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"I'll fix ya, Ixia": HP loses its NFV champion
Bethany Mayer with Martyn Warwick at ETSI NFV ISG #7 © TelecomTV
Bethany Mayer, the SVP and General Manager of HP's Network Functions Virtualisation (NFV) Division has left the job and the company to become President, CEO and Member of the Board of Directors of Ixia.
Ixia is a smallish, (1400 employees and falling fast) publicly-traded company headquartered in Calabasas, California, which is a couple of hundred miles and more south of Ms. Mayer's accustomed stomping grounds in Silicon Valley.
Currently, Ixia provides software and hardware test systems and service verification and validation platforms for for both wireless and wireline infrastructures and services as well as test equipment and services used to verify that converged IP networks are secure and operate reliably under load and pressure. Amongst Ixia's key customers are manufacturers such as Alcatel-Lucent and Cisco, telcos and service providers such as Deutsche Telekom and NTT as well as government agencies and enterprises.
Serendipitously, (or perhaps not so co-incidentally), Ixia has just published a free ebook entitled, "Demystifying NFV in Carrier Networks: A Definitive Guide to Successful Migrations."
The publisher's blurb says the ebook "helps companies fast-track NFV planning and deployment." Which is exactly what Bethany Mayer was doing at HP even though was only briefly the public face of the company's NFV initiative. She was in post for a mere six months having been "reassigned" to it in February after previously being in charge of HP's Networking Division where she had increased networking revenues by five per cent over a three year period.
Nonetheless, HP seems to have been taken by surprise by Mayer's resignation although, with the benefit of hindsight, a few days ago there were some clues that something might be afoot. In various blogs, various industry observers wrote that with the emphasis HP is currently placing on NFV, it was both odd and surprising that neither the technology nor Ms. Mayer got more than a passing, one-line and decidedly cursory mention during last week's quarterly results presentation. History shows that, in cases like this, it is often not so much what is said that matters but what is left unsaid. One thing is for sure, senior HP management will now be scrambling around to find her successor.
But first, Ixia needs the SEC's permission to get down from the Naughty Step
Bethany Mayer now has three weeks or so of kicking her heels before she can start her new job. Ixia has been either naughty or remiss in some of its corporate obligations and has "fallen behind" in filing reports and figures with the Security and Exchange Commission (SEC) in New York. It is under order to bring all and any such filings completely up-to-date by September 12 at the latest or it will be removed from the Nasdaq - and that would have immediate and potentially devastating repercussions for the company.
Bethany Mayer cannot be appointed officially as the new Ixia CEO until those filings are complete. Then, when she does take over she will face some serious challenges. Ixia lost US$18 million in Q1 this year and has announced a tranche of staff redundancies. In the first instance six per cent of the company is to get its marching orders.
Ms. Mayer's predecessor as Ixia CEO, Vic Alston, was forced to stand down after it was discovered that his cv was packed with as many porkies as a top-of-the-range Melton Mowbray pie.
Analysis & Opinion,
Business Models,
NFV,
Open Source,
Policy & Regulation,
SDN,
Wind River swaps hands (again) for $4.3 billion
TelecomTV appoints Yanitsa Boyadzhieva as Deputy Editor
Orchestrating multiple clouds
Telefónica and Wipro Engage to Offer Network Automation Through CI/CD/CT Adoption in Germany and Brazil
Automation key to CSP, vendor success in managed security services sector: Report

redpajama_set_name: RedPajamaCommonCrawl


text:
První rodina Spojených států amerických (anglicky: First Family of the United States) je neoficiální pojmenování nukleární rodiny prezidenta Spojených států amerických. Patří do ní jeho manželka – první dáma USA a jejich děti. Ochranu jim zajišťuje Tajná služba Spojených států amerických. Prezident se spolu s členy rodiny přepravuje výhradně dopravními prostředky k tomu určenými – Air Force One, Marine One a Cadillac One. Sídlem rodiny je Bílý dům. Může také využívat víkendové sídlo v marylandském Camp Davidu.

Historicky první takovou rodinou byla v letech 1789 až 1797 rodina prezidenta George Washingtona a první dámy Marthy Washingtonové. Vlastní děti společně neměli, ale vychovávali dva potomky Marthy z jejího prvního manželství, Johna Parkea Custise a Marthu Parkeovou Custisovou.

2009–2017: Obamova rodina
Obamova rodina byla první rodinou v letech 2008–2017 Spojených států, kterou se stala v den prezidentovy inaugurace 20. ledna 2009. Vedle Baracka Obamy a manželky Michelle zahrnuje jejich dvě dcery Natashu a Maliu. Bývá sem řazen i prezidentův pes Bo.

2017–2021: Trumpova rodina
Rodina Trumpových zde: Rodina Donalda Trumpa

Od 20. ledna 2017 se stala první rodinou rodina Donalda Trumpa.

2021–současnost: Bidenova rodina
Od roku 2021 je první rodinou rodina Joea Bidena. Mezi ně patří manželka Jill, syn Hunter a dcera Ashley.

Biden sice má syna Beaua a dceru Naomi, ale ti zemřeli ještě před zvolením Bidena do funkce prezidenta. Proto se nepočítají.

Odkazy

Reference

Externí odkazy

Americké rodiny
Prezident USA
První dámy USA
Bílý dům

redpajama_set_name: RedPajamaWikipedia


Current Projects/Assignments: Completing investigations and working with hunters to be successful for depredation hunts in GMU 51.
Best Thing About the Job: Being able to be outdoors protecting the states wildlife while also helping conserve wildlife for the future.
Most Common Question People Have: When is the next time you will be hiring for game wardens?
Answer: We are hiring on a frequent basis and can find out from the SPO website and our website as well.


