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Famous Northumberland castle as you've never seen it before
The now ruined Dunstanburgh Castle near Craster has been digitally reconstructed to show how the fortress would have originally looked
Tony Henderson
This is one of Northumberland's most dramatic ruined castles – as you've never seen it before.
Dunstanburgh Castle, near Craster, is one of six across the UK to be digitally "reconstructed" by NeoMam creative studios, and it shows how the Northumberland fortress would originally have looked.
"There is something wonderful about visiting castles," said Gisele Navarro, NeoMam operation director. "As you walk through the ruins, your imagination begins to fill in the blanks about the people who lived there, the history of the place and the architectural details that have been lost.
"But what if you didn't need to use your imagination?"
Dunstanburgh, which is managed by English Heritage, was built at a time when relations between King Edward II and his most powerful baron, Earl Thomas of Lancaster, had worsened.
Dunstanburgh Castle in Northumberland. Before (Image: Newcastle Chronicle)
Lancaster began the fortress in 1313, and it is thought to have been built more as a symbol of his opposition to the king than as a military stronghold.
"We consider Dunstanburgh one of the most magnificent castles in England with fantastic views and a rich history," said Gisele. "When working to identify the castles that we could digitally reconstruct, it was clear to us that Dunstanburgh had to be one of them as we had enough to bring it back thanks of years of archaeological research on the area.
"Our goal, as commissioned by On Stride, was to inspire people to rediscover the areas of the UK where they live and to get inspired to travel to those areas that perhaps they've not considered visiting before.
Dunstanburgh Castle after (Image: Newcastle Chronicle)
"Our focus was to bring these castles to life so that people could get a better feel of how they looked like in their prime."
Dunstanburgh's rebellious Lancaster was executed in 1322 and the castle passed eventually to John of Gaunt, who strengthened it by converting the great twin towered gatehouse into a keep.
During the Wars of the Roses, it was besieged and captured by Yorkist forces, but subsequently fell into decay.
The site was re-fortified during the Second World War when, following the defeat in Norway in April 1940, the North East coast was seen as being vulnerable to invasion.
The five other castles "reconstructed" by NeoMam are Dunluce Castle in Northern Ireland, Bothwell Castle and Caerlaverock Castle, both in Scotland, Kidwelly Castle in Wales and Goodrich Castle in Herefordshire.
*To see the rest of the castles digitally reconstructed by NeoMam, you can visit: www.onstride.co.uk/blog/6-ruined-castles-across-uk-reconstructed/


Maher Arar is a Syrian national who relocated to Canada in 1987, and secured Canadian citizenship in 1991. He obtained a Bachelors degree from McGill University and a Masters degree in telecommunications from INRS-Telecommunications. He had worked as a consultant for MathWorks, a Massachusetts-based high-tech firm, since 1999, and had met his wife in Canada.
Arar's rendition is notable for having taken place from US soil, whereas most renditions have involved transfers of detainees captured in other states.
On 26 September 2002, Arar was detained while attempting to make a connecting flight at New York's JFK airport, on his way to Montreal following a holiday in Tunisia with his wife and children. During the holiday he had been called home to Ottawa by his employer to meet a potential client.
Having been apprehended by immigration officials, he was handed over to FBI agents, who proceeded to question him, alongside New York Police Department staff, for several hours, before detaining him in a cell overnight. He was denied the right to make a telephone call home or to have access to a lawyer.
Among other things, he was questioned about affiliations to terrorist groups, and of knowing two other Canadians with Syrian families who were accused of terrorism. Arar barely knew the pair, Abdullah Almalki and Ahmed al-Maati.
The next morning, 27 September, Arar was taken for further interrogation throughout the day. That evening, he was taken into custody by the US Immigration and Naturalization Service (INS) and held, incommunicado, in the Metropolitan Detention Center in Brooklyn where he was held for 11 days before his rendition. On arrival at the Detention Center he was strip searched, dressed in an orange jump suit, and had a vaccination administered, although he was not told what it was for. On his second or third day at the Metropolitan Detention Center, Arar was given a document saying that he was inadmissible to the US as he was a member of al Qaeda. He was also asked to complete a form stating where he wished to be deported to, so he wrote that his choice was Canada. During his stay at the Detention Center he was visited by Canadian consul Maureen Girvan. He communicated his fears to her that he would be deported to Syria, and was reassured that this would not happen. On 5 October 2002 he was finally visited by a lawyer who advised him not to sign anything without her being present. On the night of 6 October his guards questioned him about why he did not want to go to Syria. He explained that he was afraid he would be tortured, having not completed his military service before leaving Syria for Canada, and because he is a Sunni Muslim, and because his mother's cousin was accused of involvement with the Muslim Brotherhood and had been detained by the regime for nine years. They asked him to sign a document, which he refused to do.
The notice for Arar's deportation was signed by the regional director of the INS, Scott Blackman, and was approved by Deputy Attorney General Larry Thompson of the Department of Justice. Blackman stated that the Washington DC commissioner of the INS, James Ziglar, had affirmed that Arar's deportation was consistent with the US' obligations under Article 3 of the Convention Against Torture, since 'diplomatic assurances' were obtained from Syria that the detainee would not be tortured. Arar was chained and transferred to the Teterboro airport in New Jersey, where he was put on board a small private jet, bound for Jordan. Flight data and associated documentation demonstrate that the aircraft was a Gulfstream III jet with tail number N829MG, registered at that point by MJG Aviation. Click here for our analysis of the flight data and documentation associated with Arar's rendition from the US to Syria, via Jordan.
The beating started that day and was very intense for a week, and then less intense for another week. That second and the third days were the worst. I could hear other prisoners being tortured, and screaming and screaming. Interrogations are carried out in different rooms.
One tactic they use is to question prisoners for two hours, and then put them in a waiting room, so they can hear the others screaming, and then bring them back to continue the interrogation.
The cable is a black electrical cable, about two inches thick. They hit me with it everywhere on my body. They mostly aimed for my palms, but sometimes missed and hit my wrists they were sore and red for three weeks. They also struck me on my hips, and lower back. Interrogators constantly threatened me with the metal chair, tire and electric shocks.
The tire is used to restrain prisoners while they torture them with beating on the sole of their feet. I guess I was lucky, because they put me in the tire, but only as a threat. I was not beaten while in tire.
They used the cable on the second and third day, and after that mostly beat me with their hands, hitting me in the stomach and on the back of my neck, and slapping me on the face. Where they hit me with the cables, my skin turned blue for two or three weeks, but there was no bleeding. At the end of the day they told me tomorrow would be worse. So I could not sleep.
Then on the third day, the interrogation lasted about eighteen hours.
They beat me from time to time and make me wait in the waiting room for one to two hours before resuming the interrogation. While in the waiting room I heard a lot of people screaming.
They kept beating me so I had to falsely confess and told them I did go to Afghanistan. I was ready to confess to anything if it would stop the torture. They wanted me to say I went to a training camp. I was so scared I urinated on myself twice. The beating was less severe each of the following days.
They called me up to sign and place my thumb print on a document about seven pages long. They would not let me read it, but I had to put my thumb print and signature on the bottom of each page. It was handwritten.
Another document was about three pages long, with questions: Who are your friends? How long have you been out of the country? Last question was empty lines. They answered the questions with their own handwriting except for the last one where I was forced to write that I had been to Afghanistan.
He was threatened with additional torture if he reported the abuses to Canadian consular officials that visited him while in Syrian detention. He maintained his silence from 23 October 2002 to 14 August 2003, when he cried out during the visit of a consular official that he had been tortured and was being kept in a grave.
On 20 August 2003 Arar was transferred to the Sednaya prison. There he was able to talk to other prisoners because he was held in a shared cell. During September Abdullah Almalki was also transferred to the Sednaya prison. He had also been held at Far Falestin and subjected to similar forms of torture as Arar.
On 4 October 2003, at 9pm, Arar was told he would be returning to Canada, but he did not believe it. The next morning, Arar was finally released without charge. He was shackled and driven a court where the prosecutor read his forced confession to him. His protests were ignored and he was told he must sign and finger print the document. He was not permitted to read it, but the prosecutor did say that there would be no charges and he would be released. He was then driven back to Far Falestin where the head of Syrian Military Intelligence and officials from the Canadian embassy met him. He was then escorted from the building and driven to the embassy in an embassy car.
According to a Syrian diplomat, Arar was released because they found no link between Arar and Al Qaeda, and could not substantiate the allegations against him. The diplomat stated that he was released out of goodwill towards Canada.
The Center for Constitutional Rights and co-counsel DLA Piper US LLP brought a lawsuit on behalf of Arar against former US Attorney General, John Ashcroft, FBI Director Robert Mueller and former Secretary of Homeland Security, Tom Ridge, as well as various US immigration officials. The plaintiffs were accused of violating Arar's constitutional right to due process, his right to choose a country of removal other than one in which he would be tortured, as guaranteed under the Torture Victims Protection Act, and his rights under international law. The case proceeded through the District Court for the Eastern District of New York where the case was dismissed. His lawyers filed an appeal in the Second Circuit Court on 12 September 2006. The appeals court dismissed the case on 2 November 2009, on the grounds that adjudicating Mr Arar's claims would interfere with national security and foreign policy. On 1 February 2010 Arar's lawyers petitioned the Supreme Court for review of the court of appeal decision. On 14 June 2010, the Supreme Court denied his petition, dismissing his case and ending his efforts for redress in the US courts.
In a public inquiry by the Canadian Judicial Commission into the involvement of Canadian officials in Arar's rendition and detention, it was confirmed that it was information passed to the US by Canada that led to his arrest. It was also clear that while on the one hand the Canadian government had supported Arar's wife in her campaign to secure Arar's release, it was at the same time requesting information from the Syrians obtained from Arar's interrogations. The Canadian Judicial Commission of Inquiry found that Arar was unlawfully transferred and that there was no basis at all for the allegation of terrorism-related activity. It also confirmed that Canadian authorities had transmitted faulty information to the US that contributed to his illegal transfer to Syria. Finally, it verified that Arar was tortured, concluding that 17 of the techniques he was subjected to by the Syrians at Far Falestin constituted torture. In evidence given to the Inquiry, US immigration law experts from Cornell Law School concluded that there were substantial grounds for believing Arar would be tortured, and therefore that US officials acted in contravention of Article 3. US officials refused to participate in the inquiry and the US was criticised heavily in the Inquiry report for its lack of cooperation.
Arar was awarded 10 million Canadian dollars as compensation from the Canadian government for the role it played.
Commission of Inquiry Report into Actions of Canadian Officials in Relation to Maher Arar, September 2006.


