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D.A. Invites Serial Rapist To Town
Philly D.A. Rolled Out Red Carpet For Accused Serial Rapist
He's a dreadlocked former Boston prosecutor and charismatic advocate for criminal justice reform who became a rock star after a 2016 TED talk that got nearly three million views. Now he commands up to $20,000 for a live speaking engagement.
So in February, 2018, when Philadelphia District Attorney Larry Krasner was kicking off his criminal justice revolution in Philadelphia, he invited Adam Foss to town to meet with his staff of young, eager and impressionable progressive prosecutors. Then, in September, 2018, Krasner brought back Foss in a bigger way, hiring Foss and his nonprofit to lead a nine-week training session for new ADAs.
Now, Adam Foss is under a criminal investigation announced by the county D.A.'s office in Boston where he used to work, after he was publicly accused by several women of being a serial predator and rapist who's knowingly spreading STDs.
In Boston, the authorities are hot on Foss's tail. "I am aware of and troubled by the allegations that a former [Suffolk County District Attorney's Office] employee engaged in behavior that either was inappropriate, an abuse of his authority, unethical or illegal," declared Suffolk County District Attorney Rachael Rollins. "Every allegation will be investigated thoroughly."
But meanwhile, in Philadelphia, D.A. Krasner isn't talking about how much he paid Foss. Or whether he's launched an investigation to see if any of his impressionable young female staffers were abused and/or infected by Foss, when he was here in town, training prosecutors.
Because Foss is a former prosecutor in the Suffolk County D.A.'s office, the investigation of Foss in Boston will be independently led by a private law firm, D.A. Rollins said, with the help of Boston police.
The uproar in Boston began when Raegan Sealy, a writer and singer, who said she met Foss at a conference, wrote a long blog post entitled, "The Wolf and the Network." In that tell-all, Sealy claimed that she was raped in 2017 by Foss, who posed as a "progressive, liberal, acclaimed social justice advocate" but was in reality a predator.
Foss has publicly acknowledged that his behavior has "caused many people anguish, but I deny any allegations of nonconsensual sexual relations," he said in a statement to WBUR, an NPR news station in Boston. After Sealy came out, women subsequently came forward on Twitter to claim that they too had been raped by Foss.
Foss didn't have much more to say to the NPR station in Boston.
"With respect to District Attorney Rachael Rollins' recent statement referencing a pending investigation, I do not wish to compromise that process by commenting further," he wrote in his statement. "In addition, this is a more appropriate time for me to listen than to speak."
In her blog post, Sealy described her first impression of Foss, whom she engaged in backstage small talk, as a "strikingly good looking dude in a fitted 'FEMINIST' t-shirt." Then she went on stage where she was preceded by Jennifer Garner, Cynthia Nixon and Chelsea Manning.
Foss was 12 years older when he met the then 25-year-old Sealy, who clearly had stars in her eyes. Sealy wrote that she was impressed when she heard about Foss's famous TED talk about criminal justice reform. Foss, Sealy said, was also a name dropper who bragged about a circle of friends that included Oprah, Jeff Bezos, and John Legend.
In her blog post, Sealy described exchanging nude pictures with Foss, making out with him, and during one night in his hotel room, getting naked and cuddling with him. But, she wrote, she drew the line at sex, because she claimed he was drunk. Later that night, she claims she woke up while Foss was raping her.
"I felt humiliated, used and stupid," she wrote. "Slowly, it sank in that I had not only been raped, but also essentially groomed, conned into believing I had a friendship with somebody who really had a game plan the whole time."
Then, Sealy wrote, she discovered there were at least an initial dozen other alleged victims who told her that Foss "was emotionally corrupt and inappropriately wields his power," and that he "deliberately targets married women."
The women Sealy talked to also alleged that Foss "has STDs and doesn't disclose it, and doesn't bother to use condoms."
The quotes above were from a group of professional women who "had connected over their experiences with Adam," Sealy wrote. The group wrote a joint letter of complaint to a publisher who had given Foss a book deal, but the women got no response, Sealy wrote.
Sealy concluded her blog post by saying she had learned that "behind Adam the feminist lives Adam the rapist." And "Adam the destroyer."
"Most of the women I've met who've had bad experiences with Adam are scared to speak up about them because of who he is, and because (in many cases) he technically didn't do 'anything illegal,' " Sealy wrote.
"So we're left to rely on this whisper network; privately warning other women of this destroyer, if and when we can."
"Sure, Adam knowingly spreads disease," Sealy wrote. "He deceives. He cheats. He physically and emotionally coerces women into consent they would likely not otherwise give. He withholds important information, and actively lies, further rendering the conditions of this consent invalid."
"He targets women in professional settings, leveraging his position as a leader and role model to gain our trust. He creates intense emotional ensnarements, embroiling women into not recognizing this abuse as it happens, or even being able to articulate it particularly well in retrospect."
"I wrote this for the women who've been lied to, infected, manipulated, violated, gaslit, 'played:' whose experiences sit in the grey area of legal inhumanity," Sealy wrote. "Your stories are valid. You are not alone."
"I wrote this for those who have known Adam, for those I hope never will, and for those who continue to enable and support him."
[Attention, Larry Krasner, she's talking to YOU.]
"Whispers do not stop wolves," Sealy concluded. "It's time to shout."
Here's what we know about Foss's time in Philadelphia.
D.A. Krasner hired Prosecutor Impact, a nonprofit founded by Adam Foss, to "pilot a program in the Philadelphia District Attorney's office which involves education reform for prosecutors," according to the United Way.
While Foss was in Philly, the United Way sponsored a "Poverty and the Justice System; A Public Forum With Adam Foss" on Oct. 30, 2018, at the Arch Street Presbyterian Church. In their announcement of the forum, United Way said it was "excited to welcome national Expert Adam Foss."
"Please join us for an intellectually stimulating conversation about how the justice system impacts community safety and health."
Another promoter of the forum was The Philadelphia Citizen, led by co-founder Larry Platt, which hailed Foss as a "criminal justice reformer and former prosecutor." The Citizen, under Platt's visionary activism, urged readers to "do something" by attending the public forum to hear Foss. Hopefully, there weren't too many vulnerable females there who took Platt's advice.
Foss became a celebrity for his TED talk where he detailed how prosecutors are "judged internally by our convictions and our trial wins, so prosecutors aren't really incentivized to be creative or to take risks on people we might not otherwise."
In Philadelphia, Foss and his team brought prosecutors on a field trip to visit a prison and talk to men who were given life sentences as teenagers. Foss's team also sponsored a sleepover for prosecutors at a homeless shelter.
Isn't that progressive? And Krasner was down with all of it.
"We want people who are about to exercise their discretion and deal with the realities of homelessness and addiction and mental illness and danger, to have been in the vicinity of and to have spoken to some homeless people," Krasner told medium.com.
On Feb. 7, 2018, the Philly D.A.'s office put out a series of Tweets about how Adam Foss spoke to "our staff today about the need for prosecutors to use their great power and discretion to build people up and talk to those most impacted by the criminal justice system," sent out under the hashtag #RestorativeJustice.
In another tweet, the D.A.'s office posted photos of young, impressionable prosecutors asking Foss questions after "his excellent presentation today. Thank you so much for your time, Adam!," the D.A's office wrote.
In another tweet, the Philly D.A.'s office quoted Foss as saying, "Why are we convinced that the current practice of police and prosecution are keeping us safe, when the communities most impacted by both are the most unsafe?"
Now, the question in the Philly D.A.'s office, is, were any of those impressionable young prosecutors victimized after they were exposed by Larry Krasner to an alleged serial predator, rapist and STD spreader?
Krasner isn't talking. As is typical for him, Krasner did not respond to a request for comment. Neither did Jane Roh, his alleged spokesperson.
Krasner and Roh also did not respond to a request to disclose how much public money Krasner spent to bring Foss to town, and how much money the D.A.'s office paid to Foss's organization, so he could "train" Krasner's young prosecutors.
In the Boston media, the Boston Globe, NPR and boston.com were all over the Adam Foss me too scandal. But here in Philadelphia, only Big Trial seems to think it's a story.
Ralph Cipriano can be reached at ralph@bigtrial.net.
Posted By bigtrial.net at 11:58 AM
Trials: D.A. Invites Serial Rapist To Town
Tweet Share Pin It Email It 6 comments
Razor Shines November 25, 2020 at 4:11 PM
If the Suffolk County DA does a good job at a successful prosecution he won't have to travel far to do his prison talks.
TOM KELLY November 25, 2020 at 6:45 PM
Being a gifted speaker can be inspirational or a charismatic seduction of those who are gullible, lonely and looking for the comfort of some type of affection or acknowledgment. Take the Jones event, most serial rapist have a following even in prison. Whatever the reason Krasner should have vented better and found local help if he felt the need for a speaker on Justice reform especially as that was his platform and so far it has proven a failure.
So Larry Platt was removed from his Positions at the Philadelphia Daily News and PhillyMag for the deviant behavior that pushed Jeffrey Toobin to the curb at The New Yorker and made him the Grand Poobah at the CNN Circle Jerks.
The Big Problem with Krasner is that he has a fondness for Hiring and Associating with Deviants while 'beating his meat' and wrongfully prosecuting Police who are performing their sworn duties
Rick McHale November 26, 2020 at 12:18 PM
Happy Thanksgiving, Ralph. Thank you for all the hard work you have done in speaking the truth. Honest men are hard to find. Your ancestors would be proud of you, as are the rest of us.
Stude November 26, 2020 at 2:49 PM
I heartily concur.
What a creep, let's have some justice.


