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NYCC'13: IDW Publishing Panels
By Press Release on	 October 9, 2013 0 Comments
New York Comic-Con will be the east coast's largest gathering of comic fans, creators and publishers alike! IDW will be joining the list of big names at this fan-favorite convention with a number of panels that will be the talk of the show!
In addition to these stellar panels Taran Killam and Marc Andreyko will be selling NYCC exclusive variants with cover art by the legendary Bill Willingham of the debut issue of their new series The Illegitimates, a full month before they go on sale! Find them at Kevin Sharpe's table in artist's alley, #U15!
Now, check out the list, and join IDW's all-star creators at these four-star events!
IDW: The Most Explosive Panel of 2013!
Saturday, October 12 — 11:15 AM – 12:15 PM, Room 1A10
Join IDW's Dirk Wood, Chris Ryall, and Scott Dunbier, along with acclaimed creator DARWYN COOKE and another surprise legendary guest, as they set off an explosion of exciting new projects for 2014 and beyond. Includes three announcements that are sure to set the Con on fire! This panel will be a true one-of-a-kind experience that has to be seen to be believed!
IDW Presents: Taran Killam's Illegitimates
Sunday, October 13 — 10:45 AM – 11:45 AM, Room 1A14
Fresh off his "night job" the evening before, SNL actor/comedian Taran Killam is appearing alongside co-writer Marc Andreyko and IDW's Dirk Wood and Chris Ryall, to discuss his exciting new comic-book series, The Illegitimates! Come get the lowdown on the new series, the new SNL season, and plenty o' laughs at this special panel delivered Live from New York!
YOE! Books Presents: Fiends, Ghouls & Haunted Horror!
Saturday, October 12 — 4:15 – 5:15 PM, Room 1A06
Join the award-winning comics historian Craig Yoe for a lively discussion and Q&A session about the pre-code classic comics that gave you night terrors! And make sure and swing by Craig Yoe's booth at the show, #588!
IDW Publishing: Creator Visions
Sunday, October 12 — 2:45 PM – 3:45 PM, Room 1A10
Join IDW's VP of Marketing, Dirk Wood, and a virtual cavalcade of today's most exciting creators. Featuring rising stars from David Wohl's Darby Pop imprint, the creative team from the hit digital "America's Army" comics, and much more, come down and witness Wood and company pulling back the curtain on IDW's most exciting new projects for 2014!
WILL.I.AM's Wizards & Robots
Friday, October 11 — 1:45 – 2:45 PM, Room 1A23
Musician and visionary creative Will.i.am is launching a special project with IDW, Wizards and Robots. Will is making his first-ever comic-con appearance to discuss the comic, chat about futurism, and how he just might change the comic-book world through this unique project.
Be sure to catch all of the amazing IDW panels filled with surprise guests to hear the biggest news of the year first!
IDW is an award-winning publisher of comic books, graphic novels and trade paperbacks, based in San Diego, California. Renowned for its diverse catalog of licensed and independent titles, IDW publishes some of the most successful and popular titles in the industry, including: Hasbro's The TRANSFORMERS, G.I. JOE and MY LITTLE PONY; Paramount's Star Trek; Nickelodeon's Teenage Mutant Ninja Turtles; 2000AD's Judge Dredd; The Rocketeer; Toho's Godzilla; Wizards of the Coast's Magic: The Gathering and Dungeons & Dragons; and the Eisner-Award winning Locke & Key series, created by best-selling author Joe Hill and artist Gabriel Rodriguez. IDW is also home to the Library of American Comics imprint, which publishes classic comic reprints, Yoe! Books, a partnership with Yoe! Studio, and the multiple award-winning Artist's Edition imprint.
IDW's critically- and fan-acclaimed series are continually moving into new mediums. Currently, Jerry Bruckheimer Films and Disney are creating a feature film based on World War Robot; Dwayne "The Rock" Johnson and Warner Brothers are producing a film based on Ashley Wood's Lore; and Michael Bay's Platinum Dunes and Sony are bringing Zombies vs. Robots to film.
via IDW Publishing
Comic Book Convention IDW Publishing New York Comic Con nycc
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COVID-19 – Visitor Restrictions In Place. Read Before Visiting
Search Vidant Health
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Vidant Health
Home • Volunteers
Making A Difference Every Day
Each day at Vidant hospitals, volunteers help deliver the high-quality, compassionate care our patients and their families deserve. They fill a variety of roles, from interacting with patients to helping families find their way around the hospital. Some volunteers use their skills and training to assist with administrative work or serve as lab assistants or nursing aides. We work hard to match your interests with a volunteer position that is right for you.
Our volunteers range from retirees who want to contribute years of knowledge and valuable work experience to young people who want to get a head start on a rewarding career through the VolunTEEN program. As different as our volunteers are, they share important qualities: they are caring, compassionate and willing to help others.
Benefits of Volunteering
As a Vidant Health volunteer, you'll meet new people, make new friends and gain important work experience. At the end of the day, the biggest reward of all is knowing that you helped someone who needs you.
For more information on becoming a volunteer, please choose a hospital location below.
Vidant Beaufort Hospital, a campus of Vidant Medical Center
Download the Adult Volunteer Application.
Download the Junior Volunteer Application.
Vidant Bertie Hospital
Call 252-482-6270 or complete our online application.
Vidant Chowan Hospital
High school students can apply online for our VolunTEEN program.
Already have a volunteer ID number? You can access our online Volunteer Information Center to schedule your shift.
Vidant Duplin Hospital
Adult volunteers (18 and older) are needed to help in all areas of Vidant Duplin Hospital. Service areas include administrative departments (assisting with light duties), working in the hospital gift shop, serving in the Emergency Department lobby, assisting in the Surgery Services waiting area and a variety of other units.
Volunteering is a wonderful way to give back to the community and contributes to the delivery of high-quality, compassionate care that our patients and their families deserve.
For more information, call 910-296-2629 or email [email protected] You can also apply online by clicking here.
Volunteer Chaplain Program
The Vidant Duplin Hospital Chaplain Program is an opportunity for local ordained ministers to provide spiritual and inspirational support to patients, families, visitors and team members. To learn more about this program, call 910-296-2629 and leave a message along with your email. The Volunteer Coordinator will contact you to give you more information on how you can participate in the Vidant Duplin Hospital Volunteer Chaplain Program.
VolunTEEN Program
Our student summer program is designed to teach students ages 15-17 the importance of community service. Assignments provide opportunities to learn about healthcare careers as well as careers in other areas. The atmosphere at Vidant Duplin fosters growth, maturity and responsibility. The VolunTEEN program consists of three, two-week sessions from July through August. A student can accumulate 40 hours of volunteering time during the summer sessions and will be able to volunteer throughout the school year. Applications will be accepted for the VolunTEEN Program 2020-2021 starting February 15, 2020. Due to COVID-19 the VolunTEEN Program is currently suspended. We will provide updates here as available. Thank you for your interest in our program. For more information, call 910-296-2629 or email [email protected].
A VolunTEEN application is available here.
Vidant Edgecombe Hospital
The Summer Volunteen Program is for students ages 14-17. The deadline for applicants is in May each year. To learn more about the program and how to enroll, please email Amy Dixon at [email protected].
Call 252-641-7791 for information.
Vidant Medical Center
Adults and College volunteers (18 and older) are needed to help in all areas of Vidant Medical Center (VMC).
Most of our volunteers serve within the main VMC building. However, we also have exciting volunteer positions at the Vidant SurgiCenter, Vidant Cardiovascular & Pulmonary Rehabilitation and more. Volunteers serve in three general capacities: clinical, clerical and transport. In all, we place volunteers in more than 50 different areas.
A 60-hour commitment is required, but a long-term pledge is encouraged.
Adult volunteers serve at least one four-hour shift per week. We offer flexible scheduling, with both weekday and weekend opportunities available.
College students may volunteer during the semester or summer school. A 60-hour commitment is required per semester. The application deadline for spring semester is Feb. 1 or Sept. 15 for fall semester.
For more information, call 252-847-4491 or email us at [email protected]. You can also apply online by clicking here.
*Paper applications available upon request.
This summer program is designed to teach students ages 15-17 the value of community service and to provide experiences that foster growth and maturity. Assignments provide a wonderful opportunity for students to learn and explore health care careers. Our VolunTEEN Program lasts for 8 weeks starting in June and ending in August. Vidant VolunTEENs serve in transport areas, main lobbies, surgery waiting areas, nursing units, and various other departments.
Each VolunTEEN will need to commit to serve at least one shift per week for four hours. They must complete 30 hours of service each summer in order to complete the program and be eligible to return throughout the school year/following summer. VolunTEENs must attend the mandatory orientation in June to be eligible to volunteer. Students must be 15 years of age and be on track to complete the 9th grade by the end of the current school year.
General VolunTEEN Requirements
Application must be submitted by the deadline
Submission of required VolunTEEN Reference Form by the deadline
VolunTEENS must be fully vaccinated by February 25, 2022
Complete Occupational Health Assessment – The Occupational Health Assessment will be emailed after an interview has been scheduled
TB Skin Tests will be provided after acceptance into the program
Attend Mandatory Orientation in June
Must wear appropriate VolunTEEN uniform while volunteering
VolunTEEN must remain on campus during shifts
VolunTEEN must adhere to all deadlines and Vidant/Volunteer Services rules and policies
Program Requirement
VolunTEEN is expected to commit to and be present throughout the span of the program. Only two absences will be allowed during the program. Please be aware of this commitment before applying.
School Year VolunTEEN Program
VolunTEENs will only be eligible for the school year program if they were accepted as a summer VolunTEEN and competed the 30 hours of service requirement. VolunTEENs must also not have any disciplinary marks against them to be considered a school year VolunTEEN. The school year program will run from September to May.
The Summer 2022 VolunTEEN Application is now open. The application closes on Feb. 4, 2022 at 5 p.m.
Vidant Roanoke-Chowan Hospital
Call 252-209-3290 to learn about our volunteer and VolunTEEN opportunities.
Download an adult volunteer application.
Download the VolunTEEN summer application.
Download the VolunTEEN fall application.
The Vidant Roanoke-Chowan Volunteer Chaplaincy On-Call Program is an opportunity for local ordained ministers to provide spiritual and inspirational support to patients, families, visitors and staff. To learn more about this program, call 252-209-3290, and leave a message. One of the volunteer chaplains will return your call the next business day.
The Outer Banks Hospital
Volunteers who work within the hospital are asked to give a minimum of four hours a week. They must also complete a health screen, background check and attend a one-day orientation. Additionally, there are annual requirements which must be kept up-to-date.
High school and college students are invited to participate in a 5-week summer volunteer program to get a first-hand look into the world of health care. Students must be at least 15 years of age.
For more information about volunteer opportunities at OBH, contact Kelly Divita, Volunteer and Guest Relations Coordinator, at 252-449-4550 or email [email protected].
Hands of Hope Volunteers
Hands of Hope is a partnership between The Outer Banks Hospital and the University of North Carolina Cancer Outreach. Its goal is to ensure no one walks the cancer journey alone. Volunteers complete a health screen, background check and orientation and attend bimonthly meetings. Volunteer opportunities in the program include helping patients and families with transportation, childcare, household chores, errands, meal preparation, respite care, companionship, emotional support and more.
Outreach Volunteers
The Outer Banks Hospital conducts community outreach programs where the ability to interact with the community, visitors, patients and staff is vital.
These volunteers may help plan fundraising events such as the Flights of Fancy black tie dinner dance and Beyond Pink. They may also help at community outreach events like health fairs, workshops, health initiatives and special events like Women's Heart Health.
For more information, contact Jennifer Schwartzenberg, Director of Community Outreach and Development, at 252-449-5933 or [email protected].
Vidant Education Website
For Vidant employees, please log into the LearnCenter.
For all others, please make your selection below
Physician-employed personnel
Volunteers and other non-employees
Shadowing Opportunities
For information on shadowing at Vidant hospitals and facilities, visit the Shadowing page on our Careers site.
Vidant Virtual
Online Health Records
About Vidant Health
Voices of Vidant
© 2022 Vidant Health. All Rights Reserved.
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Freeport turns to Chicago-area police chief to investigate man's arrest
Healthy Baby Born To Woman Who Received World's First Deceased Womb Transplant
by Latoya Patrick | December 07, 2018 | 03:46
The report of a Brazilian woman giving birth to a healthy baby girl a year ago, post a 10-hour long uterus transplant surgery, has added another feather in the medical world's cap and given a ray of hope to women with uterus infertility.
The transplant womb was taken from a mother-of-three who had died from bleeding on the brain.
The 32-year-old woman, who underwent the transplant in September, 2016, was born without a uterus due to Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome, which affects the reproductive system. And just seven months after the operation, doctors began in vitro fertilization, through which the woman's own fertilized eggs - which had been harvested prior to the transplant surgery - were transferred to her new uterus. She gave birth to a baby girl on December 15, 2017, by caesarean section.
This is a first success for transplantation of the uterus collected post-mortem. However, a woman who received a live uterine transplant gave birth to a baby boy in 2017, making it a first for the U.S.
The Journal cites 10 previous cases of failed uterus transplants from deceased donors, in the USA, the Czech Republic and Turkey.
A womb transplant is performed by removing the womb from the donor and transplanting it into the recipient by connecting the two major veins and a series of arteries, ligaments and vaginal canals, according to The Lancet.
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The transplanted uterus was removed during the caesarean section and showed no anomalies. They also note that it could potentially make uterus transplants more widely available, especially in countries that already regulate and distribute organ donations from deceased donors.
After surgery, the anonymous recipient remained in intensive care for two days before spending another six days on a specialised transplant ward. Uterus transplants have been happening via live donors since 2013 with 11 livebirths so far.
These are still early days for uterine transplants, says Kate O'Neill, co-lead investigator for the University of Pennsylvania's uterus transplant program, who was not part of the work in Brazil. Today, the girl and the mother are in excellent health, according to the hospital of São Paulo. It is estimated that one woman out of 500 would be the victim of an abnormality of the uterus into the world.
Five months after transplantation, the uterus showed no signs of rejection, and the recipient started having periods. Babies have been born after womb transplants from live donors, typically from a relative, but using organs from dead donors could widen the... Previous live donor transplantation have waited at least a year before attempting a pregnancy. In addition, through implanting the fertilised eggs sooner they reduced the amount of time taking immunosuppression drugs, which could help to reduce side effects and costs.
"There are still lots of things we don't understand about pregnancies, like how embryos implant", said Cesar Diaz, who co-authored an accompanying commentary in the journal. The only complication during pregnancy was a kidney infection, which was treated with antibiotics.
At the age of seven months, the baby continued to breastfeed and weighed 15lbs and 14oz (7.2 kilos).
Hamas accepts new Egyptian vision for internal Palestinian reconciliation
This is not the first time Hamas has issued and executed death sentences against suspects for collaborating with Israel. Palestinian President Mahmoud Abbas, however, has refused to sign death sentences, even in the West Bank.
Anthony Martial steps up for Manchester United in big clash yet again
United levelled immediately when Jesse Lingard caught the Arsenal defence flat-footed and blasted the ball past Leno. The goal-line technology was called into action and a vibration of Andre Marriner's watch gifted Arsenal the goal.
'Simmba' Song: Ranveer Singh-Sara Ali Khan Groove to 'Aankh Marey'
It definitely has the potential of being the flavour of the sangeet ceremonies this wedding season. The quartet drops a major hint for the possibility of the fifth Golmaal movie coming our way.
Washington Redskins Pass on Colin Kaepernick
Last week the goal was to get Colt McCoy ready for Monday Night Football in Philadelphia with a playoff spot on the line. He said if it had been Week 1 of the season, Kaepernick would have been a better possibility of joining the team.
Emery focused exclusively on squad unlike Wenger - Arsenal legend Adams
Manchester United legend, Rio Ferdinand, has named two clubs that are above the rest in the Premier League this season. I know him for a long time, when he came to Chelsea and in Italy, and he was very polite with me.
Lake in India drained after discovery of HIV-infected body
Unable to reassure the locals, authorities are now draining the 36-acre lake and planning to refill it with water from the Malaprabha canal.
Serena confirms she's heading Down Under for Australian Open
The top Croatian tennis players will fight in the first Grand Slam tournament of the 2019 season - the Australian Open . The tournament's leading sponsor is Kia, who is the main sponsor of the tournament from previous years too.
Global Narrowband IoT Market 2018
Both established and new players in Internet of Things Devices market can use report to understand the market . The report demonstrates the present trends and strategies adopted by the most leading players in the market .
EA Sports Removes Kareem Hunt From Madden NFL 19
The police announced the investigation in a statement on the department's public records website Wednesday evening. The Chiefs released Hunt on Friday and he cleared waivers on Monday , meaning he is a free agent.
GOP-led Michigan Senate to vote on bill to strip Dem's power
The campaign finance commission would assume a number of responsibilities now under the purview of the state secretary of state. Scott Walker has signaled he supports the legislation. "We don't trust Tony Evers right now in a lot of these areas".
ECG Functionality Comes to Apple Watch
Again, this could potentially save lives, as it could help users catch issues before they become serious. ECG functionality is enabled by electrodes baked into Apple Watch's back crystal and Digital Crown .
Russia Tests Iskander Ballistic Missile System Amid INF Treaty Spat
He reportedly chose not to withdraw from the treaty under pressure from European leaders, who said such a move could restart an arms race.
Microsoft will officially rebuild Edge as a Chromium-based browser
Will Microsoft's decision make it harder for Firefox to prosper? Beyond that, Edge will be coming to MacOS for the first time. As such, many web developers optimize their sites to work best in Chrome, with Firefox and Edge more of an afterthought.
Buzzcocks singer Pete Shelley dies at 63
It was at this time that Shelley talked about his bisexuality, which had been implicit in numerous Buzzcocks songs he had written. And singer Tracey Thorn, formerly of Everything But The Girl, said Shelley had been an "amazing songwriter".
OH dad makes girl walk miles to school for bullying on bus
Cox told WTVG he thinks the punishment worked, but he would give his daughter those same consequences again if he had to. Presumably she was never out in the cold for more than an hour or so at a time. "More parents should act like you".
3 astronauts set to launch to space station on Monday
This time, the mission set out to deliver a three-person Russian, American, and Canadian crew to the International Space Station. EST, the Soyuz rocket launched from the Baikonur Cosmodrome in Kazakhstan.
Alex Smith Suffering From Post-Op Infection In Leg
Trauma surgeons discovered Smith suffered a spiral fracture in his leg, defined as a long bone broken by a rotational force. Washington Redskins quarterback Alex Smith's road to recovery from a devastating leg injury appears to have gotten longer.
Sony's MEN IN BLACK Sequel Gets a Title
F Gary Gray, well-known for his work in Fate of the Furious and Straight Outta Compton , will be directing the spin-off of sorts. But no longer are we in the dark about how Sony Pictures is presenting this fourth film in the franchise.
Golden State Warriors, Cleveland Cavaliers meet in Finals rematch
The two-time defending champion Warriors make their triumphant return to Cleveland (facing a hopeless 5-18 team) tomorrow night. The Golden State Warriors ' rivalry may be over with the Cleveland Cavaliers , but Klay Thompson hasn't forgotten about it.
Chelsea's Eden Hazard Reportedly Agrees Big Move To Real Madrid
Eden Hazard looks to be on his way out of Chelsea in the summer after reports on Spanish radio said the Belgian has "agreed terms" with Real Madrid .
Spotify Wrapped 2018: Drake Dominates Their Year-End Playlists
He was Australia's most streamed male artist and his softboi banger masterpiece "God's Plan" was the most streamed track of the year.
Kurt Busch joining Chip Ganassi Racing
Ganassi's forward-thinking approach is why I have made a decision to commit all my years of Monster Energy Cup experience to CGR. According to ESPN, the deal with CGR is a one-year agreement as the 2019 season could be Busch's final season racing full-time.
Pet Supplies Plus Issues Consumer Advisory for Bulk Pig Ear Product
Mold at children's hospital killed 1, sickened 5
Swallowing Pool Water Is #1 Leading Cause Of Diarrhea Parasite
4 counties find West Nile in mosquitoes
In a first, scientists eliminate HIV from animal's genome
Pear-shaped Body in Postmenopausal Women Better for CVD Risk
CDC warns about a parasite in pools that is making people sick
What to Know About Necrotizing Fasciitis on Florida Beaches
Mum dies after cutting leg on beach and contracting a flesh-eating bug