After the finding in recent days of "vampire" burials in Sozopol, Bulgaria's archaeological vampire saga took a new twist with the unearthing of another one.
This time it was near Veliko Turnovo, in a necropolis near the St. Ivan Rilski church and is from a much later period – 18th century CE.
The head of the archaeological dig, Professor Nikolai Ovcharov, told public broadcaster Bulgarian National Radio that there was nothing sensational in such findings.
He said that all kinds of rituals protecting the deceased from turning into vampires were widely practiced up until the beginning of the 20th century CE.
"For example the deceased is pierced through the heart, or stones are piled up on top of the body", Ovcharov said.
Ovcharov said that in the grave in Veliko Turnovo, a purse was found, with about 30 silver coins so the deceased could pay for the passage to the other world and his feet were tied to prevent him from rising from the grave.
Meanwhile, the head of Bulgaria's National History Museum, Bozhidar Dimitrov, remained stuck into the story of the Sozopol "vampires", though he admitted that at least 100 similar burials had already been found in Bulgaria.
On June 11, the museum told the media that the "vampires" have arrived in Sofia over the weekend.
"With tight security measures and much to the relief of the old women of Sozopol, the vampire arrived in Sofia," read the statement, signed by Dimitrov.
According to it, the remains would be examined and will be put on display in the museum within the several next weeks. Several foreign television stations, including the BBC, History Channel and RTV Russia, are said to be already interested in making documentaries about the findings.
The "vampire" arrival in Sofia prompted jokes on internet forums and online social networks that sales of garlic and silver would increase severalfold and that visitors to the museum should be given holy water, crosses and stakes for protection.


Sixt offers great deals on car hire in Trento, Italy. We have a large variety of different cars so tourists can choose the one that suits their needs best. We offer luxury convertibles that you can cruise the streets of Trento with, cheap sedans that you and your friends can travel around with, comfortable SUVs that are great for visiting further areas and enjoying the scenery and last but not least convenient family vans for those who are traveling with kids. Sixt offers different insurance coverages for the driver and his passengers so tourists can travel felling safe and secure. We also have a very simple collecting and returning service where all you need to do is sign up on our website, and your car rental in Trento will be waiting for you upon arrival. Furthermore, there is a 24 hour return policy so you can enjoy every second of your time in Trento and return your vehicle whenever it convenes you best.
Having a car rental in Trento is convenient and economic. You can save time and money on cabs or public transport that only works during certain hours of the day and hardly on weekends. The main attraction in Trento is the Piazza Duomo, located in the old town along with other beautiful and historic buildings like the city's Gothic Cathedral and the Fountain of Neptune. There is also the Castello del Buonconsiglio, an ancient castle that used to be occupied by local bishops of the town. The castle contains impressive paintings, including ones that belong to the Fifteenth Century. Those who are interested in museums can find the impressive Trento museum of natural sciences in the city center. Driving to the Funivia Pass Pordoi is also greatly recommended by other tourists because you can park your car, take a cable ride to the top of the mountain and enjoy the beautiful view of the city. There are plenty of unique antique shops, local wineries and traditional restaurants that are located further outside the city center and are very interesting to pay a visit to. Our colleagues at Sixt are waiting to welcome you to the city and provide you with further guidance!


How do I connect/disconnect my Google Fit to the Global Challenge app? – Need help?
How do I connect/disconnect my Google Fit to the Global Challenge app?
From day 2 of the Challenge, head to your Step Entry page in the Global Challenge app to sync your step counts from your Google Fit to your Global Challenge account.
In the drop down, select the account you want to disconnect.


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How Did Valentine's Day Start?
Categories Culture
Date February 14, 2021
Valentine's Day is believed to have existed since the third Century AD, originating with a priest in Rome named St Valentine. The Emperor at the time, Claudius II, had banned marriage, but Valentine broke the rules to perform secret unions. When Claudius found out, he threw Valentine into jail and sentenced him to death. It was there that he fell in love with the jailer's daughter, and when he was taken away to be killed on the 14th of February, he sent her a love letter signed 'from your Valentine.'
'This sparked a tradition of sending love letters and cards from 'your Valentine', that became widely popular during the Victorian era. It is estimated that by the mid 1820s, around 200,000 Valentines were distributed around London. The penny postal service meant that people could send cards to their sweethearts quickly and cheaply, choosing either to sign their name, or remain anonymous.
Quirky Victorian Valentine's Day Cards
There are certainly some weird and wonderful Valentine's Day cards from the Victorian period, many of which are preserved within the Museum of London's collections. Some are beautifully illustrated, while others are teasing and silly, or just plain insulting. One thing they all have in common is that they are hand-made, using materials such as scraps of paper, lace, and even stuffed birds!
Photo: Quirky Victorian valentines | Museum of London. Date Unknown.
This design uses a silver ribbon as a border, and ornate lace. Turns out that cat pictures have always been considered cute!
Photo: Quirky Victorian valentines | Museum of London. 1860-1880.
This particular card tried (unsuccessfully) to make the lobster into a romantic symbol. It is thought that the cooked meat inside a lobster looked like a woman, however this trend quickly died down in favour of some more conventional designs!
Photo: Quirky Victorian valentines | Museum of London. 1870-85.
Here is an example of a card that was probably sent to a woman who had broken her lover's heart. These more insulting cards sometimes used short poems as mocking messages. Nice!
Why not have a go at making your own cards? And not just for Valentine's Day. Use materials that you can find around the house. Things like bubble wrap, tin foil and old buttons could make great additions to a quirky card. Share your designs with us and inspire others, too.
Tag your designs #ArtAtTheHeartValentine
Author: Francesca Rhodes
For more ideas on how to incorporate creativity into our everyday lives, head on over to the Everyday Creativity Section of our website for daily prompts on creative activities. You can download our FREE everyday creativity calendar and join our Everyday Creativity community on Facebook.
Why is Toni Morrison important?
International Women's Day: Spotlight On Our Favourite Female Artists!
Bangladesh Victory Day Project
What is Indian Miniature Painting?
Summer Solidays Success: Highlights of Art at the Heart's Summer Programme

redpajama_set_name: RedPajamaCommonCrawl


Beautifal Bridesmaid Necklaces, Earrings and Bracelets to match your bridal parties gowns. Light, airy, and never out of style. You can toast your champagne to being covered pretty in pink from stretch bracelets and pink crystal earrings to pink crystal and silver rhinestone bracelets that will work for the bride, bridesmaids, or the mother of the bride.


