text:
Protecting Patients
Published in the Summer 2011 Summa Magazine
The Role of Summa Health System's Institutional Review Board in Advancing Medicine
New drugs. New medical devices. New therapeutic protocols. Summa Health System tests them all through research and each has the potential to improve the health of different patients in different ways. But they do have one thing in common – Summa's Institutional Review Board.
Most patients who agree to participate in clinical trials have likely never heard of an institutional review board (IRB). IRBs serve as objective third parties in all discussions about medical research involving human subjects. In addition to safeguarding patient safety, these committees ensure the nature and manner in which proposed research will be conducted are both ethically and scientifically sound. Think of them as the safety net for clinical investigations.
Formal, regulated IRBs are a relatively recent directive. The National Institutes of Health's Policies for the Protection of Human Subjects, first issued in 1966, achieved regulatory status on May 30, 1974. Promulgated by the Department of Health, Education and Welfare (now the Department of Health and Human Services), the regulations established the IRB as one mechanism through which human subjects would be protected.
Despite being mandated by the Department of Health and Human Services, these committees – of which Summa has two – operate largely in the background. And their charge is more than merely greenlighting research, said Jessica Conrad, Summa's IRB manager.
"Our IRB is here to minimize the risks associated with a study and to make sure our patients know exactly what to expect if they participate," Conrad said. "We catch problems and ask questions about research projects that could affect human health – it's something we take very seriously."
Monitoring studies conducted at Summa keeps the IRB committees busy. In 2010, both groups completed approximately 1,600 reviews and approved 150 protocols. Proposals come from pharmaceutical companies and device manufacturers, as well as from Summa's physicians, nurses and other clinical staff.
Who Serves?
As a federal requirement, all IRBs share some characteristics. Every committee must have at least five members, both male and female, and they must come from varied professions. Specifically, IRBs must include at least one individual from these areas: a scientific concentration, a non-science field, and sectors wholly unaffiliated with the reviewing institution.
Summa takes extra steps, however, to guarantee that it analyzes study proposals from all angles, Conrad said. To delineate between which investigations were conceived outside the health system and which are borne of the institution, Summa operates two committees – IRB1 and IRB2. IRB1 tackles submissions from pharmaceutical companies and device manufacturers. Summa investigator-initiated study protocols fall under IRB2's jurisdiction.
Members appointed to serve on both committees reflect the nature of the studies reviewed. While both bodies have a medical librarian, a lab technician, and a nurse, that is where they part ways. IRB1 also relies on two infectious disease specialists, two oncologists, and an emergency room physician. IRB2 is rounded out with a psychologist, an obstetrician, a surgeon, an ICU doctor, a family practitioner, and a cardio-pulmonary statistician. The remaining members, such as retired nurses, school teachers, and lawyers, are recruited from the community. All members are selected based on their levels of expertise and how their knowledge can strengthen submitted protocols. They do receive instructions on how to work within the IRB framework, however.
"Once members accept the appointment to the IRB committee, we review the process and educate them so they know what IRB participation calls for," Conrad said. "Every new member is paired with an experienced member who can mentor and train them."
At Summa, simply agreeing to become a member of an IRB isn't enough. Appointees must make the time commitment to read protocols and attend monthly meetings. According to Conrad, Summa experts must attend 70 percent of the IRB meetings, and community members are expected to be present at 50 percent.
Why Community Members?
Although all research submitted for IRB approval has a solid foundation in science, not everyone who participates as a study subject has the same levelof scientific understanding. This reality makes it necessary to incorporate community members – most of whom are from outside the hard sciences – to analyze proposals through a lay person's eyes.
"Many times the medical people involved with the IRB wouldn't see any problems with a proposal, and from their perspective, they were right," said Richard Haas, a former Goodyear attorney who served on Summa's IRB committee until 1997. "But, coming from a lay person's perspective, I could see how some people could be confused about why they would be asked to participate and what they could and could not do if they did get involved."
Community members are an excellent second set of eyes – outsiders who can step into a discussion and ask for greater clarity. It is also helpful, Conrad said, to include community members from a variety of ethnic and racial groups.
"Having board members of Hispanic or other ethnic descent will definitely give you a different perspective in your approach to conducting research," she said. "You have a clearer view of what types of research will be accepted and what will positively impact your community."
In fact, the U.S. Food and Drug Administration regulations require that, as part of being qualified as an IRB, the IRB must have "… diversity of members, including consideration of race, gender, cultural backgrounds and sensitivity to such issues as community attitudes …."
On the surface, the IRB committee's job is relatively simple. It approves, disapproves, and modifies research. Through a continual review, it monitors changes and keeps a close eye on the informed consent process, as well as the methods used to move the research forward. If members don't like what they see, they can, as a group, suspend or terminate any previous IRB approval.
Giving protocols the go-ahead, however, is more involved than that, and the step-by-step process depends on whether the proposed protocol comes from a company or Summa itself.
Pharmaceutical companies and device manufacturers often handle much of the process before Conrad's office sees the proposal. They reach out directly to the physician or other investigator to gauge his or her interest in assuming the principal investigator role. If, after reading the protocol, the principal investigator chooses to move forward, a study coordinator begins working on the informed consent paperwork. For the most part, companies already have existing consent forms, and experienced coordinators will edit the documents based on the small changes Summa requires to protect its patients.
"Once the application and consent form have been prepared, the companies and principal investigator submit them to the IRB," Conrad said. "Generally, we don't ask companies to make changes to the protocols because the study could be ongoing at 600 sites. We do focus, however, on making sure the consent forms are as clear as possible."
IRB1 receives the documents via an electronic system and reviews the protocol to catch anything problematic that might remain.
This is where Summa has a unique and beneficial agreement with other hospitals and universities in the areas surrounding Akron and even some in other states. Under the agreement, Summa and other institutions involved in the same project can rely on each other's protocol review instead of conducting an additional one. Such a relationship exists between Summa and all institutions associated with the Austen BioInnovation Institute in Akron, as well as Kent State University, The Ohio State University and Duke University in North Carolina.
"This type of connection makes it easier to talk with each institution first," Conrad said. "It's easier to get things through the IRB process – it's easier to get agreement."
The process for investigator-initiated ideas, however, is frequently more involved and can take up to six months to complete. Sometimes, investigators bring Conrad a yet-to-be fleshed-out idea, giving her the opportunity to offer significant guidance before too much of the study outline is in place. Others submit what they deem to be finished proposals for the committee to read and dissect.
The Approval Process
First and foremost, the committee's main concern is mitigating risk to study participants. And, that comes in two forms – ensuring the study's potential benefits outweigh the associated risks and requiring and verifying that study coordinators secure written consent from each subject.
"Keeping track of informed consent is an important detail," said Deborah Barnbaum, Ph.D., philosophy professor at Kent State University and Summa IRB community member, "because the majority of Summa's research investigations are multi-site clinical trials in either Phase II or Phase III. With that many people involved in a study, proper documentation, and getting it done correctly, is a necessity," she said.
"In light of the Belmont Report that looked at medical ethics after the Tuskegee Syphilis Study, those of us who are involved with the IRB process hold it as our first priority to make sure people who volunteer for research studies know what they're getting into," said Barnbaum, who also chairs Kent State's IRB committee. "Are the documents detailing the study's purpose clear enough so anyone can understand it and ask the right questions?"
"Effective informed consent procedures are equally important for the community at large," said Marguerite Erme, D.O., M.P.H., Summit County Health Department medical director and member of Summa's IRB1. "Frequently, the public has a negative perception of medical research and an uninformed view about the people who choose to participate in studies."
"The people who participate in research are performing a wonderful service that may provide a benefit they'll never see," said Erme. "But they are certainly aware of the study's intent and what they have volunteered to do."
Beyond perfecting the informed consent process, IRB committees check to see if the projected study sample will be adequately varied. They also look for provisions, such as a data safety monitoring board, that will monitor data accuracy and protect patient safety and privacy throughout the investigation.
For researchers trying to determine whether to submit a protocol, Conrad offers specific advice. Make sure the proposal either furthers an existing area of research or is uncharted territory. Outline the study clearly, and include as much detail as possible.
"When, as an investigator, you think you've added enough detail, add more," Conrad said. "We want to know how your study will play out step-by-step. Tell us the dosages of drugs, the medications that exclude patients from participating, and tell us how long things are going to take. We want – and need – to know it all."
The IRB chair receives all submitted protocols and then assigns two reviewers – one scientific and one nonscientific – to each study. The research also falls into one of three categories: exempt, expedited, or full-board review.
The IRB chair can deem a study exempt from further review if there is no way to identify participants based on the data collected for research. For example, a protocol using a retrospective chart review would be exempt. Research can be expedited if it calls for minimal risk to any participants and if reviewers require only minor changes to the proposal.
Investigators must submit their study outlines for full-board review if they suggest research that puts participants at more than minimal risk and involves any protected groups, such as children, pregnant women, the elderly, psychiatric patients, or prisoners. These studies are intensely scrutinized, and occasionally they give committee members pause.
"We wouldn't be a good IRB board if we didn't sometimes have questions about whether some parts of a proposal are ethical," Erme said. "We've sent some protocols back for significant revisions, and we've denied approval to some."
Conrad agrees there have been occasions when parts of a study outline made the IRB committee uncomfortable. For example, any proposals that would deceive participants about the study's intent are circumspect. Investigators must not only prove it would be impossible to achieve their goals without deception, but they must also clearly outline their plans for revealing the truth to participants.
"Problems also arise when protocols aren't sufficiently balanced," Barnbaum said. Investigators testing a new drug or medical device must design the study to test the unverified product against a well-understood, standard-of-care therapy. Otherwise, it is impossible to determine if the proposed treatments produce better results with less risk.
"Identifying pitfalls is a large part of why IRBs exist," she said. "Ninety-five percent of the research we see has no problems. We're here for the five percent that make it through the drafting process and still raise questions."
To read the article in the original publication: http://www.thesummafoundation.org/media/11058/18979.pdf pg. 6
September 14, 2011 - Posted by wjpalmer | Healthcare | Austen Bioinnovation Institute in Akron, Belmont Report, Deborah Barnbaum, Institutional Review Board, Jessica Conrad, Marguerite Erme, Richard Haas, Summa Health System Institutional Review Board, using community members in Institutional Review Boards, what makes an Institutional Review Board submission unacceptable