Broadcom has reportedly slashed more than 300 workers at CA Technologies' facilities in the New York City area. The move comes just days after Broadcom closed on its $18.9 billion acquisition of CA Technologies.
A Newsday report cited dozens of employees who said internal documents hinted at the number of jobs being cut. A Broadcom spokesman confirmed there were layoffs but did not disclose the number of jobs being cut.
Broadcom is mostly focused on chips that power networking equipment, while CA Technologies is focused on software that powers networking devices.
A thread on the layoffs has also been posted on TheLayoff.com website.
Prior to the Broadcom acquisition, CA Technologies announced it planned to cut 800 jobs in an attempt to shift its business model. CA Technologies CEO Mike Gregoire said that the job cuts were because the company was undergoing "cross-business unit, cross-functional organization alignment" as it shifts its business to a subscription-based model.
Broadcom's acquisition of CA Technologies came on the heels of the U.S. government halting Broadcom's attempt to acquire chip giant Qualcomm. Financial analysts at that time questioned Broadcom's move on CA Technologies noting that the two firms operated on different ends of the technology spectrum with no synergistic overlap.
Broadcom CEO Hock Tan attempted to soothe investor concerns by noting that the deal would allow Broadcom to sell hardware into new customer segments and that it expects to see a boost in sales tied to 5G network deployments.
"Through CA we believe we have a big doorway to engage strategically with these customers and provide them direct access at very compelling economics to the same leading edge … storage, and compute technologies that are used to enable the cloud service providers today," Tan said during the company's third fiscal quarter conference call, according to transcripts.