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Facts So Romantic On Ideas
Here's What We'll Do in Space by 2116
Posted By Emily Lakdawalla on Feb 02, 2016
In a mere 60 years, we of Earth have gone from launching our first spacecraft, to exploring every planet and major moon in our solar system, to establishing an international, long-lived fleet of robotic spacecraft at the Moon and Mars. What will we do in the next 100 years? With such rapid expansion of capability, it may seem difficult to tell what the next 60 years will bring, much less the next century. But we never do anything in space without first imagining what we could do, so in that spirit, here is an attempt to predict—and nudge us into—the future.
An artist's conception of the Titan Saturn System Mission, including an orbiter, boat-like lander, and atmosphere-exploring balloon. European Space Agency
So far, almost all of our exploration of worlds beyond Earth has been through the senses of robotic emissaries. Everyone wants to go to Mars: It's NASA's stated goal, and rocketeer Elon Musk says he wants to retire there. But the number of humans that will land on Mars or any other planet is likely to remain small; I can tell you right now that I probably won't be one of them. The fact is that bringing humans safely to the surface of another massive planet with a thin atmosphere is mind-bogglingly difficult. In order to do it, we will have to accomplish several other, slightly less difficult things first, like surviving long-duration space flights away from Earth, somehow keeping our fragile DNA safe from cosmic rays and solar storms.
Although Mars has an environment superficially similar to Earth, it's the most difficult planet to land on. Like Earth and Venus, it has an atmosphere, but unlike those other planets, the atmosphere isn't thick enough to slow a descending spacecraft to subsonic speeds on its own. Yet the atmosphere is thick enough for incoming spacecraft to turn into fireballs as they enter with interplanetary velocity. Mars, unlike any other world in the solar system, requires both heat shields and retrorockets for safe landing; it's the worst of both worlds, so to speak. All that landing gear comes at a high cost in terms of launch mass and technology investment. Still, we could see humans on Mars in 20 years, give or take.
Some here on Earth have proposed one-way missions. This is not how NASA or ESA would do it, but it's dramatically simpler and cheaper to plan a one-way trip than a round trip. Which means it's likely that the first humans on Mars will not have been sent there by NASA or ESA. With lower budgets and less aversion to risk, private companies are also more likely than public agencies to suffer disasters in space. It is quite possible that the first humans to land on Mars will not have survived the trip.
Because of the costs and risks of physical human spaceflight, I'm personally more excited about a different kind of space exploration. Advances in miniaturization have made it relatively cheap to launch lots of microsatellites to near-Earth space. These craft will soon be sent further out, and it won't be long before there are lots of little spacecraft landing on the Moon. From our homes on Earth, we could all take virtual joyrides across the lunar surface, with these mini explorers acting as our distant eyes.
It's possible that this is how humans will first explore Mars, too—with a robotic body that needs no food, water, shelter, or sleep, serving as the avatar of human operators. The humans working the robot will still need to be located near Mars, not Earth, because of the significant delay in radio communications between the two planets. (The lag between commands sent and data received would range from eight to 42 minutes.) But the humans need not undertake the risks and challenges of landing on Mars: People in orbit at Mars could directly and immediately control Mars robots, all while staying in a ship or station tricked out with everything our delicate bodies need to survive.
Then again, depending on how technology advances, it may be that the division that we now draw between "human" and "robotic" exploration will be archaic in 50 years. To speculate ambitiously, in 100 years, we may be able to see with human consciousness from manufactured bodies. In that case, these new kinds of people may not find it so difficult to roam the alien environment of Mars.
Because of Uranus's extreme tilt, it's best to visit near its equinox, an event that happens only once in 42 years. The last equinox was in 2007; I will be sorely disappointed if we do not have an orbiter near Uranus in 2049.
Farther out than the Moon and Mars, we'll likely see only scientific, rather than human, exploration for quite some time—until we get our robot bodies, that is. The distances are so great that it takes decades, not years, to cruise from one destination to another. So we'll rely on robots to be our eyes.
Because the lead times are so long, some of this exploration plan is already mapped out. In the 2020s, we'll launch our next robotic missions to Jupiter; they'll arrive and do science in the 2030s, and will hopefully persist for a decade or so. The 2030s could see the first launch of a dedicated orbital mission to Uranus or Neptune, the "ice giants" beyond Jupiter and Saturn. These would take between 10 and 20 years to arrive, so could still be doing their science in the 2060s. Most of the planets that we've discovered beyond the solar system are Neptune-sized, so it would behoove us to understand how this size of world works by visiting one with an orbiter. Uranus is closer, so quicker and easier to get to; but because of its extreme tilt, it's best to visit near its equinox, an event that happens only once in 42 years. The last equinox was in 2007; I will be sorely disappointed if we do not have an orbiter at or approaching Uranus in 2049. But we may choose to orbit Neptune before we travel to Uranus, because Neptune has an additional draw: its moon Triton, likely a captured Kuiper belt object, and a world where Voyager 2 saw active geysers.
Speaking of the Kuiper belt, New Horizons' fast flyby of Pluto last year whetted our appetite for the exploration of more worlds out in the far reaches of the Solar System. We already know from telescopes that they're as varied in appearance as the moons of the giant planets—possibly more so. There's cigar-shaped, fast-spinning Haumea and huge, frosty-white Eris, for example. But Haumea is currently one and a half times as far away from Earth as Pluto is, and Eris is about three times as far. The announcement last week of a possible, distant, ninth planet has prompted many people to ask me if we can conceivably explore it. But if "Planet X" exists, it's likely more than 10 times farther away than Pluto; it'd take 100 years to get there, give or take, unless there is some revolution in spacecraft propulsion.
So our near future in space will likely stay closer to home, but there are plenty of enticing nearby possibilities we're already imagining and working toward. There are thousands of near-Earth asteroids, each of them unique, many of them possible prospects for mining. They could help the establishment of an in-space economy that would help us venture out further.
Some people have suggested floating balloons under the Venusian sulfuric-acid cloud deck to search for active volcanoes, or sending similar balloons under the smog of Saturn's moon Titan to watch its methane rivers flow and possibly even touch down in a Titanian ethane lake. We've dreamed of touring the populations of icy worlds that float ahead of and behind the giant planets in their orbits; many of these worlds have binary companions, and some of them have rings. We've suggested setting up lunar bases on polar crater rims where the Sun always shines, and sending rovers into crater bottoms where the Sun never does, where water ice may have been preserved over the age of the solar system.
No matter how boldy we imagine, there will always be more within our own solar system to explore.
Emily Lakdawalla is senior editor and planetary evangelist for The Planetary Society. She writes about robotic exploration of worlds beyond Earth at planetary.org/blog and as @elakdawalla on Twitter.
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redpajama_set_name: RedPajamaCommonCrawl


It took me a long time to understand where he came from. The little prince asked me many questions. I also asked him questions, but he usually didn't answer them. But from his speaking, I slowly started to understand many things.
I will not draw my plane here. It is too complicated for me.
And the little prince started to laugh very loudly. I was angry. My situation was serious. How could somebody think that it was funny?
But he didn't answer me. He only moved his head gently. He was still looking at my plane.
And he started to think deeply. He was thinking for a long time. Then he took his sheep out of his pocket. And he started to look at the sheep carefully.
So, this way, I learnt a second very important thing. The planet of the little prince was very small. It was only a little bigger than a house.
But it's true that it wasn't a big surprise. I knew very well that we don't only have planets like Earth, Jupiter, Mars, and Venus, which have names. There are also hundreds of other planets which are sometimes so small that it's difficult to see them through the telescope. When an astronomer discovers one of them, he doesn't give the planet a name, but only a number. He calls it, for example, Asteroid 3251.


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Africa's Megalopolis
Black woman with clenched fists above her head protesting with group of people on the streets.
An interesting piece in The Guardian by Harold French on Africa's megalopolis and the difficulties of pulling together five countries with very different governments and colonial histories:
There is one place above all that should be seen as the centre of this urban transformation. It is a stretch of coastal west Africa that begins in the west with Abidjan, the economic capital of Ivory Coast, and extends 600 miles east – passing through the countries of Ghana, Togo and Benin – before finally arriving at Lagos. Recently, this has come to be seen by many experts as the world's most rapidly urbanising region, a "megalopolis" in the making – that is, a large and densely clustered group of metropolitan centres.
…In just over a decade from now, its major cities will contain 40 million people. Abidjan, with 8.3 million people, will be almost as large as New York City is today. The story of the region's small cities is equally dramatic. They are either becoming major urban centres in their own right, or – as with places like Oyo in Nigeria, Takoradi in Ghana, and Bingerville in Ivory Coast – they are gradually being absorbed by bigger cities. Meanwhile, newborn cities are popping into existence in settings that were all but barren a generation ago. When one includes these sorts of places, the projected population for this coastal zone will reach 51 million people by 2035, roughly as many people as the north-eastern corridor of the US counted when it first came to be considered a megalopolis.
But unlike that American super-region, whose population long ago plateaued, this part of west Africa will keep growing. By 2100, the Lagos-Abidjan stretch is projected to be the largest zone of continuous, dense habitation on earth, with something in the order of half a billion people.
The post Africa's Megalopolis appeared first on Marginal REVOLUTION.
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Best Throws of this Past Season. Enjoy.
I'm going to do a giveaway soon. Follow me @nfl_5s on Twitter and @highl1ght_heaven on IG and dm me on what I should give away.
Aaron Rodgers dominated this list. LMAO.
Who is your favorite Qb or team?
Where's Aaron Rodgers against the jags while he threw a perfect pass while being pulled by the jersey?