The brand new video for "Gone" is up now! Watch it below. Our record River Child is available now! Get it at iTunes, Amazon, or your favorite indie record store.
We're excited to announce that our music video for "Gone" will be out early next week! In the meantime, check out this behind the scenes cut while you wait on the video.
Our second installment from Evolvement Radio! Check out our acoustic version of "Voodoo Queen".
We had a great opportunity to sit down and record a couple of acoustic songs with Evolvement Radio! Here's the first one, be on the lookout Monday for "Voodoo Child". Check out "Payback".
Red Sky Mary has a little blurb in the current issue of Revolver Magazine! Pick up the Oct/Nov issue now to see us in print.
Photos: We opened for Lynyrd Skynyrd & Blackberry Smoke!
Thanks to Mark Myers of Heathen Hill Studios for the great photos! And thanks to Lynyrd Skynyrd and Blackberry Smoke for having us. Check out all the photos by clicking here.
Our Album "River Child" Is Available Now!
We're pleased and proud to announce that RIVER CHILD is officially available in stores now! Get it at Amazon, iTunes, or at your favorite indie shop. Or, get the physical CD here.
© Red Sky Mary. All rights reserved.


These project instructions are specific to a Bernina machine; however, the project can be adapted for use on other machines.
Note: Tension settings are approximate and may need to be fine tuned for individual sergers.
Lower the presser foot; hold all three thread tails taut and serge for approximately 12". Check to see that the stitches are uniformly serged, smooth, and rounded.
If the chain is bumpy: Lengthen stitch length.
If the chain is flat, not rounded: Check to see that all thread guides are threaded.
Tighten lower looper; loosen upper looper. Slightly tighten needle. Check to see that the cutting width is set at 1.
If the chain jams at beginning: Hold taut when beginning to serge.
Note: The necklace is made of 15 strands, 10 with buttons, and 5 without. Serge each strand until it is approximately 36" in length for a finished length of 26" - 28".
Thread 8 to 10 buttons onto fishing line, leaving the line connected to reel, and placing the reel and buttons in your lap. Serge 3" - 4" of chain, then insert the fishing line to the right of the needle and to the left of the outer edge of the serged chain. While holding the thread chain and fishing line behind the foot, begin to chain over the fishing line, incorporating it into the strand.
Chain a 10" strand, with the fishing line included. Stop, lower needle into machine to hold the place in the chain. Raise the presser foot and swing 2" - 3" of fishing line to the left of the foot, out of the way of the chain. Slide up the first button.
Lower the presser foot, and chain 4 - 5 stitches without the line included. Raise the foot and swing the fishing line back into the original chaining position.
Lower the foot, and once again including the fishing line, chain for about 2". Pull down on the loop created by the fishing line when it was out of the chain. This will snug the button up against the serged chain.
Repeat the previous steps to add additional buttons onto the strand for a length of 16". Chain an additional 10" without buttons, for a total of 36". This completes the first strand.
Serge 9 more strands with buttons.
Serge five 36"-long strands with fishing line but without buttons, using the same settings as with the button strands.
On a flat surface, arrange all 15 strands atop each other. Using twist ties, temporarily tie each end 2" - 3" from the last button. Twist the necklace several times to blend strands. Hold and mark the desired length.
Bend each eye pin into a circle by placing its straight end inside the eye. At your desired ending point on each side of the necklace, insert all 15 strands into the resulting loop. Using jewelry pliers, pull down on the straight end of the eye pin, tightening the loop around the strands. Fold the excess of the strands toward the necklace, having the straight end of the eye pin pointing away from the necklace body.
Using a double length of thread, tightly wrap the strands right next to the eye pin; knot the thread and coat with clear nail polish. When dry, clip the excess strand tails close to the wrapping.
Insert the straight end of the eye pin into the hole in the cone end, and then pull the cone down to hide the wrapped necklace end inside the cone. Form a loop with the excess wire of the eye pin, and attach it to a jump ring. Remove twist ties.
Attach the jump rings to each side of the necklace clasp to complete the necklace.


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redpajama_set_name: RedPajamaGithub


We live in a world where each one of us is bombarded with at least a few hundred messages every day. Switch on the television or flip open a newspaper and there's one brand or the other selling everything from shampoo to happiness! Out in the streets gigantic hoardings insist why we should try a specific restaurant or a new, improved brand of tea. For those of us who have pre-paid cellular connections know too well the menace of a constantly beeping cell phone owing to "mobile advertisements". Then of course there is the social media where in addition to the paid advertisements the status updates, tweets and even memes have become a way to disseminate messages aimed either at promoting products and services or inspiring some sort of behavioral change.
Why are we ranting on about the constant flow of messages and media? Glad you asked! In today's blog we will discuss the many ways and means that media shapes our world view especially vis-à-vis elections – and why it has taken centre stage in the last couple of decades as an important source for educating voters.
Let's first try to understand why a free and fair media is considered essential for free and fair elections. To cut a rather long dissertation short let's put it this way: a free and fair election is not only about the freedom to vote and the knowledge of how to cast a vote, but also essentially about processes where voters engage in public debate and have adequate information about parties, policies, candidates and the election process itself in order to make informed choices. It is for this reason that the media is today considered a key actor to democratic elections, which on one hand provides eligible voters with information to make independent choices and on the other hand safeguards the transparency of the electoral process. Thus, a democratic election with no media freedom would make it very difficult to ascertain just how free and fair an election actually was.
Well, as Spiderman says, "With great power comes great responsibility" – thus it's important to understand that media's engagement in voter education as well as elections is not a one way road where media agencies only have to gather and provide a bunch of facts. In order to fulfill their roles, the media needs to maintain a high level of professionalism, accuracy and impartiality in their coverage. Regulatory frameworks, such as Pakistan Electronic Media Authority (PEMRA) can help ensure high standards. Such laws and regulations ensure freedom of information and expression while at the same time providing a code of ethics under which media outlets must operate.
The whole discussion about media ethics and regulatory authorities brings us to an important and emerging form of media, i.e. online journalism and social media. Citizen journalism is widely gaining popularity as it has provided average people with the power to share and disseminate information. In the 2013 general elections held in Pakistan we saw how images ranging from huge voter turnout to that of electoral rigging were picked up by conventional media only after they became viral on social media websites such as Facebook and Twitter.
However, since social media is still an emerging phenomenon a lot of information floating around on the internet cannot be verified. It is for this reason that conventional media must be careful before reporting anything taken from social media sources. This is especially important because a prime responsibility of media is to provide the voters with full and accurate information.
Media's role in voter education is not merely restricted to providing information to potential voters but it also serves as a platform for interaction between the political parties as well as the Election Commission of Pakistan (ECP) and the public.
The Election Commission has a need to communicate information to the people and within general public to specific groups including the political parties, candidates, women, youth and minorities. Media then serves as an important tool for the ECP to reach out to its target audiences. Thus, both during the campaign phase and on the day of the elections itself, (much like the makers of shampoos and tea brands), the ECP uses media in a variety of ways to get its various messages through.
On the other hand the media too has a great deal of interest in the ECP. The media is interested in the information that the Election Commission has to provide and at the same time will try to scrutinize the ECP's performance, efficiency and integrity of the elections.
The same formula also applies on the relationship between the media and various contesting political parties and candidates.
This two-way relationship collectively helps to keep the voters informed, engaged and encouraged to take part in the elections and safeguard against interference or corruption in the management or conduct of the electoral process.


The Volcan Turrialba Lodge lies in the valley between Irazú and Turrialba Volcanoes.
The whole area is one of great natural beauty and virtually unexplored. Ranging from 4500 to 10000 feet above sea level, it encompasses several life zones including tropical rainforest and cloud forest.
Several nature walks are available to observe the flora and fauna. Hikes to the thermal pools and fumaroles of nearby Irazú Volcano, horseback riding on Turrialba's old lava flows, and mountain biking are some of the activities available at the lodge.
Of particular interest is the "Volcán Turrialba Tour", a unique opportunity to descend to the floor of a major volcanic crater. Turrialba is the only volcano in Costa Rica where such a descent is possible.


text:
Jean d'Osta (Brussel, 1909-1993) was een Brusselse schrijver, journalist, humorist en folklorist. Zijn echte naam was Jan van Osta, want hij was van Friese afkomst. Hij schreef vele boeken over Brussel en het Brusselse dialect. d'Osta publiceerde onder meer in Le Soir en creëerde het personage Jef Kazak, dat gedurende decennia aan bod kwam in het weekblad Pourquoi Pas? en nadien in Vlan.