Tumor-associated fibroblasts promote the proliferation and decrease the doxorubicin sensitivity of liposarcoma cells. Autophagy modulates transforming growth factor beta 1 induced epithelial to mesenchymal transition in non-small cell lung cancer cells. Member feedback about List of Turkish films of Less well known among mysticetes are the numerous elongated, stiff sensory hairs vibrissae observed on the gray whale rostrum from the ventral tip to the blowhole and on the mandible. PAC down-regulates estrogen receptor alpha and suppresses epithelial-to-mesenchymal transition in breast cancer cells.
I Do High-performance beating pattern function of human induced pluripotent stem cell-derived cardiomyocyte-based biosensors for hERG inhibition recognition. We present the top 10 research priorities for hair loss excluding alopecia areata to guide researchers and funding bodies to support studies important to both patients and clinicians. Dark City.
Discourse Anaphora in Turkish as Mother Tongue. The phenomenon of low creatinine levels underlies some hitherto unexplained relevant observations, and low measurements may be attributed to inassayability secondary to involvement in autoimmune activation. The Turkish group was the most traditional; it showed the largest gender differences and the strongest meat-masculinity link. Hair loss is a commonly encountered problem in clinical practice, with men presenting with a distinctive pattern involving hairline recession and vertex balding Norwood-Hamilton classification and women exhibiting diffuse hair thinning over the crown increased part width and sparing of the frontal hairline Ludwig classification. The hairs were observed for colour, curl and calibre. The latest reports suggest that adenovirus Adv36 is related to obesity in adults and children.
None of those with low indices and only one of those with negative indices were found to have X-linked adrenoleukodystrophy; this subject had already been diagnosed because of the neurological symptoms. Fitness is known to influence arterial stiffness. Kuyucu Murad Pasha. Russell, Lori S. Characteristics of Androgenetic Alopecia in Asian.


text:
The discography of British rock band Manfred Mann's Earth Band consists of 17 studio albums, 3 live albums, 7 compilation albums, 5 video albums, and 37 singles. Manfred Mann's Earth Band have been releasing albums and singles since 1971.

The Earth Band was formed in 1971 by South African musician Manfred Mann after the break-up of Chapter Three. Mann formed the Earth Band as a progressive rock group to break away from the pop genre.

The band's albums have charted in at least 8 countries and their singles have charted in at least 5. The group is most notable remembered for their cover versions of Bruce Springsteen songs, including "Blinded by the Light", which topped the singles charts in the United States and Canada. The group's albums have been the most successful in Norway, where a total of 7 released peaking in the top 10 of the VG-lista albums chart.

Studio albums

Live albums

Compilation albums

Video albums

Singles

See also
 Manfred Mann discography

Notes

References

External links
Manfred Mann's Earth Band - Discography 1971-2005 (The Albums)

Manfred Mann's Earth Band
Blues discographies
Rock music group discographies

redpajama_set_name: RedPajamaWikipedia


text:
Engel Statement on North Korea Missile Test
WASHINGTON—Representative Eliot L. Engel, Ranking Member of the House Committee on Foreign Affairs, today made the following statement regarding the reported test of a submarine-launched ballistic missile (SLBM) by North Korea:
"Once again, North Korea's reckless and provocative behavior is threatening security and stability in the Asia Pacific region. This is the regime's fourth test this year and the latest provocation by the young dictator Kim Jung Un.
"The timing of this and other recent tests suggests that Kim is conducting them as a way to shore up domestic support in advance of the Congress of the Workers Party, scheduled for next month. Clearly Kim Jung Un will stop at nothing to secure his position at the helm of North Korea's oppressive regime.
"Despite the regime's claims, I don't believe that North Korea would abandon its troubling course if the United States stopped our joint military exercises with our allies in the region.
"It is critical, however, for China and Russia, who provide the vast majority of the regime's economic support, to do more to change the calculus of North Korea's ruling elite and bring them back to the negotiating table."
Permalink: https://foreignaffairs.house.gov/2016/4/engel-statement-north-korea-missile-test


Is bioenergy a sustainable pathway toward a low-carbon future in BC and beyond?
The Government of British Columbia has committed to reduce greenhouse gas (GHG) emissions by 80 per cent below 2007 levels by 2050. While bioenergy as a renewable energy source can play a role in achieving these reductions, questions remain over how to best harness waste to generate energy and help achieve BC's emissions reduction target.
This presentation will explore climate change and flow regulation impacts on daily and annual river discharge variations and trends into Hudson Bay, the Nechako and Fraser River Basins over the past half-century or so. The first part of the talk will focus on the 3.7 million km2 Hudson Bay drainage basin for which daily observed streamflow data from the Water Survey of Canada, Manitoba Hydro, Ontario Power Generation and Hydro-Québec for 21 rivers are used in the analyses spanning 1960-2016.
Natural gas is the source of one-quarter of the British Columbia's greenhouse gas emissions. But there is another way to generate this energy that could provide substantial emissions reductions: renewable natural gas (RNG), derived from diverse organic waste sources.
How the European Union agreed to cut its greenhouse gas emissions by 40% by 2030 and what legislation it has adopted to deliver.
Join us for a talk by Jürgen Salay, policy officer at the European Commission and 2017-2018 EU Fellow at the University of Washington, Seattle.
A video recording will be available shortly after the event.


text:
CWTS rookie Spencer Gallagher enters Iowa on a career high
By Daniel McFadinJun 19, 2015, 2:59 PM EDT
It took 20 races and a few late-race cautions for Spencer Gallagher to earn his best-carer finish in the Camping World Truck Series.
The GMS Racing rookie started 14th last weekend in the Drivin' for Linemen 200 at Gateway Motorsports Park and at the end of the night found him and his No. 23 Allegiant Travel Chevrolet in second place.
In his previous 19 races as driver in the CWTS, Gallagher, 25, had never finished better than 10th in a non-restrictor plate race.
"Our Allegiant Travel team's second-place finish last week was huge for us, and definitely gives us added confidence and something to build on," Gallagher said in a release. "(Crew chief) Jeff (Stankiewicz) is making great calls on pit road and we're making our Allegiant Travel Chevrolets better each time we go to the racetrack."
Now Gallagher enters tonight's American Ethanol 200 at Iowa Speedway looking got capitalize on his second career top-five finish.
"I'm looking forward to going back to Iowa (Speedway)," Gallagher said in a release. "It's a place we have run well in the past in the ARCA (Racing Series) and had our second-best finish of the season in the NCWTS last year. It's such a perfect match for our trucks.
"There are multiple lanes because you're always looking around for grip, and there are some sizable bumps in Turns 1 and 2," said Gallagher, who recorded the fourth best practice speed Friday morning.
In last year's Iowa race, Gallagher finished 11th after starting 21st. Gallagher will be using the same chassis he had in the Truck race at Dover International Speedway, where he ran in the top five near the race's mid-point before finishing 12th.


I discovered the talented not to mention hilarious Ayun Halliday only recently when she added my blog to the list of links on her blog and we traded some emails. On vacation in London or "holiday" as the Brits say, I read her latest book Dirty Sugar Cookies subtitled culinary observations, questionable taste. If you are looking for a book to make you laugh out loud, embarrassing yourself in front of strangers on a plane this is it.
It's hard to share how funny the book is without giving away the punchlines. Suffice it to say that Ayun's Australian Pizza Incident which takes places in Indonesia is about the funniest thing ever. Having enjoyed a Four Seasons pizza that included broccoli in Budapest (since when is broccoli a season??) I think she's really on to something. Perhaps her next book could be Pizzas Around The World.
AMY: I'm having a proper British fry up for breakfast, my version is two poached eggs, (yes I know they are supposed to be fried) toast, grilled tomato, sausages, British bacon and "chips" (fried potatoes). No beans! This was so good in London I'm still obsessed with it.
Whenever I go away on a trip I come back craving Mexican food. For lunch chilaquiles with salsa verde at Sanborn's in Mexico City.
AYUN: Your breakfast fry up sounds pretty good for lunch, but I don't want to rob you of the pleasure of trying a taste off each other's plates by copying everything you're having. I'd go for the shrimp paste grilled on sugar cane at Doyers restaurant in Chinatown, but I've mentioned that dish so many times here on the cyber road that it's going to be hard to disabuse people of the notion that I'm a major shareholder in the joint, so... goddamnit, you got me in the mood for Mexican food and now I can't think of anything else! Okay, wait, I know. I'll go to some restaurant in the French countryside and I'll go in the early 1950s, on the advice of my friend, Julia Child, who can steer me toward the menu's stand out dishes. I don't need Julia to tell me that I'll start out with a dozen raw oysters on the half shell, and I'll wash them down with a wonderful sparkling wine, something I'd never drink in real life. And I'm going to end with chocolate mousse and then some skanky-smelling cheese that's older than my children put together.
Okay, I'll have your lunch for breakfast, and I'll eat it at Frida Kahlo's house. I'm going to have two large cappuccinos with that, because I need to keep my energy up for the final week of the virtual tour.
AMY: For dinner, a plate full of papusas filled with cheese and zucchini from my favorite secret spot in the Mission district. I'll write about it someday, I promise.
AYUN: I'll hold you to that, but wait, no snack? Come on, let's at least have some edamame, salted up good. It'll be like English teatime, but Japanese, with beer.
Now back to dinner - again, I'm hearing the siren call of your Mission district, but I gave myself a hankering for Japanese too, so let's go to this restaurant that my friend Noriko's mother took me and Greg to when we were in Tokyo for the Japanese production of Urinetown. We had more than a more than a dozen courses, all exquisite, all of which featured tofu (or yuba, a by-product of the tofu making process). We were there as Japan was gearing up for its annual plum blossom festival, so we had these little fairy goblets of plum wine, and a beautiful milky soup in which tofu cut and dyed to look like plum blossoms floated. (Did I mention that we're in a private tatami room, and every ten minutes or so the waitress will slide the shoji screens open to serve us the next course, shuffling around on her knees? Yes.) I will let the chef and the tourist authority of Japan decide what we should be served, but let's try and convince them that we're very important food writers from America, the kind of important that should be comped, because I don't think we're going to want to see this bill. Then, on our way back to the ryokan (what am I talking about, the Imperial Hotel), let's stop off under the J Rail tracks for some yakitori. I have to have the eggplant, but you should have the chicken.
AMY: Many of Ayun's stories involving herself as a child or her own kids reminded me of my years as a babysitter. During that time I used to play a kind of game, challenging myself to cook something interesting with whatever was on hand in a strange kitchen. In a virtual extension of this game, Ayun provided me with a handful of her top favorite ingredients or whatever happens to be in her fridge from which I will concoct a recipe or menu.
AMY: Wow! If I was your babysitter I'd seriously think about bringing a bag lunch. But your list does intrigue me. It makes me think of a Burmese salad. You know the type with little piles of crispy garlic and stuff that you sprinkle over spinach leaves?
Place ginger and vinegar in a small bowl, then cover and chill at least 2 hours or up to 2 days; drain and discard vinegar.
In a large frying pan over medium heat, cook coconut stirring often until crispy and golden brown, about 4 minutes; pour out and set aside. Repeat with sesame seeds, cooking them 3 to 5 minutes.
Add oil to pan. When oil is hot, add peas and stir often until slightly crispy, 12 to 15 minutes. Lift out with a slotted spoon and drain on paper towels. Believe it or not fried peas are pretty tasty! Repeat with peanuts, 4 to 5 minutes.
Add garlic to oil in pan and cook, stirring often, when lightly golden, scoop garlic from pan with a slotted spoon and drain on paper towels, reserving the oil. Let oil cool. Stir fish sauce into oil; set aside.
On a platter, arrange ginger, coconut, sesame, peas, peanuts, garlic, spinach, chilies, and onion, in separate piles. At the table, pour oil mixture over salad, squeeze limes on top, and mix ingredients. Makes 6 nibble servings.
AMY: As for my pantry it's rather empty at the moment, you'll have to contend with milk, yogurt, bananas, carrots, scallions, eggs, butter, blue cheese and bread plus some bacon in the freezer. Any ideas? (you can use all the staples you want--flour, jam, ketchup, chutney, potatoes, onions, you name it, just assume I have it).
window for the pigeons. Et voila!
And so concludes our virtual blog tour date. Don't forget to check out Ayun's book. This link will let you take a sneak peek.


