Invisalign Teen is the clear way to straighten teeth as it doesn't require metal brackets or wires. Instead, this treatment uses clear, plastic aligners that are virtually invisible. This means that a patient can straighten his or her teeth without anyone else knowing. As an added benefit, the aligners are removable. Because the aligners can be removed at any time, a patient can continue to eat whatever he or she wants since there's no worry about breaking the orthodontics. Also, oral hygiene remains as simple as ever since there's no brackets or wires to clean around.
While Invisalign was previously meant only for adult patients, Invisalign Teen threw in a few modifications to make them perfectly suitable for teenagers. One of these additions was the inclusion of eruption tabs, which hold open space for an adolescent's teeth that have yet to erupt. The second improvement came in the form of compliance indicators, which are colored dots on the back of the aligners that fade away when Invisalign Teen is being worn as recommended. Many doctors worried that their teenage patients wouldn't want to keep their aligners in as required so with this new feature, doctors will know if a patient is obeying instructions.
You'll receive a series of aligners (typically somewhere between 18 and 30) from us.
You'll wear each set of aligners in a designated order for two weeks.
During this time, you should leave them in for about 22 hours per day, only taking them out to eat and clean your teeth.
You'll need to visit your orthodontist in Lawrence and Topeka once every eight or ten weeks to make sure that the treatment is proceeding as planned.
This treatment will continue until you've worn the last set of aligners, generally lasting for about six-to-15 months.
What Conditions Can Invisalign Teen Treat?
Contact Your Providers of Topeka and Lawrence, KS Invisalign!
If you have any questions about our Topeka and Lawrence Invisalign Teen treatment, please don't hesitate to contact us and let us know. We'll be glad to help you in any way that we can! If you're interested in scheduling an appointment with your orthodontist, feel free to fill out our appointment request form. We're looking forward to hearing from you!


There's growing evidence that financial planners are wilting under the heat of time-consuming processes that could be automated and executed by technology in a fraction of the time.
Take this recent Twitter poll by public speaker, author and FinalytiQ founder Abraham Okusanya.
Which of these do you consider most time-consuming?
Okay, it's just Twitter but 128 respondents is significant and they have sent a fairly clear message that onboarding clients is what's eating up the hours.
As you know, this consists largely of fact-finding, risk-profiling, setting up client agreements, arranging billing and ensuring the client reads and signs multiple documents. In 2019, these are all processes that can be sped up with software and apps, freeing up hours every month that planners could use drinking tea (or wine!) with clients and discussing their goals.
Ah, those wasted hours. We recently conducted research here at Advicefront that revealed financial advisers are spending nearly five hours a week on unnecessary tasks, costing advisers £220 per week (over £10,000 per year!) in wasted time.
So how much time are you spending on time-consuming tasks that could be automated?
It would be neglectful, at this point, not to tell you that we offer a single suite of fact-finding, risk-profiling, client agreement, billing and digital signature tools in our product, Onboard. The idea is to help financial planners automate tasks that are boring and add little value so they can concentrate on nurturing relationships with clients.
And the best part is we integrate with many of the tools you all already use for risk–profiling, cashflow-modelling and more. So you won't have to stop using them if you don't want to.
Want to see how much time you could save with Onboard? Book your free 15-minute demo!
Are financial adviser trade bodies still relevant?