Werk
 Bruxelles inconnu (Brussel, Parijs: Pierre de Méyère), 1967.
 Encyclopédie du Kitsch (Brussel: Pierre De Meyer), 1972
 Menneke-Pis au bon vieux temps (Brussel: Libro-Sciences), 1973
 Jef Kazak, professeur de belles manières (LP met sketches), 1972
 Le Livre d'Or du Vieux Marché (Brussel: Libro-Sciences), 1973
 Bruxelles d'hier et d'aujourd'hui (Brussel: Rossel), 1976
 Notre Bruxelles oublié (Brussel: Rossel), 1977
 Bruxelles, Album de Famille (Gembloux: Duculot), 1977
 Les rues disparues de Bruxelles (Brussel: Rossel), 1979
 La Belle Époque de Bruxelles (Brussel: Libro-Sciences), 1979 - met tekeningen en aquarellen van David Aranda
 Millénaire (Brussel: Rossel), 1980
 Bob Fish détectief (Brussel: Magic Strip), 1982 - stripverhaal van Yves Chaland
 Les « Flauwskes » de Jef Kazak (Brussel: Paul Legrain), 1983
 Mémoires candides d'un Bruxellois ordinaire (Brussel: Paul Legrain), 1984
 Quand Messidor était une forêt, onuitgegeven, 1985
 Dictionnaire historique et anecdotique des rues de Bruxelles (Brussel: Paul Legrain), 1986
 Le calendrier de Jean d'Osta (Brussel: Paul Legrain), 1987
 Les histoires de Jef Kazak (Brussel: Vlan), 1987
 Dictionnaire historique des faubourgs de Bruxelles (Brussel: Paul Legrain), 1989
 Phonétique et grammaire du flamand de Bruxelles, bij de Dictionnaire du dialecte bruxellois van Louis Quiévreux (Brussel), 1985
 Mémoires de Jef Kazak (Brussel: Racine), 2002

Belgisch Franstalig schrijver
Belgisch Franstalig journalist

redpajama_set_name: RedPajamaWikipedia


fwb_ipf is a firewall policy compiler component of Firewall Builder (see fwbuilder(1)). This compiler generates code for ipfilter. Compiler reads objects definitions and firewall description from the data file specified with "-f" option and generates ipfilter configuration files and firewall activation script.
All generated files have names that start with the name of the firewall object. Firewall activation script has extension ".fw" and is simple shell script that flushes current policy, loads new filter and nat rules and then activates ipfilter. IPFilter configuration file name starts with the name of the firewall object, plus "-ipf.conf". NAT configuration file name also starts with the name of the firewall object, plus "-nat.conf". For example, if firewall object has name "myfirewall", then compiler will create three files: "myfirewall.fw", "myfirewall-pf.conf", "myfirewall-nat.conf".
The data file and the name of the firewall objects must be specified on the command line. Other command line parameters are optional.
Specify working directory. Compiler creates firewall activation script and ipfilter configuration files in this directory. If this parameter is missing, then all files will be placed in the current working directory.
Be verbose: compiler prints diagnostic messages when it works.
Print version number and quit.
Generate debugging information while working. This option is intended for debugging only and may produce lots of cryptic messages.
rule options dialog provides a choice of icmp or tcp rst replies for rules with action "Reject"
compiler always adds rule "block quick all" at the very bottom of the script to ensure "block all by default" policy even if the policy is empty.


Out of this World is considered by many to be the single most powerful card trick in history. In effect, your spectator takes a shuffled deck and is able to know – with %100 accuracy – which cards are red and which cards are black.
The effect is nothing short of sensational. The method is the easiest you'll EVER see for ANY trick.
…you've never seen ANYTHING like this one.
Whether you're new to Out of this World, or you've performed it for years, this is a must see Workshop for anyone interested in performing true miracles.
We've gone through every method ever released – including the many 'trick deck' methods you may have seen. But once you see the methods CC Expert Alexander Slemmer shares in this workshop, you'll know the search is finally over!
1. Special methods that require no skill, but make the effect exponentially more deceptive and powerful.
2. The easiest 'no-fail' method of all time – you'll NEVER be suspected and they'll ALWAYS be absolutely floored by this effect.
3. Our unanimous vote for the perfect ultimate method. If you have some experience with magic or perform professionally, you'll want to start using THIS method immediately!


text:
Q: variable adapter might not have been initialized error My compiler won't have it. :( Now what? Do I have to completely rewrite the entire app?
To see the line which my compiler is rejecting, do a Ctrl+F search for recycler_menu.setAdapter(adapter);
When trying to compile, I'm told by the compiler, "error : variable celsiusOutput might not have been initialized."
recycler_menu.setAdapter(adapter);
how to slove it
private AppBarConfiguration mAppBarConfiguration;
FirebaseDatabase database;
DatabaseReference category;
TextView txtFullName;
RecyclerView recycler_menu;
RecyclerView.LayoutManager layoutManager;

@Override
protected void onCreate(Bundle savedInstanceState) {
    super.onCreate(savedInstanceState);
    setContentView(R.layout.activity_home);
    Toolbar toolbar = findViewById(R.id.toolbar);
    toolbar.setTitle("Menu");
    setSupportActionBar(toolbar);

    //Init Firebase
    database = FirebaseDatabase.getInstance();
    category = database.getReference("Category");

    FloatingActionButton fab = findViewById(R.id.fab);
    fab.setOnClickListener(new View.OnClickListener() {
        @Override
        public void onClick(View view) {
            Snackbar.make(view, "Replace with your own action", Snackbar.LENGTH_LONG)
                    .setAction("Action", null).show();
        }
    });
    DrawerLayout drawer = findViewById(R.id.drawer_layout);
    NavigationView navigationView = findViewById(R.id.nav_view);
    // Passing each menu ID as a set of Ids because each
    // menu should be considered as top level destinations.
    mAppBarConfiguration = new AppBarConfiguration.Builder(
            R.id.nav_home, R.id.nav_gallery, R.id.nav_slideshow)
            .setDrawerLayout(drawer)
            .build();
    NavController navController = Navigation.findNavController(this, R.id.nav_host_fragment);
    NavigationUI.setupActionBarWithNavController(this, navController, mAppBarConfiguration);
    NavigationUI.setupWithNavController(navigationView, navController);

    //set name for user
    View headerView = navigationView.getHeaderView(0);
    txtFullName = (TextView)findViewById(R.id.txtFullName);

    //error app broken will fix later
    /*txtFullName.setText(Common.currentUser.getName());*/

    //load menu
    recycler_menu = (RecyclerView)findViewById(R.id.recycler_menu);
    recycler_menu.setHasFixedSize(true);
    layoutManager = new LinearLayoutManager(this);
    recycler_menu.setLayoutManager(layoutManager);

    LoadMenu();

}

private void LoadMenu() {

    final FirebaseRecyclerAdapter<Category, MenuViewHolder> adapter = new FirebaseRecyclerAdapter<Category, MenuViewHolder>(Category.class,R.layout.menu_item,MenuViewHolder.class,category) {
        @Override
        protected void populateViewHolder(MenuViewHolder viewHolder, Category model, int position) {

            viewHolder.txtMenuName.setText(model.getName());

            Picasso.get().load(model.getImage())
                    .into(viewHolder.imageView);

            Category clickItem = model;
            viewHolder.setItemClickListener(new ItemClickListener() {
                @Override
                public void onClick(View view, int position, boolean isLongClick) {

                    Toast.makeText(Home.this, "", Toast.LENGTH_SHORT).show();
                }
            });

            recycler_menu.setAdapter(adapter);

        }
    };
}

A: Bring this line recycler_menu.setAdapter(adapter); outside the declaration block, as done below.
private void LoadMenu() {

        final FirebaseRecyclerAdapter<Category, MenuViewHolder> adapter = new FirebaseRecyclerAdapter<Category, MenuViewHolder>(Category.class,R.layout.menu_item,MenuViewHolder.class,category) {
            @Override
            protected void populateViewHolder(MenuViewHolder viewHolder, Category model, int position) {

                viewHolder.txtMenuName.setText(model.getName());