Hartland Hoodies are a school leavers hoodies company that supply truly bespoke hoodies to many schools, both primary and secondary, across the south of England. Wanting to break away from their current website solution Sophie English, the owner, came to us to discuss what we could bring to the table. After several meetings to formulate a solid plan of action, Sophie was confident enough to sign up to our Business Pro Package and the development commenced.
Sophie needed a robust Ecommerce solution that was both easy to administer and also supremely functional from a front end point of view. Enter ECWID, our preferred Ecommerce partner. This formed the basis of the solution for selling but it was up to Neil to design a site around it that was engaging and beautiful to both use and look at. It also needed to have fluid, simple to use forms that collected all the data prior to Sophie setting up the individual school's bespoke online shops. We injected a solution that is the perfect synthesis between functionality and design. Conditional visibility plays a large part in these forms and makes it easy to navigate through the different fields.
Once a form is submitted and Sophie has created the bespoke store (Neil taught Sophie how to create and administer in ECWID's easy to use dashboard) then the store is uploaded to the site and hidden behind a password protected page so that only the School that it is designed for can see and use it. Sophie then sends the School the link and password so that the parents and school admins can purchase their hoodies.
These orders then land in the ECWID dashboard where Sophie can collate all the information she needs before sending an order off to her printers. She can then easily update her customers on progress of the orders via the ECWID administration tools.
The results speak for themselves, at the time of writing we have set up 18 school shops with many more in development. The store is taking orders and over 70 have been bought and paid for since it's first few weeks of operating. In the future Sophie is looking to take out social media and SEO packages with Fountain Digital and also look to expand her website to sell uniforms, ski wear, university clothing and other promotional products. Watch this space.
"The Hoodies business is going really well. I have had 50 enquiries so far since the website launched and hoping to achieve quite a high conversion so am really pleased. The website is so easy and I have had really good feedback on the shop facility. I am forever grateful for the work you have done and what we have been able to create."


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This DDR4 SDRAM functions great with my ASUS Crosshair VI hero motherboard and Ryzen 1700.
Not really any noticeable performance gain over the 2666mhz but it works at the rated speed after changing BIOS settings, and I thought it was a reasonable price.This DDR4 SDRAM functions great with my ASUS Crosshair VI hero motherboard and Ryzen 1700.
Not really any noticeable performance gain over the 2666mhz but it works at the rated speed after changing BIOS settings, and I thought it was a reasonable price.
Great value RAM, runs perfectly.Great value RAM, runs perfectly.
Best ram I've ever got atm!Best ram I've ever got atm!
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Bought this ram as part of a major update. Used with AMD Ryzen 2700x and a Gigabyte x470 motherboard. The Kingston ram is very stable and have no issues.
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Opted for this RAM to get a performance machine with a bit more subtle look. Very happy, works flawlessly, and looks great!Opted for this RAM to get a performance machine with a bit more subtle look. Very happy, works flawlessly, and looks great!
Working great with Ryzen CPU.Working great with Ryzen CPU.
No problems with this so far. chunky heat sinks.No problems with this so far. chunky heat sinks.