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How Close is Doomsday?
Posted on August 11, 2017 by Rahman
The clock keeps moving but the alarm never goes off
Whenever something starts you know there's going to be an end (This is the start of the article; there will be an end). Homo Sapiens were created/evolved; there's been continuous debate of the start. No matter when or how human life on Earth started, whether it includes all life on Earth or just the planet's most annoying species, there's going to be an end. The question: when and how?
Common among religions is the prediction of the Apocalypse. It's the day or time the world will end (life); a saviour will come to those who believe and the Superior will judge everyone. Among Atheists the Apocalypse is seen as a time period when humans will go extinct. They use different words images but there's no science-religion conflict of the coming of The End.
The Atheist Apocalypse, or Doomsday, is based on science – human behaviour, adaptation, natural selection. The human race will become extinct because it will no longer be able to support itself; nature or humans themselves will bring the end. Climate Change is considered to be bringing Doomsday closer than before.
On the other hand Religion Apocalypse is defined as a day when the Superior Being decides to end the world. Religions provide signs to predict the coming of the apocalypse. It's become part of common culture. The Simpsons: Treehouse of Horror often uses the Devil as a character. Episode 221 shows the family falling asleep in Church; it's split into 4, each with their own dream, ending with the family walking out of the church into the Apocalypse, seeing the four horsemen and devils. While the Flanders Family ascends to Heaven a stairway going down opens in front of the Simpsons. Led by Homer the family walks into Hell.
There is no prediction of exactly (date and time) when The End will occur. There are continuous suggestions.
The "Y2K Bug" predicted a destruction of humans on January 1, 2000. Debated, it was predicted computers would not recognise the year 2000 and change to "January 1, 1900" instead of "January 1, 2000". At midnight on December 31, 1999 computers did move into the future. Now they and their baby phones can sometimes predict the future (what you're going to write next, what you want to see or hear). They're becoming smarter.
Mayan theology suggested the world would end on December 21, 2012. The Mayans created a calendar that ended on December 21, 2012. It was reported as accurate about many facts such as lunar cycles and eclipse. In turn, based on research, many believed the Apocalypse would come on the date the Mayan calendar suggested. According to NASA this was misinterpreted; December 21, 2012 Mayans suggested would be the day the world would be refreshed; Mayan gods would return to Earth and one of them "would conduct old rites of passage, to set space and time in order, and to regenerate the cosmos". There is debate about whether or not the world has been refreshed but there is no doubt it has not ended (unless this heaven); even if this is all a daydream because of boredom, you, Earth, humans, and life on Moon, Venus, Mars, and Pluto still exist.
A bit later than the Mayans, the Bulletin of the Atomic Scientists has been credited with creating the "Doomsday Clock". Since 1947 it has been adjusted based on how close the world is metaphorically to "Midnight" or "Doomsday"; some consider it a sign suggesting how soon an Apocalypse or Doomsday will appear. It suggests how soon the world can be destroyed. Using politics and science it has moved forward (closer to midnight) and backward (away from midnight/doomsday). It's used today.
When first created in 1947 it was set at seven minutes to midnight because of the chance of nuclear war. The first time it moved "backward", away from midnight was 1960. In 1953 the clock moved forward from 3 to 2 minutes because USA chose to pursue the Hydrogen Bomb, a more powerful weapon than the Nuclear Bomb.
Political action in 1960 suggested USA and USSR were moving further away from direct conflict; the clock moved to 7 minutes. 1991 saw the clock at 17 minutes to midnight; the official end of the Cold War brought the clock the furthest it's ever been from midnight since its creation.
Although the Cuban Missile Crisis, 1962 was a historic, fearful event, the Doomsday Clock did not move.
2015 saw the clock move from 5 minutes to 3 minutes because
Unchecked climate change, global nuclear weapons modernizations, and outsized nuclear weapons arsenals pose extraordinary and undeniable threats to the continued existence of humanity, and world leaders have failed to act with the speed or on the scale required to protect citizens from potential catastrophe. These failures of political leadership endanger every person on Earth.
On 26 January 2017 BBC reported that the clock moved to 2:30 minutes to midnight. It reported that the Bulletin of Atomic Scientists (BPA) stated the clock moved because of
President Trump's statements on climate change, expanding the US nuclear arsenal and the questioning of intelligence agencies had contributed to the heightened global risk
The clock hasn't moved since then. There hasn't been a reaction by the Doomsday Clock to the USA-North Korea Missile Standoff and President Donald Trump saying USA will respond with "fire and fury". Together with the Syrian Conflict there's a chance the clock may move closer to midnight
Today an unofficial "Doomsday Clock" app is available in Google Play Store. It shows the time of the Doomsday Clock. How close the world is to an end is unknown to everyone except the person, human or other who will bring it or has planned it.
This entry was posted in World and tagged apocalypse, clock, Doomsday Clock, religion, Science, USA by Rahman. Bookmark the permalink.
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The new study by ASPS Member Surgeon Eric Swanson, MD, Leawood, Kansas, evaluated 301 patients having liposuction, either alone or in combination with abdominoplasty, using standardized photographs and computer-assisted measurements of body dimensions obtained preoperatively and at least three months after surgery. The study revealed no evidence of fat regrowth in treated areas of the arms, abdomen, or lower body. Additionally, upper body dimensions were unchanged after surgery, indicating no fat redistribution. Average reductions in hip measurements remained significant in patients followed one year or more after both liposuction and combined liposuction/abdominoplasty procedures. The average hip reduction was significant even among a subgroup of 34 patients who gained five pounds or more after surgery.
The results of this study are in stark contrast to the one published in Obesity last year. That study revealed that fat removed via liposuction appeared to return to other unoperated areas of the body, such as the arms.
I've been performing liposuction surgery for the past 11 years. I've never had a patient claim that the fat removed from one area, such as the thighs, returned to another part of the body. I inform my patients that, in general, if you keep your weight stable, the fat that is removed via liposuction doesn't come back. It's nice to have some scientific proof of that now!
I would seriously doubt the findings of a study conducted by an ASPS member. It's too close to home and too connected to his wallet.
I agree that any plastic surgeon has a conflict of interest with plastic surgery-related studies. Unfortunately, if plastic surgeons don't do the research, then who will? All plastic surgeons are now required to disclose any relevant financial interests at our meetings, such as receiving speaking fees from laser companies.


One of the most common reasons why people choose to elope is so that they can have a wedding experience that is private and all about them and their relationship (and sometimes their closest loved ones). We strive to give our couples that experience of being completely surrounded by nature and not surrounded by strangers.
Okay, hear us out. We know no one likes waking up early, and if you are an early riser, please teach the rest of us your ways! BUT we believe that your wedding day is the perfect day to go out of you comfort zone (ahem your bed) and go experience your elopement location in a unique way. We've come up with THREE reasons why you should consider eloping at sunrise.
Most people don't want to get up before sunrise, so it is very possible that you will have your elopement location all to yourselves. There is nothing more magical than the silence and calmness of being the only people for miles. Maybe you're in love with a hike or a location that is highly trafficked during the day, and you can't get it out of you head, but you don't want to be surrounded by other people… Sunrise has got your back.
For example, we hiked one of Isle of Skye's most popular hikes with Laura and Darren at sunrise. We started the hike in the dark and the sun slowly peeked through the fog and clouds. There were no other people around, just sheep. We were able to explore the area, and focus on Laura + Darren's relationship and their adventurous experience. As we hiked down a couple hours later, other hikers were just beginning their trek to the top.
If the crowds don't convince you, then do if for the light! We all know how beautiful the light during sunset is. Well sunrise is just as gorgeous! We love to start just before sunrise, when everything is cast in a soft blue. Then as the sun rises over the horizon, on a clear or even a partly cloudy day, everything is illuminated with golden light. And even on a foggy morning, sunrise light is dreamy.
If you choose to get married in the heat of summer, sunrise can offer you a much needed escape from the heat. For locations such as Moab, Joshua Tree, or Sedona, the afternoon sun can be brutal. Sunrise allows you to hike and explore your chosen location before the heat of day and keep you from getting sweaty and sunburned.
Sunrise also offers benefits for those of you eloping in mountainous regions, like Colorado, that are known for having afternoon thunderstorms. By having your elopement ceremony in the early morning, you can beat the dangerous storms and not spend your precious time waiting out the storm in your car.
The day of the week you choose for your elopement can greatly affect the number of people that will also be around. We always suggest weekday elopements to our couples so that they can take advantage of the days when most people will be at work. Another benefit of having a weekday elopement, is that you will have less competition for getting your permit for the ceremony site you want most.
Similarly to getting married on a weekday, it is also a good idea to avoid getting married around long, holiday weekends or during Spring Break. This is when a lot of people head into nature or to National Parks to hike and explore. The days surrounding these holiday weekends can not only be busy, but also more expensive (flights, accommodation, rental cars).
Another great way to avoid crowds is to go where most people aren't going. This could be as simple as driving a little bit further than most people will drive. Or renting a 4wd vehicle and driving up the bumpy mountain road. Putting a little more effort into your elopement location will never let you down, and usually, you'll be able to find a view that's even more incredible!
Another way of getting off the beaten path is to travel somewhere that's not on many travel radars. This is exactly what Brittany and Justin did when they chose to elope in the Faroe Islands. By choosing to visit a location that is already much quieter to begin with, you'll have a much easier time avoiding other people!
Often, our couples will like the views of a busy location, but want something a bit more private. We do a lot of in-person as well as online scouting to help our couples find a location that fits their vision, but will give them more space to truly experience their elopement day. Our favorite suggestions for these off the beaten path locations are the San Juans in Colorado, the mountains and rainforests of Tasmania, Glacier National Park in Montana, North Cascades National Park in Washington, or Lofoten in Norway. But we've got many many more hidden gems where those came from!
The further you are willing to get from trailheads and easily accessible overlooks, the more space you will have to yourself. Often some of the best views can be found on hiking trails. A huge part of our location scouting is looking for trails to hike with our couples. Hiking not only makes a unique elopement day experience, but is a great way to get away from parking lots and noisy roads and immerse yourself in nature. And don't worry, you don't have to hike 10 miles up a fourteener on your wedding day to do this! We will be sure to help you choose a hike that fits your vision as well as your skill level.
We don't expect you to be able to follow all of these tips. But considering even just a couple of them in your planning will help give you privacy and the ability to truly soak your elopement day in.


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text:
\section{Introduction}

Colorectal cancer (CRC) is the most frequent pathology of the gastrointestinal tract and accounts for nearly 10\% of all forms of cancer.  When the disease reaches an advanced stage, the 5-year survival rate of CRC patients is lower than 7\%, but early diagnosis with successful treatment can dramatically increase this figure to more than 90\% \cite{c1}. Conventional colonoscopy is the reference standard for CRC screening and diagnosis. It provides direct visualization of the inner surface of the colon for acquiring biopsies and performing therapeutic procedures on early stage neoplastic lesions. Wireless and tethered endoscopic capsules and robotic endoscopes have recently been reported to overcome the pain and discomfort associated with conventional colonoscopy \cite{c1}. Despite assisted articulation or the use of wireless capsules, the success of the exam still mostly depends on the operator's skills both for dexterous maneuvering of the camera and for ensuring full exploration of the colon wall \cite{c1}. With wireless capsules the problem is more focused on the processing and analysis time of the long and often occluded video signal. As a result, even experienced endoscopists can miss polyps during examination \cite{c2}. To overcome this, computer-aided diagnostic (CAD) systems can be used to support clinical decisions with automated polyp detection algorithms.

Despite significant progress in recent years, CAD polyp detection is still far away from routine clinical use. The problem is challenging because there is large variety in the size, shape, colour and textures of colon polyps, which alongside the presence of specular reflections, endoluminal folds and blood vessels in colonoscopy recordings, impedes detection accuracy and induces a significant number of false detections. Early CAD polyp detection methods used texture and colour as the main features \cite{c3,c4}. More recently, shape information has been incorporated to increase feature complexity and discriminating power and provide improved detection models \cite{c5,c6}.