text:
Home Foreign
Winter storm leaves at least 37 dead nationwide as residents in western New York remain trapped under feet of snow
in Foreign
Martin Haslinger clears snow from the front of his home, Sunday, Dec. 25, 2022, in Buffalo, N.Y. Millions of people hunkered down against a deep freeze Sunday morning to ride out the frigid storm that has killed at least 24 people across the United States and is expected to claim more lives after trapping some residents inside houses with heaping snow drifts and knocking out power to several hundred thousand homes and businesses. (AP Photo/Bridget Haslinger)
As a massive winter storm continues to blast much of the US with brutal winter weather – leading to at least 37 deaths nationwide – parts of western New York have been buried by up to 43 inches of snow, leaving vehicles stuck and power out for thousands during the Christmas weekend.
New York Gov. Kathy Hochul told CNN the storm is the "most devastating storm in Buffalo's long storied history." The heavy snowfall and blizzard conditions made roads impassable with zero visibility, froze power substations and left at least 17 people across the state dead as of Sunday night.
Western New York is drowning in thick "lake effect" snow – which forms when cold air moves over the warm waters of the Great Lakes – just one month after the region was slammed with a historic snowstorm.
As rescue crews and hundreds of plow drivers fanned out on Christmas Day, even emergency and recovery vehicles sent out to help have gotten stuck in the snow. Eleven ambulances that had to be abandoned were dug out on Sunday, officials said.
"The rescue team was rescuing rescuers … it was so horrible," Erie County executive Mark Poloncarz said during a news conference Sunday. Many of New York's weather-related fatalities were in Erie County, where some people were found dead in cars and on the street in snowbanks, he said.
Deaths reported in Buffalo "are people found outside and in cars," a Buffalo Police statement read.
Hundreds of National Guard troops have been deployed to help with rescue efforts in New York. State police had been involved in over 500 rescues by Sunday, including delivering a baby and helping a man with 4% left on his mechanical heart, the governor said.
"We're still in the throes of this very dangerous life-threatening situation," Hochul said, urging residents to stay off the roads as a driving ban remains in place in Erie County through Monday.
A man clears snow from the front of his home, on Sunday, December 25, 2022, in Buffalo, New York.Bridget Haslinger/AP
"Our state and county plows have been out there, nonstop, giving up time and putting themselves in danger, driving through blinding snowstorms to clear the roads," Hochul said.
As blistering blizzard conditions swept the region, about 500 motorists found themselves stranded in their vehicles Friday night into Saturday morning, according to Poloncarz, who described frightening conditions on the road.
"Think about looking just a few feet in front of you at a sheet of white for more than 24 hours in a row. That's what it was like outside in the worst conditions," he said. "It was continual blizzard and white outs such that no one could see where they were going. Nobody had any idea what was happening."
While abandoned vehicles pepper the snow-covered roadways – with hundreds of cars still along the streets of Buffalo – conditions are also difficult inside homes.
Some residents have remained in their homes for the last 56 hours, some without power in the freezing cold, Hochul said during the press conference. This is not due to a lack of resources, the governor said, but rather a mobility and access challenge faced by utility companies.
Snow blankets a neighborhood, December 25, 2022, in Buffalo, New York.Carolyn Thompson/AP
As of Sunday evening, 94.5% of Erie County residents and 87% of Buffalo residents have had their power restored, Hochul said.
Still, there were 12,000 homes and businesses in Erie County without power Sunday evening, and many won't have lights and heating back until Tuesday, Poloncarz said.
Buffalo will continue to see snowfall and frigid cold temperatures Monday, with a high of 23 degrees expected in the daytime and a low of 18 at night, according to the National Weather Service.
<img src="https://media.cnn.com/api/v1/images/stellar/prod/221226054617-01-us-winter-weather-1225.jpg?c=original&q=w_1280,c_fill" alt="Snow blankets a neighborhood in Cheektowaga, New York, on Sunday, December 25. Western New York is
Snow blankets a neighborhood in Cheektowaga, New York, on Sunday, December 25. Western New York is drowning in thick "lake effect" snow — which forms when cold air moves over the warm waters of the Great Lakes — just one month after the region was slammed with a historic snowstorm.John Waller via AP
In pictures: Winter storm impacts the US
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Storm leads to loss of life across multiple states
Over the past week, the prolonged winter storm has enveloped a large swath of the US with dangerously low temperatures and wind chills, also bringing with it widespread power outages and thousands of canceled flights.
More than 10 million people were under freeze alerts across the South Monday, including residents in Orlando, Jacksonville, Tallahassee, Mobile, Montgomery and Birmingham.
Sub-freezing temperatures are expected across the affected areas, where temperatures will be in the teens and low 20s, potentially killing crops and damaging plumbing. The majority of these alerts are set to expire Monday morning as temperatures finally begin to recover from the polar air.
Nationwide, around 90,000 customers were without power early Monday, according to PowerOutage.US. Since the start of the storm, the number of outages has at times exceeded a million customers.
The storm also snarled travel in the US during the busy holiday weekend, with more than 5,000 flights canceled Friday, more than 3,400 flights canceled Saturday and more than 3,100 canceled for Christmas Day. More than 1,300 flights within, into or out of the US have already been canceled by 5 a.m. ET Monday, according to tracking site FlightAware.
Since the brutal weather's arrival, multiple storm-related deaths have been reported across several states. In addition to the deaths in New York, the fatalities include:
• Colorado: Police in Colorado Springs, Colorado, reported two deaths related to the cold since Thursday, with one man found near a power transformer of a building possibly looking for warmth, and another in a camp in an alleyway.
• Kansas: Three people have died in weather-related traffic accidents, the Kansas Highway Patrol said Friday.
• Kentucky: Three people have died in the state, officials have said, including one involving a vehicle crash in Montgomery County.
• Missouri: One person died after a caravan slid off an icy road and into a frozen creek, Kansas City police said.
• Ohio: Nine people have died as a result of weather-related auto crashes, including four in a Saturday morning crash on Interstate 75, when a semi tractor-trailer crossed the median and collided with an SUV and a pickup, authorities said.
• Tennessee: The Tennessee Department of Health on Friday confirmed one storm-related fatality.
• Wisconsin: Wisconsin State Patrol on Thursday reported one fatal crash due to winter weather.
What to expect as storm moves away
High winds and snow covers the streets and vehicles in Buffalo, on Sunday, December 25, 2022.AP
The powerful system that brought blizzard and winter weather alerts continues to move away from the Northeast, yet many cities and towns remain covered with thick snow. Over a 24-hour span, Baraga, Michigan, received 42.8 inches of snow while Watertown, New York, got 34.2 inches.
Grand Rapids, Michigan, had its snowiest Christmas Eve ever, receiving a record 10.5 inches, according to the National Weather Service.
Winter storm warnings remain in effect in New York for Buffalo, Jamestown and Watertown and will expire throughout the following couple of days. Forecasts show Jamestown could see another 8 inches of snow, Buffalo could see another 14 inches and Watertown could see another 3 feet. Winds could also gust up to 40 mph.
Lake effect snow warnings remain north of Jamestown until 10 a.m. EST Tuesday, an area where up to 18 inches are possible.
Lingering lake-effect snows blowing downwind from the Great Lakes will slowly become less intense, but the Arctic air enveloping much of the eastern half of the nation will be slow to moderate, according to the National Weather Service.
Lake-effect snows will continue to make for hazardous travel conditions for the next couple of days and conditions are expected to slowly improve over the week.
The low-pressure system is forecast to move farther away into Canada, while another system quickly across the northern US into Monday, bringing snow from the northern Plains through the Midwest.
Much of the rest of the eastern part of the country will still be in a deep freeze through Monday before a moderating trend sets in on Tuesday, forecasters said.
NDC doesn't have a solid team to compete with NPP – John Boadu
Ukraine war: Drone attack on Russian bomber base leaves three dead
Pope Francis calls for end to 'senseless' war in Ukraine
King's Christmas message to pay tribute to Queen's legacy
Ukraine war crimes investigation receives support of 45 nations