text:
Q: Prometheus keeps showing up on 9090 even if docker container not running Following a tutorial. I have the following docker compose:
version: "3.9"
services:
  grafana:
    image: grafana/grafana
    ports:
      - 3000:3000

  prometheus:
    image: prom/prometheus
    ports:
      - 9090:9090
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml:ro

  postgres:
    image: postgres:12
    ports:
      - 5432:5432
    volumes:
      - ./backup:/var/lib/postgresql/data
    environment:
      POSTGRES_PASSWORD: postgrespassword
      POSTGRES_DB: shop

  postgres-exporter:
    image: prometheuscommunity/postgres-exporter
    ports:
      - 9187:9187
    environment:
      DATA_SOURCE_NAME: "postgresql://postgres:postgrespassword@postgres:5432/shop?sslmode=disable"
    links:
      - postgres
      - prometheus

And this as my prometheus.yml:
global:
  scrape_interval: 15s
  evaluation_interval: 15s

scrape_configs:
  - job_name: prometheus
    static_configs:
      - targets: ["localhost:9090"]
  - job_name: postgres-exporter
    static_configs:
      - targets: ["postgres-exporter:9187"]

I realized I needed to make some changes, so brought down the containers. But noticed I could still access:
http://xxx.xx.xx.xxx:9090/config
In checking the containers, it seemed to restart Prometheus. I disabled it again, checked on the browser, and saw it got restarted again. Additionally, any changes I make to prometheus.yml aren't reflected in the config.
I checked running services, there is no instance of prometheus running outside of docker.
Disabling the docker service brings it down, but as soon as I re-enable it, it appears again, always with the old config.
any ideas? (have tried killing the port as well, just respawns)
CONTAINER ID   IMAGE                    COMMAND                  CREATED       STATUS       PORTS      NAMES
34c66a78b75e   prom/prometheus:latest   "/bin/prometheus --c…"   2 hours ago   Up 2 hours   9090/tcp   my-prometheus.1.iht6uk1okzlb3gc9y9jdaf2fg

redpajama_set_name: RedPajamaStackExchange


text:
\section{Introduction}
\label{sec:intro}
Traditional recognition tasks has progressed with the help of massive labeled images and deep models \cite{Simonyan2014,He2016,he2018only,fang2018attention,zheng2018fast,wang2018multimodal,xie2019automated,CPGBN_ICML2019}.
However, their major disadvantage is that they cannot recognize the images belonging to unseen categories, and it is laborious to collect sufficient labeled images for various tasks. To tackle this problem, generalized Zero-Shot Learning (ZSL) \cite{Palatucci2009,Akata2013,Lampert2014,Romera-Paredes2015a,yang2016zero,Morgado2017,Xian2018,long2018pseudo} has attracted a lot of attention in recent years.
A generalized zero-shot recognition is defined as recognizing a sample from either seen or unseen domain, which contains disjoint categories.
A general paradigm is to project the image data and semantic labels,~\emph{e.g.,} category attributes~\cite{Farhadi2009,Lampert2009}, into a joint embedding space, where recognition becomes a nearest neighbor searching problem~\cite{Song2018}.
A visual diagram is shown in Figure~\ref{fig:task}.
The major challenge of this paradigm is that the different data distributions between two domains lead to serious domain shift problems \cite{Fu2014,Kodirov2015}, which make the embedding features biased towards the seen domain.

\begin{figure*}[t]
	\begin{center}
		\includegraphics[width=0.9\linewidth]{fig_rw.pdf}
	\end{center}
	%
	\caption{Comparison of DSEN with related GZSL paradigms. a) The embedding space is spanned by visual features. b) Preserving the category relationship in the embedding space. c) the proposed DSEN, which introduces two extra $\phi_s$ and $\phi_t$ to model domain-specific knowledge and a domain division constraint $\mathcal{L}_{ddc}$ to enlarge the inter-domain difference.}
	\label{fig:rw}
\end{figure*}

To address this issue, existing methods focus on learning a robust projection between visual representations and semantic labels.
The related methods can be coarsely classified into two classes: embedding-based framework and semantic-preserving framework.
The embedding-based framework \cite{Tomasev2014,Romera-Paredes2015a,Akata2016,Jiang2017,Verma2018} aims to establish a discriminative embedding space that is shared across two domains.
Two commonly used embedding space is spanned by visual representations \cite{zhang2017learning} and semantic labels \cite{Verma2018}, respectively.
Taking Figure~\ref{fig:rw} (a) as an example, the semantic labels are projected into the visual space to match with the corresponding visual representations, which has been proved robust to Hubness problems \cite{Tomasev2014,Lazaridou2015}.
Different from the embedding-based framework, the semantic-preserving framework \cite{kodirov2017semantic,Annadani2018,Chen2018,Xian2018} focuses on preserving the semantic prototype in an embedding space through an auto-encoder architecture.
An example is shown in Figure~\ref{fig:rw} (b).
Although the above methods are effective for zero-shot problems, they all employ a single shared-projection for both seen and unseen domains.
Due to the broad domain gap, a shared projection function cannot model the full specialty of each domain, leading to biased recognition problem.

In this paper, we propose a novel Domain-Specific Embedding Network (DSEN) to alleviate the domain shift problem in ZSL by applying specific projections to different domains, as well as several domain constraints.
The novelties of DSEN over the previous methods are shown in Figure~\ref{fig:rw} (c).
Instead of a single shared projection, DSEN decomposes the projection function into three components: domain-invariant projection $\phi_c$, seen domain-specific projection $\phi_s$, and unseen domain-specific projection $\phi_t$.
The $\phi_c$ targets to capture the common projection knowledge between two domains, and $\phi_s$ and $\phi_t$ are used to capture the domain-specific projection knowledge.
Notably, $\phi_s$ and $\phi_t$ should project the semantic labels of different domains into a shared embedding space $E$ in Figure~\ref{fig:rw} (c) for cross-domain recognition.
To this end, a semantic reconstruction constraint is designed, by applying the same decoder function to both $\phi_s$ and $\phi_t$ in a cycle consistency way, to preserve the shared semantic relationship in $E$.
Compared to using a single shared projection, our domain-specific projections can generate less biased embedding features due to domain specialty modeling.

Furthermore, a domain division constraint is developed to enlarge both intra- and inter-domain discrimination of visual features, based on pseudo visual data in the unseen domain.
Besides fully supervised learning in the seen domain, our domain division constraint restricts the noisy pseudo data to have a uniform label distribution in seen categories.
The advantages of this constraint mechanism are as follows: a) being robust to the noisy pseudo visual features; b) directly enlarging the visual margin between two domains; and c) can be trained end-to-end.
Consequently, the decision boundary of visual features between two domains becomes more clear, which allows us to utilize specific classifiers in determinate searching space for unbiased recognitions.

Our contributions are threefold:
\begin{itemize}

\item We propose a novel Domain-Specific Embedding Network (DSEN) by applying specific projections to two domains, which can better capture domain similarities and differences for unbiased embedding.

\item A domain division constraint is designed to effectively enhance both intra- and inter-domain discrimination, based on real and pseudo visual data in two domains. Besides, it also enables DSEN to be trained end-to-end.

\item The proposed DSEN obtains the sate-of-the-art performance on four public datasets with an average of 9.2\% improvement in terms of harmonic mean.