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60 days in Europe Itinerary
Created using Inspirock Europe tour itinerary maker
Start: Paris, France
Train: 2h 30min
451 kms / 2h 30min
Paris CDG (CDG) — London Heathrow (LHR)
Paris — Calais-Fréthun
Calais-Fréthun — Folkestone
56 kms / 25min
Folkestone — London
London City (LCY) — Rotterdam (RTM)
3h 30min non-stop(flight time: 56min)
London — Southend Airport
Southend Airport — Southend (SEN)
1 kms / 9min
Southend (SEN) — Amsterdam Schiphol (AMS)
1h non-stop
Amsterdam Schiphol (AMS) — Berlin Tegel (TXL)
705 kms / 6h
Amsterdam — Dusseldorf (DUS)
Dusseldorf (DUS) — Berlin Tegel (TXL)
Prague (PRG) — Munich (MUC)
Fastest: 3h 15min(flight time: 50min)
Munich (MUC) — Vienna (VIE)
Munich — Salzburg Hbf
Salzburg Hbf — Salzburg (SZG)
8 kms / 12min
Salzburg (SZG) — Vienna (VIE)
Vienna (VIE) — Krakow (KRK)
Fastest: 4h(flight time: 1h)
Krakow (KRK) — Budapest (BUD)
Fastest: 4h 30min(flight time: 1h 5min)
Budapest (BUD) — Venice Treviso (TSF)
Fastest: 4h 30min(flight time: 1h 20min)
Budapest (BUD) — Bologna (BLQ)
Bologna (BLQ) — Bologna C.Le
Bologna C.Le — Venice
Venice (VCE) — Rome (FCO)
Rome (FCO) — Florence (FLR)
Florence (FLR) — Zurich (ZRH)
Florence — Venezia Mestre
Venezia Mestre — Venice (VCE)
Venice (VCE) — Zurich (ZRH)
Train + Fly: 5h
Bern — Zurich (ZRH)
Zurich (ZRH) — Nice (NCE)
1h 5min non-stop
Basel (BSL) — Nice (NCE)
Nice (NCE) — Barcelona (BCN)
Barcelona (BCN) — Lisbon (LIS)
5h non-stop(flight time: 1h 50min)
1243 kms / 12h
1378 kms / 12h 45min
Lisbon (LIS) — Paris Orly (ORY)
1602 kms / 6h 45min
Lisbon (LIS) — Beauvais (BVA)
Beauvais (BVA) — Paris - Charles de Gaulle Airport
Paris - Charles de Gaulle Airport — Paris
End: Paris, France
Destinations in Europe
Things to do in Europe
A beautiful and romantic city fit for any itinerary, Paris brims with historic associations and remains vastly influential in the realms of culture, art, fashion, food and design.
Kick off your visit on the 27th (Mon): examine the collection at Musee de l'Orangerie, get a taste of the local shopping with Champs-Elysees, then explore the activities along Seine River, and finally test your problem-solving skills at popular escape rooms. Get ready for a full day of sightseeing on the 28th (Tue): see the interesting displays at Musee Rodin and then don't miss a visit to Pont Alexandre III.
To find other places to visit, traveler tips, photos, and tourist information, refer to the Paris tour planning website .
If you are flying in from New Zealand, the closest major airports are Paris-Orly Airport, Charles de Gaulle International Airport and Paris Beauvais Tillé Airport. In April, daily temperatures in Paris can reach 18°C, while at night they dip to 6°C. Wrap up your sightseeing by early afternoon on the 28th (Tue) to allow time for the train ride to London.
Things to do in Paris
Museums Nature Parks
Find places to stay Apr 27 — 28:
Musee Rodin
10:00am, Tue Apr 28
12:00pm, Mon Apr 27
Room Escape Games
4:30pm, Mon Apr 27
The Square Mile
Vibrant and noisy, London is one of the world's leading cities, an international capital of culture, education, fashion, music, finance, trade, and politics.
London is known for museums, nightlife, historic sites, shopping, and parks. Your plan includes some of its best attractions: admire the masterpieces at National Gallery, pause for some serene contemplation at St. Paul's Cathedral, enjoy breathtaking views from Coca-Cola London Eye, and walk around Hyde Park.
To find traveler tips, other places to visit, more things to do, and other tourist information, read our London trip itinerary website .
Traveling by train from Paris to London takes 2.5 hours. Alternatively, you can fly; or drive. Due to the time zone difference, you'll gain 1 hour traveling from Paris to London. Expect a daytime high around 15°C in April, and nighttime lows around 5°C. Wrap up your sightseeing on the 3rd (Sun) to allow time to travel to Amsterdam.
Museums Historic Sites Parks
Find places to stay Apr 28 — May 3:
3:00pm, Wed Apr 29
2:30pm, Thu Apr 30
Venice of the North
Known as one of the greatest small cities in the world, Amsterdam charms visitors with its cool and cozy atmosphere.
Change things up with a short trip to Keukenhof in Lisse (about 34 minutes away). There's lots more to do: explore the world behind art at Van Gogh Museum, have some family-friendly fun at Museumphoto, wander the streets of The Jordaan, and learn about wildlife with up-close encounters at ARTIS.
To see maps, where to stay, traveler tips, and other tourist information, read Amsterdam online attractions planner .
Traveling by flight from London to Amsterdam takes 3.5 hours. Alternatively, you can take a train; or do a combination of train and flight. Due to the time zone difference, you'll lose 1 hour traveling from London to Amsterdam. In May, plan for daily highs up to 20°C, and evening lows to 10°C. Finish your sightseeing early on the 7th (Thu) to allow enough time to travel to Berlin.
Things to do in Amsterdam
Museums Parks Zoos & Aquariums
Find places to stay May 3 — 7:
Museumphoto
10:30am, Wed May 6
12:30pm, Sun May 3
1:00pm, Mon May 4
Start off your visit on the 7th (Thu): get a sense of history and politics at Reichstag Building, then appreciate the history behind The Holocaust Memorial - Memorial to the Murdered Jews of Europe, and then pause for some photo ops at Brandenburg Gate. Get ready for a full day of sightseeing on the next day: admire the masterpieces at Pergamonmuseum, then take in the local highlights with Walking tours, and then get in on the excitement at WINDOBONA Indoor Skydiving Berlin.
For traveler tips, reviews, other places to visit, and tourist information, use the Berlin trip itinerary maker app .
Getting from Amsterdam to Berlin by flight takes about 4.5 hours. Other options: do a combination of train and flight; or drive. Cap off your sightseeing on the 9th (Sat) early enough to go by car to Prague.
Tours Museums Fun & Games
WINDOBONA Indoor Skydiving Berlin
4:30pm, Fri May 8
The Holocaust Memorial - Memorial to the Murdered Jews of Europe
2:30pm, Thu May 7
City of a Hundred Spires
Prague lures millions of tourists each year with its World Heritage-listed medieval city center--a labyrinth of narrow lanes, bridges, cathedrals, and palaces overlooked by a thousand-year-old castle.
Kick off your visit on the 9th (Sat): take in the local highlights with Walking tours and then take a stroll through StaromEstske namEsti. Get ready for a full day of sightseeing on the next day: step into the grandiose world of Prague Castle, then contemplate in the serene atmosphere at St. Vitus Cathedral, then take some stellar pictures from Prague Astronomical Clock, and finally snap pictures at Charles Bridge.
For more things to do, maps, other places to visit, and other tourist information, you can read our Prague itinerary planning tool .
Traveling by car from Berlin to Prague takes 3.5 hours. Alternatively, you can take a train; or take a bus. May in Prague sees daily highs of 20°C and lows of 10°C at night. Finish your sightseeing early on the 11th (Mon) so you can travel to Munich.
Historic Sites Tours
Find places to stay May 9 — 11:
10:00am, Sun May 10
5:00pm, Sun May 10
Staromestske namesti
4:30pm, Sat May 9
Munich is known for museums, nature, historic sites, nightlife, and childrens museums. Your plan includes some of its best attractions: steep yourself in history at Nymphenburg Palace, pause for some photo ops at Moriskentanzer, get the lay of the land with Walking tours, and contemplate the waterfront views at English Garden.
To find maps, where to stay, other places to visit, and more tourist information, read our Munich road trip website .
You can fly from Prague to Munich in 3.5 hours. Alternatively, you can drive; or take a bus. May in Munich sees daily highs of 20°C and lows of 9°C at night. Wrap up your sightseeing on the 14th (Thu) to allow time to drive to Vienna.
Museums Tours Historic Sites
Find places to stay May 11 — 14:
12:00pm, Tue May 12
3:00pm, Mon May 11
10:00am, Wed May 13
The cultural, economic, and political center of the country, Vienna has developed from its early Celtic and Roman roots into a medieval and Baroque city, with an old town that was designated a World Heritage Site in 2001.
Start off your visit on the 14th (Thu): explore the historical opulence of Schonbrunn Palace, admire the masterpieces at Albertina, and then make a trip to Stephansplatz. Get ready for a full day of sightseeing on the 15th (Fri): examine the collection at Judenplatz Holocaust Memorial, see the interesting displays at The Hofburg, then explore the world behind art at Belvedere Palace, and finally don't miss a visit to St. Stephen's Cathedral.
For more things to do, photos, traveler tips, and more tourist information, go to the Vienna sightseeing tool .
Traveling by car from Munich to Vienna takes 4.5 hours. Alternatively, you can fly; or do a combination of train and flight. In May, plan for daily highs up to 22°C, and evening lows to 11°C. Finish your sightseeing early on the 16th (Sat) to allow enough time to travel to Krakow.
Things to do in Vienna
Historic Sites Museums
Schonbrunn Palace
12:30pm, Thu May 14
5:00pm, Fri May 15
The Hofburg
11:30am, Fri May 15
Slavic Rome
One of Poland's oldest cities, Krakow sits at the foot of the Carpathian Mountains, its historical town center nestled on the left bank of the Vistula River.
Step out of Krakow with an excursion to Panstwowe Muzeum Auschwitz-Birkenau in Oswiecim--about 1h 10 min away. There's still lots to do: contemplate the long history of Wawel Cathedral, get to know the fascinating history of Wawel Royal Castle, take in panoramic vistas at Town Hall Tower, and explore the galleries of Muzeum Krakowa, oddzial Fabryka Emalia Oskara Schindlera.
For photos, ratings, other places to visit, and other tourist information, refer to the Krakow trip itinerary tool .
Getting from Vienna to Krakow by flight takes about 4 hours. Other options: drive; or take a shuttle. In May, daytime highs in Krakow are 22°C, while nighttime lows are 9°C. Finish up your sightseeing early on the 19th (Tue) so you can travel to Budapest.
Historic Sites Museums Tours
Panstwowe Muzeum Auschwitz-Birkenau
Bubble Football & More by Bumper Ball Experiences
Wawel Cathedral
Pearl of the Danube
You might consider the city of Budapest, divided by the Danube River into two distinctive cities--Buda and Pest--to be essentially one large World Heritage Site.
Budapest is known for historic sites, spas, nightlife, museums, and nature. Your plan includes some of its best attractions: admire the landmark architecture of Hungarian Parliament Building, contemplate the long history of Dohány Street Synagogue, pause for some photo ops at Hősök tere, and take in the spiritual surroundings of Matthias Church.
For where to stay, traveler tips, more things to do, and other tourist information, refer to the Budapest sightseeing planning app .
Fly from Krakow to Budapest in 4.5 hours. Alternatively, you can drive; or take a bus. Expect a daytime high around 25°C in May, and nighttime lows around 11°C. Finish up your sightseeing early on the 22nd (Fri) so you can travel to Venice.
Things to do in Budapest
Historic Sites Museums Spas
Halaszbastya
2:30pm, Wed May 20
Matthias Church
1:30pm, Tue May 19
Virtually unchanged for the last six centuries, Venice is actually a group of islands made famous by a series of canals, bridges, monuments, piazzas, and narrow pedestrian lanes.
Venture out of the city with trips to Padua (Scrovegni Chapel & Basilica di Sant'Antonio) and Verona (Arena di Verona, Piazza delle Erbe, & more). And it doesn't end there: contemplate the waterfront views at Canal Grande, explore the world behind art at Gallerie dell'Accademia, enjoy breathtaking views from St Mark's Campanile, and capture your vacation like a pro with an inspiring photography tour.
To find reviews and tourist information, read our Venice itinerary planner.
Fly from Budapest to Venice in 4.5 hours. Alternatively, you can do a combination of flight and train; or drive. Expect a daytime high around 26°C in May, and nighttime lows around 15°C. Cap off your sightseeing on the 31st (Sun) early enough to travel to Rome.
Verona, Padua
12:30pm, Fri May 22
Doge's Palace
Eternal City
Aptly nicknamed the Eternal City, Rome is the birthplace of the Roman Empire, one of the world's greatest civilizations ever.
Rome is known for historic sites, nightlife, museums, classes, and shopping. Your plan includes some of its best attractions: explore the ancient world of Colosseum, admire the masterpieces at Galleria Borghese, appreciate the extensive heritage of Piazza Navona, and contemplate the long history of Palatine Hill.
To see maps, where to stay, more things to do, and other tourist information, use the Rome trip builder app .
Getting from Venice to Rome by flight takes about 3.5 hours. Other options: take a train; or drive. In May, daytime highs in Rome are 28°C, while nighttime lows are 17°C. On the 3rd (Wed), you'll have the morning to explore before heading off to Florence.
Find places to stay May 31 — Jun 3:
12:00pm, Tue Jun 2
12:30pm, Sun May 31
Birthplace of the Italian Renaissance
Considered a cultural, artistic, and architectural jewel of Italy, Florence is the birthplace of the Italian Renaissance.
Go for a jaunt from Florence to Pisa to see Leaning Tower of Pisa about 1h 10 min away. The adventure continues: admire the masterpieces at Gallerie Degli Uffizi, get engrossed in the history at Basilica di Santa Croce, admire the natural beauty at The Boboli Gardens, and snap pictures at Ponte Vecchio.
For where to stay, reviews, and more tourist information, use the Florence trip planner .
Take a train from Rome to Florence in 2 hours. Alternatively, you can drive; or fly. June in Florence sees daily highs of 33°C and lows of 21°C at night. You will have some time to spend on the 6th (Sat) before leaving for Zurich.
Find places to stay Jun 3 — 6:
Gallerie Degli Uffizi
3:30pm, Wed Jun 3
Academy Of Florence Art Gallery
5:00pm, Thu Jun 4
Cathedral of Santa Maria del Fiore
Big Small Town
The financial hub of Switzerland, Zurich is most often associated with money and banks.
On the 6th (Sat), take your sightseeing to a higher altitude at Uetliberg Mountain.
To find other places to visit, maps, ratings, and more tourist information, read Zurich visit website .
Traveling by flight from Florence to Zurich takes 4.5 hours. Alternatively, you can do a combination of train and flight; or drive. Plan for a bit cooler temperatures traveling from Florence in June, with highs in Zurich at 27°C and lows at 15°C. Cap off your sightseeing on the 8th (Mon) early enough to catch the train to Bern.
Things to do in Zurich
Nature Parks Historic Sites
10:00am, Sun Jun 7
3:00pm, Sun Jun 7
Fraumünster Church
City of Bears
The capital and political center of Switzerland, Bern sits on a peninsula formed by the U-shaped River Aare.
Change things up with these side-trips from Bern: Freddie Mercury Statue (in Montreux), Chateau de Chillon (in Veytaux) and Basel Minster (in Basel). There's still lots to do: take some stellar pictures from The Cathedral of Bern, explore the historical opulence of Castle of Gruyeres, admire the striking features of Cathedrale de Lausanne, and don't miss a visit to Clock Tower - Zytglogge.
To see reviews, where to stay, traveler tips, and more tourist information, go to the Bern day trip planning site .
Take a train from Zurich to Bern in 1.5 hours. Alternatively, you can drive; or take a bus. June in Bern sees daily highs of 28°C and lows of 13°C at night. Finish up your sightseeing early on the 11th (Thu) so you can travel to Nice.
Things to do in Bern
Lausanne, Freddie Mercury Statue, Chateau de Chillon, Gruyeres Castle, Basel Minster
Find places to stay Jun 8 — 11:
Chateau de Chillon
2:30pm, Mon Jun 8
The Cathedral of Bern
2:30pm, Tue Jun 9
Rose Garden (Rosengarten)
11:30am, Mon Jun 8
Nissa la Bella (Nice the Beautiful)
Well-known for the beautiful views of its famous Promenade des Anglais waterfront, Nice is an ethnically-diverse coastal port city on the French Riviera and the fifth most populated city in France.
Start off your visit on the 11th (Thu): take a stroll through Vieille Ville and then get great views at Colline du Chateau overlook. Get ready for a full day of sightseeing on the 12th (Fri): get up close and personal with brilliant marine life at Marineland, then don't miss a visit to Place Massena, and then admire the natural beauty at Le Jardin Exotique d'Eze.
For reviews, traveler tips, ratings, and more tourist information, read our Nice trip builder site .
You can fly from Zurich to Nice in 4.5 hours. Other options are to do a combination of train and flight; or drive. Traveling from Zurich in June, plan for somewhat warmer nights in Nice, with lows around 22°C. Finish up your sightseeing early on the 13th (Sat) so you can travel to Barcelona.
Things to do in Nice
Parks Zoos & Aquariums Historic Sites
Le Jardin Exotique d'Eze, Marineland
Find places to stay Jun 11 — 13:
10:00am, Fri Jun 12
Colline du Chateau overlook
12:30pm, Thu Jun 11
Jewel of the Mediterranean
Barcelona, the country's second-largest city, is a cosmopolitan urban center known for its throbbing youthful energy and stellar architecture.
Barcelona is known for parks, nightlife, museums, shopping, and historic sites. Your plan includes some of its best attractions: admire the striking features of Basilica of the Sagrada Familia, admire the masterpieces at Museu Picasso, make a trip to Casa Mila - La Pedrera, and indulge your senses at some of the top wineries in the area.
To see ratings, where to stay, and other tourist information, go to the Barcelona tour itinerary builder tool .
Traveling by flight from Nice to Barcelona takes 4.5 hours. Alternatively, you can drive; or take a train. In June, daily temperatures in Barcelona can reach 30°C, while at night they dip to 22°C. Wrap up your sightseeing on the 17th (Wed) to allow time to travel to Lisbon.
Historic Sites Neighborhoods Tours
12:30pm, Sun Jun 14
Built on seven hills, Lisbon has experienced a renaissance in recent years, making it the cultural star of Portugal.
Take a break from Lisbon with a short trip to Quinta da Regaleira in Sintra, about 36 minutes away. Next up on the itinerary: make a trip to Belém Tower, take in the spiritual surroundings of Mosteiro dos Jeronimos, wander the streets of Alfama, and admire the masterpieces at Museu Nacional do Azulejo.
To find maps, more things to do, ratings, and other tourist information, read Lisbon road trip app .
Getting from Barcelona to Lisbon by flight takes about 5 hours. Other options: drive; or do a combination of train and bus. The time zone difference moving from Central European Time to Western European Time is minus 1 hour. Plan for a bit cooler nights when traveling from Barcelona in June since evenings lows in Lisbon dip to 18°C. Finish up your sightseeing early on the 21st (Sun) so you can travel to Paris.
Things to do in Lisbon
Tours Historic Sites Parks
Quinta da Regaleira
Belém Tower
3:30pm, Wed Jun 17
Lisbon Oceanarium
Step out of Paris to go to Versailles and see Palace of Versailles, which is approximately 48 minutes away. Explore Paris further: steep yourself in history at Eiffel Tower, admire the landmark architecture of Arc de Triomphe, stroll the grounds of The Paris Catacombs, and take in the architecture and atmosphere at Basilique du Sacre-Coeur de Montmartre.
To see more things to do, traveler tips, where to stay, and more tourist information, read our Paris online trip itinerary builder .
Getting from Lisbon to Paris by flight takes about 5.5 hours. Other options: do a combination of flight and train; or drive. The time zone difference moving from Western European Time (WET) to Central European Time (CET) is 1 hour. Traveling from Lisbon in June, things will get slightly colder in Paris: highs are around 27°C and lows about 15°C. On the 25th (Thu), you'll have the morning to explore before heading back home.
Historic Sites Museums Neighborhoods
ClueZ Paris
19 days in Paris BY A USER FROM AUSTRALIA December, culture, outdoors, relaxing, beaches, historic sites, museums, shopping, wildlife, slow & easy, popular & hidden gems PREFERENCES: December, culture, outdoors, relaxing, beaches, historic sites, museums, shopping, wildlife ATTRACTION STYLE: Popular & hidden gems PACE: Slow & easy 12 days in Normandy & Paris BY A USER FROM UNITED STATES November, kids, culture, outdoors, relaxing, romantic, beaches, historic sites, museums, shopping, wildlife, popular & hidden gems PREFERENCES: November, kids, culture, outdoors, relaxing, romantic, beaches, historic sites, museums, shopping, wildlife ATTRACTION STYLE: Popular & hidden gems PACE: Medium 12 days in Normandy & Paris BY A USER FROM UNITED STATES November, kids, culture, outdoors, relaxing, romantic, beaches, historic sites, museums, shopping, wildlife, popular & hidden gems PREFERENCES: November, kids, culture, outdoors, relaxing, romantic, beaches, historic sites, museums, shopping, wildlife ATTRACTION STYLE: Popular & hidden gems PACE: Medium 10 days in Paris BY A USER FROM NIGERIA December, kids, culture, outdoors, relaxing, beaches, historic sites, museums, shopping, wildlife, popular sights PREFERENCES: December, kids, culture, outdoors, relaxing, beaches, historic sites, museums, shopping, wildlife ATTRACTION STYLE: Popular sights PACE: Medium 16 days in Paris BY A USER FROM INDIA December, culture, outdoors, relaxing, romantic, beaches, historic sites, museums, shopping, slow & easy, popular sights PREFERENCES: December, culture, outdoors, relaxing, romantic, beaches, historic sites, museums, shopping ATTRACTION STYLE: Popular sights PACE: Slow & easy 9 days in Paris BY A USER FROM NIGERIA December, kids, culture, outdoors, relaxing, beaches, historic sites, museums, shopping, wildlife, popular sights PREFERENCES: December, kids, culture, outdoors, relaxing, beaches, historic sites, museums, shopping, wildlife ATTRACTION STYLE: Popular sights PACE: Medium 14 days in Paris BY A USER FROM UNITED STATES December, kids, culture, outdoors, relaxing, romantic, beaches, historic sites, museums, shopping, popular sights PREFERENCES: December, kids, culture, outdoors, relaxing, romantic, beaches, historic sites, museums, shopping ATTRACTION STYLE: Popular sights PACE: Medium 8 days in Nice, Mont-Saint-Michel & Paris BY A USER FROM UNITED STATES June, teens, culture, outdoors, relaxing, beaches, historic sites, museums, shopping, wildlife, popular & hidden gems PREFERENCES: June, teens, culture, outdoors, relaxing, beaches, historic sites, museums, shopping, wildlife ATTRACTION STYLE: Popular & hidden gems PACE: Medium 23 days in Paris BY A USER FROM HONG KONG February, culture, relaxing, beaches, historic sites, museums, shopping, slow & easy, popular & hidden gems PREFERENCES: February, culture, relaxing, beaches, historic sites, museums, shopping ATTRACTION STYLE: Popular & hidden gems PACE: Slow & easy 7 days in Paris BY A USER FROM INDIA December, culture, outdoors, relaxing, romantic, beaches, historic sites, museums, shopping, popular sights PREFERENCES: December, culture, outdoors, relaxing, romantic, beaches, historic sites, museums, shopping ATTRACTION STYLE: Popular sights PACE: Medium 8 days in Paris BY A USER FROM UNITED STATES May, culture, relaxing, romantic, historic sites, museums, shopping, wildlife, popular & hidden gems PREFERENCES: May, culture, relaxing, romantic, historic sites, museums, shopping, wildlife ATTRACTION STYLE: Popular & hidden gems PACE: Medium 6 days in Paris BY A USER FROM INDIA December, culture, outdoors, relaxing, romantic, historic sites, museums, shopping, popular & hidden gems PREFERENCES: December, culture, outdoors, relaxing, romantic, historic sites, museums, shopping ATTRACTION STYLE: Popular & hidden gems PACE: Medium
Paris trip planner
Best things to do in Paris
London trip planner
Best things to do in Zurich