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Water, Water everywhere'. Of course, being surrounded by water, Anglesey has an great range of water activities to keep you busy. Whether riding down the Menai Straits in a rib, kayaking or canoeing round Holy Island, yachting, boat rides to see the dolphins and seals, paddle boarding... there is just so much to do.
On land there is also a wide range of activities to try such as biking on our cycle routes, walking along the 125 miles of the stunning Anglesey coastal path, playing golf on one of our 9 golf courses, bird watching and seeing the puffins at South Stack, horse riding, carting and of course fishing in lakes and the sea. There really is something for everyone to try and ACTIVE ANGLESEY has lots to choose from.


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benchley
Unless you live under a rock, you're probably aware that this past week was Shark Week on the Discovery Channel. It's an annual thing - basically the Discovery Channel shows the same footage they've shown for years - everything you've ever wanted to know about sharks, but with a different host...I guess to make it more watchable. Much of the stuff is available online, and some of it is on Netflix.
There are a lot of shark movies out there, too. Jaws is probably the best, but there are a number of copy-cats, and an even larger number of "monster" flicks. Most of these films are only really enjoyable because of their bad bad bad scripts and their worse special effects. There are some things I guess one just can't get enough of, and for me it's the awful special effects. But Jaws is the classic - giant shark, blood, dramatic music. It's magical. And in honor of shark week, I'm re-posting my comments on the book that started it all.
In his 2005 introduction to Jaws (1974), author Peter Benchley makes a very valid point in saying that he could not possibly write Jaws today. His reasoning has much to do with the current global standpoint on conservation and appreciation. It's true - the world has a very different perspective on Sharks than it did in the early 70s. But Benchley and his novel had had a lot to do with that shift and, if he hadn't written it, someone else would have.
Perhaps that's why it doesn't seem as scary to me as it should. There are too many movies, documentaries, etc. on great whites and other sharks to make this novel very frightening. I've seen all four Jaws films...I own all four films in the franchise. I'm also a proud owner of the Italian rip-off of the original film (The Last Shark, aka L'ultimo Squalo). I've seen Shark Attack and its two sequels. I've even seen Debbie Gibson in the never should have been made Mega Shark vs. Giant Octopus. But what am I afraid of in the ocean? Not the big sharks, no. I never go out far enough to be scared by the big sharks. I'm afraid of stepping on jelly fish and anenomes, urchins and horseshoe crabs. I'm more afraid of having to have someone pee on my leg than I am of actually losing that leg. Although that's not wholly true -- I do find myself quite fearful of bull sharks and other sharks that, through the education of Discovery Channel's Shark Week, I know like shallow water, which is where I hang out.
Jaws, as Benchley wrote it, just isn't quite so relevant anymore. Sure, big sharks are scary, but I'm much less afraid of sharks who, according to Ellen Brody in the final chapter, Jaws the Revenge, are...well...out for revenge, than I am of the people who go and hunt these beautiful creatures and cut off their fins for soup. Okay enough grandstanding, back to the book...
Benchley also goes on to say that of course the novel is not the film - the book he wrote was well-rounded, with a quiet understanding of the socio-economic undercurrents as well as a relatively solid grasp on female emotion, while the film (albeit BRILLIANT) was about a big shark. And while I already knew most of the changes made for the film, it was a treat to go back and see what the story was before it was the first summer blockbuster, and how those exclusions contributed to making it such a hit.
Labels: benchley books jaws Review sharks summer sundays


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Last week's Social Media Marketing World conference was a dream for any social marketer. Not only were some of the biggest names in social marketing there to present (Jay Baer, Chris Brogan, Mari Smith, Mark Schaefer, to name a few), but there was content to cover pretty much any social related topic one could ask for.
Michael Stelzner, founder of Social Media Examiner, opened the conference with a keynote highlighting social media trends for 2014, and showed findings from their latest study 2014 Social Media Marketing Industry Report. The following are 6 key trends derived from the report.
1. Creating visual assets is a huge focus for content. A picture is worth 1,000 words, or in the case of a social media manager, double the engagement. Images on Facebook on average get 2X more "likes" than regular status updates, according to Klout. And with other social networks like Instagram soaring in popularity, it proves that pictures/ images really are the way of the future. According to the 2014 Social Media Marketing Industry Report, 70% of marketers plan on increasing their use of visual assets this year. If you don't have one already, it might be time to start working on a visual strategy.
2. Blog, blog, and blog! Simply put, more content equals more traffic. In fact, Social Media Examiner found that by going from one blog post a day to publishing two blog posts a day, they anticipate doubling their traffic in 2014. Stelzner reported that 58% of marketers say that original written content is the most important form of content for their businesses, and that 68% of marketers plan on increasing their blogging activity in 2014.
3. Marketers are turning their focus to Google+. Optimizing Google+ is starting to top the list for social marketers, and in fact, 65% of marketers say they want to learn how to utilize Google+ better. As G+ serves as a social layer across all Google properties, there's a greater potential of getting your content seen. The other advantage of Google+ is, unlike Facebook, you're able to see every post from every person or brand that you follow. Google+ remains a free platform with no advertising allowed, making your content the star of the show, not the size of your ad budget.
4). Podcasts are gaining popularity. Only 6% of marketers podcast, but 33% plan on creating one. Of the 130 presenters at Social Media Marketing world, 30 of them have their own podcasts. As Stelzner puts it, podcast listeners are super fans. He noted that 26% of his listeners went on to purchase a ticket to SMMW after hearing him talk about it on his podcast.
One of the biggest advantages of podcasting, of course, is being able to reach your audience in their cars, a time when you capture more of your listener's attention. Stelzner believes built-in podcast support will soon be available in all cars.
5. Facebook dominates the social ad world. When it comes to social advertising, Facebook still dominates the space with 90% of marketers claiming to regularly use Facebook ads versus 20% using LinkedIn ads, and 17% using Twitter ads. When targeted correctly, these ads can prove worthwhile, but like any other ad, you need to be strategic in it's use.
6. Social Media ROI remains top priority and mystery. Proving the ROI of social media continues to be a struggle for many marketers, as so much of social media is about cultivating and nurturing relationships. But as the C-Suite continues to ask for numbers, social marketers continue to look for ways to show ROI. In the end it all comes back to being able to translate social media into your business's core objectives.
Are you investing in your social media strategy? How are you evolving your strategy to keep up and take advantage of the latest trends in social media?