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Archives For Memory
Finding South Carolina's Slavery Connection to St. Lucia
By powersb
powersb — Posted on 15 August 2019 | 4:37 am —
This summer I was fortunate to pursue my African American and diasporic research on St. Lucia, one of the Windward Islands in the eastern Caribbean. It is a lush and densely vegetated island with a rugged and mountainous topography. That makes it absolutely breathtaking to view and somewhat challenging to drive through on the winding, often narrow and alternately rising and plummeting roads. This place has a complicated history as revealed by the mixture of place names one finds. Travelers fly into Hewanorra International Airport which takes its name from the indigenous Carib inhabitants. The airport is in Vieux Fort–a town named by the French, the first European settlers–while a scenic area in the north, Rodney Bay, is named for a British naval officer. The mixture of English and French names speaks to the complicated modern history of the island colony. Until the early nineteenth century, through conquest or diplomacy, control of the island passed between Britain and France fourteen times. The British gained final control in 1814 in the aftermath of the Napoleonic Wars. Although it never became an economic powerhouse like its neighbors Barbados or Martinique, the French and British developed a slave-based plantation economy here geared to sugar production for world markets.
A waterwheel-driven sugar mill
Although many slavery-related themes are ripe for investigation in this place, on this visit my interest was very specific. I have been particularly attracted to the international ramifications of the American and French Revolutions for the institution of slavery. This is in keeping with the work we are striving to accomplish here at the Center for the Study of Slavery in Charleston (CSSC). We don't simply research slavery in this city. We want to broaden the lens through which people view slavery, its ramifications and its legacy. Our Charleston vantage point affords the opportunity to look out onto the Atlantic and the Caribbean and see slavery's many important hemispheric linkages to our city and to the state. That's how I got to St. Lucia.
When the gates of the Bastille in Paris were flung open in July 1789, French revolutionaries issued the Declaration of the Rights of Man and of the Citizen and subsequently rallied around the motto "Liberté, égalité, fraternité." This call reverberated throughout the major western colonial empires. In St. Lucia as early as 1790, free persons of color began meeting and demanding equality. Two years later, the colonial assembly extended the right to vote and hold office to free men of color. However, the enslaved population was unaffected and grew increasingly restive. Some relatively small, scattered and unsuccessful rebellions occurred on plantations by 1792. That summer the royalist governor resigned and departed the island and representatives of the new French government took control. Recognizing a new era had arrived, many slaves began abandoning the plantations by early 1793. Life on the island became increasingly unpredictable, and the British began an invasion of France and similarly threatened other parts of its empire. On February 4, 1794 the French Republic, after vacillating for years and in an effort to strengthen its military preparations, abolished slavery throughout the empire and bestowed citizenship on the freedmen. These steps were taken too late for the French to save St. Lucia, which was successfully occupied by British forces in the fall. However, the British soon discovered, just as they had previously learned during the American Revolution, that they could occupy forts and cities relatively easily, but their greatest challenges arose from rebels in the countryside.
Supporters of the French revolutionary government gathered in the countryside and organized L' Armee Francaise dans les Bois (the French Army in the Woods). Comprised mainly of fugitive slaves, the group also attracted free men of color, poor whites and some French soldiers. For the fugitive slaves, the French Revolutionary government represented emancipation; others became supporters for a variety of reasons. The British and their royalist supporters disparagingly referred to this unofficial band of insurgents as the Brigands. They were nevertheless a powerful and resourceful adversary because they were familiar with the topography and used guerilla warfare tactics that were well adapted for the mountainous, densely vegetated environment.
A 1759 map of St. Lucia showing the island's mountain ranges
After one skirmish with the Brigands in 1794 in northern St. Lucia, a British officer recalled: "the Rascals are in such Numbers and the Woods impenetrable that I am much afraid they will not be easily quelled particularly as I cannot well spare more men from this Garrison." In addition, tropical diseases sidelined many soldiers, further eroding British military strength.
St. Lucia's royalist planters opposed arming their slaves, fearing this would only invite insurrection. In order to improve their effectiveness, British Lieutenant General Sir John Vaughan reinforced his soldiers by bringing the Black Carolina Corps to St. Lucia from their base on Martinique. This group consisted of fugitive slaves and free persons of color that labored and fought for the British in South Carolina during the American Revolution. Toward the end of the war, in late 1782, at least 264 of these men evacuated Charleston with their British counterparts bound for St. Lucia. Once there they were formally organized, serving until the war ended and they were transferred to Grenada. The Carolina Corps was the first black unit to be permanently incorporated into the British West Indies peacetime military forces. They were now pressed into service on St. Lucia. They were soon joined by Malcolm's Rangers or the Black Rangers, a group comprised of royalist slaves from French Martinique. The French forces, not sitting idly, were taking some important initiatives. During the era's incessant fighting, the neighboring French island of Guadeloupe temporarily fell to the British, but by the end of 1794 it was recaptured by revolutionary France. It became a vital source of supplies and tactical support for the Brigand resistance against the British on St. Lucia.
In the fighting that ensued in 1795, the British forces continued facing protracted insurgent resistance. On multiple occasions in the southern part of the island, the insurgents successfully ambushed the invaders from the hillside and along narrow pathways. One such attack in April emanated from a Brigand settlement located on the side of Gros Piton, one of the tallest volcanic peaks on the island. The camp was led by a highly regarded and talented colored woman, Flore Bois Galliard, whose memory is enshrined in the piton named for her: Piton Flore. In another nearby campaign, the eight-hour Battle of Rabot, British forces faced withering Brigand fire and lost over one hundred men, forcing them to retreat from the southern island and take refuge in the north. Even here they only found temporary respite as Brigand attacks dislodged them from Pigeon Island and forced them from St. Lucia by mid-June.
The British occupation had lasted about a year (spanning 1794-95) and had come at considerable cost to themselves and to the island's enslaved and free black populations. The next year, 1796, British returned in force and mounted a sustained campaign, often using scorched-earth tactics to root out the dogged Brigand resistance; by early 1799 the insurgency had been substantially crushed. One factor contributing to British success at this time was greater reliance on black troops. In mid-1795 when British fortunes deteriorated on St. Lucia, steps were taken to organize more black soldiers. The Black Carolina Corps was combined with similar groups from the Caribbean–Malcolm's Rangers and the Dominica Rangers–to form the First West India Regiment. Subsequently the Second, Sixth and the Eighth West India Regiments were organized with black troops drawn from several Caribbean islands. Sometimes their ranks were bolstered by Africans imported through the Atlantic slave trade. The West India Regiments were effectively deployed in occupying and pacifying St. Lucia and would become a regular fixture in Britain's future military operations.
"A Private of The 5th West India Regiment, 1812," from the UK's National Army Museum.
Except for a brief period in 1802, St. Lucia remained under British control. This meant the emancipation decree issued by revolutionary France was reversed and Britain reestablished slavery on the island. However, the protracted fighting ensured that the slave population was diminished when peace resumed. In 1790 the slaves numbered 18,406 but by 1799 their numbers had fallen to 13,391. Many died in the fighting while some were carried off to other islands. Many also remained as fugitives in the countryside.
Part of my trip focused on the southwestern part of the island in the vicinity of the major town of Soufriere and the nearby volcanic mountains Petit Piton and Gros Piton, which comprise an important UNESCO World Heritage Site. The image of this area is the iconic representation of the island. It is possible to climb Gros Piton and I was drawn by its stark allure and especially by the opportunity to tread on a landmark that had been a refuge for fugitive slaves and a staging ground for part of the Brigand insurgency.
The Pitons on St. Lucia
The small town at the base of Gros Piton is Fond Gens Libre which means "Valley of the Free People." Inhabited as early as 1749 by slave insurrectionists that sought protection there, the place seems to have attracted additional fugitives in the 1790s.
The sign to Fond Gens Libre Village in St. Lucia
One can easily see how densely vegetated and rugged the terrain is and thus why fugitives sought out this and similar areas.
The dense and rugged terrain of St. Lucia.
Another shot of the thick vegetation on St. Lucia
At one point my guide and I departed from the regular path and entered a more secluded area. Here she showed me an example of a cave where fugitives were likely to have hidden from authorities or from which Brigand soldiers might have prepared an ambush against invaders.
Dr. Bernard Powers posing in the rocky caves of St. Lucia.
To journey to these places was at once exhilarating and exhausting. One can only imagine what those ancestors of modern day black St. Lucians must have felt in these environs. Travel to this place and immersing oneself in the environment is another way of going to the "primary sources" of history. Once you have done so, you cannot but have a deepened appreciation for the historical experience and the humans who lived it. Some of those who lived it were South Carolinians.
Dr. Bernard Powers, Emeritus Professor of History at C of C, is the director for the Center for the Study of Slavery in Charleston and a board member and Interim Director for the International African American Museum (IAAM).
Edgar, Walter, The South Carolina Encyclopedia University of South Carolina, 2006.
Gaspar, David B., "La Guerre des Bois: Revolution, War and Slavery in Saint Lucia 1793-1838" in David B. Gaspar and David P. Geggus, eds., A Turbulent Time: The French Revolution and the Greater Caribbean Indiana University, 1997.
Harmsen, Jolien et. al, A History of St. Lucia Lighthouse Publications, 2014.
Nicholas, Lorraine and Brijesh Thapa, "Tourism in the Fond Gens Libre Indigenous community in Saint Lucia: Examining impacts and empowerment." Accessed August 12, 2019.
African diaspora, Caribbean, Charleston history, Memory, Resistance to slavery
CSSC at the Reburial
Julia Eichelberger — Posted on 15 August 2019 | 4:33 am —
The Social Justice Committee of CSSC hosted the start of the May 9 Gullah Society procession on the C of C campus, in Barnet Courtyard.
Cards were inscribed with messages that were then buried with the ancestors.
C of C faculty in academic regalia joined city officials, Gullah society members, schoolchildren, and other community members in a procession down George Street to the Gaillard Complex.
Gullah Society President and founder Ade Ofunniyin, walking with the Mayor of Charleston, escorted the coffins to their final resting place. Dr. Ofuniyyin teaches African and African American studies at the College of Charleston.
Charleston Mayor John Tecklenberg and CofC's Dr. Ade Ofunniyin escorting the coffins
The program was designed by Ms. Joanna Gilmore, a Gullah Society staff member who also teaches at C of C. It included an essay by C of C professor of architectural history Dr. Nathaniel Walker and another essay by CSSC director and emeritus history professor Dr. Bernard Powers. Among the speakers during the ceremony was Dr. Kameelah Martin, chair of C of C's African American Studies department and a member of CSSC's Executive Board.
The cover of the program booklet for the reburial ceremony.
Risen, by Dr. Ade Ofunniyin
Essay by Dr. Powers
Essay from the Gullah Society
Essay from Dr. Nathaniel R. Walker.
More coverage from the Post & Courier.
Africa, African Ancestors, Charleston City Council, Charleston history, College of Charleston, DNA analysis, Gullah Society, Memory, U. S. South
Op-Ed: Honoring Charleston's Ancestors
This op-ed reflects on the significance of the May 9 event honoring those ancestors whose labor contributed so much to Charleston. Written by Julia Eichelberger, CSSC Executive Board member and director of the Program in Southern Studies.
Read op-ed on Southern Studies program blog, Studying the South
Read on Post and Courier website
https://www.postandcourier.com/opinion/commentary/commentary-honoring-charleston-s-ancestors/article_d0bf5854-6c48-11e9-a4a6-cf9ed406c0c3.html
Charleston residents have been invited to write messages to be buried with the ancestors on May 4.
African Ancestors, Charleston history, College of Charleston, DNA analysis, Gullah Society, Memory
The Enslaved Laborers who Built Randolph Hall
The Office of Institutional Diversity and other C of C faculty and students are working to produce a documentary exploring the lives of enslaved people who built Randolph Hall. Dr. Bernard Powers, CSSC director and Emeritus Professor of History, appears in this trailer for the film, which the filmmakers hope to complete in 2020.
Documentary Explores Use of Enslaved Labor
Charleston history, College of Charleston, enslaved laborers, Historic Preservation, Memory, Students
"Bourne, Bound and Battered by the Common Wind"
Julia Eichelberger — Posted on 14 August 2019 | 5:27 pm —
In February, CSSC Director and Emeritus Professor of History, Dr. Bernard Powers, delivered a lecture entitled "Denmark Vesey, South Carolina and Haiti: Borne, Bound, and Battered by the Common Wind." This was the keynote address for the first evening of the Carolina Lowcountry and Atlantic World's academic conference, "The Vesey Conspiracy at 200: Black Antislavery in the Atlantic World."
Find a copy of the conference's program here.
Carolina Lowcountry & Atlantic World (CLAW(, Charleston history, Haiti, Memory, Resistance to slavery
The Ancestors' Remains
After the remains of 36 African and African-descended people were discovered near the Gaillard Auditorium during renovations in 2013, The Gullah Society worked with city officials to study the remains and decide how they should be honorably reinterred.
DNA and isotope analysis established that these individuals were all of African descent. This research, conducted with scientists from the University of Pennsylvania and C of C student Yemi Udowole, was supported by a National Geographic Society grant.
Students in Nathaniel Walker's Architecture of Memory course imagined designs for a memorial honoring the individuals in this burial ground.
The Center for the Study of Slavery was honored to support and participate in events in which research was discussed with community members and the students' proposed designs were displayed. One event, "Rise Up," was held on campus in Randolph Hall on November 7, 2018.
Read the story in The College Today
Student Exhibit Explores Proposed Memorials to Honor Remains
More from The Post & Courier.
https://www.postandcourier.com/news/what-sort-of-monument-would-best-honor-african-americans-buried/article_c6a2ff54-f70b-11e8-a587-bf4780d4f3ac.html
African Ancestors, College of Charleston, DNA analysis, Gullah Society, Memory, Students
Op-Ed: Reasons for Studying Slavery in Charleston
In 2018 when the College announced the formation of the Center for the Study of Slavery in Charleston, this op-ed was published reflecting on the need for all Charlestonians to understand our shared past, which is profoundly shaped by slavery. The author, Julia Eichelberger, an English professor at the College, directs the program in Southern Studies and serves on CSSC's Executive Board.
https://www.postandcourier.com/opinion/commentary/commentary-charleston-must-own-its-slavery-wrongs-if-it-hopes/article_38c27ffc-c02e-11e8-af57-3f508a89293e.html
Charleston City Council, Memory, Southern literature, U. S. South, Whiteness
Juneteenth with Dr. Anthony Greene
Julia Eichelberger — Posted on 19 June 2019 | 12:30 pm —
AAST professor Dr. Anthony Greene explains the history of Juneteenth.
African American Studies Professor Explains History of Juneteenth
African American Studies, Civil War, Memory, U. S. South
Charleston's landscape of memory: Putting history in perspective
By Academic Affairs
Academic Affairs — Posted on 26 February 2019 | 7:17 pm —
Please read this commentary piece published in the Post & Courier: https://www.postandcourier.com/opinion/commentary/charleston-s-landscape-of-memory-putting-history-in-perspective/article_d782da88-0ad4-11e9-9ae2-93cbc27f6418.html.
Charleston history, Civil War, Confederate Monuments, Confederates, Jim Crow, Memory, SC, segregation, U. S. South
Call for Papers: Architectures of Slavery – Ruins & Reconstructions
Academic Affairs — Posted on 28 January 2019 | 10:04 pm — 1
The Art & Architectural History Department and the new Center for the Study of Slavery at the College of Charleston announce a symposium dedicated to the historic and ongoing relationships between slavery and architecture: A SYMPOSIUM ON THE HISTORY OF ARCHITECTURE, URBANISM AND LANDSCAPES, October 24-26, 2019, Charleston, South Carolina. We seek papers that critically explore places and times in which slavery was a legal institution, as well as papers that analyze the long-enduring memories and legacies of slavery in architecture, urbanism, and landscapes. Charleston is one of the most important sites of such history in the United States and offers an ideal setting for a confrontation with the ways that the systems and values of slavery are woven into the fabric of a place. The city was built upon the slave trade, launched the Civil War, seethed during Reconstruction, and endured decades of segregation and oppression, both in its historic center and in its modern suburbs. We acknowledge, however, the global nature of slavery and welcome relevant submissions pertaining to any corner of the planet.
Please email a 300-word abstract and a two-page CV, with the phrase Ruins and Reconstructions written in the subject line, to walkernr@cofc.edu and stiefelb@cofc.edu by April 1, 2019. Send any inquiries to the same addresses. For more information, view our Call for Papers.
Historic Preservation, Jim Crow, Memory, Reconstruction, U. S. South	architectural history