We build onto recent advances in computer vision and incorporate shape information to build a system based on a convolutional neural network (CNN) framework that can accurately detect and segment polyps and high-risk regions in colonoscopy images, while minimizing false detections. We combine state-of-the-art deep learning techniques designed for generic object detection and adapt them to endoscopic images. To our knowledge, this is the first work to fine-tune cutting edge CNN architectures, such as VGG and ResNets, for polyp segmentation. To increase detection accuracy, we also extracted a relative measure of depth from the colonoscopy images, using Shape-from-shading (SfS), and input it as an additional feature to the networks to enhance polyp representation. Our study also includes investigation into the influence of batch normalization in training convergence.

\section{Related work}

Propelled by large scale datasets such as ImageNet \cite{c8} and Pascal VOC \cite{c9}, deep learning approaches are the current paradigm for many computer vision problems, often surpassing by some way traditional methods for classification, segmentation, detection and tracking. Since the first deep CNN won the Imagenet Large Scale Visual Recognition Competition (ILSVRC) classification challenge \cite{c12} continuous breakthroughs in CNN architectures, training optimization and fast Graphic Processing Units (GPUs) \cite{c13} have led to continuous performance improvements. Studying the influence of network depth in large-scale image recognition resulted in the powerful 16 and 19 layer VGG network \cite{c14}. In the same year, the concept of "inception architecture" was developed \cite{c15} and the GoogLeNet won the Imagenet challenge. The current leader in results successfully trained a network (ResNet) 8-times deeper than VGG by using a residual learning framework \cite{c16}.  Several CNNs \cite{c21,review1,review2} were also used in semantic segmentation achieving impressive results.
Along with training on RGB data, image depth has also been used to provide a richer scene representation, in detection and segmentation tasks using a conditional random field (CRF) model \cite{rgbd_silberman_2011}. Depth can also be used directly as a fourth input channel and achieve improved results comparing to RGB-only training \cite{rgbd_couprie_2013} and can improve the performance of the fully convolutional version of VGG by performing training with RGB-D images \cite{c21,review3}.

Large scale polyp datasets, such as CVC-ColonDB \cite{c18}, that recently became publicly available, facilitated the development of deep learning techniques for polyp detection in endoscopic images. Zhu \textit{et al.} fed features extracted through a CNN, into a Support Vector Machine (SVM) to identify lesion areas in the colon \cite{c19}. On the other hand, Tajbakhsh \textit{et al.} used a variety of pre-calculated features as inputs in a set of CNNs to classify polyp candidates \cite{c20}. However, these methods are tested in different datasets, which limits comparison between the reported performance. In 2015, the MICCAI sub-challenge on automatic polyp detection provided a set of guidelines and datasets that allowed direct comparison between different methods. The best results were achieved by models learned end-to-end. The OUS participation achieved a detection precision and recall of 69.7\% and 63.0\%, respectively, in the ETIS-Larib dataset by slightly modifying the AlexNet model. The CUMED submission used a fully convolution network (FCN), initialized from models trained in large scale datasets to achieve the best overall performance, with a 72.3\% detection precision and 69.2\% recall in the same dataset \cite{c25}.

Despite the clear advantages that CNN offer, defining and training a CNN architecture from the beginning is not a trivial task. The training process typically requires an excessive amount of labeled data, something that is generally difficult to be obtained for medical images. In addition, selecting the architecture that provides the best compromise between convergence speed and inference ability is both challenging and time consuming. Finally, potential issues of convergence and overfitting often require significant tuning of the learning parameters \cite{c11}. Alternatively, it has been suggested \cite{c11} that fine-tuning deep-learned architectures for specific tasks (e.g. image detection and segmentation), provides better performance than designing and training a new network model. Such an approach can be  applied in tasks involving medical images, despite being substantially different from the natural images used in training the initial network, by using a smaller set of images to re-train (fine-tune) a pre-existing network. We adhere to this strategy and present a number of established CNNs which after appropriate fine-tuning are optimized for detecting and segmenting polyps in colonoscopy recordings.

We are expanding on the work developed in \cite{brandao2017fully} ,
where we segmented polyps using the same principles used by \textit{Long et al.}\cite{c21} to semantically segment objects in natural scene images. First, we exploit the use of residual networks \cite{c16} which significantly outperformed previous models in large scale recognition challenges. We also evaluate the effect of using depth estimations as a extra input feature for the best models. Finally, to guarantee generality, all models are evaluated in an extra publicly available dataset, the CVC-Colon  \cite{c26}.

\section{Proposed Methods}
We develop polyp segmentation CNN models, by utilizing well-known architectures initialized with weights obtained from pre-training, in large image-datasets and then fine-tune them with labeled colonoscopy images from publicly available databases (ETIS-Larib, CVC-ColonDB etc). The models we explore have optimized architectures and learned feature extractors that are capable of solving complex object detection tasks. For performing polyp segmentation instead of simple detection, we opted to convert the pre-trained CNNs into FCNs by adding deconvolution layers in order to obtain a pixel classification map as the output. Moreover, we applied an efficient SfS technique to recover depth from colonoscopy images and provide it as an additional, to the standard RGB channels, parameter in the formulation of our network models.

\subsection{CNN and FCN Basis}
Irrespective of the architecture, CNNs always integrate three basic components: convolution, activation function and pooling operation layers. They operate on local inputs, depending only on relative spatial coordinates. An example of  of the effect of convolution and activation on a colonoscopy image is depicted in Fig. \ref{fig:batchnorm}.

\begin{figurehere}
\centering
  \includegraphics[height=6cm,keepaspectratio]{batchnorm2.png}
  \caption{The basic CNN operations on a single CNN neuron from the first layer of the FCN-VGG with batch normalization. Image sequence left to right: input image, receptive field, convolution results, normalized image, and ReLU activated image.}
  \label{fig:batchnorm}
\end{figurehere}

Considering $x^{k}_{i,j}$ as the input data vector at location $(i,j)$ in layer $k$, the input of the following layer, $x_{i,j}^{k+1}$, is computed by
\begin{equation}
x_{i,j}^{k+1}=f_{S}^k ({x_{si+\delta_{i},sj+\delta_{j}}^k }  ,0 \leq \delta_{i},\delta_{j} \leq w). \label{eq1}
\end{equation}
where $S$ is the stride or subsample factor and $f^k$ represents the type of operation of the layer $k$. In classification CNNs, the network ends with one or more fully connected layers that produce non-spatial outputs \cite{c21}. A loss function $l$ compares the prediction outputs of the last layer $f^K$ to the desired result $y$  as:
\begin{equation}
l(x^K,y)=\sum_{i,j}l (x_{i,j}^K,y_{i,j} ). \label{eq2}
\end{equation}
Using the chain rule, the gradient of the loss is back-propagated throughout the network and the parameters of all layers are updated via stochastic gradient descend (SGD) \cite{c22}.

Traditional CNN architectures, such as AlexNet and VGG, are used for classification problems, which mean that take an input image and output a single classification score for all the possible classes. To obtain pixel wise segmentation, these networks need to be converted to a fully convolutional network.
A fully connected layer can be viewed as a convolution layer where the kernel has the same dimensions as the input. By replacing these with convolutions, it is possible to convert traditional classification networks into FCNs that take inputs of any size and output coarse classification maps. While the resulting maps are equivalent to the processing of the individual patches on the original network, the computational cost is highly amortized by the inherent efficiency of convolution. Even though the output maps can yield any size, these are typically reduced by subsampling within the network \cite{c21}. To connect these coarse outputs to dense pixels, an interpolation strategy needs to be used.

Convolution is a linear operation, and as such, it can be expressed in a matrix multiplication form. Assuming $\Omega$ as the map of size $W\times H$ to be convoluted by the kernel $\theta$ of size $W'\times H'$ with a stride $S$, the convolution operation can be expressed as

\begin{equation}
vec(\psi)=C vec(\Omega). \label{eq3}
\end{equation}
where $vec(\Omega)$ represents $\Omega$ flattened to a $WH$ dimensional vector, $vec(\psi)$ is a vector with size $D=\big(\frac{W-W'}{S}+1\big) \times \big(\frac{H-H'}{S}+1 \big)$  and $C$ is a sparse matrix of size $D\times WH$, where the non-zero elements are elements of $K$. The vector $vec(\psi)$ can be later reshaped to a $\big(\frac{W-W'}{S}+1\big) \times \big(\frac{H-H'}{S}+1 \big)$  convoluted map [14]. During CNN training, the loss $\psi_l$ is backward passed to the lower level layers by convolution transpose
\begin{equation}
vec(\Omega_l)=C^T  vec(\psi_l). \label{eq4}
\end{equation}

where $\Omega_l$ and $\psi_l$ have the same dimensions of $\Omega$ and $\psi$ in the forward pass, and  $\Omega_l$ connectivity pattern is compatible with $C$ by construction \cite{c23}.

If $S>1$, convolution implements a subsample operation. Intuitively, transpose convolution is a way to upsample the input by a factor of S. Following this principle, by simply reversing forward and backward pass operations, it is possible to implement in-network upsampling. The transpose convolution layer, also known as deconvolution layer, does not need to have a fixed filter (doing bilinear interpolation, for example) but can also be learned and adjusted during training. This provides very fast and effective upsampling used to structure efficient FCNs, capable of achieving state-of-the-art results in semantic segmentation \cite{c21}.

\subsection{Batch normalization}

SGD iteratively estimates the global gradient of the loss by using a limited set of samples. Changes in distribution of the inputs hinders convergence, as the parameters of each layer need to adapt to a new distribution. This slows down training by demanding lower learning rates and careful parameter initialization. In deep networks, this effect is intensified because small changes in the parameters are greatly amplified throughout the network, as the inputs of each layer are affected by parameters of all preceding layers \cite{c24}.

To overcome this, we evaluate the incorporation of batch normalization into standard CNN architectures to compensate batch distribution changes. Batch normalization layers perform in-network normalization by linearly transforming each training mini-batch to have zero mean and unit variance. This technique has proved to yield improved results on classification tasks using considerable less training iterations \cite{c24}. An example of the batch normalization process is illustrated in Fig. \ref{fig:batchnorm}.

\subsection{Shape-from-Shading}
The increased detection performance of CNNs by incorporating depth information, motivated us to employ a SfS technique \cite{sfs_ahmed_a_2007} to extract depth from colonoscopy images and include it in the formulation of our models. SfS aims to recover the 3D shape of an object by analyzing the illumination variation across the image. Subsequently, SfS is suitable for approximating depth in colonoscopy recordings with a monocular view without requiring stereo or multi-view matching \cite{mtv} and structure from motion estimation \cite{strmotion}. While limited to only relative depth, SfS does not require texture assumptions like shape-from-texture \cite{shapetexture} techniques and is useful for extracting geometric information easily from existing clinical colonoscopy systems.

