and Bananas with more relationship drama. Nany admits that she would hate to mess with a relationship he's got back home.
(Nany and Bananas Drama Below)
The exes for the first challenge were forced to strip down and get covered in paint. Admittedly all the exes horribly had to transfer said paint on to one another.
While finally transferring the pain once again to a punching bag. The teams raced to fill bubbles on the bag with the paint.
Jonna and Zach were working very well together and that surprised them both as they won the challenge. Jenna couldn't help being jealous when her knew fling got felt up by his ex. The other couple to win was Jordan and Sarah.
Yet it was Jonna and Zach that claimed the power couple spot. Meanwhile Knight and Jemmye found themselves at the bottom.
Jonna and Zach had the losers over for a talk about who the veterans would face in the dome. Knight was confident that no matter who they face it would be them on top.
Next it was Jay and Jenna that were called over and both couples agreed not to vote each other in for future dome challenges.
Zach and Jonna also discussed their relationship at this current moment in the season. Both were distant on the show so far.
The two finally got some alone time and felt mutual again. Both agreeing to be friends for the sake of the competition.
Out of nowhere there seemed to be a little drama as to who was going to face Knight and Jemmye in the dome. Jonna hesitated and thought about choosing Jenna and Jay over Adam and Brittany. Of course the risk being that choosing a veteran couple over a rookie one.
Zach wasn't fond of this idea and tried to shoot it down as quickly as possible. In the end the smart decision was made and Adam and Brittany were sent to the dome for the third time.
For the dome challenge the couples had to pull a rope together in order to break a wrecking ball through two cinder block layers. Adam was convinced that his strength was going pay off at last.
It ended up not even being close and the young couple moved to 3-0 in the dome. Possibly proving voting them back once again a failure.
Knight and Jemmye had to say goodbye for the time being. Of course the Ex-iled house is constantly awaiting the eliminated contestants. So far Dustin & Jessica and Thomas & Hailey have bit the dust in the Ex-ile challenge.
(Episode 3 Ex-ile Challenge Below)
The Challenge: Battle of the Exes 2 - Full Episodes
Unfortunately last weeks episode saw the exit of two couples. Diem has since lost her battle with cancer and CT took to Instagram and Twitter to thank all the fans for showing him support.
Hello everyone, My family, and I, want to thank you all from the bottom of our hearts for all the... http://t.co/cs9QzV9aj6
-- CT (@TheOfficial_CT) November 20, 2014
Tune in Tuesdays at 11 p.m. eastern on MTV for new episodes. Real World Skeletons airs at 10 p.m. right before. Episode 3 of The Challenge: Battle of the Exes II is available here.

redpajama_set_name: RedPajamaCommonCrawl


The Hard Rock Hotel and Casino in Atlantic City were issued a $5000 fine by the New Jersey Division of Gaming Enforcement. The casino failed to notify the regulator of its appointment of a new executive.
The group recently hired Mann Lien as vice president of Asian marketing.
According to New Jersey regulations, key personnel such as a person in a management position, or any other role in which the employee can affect the way in which a casino operates, must apply for a Key Employee License. The purpose of the license is to ensure that all staff in a position of authority meets the statutory requirements such as honesty, good character and integrity to ensure that the New Jersey casino sector stays free of organized crime.
The Hard Rock failed to make such an application for Mann Lienn, whose appointment as an executive is senior enough to require such a license.
Although the fine is a tiny one for an establishment such as the Hard Rock Café and Casino it is a matter of principal, not one of monetary value. Although Mann Lienn is licensed as a key employee, the license was not obtained through Hard Rock but his previous employment in the same position at the Tropicana. Lienn had already been in the employ of the café and casino since June before it came to the NJ regulators attention. Although he is licensed, the failure to report and the late submittal of a qualification waiver attracted the slap on the wrist.
Mann Lienn is not the only change to Hard Rock AC's human resource portfolio. The company recently appointed a new president of property and senior vice president of marketing. Both appointments came as a surprise to several industry stakeholders.
Joseph Lupo of Borgata fame will be steering the ship and Todd Moyer will fulfill one of the firms top marketing positions.
No reason for the management shuffle has been provided, however the changes were preceded by the company's new partnership with the Gaming Innovation Group (GIG). As one of only a few businesses in NJ that does not offer a sportsbook service in the quickly blossoming US sports betting market, the deal will bring the Hard Rock's product portfolio in line with its competitors.


text:
Loop Industries and SK global chemical Close $56.5M Strategic Equity Deal, Provide Further Update on Asian Joint Venture to Manufacture 100% Recycled PET Plastic
Monday, August 2, 2021 9:00 AM
Loop Industries, Inc.
http://www.loopindustries.com
$56.5 M purchase of new treasury common shares of Loop Industries at $12 per share, pursuant to the agreement announced by the two companies on June 23, 2021
Joint venture to begin planning phase for first Infinite Loop™ facility in Ulsan, South Korea, with a minimum of four manufacturing facilities planned by 2030
Mr. Jonghyuk Lee appointed to Loop's Board of Directors
MONTREAL, QC / ACCESSWIRE / August 2, 2021 / Loop Industries (NASDAQ:LOOP) ("Loop" or "The Company"), a leading innovator in clean technology whose mission is to accelerate a circular plastics economy and SK global chemical Co. Ltd. ("SKGC"), a subsidiary of South Korea's SK Group, announce the closing of a $56.5 million purchase of new treasury common shares of Loop Industries at $12 per share and warrants to purchase common shares, pursuant to the agreement which was announced by the two companies on June 23, 2021. SKGC now owns 10 percent of Loop's common shares.
The strategic partnership between the companies intends to accelerate the commercialization of Loop's technology throughout Asia to supply global CPG brand companies with virgin quality PET resin and polyester fiber made from 100% recycled material. Starting with a first facility in Ulsan, South Korea, expected to begin preparation in 2022, the joint venture intends to build a minimum of four facilities by 2030. Combined, these facilities are expected to process approximately 400,000 tons of PET plastic and polyester fiber waste annually.
Appointment of Jonghyuk Lee to Loop's Board of Directors
In conjunction with the equity investment, Mr. Jonghyuk Lee, Vice President of SKGC's Green Business Division, has been appointed to Loop's Board of Directors, effective immediately. This appointment reflects SKGC's strategic view of the importance of its investment in Loop, as part of its "Green for Better Life" global strategic vision.
About Loop Industries
Loop Industries is a technology company whose mission is to accelerate the world's shift toward sustainable PET plastic and polyester fiber and away from our dependence on fossil fuels. Loop owns patented and proprietary technology that depolymerizes no and low-value waste PET plastic and polyester fiber, including plastic bottles and packaging, carpets and textiles of any color, transparency or condition and even ocean plastics that have been degraded by the sun and salt, to its base building blocks (monomers). The monomers are filtered, purified and polymerized to create virgin-quality Loop™ branded PET resin and polyester fiber suitable for use in food-grade packaging, thus enabling our customers to meet their sustainability objectives. Loop Industries is contributing to the global movement towards a circular economy by preventing plastic waste and recovering waste plastic for a more sustainable future for all.
Common shares of Loop Industries are listed on the Nasdaq Global Market under the symbol "LOOP."
For more information, please visit www.loopindustries.com. Follow us on Twitter: @loopindustries Instagram: loopindustries, Facebook: Loop Industries and LinkedIn: Loop Industries
About SK global chemical
In 1972, SK global chemical laid the foundation for the development of petrochemical industry by operating the first naphtha-cracking facility in Korea. SK global chemical provides various automotive & packaging products and solutions that customers and markets require. Furthermore SK global chemical is growing into a technology-based global chemical company through continuous R&D efforts and global expansion.
SK global chemical will achieve its vision, "Green for Better Life", by establishing a plastics based circular economy by collaborating with various partners and stakeholders. SK global chemical will expand its portfolio of eco-friendly products and will continuously recycle beyond the amount of plastics produced in order to realize and leverage the sustainability efforts that will benefit our planet.
For more information, please visit www.skglobalchemical.com.
This news release contains "forward-looking statements" as defined in the U.S. Private Securities Litigation Reform Act of 1995. Such statements may be preceded by the words "intends," "may," "will," "plans," "expects," "anticipates," "should," "could," "projects," "predicts," "estimates," "aims," "believes," "hopes," "potential" or "continute" the negative of such terms or similar words. These forward-looking statements include, without limitation, statements about our market opportunity, our strategies, ability to improve and expand our capabilities, competition, expected activities and expenditures as we pursue our business plan, the adequacy of our available cash resources, regulatory compliance, plans for future growth and future operations, the size of our addressable market, market trends, and the effectiveness of the Company's internal control over financial reporting. Forward-looking statements are not guarantees of future performance, are based on certain assumptions and are subject to various known and unknown risks and uncertainties, many of which are beyond Loop's control, and cannot be predicted or quantified and consequently, actual results may differ materially from those expressed or implied by such forward-looking statements. Such risks and uncertainties include, without limitation, risks and uncertainties associated with among other things: (i) commercialization of our technology and products, (ii) our status of relationship with partners, (iii) development and protection of our intellectual property and products, (iv) industry competition, (v) our need for and ability to obtain additional funding, (vi) building our manufacturing facility, (vii) our ability to scale, manufacture and sell our products in order to generate revenues, (viii) our proposed business model and our ability to execute thereon, (ix) adverse effects on the Company's business and operations as a result of increased regulatory, media or financial reporting scrutiny and practices, rumors or otherwise, (x) disease epidemics and health related concerns, such as the current outbreak of a novel strain of coronavirus (COVID-19), which could result in (and, in the case of the COVID-19 outbreak, has resulted in some of the following) reduced access to capital markets, supply chain disruptions and scrutiny or embargoing of goods produced in affected areas, government-imposed mandatory business closures and resulting furloughs of our employees, government employment subsidy programs, travel restrictions or the like to prevent the spread of disease, and market or other changes that could result in noncash impairments of our intangible assets, and property, plant and equipment, (xi) the outcome of the current SEC investigation or recent class action litigation filed against us, (xii) our ability to hire and/or retain qualified employees and consultants and (xiii) other factors discussed in our subsequent filings with the SEC. More detailed information about Loop and the risk factors that may affect the realization of forward-looking statements is set forth in our filings with the Securities and Exchange Commission ("SEC"). Investors and security holders are urged to read these documents free of charge on the SEC's web site at http://www.sec.gov. Loop assumes no obligation to publicly update or revise its forward-looking statements as a result of new information, future events or otherwise.
Loop Industries
Stephanie Corrente, Director of Marketing & Communications
+1 (450) 951-8555 ext. 226
SK global chemical
Joon young Jeong, Professional Manager
SK Innovation | Value Creation Center
Media PR - Battery business
Loop Industries Investor Relations
MZ Group-MZ North America
www.mzgroup.us
SOURCE: Loop Industries, Inc.