Located in the heart of Toowoomba, this luxury hotel is within a 10-minute walk of Empire Theatre and Grand Central Shopping Centre. Toowoomba Base Hospital and City Golf Club are also within 1 mi (2 km). Toowoomba Station is 11 minutes by foot.
Situated in Toowoomba, this hotel is within a 10-minute walk of Laurel Bank Park and Grand Central Shopping Centre. Empire Theatre and Toowoomba Base Hospital are also within 1 mi (2 km). Toowoomba Station is 10 minutes by foot.
Located in the heart of Toowoomba, this luxury apartment building is within a 10-minute walk of Empire Theatre and Grand Central Shopping Centre. Queens Park and Cobb & Co Museum are also within 1 mi (2 km). Toowoomba Station is 11 minutes by foot.
Situated in the historical district, this guesthouse is within 1 mi (2 km) of Grand Central Shopping Centre, Empire Theatre, and Toowoomba Base Hospital. Queens Park and City Golf Club are also within 2 mi (3 km). Toowoomba Station is 7 minutes by foot.
The beach towns in Australia are world-famous. You know the ones: Coolangatta, Surfers Paradise, Byron Bay. The list goes on and on. But there's also some fantastic towns that lie inland, and one of them is Toowoomba, a place where the bush meets the city in a perfect combination. You can go for a stunning a nature hike, visit a railway museum, and enjoy a cold one all in one day.
Not sure what you're looking for? Let us help! When you search Toowoomba accommodation, you can filter by budget, star rating or even specific facilities. Hit the clubs, browse the shops or make friends with the locals—there are a number of hotels just a stone's throw from the action. Or do less running around and more indulging when you book a relaxing and revitalising retreat. Maybe a spa treatment is in your future! In the mood for food? Many of our listed hotels feature great bars, cafes and restaurants for laid-back brunching and suppertime feasting. Some also offer a gym or nearby outdoor activities to help burn off those extra calories.
Save time and money by using Wotif to book cheap Toowoomba accommodation. With millions of rooms available, you'll find exactly the right place to put your feet up. From bargain motels to rooms fit for blue-blooded luxury, child-friendly to pet-friendly (and definitely wallet-friendly), we make booking a hotel reservation simple.
Need insider tips on must-see attractions in Toowoomba? Check out our Toowoomba travel guide!
How Much is a Hotel Room in Toowoomba?
Hotels in Toowoomba start at AU$149 per night. Prices and availability subject to change. Additional terms may apply.
Spotlessly clean. Comfy bed. Great shower. What more could you ask for. Wish we had found this place years ago!


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Comic-Con 2018: Iron Fist Season 2 Release Date!
Michelle Alexandria
Iron Fist Season 2 features Danny Rand (Finn Jones) as he fights against the criminal element corrupting New York City with his kung-fu mastery and ability to summon the awesome power of the fiery Iron Fist.
Season 2 furthers the transformation of Danny, a character with a fish out of water coming of age story making his way in a harsh new world, battling to work out who he is. This season, Danny has promised that with Matt Murdock gone, he will step up and protect his city. But a sinister plot twist threatens his very identity and he must conquers his villains to protect the town and people he holds close to his heart.
Marvel's Iron Fist Season 2 stars Finn Jones (Danny Rand), Jessica Henwick (Colleen Wing), Sacha Dhawan (Davos), Tom Pelphrey (Ward Meachum), Jessica Stroup (Joy Meachum), Simone Missick (Misty Knight) and introduces Alice Eve (Mary Walker).
Marvel's Iron Fist Season 2 is produced for Netflix by Marvel Television and ABC Studios.
Updated: July 20, 2018 — 1:38 pm
Tags: Comic-Con 2018, Marvel's Iron Fist, Netflix, SDCC 2018


While Sammy Sosa's mark of 20 home runs captivated the nation and pushed him right into the home run race, Mark McGwire didn't have a bad third month either. Going into the last game of June, McGwire had put up a .291/.446/.658 line while hitting nine homers and, remarkably, walking 19 times while striking out just 17. Granted, seven of those were intentional passes, a number that would grow as the season wore on, but it's a good reminder that for all the focus on McGwire's prodigious power, he had a little more of an all-around game than some would acknowledge.
Interleague play was still going on as the calendar prepared to turn but the Cardinals had come home to host their rivals from the other side of Missouri. The Royals were having one of those many sub-.500 seasons that they had before 2015 came along, but even at that they were only a handful of games worse than St. Louis, who also found itself with more losses than wins.
We often talk about Manny Aybar and how rough his outings usually are, but he got through the first without incident. Glendon Rusch did the same, save a double by McGwire, which meant the game was tied when the top of the second rolled around. Former Arkansas Razorback Jeff King singled to start the inning, only to see Dean Palmer strike out looking. Jeff Conine picked up his teammate, doubling in the first run of the game. Sal Fasano fouled out, but Mendy Lopez walked. That put two on for the pitcher, Mr. Rusch. Now, you'd expect an American League pitcher not to hit all that much, so everything seemed hunky-dory. Instead, Lopez stole second and, on the throw, Conine stole home.
The Cardinals and Royals traded opportunities, but nobody broke through again until the top of the sixth. Aybar, who was still in the game, walked King on four pitches to open the frame. Tony La Russa decided not to press his luck and went to his bullpen, bringing in Curtis King. That didn't work out well, as King walked Palmer and Conine to load the bases. Fasano then singled to right, putting the third run on the board and keeping the bases loaded. Lopez followed with a double play but Palmer came in, making the score 4-0.
It stayed that way until the top of the seventh. The big redhead led things off, with a double and a single already, swung through the first pitch from Rusch. He took the second one for a ride to Big Mac Land.
Unfortunately, no one else was able to do anything that inning and the Royals got that run back in the eighth when King led off with a double, moved to third on a ground out, and scored on a sacrifice fly by Conine. The Cardinals got two runners on in the bottom of the eighth, but Gary Gaetti ended the frame with a strikeout. The Royals made it 6-1 when Hal Morris scored on a wild pitch in the ninth. McGwire grounded out to start the ninth and only a Brian Jordan single kept the ninth from being 1-2-3.
Ten homers in a month is a pretty amazing month. It was a below-average pace for McGwire in '98. These numbers are just crazy when you think of them like that.


How To Add A Photo To A Facebook Comment: You possibly knew you could post photos to Facebook in a condition upgrade, yet did you recognize you can post a picture in a comment you make on someone else's post on Facebook? It hasn't already always been feasible however. It had not been till June 2013 that the social media began supporting photo-commenting, and it's developed right into the web site and also mobile application.
Now you can make a photo comment as opposed to just typical message, or post both a message comment and a picture to illustrate it. Whatever photo you choose to upload shows up in the list of comments under the post to which it refers.
This is a specifically nice attribute to have for birthday celebrations and various other holiday desires since images commonly state greater than words.
Previously, to include a picture to a comment, you needed to submit a picture someplace on the internet and afterwards place the code that linked to the picture. It was messy and also not as simple as it is now.
The particular actions to do this are slightly various depending on just how you accessibility Facebook.
- Click Comment on your information feed below the post you wish to react to.
- Go into any type of text, if you want, and then click the camera symbol at the best side of the text box.
- Pick the image or video you want to add to the comment.
- Tap comment below the post you want to Comment on to bring up the digital keyboard.
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text:
Chuck Berry: 1926 - 2017
Chuck Berry, Keith Richards, Neil Young - 1986
Photo Lynn Goldsmith
The Photo of the Moment is Chuck Berry, Keith Richards, Neil Young in 1986 at the Rock & Roll Hall of Fame by Lynn Goldsmith.
On January 23, 1986, legendary artists James Brown, Elvis Presley, Ray Charles, Sam Cooke, Little Richard, Chuck Berry, Fats Domino, Jerry Lee Lewis, Buddy Holly, and The Everly Brothers were inducted into the first class of the Rock & Roll Hall of Fame at the Waldorf Astoria Hotel in New York City. What was expected to be only a dinner with acceptance speeches turned into a historic jam session orchestrated by promoter Bill Graham featuring Berry, Lewis, Domino, Billy Joel, Keith Richards and Ron Wood of The Rolling Stones, Neil Young, Steve Winwood, John Fogerty, and Chubby Checker. Paul Shaffer led the "World's Most Dangerous Band" (from Late Night with David Letterman) including saxophonist David Sanborn, guitarist Sid McGinnis, bassist Will Lee, and drummer Steve Jordan.
Highlights included Winwood performing "Gimme Some Lovin' (The Spencer Davis Group) on organ, Forgery playing "Proud Mary"' for the first time in 14 years, and Checker singing and dancing "The Twist." Berry led the closing jam, duckwalking across the stage to his classic "Roll Over Beethoven" as Joel, Lewis, Fogerty, Richards, Young and Winwood played behind him.
From "30th Anniversary of the 1986 Rock & Roll Hall of Fame Induction Ceremony.":
Chuck Berry is one of the pioneers of rock and roll. His accolades include a Grammy Lifetime Achievement Award, the Kennedy Center Honors, and being named seventh on Time magazine's 2009 list of the 10 best electric guitar players of all time. On May 14, 2002, Berry was honored as one of the first BMI Icons at the 50th annual BMI Pop Awards.
The legendary guitarist is known for his duckwalk on stage, which he introduced during a 1956 concert in New York. His list of classics includes "Sweet Little Sixteen," "Rock and Roll Music," "Maybellene," "School Day," "Johnny B. Goode," and "Back in the U.S.A." Berry also appeared in several rock and roll movies in the 1950s, including Rock, Rock, Rock!, Mister Rock and Roll, and Go, Johnny, Go!
Berry was inducted by Keith Richards, who stated, "It's very difficult for me to talk about Chuck Berry 'cause I've lifted every lick he ever played. this is the gentleman who started it all!" Berry was the centerpiece of the closing jam session, performing "Roll Over Beethoven" and "Johnny Be Goode" with Richards, Ron Wood, Billy Joel, Jerry Lee Lewis, John Fogerty, Neil Young, Steve Winwood, Hank Williams Jr., and Fats Domino.
Watch Chuck Berry perform "Johnny B. Goode" live in 1958 here.
Labels: chuck berry, keith richards, neil young
At 3/23/2017 05:57:00 PM, Mother Nature on the Run said...
I've known this guy practically all my life as the original RnR King. Condolences to his family and friends. God bless Rock'n'Roll.
Comment of the Moment: Silver & Gold: A Track By T...
NEW - Official Lyric Video: "Indian Givers" by Nei...
Neil Young's "Ditch Quadrilogy" Re-Re-Release is F...
Silver & Gold: A Track By Track Commentary by Neil...
CONTEST: BOOKAZINE: UNCUT NEIL YOUNG - THE COMPLET...
Neil Young Interview Transcript: Companion Bonus T...
Pegi Young: Charitable Work and Songwriting Help t...
1989 Neil Young Interview: Village Voice by Jimmy...
New Official Video: "Peace Trail" by Neil Young


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text:
\section{Introduction}
\label{sec:intro}
Image super-resolution (SR) is a fundamental problem in image processing.
\yukai{Single image SR approaches, which aim at restoring a high-resolution (HR) image only from a single low-resolution (LR) image, have been applied to many}
image and video analysis tasks, such as
video surveillance~\cite{SR_app_TMM14},
image-based medical analysis~\cite{SR_App_TMI16},
and image/video streaming~\cite{shi2016real,romano2017raisr}.