I wrote to you several years ago after my nephew, Ryan Means, died. His widow, Heather, has just self- published a book. Please check out the website. It is worth reading.
I would love to send you a copy of Heather's book. Please send me an address where to mail it to.


You can now order your books online!
The ESCC Bookstore is managed by Follett, which provides opportunities for students to rent textbooks** and eBooks for lower cost. You can also order textbooks from the ESCC bookstore online at esvccsshop.com. In addition, students have a pre-pay option when they order books from the ESCC bookstore for SSDL courses along with regular courses offered here on campus via credit card. Students may use their financial aid to pre-pay and must do so within the 2-week time period that financial aid can be used each semester. Students who wish to use their financial aid to prepay for the SSDL course materials must do so in person.
**For textbook rentals, you must have a major credit card or debit card to put on file for collateral. Individuals who rent texts this way must be at least 18 years of age. You may pre-register by going to www.rental.bkstr.com to create a rental account. Please retain a copy of your username, password, and patron ID number for your record.


View a selection of German Romantic prints that reflect new trends in the arts such as the rediscovery of Gothic art, medieval sagas, and age-old fairy tales.
Prints created by Austrian, German, and Swiss artists included in this exhibition reflect the dramatic shifts in taste in the arts during a time of significant cultural and political transformation throughout the German-speaking regions of Central Europe during the Romantic period. The selection of 125 prints reflects a number of the artistic enthusiasms of the Romantic period, such as the emerging taste for wild, untamed landscapes; for intimate family scenes and friendship portraits; and for recently rediscovered ancient Nordic sagas and age-old fairytales. Offering a broad overview of a vital chapter in the history of European printmaking, The Enchanted World of German Romantic Prints will illuminate one of the richest areas of the Philadelphia Museum of Art's collections.
On view in the exhibition will be major prints by important artists of the German Romantic era such as Caspar David Friedrich (1774–1840); Joseph Anton Koch (1768–1839); Carl Wilhelm Kolbe the Elder (1759–1835); Johann Heinrich Lips (1758–1817); Eugen Napoleon Neureuther (1806–1882); Ferdinand Olivier (1785–1841); Ludwig Richter (1803–1884); Philipp Otto Runge (1777–1810); Johann Gottfried Schadow (1764–1850); and Karl Friedrich Schinkel (1781-1841) as well as Ludwig Emil Grimm (1790–1863), the younger brother of the famous Brothers Grimm, Jacob and Wilhelm.
The Museum's holdings of some 8,500 prints from the German Romantic period is the largest in the country, and includes many rare examples seldom seen even in the great European collections.
The majority of the works in this exhibition were selected from the remarkable group of around 40,000 European old master and modern prints assembled in Philadelphia in the mid-nineteenth century by John S. Phillips (1800–1876). Phillips was able to acquire an especially rich representation of the prints of Ludwig Emil Grimm, Carl Wilhelm Kolbe the Elder, Eugen Neureuther, and other significant Austrian, German and Swiss artists of the Romantic generation. When he died in 1876, Phillips left his collection to the Pennsylvania Academy of the Fine Arts. The Philadelphia Museum of Art acquired it from the academy in 1985 through an exchange of works of art and a gift of funds from the Museum's former Chairman of the Board Philip Berman and his wife Muriel.
In recent decades the Museum has been able to acquire a number of rare masterpieces of German Romantic printmaking that were lacking in the Phillips collection. These include a rare self-portrait etching of the artist and his family by Neureuther, an album of etchings and a lithograph by Johann Gottfried Schadow, as well as rare sets of prints by Johann Heinrich Lips, Ferdinand Olivier, and Philipp Otto Runge.
The exhibition is generously supported by The Robert Montgomery Scott Fund for Exhibitions and The Pew Charitable Trusts.