The majority of SfS approaches \cite{sfs_ahmed_a_2007, sfs_ahmed_b_2007, sfs_zhang_2009} assume a light source either coinciding with the optical center or infinitely far away from the scene. These conditions are unrealistic in the case of colonoscopy even though the light source and the camera are both at the tip of the instrument. This is because despite the small distance between the camera and the light, the observed tissue is also very close and highly dependent on small illumination changes. To overcome this limitation, we use a method which approximates the position of the lightsource at the tip of the endoscope and uses the position directly in the SfS problem formulation \cite{sfs_stoyanov_2012}.

Assuming the camera positioned at $\mathbf{y} = (a, b, c)$ and a set $\mathcal{A}$ of all image points $\mathbf{x} = (x, y)$, the SfS problem is formulated as a Hamiltonian-Jacobi non-linear Partial Differential Equation (PDE),
\begin{equation}
    H(\mathbf{x}, \nabla v)=\frac{1}{\rho}I(\mathbf{x})
    \sqrt{(v_x^2 + v_y^2) + J(\mathbf{x}, \nabla v)^2}
    \cdot
    Q(\mathbf{x})^{\frac{3}{2}},\label{eq5}
\end{equation}
where
\begin{equation}
Q(\mathbf{x}) = (x + a)^2 + (y + b)^2 + (f + c)^2,
\end{equation}
\begin{equation}
J(\mathbf{x}, \nabla v)=\frac{\left(x + a\right)v_x + \left(y + b\right)v_y + 1}{f + c},
\end{equation}
\begin{equation}
\textbf{I}(x) = \rho \frac{I.\mathbf{n}}{r^2},
\end{equation}
\begin{equation}
v = \log d(x) . \label{eq9}
\end{equation}
$d(\mathbf{x})$ is the depth of point $\mathbf{x}$, $v_x$ and $v_y$ are the spatial derivatives of $v$, $\rho$ is the surface albedo and $\mathbf{I}$ and $\mathbf{n}$ are the light and surface normal vectors, respectively. The Lax-Friedrichs method \cite{sfs_lax_friedrichs_2004} is used to solve the resulting PDE and a specular highlight triangulation method is used to solve for the unknown albedo. An example of depth extraction from a single colonoscopy image can be seen in Fig.
\ref{fig:sfs}.

\begin{figurehere}
 \centering
 \minipage{0.14\textwidth}
 \centering
    \includegraphics[width=1.0\textwidth]{cvc_clinic_1.png}\           (a)

\endminipage \hfill
 \minipage{0.14\textwidth}
    \centering
    \includegraphics[width=1.0\textwidth]{cvc_clinic_1_depth.png}\           (b)

\endminipage
 \minipage{0.19\textwidth}
    \centering
    \includegraphics[width=1.0\textwidth]{cvc_clinic_1_3d_shape.png}\           (c)

\endminipage
\caption{SfS method employed. (a) image from the CVC-ClinicDB dataset; (b) depth estimation from SfS; (c) 3D surface recovered from SfS depth.}
\label{fig:sfs}
\end{figurehere}

\begin{figure*}[!ht]
\centering
  \includegraphics[width=1.0\textwidth,keepaspectratio]{vgg.png}
  \caption{  Illustration of the proposed BN-FCN-VGG architecture with batch normalization for a $500\times 500$ size image. The values on the top array represent the output size of each layer underneath.  The fully connected and scoring layers of the original VGG were removed. Grey coloured layers were loaded from the original model while blue coloured layers were added or modified for polyp segmentation.}
  \label{fig:vgg}
\end{figure*}

\subsection{Proposed architectures for polyp detection}
We investigated several state-of-the-art convolution architectures and adapt them through fine-tuning, for polyp detection. Specifically, we test six different architectures: AlexNet \cite{c12}, GoogLeNet \cite{c15}, VVG \cite{c14} and three version of the ResNets architecture with 50, 101 and 152 layers of depth \cite{c16}. The AlexNet and VGG are converted into FCNs (FCN-AlexNet and FCN-VGG) by discarding the two fully connected layers and replacing them with $1\times 1$ convolution layers with  the same 4096 dimensions of the fully connected layers. The final scoring layers were also replaced with a 2D, $1\times 1$ convolution to produce the background and polyp pixel classification maps as the output. The conversion of GoogLeNet and ResNets into FCNs only requires the replacement of the scoring layers with a 2D convolution. We also increased the resolution of the output coarse map by discarding the final averaging layer. ResNets already incorporate batch normalization, while to apply this to the remaining networks, we added a regularization operation between every convolution and activation layer, as illustrated in Fig. \ref{fig:batchnorm}. Every network is finalized with a deconvolution layer with stride $S=32$ and a kernel of size $W'= H'= 64$ , responsible for upsampling the coarse output to a dense scoring map with the same dimensions as the input. Even though the CNNs output a coarse segmentation map, a single deconvolution layer can accurately upsample the blob-like structures of most polyps. We verified that adding extra deconvolution layers from the finer levels of the models did not improve the results.    An example the proposed fully connected version of VGG with batch normalization (BN-FCN-VGG) is illustrated in Fig. \ref{fig:vgg}.

\subsection{Implementation and training details}
Developed networks were optimized by SGD with a 0.99 momentum and all layers were updated by back-propagation. Classes probabilities are calculated with Softmax function and cross-entropy was used as the loss function and the learning rate for convolution bias was doubled. In GoogLeNet, the two deeper loss function were discarded and only the last one was used for fine-tuning. For FCN-Alexnet and FCN-VGG, the convolution filters were initialized by copying weights from public available models trained on the PASCAL segmentation dataset. Because no trained segmentation models are publicly available,  the fully convolutional GoogLeNet (FCN-GoogLeNet) and the three fully convolution ResNets (FCN-ResNet)  were initialized by loading classification models trained on the Imagenet dataset. New convolution layers were zero-initialized and the learning rate of scoring layers was increased by a factor of 10. We fine-tune the networks with the highest fixed learning rate that did not cause loss divergence. For FCN-GoogLeNet this corresponds to a learning rate of $10^{-12}$, while all other FCNs were optimized with a learning rate of $10^{-10}$. Convergence was achieved after 30K iterations for FCN-ResNet-51 and FCN-ResNet-101, 40K for FCN-GoogLeNet, 50K for FCN-VGG and FCN-ResNet-151 and 120K for FCN-AlexNet.

Images were  resized to $500 \times 500$ and random flipping was used for data augmentation during training. Non residual FCNs were trained with a random single image per batch. All FCN-ResNets were trained with $224\times224$ patches randomly sampled from the training images. This allowed to increase the batch size even with limited memory resources. The same type of sampling was performed during training of the batch normalization versions (BN-FCNs) of the non residual networks. Batch sizes of 20 were used for BN-FCN-AlexNet, BN-FCN-GoogLeNet and FCN-ResNet-51. Due to memory constrains, smaller batch sizes were used for other FCNs: 16 for FCN-ResNet-101, 8 for FCN-ResNet-151 and 5 for BN-FCN-VGG. When training networks with depth (D-FCN), the SfS values are concatenated to the RGB channels to create a new 4-channel input. A new channel is added to every first layer convolution filter and it is initialized by averaging the values of the other filter dimensions. The learning rate of this layer is increased by a factor of 10.  All models were trained and tested using the Caffe \cite{c30} software library in a single NVIDIA Tesla K40 GPU.

\begin{figure*}
\centering
  \includegraphics[width=1.0\textwidth,keepaspectratio]{res1_1.png}
  \caption{Example of three different scored segmentations produced by the six proposed FCN networks. The colorbar defines the scoring probability of each pixel to belong to the polyp class. Third image results are best viewed in colour electronically because the FCN-ResNet-152 detection is very small.}
  \label{fig:res1}
\end{figure*}

\begin{table*}[ht]
\centering
\tbl{Segmentation and detection precision (prec) and recall (rec) in \% obtained by the proposed FCNs. Mean interception over union (IU) is also presented for segmentation. The best result for each metric is highlighted \label{tab:res1}}{

\begin{tabular}{ccccccccccc}
\hline
                                                                      & \multicolumn{5}{c}{\textbf{ETIS-Larib}}                                                                                & \multicolumn{5}{c}{\textbf{CVC-ColonDB}}                                                                                \\
                                                                      & \multicolumn{3}{c}{\textbf{Segmentation}}                                                                 & \multicolumn{2}{c}{\textbf{Detection}}                                 & \multicolumn{3}{c}{\textbf{Segmentation}}                                                                  & \multicolumn{2}{c}{\textbf{Detection}}                                 \\
\multicolumn{1}{l}{}                                                 & \multicolumn{1}{c}{\textbf{Prec}} & \multicolumn{1}{c}{\textbf{Rec}} & \multicolumn{1}{c}{\textbf{IU}} & \multicolumn{1}{c}{\textbf{Prec}} & \multicolumn{1}{c}{\textbf{Rec}} & \multicolumn{1}{c}{\textbf{Prec}} & \multicolumn{1}{c}{\textbf{Prec}} & \multicolumn{1}{c}{\textbf{IU}} & \multicolumn{1}{c}{\textbf{Prec}} & \multicolumn{1}{c}{\textbf{Rec}} \\ \hline

\multicolumn{1}{c}{\textbf{FCN-AlexNet}}    &27.87	& 35.54 &	15.7	 & 44.08 &	63.78 &	40.3 &	20.71 &	15.77 &	45.29 &	54.68
                         \\
\multicolumn{1}{c}{\textbf{FCN-GoogLeNet}}                          & 25.83	 & 29.82 &	12.29 &	41.85 &	62.76 &	37.46 &	12.93 &	12.71 &	42.26 &	45.25
                           \\

\multicolumn{1}{c}{\textbf{FCN-VGG}}        & \textbf{70.23} &	\textbf{54.2} &	\textbf{44.06} &	73.61 &	86.31 &	\textbf{76.06} &	\textbf{60.46}&	\textbf{54.01} &	79.57 &	86.01
                          \\

\multicolumn{1}{c}{\textbf{FCN-ResNet-50}}                          & 55.75                              & 23.43                             & 19.72                                 & 73.84                              & 76.53                              & 67.76                              & 25.64                              & 22.74                                 & 82.89                              & 82.38                              \\