Common techniques for single image SR can be roughly categorized into reconstruction-, example- and interpolation- based approaches. Reconstruction-based approaches~\cite{irani1991improving,shan2008fast,michaeli2013nonparametric}, which restore HR images by deconvolutional methods~\cite{shan2008fast} with a global blur degradation model, usually introduce ringing artifacts around salient structures~\cite{michaeli2013nonparametric} due to inaccurate blurring kernels in the inverse problem. \yukai{Example-based approaches~\cite{yang2010sc} boost the amplification factor by using internal or external patch data to guide the image restoration. Recently, Huang \textit{et al.}~\cite{huang2015single}}
 proposed to exploit self-similarity for single image SR,
which greatly expands the internal patch searching space. Hu \textit{et al.}~\cite{hu2016serf} proposed a cascaded linear regression technique to model the relationship between HR and LR images. Interpolation-based approaches can achieve acceptable trade-off between performance and efficiency with a pre-defined kernel. However, pre-defined kernels use fixed weights for interpolation, which will inevitably cause blur when the weight definition is inconsistent with image structures. To address issue, various adaptive interpolations~\cite{chu2008gradient,van2012polygon, pisa15PAMI} are proposed. But the improvements in restoration quality are still limited.

The success of deep convolutional neural network (CNN) in computer vision tasks has inspired novel trends in low-level image restoration researches, such as rain/dirt removal~\cite{eigen2013restoring}, noise removal~\cite{jain2009natural}, face hallucination~\cite{wang2014comprehensive,cao2017face}, hashing~\cite{zhang2015bit} and image inpainting~\cite{xie2012image}. \chongyu{Focusing on learning an end-to-end mapping between the LR images and their corresponding HR images, several CNN-based methods~\cite{dong2014srcnn, NIPS2015xuli, wang2015deep,kim2015accurate} have been proposed to perform image SR} in a pure data-driven manner. That is, they directly minimize the mean squared error (MSE) between the predicted and ground-truth images in the training stage.
Although the restoration performance is significantly improved, the structural
inconsistency between the LR input and HR output still exists.
This is because human visual system is more sensitive to structural changes,
which are difficult to be exploited from MSE-based loss functions.
Recent advances in image SR try to address this
issue~\cite{bruna2015super,ledig2016photo,johnson2016perceptual} by introducing
feature-based perceptive loss functions to the training stage. However, unwanted artifacts and unreal details are also introduced, which make their SR results look unrealistic.

\chongyu{
Considering single image SR is an ill-defined problem, it is necessary to exploit the priors
of natural image to further improve the SR performance.
Motivated by recent advances in deep learning researches
that exploit priors in the form of context information in designing
neural networks~\cite{VideoContex_TMM15, Parsing_Contex_ICCV15},
in this work, we propose to design neutral networks to investigate two types of image
structural information, i.e.,} \textit{global structural information} which corresponds to salient
boundaries in a global perspective and \textit{residual structural information} which contains
noticeable details that are critical to visual quality.
\chongyu{The success of multi-task learning framework inspires us
to \keze{leverage} such structural information in a unified manner. \keze{For instance, Yang \textit{et al.}~\cite{yang2013feature} proposed to utilize the common knowledge (e.g., feature selection functions) of multiple tasks as supplementary information to facilitate decision making. Considering aforementioned structural information are usually considered as complementary context rather than common knowledge, in this work, we concentrate on complimentary contextualized multi-task learning for structure-preserving single image SR.}
In particular, we propose a deep
joint contextualized multi-task learning framework\keze{, where} three types of image components are imposed as complimentary contexts and jointly learned,
i.e., the base image content, the boundary map, and the residual map.
Besides a convolutional network that learns content-adaptive interpolations to produce
the intermediate base image, we impose an auxiliary task to back-propagate the
global boundary structural context.
Meanwhile, an independent sub-network is introduced to explicitly model
the noticeable details to provide residual structural context.
}

\chongyu{
The major contribution of this work is the proposed contextualized multi-task learning
framework, which is the first attempt to incorporate joint learning
of local, global, and residual contexts into CNNs for single image SR.
Other contributions mainly come from the proposed content-adaptive interpolation
and the sub-networks for capturing complementary image contents, which enables
better trade-off between restoration quality and the number of network parameters.
}
\yukai{Extensive experiments on several benchmarks datasets (e.g. \textit{Set5}, \textit{Set14}, \textit{BSD500}) demonstrate that the proposed framework shows superior performance to most learning-based approaches in the perspective of both visual quality and quantitative metrics, which
facilitates \keze{the} real-time image SR process.}

\chongyu{
We would like to point out that a preliminary version of this work is reported
in~\cite{This_ICME16}, which coarsely \keze{concatenates} content-adaptive interpolation
and holistic edge context.
In this paper, we inherit the idea of preserving structures and \keze{refining} the network architecture.
A simple yet powerful sub-network is further employed to capture noticeable
image details for better visual quality.
The whole framework is re-interpreted from the aspect of joint context learning and
multi-task learning.
Besides, more comparisons with state-of-the-art approaches and more detailed \keze{analyses}
of the proposed modules are added to further verify our statements.}

The rest parts of this paper are organized as follows. Section~\ref{sec:Related} briefly reviews existing machine learning-based SR approaches which motivate this work. Section~\ref{sec:proposed} presents the details of the proposed framework, with thorough analysis of every component. Section~\ref{sec:exp} demonstrates the experimental results on several public benchmarks, comparing with state-of-the-art alternatives. Finally, \keze{Section}~\ref{sec:con} concludes this paper.

\section{Related Work}
\label{sec:Related}

\subsection{Interpolation-based image super-resolution}
Interpolation-based approaches typically start from evenly placing the pixels of LR image to the HR grid (the integral coordinates in the HR image domain).
The basic idea of these approaches is to estimate the unknown pixel values in
the HR grid by weighted average of surrounding known pixels.
Considering common pixel changes in a local region can be approximated by continuous functions,
people have proposed various weight definitions for image interpolation. For example, bilinear
interpolation is proposed to utilize local linearity, and bicubic interpolation is proposed to exploit the
high-order continuity~\cite{Cubic_TASSP81}.
However, there are plenty of pixel changes that cannot be described by these pre-defined functions,
especially for regions with rich image structures. In this case, structures will be blurred
due to improper pixel averaging. To address this problem, various adaptive interpolation~\cite{chu2008gradient,van2012polygon} are proposed. For instance, Walt~\textit{et al.}~\cite{van2012polygon} proposed to express polygonal pixel overlap as a linear operator to improve the interpolation performance. But the improvements are still limited.

\subsection{Multi-task learning in image super-resolution}
Decades of researches on multi-task learning have demonstrated that learning multiple correlated tasks simultaneously can significantly improve the performance of the main task~\cite{multitask_ML97, mt2016mm, al2016TMM, mt2016cvpr, yu2017iprivacy}.
In single image SR, there is also a trend of utilizing multi-task learning.
For example, Yang~\textit{et al.}~\cite{multitask_IWMR11} proposed a multi-task K-SVD
learning for image SR, in which example image patches are divided into different groups
and K-SVD is applied to every group.
It is shown that simultaneous learning multiple dictionaries can lead to better SR quality.
Liang~\textit{et al.}~\cite{multitaskSR__ICIP15} proposed a multi-task learning framework that jointly
considers image SR process and the image degeneration process.
These works claim that the multi-task learning framework is a feasible way of utilizing priors in learning-based image SR.

\subsection{Deep learning in image super-resolution}
\chongyu{Recently, deep learning has achieved
significant quality improvements in image SR}.
For example, Dong~\textit{et al.}~\cite{dong2014srcnn} utilized a three-layer fully convolutional
network to learn the non-linear mapping between HR and LR patches,
which has a close relationship to sparse coding.
\yukai{Ren~\textit{et al.}~\cite{NIPS2015xuli} introduced Shepard CNNs
to facilitate translation variant interpolation, which gives a solution to both inpainting and SR.
Wang~\textit{et al.}~\cite{wang2015deep} proposed a sparse coding based network for
image SR. Based on learned iterative shrinkage and thresholding algorithm(LISTA)~\cite{gregor2010learning}, they employ a set of neural networks to restore images.
Zeng~\textit{et al.}~\cite{zeng2017coupled} proposed a deep autoencoder for SR,}
\chongyu{which explores the consistent representations of HR and LR images and
demonstrate a superior efficiency compared to similar methods based on sparse
representation.
Kumar~\textit{et al.}~\cite{TMM_SR_Kumar} studied on several factors that affect the
training phase
to facilitate learning-based SR with fewer training samples.}
The models of these methods, although being proposed from different aspects, are trained
to minimize the squared error w.r.t. the ground-truth HR image, which is not necessarily correlated
to good perceptual quality. Bruna~\textit{et al.}~\cite{bruna2015super} referred this problem
as \textit{regression to mean}. Their proposed solution is a conditional generative model,
which demonstrates improvement over visual quality, but with high time cost in both training
and testing.

\yukai{More recently, researchers notice the importance of image details and make various of attempts for exploration. Kim \textit{et al.}~\cite{kim2015accurate,kim2015deeply} further improved the SR quality by different network architectures such as very deep and recursive network structures. However, these methods heavily rely on very deep networks with plenty of parameters. e.g., a 20-layer convolutional neural network~\cite{simonyan2014vgg}. In addition, perceptual losses have
been proposed for CNNs~\cite{bruna2015super,johnson2016perceptual}, which conduct the loss from the image space to high-level feature space of a pre-trained VGG-net~\cite{simonyan2014vgg}.
At the same time, \keze{Ledig \textit{et al.}}~\cite{ledig2016photo} proposed to apply adversarial network to the task of SR, which results in more image details but lower PSNR score.
More related to our work, there are several attempts to accelerate image SR. By developing a sub-pixel convolutional layer, Shi \textit{et al.}~\cite{shi2016real} used a single model to handle real-time image SR. Similarity, Dong \textit{et al.}~\cite{dong2016sr} applied convolutional layers on LR image and \keze{upscaled} it with deconvolution. They both promise low computational complexity, but there still exists plenty of room for performance improvement.}

\section{Contextualized Multi-task Learning}~\label{sec:proposed}

\begin{figure*}[ht]
\centering
\includegraphics[width=1\textwidth]{01_overview_of_framework}
\caption{ The architecture of our contextualized multi-task deep learning framework for single image super-resolution. Given an input LR image, our framework first extracts its convolutional features and applies one deconvolutional module to interpolate the feature maps in a content-adaptive way. The resulting maps are then fed into two branched sub-networks, which incorporate global boundary context and residual context, respectively. Specifically, during the neural network training, one sub-network outputs salient image boundaries and the intermediate HR image; the other sub-network outputs the local residual map, i.e., the residual difference of the generated HR image and ground-truth image. The final HR estimation is obtained by fusing the intermediate HR image and the local residual map.}
\label{fig:overall-pipeline}
\end{figure*}

In this section, we present \chongyu{the details of our} framework. As sketched in Fig.~\ref{fig:overall-pipeline}, the proposed framework \chongyu{includes} three components: feature extraction, content-adaptive interpolation, and multi-task estimation.