redpajama_set_name: RedPajamaCommonCrawl


At the Weekly Standard, Dan Darling, a counter-terrorism consultant for the Manhattan Institute Center for Policing Terrorism, focuses on one interesting paragraph on page 9 of the al-Zawahiri letter.
And do the brothers forget that we have more than one hundred prisoners - many of whom are from the leadership who are wanted in their countries - in the custody of the Iranians? And even if we attack the Shia out of necessity, then why do you announce this matter and make it public, which compels the Iranians to take counter measures? And do the brothers forget that both we and the Iranians need to refrain from harming each other at this time in which the Americans are targeting us?
Not only is it clear that the Iranians are once again very much involved in stirring up the troubles in the Middle East, but it is also equally clear they are playing both ends against the middle again. The Iranians are using their "custody" of senior al Qaeda officials, something they have never denied, to restrain al Qaeda from interfering with their own Shia interests in Iraq.
Now that we are done bombing Baghdad, maybe it is time to start Tomahawking Tehran.


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Beautiful accommodation facility for sale.
Abbotsford Country House is set on the 50 acres of farmland with a small vineyard in the heart of the Barossa.
The Barossa has a well-earned international reputation for its wines accompanied by delicious food, regarded by some as Australia's greatest wine region and amongst the greatest in the world.
Abbotsford is run as a fully hosted, traditional and luxurious country retreat and provides a home away from home in the Barossa for it's Australian and international visitors alike.
Abbotsford Country House offers 8 individually decorated and beautifully furnished suites, each with an en-suite bathroom featuring rainwater shower and luxury bathrobes.
Abbotsford Country House continues to remain one of Australia's top 20 Fodor's Choice properties, the only Fodor's Choice property in the Barossa, has a AAA Tourism 4.5 star rating and is one of Andrew Harper's Top 1000 Hotels of the World.
Buy and upgrade to the level of Wellness retreat (Spa facilities, yoga, massage, detox, etc.) and leverage the unique picturesque location and beautiful house design to deliver hi-end health and wellness services. There is a demand for such type of facilities which will offer a package of accommodation, luxury SPA and health services for those who need a recovery and relaxation or just prefer to maintain a healthy lifestyle while travelling.