text:
\section{Introduction}
In recent year, various convolutional networks have been developed for
the applications from low to high level vision problems such as
classification, object detection, image restoration and segmentation problems~\cite{krizhevsky2012imagenet,simonyan2014very,
szegedy2015going,7115171,wang2015deep,girshick2014rich,long2015fully,wang2015designing}.
Among many problems, this paper focuses on developing a
convolutional network that works for image segmentation and filtering problems.
For the semantic segmentation, Long \etal proposed a
convolutional network~\cite{long2015fully}, which adopts a classification network
as an encoder part for exploiting pre-trained features. Then the entire
image is observed by using a pooling and converting the fully connected
layer to a convolution layer. However, the labeling results seem somewhat
coarse due to the limitation of reconstruction part which is consist of
deconvolution layer and up sampling layer. Some researchers tried to alleviate
this weakness by cascade deconvolutional network training~\cite{noh2015learning}
or indexing up sampling layer~\cite{badrinarayanan2015segnet}.

\begin{figure}[t]
	\centering
		\includegraphics[width=0.95\linewidth]{fig/procedure/intro.png}
	\caption{Example of image-to-image deep learning problems where dimension of input and output (label) is same: (from left to right) skin detection, semantic segmentation, and compression artifact reduction}
	\label{fig:Skin Inception23123}
\end{figure}

Meanwhile, convolutional network approaches for lower class pixel-wise
classification such as salient region detection~\cite{wang2015deep,zhao2015saliency},
surface normal classification, and edge orientation classification \cite{wang2015designing}
have also been developed, which may be regarded as specific applications of
semantic segmentation. In these works, they tried to find appropriate convolutional networks
and post processing steps to the convolutional network output.
In many of the above stated works and many other recent works,
a classification network model~\cite{liu2016dhsnet} is usually adopted
for exploiting the pre-trained features.
However, it is not clear whether adopting a classification network
is also effective for the pixel-wise labeling problems with small number of labels.
Further, for the problems where the input and output have the
same dimension as in many of image restoration and labeling problems discussed so far,
since successive dimension reduction by pooling reduces specific pixel information,
it is not helpful at the image reconstruction stage.

From these reasoning, we propose a new convolutional network-in-network structure
that can be applied the image-to-image deep learning problems as in
Figure~\ref{fig:Skin Inception23123}.
The proposed structure can be considered a modification of inception network,
where the pooling is
removed and a larger kernel is added instead.
Our main contributions are summarized as follows.

\begin{enumerate}
\item [$\bullet$]We propose a new inception-like convolutional network-in-network structure, which
consists of convolution and rectified linear unit (ReLU) layers only.  That is, we exclude
pooling and subsampling layer that reduce feature map size, because decimated features
are not helpful at the reconstruction stage. Hence, it is able to do one-to-one (pixel wise)
matching at the inner network and also intuitive analysis of feature map correlation.

\item [$\bullet$] Proposed architecture is applied to several pixel-wise labeling and restoration
problems and it is shown to provide comparable or better performances compared to
the state-of-the-art methods.
\end{enumerate}

In the rest of this paper, overview of related methods is introduced in section 2,
and the proposed structure is presented in section 3.
Analysis and comparison are provided in section 4 and this paper is concluded in section 5.

\begin{figure*}[ht!]
\includegraphics[width=\textwidth]{fig/procedure/overview6.png}
\caption{Proposed Network-in-Network Structure applied to skin detection. The architecture is
composed of 4 convolution layers with nonlinear layers and 8 modified inception modules.
Input image goes through the convolutional network to be a skin probability map at the output.}
\label{fig:Overview Global Network}
\end{figure*}

\section{Related Works}
Since the proposed architecture is very simple in that it uses only convolution layer and
ReLU, which are widely known and used, we skip the review of these. Instead we
review the areas that we apply our architecture: skin detection, semantic segmentation, and compression artifacts reduction as illustrated in Figure~\ref{fig:Skin Inception23123}.

\subsection{Skin Detection}
Skin detection is to locate skin pixels or regions in images, which is an important
pre-processing step in many image processing and computer vision tasks.
For example, it can be used in image enhancement~\cite{zafarifar2013application},
face and human detection~\cite{hsu2002face}, gesture analysis~\cite{Francke2007real},
pornographic contents filtering~\cite{jones2002statistical},
surveillance systems~\cite{zhang2009head}, etc.
Hence there have been a large number of skin detection algorithms,
which is well summarized in~\cite{kakumanu2007survey,vezhnevets2003survey,bianco2015adaptive}.
According to these works, the conventional methods find skin pixels that fit to parametric models  ~\cite{hsu2002face,yang1998gaussian,lee2002elliptical},
nonparametric models~\cite{sigal2004skin,jones2002statistical,khan2010skin},
or some skin-cluster defined regions in certain color spaces~\cite{yang1998skin}.
There are also some methods that detect human related features (hands, faces, and body)
first for finding skin pixels~\cite{liao2008estimation, bianco2015adaptive},
while most of the existing works detect skin pixels for finding human.
In addition, there are neural network methods that try to find skin areas
such as adaptive neural networks~\cite{phung2001universal} and self-organizing
maps~\cite{BMVC.15.51}.
But they are quite old dated and
do not use open database so that direct comparison with existing neural network methods seems
very difficult. Even though there have been many algorithms,
the skin detection is still considered a challenging problem due to diverse
variations of images, illumination conditions, skin color variations
of races and makeup, and skin-like background.

\subsection{Semantic Segmentation}
Pixel-wise semantic segmentation problem is to classify each pixel, \ie, to
label a pixel as one of classes.
As some examples of non-convolutional network approaches,
hand-crafted features are extracted from image patches and they are
trained by random forest classifier
~\cite{shotton2008semantic,brostow2008segmentation} or boosting~\cite{sturgess2009combining}.
After the success of convolutional network to classification problems~\cite{krizhevsky2012imagenet,simonyan2014very,szegedy2015going},
many researchers tried to apply convolutional network to semantic segmentation
problem with patch based method~\cite{NIPS2012_4741,farabet2013learning,icml2014c1_pinheiro14}.
More recently, FCN~\cite{long2015fully,7478072} which contains classification network
and deconvolution layers as encoder and decoder has been proposed. The encoder is a modified VGG~\cite{simonyan2014very} network which converts the original fully connected layer to a
convolution layer. Therefore, the encoder fully reflects entire image and thus obtained the best
performance on benchmark PASCAL VOC 2012 dataset.
Many researchers also proposed new networks to improve the performance of FCN
~\cite{noh2015learning,badrinarayanan2015segnet}.