\begin{table*}[t] \centering \footnotesize
	\center
	 \vspace{0.05cm}

	\begin{tabular}{c|c|c|c|c|c|c|c|c|c|c}
		\hline
	Component   & \multicolumn{4}{c|}{Feature Extraction} & Interpolation-1 & \multicolumn{2}{c|}{BCN} & Interpolation-2 & \multicolumn{2}{c}{RCN} \\ \hline \hline

    \textbf{\textit{layer}}   & \textit{\textbf{conv}} & \textit{\textbf{conv}} & \textit{\textbf{conv}} & \textit{\textbf{conv}}& \textit{\textbf{deconv}} & \textit{\textbf{conv}} & \textit{\textbf{conv}} & \textit{\textbf{deconv}} & \textit{\textbf{conv}} & \textit{\textbf{conv}} \\
    \textbf{\textit{filter}} & 5 & 3 & 3 & 1 & 11 & 3 & 3 & 11 & 3 & 3 \\
    \textbf{\textit{channels}} & 16 & 32 & 128 & 8 & 8 & 12 & 2 & 8 & 12 & 1 \\
    \textbf{\textit{size}} & 128 & 124 & 124 & 124 & 372 & 372 & 370 & 372 & 372 & 370 \\
	\textbf{\textit{parameters}} & 400 & 4,608 & 36,864 & 1,024 & 7,744 & 864 & 216 & 7,744 & 864 & 108 \\
	\hline
	\end{tabular}
	\caption{\chongyu{Detailed setup} \yukai{of each component in our framework. The five rows of the table represent the ``layer type'', ``filter size'', ``output channels'', ``size of output feature maps'' and ``number of parameters'', respectively.}
\chongyu{The content-adaptive interpolation layers for
RCN and BCN are
``Interpolation-1'' and ``Interpolation-2'', respectively.
Note that this table takes
the magnification factor of 3 and input images of resolution $128\times128$
as an example of parameter setup. }}
	\vspace{0.1cm}
\label{table:Network_complexity}
\end{table*}

\subsection{Feature Extraction}\label{sec:FE}
\yukai{Inspired by the Pyramid-Net~\cite{han2016deep}, we design a pyramid network
structure for feature extraction.}
\chongyu{That is, there are 3 convolutional layers with
16, 32 and 128 kernels, respectively.
Detailed setup is summarized in Table~\ref{table:Network_complexity}.
The first layer with kernel size $5\times5$ is designed as a spacious receptive field to capture as much image information as possible, as illustrated in~\cite{he2016deep}.}
\yukai{
The other two layers with $3\times3$ kernel are adopted for better efficiency
as~\cite{chetlur2014cudnn}. Note that we focus on extracting features
from original LR images instead of the interpolated images.
Thanks to the decreased computations of convolutional operations caused by the small size of feature maps,
the proposed feature extraction can significantly accelerate the speed without
obvious quality drop.
Since the LR image has been represented as high-dimension feature maps through
the first 3 layers, the computation cost may become pretty high if we
import the high-dimension feature maps to content-adaptive interpolation directly.}
\chongyu{
Therefore, we apply a shrinking layer with 8 kernels of size $1\times1$ to reduce
the feature dimension. Note that the kernel number is empirically chosen for a reasonable trade-off between effectiveness and efficiency.}
\yukai{
Benefitting from the shrinking layer, our model not only \keze{avoids} parameter explosion but also \keze{promotes} the restoration efficiency.
}

\begin{figure}[ht]\centering
\subfloat[][ \centering Bicubic kernel \par PSNR: 32.71 dB] {
\includegraphics[width=0.24\textwidth] {NB-convolution-kernel-and-result_a.png}
}
\subfloat[][ \centering Learned kernel \par PSNR: 33.10 dB] {
\includegraphics[width=0.24\textwidth]{NB-convolution-kernel-and-result_b.png}
}
\caption{A comparison between image interpolations by bicubic and learned kernels. }
\label{fig:LSP_Kernels}
\end{figure}

\subsection{Content-adaptive Interpolation}\label{sec:LSPM}
The second component is one deconvolutional layer, which is used to interpolate the LR feature maps in a content-adaptive way. The deconvolutional layer has 8 kernels of size $n \times n$.
Note that in this work, $n$ is determined by the upscaling factor,
which follows the principles of bicubic interpolation.
That is, the kernel should be large enough to cover the second pixel
around the anchor pixel in the HR grid.
For example, the deconvolutional kernel is of size $8\times8$,
$11\times11$, and $16\times16$ for the upscaling factors of
2, 3 and 4, respectively.
In this way, the deconvolutional layer can be regarded as
a neural network implementation of
standard image interpolation. Let $\mathbf{y}$ be the HR image with a HR grid.
We construct another HR image $\mathbf{x}$ by evenly placed the LR image in
the HR grid with identical pixel intervals. Then, standard interpolation can be written as:
\begin{equation} \mathbf{y}_j = \sum_{i \in \Omega_j}{ \mathbf{x}_{i}~\omega_{ji}},
\end{equation}
where $i$ and $j$ are the pixel indices in the HR grid,
$\Omega_j$ represents the subset of $n\times n$ neighbouring pixels around pixel $j$,
and $\omega_{ji}$ is the pre-defined weight for interpolation.
Note that $ \mathbf{x}_i$ is non-zero only when it comes from a pixel in the LR image.

With these definitions, we re-formulate the interpolation process
as a basic component of a deconvolutional layer, i.e.,
\begin{equation}
\mathbf{y}_j = \delta (\sum_{i\in \Omega_j} \mathbf{x}_i W(i') + b)
\end{equation}
where $\delta(\cdot)$ represents the activation function, $W$ is the deconvolutional kernel,
$i'$ represents the pixel of $W$ that contributes to pixel $j$, and $b$ is the bias.

In the proposed content-adaptive interpolation, we use multiple deconvolutional kernels in a similar fashion.
That is, we evenly place the LR image in the HR grid to construct $\mathbf{h}^l$. Then,
\begin{equation}
\mathbf{h}^{l+1}_k = \delta ( \mathbf{h}^{l} \otimes W_k
+ b_k ), \end{equation}
where the subscript $k$ represents the kernel index, ``$\otimes$'' represents the
convolutional operator, and $\mathbf{h}^{l+1}$ is the output image of the
$l^{th}$ layer.
In this way, content-adaptive image interpolation can be accomplished via a deconvolutional layer,
whose kernels are learned from sufficient training data.
\yukai{Note that the deconvolutional layer is in the middle of the proposed network, which is different from other CNN-based SR methods~\cite{NIPS2015xuli, dong2014srcnn} that use deconvolution as the last layer. It is shown empirically that the proposed network can achieve nice restoration quality with reasonably increasing network parameters.}

\yukai{To compare the proposed network with the bicubic interpolation, we construct a small network which only has one deconvolutional layer to learn an adaptive kernel, taking BSD300 as training data and bicubic interpolation parameters for initialization. The intensity changes of bicubic and our learned kernels are visualized in Fig.~\ref{fig:LSP_Kernels}, which illustrates that the learned kernel contains more high-frequency components. Meanwhile, the restoration results also indicate that the learned kernel leads to a superior restoration quality with more recovered details compared to the bicubic kernel. Thus, the effectiveness of the proposed adaptive interpolation is verified.}

\subsection{Contextualized Multi-task Learning}\label{sec:Multi}

\begin{figure} [t]\centering
\subfloat[][ \centering original images] {
\includegraphics[width=0.22\textwidth] {holistic_boudaries_1.png}
}
\subfloat[][ \centering boundary maps] {
\includegraphics[width=0.22\textwidth]{holistic_boudaries_2.png}
}
\caption{Example images with salient boundaries. (a) Original images. (b) Manually labeled edge
maps.}
\label{fig:HSP-boundaries}
\end{figure}
In spired by the multi-task learning principles, we make an attempt to introduce auxiliary knowledge to SR issue.

\begin{figure}[t] \centering
\centering
\includegraphics[width=0.115 \textwidth]{LSP_eg_conv_5.png}
\includegraphics[width=0.115 \textwidth]{LSP_eg_conv_6.png}
\includegraphics[width=0.115 \textwidth]{LSP_eg_conv_7.png}
\includegraphics[width=0.115 \textwidth]{LSP_eg_conv_8.png}
\includegraphics[width=0.115 \textwidth]{LSP_eg_conv_1.png}
\includegraphics[width=0.115 \textwidth]{LSP_eg_conv_2.png}
\includegraphics[width=0.115 \textwidth]{LSP_eg_conv_3.png}
\includegraphics[width=0.115 \textwidth]{LSP_eg_conv_4.png}
\caption{Illumination of several representative feature maps produced by the first three
layers of feature extraction. The top row and bottom row show image-like and edge-like features, respectively.
}
\label{fig:LR_fmap}
\end{figure}

\textit{\textbf{Global Boundary Context}}: We develop a Boundary Context sub-Network (BCN) to preserve salient boundaries that represent global image structures.
BCN consists of two convolutional layers with $3\times3$ kernels, where one layer is with 12 kernels and the other layer is with 2 kernels. In the training phase of BCN, we propose to exploit salient image boundaries by regarding edge detection as a joint task of HR image restoration. In particular, we introduce an auxiliary term into the objective function, which computes the error between predicted and human-labeled edge/boundary maps. These boundary maps are from Berkeley Segmentation Dataset (BSD)~\cite{amfm_pami2011}. Note that there are multiple boundary maps in BSD500 data set, we use their summation for better visualization and \keze{show} the examples in Fig.\ref{fig:HSP-boundaries}.

With the two tasks of image restoration and edge detection, image components and
structural features are firstly extracted and enlarged by content-adaptive interpolation before being fed into the BCN. Several representative samples of the extracted feature maps are shown in Fig.~\ref{fig:LR_fmap}, in which the top row and bottom row show image-like and edge-like features, respectively. This implies that these layers simultaneously extract redundant components and features, making it possible to produce base image and boundary maps in the HR image domain.

Through joint optimization in an end-to-end manner, feature extraction, content-adaptive interpolation and BCN can provide complimentary context information to each other. In this way, structure-aware feature representations can be learned with the content-adaptive interpolation.

\textit{\textbf{Residual Context}}: As a result of paying close attention to generating the HR image with salient boundaries, the concatenated BCN might fail to restore some subtle but noticeable structures. Motivated by the recent residual learning paradigm~\cite{kim2015accurate, csc_sr}, we make an attempt to address this issue by employing a Residue Context sub-Network (RCN). The objective of the RCN is to synthesize a residual image, which is defined as the difference between the interpolated HR image and the ground-truth HR image. In contrast to using the bicubic interpolated HR image as in \cite{kim2015accurate} and \cite{csc_sr}, our model uses the intermediate HR image provided by BCN. This can bring us two benefits: i) Higher image SR performance. As the HR image provided by BCN achieves comparable performance to the state-of-the-art methods, RCN can focus on remedying the overlooked information for higher SR quality; ii) A lightweight network architecture for RCN. Our used interpolated image contains significantly richer information than the bicubic one. Hence, compared with \cite{kim2015accurate} and \cite{csc_sr}, the synthesization of residual images is much easier. As illustrated in Fig.~\ref{fig:overall-pipeline}, the architecture of RCN is the same as that of the concatenated BCN.