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text:
Battle Of Santa Cruz De Rosales
The Battle of Santa Cruz de Rosales was an engagement of the Mexican-American War that took place after the Treaty of Guadalupe Hidalgo had been signed.
Brigadier General Sterling Price, commander of U.S. forces in New Mexico, received reports of an alleged Mexican advance into New Mexico. These reports would eventually prove to be false, but in the meantime Price had moved his command to El Paso, where he received orders from the adjutant general Roger Jones to remain there and, if possible, send reinforcements to California. Despite this and scouting reports which found no evidence of any Mexican invading army, Price advanced towards the city of Chihuahua.
Source: Wikipedia Added by: Aimee Lucido
Sterling Price advances into Chihuahua after the Treaty of Guadalupe Hidalgo was already signed.
Wikipedia: Battle Of Santa Cruz De Rosales
Wikipedia: Mexican-American War
View other events that happened on March 16


I am looking for source code of ML-T2 algorithm by Jiawei Han and Yongjian Fu for mining multilevel association rules. Can you kind provide me or can give me the link of source code? I will be very thankful for help.
This is veda, 'm persuing my masters.. i need separate code for fp-growth, eclat,charm. can u pls mail it as soon as possible.
in the section "Copying the source code of an algorithm in another Java project"
Basically, you need to copy the file(s) that you need and remove the package statement from each file.
Edited 1 time(s). Last edit at 01/08/2014 10:10AM by webmasterphilfv.
This is Raj, 'i m persuing my masters.. i need separate code for fp-growth, Multilevel,Apriori Algorithm. can u pls mail it as soon as possible.
The code of FPGrowth can be found in SPMF. You can easily extract it from SPMF. The main file is AlgoFPGrowth. Then you can check the files that are used by that file and extract it.
For multi-level association rules, I do not have the code.


text:
Q: ARM Change string to different length? Ghidra, Ida Pro I'm trying to translate compiled script to native language. I'm using ida pro and ghidra, but I've stuck on problem. I can change string values to any, if string length is same. But if string length is bigger or smaller, I have problems. I'm newbie here and can't post photos, so I will upload them to imgur. https://imgur.com/a/Rbv67lJ
When I insert additional byte to current string, IDA not wants to open file and says error. I understand, probably somewhere it is stored that string length, but I can't find anything. And at all, maybe it is not possible? Thanks in advance.

A:
probably somewhere it is stored that string length, but I can't find anything

From the screenshot it looks like they are NUL-terminated strings so the length is not stored but determined by checking for the NULL byte. Shorter strings can be inserted by inserting a NULL byte earlier in the string, but for longer ones you would have to do more (like move stuff around) so there's room for them. This might be really time consuming (would need to update references to those)