\subsection{Reduction of Compression Artifacts}

Lossy compression leads to image degradation which is called compression artifacts
such as blocking, ringing, and blurring artifacts.
Conventional non-convolutional network approaches manipulate the artifacts in spatial domain~\cite{reeve1984reduction,Xu:2011:ISV:2070781.2024208,wang2013adaptive} or
frequency domain~\cite{foi2007pointwise}, and more recent work using convolutional network
has shown that deep learning approach provides better results than the
above state methods~\cite{dong2015compression}.
After this work, many researchers also proposed
various kinds of artifact reduction networks~\cite{svoboda2016compression,yu2016deep}.

 \section{Proposed Convolutional Network}
After the convolutional network with inception module~\cite{szegedy2015going} has
been developed, there have been many variants and applications adopting
the inception module for the classification problems. However,
it seems there are not much evidences that the inception based network
is also effective for the image-to-image problems. To be precise,
although the inception module has the advantage that it extracts scale
invariant features by using multi-size kernels and thus shows
good performance on classification problems, it cannot be effectively
used for image-to-image problems due to decimated features by
pooling layers. As an example with the image restoration problems,
the pooling and decimation are generally not used because
we need to keep the features from the original size image for the
successful reconstruction of output image~\cite{Kim_2016_CVPR,dong2015compression,7115171}.
Further the restoration problems do not employ dense prediction
method which is usually used in classification problems, because dense
prediction needs lots of train parameters.
Hence, for taking advantage of network-in-network
structure for the image-to-image problems,
we propose a  new convolutional module which can be
considered a modification of inception module.
Specifically, we remove pooling in the inception module and add a larger size kernel instead
to widen the receptive field which might have been reduced by the
removal of pooling.

The overall convolutional network architecture using the proposed module is shown in
in Figure~\ref{fig:Overview Global Network}, which is consisted of
several convolutional modules and the cascade of proposed multi-kernel
network-in-network modules. The figure shows the application of the
architecture in skin detection problem, and the same architecture is
also applied to semantic segmentation and compression artifacts removal
except that the input can be a patch or decimated image, and also that
the depth of the network can be changed. In the following, we explain
the details of proposed module and overall architecture.

\subsection{Proposed Inception Module}

Christian \etal~\cite{szegedy2015going} proposed GoogLeNet with an inception module
that extracts multi-scale feature maps by using multiple filters with different sizes,
\eg, $1\times1$, $3\times3$, $5\times5$, and also by using pool projection layer
which is composed of $1\times1$ convolution filter and max pooling to make pooling
feature maps. To reduce the number of parameters, an additional $1\times1$
convolution filter (named as \textquotedblleft reduction layer \textquotedblright) is followed by comparably expensive
$3\times3$ and $5\times5$ filters. All the feature maps extracted from the filters are
integrated at the end of the inception module.
Since object regions change considerably in sizes according to scales and perspective views,
the inception module is designed to reflect diverse scales of objects by incorporating
multi-size filters. A filter with small kernel has the role of detecting small object
regions while a larger filter contributes to not only detecting large object regions
but also effectively suppressing false positive regions that have similar properties
as the targeting object.

\begin{figure}[t]
	\centering
	\begin{subfigure}[]{0.5\linewidth}
		\centering
		\includegraphics[width=0.95\linewidth]{fig/procedure/new/inception1.png}

		\caption{\small Original Inception}
	\end{subfigure
	\begin{subfigure}[]{0.5\linewidth}
		\centering

		\includegraphics[width=0.95\linewidth]{fig/procedure/new/inception2.png}
		\caption{\small Modified Inception}
	\end{subfigure}
	\vspace{0.3cm}
	\caption{Inception modules}
	\label{fig:Skin Inception}
\end{figure}

We modify the inception module for image-to-image tasks by adding a larger kernel
($7\times7$) and excluding the pool projection layer as shown in
Figure~\ref{fig:Skin Inception} (b), where the original inception module is shown
in Figure~\ref{fig:Skin Inception} (a) for comparison.
The original inception module~\cite{szegedy2015going} collects information from a wide range
of images due to the overall pooling layers in the network, \ie, the pooling layers enlarge
receptive fields by reducing the size of feature dimension.
However, since we remove the combination of pooling and
fully connected layers in order not to decimate the feature map,
we need to include a larger filter for keeping the receptive field large.
As a specific example for this,
Figure~\ref{fig:Filter} shows how many pixels are involved in detecting
a certain pixel according to filter size and the existence of the pooling layer.
The first row of Figure~\ref{fig:Filter} shows that the receptive range
becomes $5\times5$ pixels at the second layer when we use $3\times3$ kernels
consecutively. The second row shows that the receptive range becomes wider to
$10\times10$ (at the image before decimation) when we add pooling to the
above result. However, since we do not have pooling, we add a larger kernel ($7\times7$)
to keep the receptive range as shown in the last row of Figure~\ref{fig:Filter}.

In summary, the proposed module is composed of a set of filters with different
sizes ($1\times1$, $3\times3$, $5\times5$, $7\times7$) as illustrated in
Figure~\ref{fig:Skin Inception}, each of which consists of 8, 32, 16, and
8 filters respectively. We determine the number of filters in modified inception depending
on the size of the filter in order to reduce the number of parameters.

\begin{figure}[t]
\includegraphics[width=\linewidth]{fig/procedure/filter_example.png}
\caption{Example of the range of input pixels according to the filter size and pooling layer.
It compares the receptive field when the network consists of
$3\times3$ kernel (first row), $3\times3$ kernel with pooling (second row)
and $7\times7$ kernel (third row).
First column is the input,  second is the receptive area at the first layer,
and the third shows the receptive field after the second layer with the
same size kernels or pooling.}
	\label{fig:Filter}
\end{figure}

\subsection{Overall Architecture}
The proposed method is applied to end-to-end mapping problems,
which directly generate the overall output map.
The network is composed of deep
convolution layers and proposed inception modules in order to exploit global
evidence from the entire image. The number of overall parameters is 300K,
which is much smaller than the same-depth inception networks adopted
for classification problems.

\begin{table*}[t]
	\centering
	\caption{Proposed Network-in-Network Architecture based on Modified Inception}

	\begin{adjustbox}{width=0.8\textwidth}
		\renewcommand{\arraystretch}{1.5
		\begin{tabular}{|c| c| c| c| c| c| c| c| c| c| c| c|}
			\hline
			type & \begin{tabular}{@{}c@{}}Kernel Size\end{tabular} & Output size & Depth & \# $1\times1$ & \begin{tabular}{@{}c@{}}\# $3\times3$ \\ Reduce \end{tabular}   & \# $3\times3$   & \begin{tabular}{@{}c@{}}\# $5\times5$ 5\\ Reduce \end{tabular} & \# $5\times5$  & \begin{tabular}{@{}c@{}}\# $7\times7$ \\ Reduce \end{tabular} & \# $7\times7$  & Params   \\ [0.5ex]
			\hline\hline
			Convolution 1 &  $7\times7$  & H $\times$ W $\times$ 64 & 1 & & & & & & & &9K  \\
			\hline
			Convolution 2 &  $3\times3$  & H $\times$ W $\times$ 64 & 2 & &64 &64 & & & & &41K  \\
			\hline
			Inception 1 &  & H $\times$ W $\times$ 64 & 2 & 8 &  32 & 32 & 16 & 16 & 8 & 8&23K  \\
			\hline
			Inception 2 &  & H $\times$ W $\times$ 64 & 2 & 8 &  32 & 32 & 16 & 16 & 8 & 8&23K \\
			\hline
			Inception 3 &  & H $\times$ W $\times$ 64 & 2 & 8 &  32 & 32 & 16 & 16 & 8 & 8&23K \\
			\hline
			Inception 4&  & H $\times$ W $\times$ 64 & 2 & 8 &  32 & 32 & 16 & 16 & 8 & 8&23K  \\
			\hline
			Inception 5 &  & H $\times$ W $\times$ 64 & 2 & 8 &  32 & 32 & 16 & 16 & 8 & 8&23K  \\
			\hline
			Inception 6&  & H $\times$ W $\times$ 64 & 2 & 8 &  32 & 32 & 16 & 16 & 8 & 8&23K \\
			\hline
			Inception 7&  & H $\times$ W $\times$ 64 & 2 & 8 &  32 & 32 & 16 & 16 & 8 & 8&23K \\
			\hline
			Inception 8&  & H $\times$ W $\times$ 64 & 2 & 8 &  32 & 32 & 16 & 16 & 8 & 8&23K \\
			\hline
			Convolution 3&  $5\times5$  & H $\times$ W $\times$ 1 & 1 & & & & & & & &16K \\
			\hline
			Euclidean &  & H $\times$ W $\times$ 1 &  1 & & & & & & & & \\
			\hline
		\end{tabular}
	\end{adjustbox}
	\label{table:kysymys}
\end{table*}

\subsubsection{Input and Output Design for the Proposed Architecture}
As stated previously, we apply the proposed architecture to three problems: skin detection,
semantic segmentation and compression artifacts reduction.
In the case of semantic segmentation, since the images in the dataset have
the same size, we just put the overall image as the input. In the
case of compression artifacts reduction, we set the input
and output as $37 \times 37$ image patches. For the
skin detection problem, the overall image should also be the
input because we wish to capture the wide range features rather
than just color and texture of small patches. However, since
the images in the dataset have various sizes and aspect ratios,
we design the input and output as follows:

\noindent \textbf{Input for the training}: Decimate the image to a fixed size such that smaller
 side (horizontal/vertical) is reduced to 50, and stride the $50 \times 50$ image
 to other (larger side) direction.