\multicolumn{1}{c}{\textbf{FCN-ResNet-101}} & 63.26                              & 53.88                              & 41.35                                 & 75.32                              &\textbf{ 91.66}                              & 73.85                              & 50.73                              & 46.23                                 & \textbf{83.70 }                             & 88.20                              \\
\multicolumn{1}{c}{\textbf{FCN-ResNet-152}}                         & 65.26                              & 38.24                             & 33.19                                 & \textbf{79.42 }                             & 89.75                              & 72.85                              & 50.72                              & 43.28                                 & 82.08                              & \textbf{93.27}                              \\ \hline
\end{tabular}
}
\end{table*}

\section{Experiments and Results}

We used the public datasets from the MICCAI 2015 polyp detection challenge \cite{c25}. For comparison purposes, we divided the dataset for training and testing as it was suggested in the MICCAI challenge guidelines: CVC-CLINIC and ASU-Mayo for training and ETIS-Larib for testing  . Furthermore, we also report results from a second public available dataset (CVC-ColonDB) \cite{c26}. The datasets were obtained with different imaging systems and contain manual segmentations of every detected polyp. We specifically used the following grouping of images for training (fine-tuning) and testing:

\begin{itemlist}
\item {CVC-CLINIC: 612 SD training frames with at least one polyp each;}
\item {ETIS-Larib: 196 HD testing frames with at least one polyp each;}
\item {ASU-Mayo: 36 small SD and HD videos sequences, divided into training frames with and without polyps.}
\item {CVC-ColonDB: 379 testing frames from 15 different colonoscopy sequences with at least one polyp each.}
\end{itemlist}

In total, the MICCAI challenge training data has 19514 frames from CVC-CLINIC and ASU-Mayo datasets. However, only 4664 of these corresponds to images with polyps. We verified that networks trained with the full dataset did not converge satisfactory in practical time and performance was substantially worst. To avoid this, we fine-tuned all proposed FCNs only with images containing polyps, achieving better performance.

The developed FCNs were formulated to produce dense pixel-wise polyp segmentations. As such, we report results using three common segmentation evaluation metrics: mean pixel precision, mean pixel recall and interception over union (IU). If a pixel of polyp is correctly classified it is counted as a true positive (TP). Every pixels segmented as polyp that fall outside of a polyp mask counts as a false positive (FP). Finally, Every polyp pixel that has not been detected counts as a false negative (FN). The evaluation metrics are calculated according Equation \ref{eq11}.

\begin{equation}
 \label{eq11}
\resizebox{0.4\textwidth}{!}{
$Prec =  \frac{TP}{TP+FP}  \qquad Rec =  \frac{TP}{TP+FN} \qquad IU=\frac{TP}{TP+FP+FN}.$
}
\end{equation}

Since, in the MICCAI challenge results are reported in terms of polyp detection, we also evaluate the polyp detection rate using the metrics advocated by the challenge directives \cite{c25}; detection precision and recall. If a segmented blob falls within the polyp mask it is counted as a TP. If the detected blob falls outside the ground truth mask it is a FP. Every polyp in the image that has not been detected counts as a FN. Only one TP is considered for polyp, no matter how many detections fall within the polyp mask. Detection precision and detection are calculated with the same formulas of Equation \ref{eq11}

\begin{figure*}
\centering
  \includegraphics[width=0.7\textwidth,keepaspectratio]{batch_results.png}
  \caption{Segmentation comparison obtained by the three non-residual architectures with and without batch normalization. FCN-VGG results are best viewed in colour electronically the detection is very small.}
  \label{fig:batch_results}
\end{figure*}
\subsection{Results from RGB data}

\begin{table*}
\centering
\tbl{Segmentation and detection precision (prec) and recall (rec)  in \% obtained by the non residual FCNs trained with batch normalization. Mean interception over union (IU) is also presented for segmentation. Metrics improved by adding BN are highlighted in bold  \label{tab:res2}}{

\begin{tabular}{ccccccccccc}
\hline
                                                               & \multicolumn{5}{c}{\textbf{ETIS-Larib}}                                    & \multicolumn{5}{c}{\textbf{CVC-ColonDB}}                                   \\
                                                               & \multicolumn{3}{c}{\textbf{Segmentation}} & \multicolumn{2}{c}{\textbf{Detection}} & \multicolumn{3}{c}{\textbf{Segmentation}} & \multicolumn{2}{c}{\textbf{Detection}} \\
                                                               & \textbf{Prec}      & \textbf{Rec}       & \textbf{IU}        & \textbf{Prec}           & \textbf{Rec}           & \textbf{Prec}      & \textbf{Rec}       & \textbf{IU}        & \textbf{Prec}           & \textbf{Rec}           \\ \hline

\multicolumn{1}{c}{\textbf{BN-FCN-AlexNet}}   &
\textbf{30.05} &	29.07 &\textbf{	17.41} &	38.95 & 62.76 &\textbf{	46.22} &	\textbf{28.78} &	\textbf{21.19 }&	43.69 &	\textbf{80.87  }      \\
\multicolumn{1}{c}{\textbf{BN-FCN-GoogLeNet}} & \textbf{49.36}        &23.85       & \textbf{20.36}        & \textbf{53.96 }            & \textbf{63.10 }          & \textbf{63.87}        & \textbf{25.92}        & \textbf{23.04 }      &   \textbf{ 62.56}          & \textbf{75.99}           \\

\multicolumn{1}{c}{\textbf{BN-FCN-VGG}}       & 56.87	&\textbf{ 66.59} &	42.32 &	56.24 &	\textbf{94.01} &	66.8 &	\textbf{61.3} &	47.18 &	61.57 &	\textbf{95.16}
    \\ \hline
\end{tabular}
}
\end{table*}

We first train every FCNs using only RGB data and compare their performance on both testing datasets. Table \ref{tab:res1} presents the segmentation and detection results for all proposed network architectures and Fig. \ref{fig:res1} illustrates representative examples of polyp segmentation for each network. FCN-ResNet-152 and FCN-ResNet-101 proved to be the best polyp detectors achieving the highest recalls in both databases. In some situations (third row in Fig. \ref{fig:res1}), FCN-ResNet-152 was the only network capable of correctly detecting the polyp, even with limited segmentation accuracy.  Both deep architectures (FCN-ResNet-101, FCN-ResNet-152) proved to be able to learn complex filters capable of 90\%detection recall in the testing datasets. FCN-ResNet-50 resulted in less accurate detections than its deeper counterparts, with approximately 10\% lower detection recall.  These observations indicate that, while more than 50 layers are essential in handling the high complexity of detecting polyps of various sizes, shapes and textures, as indicated by the improved performance of FCN-ResNet-100 against FCN-ResNet-50, the addition of even more layers in FCN-ResNet-152 does not improve the detection performance.

In the non residual architectures, FCN-VGG outperforms the other FCNs by achieving detection recalls of 86\% in both datasets. The simpler FCN-AlexNet successfully detected 63.78\% of the ETIS-Larib polyps, and 54.68\% of the CVC-ColonDB, and resulted in a considerable amount of false positives, as exemplified by the second and third segmentations of Fig. \ref{fig:res1}. Finally, the FCN-GoogLeNet produced the worst detection performance of all networks studied. Although, GoogLeNet is a deeper architecture than the other two, this does not necessarily translate to better inference ability, as the network is notoriously hard to optimize. Without the two loss functions that allow better convergence in the deeper modules, the network is not able to completely adapt to the new learning problem.

\begin{figure*}
\centering
 \includegraphics[width=1.0\textwidth,keepaspectratio]{rgbd_results2.png}
  \caption{Comparison between segmentations obtained by the three top-performing architectures trained with and without depth.}
  \label{fig:rgbd_results}
\end{figure*}

\begin{table*}
\centering
\tbl{Segmentation and detection precision (prec) and recall (rec)  in \% obtained by the three FCNs with the best performance trained with RGB-D data (D-FCNs). Mean interception over union (IU) is also presented for segmentation. Metrics improved by adding depth information are highlighted in bold \label{tab:res3}}{

\begin{tabular}{ccccccccccc}
\hline
                                                               & \multicolumn{5}{c}{\textbf{ETIS-Larib}}                                    & \multicolumn{5}{c}{\textbf{CVC-ColonDB}}                                   \\
                                                               & \multicolumn{3}{c}{\textbf{Segmentation}} & \multicolumn{2}{c}{\textbf{Detection}} & \multicolumn{3}{c}{\textbf{Segmentation}} & \multicolumn{2}{c}{\textbf{Detection}} \\
                                                               & \textbf{Prec}      & \textbf{Rec}       & \textbf{IU}        & \textbf{Prec}           & \textbf{Rec}           & \textbf{Prec}      & \textbf{Rec}       & \textbf{IU}        & \textbf{Prec}           & \textbf{Rec}           \\ \hline

\multicolumn{1}{c}{\textbf{D-FCN-VGG}}   &
68.68	& \textbf{62.16} &	\textbf{47.78} & 73.32 &	\textbf{88.01} &		74.94 &	\textbf{68.02} & \textbf{56.95} & 	76.85 &	\textbf{91.03}	    \\
\multicolumn{1}{c}{\textbf{D-FCN-ResNet-101}} & 55.63 & 	\textbf{61.11} &	40.99 &	70.62 &\textbf{	95.83} &	\textbf{74.31} &	\textbf{58.15} &	\textbf{49.65} &	83.17 &	\textbf{90.47}
          \\

\multicolumn{1}{c}{\textbf{D-FCN-ResNet-152}}       & \textbf{67.66} &	\textbf{39.78} &	\textbf{33.77} &	77.95 &	\textbf{90.2} &	71.16 &	47.95 &	41.32 &	80.78 &	92.5

    \\ \hline
\end{tabular}
}
\end{table*}

In terms of the segmentation results, FCN-VGG outperformed all other networks with an IU of 44.06\% and 54.01\% for ETIS-Larib and CVC-ColonDB datasets, respectively. Subsequently, even though FCN-VGG detects a smaller number of polyps, the overall quality of the segmentation it provides is superior to other networks. An example of this is depicted in the second polyp of Fig. \ref{fig:res1}. Similar levels of segmentation quality were achieved by FCN-ResNet-101, which indicates that it learned more general filters than its deeper residual counterpart (FCN-ResNet-151). Finally, similar to the detection results, FCN-AlexNet and FCN-GoogLeNet achieved the worst performance in segmenting the polyps.