                Picasso.get().load(model.getImage())
                        .into(viewHolder.imageView);

                Category clickItem = model;
                viewHolder.setItemClickListener(new ItemClickListener() {
                    @Override
                    public void onClick(View view, int position, boolean isLongClick) {

                        Toast.makeText(Home.this, "", Toast.LENGTH_SHORT).show();
                    }
                });
           }
        };

        recycler_menu.setAdapter(adapter);
    }

A: Initialize recycler_menu with null
    RecyclerView recycler_menu = null;

then in your method LoadMenu(), wrap it with null check
    if (recycler_menu != null) {
        recycler_menu.setAdapter(adapter);
    }

The issue is compiler cant check if recycler_menu is initialized since the initialization process is in another method onCreate

redpajama_set_name: RedPajamaStackExchange


Seura published an article about Russian environment- "Venäjän metsäpaloissa pääsee ilmaan mustaa hiiltä, joka nopeuttaa ilmastonmuutosta – "Palokaudesta tulee hyvin vaarallinen"" (In Russian forest fires black carbon is released into the air, which speeds up climate change – "Fire season becomes very dangerous").
The fire season starts already in February and lasts for months. In addition to the forest, peat bogs dry.
In the fire, carbon dioxide and black carbon are released into the air, which is the third largest source of climate change after carbon dioxide and methane. Black carbon is thought to cause up to a quarter of global warming in the Arctic.
Climate change has accelerated further still due to black carbon. In Russia, the Siberian permafrost melts, and dozens of large craters have been created in the tundra, which are likely to cause methane explosions underground.
The article contains a commentary on the issue by Veli-Pekka Tynkkynen, read it here.
This entry was posted in 2018, Media on 24.7.2018 by Elena Gorbacheva.


Join us in our annual tribute to Elvis with the Beer Me Tender raft-up and party. Bring your drinking shoes and take part in floating the keg dinghy. This is your chance to make Elvis proud and show off those sick dance moves you do in front of the mirror when (you think) no one is watching you. Location and final details to be posted on social media.
The 2nd installment of the Firefly is on August 20th. Please come out and enjoy this awesome time to sail into the sunset and under full moon. If you are new to the club, just contact me. We always are looking for crew. We want to share this with as many as we can! Just call me at 404-435-6051 or email me at racecommodore@southernsailing.org.


Released as the album's second single, the track featuring Musiq Soulchild was unveiled earlier this week, and showcases the diva putting her classic chops to modern-day RnB.
The album's first single 'What The World Needs Now' was released with an accompanying music video in late March and was warmly received by her fans. Interestingly Warwick had previously recorded a number of versions of this song during different phases in her career.
'She's Back' – the legend's 39th studio album, is set for a release on May 10th.


text:
\section{Introduction}
\input{intro}

\section{Publicly available databases}
\input{datasets}

\section{Selected Baseline Algorithms}
\input{baselines}

\section{Experimental assessment of selected baselines}
\input{experiments}

\section{Conclusion}
In this contribution, three state-of-the-art rPPG algorithms were selected and evaluated.
For this purpose, a new, publicly available database containing 40 subjects captured under two different
illumination conditions has been introduced. A thorough experimental evaluation of the selected approaches has been conducted
using different datasets and their associated protocols. Our reproducible research framework allows assessing performance in a principled and
unbiased way. Obtained results show that only one rPPG algorithm has a stable behaviour, but overall it has been noticed that performance
is highly dependent on a careful optimization of parameters. Conducted experiments also shows that generalization across conditions (i.e. resolution, illumination)
should be of high concern when assessing rPPG approaches.
The data, the experimental protocols and the implementation of the
algorithms used in this study have been made available as open source free software\footnote{https://pypi.python.org/pypi/bob.rppg.base}.
We hope that this will help standardizing the comparison of remote heart rate measurement algorithms and advance the progress in this field.

\section*{Acknowledgment}
This work was supported by the Hasler foundation through the COHFACE project
and by the FP7 European project BEAT (284989).
The authors would like to thank Sushil Bhattacharjee at Idiap Research Institute for his help with the recordings and for providing the landmarks,
Xiaobai Li at the University of OULU for sharing part of his code, and Weijin Wang at the Technical University of Eindhoven for fruitful discussions.

\bibliographystyle{plain}

\subsection{CHROM }
The so-called CHROM approach \citep{dehaan-tbe-2013} is relatively simple, and has been shown to outperfom
previous baselines such as ICA \citep{poh-oe-2010} and PCA \citep{lewandowska-ccsis-2011}. The algorithm first
applies a color filter based on a one-class SVM to find skin-colored pixels in each frame of the input sequence.
Then, the mean skin color value is projected onto the proposed chrominance subspace, which aims to reveal
subtle color variations due to blood flow. The final pulse trace is obtained by first bandpass filtering
the temporal signals in the XY chrominance colorspace, and then combining the two dimensions of this colorspace
into a one-dimensional signal (see Figure~\ref{fig:block-chrom}). Note that in our implementation, we did not use a one-class SVM as the skin color
filter, but a recent approach proposed by Taylor \citep{taylor-spie-2014}. An example of the obtained skin mask
with this algorithm is shown in Figure~\ref{fig:block-chrom}.

\begin{figure}[!h]
\centering
\includegraphics[width=0.8\textwidth]{./figures/block-diagram-chrom.png}
\caption{Flowchart of the CHROM algorithm to retrieve the pulse signal.}
\label{fig:block-chrom}
\end{figure}

This approach was applied to no less than 117 static subjects, where different skin tones were represented.
The recordings have been made in a controlled environment and using professional studio illumination.
Obtained performances for the CHROM algorithm, as well as for the baselines used for comparison
are almost perfect (with a Pearson's correlation coefficients between 0.97 and 1). Also, another experiment
was presented on exercising subjects, either on a bike or on a stepping device.
As expected, performance degrades, but the CHROM algorithm was shown
to better mitigate the effect of subject's motion on the rPPG signal, as compared to previous baselines.

\subsection{LiCVPR}
The algorithm pusblished by Li et al. \citep{li-cvpr-2014} has been included in selected baselines since it is the only rPPG approach
which reports results on a publicly available database \citep{soleymani-tac-2012}. Hence, it provides
a reference and allows comparison between published material and our implementation of the different
algorithms. This approach relies on the tracking of the bottom part of the face, defined
by keypoints as shown in Figure~\ref{fig:block-li}. The keypoints are detected thanks to the DMRF algorithm
\citep{asthana-cvpr-2013} in the first frame of the sequence and are used to build the mask. The position of this mask
is updated at each subsequent frame by applying an affine transformation found by a KLT tracker on features
detected by a Shi-Tomasi detector \citep{shi-cvpr-1994} inside the face region. Once the temporal signal corresponding to
the mean green value inside the tracked area has been built, it is corrected for illumination using
the Non-Linear Mean Square algorithm with the mean green value of the background as reference. Large variations
in the corrected signals, probably due to subject motion, are removed according to statistics computed on the entire database. Finally,
the pulse signal is obtained by applying a detrending filter \citep{tarvainen-tbe-2002} to reduce slow trends
in the signal, a moving average filter to remove noise and a bandpass filter to retain
the range of interest corresponding to plausible heart rate values.  A flowchart of the whole procedure is
given in Figure~\ref{fig:block-li}.\\
Experiments are performed on two different datasets. The first one has been built with 10 subjects
recorded under controlled conditions, and individuals were explicitly asked to avoid any movement. Note that
this dataset has not been made public by the authors. The second
one is a subset of the Manhob HCI-Tagging dataset, containing only sequences lasting more than 30 seconds.
The performance of the proposed algorithm on the first, controlled video sequences is almost perfect, reaching a Pearson's correlation
coefficient of 0.99. However, on the less controlled data from the Manhob HCI-Tagging database, there is
a slight drop in performance with a reported correlation coefficient of 0.81.

\begin{figure*}[!h]
\centering
\includegraphics[width=0.8\textwidth]{./figures/block-diagram-li.png}
\caption{Flowchart of the LiCVPR algorithm to retrieve the pulse signal.}
\label{fig:block-li}
\end{figure*}

\subsection{2SR}
Wang et al. recently proposed a novel algorithm termed as Spatial Subspace Rotation (2SR)
\citep{wang-tbe-2015-2}. In their approach, the authors consider the subspace of skin pixels
in the RGB space and derive the pulse signal by analyzing the rotation angle of
the skin color subspace in consecutive frames. To do so, the eigenvectors of the
skin pixels correlation matrix are considered. More precisely, the angle between the
principal eigenvector and the hyperplane defined by the two others is analyzed
across a temporal window. As claimed by the authors, this algorithm is able to directly retrieve
a reliable pulse signal, and hence no post-processing step (i.e. bandpass filtering) is required.
However, this algorithm needs an accurate estimate of the skin color, since it is implicitly
assumed that the skin colored pixels form a single cluster in the RGB space.
In our work, the same skin color filter \citep{taylor-spie-2014}
as in the CHROM method is used, allowing a fair comparison between these two approaches.\\
Experimental results obtained on a private dataset containing 54 video sequences
show better overall performances than all the approaches considered for comparison,
including CHROM. A correlation coefficient of 0.94 is reported
across a wide variety of conditions, including skin tone, motion of the subject and recovery after exercise.