With the calendar saying that it's almost Halloween (even though the recent weather has been more Labor-day like), Allie has been in her soups-and-stews mode. The thick coastal fog we've had off-and-on for the past couple of days have added to the fall feeling. Well, as "fall" as it gets here in North County.
...and a wicked chicken lentil stew with turkey bacon (further enhanced by copious amounts of Cholula - nectar of the gods).
I'm fortunate to be married to a woman with mad cooking skillz (among her other skillz). Good eatin' here in Carlsbad tonight; can't wait to bring leftovers for lunch.


text:
See Codes of Professional Conduct information posted in the above section of this catalog. Unique information that applies to Graduate Studies differently, is listed below.
All students who enter the graduate program at the University of Providence must understand that professional, ethical, and mature behavior is expected of each student at all times. Every student must conduct himself or herself with maturity and professionalism. It is expected that students will:
Maintain a high standard of professionalism in graduate classes
Act in a professional manner at all internship placements and experiential learning classes
Comply with the professional ethics code of their chosen profession and
Do their own work and properly credit all sources.
An alleged violation of the Code of Professional Conduct shall be promptly reported to the applicable Department Chair or Division Chair or another graduate faculty member. If an alleged violation occurs within a course, the instructor is supported in addressing the resolution of a violation within the scope of the course syllabus. Should the instructor believe the violation, wholly or in part, is beyond the parameters of the course, a referral may be made either to the degree Program Coordinator or to the applicable Department Chair or Division Chair.
The complainant shall file a written complaint setting out the name of the alleged violator, date and time of the alleged violation, and facts outlining the alleged violation. The complainant must sign the complaint. The applicable Department Chair or Division Chair will review the complaint and may conduct an investigation of the reported violations. If the applicable Department Chair or Division Chair finds sufficient grounds for an alleged violation, the accused student will have a choice of an administrative hearing before the applicable Department Chair or Division Chair or a Hearing Committee composed of two full-time graduate faculty members. The process and all procedures will comply with the Code of Student Conduct found in the Student Handbook.
If the Hearing Committee determines the complaint is factually accurate, they may recommend any, or a combination of any, of the following:
failure or incomplete be awarded in the course in which the violation occurred;
require replacement, repair, or payment for any property defaced;
an oral reprimand;
a written reprimand, to be placed either permanently or temporarily in the student's official file;
placement of the student on disciplinary probation for one or more semesters;
suspension from Graduate Studies for one or more semesters;
permanent expulsion.
An appeal of failure to abide by due process may be made to the applicable Department Chair or Division Chair.
For a complete list of student rights and responsibilities, please refer to the Code of Academic Conduct.


text:
\section{Introduction}
Different from traditional classification tasks, the goal of verification tasks is to determine whether two samples belong to the same class or not, without predicting the class directly~\cite{chopra2005learning}. Verification tasks arise from applications where thousands or millions of classes are present with very few samples within each category (in some cases just one). For example, in face and signature verification, faces and signatures of a person are considered to belong to a class. While there can be millions of persons in the database, very few examples for each person are available. In such applications, it is also necessary to handle new classes without the need to train the model from the scratch. It is not trivial to address such challenges with traditional classification techniques.

Motivated by the impressive performance brought by deep networks to many machine learning tasks~\cite{lecun2015deep,bahaadini2017deep,zheng2017joint}, we pursue a deep learning model to improve existing verification models. However, deep networks require a large amount of labeled data for each class, which are not readily available in verification. There are semi-supervised training methods for deep network to tap on the large amount of unlabeled data. These semi-supervised methods usually have separate learning stages \cite{sun2017weakly,nair2010rectified}. They first pre-train a model using unlabeled data and then fine-tune the model with labeled data to fit the target tasks. Such two-phase methods are not suitable for verification. First, the large number of classes and the lack of data (be it labeled or unlabeled) within each category prohibit us from any form of within class pre-training and fine-tuning. Second, if we pool data from all categories for pre-training, the learned features are general but not specific towards each category, and the later fine-tuning within each category may not be able to correct such bias due to the lack of labeled data.

To address such challenges, we propose Deep SEmi-supervised VErification Networks (SEVEN) that consists of a generative and a discriminative component to learn general and category specific representations from both unlabeled and labeled data simultaneously. We cross the category barrier and pool unlabeled data from all categories to learn salient structures of the data manifold. The hope is that by tapping on the large amount of unlabeled data, the structures that are shared by all categories can be learned for verification.

SEVEN then adapts the general structures to each category by attaching the generative component to the discriminative component that uses the labeled data to learn category-specific features. In this sense, the generative component works as a regularizer for the discriminative component, and aids in exploiting the information hidden in the unlabeled data. On the other hand, as the discriminative component depends on the structures learned by the generative component, it is desirable to inform the generative component about the subspace that is beneficial to the final verification tasks. Towards this end, instead of training the two components separately or sequentially,  SEVEN chooses to train the two components simultaneously and allow the generative component to learn more informative general features.

We evaluate SEVEN on four datasets and compare it to four state-of-the-art semi-supervised and supervised algorithms. Experimental results demonstrate that SEVEN outperforms all the baselines in terms of accuracy. Furthermore, it has shown that by using very small amount of labeled examples, SEVEN reaches competitive performance with the supervised baselines trained on a significantly larger set of labeled data.

The rest of this paper is organized as follows. In Section \ref{sec:rw} we give an overview of the related works. In Section \ref{sec:seven} we present SEVEN in detail. Section \ref{sec:ex} gives the experimental evaluation and analysis of the proposed model, followed by a conclusion.

\section{Related Work}
\label{sec:rw}
SEVEN can serve as a metric learning algorithm that is commonly employed in verification. The goal of metric learning is to learn a distance metric such that samples in any negative pair are far away and those in any positive pair are close. Many of the existing approaches \cite{sun2017weakly,ye2015,zagoruyko2015learning,hu2014discriminative} learn a linear or nonlinear transformation that maps the data to a new space where the distance metric satisfies the above requirements. However, these methods do not address the large number of categories with scarce supervision information.

One of the earliest works in neural network-based verification is proposed by Bromley et al. for signature verification \cite{bromley1993signature}. The proposed architecture, named Siamese networks, uses a contrastive objective function to learn a distance metric with Convnets. Similar approaches are employed for many other tasks such as face verification or re-identification \cite{koch2015siamese,sun2014deep,wolf2014deepface}. It is worthy to mention that all these works are supervised and do not exploit unlabeled data.

Great interest in deep semi-supervised learning has emerged in applications where unlabeled data are abundant but obtaining labeled data is expensive or not feasible \cite{li2016semi,hoffer2016semi,rasmus2015semi,kingma2014semi,lee2013pseudo}. However, most of such approaches are designed for classification. To the best of our knowledge, there exists no deep semi-supervised learning to address the above two challenges in verification.

A key difference between SEVEN and most of the previous semi-supervised deep networks lies in the way that unlabeled and labeled data are exploited. Lee \cite{lee2013pseudo} has presented a semi-supervised approach for classification tasks called Pseudo-Label based on self-training scheme. It predicts the labels of unlabeled samples by training the model with the available labeled samples. Then they bootstrap the model with the highly confident labeled samples. This approach is prone to error because it may reinforce wrong predictions especially in problems with low confident estimation.

A more common semi-supervised approach is to pre-train a model with unlabeled samples and then the learned model is fine-tuned using the labeled samples. For example, \cite{nair2010rectified} have pre-trained a Restricted Boltzmann Machine (RBM) with noisy rectified linear units (NReLU) in the hidden layers, then they used the learned weights to initialize and train a siamese network \cite{chopra2005learning} in a supervised way. The problem with pre-training based approaches is that the supervised part of the algorithm can ignore or lose what the model has learned in the unsupervised step. Another problem with pre-training based approaches is that they still need enough labeled examples for the fine-tunning step.

Recently, some works have tried to alleviate such problems by performing the learning process from all the labeled and unlabeled data in a joint manner for \textit{classification} tasks \cite{li2016semi,hoffer2016semi,maaloe2016auxiliary,rasmus2015semi}. They make the unsupervised model involved in the learning as a regularizer for the supervised model. It should be considered that all such techniques are designed for classification tasks and can not handle the cases mentioned in the introduction such as the few samples per each class and the high number of classes.

Another line of work that handles a large number of categories is extreme multi-label learning~\cite{xie2017active}. The most popular assumption is that all classes have sufficient amount of labeled data, and this is clearly different from our problem setting. Recently, there are methods focusing on predicting the tail labels~\cite{Jain2016}, but they are proposed for traditional classification task and can not handle new classes in the test data.

\section{Proposed Model}
\label{sec:seven}
\subsection{Problem Formulation}
The training set is represented as ${\cal{X}} = \big\{(x_1^i,x_2^i)\big\}_{i=1}^{N}$, where $(x_1^i,x_2^i)$ is a pair of training samples $x_j^i \in \mathbb{R}^m$, $N=L+U$ is the total number of training pairs consisting of $L$ labeled and $U$ unlabeled pairs. The relation, i.e., label set denoted by ${\cal{Y}} = \{y^i|y^i \in \{ pos,neg \}\}_{i=1}^{L}$ specifies the relation between the samples of each pair. A positive relation indicates that two samples of the pair belong to the same class and a negative relation indicates the opposite. The relations for the unlabeled pairs are unknown.

Our goal is to learn a nonlinear function $r_{\theta_e}(x_1,x_2) \colon \mathbb{R}^m \times \mathbb{R}^m \rightarrow \{pos,neg\}$ parameterized by $\theta_e$ that predicts the relation between the two data samples $x_1$ and $x_2$.
In other words, function $r_{\theta_e}(x_1,x_2)$ verifies if two samples are similar or not.

We define $r_{\theta_e}(.,.)$ based on the distance of $x_1$ and $x_2$ estimated by a metric distance function as:
{\begin{equation}
r_{\theta_e}({x_1},{x_2}) = \left\{ \begin{array}{l}
neg{\rm{  \qquad if \quad}} d_{\theta_e}(x_1,x_2) > \tau \\
pos{\rm{   \qquad if \quad}}d_{\theta_e}(x_1,x_2) \le \tau
\end{array} \right.
\label{eq:ffunc}
\end{equation}}

\noindent
where $d_{\theta_e}(.,.)$ is the metric distance function and threshold $\tau$ specifies the maximum distance that samples of a class are allowed to have.
We define a nonlinear embedding function $f_{\theta_e}(.)$ that projects data to a new feature space and $d_{\theta_e}(x_1,x_2)=\|f_{\theta_e}({x_1}) - f_{\theta_e}({x_2})\|_2$ is the Euclidean distance between $x_1$ and $x_2$ in the new space. An arbitrary distance function can be also used instead of the Euclidean distance.

\subsection{Model Description}
Our proposed model consists of discriminative and generative components. The model learns a non-linear function for each component. For the discriminative component, the nonlinear embedding function $f_{\theta_e}()$ is learned to yield ``discriminative" and ``informative" representation. In a discriminative feature space, similar samples are mapped close to each other while dissimilar pairs are far from each other. Such property is crucial for a good metric function. The generative component of the model is designed to exploit the information hidden in the unlabeled data. The desired representation should keep the salient structures shared by all categories as much as possible. We define a probabilistic framework of the problem along with the discriminative and generative modelings of our algorithm.