\noindent \textbf{Label image for the training}: Decimated and stridden
ground truth binary map in the same way as the input.

\noindent \textbf{Input for inference}: Image that is decimated such that smaller side (horizontal/vertical) is reduced to 50.

\noindent \textbf{Final output}: Probability or binary maps are obtained from the network,
and they are interpolated to the original size.

\subsubsection{Architecture Details for the Skin Detection Problem}
We explain the details only for the skin detection problem, because other
problems use almost the same architecture.
The proposed network for the skin detection consists of 4 convolution layers
and 8 modified inception modules where
each module is composed of 2 convolution layers, so that the depth of the network
adds up to 20 layers as described in Table~\ref{table:kysymys} where \textit{H}
 and \textit{W} are decimated height and width.
It is notated that ``$3\times3$ \textit{reduction}, $5\times5$ \textit{reduction},
and $7\times7$ \textit{reduction}'' are the reduction layers for $3\times3$, $5\times5$
 and $7\times7$ respectively.
The ReLU layer is used as an activation function
which is defined as:
\begin{equation}
f(x) = max(0,x)
\end{equation}
which follows each convolution layer except for the last.

The proposed network can be divided into four parts.
The first part consists of 3 convolution layers of $7\times7$, $1\times1$, and $3\times3$ kernels.
The second part is a set of modified inception modules which are described in section 3.1 where all
modules are identical. The number of modified inception modules is set to 8 which will be
discussed in section 4.2. Then, one convolution layer is used to integrate all the
feature maps in a single
channel which is a skin map produced by the proposed method.
At last, the loss function is included for the training phase.

\subsubsection{Loss function}
Given training images and ground truth $\left\{\mathbf{X}_i,\mathbf{Y}_i\right\}_{i=1}^{N}$,
our goal is to learn the parameters that minimize the loss function which is defined
as Euclidean loss:
\begin{equation}
\mathbf{L}(\Theta) = \frac{1}{N}\sum_{i=1}^{N} \| \mathbf{Y}_i - F(\mathbf{X}_i;\Theta) \|^2,
\end{equation}
where $N$ is the number of training images and $\Theta$ is the parameters of the network.
convolutional network with the Euclidean loss function generally employs low learning rate because it
easily fails to converge. As low learning rate needs much time for convergence, we employ
an adaptive momentum approach~\cite{kingma2014adam}.

\section{Experiments}
For validating the effectiveness of the proposed architecture
to image-to-image problems, we select some of typical such
problems as skin detection, semantic segmentation, and compression
artifacts reduction.

\subsection{Experiment Setup}
Our network is implemented with the Caffe tool box~\cite{jia2014caffe}, and
GTX 980 with 4GB memory is employed both for training and testing.
The input images are subtracted by the average intensity to alleviate
vanishing and exploding gradient problem in the training phase
~\cite{lecun2012efficient}, and all the initial parameters for the
convolution layers are set to zeros.
There are some hyper parameters for the proposed network which
are decided empirically. We set the learning parameters as: initial
learning rate = 0.001, weight decay = 0.0002.
The depth of our network is set to 20 convolution layers,
which is discussed in section 4.2.

\subsection{Application to Skin Detection}
We adopt ECU dataset~\cite{phung2005skin} for training and validation, because it is the largest reliable
set for the skin detection.
It contains various ethnic groups, illumination variation, and complex
non-skin but skin-colored regions/objects which often occlude skin regions.
The dataset consists of 4,000 images, half of which are exploited for
training and other 2,000 images are used for evaluation.
We adopt the precision recall (PR) and Receiver Operating Characteristic
(ROC) curves in evaluating the performance. Moreover, the quality
of binarized skin probability map is evaluated with four metrics: \textit{Accuracy},
\textit{Precision}, \textit{Recall}, and \textit{F-measure} in a fixed threshold
which are determined to make the maximum F-measure of the dataset.

\begin{figure}[t]
	\centering
	\begin{subfigure}[]{0.5\linewidth}
		\centering
		\includegraphics[width=0.95\linewidth]{fig/result_plot/fig/ECU2_ours_depth_ROCcurve2.jpg}
	\end{subfigure
	\begin{subfigure}[]{0.5\linewidth}
		\centering
		\includegraphics[width=0.95\linewidth]{fig/result_plot/fig/ECU2_ours_inception_ROCcurve2.jpg}
	\end{subfigure}
	\vspace{0.1cm}
	\caption{Quantitative comparison of proposed method with
		variations in network structure: ROC curves depth variation (left), filter set variation (right).}
	\label{fig:comparison_ours}
\end{figure}
We conduct two experiments to determine the optimal network structure
for skin detection. First we evaluate the performance according
to network depth: 8, 14, 20, and 26, where the number of filters and
their sizes are all the same. We show the comparison results with ROC
curve in Figure~\ref{fig:comparison_ours} at left the column.
It can be seen that the performance increases as the depth grows up to 20,
but the performance is lowered when 26. More fine variation in depth (not
shown here) shows that performance saturates from the depth of 20.
We think that 20 layers are enough, because this number can cover entire
region as the receptive field when the input image size is $50 \times 50$ as
stated previously. Hence, we set the depth of layers to 20 in the rest of skin
detection experiments.

Second, we show the effectiveness of filter composition
of network module (number of kernels and sizes at each layer).
We conduct experiments with four different variations.
The first module is composed of $1\times1$, $3\times3$, $5\times5$, and
pool projection which are the same composition as GoogLeNet,
so we denote it as \textit{GoogLeNet inception}.
The second module is composed of $1\times1$, $3\times3$, $5\times5$
which removed pool projection layer in \textit{GoogLeNet inception},
so we denote it as \textit{w/o pool projection} and the third consists of
$1\times1$, $3\times3$, $5\times5$, and $7\times7$ in addition,
so we denote it as \textit{network with $7\times7$}.
The final filter set consists of $1\times1$, $3\times3$, $5\times5$,
$7\times7$, and $9\times9$ filters, so it is denoted as
\textit{network with $7\times7$ and $9\times9$}.
The comparison results with ROC curve is shown in
Figure~\ref{fig:comparison_ours} at the right column.
It can be seen that the \textit{GoogLeNet inception} case has lower
performance than the others. It means that pool projection does not help
to make effective feature maps, because
some of spatial information can be lost due to max pooling.
Since it is observed that \textit{network with $7\times7$} and
\textit{network with $9\times9$} have similar performance on ROC
curves, we determine to use \textit{network with $7\times7$}
in the rest of experiments.

\begin{figure}[h]
	\centering
	\includegraphics[width=0.95\linewidth]{fig/procedure/feature.png}\hspace{0.5em
	\caption{Visualization of feature maps.}
	\label{fig:featuremap}
\end{figure}

We also visualize feature maps in Figure~\ref{fig:featuremap} to show how
our inception module works. The first row shows $1\times1$ feature maps, second to fifth
show $3\times3$, sixth to seventh $5\times5$, and eighth shows $7\times7$ feature maps.
We can see in ``Inception 4'' feature maps that small filters usually make features
from texture information, while large filters ($7\times7$ filter) group skin and
non skin region from shape information.
At ``Inception 6'' feature maps, we can find that small filters can suppress small
non skin region such as pupils and mouth, and larger filters can
find overall face as skin regions which were missed in small filters (red box).
From these observation, adding a $7\times7$ filter helps to group
skin region more effectively.