\end{itemize}

\section{Related Work}
Three types of related techniques are discussed in this section.
\subsection{Embedding-based Zero-Shot Learning}
A general paradigm of zero-shot recognition targets to project the image representations and semantic labels into a joint embedding space, where the recognition becomes a nearest neighbor searching problem \cite{Song2018}.
This process is called as an embedding-based method, which is one of the most popular ZSL strategies.
As the seen and unseen domains have disjointed categories, the additional semantic information, such as attributes \cite{Farhadi2009,Lampert2009} and word vectors \cite{Pennington2014,Niu2017}, are used to construct a relationship between these two domains.
Among these methods, Frome~\emph{et al.}~\cite{Frome2013} and Akata~\emph{et~al.}~\cite{Akata2016} use the bilinear embedding model trained with a pairwise ranking loss.
The ESZSL model \cite{Romera-Paredes2015a} constructs an embedding space with a Frobenius norm regularization, and Qiao~\emph{et~al.}~\cite{Qiao2016} extend this work to online documents by suppressing the noise with an extra $l_{1,2}$ norm.
In addition, Akata~\emph{et al.}~\cite{Akata2015} build a joint embedding space with several compatibility functions, which is improved in \cite{Xian2016} by incorporating latent variables.
Zhang~\emph{et al.}~\cite{Zhang2018} employ a non-linear kernel to generate a mapping between visual representations and attributes.
In spite of the promising performance, the above methods directly project the visual representations into space spanned by semantic labels, which suffer from Hubness problems \cite{Radovanovic2010,Tomasev2014,Lazaridou2015}.
The Hubness problem is defined as a few points being the nearest neighbors of most of the other points, which is caused by that projecting a visual feature with high dimensions into an attributes space with low dimensions shrinks the variance of the projected data points \cite{zhang2017learning}.
Therefore, a few methods \cite{Shigeto2015,zhang2017learning,Xian2018} use an embedding space spanned by visual features, which is defined as a semantic-visual embedding.
Although the previous methods are effective, insufficient semantic embedding limits their further applications due to serious domain shift problems.
For example, a testing sample from an unseen domain tends to be recognized from one of the seen categories.

\subsection{Semantic-Preserving Framework}
To alleviate the domain shift problems, many recent works target to preserve the semantic prototype in an embedding space.
The motivation under this tactic is that the semantic prototype is robust to domain change, which is beneficial to train a robust projection function.
Among these methods, SAE \cite{kodirov2017semantic} and SP\_AEN~\cite{Chen2018} use an auto-encoder architecture on the embedded space to make their embedding features discriminative.
Jiang~\emph{et al.}~\cite{Jiang_2018_ECCV} propose a coupled dictionary learning model to preserve the visual-semantic structures with semantic prototypes.
Especially, Annadani~\emph{et al.}~\cite{Annadani2018} preserve the semantic relationships in visual space by decomposing the relation between categories into three groups.
Although the embedding-based methods and semantic-preserving framework can effectively solve the ZSL problems, they are mainly based on using a single shared projection across two domains, which ignore the large domain differences.

\subsection{Synthetic Data-Based Methods}
Recently, synthetic data-based methods \cite{bucher2017generating,Mishra_2018_CVPR_Workshops,long2018pseudo,Verma2018,xian2018feature} have been proposed, and they have obtained state-of-the-art performance.
In contrast to embedding-based methods, they train a softmax classifier with full supervision on the union of real and synthetic visual data from seen and unseen domains.
The synthetic visual data is obtained by a specific generator, such as GAN \cite{Goodfellow2014} and its variants, based on the unseen domain attributes.
Consequently, their models are more robust to domain shifts than embedding-based methods, based on both seen and unseen visual features.
However, fully-supervised learning is sensitive to noisy synthetic visual data, which has not been fully exploited.

\begin{figure*}[t]
	\begin{center}
		\includegraphics[width=0.88\linewidth]{fig_pipeline.pdf}
	\end{center}
	%
	\caption{The pipeline of training Domain-Specific Embedding Network. Besides the domain-shared projection $\phi_c$, DSEN trains two extra domain-specific $\phi_s$ and $\phi_t$ to better capture domain specialties. Furthermore, the domain division constraint $\mathcal{L}_{ddc}$ makes the visual embedding features in two domains distinguishable. The whole network is trained end-to-end.}
	\label{fig:pipeline}
\end{figure*}
\section{Domain-Specific Embedding Network}
\label{sec:method}
We first describe the problem formulation of the Domain-Specific Embedding Network in Sec.~\ref{sec:pf} and then provide a detailed implementation of DSEN. The pipeline is shown in Figure~\ref{fig:pipeline}.

\subsection{Problem Formulation}
\label{sec:pf}
Let $\mathcal{S}=\{(x_{s},y_{s},\boldsymbol{a}_{s})|x_{s}\in\mathcal{X}_s, y_{s}\in\mathcal{Y}_s,\boldsymbol{a}_{s}\in\mathcal{A}_s\}$ represents the seen domain dataset, where $y_{s}$ and $\boldsymbol{a}_{s}$ are the class labels and semantic attributes for each image $x_{s}$, respectively. $\mathcal{T}=\{(x_{t},y_{t},\boldsymbol{a}_{t})|x_{t}\in\mathcal{X}_t, y_{t}\in\mathcal{Y}_t,\boldsymbol{a}_{t}\in\mathcal{A}_t\}$ is similarly defined as the unseen domain dataset, where $\mathcal{Y}_s\cap \mathcal{Y}_t = \varnothing$.
Given the seen domain data $\mathcal{S}$ and unseen domain labels $\mathcal{Y}_t$ with attributes $\mathcal{A}_t$, the target of a generalized ZSL task is to recognize an image from either $\mathcal{X}_s$ or $\mathcal{X}_t$.

Based on the above definition, a basic objective for our DSEN is to:
\begin{eqnarray}
\min_{W_{\phi}}\sum_{x_s\in\mathcal{X}_s}d(f(x_s),\phi(\boldsymbol{a}_s)),
\label{eq:obj}
\end{eqnarray}
where $f(\cdot)$ is the visual feature extractor for visual images.
$\phi(\cdot)$ is a semantic-visual projection function with trainable weights $W_{\phi}$.
Notably, $\phi(\boldsymbol{a}_s)$ is the semantic embedding.
The distance function $d(\cdot)$ computes the negative cosine distance between two features $\boldsymbol{v}_1$ and $\boldsymbol{v}_2$ by:
\begin{eqnarray}
d(\boldsymbol{v_{1}},\boldsymbol{v_{2}})=-\frac{<\boldsymbol{v_{1}},\boldsymbol{v_{2}}>}{||\boldsymbol{v_{1}}||_2||\boldsymbol{v_{2}}||_2}.
\label{eq:s}
\end{eqnarray}

In most existing methods~\cite{Shigeto2015,zhang2017learning,Annadani2018}, $\phi$ is trained on $\mathcal{S}$ and directly adapted to $\mathcal{T}$ and $f(\cdot)$ is fixed by using pre-trained visual feature extractor.

\subsection{Domain-Specific Projections}
One leading cause of the domain shift problem is that a shared $\phi_c$ cannot model the full differences between two domains, thereby making generated embedding features towards the seen domain.
Targeting to model the differences between two domains, we decompose the projection function into three parts, which are domain-invariant projection $\phi_c$, seen domain-specific projection $\phi_{s}$, and unseen domain-specific projection $\phi_{t}$.
Thus, the embedding features from seen and unseen domains become the combination of two sub-features:
\begin{eqnarray}\label{eq:DSP}
\phi(\boldsymbol{a})=\left\{
\begin{aligned}
\phi_s(\boldsymbol{a})+\phi_c(\boldsymbol{a}) \qquad if~ \boldsymbol{a}\in\mathcal{A}_s, \\
\phi_t(\boldsymbol{a})+\phi_c(\boldsymbol{a}) \qquad if~ \boldsymbol{a}\in\mathcal{A}_t,
\end{aligned}
\right.
\end{eqnarray}
where $\phi_c$ is used to capture the common knowledge between two domains, and $\phi_s$ and $\phi_t$ capture the specific characteristics for seen and unseen domains, respectively.
Compared to existing methods that use a single shared projection, the additional domain-specific projections can better accommodate domain differences, yielding more discriminative embedding features.
By taking the $\phi_c$ and $\phi_s$ into consideration, Eq.~\eqref{eq:obj} becomes minimizing:
\begin{eqnarray}
\mathcal{L}_{svs}=\sum_{x_s\in\mathcal{X}_s}d(f(x_s),\phi_s(\boldsymbol{a}_s)+\phi_c(\boldsymbol{a}_s)).
\label{eq:obj_svs}
\end{eqnarray}

Different from $\phi_{s}$, $\phi_{t}$ is difficult to train due to unavailable $f(x_t)$.
Specifically, it is hard to constrain $\boldsymbol{a}_s$ and $\boldsymbol{a}_t$ to be projected into a shared embedding space using different $\phi_s$ and $\phi_t$, for cross domain recognition.
To achieve this goal, a semantic reconstruction constraint $\mathcal{L}_{sr}$ is designed by applying the same decoder function to both $\phi_s+\phi_c$ and $\phi_t+\phi_c$ for semantic label reconstruction.
The motivation is that the semantic labels are shared across two domains; thus $\mathcal{L}_{sr}$ can constrain the semantic embedding from two specific projections to be associated in a shared embedding space.
First, $\phi_t$ is initialized on the well-trained $\phi_s$ for projection knowledge transfer.
Then, the semantic information in $\boldsymbol{a}_s$ and $\boldsymbol{a}_t$ are simultaneously encoded into $\phi_s$ and $\phi_t$ in a semantic cycle consistency way.
Notably, using $\phi_s$ as the initialized $\phi_t$ can facilitate the convergence.
Consequently, $\mathcal{L}_{sr}$ enables $\phi_t$ to capture the effective projection knowledge in the unseen domain based on $\mathcal{A}_t$, which will be illustrated subsequently.

Inspired by the applications of auto-encoding architecture in unsupervised representation learning, a domain-specific auto-encoder architecture is used to encode the semantic information in both $\phi_t$ and $\phi_s$ by:
\begin{eqnarray}\label{eq:L_rec}
\begin{split}
\mathcal{L}_{sr} = &\sum_{\boldsymbol{a}_s\in\mathcal{A}_s}||\phi_{sr}(\phi_s(\boldsymbol{a}_s)+\phi_c(\boldsymbol{a}_s))-\boldsymbol{a}_s||^2_2\\
&+\sum_{\boldsymbol{a}_t\in\mathcal{A}_t}||\phi_{sr}(\phi_t(\boldsymbol{a}_t)+\phi_c(\boldsymbol{a}_t))-\boldsymbol{a}_t||^2_2,
\end{split}
\end{eqnarray}
where $\phi_{sr}$ is a shared decoder function for both domains.
From Eq.~\eqref{eq:L_rec}, $\phi_c$ has access to the semantic information in two domains, which can capture the domain similarity information.
$\phi_s$ and $\phi_t$ only have access to domain-specific information, thereby rendering them to capture specific characteristics of two domains.

Finally, the objective function for domain-specific projections becomes:
\begin{eqnarray}
\min_{W_{\phi_c},W_{\phi_s},W_{\phi_t},W_{\phi_{sr}}}\mathcal{L}_{svs}+\lambda_1\mathcal{L}_{sr},
\label{eq:obj_dsp}
\end{eqnarray}
where $\lambda_1$ is a hyper-parameter used to balance different constraints.
All the encoders $\phi_c$, $\phi_s$, $\phi_t$ and decoder $\phi_{sr}$ are implemented with two fully connection layers followed by ReLU activation.