For the joint optimization of content-adaptive interpolation, BCN and RCN, we develop a fusion layer to merge the intermediate output of RCN and BCN in a data-driven way. In particular, the final HR image $\mathbf{y}$ of our framework is obtained by:
\begin{equation}
\mathbf{y}=\mathbf{f} \otimes \mathbf{I}_{interHR} + \mathbf{I}_r,
\label{equation:the-whole-obj2}
\end{equation}
where $\mathbf{f}$ denotes a $3\times3$ convolutional filter, $\mathbf{I}_{interHR}$ is the intermediate HR image provided by BCN, and $\mathbf{I}_r$ is the residue image synthesized by RCN. In this way, the parameters of $\mathbf{f}$ can be adaptively \keze{updated} during the learning process.

\section{Framework Training}
The proposed framework is jointly optimized on a set of ``LR image, HR image and HR edge
map\footnote{In BSD data sets, more than one boundary maps are provided for every image, which
are all used in our training process. Since multiple boundary maps are used in the same way, in this
subsection, we focus on the case of one boundary map for simplicity.}'' triplets.
For convenience, we use $\mathbf{I}_l$,  $\mathbf{I}_h$ and $\mathbf{I}_b$ to represent
the LR image, HR image and boundary map, respectively.
Given the input $\mathbf{I}_l$, the objective of our model is to reconstruct a HR image similar to $\mathbf{I}_h$ and predict a boundary map similar to $\mathbf{I}_b$.

The parameter $\mathbf{W}$ of our model can be divided into 4 disjoint parts, i.e., $\mathbf{W}=\{\mathbf{W}_{s}, \mathbf{W}_{h}, \mathbf{W}_{b}, \mathbf{W}_{d}\}$, where $\mathbf{W}_{s}$ and $\mathbf{W}_{d}$ denote the parameters of content-adaptive interpolation and RCN, respectively. We denote the parameter of feature extraction stage has combined into content-adaptive interpolation part. For BCN, we use $\mathbf{W}_{h}$ and $\mathbf{W}_{b}$ to represent the specific weights for generating the intermediate HR image and the boundary maps, respectively.
Since the parameters are separable, we propose to train our model in three iterative steps.
First, we jointly train content-adaptive interpolation and BCN until their convergence; Second, fixing the parameters of content-adaptive interpolation and BCN, we update the parameters of RCN. Third, we jointly optimize content-adaptive interpolation, BCN and RCN. Specifically, content-adaptive interpolation and BCN are trained according to the following objective function:
\begin{equation}
\begin{array}{rl}
L(\mathbf{I}_l,\mathbf{I}_h, \mathbf{I}_b, \mathbf W)  = & L_{h}(\mathbf{I}_{l},\mathbf{I}_{h}, \mathbf W_{s}, \mathbf W_{h})  +\\ & \alpha \cdot L_{b} (\mathbf{I}_l,\mathbf{I}_b, \mathbf W_{s}, \mathbf W_{b}),
\end{array}
\label{equation:the-whole-obj}
\end{equation}
where $L_{h}$ and $L_{b}$ represent the HR image reconstruction objective and the boundary prediction objective, respectively.
The balance weight $\alpha$ is used to control the importance of $L_{h}$ and $L_{b}$, which is empirically set to 1 in all our experiments.
Both $L_{h}$ and the $L_{b}$ are in the form of mean squared error (MSE), i.e.,
\begin{equation}
L_{h} = \frac{1}{N}\sum_{i=1}^{N}\left ( \mathbf{I}_h^{i} - f_{h}( \mathbf{W}_{s}, \mathbf{W}_{h}, \mathbf{I}_l^i) \right ) ^ 2,
\label{equation:the-hr-obj}
\end{equation}
and
\begin{equation}
L_{b} = \frac{1}{N}\sum_{i=1}^{N}\left ( \mathbf{I}_b^{i} - f_{b}( \mathbf{W}_{s}, \mathbf{W}_{b}, \mathbf{I}_l^i) \right ) ^ 2,
\label{equation:the-boundaries-obj}
\end{equation}
where $f_{h}(\cdot)$ and
$f_{b}(\cdot)$
denote the reconstructed HR image and the predicted boundary map, respectively,
$i$ represents the sample index, and $N$ is the number of training triplets.
For simplicity, we use $\mathbf{I}_\omega$ to denote $f_{b}( \mathbf{W}_{s}, \mathbf{W}_{b}, \mathbf{I}_l)$.
Note that when multiple boundary maps are available, there will be more edge prediction objectives.

\begin{algorithm}[t]
\caption{Contextualized Multi-task Learning.}
\label{alg}
\label{alg:batchTraining}
\begin{algorithmic}[1]
\raggedright

\Require
Training LR images $I_l$; HR images $I_h$; boundary images $I_b$;
\While    {$t<T$}
\State $t\leftarrow t+1$;
\State Randomly select a subset of LR images, HR images and boundary images $\mathbf{I}'_l,\mathbf{I}'_h,\mathbf{I}'_b$ from the training set;
\State \textbf{for all} {$\mathbf{I}_l^{'i}$} \textbf{do}
\State Obtain $f_{h}(\mathbf{W}_{s}, \mathbf{W}_{h}, \mathbf{I}_l^{'i})$ and $f_{b}(\mathbf{W}_{s}, \mathbf{W}_{b}, \mathbf{I}_l^{'i})$ via forward propagation;
\State Update $\mathbf{W}_s^t, \mathbf{W}_h^t, \mathbf{W}_b^t$ via the intermediate HR output and boundary output: $\frac {\partial L_h}{\partial f_{h}(\mathbf{W}_{s}, \mathbf{W}_{h}, \mathbf{I}_l^{'i})}$,$\frac {\partial L_b}{\partial f_{b}(\mathbf{W}_{s}, \mathbf{W}_{b}, \mathbf{I}_l^{'i})}$;
\State \textbf{end for}
\EndWhile
\While    {$t<2T$}
\State $t\leftarrow t+1$;
\State \textbf{for all} {$\mathbf{I}_l^{'i}$} \textbf{do}
\State Obtain $f_{d}(\mathbf{W}_{s}, \mathbf{W}_{d}, \mathbf{I}_l^{'i})$ via forward propagation;
\State Update $\mathbf{W}_d^t$ via the residual output and intermediate HR output: $\frac {\partial L_d}{\partial( f_{d}(\mathbf{W}_{s}, \mathbf{W}_{d}, \mathbf{I}_l^{'i}) + f_{h}(\mathbf{W}_{s}, \mathbf{W}_{h}, \mathbf{I}_l^{'i}))}$;
\State \textbf{end for}
\EndWhile
\end{algorithmic}
\end{algorithm}

The loss function for training RCN is defined as:
\begin{equation}
L_d =\frac{1}{N}\sum_{i=1}^{N}(\mathbf{I}_h^i-\mathbf{I}_\omega^i-f_d(\mathbf{W}_s,\mathbf{W}_d, \mathbf{I}_l^i))^2.
\label{equation:the-whole-obj3}
\end{equation}

Finally, the whole framework is optimized by employing the standard back propagation algorithm, i.e.,
\begin{equation}
L = \frac{1}{N} \sum_{i=1}^N (\mathbf{I}_h^i - y)^2,
\end{equation}
where $y$, the output of fusion layer, is the final HR image in the testing phase.

The whole training phase is summarized as Algorithm~\ref{alg},
which accords with the pipeline of our proposed framework in Fig.~\ref{fig:overall-pipeline}.

\section{Experiments}\label{sec:exp}

\subsection{Experiment Setting}
\indent \textit{\textbf{Datasets}}: All experiments are evaluated on three challenging benchmarks, i.e., \textit{Set5}~\cite{bevilacqua2012low},
\textit{Set14}~\cite{zeyde2012single} and \textit{BSD500}~\cite{amfm_pami2011}. The \textit{BSD500} dataset consists of 500 natural images and human annotations for corresponding boundaries. We use the 300 images \keze{from} its training and validation set for training.
The rest of 200 images in \textit{BSD500} dataset form a widely used benchmark called \textit{BSD200}. Besides, the \textit{Set5} and \textit{Set14} datasets are also adopted
as testing sets in other state-of-the-art methods such as~\cite{dong2014srcnn,wang2015deep,kim2015accurate}. Thus, we conduct experiments on the three
benchmarks.

\begin{table*}[t] \centering
\begin{center}
\begin{tabular}{|c|*{10}{>{\hfil}p{33pt}<{\hfil}| }}
\hline
Test set & \multicolumn{3}{c|}{Set5} & \multicolumn{3}{c|}{Set14} & \multicolumn{3}{c|}{BSD200} \\
\hline
Scaling factor & $\times2$ & $\times3$ & $\times4$ & $\times2$ & $\times3$ & $\times4$ & $\times2$ & $\times3$ & $\times4$\\
\hline \hline
Bicubic & 33.66 & 30.39 & 28.42 & 30.23 & 27.54 & 26.00 & 29.43 & 27.18 & 25.92\\
A+~\cite{a+} & 36.55 & 32.59 & 30.28 & 32.28 & 29.13 & 27.32 & 31.44 & 28.36 & 26.83\\
SRCNN~\cite{dong2014srcnn} & 36.34 & 32.59 & 30.09 & 32.18 & 29.00 & 27.20 & 31.38 & 28.28 & 26.73 \\
SRF~\cite{schulter2015fast} & 36.89 & 32.72 & 30.35 & 32.52 & 29.23 & 27.41 & 31.66 & 28.45 & 26.89\\
FSRCNN~\cite{dong2016sr} & \underline{36.94} & 33.06 & 30.55 & 32.54 & 29.37 & 27.50 & 31.73 & 28.55 & 26.92 \\
SCN~\cite{wang2015deep} & 36.93 & \underline{33.10} & \underline{30.86} & \underline{32.56} & \underline{29.41} & \underline{27.64} & 31.63 & 28.54 & \underline{27.02} \\
ShCNN~\cite{NIPS2015xuli} & 36.83 & 32.88 & 30.46 & 32.48 & 29.39 & 27.51 & \underline{31.75} & \underline{28.60} & 26.95 \\ \hline
Proposed & \textbf{37.17} & \textbf{33.45} & \textbf{31.11}& \textbf{32.77} & \textbf{29.63} & \textbf{27.79} & \textbf{31.81} & \textbf{28.67} & \textbf{27.11} \\
\hline
\end{tabular}
\end{center}
\caption{Quantitative comparisons among different methods in terms of PSNR (dB),
in which the underline indicates the second place and bold face represents the first place.}
\label{table:PSNR-on-different-method}
\end{table*}