As far as we know, our method is the first to produce dense polyp segmentations, which only allow comparison with other algorithms with the use of detection metrics. The current state of the art was set by the top deep learning method in the 2015 MICCAI polyp detection challenge (OUS), which achieved 73.3\% detection precision and 69.2\% recall in the ETIS-Larib dataset \cite{c25}. As seen in Table \ref{tab:res1}, four of our models (FCN-VGG and all three FCN-ResNets) surpass this results, with improvements in precision and increases in recall as high as 20\%. The OUS methodology was not made publicly available yet, so direct comparison is not possible. However, the huge difference in accuracy shows how important proven CNN architectures and a good initialization are to achieved a better solution.

\subsection{Adding batch normalization}
Batch normalization is not implemented in the original AlexNet, VGG and GoogLeNet. We investigate the influence of adding batch normalization in these FCNs and list the results in Table \ref{tab:res2}. During training of the batch-normalized (BN-FCNs) versions, we decreased the learning rate by a factor of 100 and convergence was achieved after 30K iterations for all BN-FCNs. Due to memory limitations, relatively small batch sizes were used. BN-FCN-AlexNet resulted in a slight increase in IU segmentation and detection recall for ETIS-Larib, while in CVC-ColonDB, every single evaluation metric was improved, especially for detection, where the recall increased by more than 25\%. Similar improvements in segmentation IU and detection accuracy, are observed with BN-FCN-GoogLeNet for both datasets. Examples of improved segmentations are illustrated in Fig. \ref{fig:batch_results} for all three non-residual networks. Batch normalization enabled BN-FCN-VGG to increase the amount of polyps detected, with recalls higher than 94\% for both datasets. However, this was accompanied with an decrease in precision.  The third row in Fig. \ref{fig:batch_results} shows an example of a polyp being misdetected without batch normalization (FCN-VGG) while being successfully recovered in the batch-normalized version (BN-FCN-VGG).

\subsection{Results from RGB-D data}
To evaluate the addition of SfS-extracted depth, as an additional feature, we restricted ourselves to the three architectures that achieved the best detection and segmentation results with RGB data; FCN-VGG, FCN-ResNet-101 and FCN-ResNet-152. The results after the inclusion of depth information are listed in Table \ref{tab:res3}.

The addition of depth information allowed D-FCN-VGG to perform slightly better than its RGB counterpart. Segmentation IU and detection recall improved approximately by 2\% for both datasets. Similar increases were verified with D-FCN-ResNet-101, elevating its detection recall to more than 95\% for the ETIS-Larib. Fig \ref{fig:rgbd_results} illustrates three examples where depth information allowed the networks to either detect a polyp that would otherwise miss (D-FCN-VGG, D-FCN-ResNets-101) or improve segmentation accuracy (D-FCN-ResNets-152).

With the current architectures, bigger improvements are hampered by the difficulty of propagating meaningful gradients through the model. This issue is more evident in the FCN-ResNet-152, which had comparable detection performance with and without depth data. Alternative ways to incorporate depth information into the models might facilitate the learning of more meaningful RGB-D feature extractors.

\subsection{Computation speed}
The inference speed of each network highly depends of the amount of learned parameters and the number of layers. Table \ref{tab:speed} lists the average time required for each FCN to segment a single $500\times500$ image. The addition of batch normalization and the use of depth features slows down inference, as more operations are required to produce the final segmentation maps. The VGG architecture has the highest number of receptive fields, which results in the slowest average inference speed of all networks. On the other end, FCN-AlexNet has the fastest average inference both with (136ms) and without (51ms) batch normalization.

\begin{tablehere}
\tbl{Average inference time in milliseconds (ms) for a $500\times500$ image. If applicable, average inference time is shown , without batch normalization (no BN), with batch normalization (BN) and with the inclusion of depth (Depth)\label{tab:speed}}{
\begin{tabular}{cccc}
\hline                                                      & \multicolumn{3}{c}{\textbf{Average Inference Speed (ms)}} \\
\multicolumn{1}{c}{\textbf{Networks}}                               & \textbf{no BN}    & \textbf{BN}    & \textbf{Depth}   \\ \hline
\multicolumn{1}{c}{\textbf{FCN-AlexNet}}                            & 51                & 136            & \textbf{-}            \\

\multicolumn{1}{c}{\textbf{FCN-GoogLeNet}}  & 60                & 193              & -                     \\
\multicolumn{1}{c}{\textbf{FCN-VGG}}                                & 295               & 412            & 536                    \\

\multicolumn{1}{c}{\textbf{FCN-ResNet-51}}  & -                 & 164            & -                     \\
\multicolumn{1}{c}{\textbf{FCN-ResNet-101}}                         & -                 & 206            & 265                     \\

\multicolumn{1}{c}{\textbf{FCN-ResNet-152}} & -                 & 319            & 387                     \\ \hline
\end{tabular}}
\end{tablehere}

\section{Conclusion}\vspace{-2ex}
We have presented a deep learning framework for automatically detecting and segmenting polyps in colonoscopy images. This is achieved by taking advantage of very rich representations available in CNNs trained on large databases which we fine-tune to perform polyp detection and adapt them, by converting them to FCNs for achieving segmentation. We compare the networks' ability to accurately detect and segment polyp structures in experiments on publicly available datasets with annotated ground truth. Obtained results suggest that the two deepest residual architectures (ResNet-101, ResNet-152) were able to cope with the complexity of polyp structures and achieve the best detection results. On the other hand, the higher number of receptive fields in the VGG, allowed the network to achieve a better overall segmentation output. These three networks achieved detection recall rates around 90\% both in the ETIS-Larib and CVC-ColonDB, considerably surpassing the state-of-the-art in polyp detection. We also introduce relative depth information, derived from SfS as an additional input channel. Results show that including depth can improve polyp representation and lead to increased detection rates and segmentation accuracy. We note thought that it is hard to propagate meaningful gradients through deep networks, thus the directly visible improvement was small. In the future, a modeling strategy that facilitates learning from depth might yield better prediction and inference rates rather than direct SfS embedding. Inference times are still a limitation for the use of CNNs in real-time CAD systems suitable for clinical practice. Strategies to speed up the inference process, such as the use of FFT convolutions, could be used for achieving minimum latency, required in clinical applications. Furthermore, a more detailed analysis of receptive field activation might allow the compression of large scale CNNs for simpler problems, providing even faster inference times.

\bibliographystyle{IEEEtran}

\section{Usage}

\begin{verbatim}
\documentclass{ws-jmrr}
\usepackage{cite}
\usepackage{url}
\begin{document}
\section{Output}
Sample output using ws-jmrr bibliography ...
text\cite{best,pier,jame} ...
\bibliographystyle{ws-jmrr}

redpajama_set_name: RedPajamaArXiv


Spring has sprung in Dufferin County!
The County of Simcoe will be conducting a controlled burn on 40 hectares (100 acres) of Simcoe County Forests within the Tosorontio Tract in the Township of Adjala Tosorontio.
Dufferin County was contacted in mid January by a tenant with possible concerns.


DHAKA, Nov 19, 2014,- Prime Minister Sheikh Hasina today gave financial support to eminent poet Nirmalendu Goon and three noted female film artistes.
She also gave assistance to an organization working for autistic and physically challenged persons and a poor female living on selling flower.
The Prime Minister distributed the cheques of the financial support to the recipients at her official residence Ganobhaban here this evening.
Poet Nirmalendu Goon, artistes Shahina Sikder Banasri, Amirunnesa Khanam (Rani Sarker) and Lainun Nahar Mala received Taka 20 lakh each while flower seller Dil Ara Begum of North Road, Dhaka, received Taka 25 lakh.
Najnin Begum Eva, Chairman of Rehabilitation Nibas for the Autistic and Physically Challenged Persons working in Manikganj, was given Taka five lakh.
The Prime Minister warmly greeted the ailing artistes and enquired about their wellbeing and treatment.
Minister for Cultural Affairs Asaduzzaman Noor, PMO Secretary Abul Kalam Azad and Press Secretary AKM Shamim Chowdhury were present.
The Prime Minister came to know about the inhuman living condition of the female artistes through newspaper and invited them to her residence.


Black and Special Voltage are Available Now!
The new EU2L emergency light, ECC emergency exit combination light and ERE remote light are designed for installation in small spaces at low mounting heights while providing 90-minutes of illumination upon the loss of AC power.
These products are now available in black and special voltage.
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Thank you for visiting our website and for those of you who have ever been in our stores, we extend an extra thanks. We have been in business since 1958 and feel grateful for the incredible customers that we get to interact with each day. We are here because of those customers and want to extend to you an invitation to let us be of service to you.
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text:
> TV & Film
Good Morning Britain presenter runs after gunfire interrupts broadcast
Protests have been taking place in the city following the death of Daunte Wright
April 13, 2021 - 10:50 BST Emmy Griffiths Noel Phillips was reporting on protests in the city of Brooklyn Centre in Minnesota when the terrifying moment took place
Good Morning Britain presenter Noel Phillips' was forced to cut a live broadcast short, run and take cover after gunfire sounded out while reporting on the protests following the death of Daunte Wright, 20, by a police officer.
MORE: Kate Garraway reveals real reason husband Derek Draper is at home
While doing a piece to camera in Minnesota for the breakfast show, Noel said: "There's a very tense standoff between protesters and police, as you can hear several gunshots." Off-camera, someone can be heard saying: "We've got gunfire over there," as another person shouts: "Behind the cars. Now! Now!"
WATCH: Noel Phillips' broadcast is interrupted by gunfire
The presenter can be seen running and ducking away from the camera, but later finished his report, saying: "We heard 20-30 gunshots from a group of men towards the police." He also spoke to a protester named Anthony about Daunte's death, who was killed after an officer mistakenly shot him with a gun rather than her taser.
MORE: Kate Garraway takes control of husband Derek Draper's business after 'financial' woes
MORE: Holly Willoughby's advice to Piers Morgan after GMB exit revealed
Speaking about Daunte's death, Chief Tim Gannon told reporters: "It is my belief the officer meant to deploy their Taser but shot him with a single bullet. There's nothing I can say to lessen the pain." The officer has been placed on administrative leave during the investigation into the incident.
Daunte Wright was killed by a police officer
Brooklyn Centre Mayor Mike Elliott said: "Our hearts are aching right now, we are in pain right now. We recognise this couldn't have happened at a worse time. This is happening at a time that all of our community, all of America, indeed all of the world is watching." He added that they will "ensure justice is done for Daunte Wright".
This collagen drink has over 2,000 5* reviews - here's why
Cyrano movie star Peter Dinklage is breathtakingly good - here's your special screening invite
More about good morning britain
Gavin and Stacey star breaks silence over historic sexual harassment
Good Morning Britain's Ben Shephard and Kate Garraway apologise after on-air blunder
Kate Garraway reveals at-home hair dye disaster - and son Billy is not impressed
Prince Philip once refused to accept a picture of the Queen - see his funny remark
Marks & Spencer's new menopause skincare range is a hit with fans - the reviews are in
Piers Morgan's son rejects his dad's clever way they can see each other during lockdown
Viewers defend Dr Hilary in heated spat against Good Morning Britain presenter co-star
Ben Shephard reveals he worked below minimum wage job ahead of television career
Dr Hilary Jones takes break from Good Morning Britain