Consequently, the domain-specific projections $\phi_s$ and $\phi_t$ assist $\phi_c$ to generate less biased embedding features during semantic-visual projection.
The detailed architecture of our domain-specific projections is shown in Figure.~\ref{fig:pipeline}.

\subsection{Domain Division Constraint}
Based on the embedding features from semantic attributes, we further propose a domain division constraint to make the embedding features between two domains distinguishable.

To achieve this goal, we first generate pseudo visual features from category attributes $\boldsymbol{a}_t$ for the unseen domain.
Especially, we regard $\phi(\boldsymbol{a}_t)$ as pseudo visual features, because $\phi(\boldsymbol{a}_t)$ and $f(x_t)$ have similar distributions based on well-trained semantic-visual projections.
With the real visual features $f(x_s)$ and pseudo visual features $\phi(\boldsymbol{a}_t)$, it is intuitive to train a $|\mathcal{Y}_s\cup\mathcal{Y}_t|$-way softmax classifier, which can recognize visual samples from either seen or unseen domain.
However, $\phi(\boldsymbol{a}_t)$ is usually too noisy for a model to use fully supervised learning, which may deteriorate the model performance in the seen domain.
Therefore, DDC just constraints the noisy pseudo features $\phi(\boldsymbol{a}_t)$ to be far away from the seen categorizes, because $\mathcal{Y}_s$ and $\mathcal{Y}_t$ are disjoint.
Thus, a $|\mathcal{Y}_s|$-way softmax classifier $p$ is trained by minimizing:
\begin{eqnarray}\label{eq:L_cls}
\mathcal{L}_{ddc} = -\sum_{x_s\in\mathcal{X}_s}ln~p_{y*}(f(x_s))+\alpha\sum_{\boldsymbol{a}_t\in\mathcal{A}_t}ln~\hat{p}({\phi}(\boldsymbol{a}_t)),
\end{eqnarray}
where $p_{y}(\cdot)$ is the classification score in terms of the ground truth label $y*$, and $\hat{p}(\cdot)$ is the maximum classification score in $\mathcal{Y}_s$.
The first term in Eq.~\eqref{eq:L_cls} is a general cross-entropy softmax loss in the seen domain.
The second term forces $\phi(\boldsymbol{a}_t)$ to have a uniform label distribution in $\mathcal{Y}_s$, which means that the $\phi(\boldsymbol{a}_t)$ should not be recognized as a seen category.
\begin{table}[h]
\begin{center}
\caption{The details of the experimental datasets. $|\mathcal{Y}_{s}|$ and $|\mathcal{Y}_{t}|$ indicate the class numbers in the seen and unseen domains. The train/val/test indicates the image number of the respective split.} \label{tab:datasets}
\begin{tabular}{lcccccc}
  \hline
  Datasets &Attributes&$|\mathcal{Y}_s|$&$|\mathcal{Y}_t|$&train&val&test \\
  \hline
  \hline
  CUB&312&150&50&7,057&1,764&2,967\\
  SUN&102&645&72&10,320&2,580&1,440\\
  AWA2&85&40&10&23,527&5,882&7,913\\
  aPY&64&20&12&5,932&1,483&7,924\\
  \hline
\end{tabular}
\end{center}
\end{table}
$\alpha$ is a hyper-parameter that is used to balance the training effects between real and pseudo visual features on classifier $p$.

Based on Eq~\eqref{eq:L_cls}, the decision boundary between $f(x_s)$ and $f(x_t)$ can be determined by judging whether the label distribution of an input sample is smooth in $\mathcal{Y}_s$.
Especially for an input image from the seen domain, $\hat{p}(f(x))$ should be extremely large in terms of the true label. Conversely, the $\hat{p}(f(x))$ should be small, indicating a uniform label distribution for an image from the unseen domain.
Furthermore, since the well-trained classifier $p$ can only do categorization in the seen domain, we employ a ranking-based classifier, which is proposed based on nearest neighbor searching, to those samples that are suspected from the unseen domain.
Thus, the final inference of our DSEN can be expressed by:
\begin{eqnarray}\label{eq:zsl_infer}
\hat{y}=\left\{
\begin{aligned}
&\arg\max_{y\in\mathcal{Y}_s} p_y(f(x))\qquad if~ \hat{p}(f(x))>\tau \\
&\arg\min_{y\in\mathcal{Y}_t} d(f(x),\phi(\boldsymbol{a}_t))\quad else,
\end{aligned}
\right.
\end{eqnarray}
where $x\in\mathcal{X}_s\cup\mathcal{X}_t$, and $\hat{y}$ is the final prediction.
$\tau$ is a threshold to determine the domain of an input sample.

With Eq.~\eqref{eq:zsl_infer}, we can divide the searching space for any samples into two sub-spaces.
Once the samples coming from the unseen domain, the ranking-based classifier is used for recognition, of which the search space has been reduced to the unseen domain.
For samples from the seen domain, the softmax classifier $p$ can directly give the confident category predictions.
By reducing the search space, the recognitions in both domains will be measurably improved, which is attributed to our domain division constraint $\mathcal{L}_{ddc}$.

\subsection{Overall Objective}
Finally, the overall objective function of DSEN becomes:
\begin{eqnarray}
\min_{W_{\phi_c},W_{\phi_s},W_{\phi_t},W_{\phi_{sr}},W_{f}}\mathcal{L}_{svs}+\lambda_1\mathcal{L}_{sr}+ \lambda_2\mathcal{L}_{ddc},
\label{eq:obj_daen}
\end{eqnarray}
where $W_{f}$ is the trainable parameters of visual feature extraction function $f(\cdot)$.
$\lambda_1$ and $\lambda_2$ balance different constraints.
Notably, in many existing ZSL methods \cite{Jiang_2018_ECCV,Chen2018,xian2018feature}, $f(\cdot)$ is fixed across different datasets, leading to a weak visual representation $f(x)$.
Instead, $\mathcal{L}_{ddc}$ enables DSEN to be trained end-to-end with a trainable $f(\cdot)$.
Therefore, both visual representations and embedding features from our DSEN are powerful and discriminative.

\section{Experiments}
In this section, experimental analysis on four benchmarks is given to evaluate the proposed DSEN.

\subsection{Experimental Settings}
\noindent\textbf{Datasets.}
We evaluate the proposed method on four widely used benchmarks: Caltech-USCD Birds-200-2011 (CUB) \cite{Welinder2010}, SUN \cite{Patterson2012}, Animals with Attributes 2 (AwA2) \cite{Xian2018}, and Attribute Pascal and Yahoo (aPY) \cite{Farhadi2009}. All the datasets provide annotated attributes. The newly proposed splits of seen/unseen classes in \cite{Xian2018} are used for fair comparisons, which ensure that the test categories are strictly unseen in the pretrained visual projection network \cite{Russakovsky2015}.
The details of the datasets are listed in Table~\ref{tab:datasets}.

\begin{table}
\begin{center}
\caption{The detailed implementations of three baselines.} \label{tab:baselines}
\begin{tabular}{lccc}
  \hline
  Setting&$L_{svs}$&$L_{sr}$&$L_{ddc}$ \\
  \hline
  \hline
  S2V&$\surd$&&\\
  DSP&$\surd$&$\surd$&\\
  DDC&$\surd$&&$\surd$\\
  \hline
  \hline
  DSEN (DSP+DDC)&$\surd$&$\surd$&$\surd$\\
  \hline
\end{tabular}
\end{center}
\end{table}

\noindent\textbf{Implementation details.}
The input images are resized to $480$ along the short side, with data augmentation of $448\times 448$ random cropping and horizontal flipping.
The visual feature extraction network $f(\cdot)$ is based on the ResNet-101 architecture, which is pre-trained on the ImageNet dataset.
The rest of the networks uses MSRA random initializer \cite{He2016}.
In this work, we employ a two-stage training strategy to train the proposed DSEN.
It first fixes $f(\cdot)$ and trains the rest with a large learning rate $lr=1\times e^{-3}$, and then it uses a small $lr=1\times e^{-5}$ to train the whole DSEN. The Adam optimizer is used with $\beta=(0.5,0.999)$ and weight decay $5\times e^{-5}$.
For the hyper-parameters in DSEN, we set $\lambda_1=5$ and $\lambda_2=1$ to balance $\mathcal{L}_{svs}$, $\mathcal{L}_{sr}$, and $\mathcal{L}_{ddc}$, and $\alpha=0.1$ in $\mathcal{L}_{ddc}$.
The above hyper-parameter settings are determined according to experiments, and they are applicable to all of our experimental datasets.
$\tau$ will be analyzed in the ablation study.

\noindent\textbf{Evaluation metrics.}
Similar to \cite{Xian2018}, the harmonic mean ($H$) is denoted in Eq.~\eqref{eq:H} to evaluate a model by:
\begin{eqnarray}\label{eq:H}
\begin{split}
H = \frac{2\times \textit{MCA}_t\times \textit{MCA}_s}{\textit{MCA}_t+\textit{MCA}_s},
\end{split}
\end{eqnarray}
where \textit{MCA}$_s$ and \textit{MCA}$_t$ are the Mean Class top-1 Accuracy for the validation (seen) and testing (unseen) sets, respectively.

In the following parts, the experiments are mainly conducted under generalized ZSL settings, where the testing images come from either the seen or unseen domain.