\begin{table*}[t] \centering
\begin{center}
\begin{tabular}{|c|*{10}{>{\hfil}p{33pt}<{\hfil}| }}
\hline
Test set & \multicolumn{3}{c|}{Set5} & \multicolumn{3}{c|}{Set14} & \multicolumn{3}{c|}{BSD200} \\
\hline \hline
Scaling factor & $\times2$ & $\times3$ & $\times4$ & $\times2$ & $\times3$ & $\times4$ & $\times2$ & $\times3$ & $\times4$\\
\hline
Bicubic & 0.9299 & 0.8682 & 0.8104 & 0.8687 & 0.7736 & 0.7019 & 0.8524 & 0.7469 & 0.6727\\
A+~\cite{a+} & 0.9544 & 0.9088 & 0.8603 & 0.9056 & 0.8188 & 0.7491 & 0.8966 & 0.7945 & 0.7171\\
SRCNN~\cite{dong2014srcnn} & 0.9521 & 0.9033 & 0.8530 & 0.9039
 & 0.8145 & 0.7413 & 0.8835 & 0.7794 & 0.7018 \\
SRF~\cite{schulter2015fast} & 0.9536 & 0.9046 & 0.8529 & 0.9042 & 0.8168& 0.7457 & 0.9011 & 0.8053 & 0.7332\\
FSRCNN~\cite{dong2016sr} & 0.9552 & \underline{0.9128} & 0.8619 & 0.9080 & 0.8231 & 0.7509 & 0.9064 & 0.8123 & 0.7378 \\
SCN~\cite{wang2015deep} & \underline{0.9571} & 0.9112 & \underline{0.8644} & \underline{0.9093} & \underline{0.8246} & \underline{0.7541} & 0.9058 & 0.8139 & 0.7403 \\
ShCNN~\cite{NIPS2015xuli} & 0.9551 & 0.9109 & 0.8638 & 0.9079 & 0.8239 & 0.7530 & \underline{0.9069} & \underline{0.8144} & \underline{0.7407} \\
 \hline
Proposed & \textbf{0.9583} & \textbf{0.9175} & \textbf{0.8736}& \textbf{0.9109} & \textbf{0.8269} & \textbf{0.7594} & \textbf{0.9074} & \textbf{0.8182} & \textbf{0.7460} \\
\hline
\end{tabular}
\end{center}
\caption{Quantitative comparisons among different methods in terms of SSIM,
in which the underline indicates the second place and bold face represents the first place.}
\label{table:SSIM-on-different-method}
\end{table*}

\textit{\textbf{Implementation details}}: In the training phase, we first convert the original color image to grayscale image by extracting the luminance component in YCbCr color space.
Then, we downscale the training images by requested scaling factors (e.g., 2, 3, and 4) to obtain the LR images. The LR images are cropped into a set of patches with a stride of 4. { The size of patches \keze{is} set to be same as receptive field.}
The corresponding HR images and boundary maps are cropped with respect to the scaling factors.
Before training, we initialize the network parameters by a zero-mean Gaussian distribution with a standard deviation of $1\times10^{-4}$.
For the pre-training of the proposed model, we use the 91-images~\cite{yang2010sc}
and PASCAL VOC2012~\cite{voc2012} datasets, which totally contain 13,487 clear images.
Specifically, the model using LR and HR image pairs is pre-trained following the same strategy as~\cite{dong2014srcnn}. Since the feature extraction stage employ pyramid structure, we speed it up with the help of Factorized CNN~\cite{Wang2016Factorized}. In the training on \textit{BSD300} dataset, The learning rate of the last layer is set to $1\times10^{-5}$, while the rest layers are using a fixed learning rate of $1\times10^{-4}$.
To increase the number of training samples, we also employ data augmentation for \textit{BSD300}
dataset, as reported in~\cite{wang2015deep}.

\begin{table}[tbp] \centering \smal
\center
\begin{tabular}{|*{4}{c|}}
\hline
Methods & Parameter number & PSNR \\
\hline\hline
SRCNN \cite{dong2014srcnn} & 57,184 & 32.59 \\
FSRCNN \cite{dong2016sr}& 15,740 & 33.06 \\
VDSR \cite{kim2015deeply}& 664,704 & 33.66 \\
\hline
Ours & 60,436 & 33.45 \\
Deeper ours & 594,964 & \textbf{33.80}\\
\hline
\end{tabular}
\caption{Comparison on parameter number and PSNR performance on \textit{Set5} with a scaling factor of 3.}
\label{table:Parameter_Comparison}
\end{table}

\begin{figure} [tbp]
\centering
\includegraphics[width=1 \columnwidth]{efficiency_comparison}
\caption{The efficiency analysis for the scaling factor of 3 on the \textit{Set5} dataset.
}
\label{fig:efficiency_comparison}
\end{figure}

\textit{\textbf{Methods and metrics}}: We compare our model with several recent state-of-the-art methods, including a three-layer CNN (SRCNN)~\cite{dong2014srcnn}, super-resolution forest (SRF)~\cite{schulter2015fast}, sparse coding-based network (SCN)~\cite{wang2015deep}, anchored neighborhood regression (A+)~\cite{kim2015accurate}, shepard interpolation neural network (ShCNN)~\cite{NIPS2015xuli}, very deep convolutional network (VDSR)~\cite{kim2015accurate}, and fast convolutional network for SR (FSRCNN)~\cite{dong2016sr}.
For fair comparisons, we employ the popular PSNR and SSIM metrics for evaluation.
To evaluate the structure-preserving capability, we introduce a new metric called ``EPSNR'', which can be formulated as:
\begin{equation}
EPSNR = 10\log_{10} {\left(\frac{MAX_I^2}{\frac{1}{|E|}\sum\limits_{i \in E}{\left(G_i-P_i\right)^2}}\right)},
\end{equation}
where $MAX_I=255$ is used for 8-bit images, $G$ and $P$ denote the ground-truth and the produced HR images, respectively, $E$ indicates the pixels whose distances to their closest boundary are less than 2 pixels, and $i$ is the pixel index. It is believed that EPSNR can better exploits image fidelity on edge regions.

\begin{figure*}[htbp]\centering
\subfloat[][ \centering Bicubic \par 26.64 / 0.8232] {
\includegraphics[width=0.21\textwidth]{set5_bf_bic.png}
}
\subfloat[][ \centering A+ \cite{a+} \par 29.11 / 0.8462] {
\includegraphics[width=0.21\textwidth]{set5_bf_a+.png}
}
\subfloat[][ \centering SRF \cite{schulter2015fast} \par 29.23 / 0.8483] {
\includegraphics[width=0.21\textwidth]{set5_bf_srf.png}
}
\subfloat[][ \centering SRCNN ~\cite{dong2014srcnn}\par 29.34 / 0.8513] {
\includegraphics[width=0.21\textwidth]{set5_bf_srcnn.png}
}
\\
\subfloat[][ \centering SCN ~\cite{wang2015deep}\par 29.58 / 0.8499 ] {
\includegraphics[width=0.21\textwidth]{set5_bf_scn.png}
}
\subfloat[][ \centering ShCNN ~\cite{NIPS2015xuli}\par 29.61 /  0.8521 ] {
\includegraphics[width=0.21\textwidth]{set5_bf_shcnn.png}
}
\subfloat[][ \centering Proposed \par \textbf{29.80} / \textbf{0.8589} ] {
\includegraphics[width=0.21\textwidth]{set5_bf_our.png}
}
\subfloat[][ \centering Original \par PSNR / SSIM ] {
\includegraphics[width=0.21\textwidth]{set5_bf_gt.png}
}
\caption{\yukai{Visual comparison on the ``Zebra'' image from \textit{Set14} (factor 3), where the PSNR
and SSIM are separated by ``/''.}}
\label{fig:set5-visualize}

\subfloat[][ \centering Bicubic \par 22.18 / 0.7376] {
\includegraphics[width=0.21\textwidth] {set5_con_bic.png}
}
\subfloat[][ \centering A+ \cite{a+} \par 24.68 / 0.8402] {
\includegraphics[width=0.21\textwidth] {set5_con_a+.png}
}
\subfloat[][ \centering SRF \cite{schulter2015fast} \par 24.60 / 0.8280] {
\includegraphics[width=0.21\textwidth] {set5_con_srf.png}
}
\subfloat[][ \centering SRCNN \cite{dong2014srcnn} \par 25.31 / 0.8677 ] {
\includegraphics[width=0.21\textwidth] {set5_con_srcnn.png}
}
\\
\subfloat[][ \centering SCN \cite{wang2015deep} \par 25.98 / 0.8821 ] {
\includegraphics[width=0.21\textwidth] {set5_con_scn.png}
}
\subfloat[][ \centering ShCNN \cite{NIPS2015xuli} \par 25.85 /  0.8677  ] {
\includegraphics[width=0.21\textwidth] {set5_con_shcnn.png}
}
\subfloat[][ \centering Proposed \par \textbf{26.05} / \textbf{0.8830} ] {
\includegraphics[width=0.21\textwidth] {set5_con_our.png}
}
\subfloat[][ \centering Original \par PSNR / SSIM ] {
\includegraphics[width=0.21\textwidth] {set5_con_gt.png}
}
\caption{Visual comparisons on the ``Butterfly'' image from \textit{Set5} (factor 4), where the PSNR
and SSIM are separated by ``/''.}
\label{fig:internet-visualize}
\end{figure*}

\begin{figure}[t] \centering
\centering

\subfloat[][ \centering Bicubic \par 21.51 dB] {
\includegraphics[width=0.14\textwidth] {GAN_bic.png}
}
\subfloat[][ \centering ShCNN\cite{NIPS2015xuli} \par 22.54 dB ] {
\includegraphics[width=0.14\textwidth]{GAN_SHCNN.png}
}
\subfloat[][ \centering SRGAN-1~\cite{ledig2016photo} \par 20.45 dB] {
\includegraphics[width=0.14\textwidth]{GAN_MSE.png}
}
\\
\subfloat[][ \centering SRGAN-2~\cite{ledig2016photo} \par 19.07 dB] {
\includegraphics[width=0.14\textwidth]{GAN_VGG54.png}
}
\subfloat[][ \centering Proposed \par 22.72 dB] {
\includegraphics[width=0.14\textwidth]{GAN_our.png}
}
\subfloat[][ \centering Ground Truth ] {
\includegraphics[width=0.14\textwidth]{GAN_gt.png}
}
\caption{Visual comparison on Bicubic, ShCNN, our proposed and SRGAN methods. Note that, \keze{`SRGAN-1'} represents the adversarial network with MSE-based content loss only. \keze{`SRGAN-2'} is the adversarial network with perceptual loss as mentioned in~\cite{ledig2016photo}.
}
\label{fig:GAN}
\vspace{-8pt}
\end{figure}

We have also investigated the model complexity from the aspect of parameter number. Two profiles of our model are used, i.e., the common model (denoted as ``ours'') used in the above comparisons, and the model with a much deeper architecture (denoted as ``deeper ours''). In the ``deeper ours'' profile, we only increase the convolutional layer number of feature extraction stage from 4 to 18. Thus our model has similar number of parameters compared to VDSR. Both profiles can be accelerated by cuDNN~\cite{chetlur2014cudnn}. All the CNN-based methods are compared using the \textit{Set5} dataset with a scaling factor of 3. The results illustrated in Table~\ref{table:Parameter_Comparison} \keze{demonstrate} that the performance of our model keeps increasing as the parameter number increases. Using comparable network parameters, our model can achieve a PSNR gain of 0.14~dB compared to VDSR.
Since fewer parameters can benefit both the training and testing phases, we recommend our model with the common profile. Fig.~\ref{fig:efficiency_comparison} illustrates the efficiency of all the compared methods using the ``time-quality'' diagram. It is demonstrated that our model with common profile runs nearly 2 times faster than VDSR while maintaining the second best SR performance, which is quite suitable for lightweight and fast implementation on consumer-grade devices.
For applications that require extremely high SR quality, deeper ours will be a nice choice.

\begin{figure}[t] \centering
\centering

\subfloat[][ \centering Original ] {
\includegraphics[width=0.24\textwidth]{REAL1_WO.png}
}
\subfloat[][ \centering Proposed] {
\includegraphics[width=0.24\textwidth]{REAL1_W.png}
}
\\

\subfloat[][ \centering Original] {
\includegraphics[width=0.24\textwidth]{REAL2_WO.png}
}
\subfloat[][ \centering Proposed ] {
\includegraphics[width=0.24\textwidth]{REAL2_W.png}
}
\caption{ Visual results of our model on real-world cases. The upper row shows the case of video surveillance and the lower row shows the case of mobile device. To see clear comparisons, it is better to zoom in the electronic version of this paper.
}
\label{fig:realworld}
\vspace{-8pt}
\end{figure}

Some promising examples are visualized in Fig.~\ref{fig:set5-visualize} and Fig.~\ref{fig:internet-visualize}. For better viewing, we interpolate the chrominance components by the bicubic method to generate color images. To clearly demonstrate the difference, we choose one
patch from each image and attach them below. Compared to other methods, our model can produce images with sharper and clearer boundaries.