The conditional probability distribution of the relation variable $y$ given the $i^{\textrm{th}}$ pair can be estimated as:
\begin{equation}
p(y^i|x_1^i, x_2^i)= 1- tanh(d_{\theta_e}(x_1,x_2))
\label{eq:p}
\end{equation}
\noindent
which can be written as the following.
\begin{equation}
p(y^i|x_1^i, x_2^i)= \frac{2}{1 + \exp(2d_{\theta_e}(x_1,x_2))}
\label{eq:p}
\end{equation}
\noindent

Here we use a $tanh$ function to map the distance between samples to $[0, 1]$. However, any monotonic increasing function $u(.)$ which gives $u(0)=1$ and $u(\inf)=0$ can be also used for this purpose.

We define $\tilde{p}$ as the ground truth distribution to be approximated by $p$ in~(\ref{eq:p}) as $\tilde{p}(y^i|x_1^i,x_2^i)=1$ if $y_i=pos$, and $\tilde{p}(y^i|x_1^i,x_2^i)=0$ otherwise. In the rest of the paper, the conditional distributions $p(y^i|x_1^i,x_2^i)$ and $\tilde{p}(y^i|x_i^1,x_i^2)$ are denoted by $p_i$ and $\tilde{p}_i$, respectively. Due to the probabilistic nature of such distributions, we approximate $\tilde{p}$ with $p$ by minimizing the Kullback-Leibler divergence between them and introduce the following discriminative loss function $\mathcal{L_D}({\cal{X}},{\cal{Y}};{\theta _e})$ defined over all the labeled pairs as:
{\small \begin{equation}
{\small \mathcal{L_D}({\cal{X}},{\cal{Y}};{\theta _e}) =  \sum\limits_{i = 1}^L {{l_d}(x_1^i,x_2^i;{\theta _e})}} = \sum\limits_{i = 1}^L KL(\tilde{p_i}\|p_i)
\end{equation}}

\noindent
where ${{l_d}(x_1^i,x_2^i;{\theta _e})}$ denotes the discriminative loss for the $i^{\textrm{th}}$ pair, and $KL(\tilde{p_i}\|p_i)$ denotes the KL-divergence between $\tilde{p_i}$ and $p_i$. $KL(\tilde{p_i}\|p_i)$ can be substituted by $H(p_i,\tilde{p_i}) - H(\tilde{p_i})$ where $H(p_i)$ specifies the entropy of $p_i$, and $H(p_i,\tilde{p_i})$ defines the cross entropy between $p_i$ and $\tilde{p_i}$. Considering that the loss function is optimized with a gradient based optimization approach and $H(\tilde{p_i})$ is a constant with respect to the the parameters, we simplify the discriminative loss function as:
{\small \begin{multline}
{{l_d}(x_1^i,x_2^i;{\theta _e})} = \\- I\{y_i=pos\}log(p_i) - I\{y_i=neg\}log(1-p_i)
\end{multline}}

\noindent
where $I\{.\}$ is the identity function. The loss function becomes equivalent to the the cross entropy over $p_i$ and $\tilde{p_i}$. It penalizes large distance (similarity) between samples from the same (different) class to make the new space discriminative. $\mathcal{L_D}$ attains its minimum when $p_i=\tilde{p_i}$ over all the labeled pairs.

To alleviate the insufficiency of the unlabeled data for verification task, through generative modeling, we encourage the embedding function $f_{\theta_e}(.)$ to learn the salient structures shared by all categories. We define a nonlinear function $g_{\theta_d}(.)$ parametrized by $\theta_d$ to project back the samples from new representation obtained from $f_{\theta_e}(.)$ to the original feature space.

The generative loss for the $i^{\textrm{th}}$ pair $(x_1^i, x_2^i)$ is defined as the reconstruction error between the original input and the corresponding reconstructed output as:

\begin{multline}
j_g(x_1^i,x_2^i;\theta_e,\theta_d) =
\|g_{\theta_d}(f_{\theta_e}(x_1^i)) - x_1^i\|_2 +
\\ \|g_{\theta_d}(f_{\theta_e}(x_2^i)) - x_2^i\|_2
\end{multline}

\noindent
where $g_{\theta_d}(f_{\theta_e}(x_j^i))$ indicates the reconstruction of the input of the $x_j^i$ and is denoted by $\hat{x}_j^i$. The generative loss function $\mathcal{L_G}$, over all pairs including labeled and unlabeled, is defined as:
\begin{equation}
{\small
\mathcal{L_G}({\cal{X}};{\theta _e},{\theta _d}) = \sum\limits_{i = 1}^{L+U} l_g(x_1^i,x_2^i;\theta_e,\theta_d)}
\end{equation}

We combine the generative and discriminative components into a unified objective function and write the optimization problem of SEVEN as:
{\small \begin{multline}
 \mathcal{L}({\cal{X}},{\cal{Y}};{\theta _e},{\theta _d}) =  \sum\limits_{i = 1}^L {{l_d}(x_1^i,x_2^i;{\theta _e})}  + \\
 \alpha \sum\limits_{i = 1}^{L + U} {{l_g}(x_1^i,x_2^i;{\theta _e},{\theta _d})}  +
 \beta (\left\| {{\theta _e}} \right\|_2 + \left\| {{\theta _d}} \right\|_2)
 \label{eq:eqj}
 \end{multline}}

\noindent
where $\|\theta_e\|$ and $\|\theta_d\|$ are the regularization terms on the parameters of the functions $f_{\theta_e}(.)$ and $g_{\theta_d}(.)$. The parameter $\beta$ controls the effect of this regularization, parameter $\alpha$ controls the trade off between the discriminative and generative objectives.

\begin{figure}[t!]
\centering\includegraphics[width=0.45\textwidth]{det.pdf}
\caption{The overall architecture of SEVEN.}
\label{fig:det}
\end{figure}

\subsection{Model Architecture and Optimization}
We choose deep neural networks for parameterizing $f_{\theta_e}(.)$ and $g_{\theta_d}(.)$. The schematic representation of SEVEN is illustrated in Figure \ref{fig:det}.
The input pair is given to two neural networks denoted by $F_1$ and $F_2$ with shared parameters $\theta_e$. They represent the discriminative component of the SEVEN (nonlinear embedding function $f_{\theta_e}(.)$). They project the input samples to the discriminative feature space. A layer, denoted by $d$, is added on top of the networks $F_1$ and $F_2$ that estimates the distance between the two samples of the input pair in the discriminative space.

It can be considered as the metric distance function $d_{\theta_e}(.,.)$ which networks $F_1$ and $F_2$ are supposed to learn. The final layers of $F_1$ and $F_2$ are connected to two other subnetworks denoted by $G_1$ and $G_2$ in Figure \ref{fig:det} with shared parameters $\theta_d$. They model the generative component of SEVEN ($g_{\theta_d}(.)$). They project back the samples to the original space. In other words, they can be considered as decoders for the encoders $F_1$ and $F_2$. The outputs of $G_1$ and $G_2$ shown as $\hat{x}_1$ and $\hat{x}_2$ are the reconstructions of the corresponding inputs $x_1$ and $x_2$.

Subnetworks $F_1$ and $F_2$ are ConvNets built with convolutional and max-pooling layers. $G_1$ and $G_2$ are made with transposed convolutional and upsampling layers which perform the reverse operations of convolutional and max-pooling layers, respectively. More detail of the transposed convolutional layer can be found in \cite{dumoulin2016guide}. The complete specifications of the models are presented in~Table \ref{tab:spec}.

The whole model is trained using backpropagation with respect to the objective function in Equation \ref{eq:eqj}. Given a set of $N$ pairs, we optimize the model through an adaptive version of gradient descent called RMSprop \cite{dauphin2015equilibrated} over shuffled mini-batches.

We employ $l2$-regularization and dropout \cite{srivastava2014dropout} strategy to the convolutional and fully connected layers of the subnetworks to prevent overfitting. Batch normalization \cite{ioffe2015batch} technique is also applied after each convolutional layer to normalize the output of each layer. It can improve the performance in some cases. The training procedure of SEVEN is illustrated in Algorithm \ref{alg:sevencode}.

\begin{algorithm}[h!]
 \caption{Training procedure of SEVEN}
 \label{alg:sevencode}
\SetAlgoLined {
\KwIn{Training set: ${\cal{X}} = \big\{ (x_1^i,x_2^i) \big \}_{i=1}^{N}$, label set ${\cal{Y}} = \{y^i\}_{i=1}^{L}$,
number of iterations $T$, and batch size $m$.}
\KwOut{Model's parameters: $\Theta$}

$B = \frac{|{\cal{X}}|}{m}$; \quad // number of batches

Randomly split the training set {\cal{X}} into $B$ batches\;
\For{${t}=1,2,\cdots,T$}{
 \For{$b=1,2,\cdots,B$}{
Feedforward propagation of the $b^{\textrm{th}}$ batch\;
Calculate $\mathcal{L}^{b}$ according to Equation~(\ref{eq:eqj})\;
Estimate gradients $\frac{{\partial \mathcal{L}^{b}}}{{\partial \Theta_t }}$ by back propagation\;
Calculate $\Theta_{t+1}$ using RMSProp;
}
}

\KwRet{$\Theta_{T}$};}
\end{algorithm}

\section{Experiments}
\label{sec:ex}
\subsection{Datasets}
We evaluate the proposed algorithm on the following four datasets.

\textbf{MNIST}: It is a dataset of $70000$ grayscale images of handwritten digits from $0$ to $9$. We use the original split of $60000/10000$ for the training and test sets. A uniform random noise of $[0, 1]$ is added to each pixel to make it noisy and more challenging.

\textbf{US Postal Service (USPS)} \cite{hull1994database}: It is a dataset of $9298$ handwritten digits automatically scanned from envelopes by the US Postal Service. All images are normalized to $16 \times 16$ grayscale images. We selected randomly $85\%$ of the images for the training set.