redpajama_set_name: RedPajamaStackExchange


text:
Movie Review: Maleficent
Dani and the Screen — August 7, 2014
What if I told you that Maleficent, known as the greatest, most powerful, and most evil Disney Villain was… a Fairy Godmother? What? Oh, dear… what an awkward situation…
Maleficent is the re-telling of the Disney classic Sleeping Beauty, with the focus on the main villain of that film. People constantly compare this to the story of Wicked: they share the similar concept of the villain never truly being evil. Unlike Wicked, which was more of a "what you didn't see" story, Maleficent has more of a "what-if" scenario, since they change almost every element of the original tale.
So, in this version, Maleficent (played by Angelina Jolie) started out as a kind and innocent fairy (who was somehow named Maleficent… if she's not supposed to be evil, don't give her a name which literally says "EVIL"!) who protected the land of the Moors, in a battle between the humans and the magical creatures. When she was younger, she encountered a peasant boy named Stephan (played by Sharlto Copley as an adult), who had the ambition of becoming the ruler of the land. They fall in love and share what Stephan called a "true love's kiss". However, Stephan was tempted by power, and in order to obtain the succession to the throne, he had to kill Maleficent. He instead cuts-off and steals her wings, claiming that he has killed her, and is crowned King. Maleficent, feeling betrayed and heartbroken, vows for revenge. Some time later, King Stephan hosts a christening for his newborn daughter, Aurora. As her revenge on Stephan, Maleficent arrives uninvited and curses the princess: on her 16th birthday, Aurora will prick her finger on the spindle of a spinning wheel and die (though she says "sleep-like death", unlike the original story where one of the fairies changed death to sleep). To the King's pleading, Maleficent adds that only a true love's kiss can break the curse, but only because she doesn't believe in true love's kiss. You know the gist: Stephan sends his daughter away for her own protection to be raised by the three good fairies (played by Imelda Staunton, Juno Temple and Lesley Manville). Maleficent, seeing the princess grow over the years, begins to care for her, and at 15, Aurora (played by Elle Fanning) meets her and mistakes Maleficent for her Fairy Godmother.
The more I think about Maleficent, the less and less I like it. The idea is to change the way we see good and evil, but the film fails on that aspect, except for one thing (and you already know what I'm talking about): Angelina Jolie as Maleficent. And I don't just mean the performance. I also like the way they re-told her backstory.
Some people were bothered by the whole "evil ex-girlfriend" revenge story, and some were bothered by the forced "rape" metaphor with the wings, but I think that it works here. From her betrayal, Maleficent feels love is nothing but a lie, and a tool used against her, which made her feel foolish and vulnerable, and has her lose something precious. When we see her form a relationship with Aurora, it makes her slowly realise that love is real, and that love is not a weakness, but a strength. Jolie's performance adds to the emotion of the character, like the pain and sorrow when she loses her wings, making it understandable why it leads her to doing what she does. It's also believable when we see her bond with Aurora (and I like that they used Jolie's real-life daughter as infant-Aurora in one scene, which was really cute). The backstory also works, because it explains why the terms of the curse were included: why Aurora had to be 16, since she was 16 when she lost her wings, and why it was a sleep-like death, because Stephan drugged her. And the addition of the "true love's kiss" to the curse was clever.
Jolie does a fantastic job playing Maleficent, whether she was playing the kind and playful girl, or the evil villainess we all know from the original Disney classic. Her performance and motivations were very consistent. On top of that, I loved her outfits and the make-up work they used on her (specifically the Lady-Gaga-inspired cheeks). When I went to the London Film and Comic Con, I wasn't surprised to see about four different girls dressing-up as Maleficent. Angelina Jolie just made that character her own, just like how Johnny Depp made Jack Sparrow his own.
Anything else I liked before I get into the negative aspects? Well, the film looks very nice. It does look beautiful and stylistic. It keeps a glow-y, fairy tale look. I also liked how Maleficent can put a spell which puts people to sleep, and making them float mid-air when they're unconscious, like they're floating on water. It looks really cool. And I liked Maleficent's raven Diaval, played by Sam Riley, who gets to be turned into a human (and other animals) so that Maleficent gets a buddy to talk to and assist her.