\textbf{\textit{Visual Comparison with SRGAN:}} We compare our method with the super-resolution generative adversarial network (SRGAN)~\cite{ledig2016photo}. Because of their proposed adversarial loss, SRGAN has obtained promising performance. However, it still has
 problems in recovering real details, which is verified by
the comparisons shown in Fig.~\ref{fig:GAN}. It is shown in the enlarged patches of Fig.~\ref{fig:GAN}~(c) and (d) that some waterdrops exist in the ground-truth image disappear, which are produced by SRGAN methods. But these waterdrops are captured by our method and ShCNN.
As pointed out in~\cite{Mehdi2016enhance}, SRGAN tends to bring in similar textures instead of recovering real details.
Therefore, our proposed framework performs better than SRGAN on recovering more accurate details.

{\textbf{\textit{Discussion on real-world cases:}} To justify the effectiveness of our method, we move one step forward to deal with images from video surveillance and mobile device. Specifically, we apply our model on real-world images with a scaling factor of 3. As reported in Fig.~\ref{fig:realworld}, ``Original'' indicates the original images and ``Proposed'' represent the images processed with our model. As one can observe from results shown in Fig.~\ref{fig:realworld}, ``Proposed'' have fewer artifacts compared with ``Original''. This demonstrates the robustness of our method towards real-world challenges. }

\subsection{Ablation Study}~\label{sec:module_analysis}
In this subsection, we conduct detailed analyses on the proposed modules, i.e., content-adaptive interpolation, BCN and RCN, for better understanding of our framework.
We hope such analysis can lead to new insights into image restoration researches.

\textit{\textbf{Content-adaptive interpolation}}:
One of the major differences between our model and SRCNN~\cite{dong2014srcnn} is the employment of the deconvolutional layer. To demonstrate the superiority of our design,
we train several fully convolutional networks (FCNs) with various layer numbers for comparisons. Specifically, we increase the number of middle layers from 5 to 16, resulting in FCN-5, FCN-9, FCN-12, and FCN-16. These FCNs follow the bicubic upsampling strategy as in SRCNN~\cite{dong2014srcnn}. Our content-adaptive interpolation consist of 5 convolutional layers and one deconvolutional layer, which contain feature extraction stage, content-adaptive interpolation and BCN. We remove the task of boundary objective to address the effectiveness of content-adaptive interpolation. By comparing content-adaptive interpolation with these FCNs on \textit{Set5} dataset with a scaling factor of 3, we obtain the results shown in Table~\ref{table:LSP-vs-FCN}.

\begin{figure} [ht]\centering
 {
\includegraphics[width=0.41\textwidth]{hsp} }
\caption{The PSNR curves
generated by models trained with and without edge prediction objective.}
\label{fig:comparison-on-HSP-and-noHSP}
\end{figure}

It is indicated in these results that although the SR performance of FCN keeps increasing as the network depth increases, it still cannot outperform content-adaptive interpolation even when there are 16 layers.
Nevertheless, our content-adaptive interpolation network, which only has 6 layers, surpasses these FCNs by a clear margin. More specifically, content-adaptive interpolation network outperforms FCN-16 by 0.32~dB. This explicitly verifies the superiority of the content-adaptive interpolation.
\begin{table}[t] \centering \footnotesize %
\center
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
Module  & FCN-5 & FCN-9 & FCN-12 & FCN-16 & LSPM \\ \hline \hline
PSNR (dB) & 32.75 & 32.82 &  32.86  &  32.97  & \bf33.29 \\ \hline
\end{tabular}
\caption{Comparison between content-adaptive interpolation and
FCNs on \textit{Set5} dataset with a scaling factor of 3. We remove the edge prediction objective to justify the effectiveness of content-adaptive interpolation.}
\label{table:LSP-vs-FCN}
\end{table}

\textit{\textbf{Global Boundary Context}}:
The proposed BCN is motivated by the paradigm of mult-task learning, which incorporates edge estimation as a co-task of HR image generation. Therefore, its analysis is conducted by comparing the SR performance between with and without the edge prediction objective.
Since the \textit{BSD200} dataset contains manually labeled boundary maps, based on which we can easily compute the EPSNR. We compare two profiles of our model on this dataset with a scaling factor of 3 using both PSNR and EPSNR metrics.
By removing the boundary prediction objective, we degrade BCN into single-task learning and denote it as ``ours w/o boundary''. As illustrated in Table~\ref{table:EPSNR}, the PSNR and EPSNR gains indicate the benefit of multi-task learning.
Because the boundaries only occupy a small portion of the whole image, the improvement on overall PSNR is minor. However, the large improvement on EPSNR verifies the effectiveness of BCN.
Another benefit of incorporating boundary prediction objective is the acceleration of training process.
As shown in the PSNR curves of Fig.~\ref{fig:comparison-on-HSP-and-noHSP}, the edge prediction objective not only accelerates the convergence, but also contributes to a higher restoration quality.
\begin{table}[t] \centering
\center
\begin{tabular}{|*{3}{c|}}
\hline
Methods & PSNR (dB) & EPSNR (dB) \\
\hline\hline
Bicubic & 27.18 (+0.00) & 22.71 (+0.00) \\
A+~\cite{a+} & 28.36 (+1.21) & 24.28 (+1.57) \\
SRCNN~\cite{dong2014srcnn} & 28.28 (+1.1) & 24.24 (+1.53) \\
SRF~\cite{schulter2015fast} & 28.45 (+1.27) & 24.27 (+1.56) \\
SCN~\cite{wang2015deep} & 28.54 (+1.36) & 24.29 (+1.58) \\
ShCNN~\cite{NIPS2015xuli} & 28.60 (+1.42) & 24.32 (+1.61) \\
\hline
Ours w/o boundary & 28.68 (+1.46) & 24.36 (+1.65) \\
Ours & \textbf{28.69 (+1.47)} & \textbf{24.43 (+1.72)} \\
\hline
\end{tabular}
\caption{Comparisons on \textit{BSD200} dataset with a scaling factor of 3.}
\label{table:EPSNR}
\end{table}

\textit{\textbf{Local Residue Context}}:
We design RCN to provide complementary information for image SR. Therefore, the SR performance
of our model will be degraded if RCN is removed.
To verify our statement, we use another profile named ``ours w/o RCN'', which is very similar to the previous version of this work~\cite{This_ICME16},  to conduct more comparisons on the aforementioned datasets with a scaling factor of 3.
Table~\ref{table:dcm} reports the comparison results. It is shown that, although content-adaptive interpolation and BCN can produce HR image of high quality, the SR performance can still be further improved.
The improvement on PSNR is minor because PSNR is a squared error-based metric, which is difficult to reveal subtle structure differences. In contrast, because SSIM concentrates on structure similarity, the improvement on SSIM is more significant.
\begin{table}[t] \centering \smal
\center
\begin{tabular}{|*{4}{c|}}
\hline
Test set & Set5 & Set14 & BSD200\\
\hline\hline
Ours w/o RCN & 33.36~dB & 29.57~dB & 28.63~dB \\
Ours & \textbf{33.47}~dB & \textbf{29.64}~dB & \textbf{28.69}~dB\\
\hline
Ours w/o RCN & 0.9162 & 0.8255 & 0.8176 \\
Ours & \textbf{0.9176} & \textbf{0.8273} & \textbf{0.8183} \\
\hline
\end{tabular}
\caption{Comparisons between our model with and without RCN on the PSNR (top) and SSIM (bottom) metrics.}
\label{table:dcm}
\end{table}

\section{Conclusion and Future Work}~\label{sec:con}
In this paper, to address single image super-resolution, we have proposed a novel contextualized multi-task deep learning framework. Our neural network model incorporates global boundary context and residual context to super-resolve images while well preserving their structural details.
{ Moreover, we have introduced ``content-adaptive interpolation", which leverages a set of filters that are adaptive to the training samples. Different from the kernel estimation in blind image SR which usually employs only a single filter, our proposed content-adaptive interpolation has more filtering parameters and better convenience of being embedded into CNNs.} Our extensive experiments suggest that the proposed method outperforms other leading image super-resolution approaches, and achieves state-of-the-art performances on both popular evaluation metrics and visual quality comparison.

There are several directions to extend our method. First, we are considering to introduce a perceptual loss into the multi-task optimization, aiming to better capture realistic and meaningful image details. Second, we shall generalize this framework to adapt to video data by taking spatio-temporal coherency into consideration.
Third, considering that additional common knowledge in deep neural networks would be an interesting trial, we intend to utilize complementary spatial-temporal contexts as privileged information for video SR, as suggested by Yang \textit{et al.}~\cite{al2016TMM}.

\section*{Acknowledgements}
This work is partially supported by NSFC (No. 61602533), The Fundamental Research Funds for the Central Universities, in part by Hong Kong Scholars Program and Hong Kong Polytechnic University Mainland University Joint Supervision Scheme. We are grateful to acknowledge NVIDIA for GPU donations.

\bibliographystyle{IEEEbib}

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text:
Q: Error Placement of a Form I'm trying to use the following plugin wiht the second form where it places all the error messages inside of its separate div. Only difference is they place it above it and I'm placing mind below. However its still not displaying the messages inside the div for me. Not sure why.
http://jquery.bassistance.de/validate/demo/errorcontainer-demo.html
Demo: http://jsfiddle.net/xtremer360/KHSKm/1/
Edit: I updated my code want to show what I have now to how it's not placing the error messages inside of the div I have made for errors when there are errors for the form submission.
Updated Demo: http://jsfiddle.net/MtRn6/

A: You need to use errorPlacement to put the error messages where you want them, otherwise it will use the default method of appending them to the input:
// container is the element which should hold errors,
// You have already definied it in your demo
errorPlacement: function(error, element) {
        error.appendTo(container);
},

Updated demo: http://jsfiddle.net/MtRn6/1/
Check the docs for more info: http://docs.jquery.com/Plugins/Validation/validate

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