\subsection{Mahnob HCI-Tagging}
The Mahnob HCI-Tagging dataset was not designed for
rPPG benchmarks, but rather for the characterisation of multimedia content
based on human emotions. For that purpose, dataset authors collected video and
physiological data from 30 subjects being exposed to different audio-visual
stimuli such as the display of pictures and short to medium-sized video
sequences.  \\
The Manhob HCI-Tagging dataset may be used to evaluate rPPG techniques by ignoring
most of its data contents and manually correlating physiological signals with
the data from the professional camera located in front of the subject being
tested. An example image capture for the Mahnob dataset is shown on
Figure~\ref{fig:block-li}.
Authors indicate professional lighting
was used so as to maximize the video data quality. The professional camera used
outputs videos of size 784 (h) by 592 (w) pixels at a rate of 60 Hz. Three
electro-cardiogram (EKG) sensors placed on the participants chest measure the
individual heart rate. The data extracted from those sensor readings is also
made available in the dataset and are synchronized with the video recordings.\\
Figure~\ref{fig:mahnob-ekg} shows the outputs of the three EKG sensors
(in blue). The estimated heart rate for the
subject on the video must be estimated from these by first running a QRS
detector \citep{pan-tbe-1985} which estimates the location of vertical (red) bars
corresponding to true heart-beat events\footnote{the MNE package was used for this
purpose. \url{http://mne-tools.github.io/stable/index.html}}, followed-up by a consensus heuristic
(voting) for selecting the estimate rate from one of the channels.
Because sequences are rather short (30-60 seconds)
and the subject is comfortably sitting, it is assumed the heart rate does not
vary greatly during the session. Once the heart rate is estimated for every
sequence in the Mahnob HCI-Tagging database (there are 3'490 videos on
the dataset), it is possible to use it for rPPG benchmarking.

\begin{figure}[!h]
  \centering
  \includegraphics[width=\linewidth]{figures/client-ekg-example}
  \caption{Example of collected electro-cardiogram signals. Signals provided with the database are
  depicted in blue. Red stripes indicate the result of QRS detection. The
  estimated heart rate is shown on the top of each graph in terms of bpm.}
  \label{fig:mahnob-ekg}
\end{figure}

\subsection{The COHFACE dataset}
The COHFACE dataset\footnote{\url{https://idiap.ch/dataset/cohface}} is composed of 160 videos and physiological signals collected from 40 healthy individuals.
The data collection campaign spawned several days. The data acquired in this new corpus
includes more realistic conditions as compared to the well-controlled setup
for recording the Manhob HCI-Tagging data.\\
The age distribution from the 40 individuals is shown in
Figure~\ref{fig:cohface-age-distribution}. The average subject age is 35.6
years old, with a standard deviation of 11.47 years. Gender-wise, there were 12
women (30\%) and 28 men (70\%).\\

\begin{figure}[!h]
  \centering
  \includegraphics[width=0.8\linewidth]{figures/cohface-age}
  \caption{Age distribution for subjects at the COHFACE dataset.}
  \label{fig:cohface-age-distribution}
\end{figure}

\subsubsection{Data collection}

Individuals were asked to look into a conventional webcam connected to a laptop
during a period of approximately 60 seconds. We registered both video and physiological signals (contact
photoplethysmography and respiration) using a biomedical toolkit that
synchronized the 3 input channels. The physiological data shall be used as
ground-truth for benchmarking rPPG algorithms. \\
For each of the 160 videos, the subject's face is recorded by a commodity
webcam (Logitech HD Webcam C525) during a full minute, while physiological
readings are taken by a Blood-Volume Pulse (BVP) sensor and a respiration belt,
both from Thought Technologies (BVP model SA9308M, belt model SA9311M). The BVP
sensor measures changes in skin reflectance to near-infrared lighting caused by
the varying oxigen level in the blood due to heart beating. The respiration
belt is composed of a mechanical coiling system that simply measures thoracic
stretch. Both sensors are connected, following advice from Thought
Technologies, to a computer running Microsoft Windows via their 2-channel
USB-based acquisition system (ProComp2). Data from the physiological sensors
together with the video stream from the webcam is synchronized and recorded
using Thought Technologies' BioGraph Infiniti Software suite, version 5. With
the standard settings, BioGraph Infiniti v5 was able to output a video stream
with a resolution of 640 x 480 pixels at 20 frames per second, together with
BVP measurements 256 times per second and, respiration sensor readouts 32 times
per second. Figure~\ref{fig:db-lighting} shows two images as captured by
the webcam. Figure~\ref{fig:cohface-db-signals} shows typical signals acquired
in sync with the video data, corresponding to the subject's pulse (top, in
blue) and the respiration readings (bottom, in blue).

\begin{figure}[!h]
\centering
\subfloat[][]{\includegraphics[width=0.4\textwidth]{./figures/cohface-db-face-frontal.jpg}\label{fig:cohface-office}}%
\hfil
\subfloat[][]{\includegraphics[width=0.4\textwidth]{./figures/cohface-db-face-natural.jpg}\label{fig:cohface-natural}}%
\caption{Example images of the COHFACE dataset.
  \protect\subref{fig:cohface-office} Studio
  \protect\subref{fig:cohface-natural} Natural}
\label{fig:db-lighting}
\end{figure}

\begin{figure}[!h]
  \centering
  \includegraphics[width=\linewidth]{figures/cohface-client-1-signals.pdf}
  \caption{Example signals acquired for each session in the COHFACE dataset.}
  \label{fig:cohface-db-signals}
\end{figure}

To estimate the heart and respiratory rates from the signals read-out
from our physiological sensors, a simple peak detector was
deployed. While the Mahnob HCI-Tagging dataset contains EKG signals from which
a QRS complex can be detected, pulse signals readout by the contact PPG sensor
used at the COHFACE dataset acquisition look sensibly simpler (see
Figure~\ref{fig:cohface-db-signals}) and just applying a Pan
and Tompkins detector \citep{pan-tbe-1985} would not work as expected.\\
After testing, we decided to adopt a simple peak-detector available as free
software\footnote{\url{https://github.com/demotu/BMC/blob/master/functions/detect_peaks.py}},
which deployment results in the detected peaks (bars in red) also shown in
Figure~\ref{fig:cohface-db-signals}. The estimated heart and respiratory rates
can then be easily calculated from the peak positions as shown on the title
bars of the same figure.\\

\subsubsection{Acquisition Protocol and Illumination Changes}

Each of the 40 individuals was asked to sit still in front of the webcam so
that the face area is captured in full, for four sessions that lasted about 1
minute. Illumination on the scene was changed once as to create two types of
lighting conditions:

\begin{enumerate}
  \item \textbf{studio}, for which we closed the blinds, avoiding natural
    light, and used extra light from a spot to well illuminate the
    subject's face, and

  \item \textbf{natural}, in which all the lights were turned off and the
    blinds were opened.
\end{enumerate}

The four video sequences (2 with studio lighting and 2 with natural lighting)
can be used to evaluate the performance of rPPG algorithms in either matched or
unmatched settings. Figure~\ref{fig:db-lighting} shows the differences in
illumination for the two conditions of acquisition for the COHFACE dataset.\\

\subsubsection{File Formats and Metadata}

The choice of formats for data distribution aimed on improving readability and
access using free software tools. Data for the video stream in each session and
for each individual is shipped in MP4 format inside a commonly available AVI
movie container. Data from the physiological sensors is distributed using
standard HDF5 containers, with information such as the date of birth of the
subject, his gender, the illumination condition and
of course the recorded pulse and respiration signals.\\