\noindent\textbf{Baselines.}
To demonstrate the effectiveness of different components in DSEN, three baselines are defined:
\begin{itemize}
\item S2V is a general semantic-visual structure with shared projection function $\phi_c$. The visual feature extraction function $f(\cdot)$ is fixed.
\item DSP adds two extra domain-specific projections $\phi_s$ and $\phi_t$ to S2V.
\item DDC applies the domain division constraint $\mathcal{L}_{ddc}$ to S2V, which makes the visual feature extractor $f(\cdot)$ trainable.
\end{itemize}
Finally, DSEN uses both domain-specific projections and $\mathcal{L}_{ddc}$ with a trainable $f(\cdot)$. The details of each baseline are listed in Table~\ref{tab:baselines}.

\begin{figure*}
	\begin{center}
		\includegraphics[width=1\linewidth]{fig_c.pdf}
	\end{center}
	%
	\caption{Distributions of maximum classification score on four datasets. The vertical axis indicates the percentage of unseen domain samples. FSL and DDC represent the fully-supervised learning and our domain division constraint, respectively.}
	\label{fig:c}
\end{figure*}

\begin{table}
\begin{center}
\caption{The effects of each domain-specific projection on CUB.} \label{tab:phi_s_t}
\begin{tabular}{c|ccc|cc}
  \hline
  Baseline&$\phi_c$&$\phi_s$&$\phi_t$&\textit{MCA}$_t$&\textit{MCA}$_s$ \\
    \hline
    \hline
  \multirow{5}{*}{S2V}&$\surd$&&&25.6&56.6\\
  &$\surd$&$\surd$&&27.5&61.9\\
   &$\surd$&&$\surd$&28.3&57.4\\
  &&$\surd$&$\surd$&29.7&60.1\\
  &$\surd$&$\surd$&$\surd$&30.8&62.7\\
  \hline
\end{tabular}
\end{center}
\end{table}

\subsection{Ablation Studies}

\noindent\textbf{Effects of $\phi_c$, $\phi_s$, and $\phi_t$.}
As the domain-specific projections consist of one domain-shared $\phi_c$ and two domain-specific $\phi_s$ and $\phi_t$, we explore their effects by individually applying them to the baseline S2V.
Table~\ref{tab:phi_s_t} shows the results.
From Table~\ref{tab:phi_s_t}, it is observed that applying $\phi_s$ and $\phi_t$ individually to $\phi_c$ yields improvements by $5.3\%$ and $2.7\%$ in terms of $MAC_s$ and $MAC_t$.
This proves that the domain-specific projections effectively capture their characteristic domain information via semantic reconstruction constraint $\mathcal{L}_{sr}$.
Then, we further explore the effects by using totally separated $\phi_s$ and $\phi_t$ without $\phi_c$.
The results show a slight improvement of $1.5\%$ on $MAC_t$.
The reason is that the connection between $\phi_s$ and $\phi_t$ is too weak to guarantee the projected embedding features to be in the same embedding space.
Finally, combining $\phi_s$, $\phi_t$, and $\phi_c$ offers the best performance, which indicates that $\phi_c$ successfully captures the domain similarities during semantic-visual projection.
These experiments prove the effectiveness of domain-specific projections in generating discriminative embedding features.
In addition, random initialization of $\phi_t$ yields a relatively slow convergence speed.

\noindent\textbf{Effects of domain-specific projections.}
As the domain-specific projections play a critical role in the proposed DSEN, we analyze the effect of applying domain-specific projections to different baselines. The related results are summarized in Table~\ref{tab:gzsl}. From Table~\ref{tab:gzsl}, we observe that applying domain-specific projections achieves better performance than using a single shared projection for all datasets, \emph{e.g.,} the DSP and DSEN both achieve $6.0\%$ and $1.9\%$ improvements on the S2V and DDC baselines in terms of $H$ on CUB, respectively. Table~\ref{tab:gzsl} further shows that the domain-specific projections improve the recognition performance on both seen and unseen domains by about $2\%\sim 6\%$ on CUB.
These achievements demonstrate the effectiveness of the proposed domain-specific projections.

\begin{figure*}
	\begin{center}
		\includegraphics[width=1\linewidth]{fig_t.pdf}
	\end{center}
	%
	\caption{The performance of DSEN with varying $\tau$ on different datasets. }
	\label{fig:t}
\end{figure*}

\noindent\textbf{Comparison of fully-supervised learning and $\mathcal{L}_{ddc}$.}
We further analyze the superiority of our domain division constraint $\mathcal{L}_{ddc}$ to fully-supervised learning.
The analysis is performed by using noisy pseudo visual data for supervised training.
Given a ZSL model, we denote $\hat{p}(f(x))$ as the maximum score of an image among seen categories.
Thus, for an image from the seen domain, the $\hat{p}(f(x))$ should be extremely large in terms of the true label. Conversely, the $\hat{p}(f(x))$ should be small, indicating a uniform label distribution for an image from the unseen domain.
To this end, we compare the $\hat{p}(f(x))$ for all unseen domain samples by individually applying fully-supervised learning and our $\mathcal{L}_{ddc}$ to baseline DSP with noisy pseudo data.
The results are reported in Figure~\ref{fig:c}.

From Figure~\ref{fig:c}, it can be observed that, compared to fully supervised learning, $\mathcal{L}_{ddc}$ improves the percentage of samples with a small $\hat{p}(f(x))<0.5$ from $50\%$ to $70\%$ on CUB, $24\%$ to $42\%$ on AWA2, and $30\%$ to $50\%$ on aPY, approximatively.
On SUN, the percentage of samples with $\hat{p}(f(x))<0.3$ is improved from $22\%$ to $43\%$.
Notably, more samples with a small $\hat{p}(f(x))$ in Figure~\ref{fig:c} mean that more unseen domain samples can be distinguished from the seen domain samples.
Therefore, compared to fully supervised learning, $\mathcal{L}_{ddc}$ makes the embedding features between two domains more distinguishable, which accounts for our impressive performance.
Furthermore, it also shows that the softmax classifier $p$ can model the decision boundary between two domain.
Thus, using domain-specific classifiers are reasonable.

\noindent\textbf{Effects of varying $\tau$ values.}
$\tau$ is a critical parameter to judge whether a testing sample is from an unseen domain. The results of varying $\tau$ values are shown in Figure~\ref{fig:t}.
It can be found that, for the seen domain samples, a higher $\tau$ leads to a lower $MCA_s$.
The reason is that a higher $\tau$ may mistakenly give some seen domain samples to the ranking-based classifier, which degrades the $MCA_s$.
Conversely, in the unseen domain, the larger value the $\tau$ is, the higher $MCA_t$ our DSEN obtains.
The reason is that a higher $\tau$ will feed a large number of unseen domain samples to the ranking-based classifier, which is good at unseen domain categorization and consequently improves the $MAC_t$.
As the metric $H$ is the combination of \textit{MCA}$_s$ and \textit{MCA}$_t$, $H$ does not have a consistent trend.
With an increase of $\tau$, $H$ first increases to the optimal value and then drops.
From Figure~\ref{fig:t}, it can be found that the optimal values for $\tau$ in different datasets are different, \emph{e.g.,} the optimal values for $\tau$ are 0.8, 0.5, 0.9, 0.8 for CUB, SUN, AWA2,and aPY, respectively.

\noindent\textbf{Effects of domain division constraint $\mathcal{L}_{ddc}$.}
We then verify the effectiveness of using domain division constraint $\mathcal{L}_{ddc}$. As shown in Table~\ref{tab:gzsl}, the baseline DDC obtains a higher harmonic mean ($H$) than S2V on all four datasets.
For example, on the AWA2 dataset, the DDC raises a harmonic mean ($H$) from $39.8\%$ to $60.1\%$ over S2V, which is mainly attributed to the significant $24.5\%$ improvement of $MAC_t$. Further, with the domain division constraint $\mathcal{L}_{ddc}$, the DSEN obtains a higher performance than DSP.
These improvements confirm that the $\mathcal{L}_{ddc}$ can effectively make the embedding features more distinguishable, thereby rendering improved recognition in challenging unseen domains.

Furthermore, two domain-specific classifiers, based on $\mathcal{L}_{ddc}$, also yield significant contributions to our impressive performance.
As shown in Table~\ref{tab:gzsl}, the main improvements of our DDC come from the high $MAC_t$ in the unseen domain, which shows that the searching space reduction of the ranking-based classifier is an important factor of performance improvements.
Especially, on the CUB dataset, the metric H of DDC is 12.9\% higher than FGN \cite{Verma2018} in terms of H, where the FGN uses a single softmax classifier in two domains.
This also proves that using two domain-specific classifiers based on $\mathcal{L}_{ddc}$ is superior to using single shared classifier.

\noindent\textbf{Feature visualizations of DSEN.}
Figure~\ref{fig:tsne} shows the t-SNE of generated visual features by DSEN on CUB and AWA2 datasets, respectively.
In each dataset, total $10$ categories are randomly selected from the unseen domain.
From the results, DSEN can not only preserve the semantic relationship in the embedding space but also obtain a large inter-class discrimination.
This is attributed to DSP that captures accurate domain difference and DDC that enlarges the domain difference.

\begin{figure}
	\begin{center}
		\includegraphics[width=0.9\linewidth]{fig_tsne.pdf}
	\end{center}
	%
	\caption{The t-SNE of visual features from DSEN on CUB and AWA2, respectively. }
	\label{fig:tsne}
\end{figure}
\subsection{Comparison with existing methods}
\noindent\textbf{Comparison with generalized zero-shot learning.}
Table~\ref{tab:gzsl} illustrates comparison with previous methods on generalized ZSL. As shown in Table~\ref{tab:gzsl}, our DSEN significantly outperforms existing methods on four datasets, \emph{e.g.,} DSEN obtains $15.0\%$, $1\%$, $3.5\%$, and $16.8\%$ improvement in terms of metric $H$ on CUB, SUN, AWA2, and aPY, respectively.

\begin{table*}
\begin{center}
\caption{Evaluation performance under generalized zeros-shot learning. NG indicates non-generative methods, and G indicates generative methods that use GAN.} \label{tab:gzsl}
\begin{tabular}{c|l|c|c|c|c|c|c|c|c|c|c|c|c}
  \hline
&\multirow{2}{*}{Methods}&\multicolumn{3}{c|}{CUB~\cite{Welinder2010}}&\multicolumn{3}{c|}{SUN~\cite{Patterson2012}}&\multicolumn{3}{c|}{AWA2~\cite{Xian2018}}&\multicolumn{3}{c}{aPY~\cite{Farhadi2009}}\\
\cline{3-14}
&&\textit{MCA}$_t$&\textit{MCA}$_s$&$H$&\textit{MCA}$_t$&\textit{MCA}$_s$&$H$&\textit{MCA}$_t$&\textit{MCA}$_s$&$H$&\textit{MCA}$_t$&\textit{MCA}$_s$&$H$\\
\hline
\hline
\multirow{8}{*}{NG}&CMT\cite{Socher2013}&7.2&49.8&12.6&8.1&21.8&11.8&0.5&90.0&1.0&1.4&$\textbf{85.2}$&2.8\\
&SYNC\cite{Changpinyo2016}&11.5&70.9&19.8&7.9&$\textbf{43.3}$&13.4&10.0&90.5&18.0&7.4&66.3&13.3\\
&SAE\cite{kodirov2017semantic}&7.8&54.0&13.6&8.8&18.0&11.8&1.1&82.2&2.2&0.4&80.9&0.9\\
&KL\cite{Zhang2018}&19.9&52.5&28.9&19.8&29.1&23.6&17.6&80.9&29.0&11.9&76.3&20.5\\
&PTZSL\cite{long2018pseudo}&23.0&51.6&31.8&19.0&32.7&24.0&-&-&-&15.4&71.3&25.4\\
&CDL\cite{Jiang_2018_ECCV}&23.5&55.2&32.9&21.5&34.7&26.5&-&-&-&19.8&48.6&28.1\\
&PSR-ZSL\cite{Annadani2018}&24.6&54.3&33.9&20.8&37.2&26.7&20.7&73.8&32.2&13.5&51.4&21.4\\
&SP-AEN\cite{Chen2018}&34.7&70.6&46.6&24.9&38.6&30.3&23.3&$\textbf{90.9}$&37.1&13.7&63.4&22.6\\
\hline
\multirow{2}{*}{G}&SE-ZSL\cite{Verma2018}&41.5&53.3&46.7&40.9&30.5&34.9&$\textbf{58.3}$&68.1&62.8&-&-&-\\
&FGN\cite{xian2018feature}&43.7&57.7&49.7&$\textbf{42.6}$&36.6&39.4&-&-&-&-&-&-\\
\hline
\multirow{4}{*}{}&S2V&25.6&56.6&35.3&20.1&35.3&26.2&25.6&88.9&39.8&15.5&73.6&25.7\\
&DSP&30.8&62.7&41.3&30.0&40.3&34.4&31.2&87.9&46.1&18.1&73.1&29.0\\
&DDC&57.1&69.2&62.6&40.1&39.2&39.6&51.3&75.2&61.0&30.9&44.9&36.6\\
&DSEN&$\textbf{59.1}$&$\textbf{71.1}$&$\textbf{64.5}$&39.4&41.4&$\textbf{40.4}$&56.4&80.4&$\textbf{66.3}$&$\textbf{31.6}$&52.1&$\textbf{39.4}$\\
\hline