\subsection{Comparison with Other Skin Detection Methods}

We compare the proposed method using benchmark skin dataset with
other methods: Bayesian~\cite{jones2002statistical},
FPSD~\cite{kawulok2013fast}, DSPF~\cite{kawulok2014spatial},
FSD~\cite{tan2012fusion}, and LASD~\cite{hwang2013luminance}.
FPSD and DSPF are based on seed propagation over the graph
representation of images.
FSD is the mix of dynamic threshold and Gaussian model method.
LASD is a luminance adapted color space method which optimizes least square error.
We also test the proposed method only with gray channel input
to show that our method effectively exploits
structure information, and this structure is denoted as \textquotedblleft Proposed (gray)\textquotedblright.

\begin{figure}[h]
	\centering
	\includegraphics[width=0.48\linewidth]{fig/result_plot/fig/ECU2_PRcurve.jpg}\hspace{0.5em}%
	\includegraphics[width=0.48\linewidth]{fig/result_plot/fig/ECU2_ROCcurve2.jpg
	\caption{Comparison of PR (left) and ROC (right) curves on ECU dataset.}
	\label{fig:comparison_others_ECU}
\end{figure}

\begin{table}[h]
	\caption{Evaluation of skin detection on ECU dataset at peak F-measure.}

	\begin{center}
		\resizebox{0.45\textwidth}{!}
		{
			\begin{tabular}{|l|c|c|c|c|c|}
				\hline
				Method &  Accuracy & Precision & Recall & F-measure\\
				\hline\hline
				Bayesian~\cite{jones2002statistical} 	&	0.8910 & 0.7292		& 0.8220	& 0.7728\\
				FPSD~\cite{kawulok2013fast}     & 0.9106		& 0.7948	& 0.8534	& 0.8231\\
				DSPF~\cite{kawulok2014spatial}   &  0.9190 		& 0.7713	& 0.8864 &  0.8249\\
				Proposed (gray) & 0.9276			& 0.8137	& 0.8087	& 0.8112 \\
				Proposed & \textbf{0.9562}	& \textbf{0.8720}	& \textbf{0.9122}	& \textbf{0.8917}\\
				\hline
			\end{tabular}
		}
	\end{center}
	\label{table:comparison_ECU}
\end{table}
We conduct test for two datasets: ECU~\cite{phung2005skin}
(without trained images) and Pratheepan~\cite{yogarajah2010dynamic}.
The Pratheepan is 32 facial images, each of which has a single face.
First we compare the proposed method and other methods for ECU
dataset which are plotted in Figure~\ref{fig:comparison_others_ECU}.
 Our method outperforms other methods by a large margin in terms of
 PR and ROC curves. We can also find that using a single gray channel
 image is also slightly better than Bayesian method which employ color
 information. It shows that our approach uses shape of human parts (body,
 hands, and faces) as well as color information.

We also evaluate the quality of binary map obtained by thresholding the above
probability map. The threshold is set as to maximize F-measure which is differently
selected at each method. Table~\ref{table:comparison_ECU} shows this on ECU dataset,
and our method has the best performance.

Subjective comparison is presented in Figure~\ref{fig:comparison_others_vis1}.
It shows that our method robustly detects the skins of various races,
rejects skin-like background region (non-skin region), and works robustly
to illumination variations. Specifically, at the first row of Figure~\ref{fig:comparison_others_vis1},
the illumination variation on the baby's face is very severe, which lowered the
performance of other methods, while our method works robustly as it uses color and
shape information effectively as illustrated in Figure~\ref{fig:featuremap}.
It is also shown that other methods often fail to suppress skin-like background
clutter (row 2,3, and 4), whereas our method can overcome these difficulties very well.

\begin{figure}[h]
	\centering
	\includegraphics[width=0.48\linewidth]{fig/result_plot/fig/Pratheepan_PRcurve.jpg}\hspace{0.5em}%
	\includegraphics[width=0.48\linewidth]{fig/result_plot/fig/Pratheepan_ROCcurve2.jpg
	\caption{Comparison of PR (left) and ROC (right) curves on Pratheepan dataset.}
	\label{fig:comparison_others_PRA}
\end{figure}

\begin{table}[h]
	\caption{Evaluation of skin detection on Prateepan dataset at peak F-measure.}

	\begin{center}
		\resizebox{0.45\textwidth}{!}
		{
			\begin{tabular}{|l|c|c|c|c|c|}
				\hline
				Method &  Accuracy & Precision & Recall & F-measure\\
				\hline\hline
				Bayesian~\cite{jones2002statistical}   &	0.8237  & 0.6881		& 0.8972	& 0.7788 	\\
				FSD~\cite{tan2012fusion} &	0.8255  & 0.8077		& 0.6851	& 0.7414\\
				LASD~\cite{hwang2013luminance} &	 0.8361 & 0.7954& 0.8275 & 0.8111\\
				FPSD~\cite{kawulok2013fast}     &  0.8419		& 0.7837	& \textbf{0.8991}	& 0.8070	\\
				DSPF~\cite{kawulok2014spatial}    &   0.8521  		& 0.7543	& 0.8436	& 0.7964	\\
				Proposed (gray)  & 0.9211	& 0.8296 & 0.8379 & 0.8337 \\
				Proposed & \textbf{0.9483}	& \textbf{0.9003} & 0.8912 & \textbf{0.8957} \\
				\hline
			\end{tabular}
		}
	\end{center}
	\label{table:comparison_Prateepan}
\end{table}

Comparison on Pratheepan dataset is shown in Figure~\ref{fig:comparison_others_PRA},
where it can be seen that the proposed method has the best performance on both
PR and ROC curves. Moreover, it has much better performance than others even
in the case of using only gray channel image.
Table~\ref{table:comparison_Prateepan} also shows that proposed method yields
good results in terms of many performance measures.
Finally, the subjective comparison for Pratheepan dataset is
shown in Figure~\ref{fig:comparison_others_vis2}.

\begin{figure}[h]
	\centering
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02011.jpg}\hspace{0.1em}%
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02011_Bayes.png}\hspace{0.1em}%
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02011_DSPF.png}\hspace{0.1em}%
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02011_FPSD.png}\hspace{0.1em}%
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02011_ECU2_gray.png}\hspace{0.1em}%
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02011_ECU2_22_ori.png}\hspace{0.1em
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02011.png}\vspace{0.1em}\\

	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02008.jpg}\hspace{0.1em}%
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02008_Bayes.png}\hspace{0.1em}%
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02008_DSPF.png}\hspace{0.1em}%
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02008_FPSD.png}\hspace{0.1em
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02008_ECU2_gray.png}\hspace{0.1em}%
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02008_ECU2_22_ori.png}\hspace{0.1em
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02008.png}\vspace{0.1em}\\

	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02013.jpg}\hspace{0.1em}%
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02013_Bayes.png}\hspace{0.1em}%
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02013_DSPF.png}\hspace{0.1em}%
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02013_FPSD.png}\hspace{0.1em
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02013_ECU2_gray.png}\hspace{0.1em}%
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02013_ECU2_22_ori.png}\hspace{0.1em
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02013.png}\vspace{0.1em}\\

\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02335.jpg}\hspace{0.1em}%
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02335_Bayes.png}\hspace{0.1em}%
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02335_DSPF.png}\hspace{0.1em}%
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02335_FPSD.png}\hspace{0.1em}%
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02335_ECU2_gray.png}\hspace{0.1em}%
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02335_ECU2_22_ori.png}\hspace{0.1em
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02335.png}\vspace{0.1em}\\

	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02214.jpg}\hspace{0.1em}%
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02214_Bayes.png}\hspace{0.1em}%
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02214_DSPF.png}\hspace{0.1em}%
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02214_FPSD.png}\hspace{0.1em
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02214_ECU2_gray.png}\hspace{0.1em}%
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02214_ECU2_22_ori.png}\hspace{0.1em
	\includegraphics[width=0.12\linewidth]{fig/intra_results/ECU2_ImgGt/ECU2_ImgGt/im02214.png}\vspace{0.1em}\\