\subsubsection{Comparison to Mahnob HCI-Tagging}

Table~\ref{tab:dataset-diff} summarizes the differences between the only two
public datasets for rPPG benchmarking. While the Mahnob HCI-Tagging dataset
provides more video samples, the COHFACE dataset allows more realistic testing
for the use of rPPG due to its increased data
variability (more subjects and more realistic data acquisition conditions).

\begin{table}[!h]
  \centering
  \caption{Key figures of the COHFACE dataset when compared to the Mahnob
  HCI-Tagging dataset for the purpose of remote non-contact PPG benchmarking.}
  \label{tab:dataset-diff}
  \begin{tabular}{|c|c|c|}
    \hline
     & \textbf{HCI-Tagging} & \textbf{COHFACE}\\ \hline\hline
    \textbf{Subjects} & 30 & 40 \\ \hline
    \textbf{Samples} & 3490 & 160 \\ \hline
    \textbf{Camera} & 784x592@60Hz & 640x480@20Hz \\ \hline
    \textbf{Physiological Signals} & 3 $\times$ EKG & BVP + Respiratory Belt \\ \hline
    \textbf{Illumination} & Studio & Studio + Natural \\ \hline
  \end{tabular}
\end{table}

\subsection{Baselines}

As a starting point, obtained results on the same subset of the Manhob HCI-Tagging database as used by Li et al. \citep{li-cvpr-2014} are presented.
This data subset consists in 527 sequences of 30 seconds. These sequences are obtained by considering
selected video files from frame 306 to frame 2135. Table~\ref{tab:results-baseline-527-cut} report the
RMSE and the correlation coefficient with the ground truth on these 527 sequences.
We also report, for each algorithm, the number of free parameters that have been optimized directly on this dataset.\\
Since testing all possible combinations of the different parameters is virtually impossible, the
following strategy was adopted: because investigated approaches generally involve a serie of sequential steps, we
first optimize the parameters of the first step (the threshold on the skin color probability, for instance), fix them, and
keep on with the optimization of the parameters of the second step, and so on.

\begin{table}[!h]
\renewcommand{\arraystretch}{1.3}
\caption{Results of our implementations of baselines on 527 sequences of the Manhob HCI-Tagging database, from frame 306 to 2135}
\label{tab:results-baseline-527-cut}
\centering
\begin{tabular}{|c||c|c|c|}
\hline
& \textbf{LiCVPR} & \textbf{CHROM} & \textbf{2SR}\\
\hline
\hline
\textbf{RMSE} &  \textbf{8.12} & 15.40 & 18.4 \\
\textbf{Pearson's $\mathbf{\rho}$} & \textbf{0.70} & 0.33 & 0.43 \\
\hline
\textbf{\# of parameters} & 12 & 6 & 4\\
\hline
\end{tabular}
\end{table}

The first point to note here is that we were unable to reproduce results reported by Li et al. \citep{li-cvpr-2014},
where a RMSE of $7.62$ and a correlation coefficient of $0.82$ are presented. We can say with high confidence that these differences
are only due to the tracking procedure and the considered background area. Indeed, the authors of \citep{li-cvpr-2014} shared
their source code, except the tracking part, hence making their algorithm only partially reproducible.
However, this algorithm still significantly outperforms the two other baselines. Note also that
our own implementation of the spatial subspace rotation (2SR) algorithm does
reflect findings reported in \citep{wang-tbe-2015-2}, where the authors empirically showed that their approach outperforms CHROM
\citep{dehaan-tbe-2013}, which is also the case here.
Finally, it is important to point out that the various parameters, such as the threshold on motion elimination \citep{li-cvpr-2014}, or
the window size in the overlap-add procedure \citep{dehaan-tbe-2013}, have been tuned \textit{directly on the test data},
following the practice in \citep{li-cvpr-2014}.
Doing so introduces a severe bias, since the generalization ability of each algorithm to unseen data could not be
established. As a consequence, we devised strict experimental protocols for subsequent experiments, specifying data for training (i.e. optimizing
the free parameters) and for testing. These various protocols are presented in the next subsections.

\subsection{Unbiased performances in clean illumination conditions}
In a pattern recognition framework, such as the one here, it is important
to train and test proposed algorithms on different datasets.
This is done to assess the generalization ability of the different approaches
to unseen data. Indeed, it could be the case that some combination of free parameters
would perform well on certain kind of data (say, a particular skin tone, for instance), while failing
in other conditions. Using different datasets for tuning the algorithms and evaluating
their performances is hence critical to have a clear insight of their generalization capabilities.
In rPPG research however, there are - to the best of our knowledge -
no published results following this paradigm. This could be due to the fact
that databases are generally expensive to collect and hence rather small.\\

\subsubsection{Manhob HCI-Tagging}
The complete Manhob HCI-Tagging database contains 3490 video sequences recorded during 4 sessions,
with constant acquisition conditions. The only difference between the sessions consists in the
task assigned to the participants, and that should not affect the performance of any rPPG algorithm.
However, there is a great amount of variation in sequence length, since the smallest sequence
contains 556 frames and the largest 15780, the mean being 1789 frames. Different sets were built based
on subjects identities, ensuring no overlaps between training and test sets.
Also, since the sequence duration is usually correlated with the session,
all sessions corresponding to selected subjects were included in the set. This is done
to balance the mean sequence duration across the different sets. The training set has been built
using data from 19 subjects and the test set with data of the remaining 10 subjects.
Table~\ref{tab:manhob-sets}  summarizes relevant information about this partitioning.

\begin{table}[!h]
\renewcommand{\arraystretch}{1.3}
\caption{Statistics for training and test sets of the Manhob HCI-Tagging database. The minimum,
maximum and mean length are given in number of frames.}
\label{tab:manhob-sets}
\centering
\begin{tabular}{|c||c|c|c|c|}
\hline
 Set & \# of sequences & min length & max length & mean length\\
\hline
\hline
Train & 2302 & 556 & 13106 & 1812\\
Test & 1188 & 579 & 15780 & 1745\\
\hline
\end{tabular}
\end{table}

We then applied the different baseline algorithms to the different sets
of the Manhob HCI-Tagging database. Table~\ref{tab:hci-protocols-opt-train} shows the
Pearson's correlation coefficients obtained when parameters are optimized on the training set.

\begin{table}[!h]
\renewcommand{\arraystretch}{1.3}
\caption{Pearson's correlation coefficient on different sets of the Manhob HCI-Tagging database}
\label{tab:hci-protocols-opt-train}
\centering
\begin{tabular}{|c||c|c|}
\hline
& \textbf{Training set} & \textbf{Test set} \\
\hline
\hline
\textbf{LiCVPR} & \textbf{0.49} & \textbf{0.45}\\
\textbf{CHROM} & 0.15 & 0.14\\
\textbf{2SR} &  0.17 & 0.05\\
\hline
\end{tabular}
\end{table}

As can be seen on Table~\ref{tab:hci-protocols-opt-train}, obtained performance is generally better on the training set than on the test set, as expected.
Also, it can be observed that performance is not as good as in the previous experiment (Table~\ref{tab:results-baseline-527-cut}). This can be mainly explained by considering
two points. First, the length of the different sequences greatly varies, and some parameters may
yield to an overall better behaviour if the sequences are of the same duration (i.e. some parameters value
may be best suited to long sequences). As a matter of fact,
optimal parameters on the training set are different than the ones obtained in the previous experiments.
Secondly, and a more obvious reason for this performance drop is that more sequences introduces more variations.\\

\subsubsection{COHFACE}
Since the COHFACE dataset contains two distinct illumination conditions, different
experimental protocols have been devised. For this test however, we only consider
studio conditions, i.e. when the face is well-lit by the spot (see Figure~\ref{fig:cohface-office}).
To generate training and test sets, the same approach
as with the Manhob HCI-Tagging database has been applied: 24 subjects were selected to build the training set, and the
16 remaining subjects form the test set. Considering studio conditions only, this partitioning results in
48 sequences for the training set and 32 for the test set (there are 2 sequences for each subject).\\

\begin{table}[!h]
\renewcommand{\arraystretch}{1.3}
\caption{Pearson's correlation coefficient for the \textit{studio} protocol on different sets of the COHFACE database.}
\label{tab:cohface-protocols-clean}
\centering
\begin{tabular}{|c||c|c|}
\hline
& \textbf{Training set} & \textbf{Test set} \\
\hline
\hline
\textbf{LiCVPR} & -0.16 & -0.61\\
\textbf{CHROM} & \textbf{0.23} & \textbf{0.43}\\
\textbf{2SR} & 0.07 & -0.31\\
\hline
\end{tabular}
\end{table}

Results in Table~\ref{tab:cohface-protocols-clean} should be compared to the ones reported in Table~\ref{tab:results-baseline-527-cut}: sequences are
approximatively of the same length and recorded in clean illumination conditions.
In this case however, obtained results are much worse for two out of the three studied algorithms.
On this database, they prove to be incapable at inferring the heart-rate reliably, since their
Pearson's correlation coefficient is close to zero and even negative in the case of LiCVPR. This result,
although suprising, is interesting: it emphasizes the sensitivity of these approaches to
different acquisition conditions. Indeed, the COHFACE database has both lower resolution and framerate.
On the other hand, the performance of the CHROM algorithm remains
consistent across these different datasets. An interesting point to note is that it actually
performs better on the test set than on the training set. This could mean that its performance
is less sensitive to its parameters, which is a nice feature: it suggests a good
generalization ability to unseen video sequences.
However, it should be noted that optimal
parameters for the training set of the COHFACE database are generally not the same as the one used in the
very first experiment.