\textbf{Labeled Faces in the Wild (LFW)}~\cite{Huang2012a}: It is a database of face images that contains $1100$ positive and $1100$ negative pairs in the training set, and $500$ positive and $500$ negative pairs in the test set. All images are resized to $64\times 48$.

\textbf{BiosecurID-SONOF (SONOF)} \cite{galbally2015line}: We use a subset of this dataset comprising signatures collected from $132$ users, each user has $16$ signatures. Signature images are normalized and converted to $80\times 80$ grayscale images. We divided the users randomly into $100/32$ for the training and test purposes.

In SONOF and LFW datasets, classes in the training and test samples are disjoint, while in MNIST and USPS classes are common between test and train sets. The samples of LFW are already in the form of pairs. For other datasets, we create the pairs by first splitting samples into two distinct sets for the training and test. We split the train set randomly into labeled and unlabeled samples. Then, each sample gets paired with two other samples randomly. One sample is selected from the same class to form a positive pair, and another one from a different class to form a negative pair.

\begin{table}[t]
\caption{Models specifications for different datasets. BN: batch normalization, ReLu: rectified linear activation function, Conv: convolutional layer, TransConv: transposed convolutional layer, Upsampling: Upsampling layer, Dense Layer: fully connected layer, and Max-pooling: max-pooling layer.}
\centering
\begin{tabular}{ll}
\multicolumn{2}{c}{\textbf{MNIST and USPS}} \\ \hline
\multicolumn{1}{c|}{\textbf{Network $F_i$}} & \multicolumn{1}{c}{\textbf{Network $G_i$}} \\ \hline
\multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}MNIST: $28 \times 28$\\ USPS:$16 \times 16$\end{tabular}} & \multicolumn{1}{l}{Input $128 \times 1$} \\
\multicolumn{1}{l|}{$3\times 3$ Conv ($8$)} & \multicolumn{1}{l}{Dense Layer} \\
\multicolumn{1}{l|}{ReLu} & \multicolumn{1}{l}{ReLu}\\
\multicolumn{1}{l|}{$2\times 2$ Max-pooling} & \multicolumn{1}{l}{Reshape layer}\\
\multicolumn{1}{l|}{Dropout(0.5)} & \multicolumn{1}{l}{$2\times 2$ Upsampling}\\
\multicolumn{1}{l|}{$5 \times 5$ Conv ($8$)} & \multicolumn{1}{l}{$5\times 5$ TransConv ($8$)} \\
\multicolumn{1}{l|}{ReLu} & \multicolumn{1}{l}{ReLu} \\
\multicolumn{1}{l|}{$2\times 2$ Max-pooling} & \multicolumn{1}{l}{Dropout(0.5)}\\
\multicolumn{1}{l|}{Dropout(0.5)} & \multicolumn{1}{l}{$2\times 2$ Upsampling}\\
\multicolumn{1}{l|}{Dense Layer ($128 \times 1$)} & \multicolumn{1}{l}{$3\times 3$ TransConv ($1$) }\\
\multicolumn{1}{l|}{ReLu} & \multicolumn{1}{l}{Sigmoid} \\
\multicolumn{1}{l|}{} & \multicolumn{1}{l}{Dropout(0.5)}\\
\\

\multicolumn{2}{c}{\textbf{LFW and SONOF}} \\ \hline
\multicolumn{1}{c|}{\textbf{Network $F_i$}} & \multicolumn{1}{c}{\textbf{Network $G_i$}} \\ \hline
\multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}LFW: $ 64 \times 48$\\ SONOF: $100 \times 100$\end{tabular}}& Input $128 \times 1$ \\
\multicolumn{1}{l|}{$4\times 4$ Conv ($32$)} & Dense Layer \\
\multicolumn{1}{l|}{BN-ReLu} & ReLu \\
\multicolumn{1}{l|}{Dropout(0.5)} & Reshape layer \\
\multicolumn{1}{l|}{$2\times 2$ Max-pooling} & $3\times 3$ TransConv ($64$) \\
\multicolumn{1}{l|}{Dropout(0.5)} & BN-ReLu \\
\multicolumn{1}{l|}{$3\times 3$ Conv ($64$)} & Dropout(0.5) \\
\multicolumn{1}{l|}{BN-ReLu} & $2\times2$ Upsampling \\
\multicolumn{1}{l|}{$2\times 2$ Max-pooling} & $3 \times 3$ TransConv ($32$) \\
\multicolumn{1}{l|}{Dropout(0.5)} & BN-ReLu \\
\multicolumn{1}{l|}{$3\times 3$ Conv ($128$)} & Dropout(0.5) \\
\multicolumn{1}{l|}{BN-ReLu} & $2\times 2$ Upsampling \\
\multicolumn{1}{l|}{Dropout(0.5)} & $3 \times 3$ TransConv ($1$) \\
\multicolumn{1}{l|}{Dense Layer ($128 \times 1$)} & BN-Sigmoid \\
\multicolumn{1}{l|}{ReLu} & Dropout(0.5)
\end{tabular}
\label{tab:spec}
\end{table}

\subsection{Baselines}
We compare the performance of SEVEN with the following baselines. It should be considered that we can not compare SEVEN with classification techniques because they are not usually designed to handle new classes in the test data which happens in verification applications. Since there are no other deep semi-supervised works for verification tasks, we adopt the common deep semi-supervised techniques to verification networks as our baselines.

\textbf{Discriminative Deep Metric Learning (DDML)} ~\cite{hu2014discriminative}: They developed a deep neural network that learns a set of hierarchical transformations to project pairs into a common space by using a contrastive loss function. It is a supervised approach and can not use unlabeled data.

\textbf{Pseudo-Label} \cite{lee2013pseudo}: It is a semi-supervised approach for training deep neural networks. It initially trains a supervised model with the labeled samples. Then it labels the unlabeled samples with the current trained model before each iteration, and use the high confidence ones along with the labeled samples for training in the next iteration. We followed the same approach for training a siamese network \cite{bell2015learning} to extend their approach to the verification tasks.

\textbf{Convolutional Autoencoder + Siamese Network (PreConvSia)}: We pre-train a siamese network \cite{bell2015learning,chopra2005learning} with an convolutional autoencoder model \cite{masci2011stacked}. Then we fine-tune the network with labeled pairs. The network uses ConvNets as the underlying network for the modeling.

\textbf{Autoencoder + Siamese Network (PreAutoSia)}: It is similar to PreConvSia, but uses MLP as the underlying network for the modeling. It is significantly faster in training compared to PreConvSia.

\textbf{Principle Component Analysis (PCA)}: We use PCA as an unsupervised feature learning technique. The distance between samples in the new space learned by PCA indicates their relations. The threshold on the distance is selected for each dataset separately based on the performance on the training data.

\begin{table*}[]
\caption{Performance of different methods on LFW and SONOF in terms of accuracy.}
\centering
 \begin{adjustbox}{max width=0.97\textwidth}
{\small
\begin{tabular}{l|llllll|llllll}
\multicolumn{1}{c|}{Dataset} & \multicolumn{6}{c|}{LFW}                            & \multicolumn{6}{c}{SONOF}   \\
\# of labeled pairs          & $110$ & $440$   & $880$  & $1320$ & $1760$ &$All$  & $160$   & $320$   & $640$   & $960$   & $1280$  & $All$ \\ \hline
SEVEN  & $\textbf{61.2}$ & $\textbf{64.1}$ & $\textbf{65.7}$ & $\textbf{66.3}$ & $\textbf{67.0}$ & $68.7$ & $\textbf{72.7}$  & $\textbf{74.6}$  & $\textbf{79.3}$  & $\textbf{83.1}$  & $\textbf{84.1}$  & $85.3$  \\
PCA                          & -      & -      & -      & -      & -      & $64.5$ & -       & -       & -       & -       & -       & ${67.61}$  \\DDML & $51.5$ & $54.2$ & $61.9$ & $63.8$ & $64.8$ & $\textbf{71.1}$ & $58.5$  & $67.7$  & $72.5$  & $78.4$  & $82.9$  & $\textbf{86.1}$ \\
Pseudo-label                 & $52.0$ & $52.2$ & $53.9$ & $57.4$ & $57.9$ & $70.1$ & $53.8$  & $59.9$  & $63.2$  & $71.0$  & $80.5$  & $84.5$ \\
PreConvSia                   & $55.1$ & $62.3$ & $63.5$ & $63.2$ & $64.2$ & $66.0$ & $61.9$  & $67.1$  & $70.4$  & $71.5$  & $78.8$  & $82.1$ \\
PreAutoSia                   & $51.1$ & $62.9$ & $63.0$ & $63.5$ & $64.2$ & $66.1$ & $57.2$  & $62.7$  & $66.4$  & $70.1$  & $73.1$  & $79.0$
\end{tabular}}
\end{adjustbox}
\label{table:lfw}
\end{table*}
\begin{table*}[h!]
\caption{Performance of different methods on MNIST and USPS in terms of accuracy.}
\centering
 \begin{adjustbox}{max width=0.97\textwidth}
 {\small
\begin{tabular}{l|llllll|llllll}
\multicolumn{1}{c|}{Dataset} & \multicolumn{6}{c|}{MNIST}         & \multicolumn{6}{c}{USPS}     \\ \# of labeled pairs & \multicolumn{1}{c}{$30$} & \multicolumn{1}{c}{$60$} & \multicolumn{1}{c}{$120$} & \multicolumn{1}{c}{$600$} & \multicolumn{1}{c}{$2400$} & \multicolumn{1}{c|}{$All$} & \multicolumn{1}{c}{$40$} & \multicolumn{1}{c}{$80$} & \multicolumn{1}{c}{$160$} & \multicolumn{1}{c}{$300$} & \multicolumn{1}{c}{$800$} & \multicolumn{1}{c}{$All$} \\ \hline
SEVEN  & $\textbf{75.5}$ & $\textbf{76.9}$ & $\textbf{79.8}$ & $\textbf{84.8}$ & $90.7$                   & $96.8$                    & $\textbf{76.2}$ & $\textbf{77.3}$& $\textbf{80.2}$ & $\textbf{80.7}$                  & $82.8$                  & $93.1$                   \\
PCA   & -   & -                     & -           & -  & -                       & 65.84                & -    & -                     & -              & -   & -  & 70.96                       \\
DDML                         & $61.1$                 & $65.9$                 & $75.7$                 & $84.0$                  & $90.4$                   & $96.8$                    & $69.0$                 & $71.8$                 & $75.7$                 & $75.9$                  & $80.8$                  & $92.7$                   \\
Pseudo-label & $59.8$ & $67.9$  & $76.8$         & $83.2$  & $89.3$ & $95.2$ & $70.1$      & $57.4$  & $57.9$  & $77.2$ & $78.3$  & $\textbf{93.3}$  \\
PreConvSia    & $64.4$  & $73.0$ & $77.2$  & $82.7$  & $\textbf{90.8}$                   & $\textbf{97.2}$ & $72.2$   & $78.2$   & $77.6$                 & $78.1$                  & $\textbf{82.9}$  & $93.0$                   \\
PreAutoSia   & $61.5$   & $68.0$ & $71.9$& $78.9$ & $84.7$  & $93.1$  & $70.6$ & $73.9$   & $69.0$   & $75.0$  & $82.0$ & $90.2$
\end{tabular} }
\end{adjustbox}
\label{table:usps}
\end{table*}
\subsection{Experimental Settings}
The architectures of SEVEN for all datasets are presented in Table \ref{tab:spec}. All the parameters of SEVEN and also other baselines are selected based on a validation on a randomly selected $20\%$ subset of the training data. The $l2$-regularization parameter $\beta$ is selected from $\{1e-4, 1e-3, 0.01, 0.1\}$ for each dataset separately. The parameter $\alpha$ that controls the trade-off between generative and discriminative objectives is selected from $\{0.01, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0\}$. It is set to $0.05$, $0.1$, $0.05$, and $0.2$ for MNIST, LFW, USPS and SONOF, respectively. Parameter $\tau$ is set to $0.5$ for all the four datasets.