A performance that took me by surprise was Elle Fanning as Aurora. In her writing, she's follows the "generic good" character. She isn't as opinionated or rebellious as the original Aurora, who did respect and care for her aunts, but also wanted to get out and meet new people. Even so, I did get attracted to Aurora's character in this film. In terms of performance, she has a very playful and lively personality that it's hard not to like her. And like the original, she's very kind, graceful and even a bit mischievous. Fanning was just lovely on screen and really makes a great Disney Princess.
Sadly, I can't say the same for the rest of the characters in the film. Not only are they poorly written, but they are very unlikeable. It wasn't the performances that ruined it, but the characters themselves.
King Stephan's motivation is very confusing. He became corrupted by power, but he still loved Maleficent and never wanted to hurt her, but when we see him later, she somehow drives him mad, and he desperately wants her to be killed. Initially he wants his daughter to be protected, and we even see that later, but then he puts her daughter aside so he can try to kill Maleficent? Do you two even remember that you were once in love? Why is Stephan so psychotic? And what's his priority: his daughter or killing Maleficent? In the end, he was just the bad guy and nothing else.
Prince Phillip has little to nothing to do in the film. Like the other characters, they downgraded him in order to focus on Maleficent's relationship with Aurora. The original Prince Phillip was more developed (really the first developed Disney Prince), and he had a very big role in the story. Here, he just seems to be a decoy, if you know what I mean. It's not the actor's fault, as he has little to nothing to work with.
The worst offense has to be what they did to the three good fairies. They're the worst thing about this film! They have them as small pixie versions of themselves, and the CGI faces look very fake and unnatural. Though later they use real actresses when they turn human size anyway, so what's the point of turning them into fake CGI pixies? It's also weird, because Maleficent is also a fairy, so she doesn't have to turn herself into a pixie (though maybe there are different kinds of fairies, but they never touch upon it). But what's worse is that these fairies are just horrible people. I know the idea was to make them so bad at their job so that they can make Maleficent become the surrogate mother for Aurora, but these fairies are just stupid and so inept at their job that it's annoying and frustrating. No wonder Maleficent found Aurora so quickly! Sometimes Maleficent is using her magic on them, but none of them question anything. Nor do they seem to know where Aurora is going, because they're so neglectful and are terrible at keeping Aurora safe, as it doesn't even feel like they care about her at all. They do nothing but complain about having to take care of her, and having to live together with the fairies. No wonder Aurora doesn't immediately tell them about Maleficent!
It's such a shame, because the three fairies in the original were likable, funny, and were practically the main characters of the story! It was the fairies who have the idea to protect and raise Aurora, and eventually they rescue Phillip and assist him to defeating Maleficent, even enchanting the sword in order to kill the dragon! They're the real heroes of the story! So to downgrade the fairies from the original film to such a degrading level is just… depressing.
The rest of the writing is just flat, and some of the logic doesn't add up. If Maleficent's able to make her crow transform into any creature she wants, including a dragon, why not grow her wings back herself? And if the humans and the magical creatures are at war, why were the three fairies allowed to bless the child during the christening? Why are the humans so against the Moors?
This film had so much potential and could have been something fantastic, but was just mediocre. Maleficent had so much going for it, because there was so much that was done with Angelina Jolie's version of the character. The worst part is that there is so much that could have been fixed, but they didn't, because they put Maleficent so front-and-centre that they forgot about everything else. While I did care for Maleficent and Aurora's relationship, it's so important to the story that it makes the rest of the characters less developed or shoved aside. They even just had Aurora's real mother just dying off-screen during the film, like she's not even important. How sad that Maleficent robbed her of that chance, and that it's not even touched upon in the story!
It didn't bother me that it's a loose adaptation of Disney's Sleeping Beauty, and that they're trying to make Maleficent a sympathetic character instead of the Disney Villain that we know, but if you want to see a film about Maleficent being evil, there's very little of it here, and it might offend some Maleficent fans. I say stick to the original unless you'd like to see something different, or at least stick around to see Jolie at one of her best performances.
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text:
\section{Introduction}
\label{sec:intro}