\subsection{Evaluation on umatched conditions}
An interesting and realistic scenario is to use rPPG algorithms across a wider range of aquisition
conditions. Think, for instance, of a usage on a mobile device in different rooms, where the lighting conditions
are not the same. It would be an additional burden to readjust different parameters each time there is a
change in the environment. Hence it is important to assess the behaviour of the
different approaches in various settings. Such cases have been emulated with two different experiments presented below.\\

\subsubsection{Cross database}
Although the two considered databases were partly recorded in quite similar conditions (indoor, controlled illumination),
it is of interest to test the behaviour of the different approaches in a cross-database setting. For this purpose,
we first tune the algorithms on the training set of the Manhob HCI-Tagging database and test them on the studio test set of
the COHFACE database. For the second experiments, algorithms are optimized on the studio training set of the COHFACE database
and tested on the test set of the Manhob HCI-Tagging database.

\begin{table}[!h]
\renewcommand{\arraystretch}{1.3}
\caption{Pearson's correlation coefficient in cross-database testing.}
\label{tab:results-xdb}
\centering
\begin{tabular}{|c||c|c|c|}
\hline
& \textbf{LiCVPR} & \textbf{CHROM} & \textbf{2SR}\\
\hline
\hline
\textbf{HCI $\rightarrow$ COHFACE} & -0.20  & \textbf{0.51} & -0.15\\
\hline
\textbf{COHFACE $\rightarrow$ HCI} & \textbf{0.25} & 0.10 & 0.00\\
\hline
\end{tabular}
\end{table}

Results reported in Table~\ref{tab:results-xdb} shows an interesting behaviour. Indeed,
when algorithms are tuned on the Manhob HCI-Tagging database, their performance on the test set of the COHFACE
database is better than when tuned with the same database. However, results for both LiCVPR and 2SR are
still not satisfactory. While such a behaviour may seem surprising,
it only means that optimal parameters on the training set of Manhob HCI-Tagging also suit well the data
in the COHFACE studio test set. This can be due to the wider variety of sequences to tune parameters, since
the training set of the Manhob HCI-Tagging database contains much more sequences than the training set of
the COHFACE database. In the second experiment however, one can see that performance is
generally worse than the one reported in the second column of Table~\ref{tab:hci-protocols-opt-train}, as expected.
In this case, the limited amount of sequences in the COHFACE studio training set prevents to find optimal
parameters for the more generic conditions in the test set of the Manhob HCI-Tagging database.
Again, these experiments underline the extreme sensitivity of the different approaches to their respective parameters. Note finally
that the performances of CHROM on both test sets are comparable, suggesting its higher stability to different configurations.\\

\subsubsection{Illumination conditions}
Since the COHFACE database contains two distinct illumination conditions (see Figure~\ref{fig:db-lighting}), one is now able
to test the generalization of rPPG algorithms to different illumination conditions. The number of
recordings under natural lighting is the same as in well-lit conditions, meaning that the natural test set contains
16 subjects and 32 video sequences. To test the behaviour in mismatched conditions, parameters were tuned on the
studio training set, and applied to the natural test set. Performance in unmatched illumination conditions are reported
in Table~\ref{tab:results-illumination-mismatch}.\\

\begin{table}[!h]
\renewcommand{\arraystretch}{1.3}
\caption{Pearson's correlation coefficient on the natural test set of the COHFACE database.}
\label{tab:results-illumination-mismatch}
\centering
\begin{tabular}{|c||c|c|c|}
\hline
& \textbf{LiCVPR} & \textbf{CHROM} & \textbf{2SR}\\
\hline
\hline
\textbf{Studio $\rightarrow$ Natural} & -0.24 & -0.03 & \textbf{0.00}\\
\hline
\end{tabular}
\end{table}

As expected, obtained results generally show a drop in performance as compared to the test set in studio conditions (Table~\ref{tab:cohface-protocols-clean}).
This denotes again the need for a careful selection of the parameters, and suggest that none of the selected algorithms are suitable
to achieve acceptable performance in realistic scenarios, where the acquisition conditions are unknown \textit{a priori}. \\

\subsection{Importance of the ROI}
In the next experiments, we consider the whole COHFACE dataset, with both lighting conditions. Doing so will
allow to get insights on the performances in a more generic setting in terms of illumination, corresponding to
more realistic conditions. Using the full dataset, there are now 96 sequences in the training set, and 64
in the test set. Note that both sets contain sequences recorded under the two different lightings,
there is no mismatch between training and testing, but rather a wider range of variability. \\

Since all investigated rPPG algorithms first extract skin pixels, it is interesting to see which method is best suited
to perform this task. As a baseline, the complete face bounding box is used. We then compare the different algorithms
when the skin pixels are either extracted using a mask on the bottom part of the face (see Figure~\ref{fig:block-li}), or using the
skin color filter \citep{taylor-spie-2014}. Performance using different ROI to extract skin pixels are presented in Table~\ref{tab:pearson-ROI}.\\

\begin{table}[!h]
\renewcommand{\arraystretch}{1.3}
\caption{Pearson's correlation coefficient for different skin color ROI on the test set of the COHFACE database.}
\label{tab:pearson-ROI}
\centering
\begin{tabular}{|c||c|c|c|}
\hline
& \textbf{Face bounding box} & \textbf{Skin pixels} & \textbf{Mask} \\
\hline
\hline
\textbf{LiCVPR} & -0.29 & -0.16 & -0.44\\
\textbf{CHROM} &  \textbf{0.30} & \textbf{0.27} & 0.30 \\
\textbf{2SR} & 0.26 & 0.09 & \textbf{0.65}\\
\hline
\end{tabular}
\end{table}

In this case, the performance of the CHROM algorithm remains stable and comparable to the one obtained on the studio dataset
(see Table~\ref{tab:cohface-protocols-clean}), no matter which ROI is considered.
This could be explained by the fact that the mean color is used, which should not vary much across
different ROIs. This also shows that when parameters are tuned using conditions known \textit{a priori},
this algorithm is able to cope with variability in illumination.
It is interesting to see that the 2SR algorithm performs better when the mask is considered. Indeed, one
could expect that it would work best when the color subspace of the considered skin pixels are compact in the RGB space, since
it has been designed this way. This result suggests that with this algorithm the ROI has a tremendous influence
on the results, as evidenced by the variation in performance across the different regions.
Finally, one should notice that LiCVPR's algorithm does not achieve acceptable performance no matter the ROI. This shows
that this algorithm is very sensitive to variations in terms of both image resolution and illumination conditions.

redpajama_set_name: RedPajamaArXiv


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Lincoln, Washington, You – stature and leadership
Friends, To paraphrase Tina Turner, "What's height got to do with it?" Well, it seems like a lot. When George Washington at 6'1″ served as first president he was literally a head taller than many of his cohorts: John Adams was 5'7″ and James Madison a diminutive 5'4″. (Independence Hall in Philadelphia has a wonderful … Continued
She/he asks: What can we learn when things don't go as expected?
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Special 12th Anniversary Guest Column By Katherine Crowley and Kathi Elster We recently appeared on a late-night television show to promote our new book, Mean Girls at Work. Our hosts, two male comedians, were very surprised to learn that women react to teasing, sparring and criticism very differently from men. "When you insult or yell at one of … Continued