	\caption{Subjective comparison of skin detection methods
	on ECU dataset: (from left to right) input, Bayesian, DSPF, FPSD, proposed (gray), proposed,
	and ground truth.}
	\label{fig:comparison_others_vis1}
	\vspace{0.5cm}
	\includegraphics[width=0.10\linewidth]{fig/intra_results/Prateepan/dulani_anuradha7.jpg}\hspace{0.1em}%
	\includegraphics[width=0.10\linewidth]{fig/intra_results/Prateepan/dulani_anuradha7_Bayes.png}\hspace{0.1em}%
	\includegraphics[width=0.10\linewidth]{fig/intra_results/Prateepan/dulani_anuradha7_DSPF.png}\hspace{0.1em}%
	\includegraphics[width=0.10\linewidth]{fig/intra_results/Prateepan/dulani_anuradha7_FPSD.png}\hspace{0.1em}%
	\includegraphics[width=0.10\linewidth]{fig/intra_results/Prateepan/dulani_anuradha7_FSD.png}\hspace{0.1em}%
	\includegraphics[width=0.10\linewidth]{fig/intra_results/Prateepan/dulani_anuradha7_LASD.png}\hspace{0.1em}%
	\includegraphics[width=0.10\linewidth]{fig/intra_results/Prateepan/dulani_anuradha7_gray.png}\hspace{0.1em}%
	\includegraphics[width=0.10\linewidth]{fig/intra_results/Prateepan/dulani_anuradha7_re.png}\hspace{0.1em}%
	\includegraphics[width=0.10\linewidth]{fig/intra_results/Prateepan/dulani_anuradha7.png}\hspace{0.1em

	\includegraphics[width=0.10\linewidth]{fig/intra_results/Prateepan/josh-hartnett-Poster-thumb.jpg}\hspace{0.1em}%
	\includegraphics[width=0.10\linewidth]{fig/intra_results/Prateepan/josh-hartnett-Poster-thumb_Bayes.png}\hspace{0.1em}%
	\includegraphics[width=0.10\linewidth]{fig/intra_results/Prateepan/josh-hartnett-Poster-thumb_DSPF.png}\hspace{0.1em}%
	\includegraphics[width=0.10\linewidth]{fig/intra_results/Prateepan/josh-hartnett-Poster-thumb_FPSD.png}\hspace{0.1em}%
	\includegraphics[width=0.10\linewidth]{fig/intra_results/Prateepan/josh-hartnett-Poster-thumb_FSD.png}\hspace{0.1em}%
	\includegraphics[width=0.10\linewidth]{fig/intra_results/Prateepan/josh-hartnett-Poster-thumb_LASD.png}\hspace{0.1em}%
	\includegraphics[width=0.10\linewidth]{fig/intra_results/Prateepan/josh-hartnett-Poster-thumb_gray.png}\hspace{0.1em}%
	\includegraphics[width=0.10\linewidth]{fig/intra_results/Prateepan/josh-hartnett-Poster-thumb_re.png}\hspace{0.1em}%
	\includegraphics[width=0.10\linewidth]{fig/intra_results/Prateepan/josh-hartnett-Poster-thumb.png}\hspace{0.1em

\caption{Subjective comparison of other methods on Prateepan dataset: (from left to right) input, Bayesian, DSPF, FPSD, FSD, LASD, proposed (gray), proposed, and ground truth.}
\label{fig:comparison_others_vis2}
\end{figure}

\subsection{Application to Semantic Segmentation}

We also apply the proposed network to semantic segmentation problem, where
the only difference from the above skin detection network is the loss function.
Specifically, we use softmax as a loss function and the other parameters
(depth, number of filters, learning rate, etc) are kept the same as the skin detection work.
We train and evaluate proposed architecture using CamVid~\cite{brostow2009semantic}
set which is a 11 class set (mainly road, building, sky, and car).
We train our network employing 367 training images and test with 233 RGB images
at 360 $\times$ 480 resolution.
It is compared with FCN~\cite{long2015fully} and SegNet~\cite{badrinarayanan2015segnet}
which are the state of the art semantic segmentation convolutional networks.
We adopt three commonly employed evaluation measure: \textit{Accuracy} for global
pixel-wise accuracy, \textit{Class Mean} for class average accuracy
, and \textit{I/U} is the mean of intersection over union.

\begin{figure*}[t]
	\centering
	\includegraphics[width=0.19\linewidth]{fig/jpeg_results/caps_jpeg.png}\hspace{0.1em}%
	\includegraphics[width=0.19\linewidth]{fig/jpeg_results/caps_arcnn.png}\hspace{0.1em}%
	\includegraphics[width=0.19\linewidth]{fig/jpeg_results/caps_resi.png}\hspace{0.1em}%
	\includegraphics[width=0.19\linewidth]{fig/jpeg_results/caps_propo.png}\hspace{0.1em}%
	\includegraphics[width=0.19\linewidth]{fig/jpeg_results/caps_ori.png}\hspace{0.1em}\vspace{0.1em}\\

	\includegraphics[width=0.19\linewidth]{fig/jpeg_results/ocean_jpeg.png}\hspace{0.1em}%
	\includegraphics[width=0.19\linewidth]{fig/jpeg_results/ocean_arcnn.png}\hspace{0.1em}%
	\includegraphics[width=0.19\linewidth]{fig/jpeg_results/ocean_resi.png}\hspace{0.1em}%
	\includegraphics[width=0.19\linewidth]{fig/jpeg_results/ocean_propo.png}\hspace{0.1em}%
	\includegraphics[width=0.19\linewidth]{fig/jpeg_results/ocean_ori.png}\hspace{0.1em}\vspace{0.1em}\\

	\caption{Subjective comparison of the proposed method with others:
		(from left to right) JPEG, AR-CNN, RAR-CNN, proposed,
		and original.}
	\label{fig:comparison_arcnn}
\end{figure*}

\begin{figure}[h]
	\centering
	\includegraphics[width=0.3\linewidth]{fig/semantic_results/input/0001TP_008550.png}\hspace{0.3em}%
	\includegraphics[width=0.3\linewidth]{fig/semantic_results/result/0001TP_008550.png}\hspace{0.3em}%
	\includegraphics[width=0.3\linewidth]{fig/semantic_results/gt/0001TP_008550.png}\vspace{0.1em}\\

	\includegraphics[width=0.3\linewidth]{fig/semantic_results/input/0001TP_009930.png}\hspace{0.3em}%
	\includegraphics[width=0.3\linewidth]{fig/semantic_results/result/0001TP_009930.png}\hspace{0.3em}%
	\includegraphics[width=0.3\linewidth]{fig/semantic_results/gt/0001TP_009930.png}\vspace{0.1em}\\

	\includegraphics[width=0.3\linewidth]{fig/semantic_results/input/Seq05VD_f03090.png}\hspace{0.3em}%
	\includegraphics[width=0.3\linewidth]{fig/semantic_results/result/Seq05VD_f03090.png}\hspace{0.3em}%
	\includegraphics[width=0.3\linewidth]{fig/semantic_results/gt/Seq05VD_f03090.png}\vspace{0.1em}\\

	\caption{Visualization on CamVid dataset: (from left to right) input, proposed, and ground truth.}
	\label{fig:visualization_cam}
\end{figure}

\begin{table}[h]
	\caption{Evaluation of semantic segmentation on CamVid dataset.}
	\begin{center}
		\resizebox{0.35\textwidth}{!}
		{
			\begin{tabular}{|l|c|c|c|c|c|}
				\hline
				Method &  Accuracy & Class Mean & I/U\\
				\hline\hline
				FCN~\cite{long2015fully} 	&	0.839 & 0.556		& 0.450\\
				SegNet~\cite{badrinarayanan2015segnet}     & 0.842		& 0.565	& 0.477	\\
				Proposed    &  \textbf{0.855} 		& \textbf{0.651}	& \textbf{0.517} \\
				\hline
			\end{tabular}
		}
	\end{center}
	\label{table:comparison_CamVid}
\end{table}
Quantitative results in Table~\ref{table:comparison_CamVid} show that
the proposed method
yields better performance than the others in terms of all measure metrics.
Visualization of output is presented in Figure~\ref{fig:visualization_cam}.
Proposed method classifies road and car very well, but it has limitations when the trees occlude buildings.