All the neural network models are trained for $150$ epochs. The pre-training is also performed for $150$ epochs for the baselines which require pre-training. RMSProp optimizer is used for the training of all the neural networks with the default value $\lambda=0.001$ recommended in the original paper.

\subsection{Performance Evaluation}
We report the performance in terms of accuracy which is the number of pairs in the test set verified correctly divided by the total number of pairs in the test set. The performance of SEVEN and all baselines are presented in Tables \ref{table:lfw} and \ref{table:usps}. The results are reported for different number of labeled pairs and the best accuracy for each case is depicted in bold. The last column indicates the case where all labeled training pairs are used. PCA is a fully unsupervised method, thus one performance is reported for each dataset.

As it can be seen from the tables, SEVEN outperforms other baselines in cases where a limited number of labeled pairs are used and the differences in performance are more significant where the number of labeled pairs is lower, and thus SEVEN can address the scarcity of labeled data better.

\begin{figure*}[h!]
\begin{center}
\begin{subfigure}{.24\textwidth}
  \centering

\includegraphics[width=.98\linewidth]{mnist_cmr}
\caption{MNIST}
  \label{fig:mnistS}
\end{subfigure}%
\begin{subfigure}{.24\textwidth}
  \centering
  \includegraphics[width=.98\linewidth]{lfw_cmr}
  \caption{LFW}
  \label{fig:lfwS}
\end{subfigure}
\begin{subfigure}{.24\textwidth}
  \centering
  \includegraphics[width=.98\linewidth]{usps_cmr}
  \caption{USPS}
  \label{fig:uspsS}
\end{subfigure}
\begin{subfigure}{.24\textwidth}
  \centering
  \includegraphics[width=.98\linewidth]{sono_cmr}
  \caption{SONOF}
  \label{fig:sonof}
\end{subfigure}
\end{center}
\caption{The accuracy of SEVEN for different values of parameter $\alpha$ for (a) MNIST, (b) LFW, (c) USPS and (d) SONOF.}
\label{fig:alpha}
\end{figure*}
Algorithm DDML can give good performance when we have enough labeled data but its performance is significantly lower compared to SEVEN in cases with few labeled samples. DDML does not use the unlabeled data while other baselines benefit from the information hidden in the unlabeled data. By increasing the number of labeled pairs, the difference in accuracy decreases.

SEVEN outperforms all the semi-supervised baselines. One of the main advantages of SEVEN over other semi-supervised methods is that they perform supervised step after pre-training with unlabeled data is finished. This may cancel out some of the learned information from unlabeled data through a supervised process. There is no guarantee that the supervised process can benefit from the unsupervised learning \cite{rasmus2015semi}. Among the semi-supervised baselines, Pseudo-Label not only gives worse results compared to SEVEN, but also it shows lower performance than PreConvSia and PreAutoSia in many cases. It can be related to the noise and error in estimating the labels for unlabeled pairs.

\subsection{Model Analysis}
We perform some experiments to analyze the effect of the different components of SEVEN. The performances of different variants of SEVEN are given in Table \ref{tab:psens}. The number of labeled pairs for each dataset is indicated in front of the name of the dataset. DisSEVEN indicates SEVEN with $\alpha=0$ in Eq. (\ref{eq:eqj}) which disables the $G_i$ networks and the generative aspect of the model. This variant does not consider the unlabeled data during the learning. GenSEVEN corresponds to a model that does not have the discriminate component. In other words, it does not have the contrastive layer and does not use the label information. SEVEN indicates the full variant of SEVEN with both generative and discriminative components. The variant SEVEN (MLP) is similar to the regular SEVEN, except that it uses fully connected layers instead of convolutional and transposed convolutional layers.

Among all the different variants, full SEVEN gives the best performance. It shows the effectiveness of both the generative and discriminative components. It also verifies the effectiveness of using the information hidden in the unlabeled data. The results show that the discriminative component has the broader impact compared to the generative component. SEVEN (MLP) gives weaker performance compared to SEVEN. It is mainly because of the capabilities of convolutional layers in modeling image data as it has also been shown by ConvNets in image processing applications.
\subsection{Parameter Sensitivity}
We analyze the effect of the parameter $\alpha$ in Equation~ (\ref{eq:eqj}) on the performance of SEVEN on all the four datasets. Parameter $\alpha$ of SEVEN controls the trade-off between the generative and discriminative aspects of the model. In Figure~\ref{fig:alpha} the performance of SEVEN for different values of $\alpha$ is plotted. For each dataset, the performance is plotted for three different values of $L$ (number of labeled pairs). There exists a trade-off between the two generative and discriminative aspects of SEVEN on all of the four datasets. As it can be seen, the optimum value of this parameter is dependent to the dataset and also to the ratio of labeled data to some extent.
\begin{table}[h!]
\caption{Performance of SEVEN variants in accuracy.}
\label{tab:psens}
\centering
\begin{adjustbox}{max width=0.5\textwidth}
{ \begin{tabular}
{l||llll}Method & MNIST (120)& LFW (440) & USPS (80) & SONOF (320) \\\hline
\multicolumn{1}{l||}{DisSEVEN} &$75.7$& $54.2$& $73.9$ & $70.3$ \\
\multicolumn{1}{l||}{GenSEVEN} & $73.0$ &$58.2$ & $60.0$ & $62.5$    \\
\multicolumn{1}{l||}{SEVEN} &$\textbf{79.8}$  &$\textbf{64.1}$  &$\textbf{77.3}$&  $\textbf{74.6}$     \\
\multicolumn{1}{l||}{SEVEN (MLP)} &$73.1$  &$60.0$  & $77.3$ & $70.9$
\end{tabular} }
\end{adjustbox}
\end{table}
\noindent

\vspace{-3mm}
\section{Conclusion}
Benefiting from the salient structures hidden in the unlabeled data and the ability of deep neural networks in nonlinear function approximation, we propose a semi-supervised deep SEmi-supervised VErification Network (SEVEN) for verification tasks. SEVEN benefits from both generative and discriminative modelings in a unified model. These two components are simultaneously trained which lead them to closely interact and influence each other. Extensive experiments demonstrate that SEVEN outperforms other state-of-the-art deep semi-supervised techniques in a wide spectrum of verification tasks. Furthermore, SEVEN shows competitive performance compared with fully supervised baselines that require a significantly larger amount of labeled data, indicating the important role of the generative component in SEVEN.
{\small\bibliographystyle{named}

redpajama_set_name: RedPajamaArXiv


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text:
Q: How to update requirements? I would like to know how to update outdated requirements with the multi requirements files setup that cookie cutter uses (base, local, etc...) ?
If i launch a "pip freeze" it will write the requirements in a single file. It will not dispatch them in correct files
Do I miss something ?

A: You could update all outdated python modules with this command:
Linux:
pip list --outdated --format=freeze | grep -v '^\-e' | cut -d = -f 1 | xargs -n1 pip install -U

Windows (Powershell):
pip freeze | %{$_.split('==')[0]} | %{pip install --upgrade $_}

After this you have to update your requirements.txt with:
pip freeze > requirements.txt

If you use Pipenv:
pipenv shell
pipenv update

redpajama_set_name: RedPajamaStackExchange


text:
Rap music star facing a child support lawsuit
On behalf of The Law Offices of Lorrie J. Zahodnic, P.C. | Apr 8, 2020 | Child Custody and Support
In Michigan and all other states, if the parent of a minor wants a co-parent to be legally ordered to financially provide for the child in question, the proper legal steps must be taken to file a petition in a family court. A judge will then review the case and determine whether child support is warranted, as well as the amount of payments, frequency of payments and venue through which payments should be made. In some cases, a petitioner must first establish the paternity of the co-parent, particularly if they were not married at the time of the child's birth. Rap music star, Future, is said to be the father of six children from five different women; in fact, he is currently facing a paternity lawsuit as well.
Two of the women with whom Future reportedly had past romantic relationships apparently know each other. At some point, they are said to have tested the DNA of their children, and the results noted a nearly 100% chance that the toddlers were related. This led to both women to file petitions in court to establish Future's paternity and to request child support.
One of the women has since withdrawn her previous legal claim. It is not uncommon for litigants to do this when they have achieved private agreements outside a courtroom. No details were provided in this particular case as to why one of the women had her case dismissed.
In some Michigan cases, as well as in family courts in other states, it sometimes happens that a paternity test produces negative results. In such cases, this can benefit a man who believes he is being asked to pay child supporteven though he is not the biological father of the child in question. Such cases can be complex and stressful to resolve, which is why it is always a good idea to rely on experienced legal representation in court to help protect one's rights and interests.
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