\end{tabular}
\end{center}
\end{table*}

\begin{table}
\begin{center}
\caption{Conventional zeros-shot learning. The \textit{MCA} ($\%$) metric is used for comparison.} \label{tab:czsl}
\begin{tabular}{lcccc}
  \hline
  Methods&CUB&SUN&AWA2&aPY \\
  \hline
  \hline

  CAV\cite{zhang2017learning}&52.1&61.7&65.8&-\\
  FGN\cite{xian2018feature}&61.5&62.1&-&-\\
  SE-ZSL\cite{Verma2018}&59.6&63.4&69.2&-\\
  PSR-ZSL\cite{Annadani2018}&56.0&61.4&63.8&38.4\\
  CDL\cite{Jiang_2018_ECCV}&54.5&63.6&-&43.0\\
  SP-AEN\cite{Chen2018}&55.4&59.2&58.5&24.1\\
  LDF\cite{li2018discriminative}&70.4&-&-&-\\
  \hline
  S2V&52.4&58.2&65.8&40.5\\
  DSP&56.2&62.6&69.1&41.7\\
  DDC&$\textbf{71.8}$&$\textbf{64.0}$&71.2&43.1\\
  DSEN&$\textbf{71.8}$&62.2&$\textbf{72.3}$&$\textbf{43.5}$\\
  \hline
\end{tabular}
\end{center}
\end{table}

To evaluate the effectiveness of domain-specific projections, we compare the DSP baseline with two representative methods~\cite{Annadani2018,Jiang_2018_ECCV} which both employ a single shared semantic-visual projection. From Table~\ref{tab:gzsl}, we see that the DSP baseline performs best on all four datasets in terms of metric $H$.
The high performance demonstrates the superiority of our domain-specific projections to the single shared semantic projection. Comparing with \cite{Annadani2018,Jiang_2018_ECCV}, the other advantage of DSEN is that it makes the visual features more discriminative. In this work, we define the domain shift degree as $|\textit{MCA}_s-\textit{MCA}_t|$.
As the CUB for examples, we find that the domain shift degree for PSR-ZSL \cite{Annadani2018} and CDL \cite{Jiang_2018_ECCV} are both larger than $30\%$. However, our DSEN only has a $12\%$ domain shift degree.
This low domain shift degree proves that our domain-specific projections can generate domain-robust embedding features.

Different from embedding-based PSR-ZSL and CDL, SE-ZSL \cite{xian2018feature} and FGN \cite{Verma2018} obtain state-of-the-art performance by alleviating the domain shift problem with synthetic visual data in an unseen domain. However, they all employ the widely used fully supervised learning that can degrade the recognition performance on real seen domain data, \emph{i.e.,} FGN \cite{Verma2018}  obtains a 10\%  drop of $\textit{MCA}_s$ with synthetic data.
Instead, the $\mathcal{L}_{ddc}$ used in our DDC can reduce the influence of noisy synthesized data.
For example, in CUB dataset, the DDC obtains \textit{MCA}$_{s}$ of 69.2\%, which is higher than the values of 55.7\% and 46.7\% for FGN and SE-ZSL, respectively.
As a consequence, the high \textit{MCA}$_t$ and \textit{MCA}$_s$ make DSEN obtain the highest $H$ among all datasets, which also demonstrates its effectiveness in generalized ZSL.

\noindent\textbf{Comparison with conventional zero-shot learning.}
Comparison with conventional ZSL setting is shown in Table~\ref{tab:czsl}, where the testing images only come from an unseen domain.
Notably, conventional ZSL setting is easier than generalized ZSL due to it ignores the searching space from the seen domain.
From Table~\ref{tab:czsl}, we can observe that the proposed DSEN obtains the best performance on four datasets.
Also, the DDC has achieved higher performance than the existing methods on four datasets.
It proves that the powerful and discriminative visual representations by the end-to-end trainable visual network are significant.
Furthermore, compared to the $MCA_t$ in Table~\ref{tab:gzsl} in generalized zero-shot learning, we have found that the four baselines all obtain higher performance.
The reason is that the conventional ZSL know prior information for the testing images belonging to which domains, which mitigates the projection domain shift problem.

\noindent\textbf{Discussion.}
As shown in Table~\ref{tab:gzsl}, DSEN achieves impressive improvement on CUB, aPY, and AWA2. However, it cannot obtain a consistent improvement on SUN dataset.
The reason is that too many categories in SUN make it hard to generate good visual features from semantic attributes of low dimensions.
More specifically, FGN uses GAN to generate synthetic visual features for an unseen domain, which is much more powerful than our two-layer generator.
Thus, the distance between two generators is hard to remedy with domain-specific projections and classifiers, since there is a total of 717 categories in SUN.
However, our DSEN finally obtains a slightly higher $H$ value than FGN, due to an obviously higher $MAC_s$ in the seen domain, which ensures the robustness of DSEN.

\section{Conclusion}
With an aim to solve the domain shift problem in generalized zero-shot learning, we propose a novel Domain-Specific Embedding Network by applying specific projections to seen and unseen domains based on domain characteristics.
In contrast to existing methods using a single shared projection, we demonstrate that domain-specific projections can better capture domain similarities and differences, leading to more robust embedding features.
To avoid domain-separated embedding space, a semantic reconstruction constraint is designed by using semantic labels to associate two specific projections in a cycle consistency way.
Furthermore, a domain division constraint is developed to make the generated embedding features more distinguishable.
Experiments on four benchmarks demonstrate the effectiveness of the proposed method.

In the future, powerful generators will be explored to provide more reliable synthetic visual representations, \emph{e.g.,} GAN.
Also, domain-specific projection architectures will be explored by using autoML, which may yield further improvements.

\section{Acknowledgement}
This work is supported by the National Key Research and Development Program of China (2017YFC0820600), National Defense Science and Technology Fund for Distinguished Young Scholars (2017-JCJQ-ZQ-022), the National Nature Science Foundation of China (61525206,61771468,61622211,61620106009),the Youth Innovation Promotion Association Chinese Academy of Sciences (2017209), National Postdoctoral Programme for Innovative Talents (BX20180358), and the Fundamental Research Funds for the Central Universities (WK2100100030).

  \bibliographystyle{ACM-Reference-Format}

redpajama_set_name: RedPajamaArXiv


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text:
Q: HTML white background not displaying properly
I downloaded an already set-up dashboard ( https://github.com/app-generator/django-dashboard-argon ). Of course, after downloading it I tried to customize it according to my needs. I need the background to be fully white but I am not able to change the standard greyish color (right in the picture). I tried the following:

*

*Inspect the argon.css file and inspect the classes

*Inspect the root colors and tried to change them

*Tried to set the background on the body via the style attribute setting the background-color: #FFFFFF but this is working for all colors except for white :D

My opinion is that something is affecting the way white is displayed and the final result is not a simple white but it is something more grey. I attach an example.

I would like my background to be fully white as it is inside the table(left) but at the moment is light grey as it is on the right.
EDIT 1:
This is the CSS
:root {
  --blue: #5e72e4;
  --indigo: #5603ad;
  --purple: #8965e0;
  --pink: #f3a4b5;
  --red: #f5365c;
  --orange: #fb6340;
  --yellow: #ffd600;
  --green: #2dce89;
  --teal: #11cdef;
  --cyan: #2bffc6;
  --white: #fff;
  --gray: #8898aa;
  --gray-dark: #32325d;
  --light: #ced4da;
  --lighter: #e9ecef;
  --primary: #5e72e4;
  --secondary: #f7fafc;
  --success: #2dce89;
  --info: #11cdef;
  --warning: #fb6340;
  --danger: #f5365c;
  --light: #adb5bd;
  --dark: #212529;
  --default: #172b4d;
  --white: #fff;
  --neutral: #fff;
  --darker: black;
  --breakpoint-xs: 0;
  --breakpoint-sm: 576px;
  --breakpoint-md: 768px;
  --breakpoint-lg: 992px;
  --breakpoint-xl: 1200px;
  --font-family-sans-serif: Open Sans, sans-serif;
  --font-family-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
}

*,
*::before,
*::after {
  box-sizing: border-box;
}

html {
  font-family: sans-serif;
  line-height: 1.15;
  -webkit-text-size-adjust: 100%;
  -ms-text-size-adjust: 100%;
  -ms-overflow-style: scrollbar;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}

body {
  margin: 0;
  font-family: Open Sans, sans-serif;
  font-size: 1rem;
  font-weight: 400;
  line-height: 1.5;
  color: #525f7f;
  text-align: left;
  background-color: #FFFFFF;
}

Index.html
<body class="">
</body>

The body is just initialized with no class but somehow already has a greyish color.


Jeff Tyzik conducts the Toronto Symphony Orchestra at Roy Thomson Hall. A co-production with Greenberg Artists, an evening featuring hits made famous by the reigning divas of Soul and R&B: Aretha Franklin, Tina Turner, Patti LaBelle, Gladys Knight, Nina Simone, Whitney Houston, Jennifer Hudson, Amy Winehouse, Alicia Keys and Adele. Features three female singers performing popular hits made famous by these powerful and soulful songstresses.


text:
Ken Boyd may refer to:

 Ken Boyd (basketball) (born 1952), American basketball player
 Ken Boyd (footballer) (born 1938), Australian rules football player
 Ken Boyd (politician) (born 1947), Virginia politician
Kenneth Boyd, founder of thinktank Boyd Group