\subsection{Application to Compression Artifacts Reduction}
Our network is also applied to reducing the compression artifacts,
especially the compression noise in JPEG images.
The number of training images is 400 and test dataset is 5 classical
images and LIVE1 which contains 29 images.
We compare the proposed algorithm to SA-DCT~\cite{foi2007pointwise} and convolutional network such as AR-CNN~\cite{dong2015compression} and RAR-CNN~\cite{svoboda2016compression}.
SA-DCT is deblocking oriented method at frequency domain which is regarded as the state-of-the-art method before using a convolutional network.
AR-CNN consist of four convolution layers and RAR-CNN is composed of
four convolution layer which is trained with residual learning.
\begin{table}[h]
	\caption{Image reconstruction quality on 5 classical validation images for JPEG quality 10 and 20.}
	\begin{center}
		\resizebox{0.4\textwidth}{!}
		{
			\begin{tabular}{|l|c|c|c|c|c|c|}
				\hline
				\multirow{2}{*}{Methods} &\multicolumn{2}{c}{Q = 10} & \multicolumn{2}{c}{Q = 20} \vline \\\cline{2-5}
				&  \textit{PSNR} & \textit{SSIM}	&	\textit{PSNR}	& \textit{SSIM}\\\hline\hline
				JPEG  	&	27.82 & 0.780		& 30.12 & 0.854\\
				SA-DCT~\cite{foi2007pointwise}     & 28.88		& 0.807	& 30.92 & 0.8663	\\
				AR-CNN~\cite{dong2015compression}     & 29.04		& 0.811	& 31.16& 0.869	\\
				Proposed  & \textbf{29.34} &  \textbf{0.820} 		& \textbf{31.51}	& \textbf{0.876} \\
				\hline
			\end{tabular}
		}
	\end{center}
	\label{table:comparison_5classic_arcnn}
\end{table}
\begin{table}[h]
	\caption{Image reconstruction quality on LIVE1 validation dataset for JPEG quality 10 and 20.}
	\begin{center}
		\resizebox{0.4\textwidth}{!}
		{
			\begin{tabular}{|l|c|c|c|c|c|c|}
				\hline
				\multirow{2}{*}{Methods} &\multicolumn{2}{c}{Q = 10} & \multicolumn{2}{c}{Q = 20} \vline \\\cline{2-5}
				&  \textit{PSNR} & \textit{SSIM}	&	\textit{PSNR}	& \textit{SSIM}\\\hline\hline
				JPEG  	&	27.77 & 0.791		& 30.07 & 0.868\\
				SA-DCT~\cite{foi2007pointwise}     & 28.65		& 0.809	& 30.81 & 0.878	\\
				AR-CNN~\cite{dong2015compression}     & 28.98		& 0.822	& 31.29& 0.887	\\
				RAR-CNN~\cite{svoboda2016compression}     & 29.08		& 0.824	& 31.42	&0.891\\
				Proposed  & \textbf{29.25} &  \textbf{0.828} 		& \textbf{31.60}	& \textbf{0.894} \\
				\hline
			\end{tabular}
		}
	\end{center}
	\label{table:comparison_arcnn}
\end{table}
Quantitative results evaluated on 5 classical images and LIVE1 dataset are presented in Table~\ref{table:comparison_5classic_arcnn} and~\ref{table:comparison_arcnn}, which
show that the proposed method yields the best performance for the
compressed images at the quality factor of 10 and 20 in
terms of PSNR and SSIM.
Subjective results are shown in Figure~\ref{fig:comparison_arcnn}, where
enlarged image can be found in the supplementary file.

\section{Conclusions}
We have proposed a network-in-network structure based on the
inception module of GoogLeNet, which can be effectively used for
the problems of reconstructing an image as the output.
Specifically, the pool projection is removed from the original inception
module in order not to decimate the features, and $7\times7$ convolutional network
is added instead to keep the receptive field wide.
The proposed architecture is
applied to skin detection, semantic segmentation
and compression artifacts reduction, and it is shown to yield competent results compared
with the state-of-the-art methods.
We believe that the proposed network widens the application
areas of inception-like modules to the image-to-image problems, \ie,
it can be adopted to many other image restoration problems with
some modification and tuning.

{\small
	\bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


text:
7 Book Sub-Genres That Are So Extremely Specific You Probably Haven't Heard Of Them Before
By Charlotte Ahlin
Stocksy/Good Vibrations Images
So you've just opened up a new indie bookshop (congratulations!) and you've decided to organize your shelves by genre. Sure, you might personally believe that book genres can be a limiting and somewhat outmoded way of unnecessarily dividing up our diverse world of literary brilliance... but sometimes a girl just wants to know where the romance section is. This should be easy enough. There are only like five or six book genres, right? But then you start to wonder if you should really shelve sci-fi and fantasy together, or if they each deserve their own section. And while you're on the subject, should cyberpunk get its own section within sci-fi? What about YA dystopia cyperpunk? What about alternate history YA dystopia cyperpunk? What about romantic alternate history YA dystopia cyperpunk? Where did all of these sub-genres come from, and how did they get so specific?!
I mean yes, at the end of the day it is pretty subjective where one sub-genre ends and another one begins. But there's no denying that we live in a beautiful new world of possibilities when it comes to finding a book genre that is perfectly tailored to your own weird reading tastes. Here are a few extremely specific sub-genres to explore, even if you've never heard of them before.
1. Sky Pirates
Sky pirates, my friends. Sky. Pirates. The Sky Pirates genre seems to be a descendant of Steampunk fiction, which in turn is an offshoot of Sci-Fi (basically, what if Victorian-era steam technology was way cooler and everyone had airships?). Throw in some good old fashioned pirates and plundering, but in the sky instead of the sea, and you've got a weird, wonderful genre full of buccaneers in blimps. Notable Sky Pirates books include Jules Verne's classic Robur the Conqueror, The Edge Chronicles by Paul Stewart, and, of course, Sky Pirates by Liesel Schwarz.
2. Cashier Memoirs
You've read Showbiz Memoirs, sure, but have you ever entered the glamorous and rarefied world of the Cashier Memoir? OK, so most of these books aren't all that glamorous. Rather, the sub-genre of the Cashier Memoir explores the frustration (and, at times, straight up cruelty) that most cashiers deal with on the daily. If you've ever worked as a cashier, these books are highly relatable. And if you haven't... you'd better check them out to make sure that you're not one of the nightmare customers. Cashier Memoirs include Checkout by Anna Sam, Memoir of a Cashier: Korean Americans, Racism, and Riots by Carol Park, and Letters From Your Friendly Cashier by Carrie Evans.
3. Solarpunk
If Steampunk is a vision of neo-Victorian tech, and Cyberpunk is a hellscape of creepy virtual tech, then Solarpunk is a beautiful fantasy of environmentally friendly tech. That doesn't mean that all Solarpunk books are plotless manifestos about urban rooftop farming. But in Solarpunk stories, there is always at least a glimmer of hope that humanity might be able to use some of our wacky technology to actually help the Earth instead of destroying it. Examples of Solarpunk include the anthology Glass and Gardens edited by Sarena Ulibarri, the anthology Wings of Renewal edited by Claudie Arseneault and Brenda J. Pierson, and the novel New York 2140 by Kim Stanley Robinson
4. Counter-Lovecraft
Is it just me, or are there a lot of Counter-Lovecraft books coming out these days? By "Counter-Lovecraft" I mean, essentially, stories based on the writing of H.P. Lovecraft that H.P. Lovecraft would hate. See, Lovecraft's writing was hugely influential on the genres of Horror and Science Fiction. His tales of unimaginable evil and alienation are sometimes called Cosmic Horror, playing on humanity's insignificance in the cosmic scheme of things. Lovecraft was a very scary writer. He was also an Olympic level racist, sexist, homophobe, white supremacist, and generally awful human being. However, there's a current trend of taking Lovecraft's stories and using them to explore issues of oppression and real life alienation, subverting Lovecraft's bigotry with his very own tentacle monsters. Examples include Lovecraft Country by Matt Ruff, The Ballad of Black Tom by Victor LaValle, and Winter Tide by Ruthanna Emrys.
5. Bangsian Fantasy
Most Bangsian stories start out like a joke: *insert famous person here* walks into Heaven. Or Hell. Or Limbo. Or some combination of the three. But rather than ending with a punchline, a Bangsian book will explore that person's adventures as they wander around the afterlife, usually (but not always) with a humorous slant. The genre is named for author John Kendrick Bangs, who loved a good story about a famous dead person. Examples include Heroes in Hell by Janet Morris, Chris Morris, and C.J. Cherryh, the Riverworld series by Philip José Farmer, A House-Boat on the Styx by John Kendrick Bangs, and Dante's Inferno.
6. Mannerpunk
Look, we all love a good comedy of manners. Jane Austen is still hugely popular, and most modern sitcoms bank on our common understanding of social norms and unwritten rules of conduct. But what if I told you that you could have a comedy of manners... with zombies? Mannerpunk is a genre that mashes up fantasy and manner-based comedy. Stories take place in an elaborate social structure, battling primarily with wits instead of monsters, even though the world around them might include magic and mayhem of every kind. Examples include Swordspoint by Ellen Kushner, Pride and Prejudice and Zombies by Seth Grahame-Smith, and Soulless by Gail Carriger.
7. Mr. Darcy POV Books
I want to be clear here: I don't mean modern re-tellings of the novel Pride and Prejudice, or mash-up Mannerpunk stories that add zombie vampires to Pride and Prejudice, or even prequels or sequels to Pride and Prejudice. Those all exist in abundance, too. But what I'm talking about is the entire sub-genre of books that more or less tell Jane Austen's original story from Mr. Darcy's perspective. There are many of them. Some of the most popular include His Good Opinion: A Mr. Darcy Novel by Nancy Kelley, Mr. Darcy's Diary by Amanda Grange, Into Hertfordshire by Stanley Michael Hurd.