text:
import Jumbotron from './Jumbotron'

export default Jumbotron

redpajama_set_name: RedPajamaGithub


ESPN and SportsCenter (the real one) notoriously "accidentally" lift things all the time. Guys like Jay Glazer and Adrian Wojarnowski become "sources" or "reports" or "the internet" or "some bald idiot," anything they can possibly think of besides actually sourcing the person himself.
Daniel Tosh recently became ESPN's latest "accidental" victim, when they just happened to do a segment that is almost identical to Tosh's Web Redemption, just with a much more bland name that's supposed to be hip but is anything but (in true ESPN form).
Nailed it, except the LeBron part. Only 39%? Maybe during commercials. He probably left off the 1 by accident and meant 139%. That makes more sense.


text:
Ed Sheeran & Justin Bieber 2019
÷ (Deluxe) 2017
No.6 Collaborations Project 2019
Ed Sheeran & Travis Scott 2019
x (Wembley Edition) 2014
+ (Deluxe Version) 2010
Essential Albums
About Ed Sheeran
There's a lovely moment a little ways into Apple Music's Songwriter documentary about Ed Sheeran in which Sheeran, jet-lagged and carrying a cup of tea, straggles out into a yard in Malibu at dawn, sits down at his laptop, and begins writing a song about the moment right in front of him: the crisp air, the birdsong, the hot tea, the day ahead. He gets a full verse in, melody and all, then pauses. "Songs are weird things," he says. "How so?" an interviewer asks. "They just come and go," Sheeran says, smiling. "And they never give you any warning."
For Sheeran, they seem to come pretty reliably. Born in 1991 in Halifax, England, he started out gigging on the UK pub circuit before releasing his first studio album, +, in 2011, and has since become one of the most unstoppable singer-songwriters in music, forging a light blend of folk, pop, hip-hop, and dance that feels slick but lived-in, intimate but universal.
Whether on his own ("Sing," "The A Team," "Shape of You," "Perfect") or in collaboration with artists from Taylor Swift to Eminem to Justin Bieber, Sheeran has a unique ability to strike a chord that feels both bittersweet and redemptive, good-natured and genuine: the hopeless romantic who convinces you he might just be right.
In 2017, he was made a Member of the Most Excellent Order of the British Empire, touching Prince Charles on the arm while shaking his hand—a breach of royal protocol, and an appealingly human one at that.
Halifax, West Yorkshire, England


text:
Q: Debezium server docker cannot access AWS credentials My goal is to use Debezium for real-time CDC from MongoDB to AWS Kinesis. I am using the official Debezium server 1.8 docker image. I'm able to run the image locally and I've configured the settings via environment variables that I am passing into the docker container. Following the official documentation for debezium server.
It runs for a bit but then seems to fail when it tries to connect to AWS and can't find my credentials.
Error:
software.amazon.awssdk.core.exception.SdkClientException: Profile file contained no credentials for profile 'default': ProfileFile(profiles=[])
        at software.amazon.awssdk.core.exception.SdkClientException$BuilderImpl.build(SdkClientException.java:98)
        at software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider.lambda$new$1(ProfileCredentialsProvider.java:76)
        at java.base/java.util.Optional.orElseThrow(Optional.java:408)
        at software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider.<init>(ProfileCredentialsProvider.java:73)
        at software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider.<init>(ProfileCredentialsProvider.java:43)
        at software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider$BuilderImpl.build(ProfileCredentialsProvider.java:211)
        at software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider.create(ProfileCredentialsProvider.java:113)
        at io.debezium.server.kinesis.KinesisChangeConsumer.connect(KinesisChangeConsumer.java:82)
        at io.debezium.server.kinesis.KinesisChangeConsumer_Bean.create(Unknown Source)
        at io.debezium.server.kinesis.KinesisChangeConsumer_Bean.create(Unknown Source)
        at io.debezium.server.DebeziumServer.start(DebeziumServer.java:119)
        at io.debezium.server.DebeziumServer_Bean.create(Unknown Source)
        at io.debezium.server.DebeziumServer_Bean.create(Unknown Source)
        at io.quarkus.arc.impl.AbstractSharedContext.createInstanceHandle(AbstractSharedContext.java:101)
        at io.quarkus.arc.impl.AbstractSharedContext$1.get(AbstractSharedContext.java:29)
        at io.quarkus.arc.impl.AbstractSharedContext$1.get(AbstractSharedContext.java:26)
        at io.quarkus.arc.impl.LazyValue.get(LazyValue.java:26)
        at io.quarkus.arc.impl.ComputingCache.computeIfAbsent(ComputingCache.java:69)
        at io.quarkus.arc.impl.AbstractSharedContext.get(AbstractSharedContext.java:26)
        at io.quarkus.arc.impl.ClientProxies.getApplicationScopedDelegate(ClientProxies.java:18)
        at io.debezium.server.DebeziumServer_ClientProxy.arc$delegate(Unknown Source)
        at io.debezium.server.DebeziumServer_ClientProxy.arc_contextualInstance(Unknown Source)
        at io.debezium.server.DebeziumServer_Observer_Synthetic_d70cd75bf32ab6598217b9a64a8473d65e248c05.notify(Unknown Source)
        at io.quarkus.arc.impl.EventImpl$Notifier.notifyObservers(EventImpl.java:320)
        at io.quarkus.arc.impl.EventImpl$Notifier.notify(EventImpl.java:302)
        at io.quarkus.arc.impl.EventImpl.fire(EventImpl.java:73)
        at io.quarkus.arc.runtime.ArcRecorder.fireLifecycleEvent(ArcRecorder.java:128)
        at io.quarkus.arc.runtime.ArcRecorder.handleLifecycleEvents(ArcRecorder.java:97)
        at io.quarkus.deployment.steps.LifecycleEventsBuildStep$startupEvent1144526294.deploy_0(Unknown Source)
        at io.quarkus.deployment.steps.LifecycleEventsBuildStep$startupEvent1144526294.deploy(Unknown Source)
        at io.quarkus.runner.ApplicationImpl.doStart(Unknown Source)
        at io.quarkus.runtime.Application.start(Application.java:101)
        at io.quarkus.runtime.ApplicationLifecycleManager.run(ApplicationLifecycleManager.java:103)
        at io.quarkus.runtime.Quarkus.run(Quarkus.java:67)
        at io.quarkus.runtime.Quarkus.run(Quarkus.java:41)
        at io.quarkus.runtime.Quarkus.run(Quarkus.java:120)
        at io.debezium.server.Main.main(Main.java:15)

Here's what I've tried:

*

*Pass credentials as environment variables AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY

*Mounting my local AWS credentials file to the docker container: docker run -it --name debezium -p 8080:8080 --env-file .env -v $PWD/conf:/debezium/conf -v $PWD/data:/debezium/data -v "C:\Users\jon\.aws\credentials":/root/.aws/credentials:ro debezium/server bash

*^Related to above, tried setting the environment variable AWS_CREDENTIAL_PROFILES_FILE=/root/.aws/credentials

*Logged into the docker container once it's running and confined the environment variables are there and the credentials file was there.

My .env file:
DEBEZIUM_SINK_TYPE=kinesis
DEBEZIUM_SINK_KINESIS_REGION=us-east-1
DEBEZIUM_SINK_KINESIS_CREDENTIALS_PROFILE=default
DEBEZIUM_SINK_KINESIS_ENDPOINT=test

DEBEZIUM_SOURCE_CONNECTOR_CLASS=io.debezium.connector.mongodb.MongoDbConnector
DEBEZIUM_SOURCE_OFFSET_STORAGE_FILE_FILENAME=data/offsets.dat
DEBEZIUM_SOURCE_OFFSET_FLUSH_INTERVAL_MS=0

DEBEZIUM_SOURCE_MONGODB_NAME=test
DEBEZIUM_SOURCE_MONGODB_HOSTS=rs0/test-mongodb-shard-00-00.test.mongodb.net:27017
DEBEZIUM_SOURCE_MONGODB_USER=test
DEBEZIUM_SOURCE_MONGODB_PASSWORD=test
DEBEZIUM_SOURCE_MONGODB_SSL_ENABLED=true

DEBEZIUM_SOURCE_DATABASE_INCLUDE_LIST=test
DEBEZIUM_SOURCE_COLLECTION_INCLUDE_LIST=test.test

DEBEZIUM_SOURCE_CAPTURE_MODE=change_streams_update_full
DEBEZIUM_SOURCE_DATABASE_HISTORY=io.debezium.relational.history.FileDatabaseHistory
DEBEZIUM_SOURCE_DATABASE_HISTORY_FILE_FILENAME=history.dat

QUARKUS_LOG_CONSOLE_JSON=false

AWS_ACCESS_KEY_ID=test
AWS_SECRET_ACCESS_KEY=test
AWS_CREDENTIAL_PROFILES_FILE=/root/.aws/credentials

My AWS credentials file:
[default]
aws_access_key_id = test
aws_secret_access_key = test

According to the documentation for this AWS class, either of the above methods should work: https://www.javadoc.io/doc/software.amazon.awssdk/auth/2.13.54/software/amazon/awssdk/auth/credentials/DefaultCredentialsProvider.html

A: I had the same problem. I ended up solving it by opening a shell in the container (eg using docker exec -it {container id} /bin/bash) and realizing that the user was not root but jboss. When I mounted the credentials file to /home/jboss/.aws/credentials, it started working.

redpajama_set_name: RedPajamaStackExchange