Samsung, Xiaomi and Meizu better watch out. The future Apple is coming after them. Or something like that.
It's always fun to pick around and see how the fortune tellers in office buildings see the future. In our case, it's especially fun to talk about some stellar hardware. Ming-chi Kuo has been a highly regarded analyst for a while at KGI Securities and he got the iPhone SE (mostly) in the bag. Didn't catch that part about the iPhone 5s dropping out of the race, huh?
He believes that Apple will revisit material options and choose glass over ceramic or plastic. Specifically, glass and metal will help attain a "completely new form factor design" akin to the iPhone 4, but thinner and lighter than what a mass production of plastic or ceramic phones would successfully yield. Bezels would shrink and users would "more comfortably grip" the device.
Apple may also look to see if it can get enough AMOLED panels sized 5.8 inches to bump up its Plus model. This would replace the current 5.5-inch iPhone Plus scheme and complement the regular 4.7-inch version. The hope is that with the new build, the 5.8-inch screen will fit into a body that's smaller than the current iPhone 6s Plus.
On new antennas, we find wireless charging and more biometrics on the agenda. No specifics have been mentioned and gone is talk about any long-range wireless charging.
We have 18 months before we walk into the future, friends. Let's tuck this in the back of our minds and see if this note will burn.


text:
Q: InvalidOperationException: Collection Was Modified I know that I can't modify a collection while iterating over it.
Normally I make a copy of the collection I want to iterate over, then iterate over the copy to avoid errors.
My program; however, makes lots of fast modifications to the collection I'm trying to make a copy of.
Could the error be occurring because I am making a copy while the parent collection is being modified?
I know ConcurrentDictionary provides some sort of locking mechanism for cross threading. Is there something similar I can use to prevent this error?
    // Original collection:
    Dictionary<string, Enemy> Dict_Enemies = new Dictionary<string, Enemy>();

    // Copy of original collection: (this line throws the exception)
    Dictionary<string, Enemy> Dict_Enemies_Copy = new Dictionary<string, Enemy>(Dict_Enemies);

A: Try using a ConcurrentDictionary for Dict_Enemies to allow it to be modified from multiple threads. Then make a copy of it in a thread-safe operation using the ToArray() method:
// Original collection:
var Dict_Enemies = new ConcurrentDictionary<string, Enemy>();

// Thread-safe copy:
ICollection<KeyValuePair<string, Enemy>> Dict_Enemies_Copy = Dict_Enemies.ToArray();

A: A Dictionary<K,V> does not support multiple concurrent writers, which means you must already have a lock. Just make sure that you create your copy while holding that lock.

A: Encapsulate this dictionary as a private member of a class, and use a lock (with the same lock object in both places, possibly the dictionary itself) to ensure that writes and copies don't happen at the same time.  Don't allow direct access to the dictionary anywhere else.

A: But similar code is perfeclty working for me.....Could you give more explanation of your code???
  Dictionary<int, Test> dicOrg = new Dictionary<int, Test>();//Original
  dicOrg.Add(1, new Test() {id= 1,name="name",add="add"});
  Dictionary<int, Test> dicCopy = new Dictionary<int,Test>(dicOrg);//Copy of dicOrg

redpajama_set_name: RedPajamaStackExchange


coloring page butterfly free printable butterfly coloring sheets for preschoolers printable butterfly coloring page printable butterfly coloring pages color butterfly coloring page easy.
africa coloring pages africa flag coloring pages.
free abstract coloring pages coloring pages abstract designs patterns coloring pages abstract adorable difficult geometric printable coloring pages abstract designs free printable abstract coloring pa.
emo coloring pages coloring book pages printable coloring pages printable emo coloring pages emo coloring book also emo emo heart coloring pages.
easter coloring pages religious religious coloring pages risen religious easter coloring pages for toddlers.
berenstain bears coloring pages bears coloring pages berenstain bears forget their manners coloring pages.
pocahontas coloring page and smith coloring pages pocahontas super coloring pages.
presidents day coloring pages presidents day printable coloring pages presidents day coloring pages luxury ties president to print presidents day coloring page pages printable presidents free printabl.
ocean animal coloring pages ocean animal coloring pages for preschoolers under the sea coloring page ocean sheets life pages preschool animals pictures co sea animals coloring pages cute sea animal co.
chick coloring pages chick coloring page girl coloring pages.


text:
Murals
 Broadway Street: 1910–1920 (2011), Bev Ecker
 Forest for the Trees murals (2017); three murals by Meg Adamson and Jennifer Parks, Blaine Fontana, and Drew Merritt
 Hernandez Mural (2009), Hector Hernandez and Merlo Station Students
 Larry Kangas Mural (29014), Larry Kangas (completed by Sandy Kangas and Allison McClay)
 Rather Severe Mural (2016), Rather Severe
 The School of Outdoor Learning (2011), Angelina Marino
 Steps to Wellness (2012), Angelina Marino
 Wilson Mural (2009), Gina Wilson

Source: City of Beaverton, Oregon

Sculpture
 embrace your inner light (2016), Angela Ridgway
 Just the Two of Us, Katy McFadden
 Ribbon Candy (2014), Paul Vexler
 Singing Sky (2010), Richard Taylor
 Spinning Progress (2017), Reven Swanson
 Sun (2016), Jesse Swickard
 Three Creeks, One Will (2013), Devin Laurence Field

Source: City of Beaverton, Oregon

References

External links

 Downtown Beaverton Public Art Tour Map (PDF), City of Beaverton, Oregon

Culture of Beaverton, Oregon
Beaverton, Oregon
Beaverton
Outdoor sculptures in Beaverton, Oregon

redpajama_set_name: RedPajamaWikipedia


text:
Q: regex substring not containing words I'm looking for a regex that matches strings from multiple lines that do not include certain words/characters.
In my case it is for refactoring of HTML template files. I have to remove inline stylings except when they contain a display:none; or $TEMPLATE_VARIABLE. For this I'm trying to use the search and replace function with regex from Netbeans.
What I had first is following:
    style="[^"(?!\$)]*"

Regex Test 1 This matches all style declarations that does not include template variables, but unfortunately does include display:none.
After some research I came up with the following:
    style="(?!display\s*:\s*none)[^"(?!\$)]*"

Regex Test 2 This works until something in the style declaration preceedes the display:none style.
Trying different approaches with negative lookbehinds and lookaheads did not result in success. For example:
    style="(?!.*(\$|display)).*"

Regex Test 3 This seemed to work at first glance but has several problems: other HTML element attributes that follow a style definition are matched together with the style definition and if there is a template variable used somewhere after the style definition there is no match for that style.
Does anyone have an idea how the regex has to look so that it turns this
    Test
    <form style="border: 1px solid black" method="POST">
        TestTest

            Test
            Test $NOT_STYLING_TEMPLATE_VARIABLE

    </form>

into this?
    Test
    <form method="POST">
        TestTest

            Test
            Test $NOT_STYLING_TEMPLATE_VARIABLE

    </form>

The remaining stylings where display:none or template variables are used will be cleaned by hand.
Thanks in advance!

A: Brief
You shouldn't be using regex to parse HTML, but I'll answer it in regex anyway since you are specifying an answer in regex and haven't specified any other language.
Also, I'd suggest changing \$ in the regex to \$\w+ since a[href$=".pdf"] is valid CSS and you might magically catch something like that (although I'm not sure how, but I'm sure you can be creative). It does add a somewhat preventative measure.
P.S Your regex was very close. In regex . will match any character. I've changed that to [^"] since the issue is the . was also capturing ".

Code
See this code in use here
\s*style="(?![^"]*(\$|display:\s*none))[^"]*"(?:\s*(?=>))?

Results
Input
Test
<form style="border: 1px solid black" method="POST">
    TestTest

        Test
        Test $NOT_STYLING_TEMPLATE_VARIABLE

</form>

Output
Test
<form method="POST">
    TestTest

        Test
        Test $NOT_STYLING_TEMPLATE_VARIABLE

</form>

Explanation

*

*\s* Match any whitespace character any number of times

*style=" Match this string literally

*(?![^"]*(\$|display:\s*none)) Negative lookahead ensuring that what follows does not match the following

*

*[^"]* Match any character except "

*(\$|display:\s*none) Match either of the following

*

*\$ Match $ literally

*display:\s*none Match display: literally, followed by any number of whitespace characters, followed by none literally

*[^"]* Match any character except "

*" Match " literally

*(?:\s*(?=>))? Potentially match any following whitespace characters if the positive lookahead is true (if the following character is >) - This removes extra whitespace when it's not followed by any other attributes