Designing algorithms for solving ill-posed imaging inverse problems that both deliver high precision reconstructions and scale to large data volumes is a significant challenge of imaging sciences.
A problem where such considerations are paramount is aperture synthesis by Radio Interferometry (RI) in astronomy, an iconic example of linear inverse Fourier imaging problems \cite{wiaux2009compressed}.
In this context, the acquisition of an unknown target radio image $\overline{x}\in\mathbb{R}^N$ from observed data $z\in\mathbb{C}^M$ follows the observation model
\begin{equation}
z = H\overline{x} + e,
\label{eq:inv_pb}
\end{equation}
where $e\sim\mathcal{N}(0,\sigma^2)$ is the realisation of a complex Gaussian random noise, and where the linear measurement operator $H\colon \mathbb{R}^N \to \mathbb{C}^M$ represents (in its simplest form, see \cite{terris2022image} for details) a non-uniform Fourier transform.
More precisely, $H = UFZ$, where $Z$ is a zero-padding operator, $F$ denotes the 2D discrete Fourier transform, and $U$ is an interpolation matrix mapping the discrete Fourier coefficients onto the measured values of the continuous Fourier transform. The set of sampled values is characterised by a so-called sampling pattern. With the advent of a whole new generation of radio telescopes arrays aiming to probe the sky with much higher resolution and sensitivity, such as the flagship MeerKAT (\href{https://www.sarao.ac.za/}{sarao.ac.za}) and SKA (\href{https://www.skatelescope.org/}{skatelescope.org}), extreme image dimensions and data volumes are to be expected, along with dynamic ranges (\emph{i.e.}~the ratio between the brightest and faintest sources in $\overline{x}$) spanning multiple orders of magnitude.

Deep Neural Networks (DNNs) have shown outstanding results in solving linear inverse imaging problems. On the one hand, end-to-end approaches provide extremely fast reconstruction. They are widespread in other imaging communities \cite{ahmad2020plug, muckley2021results, liang2021swinir}, but their use remains limited in RI imaging \cite{terris2022image, gheller2021convolutional, connor2022deep}. This is mainly due to the lack of ground-truth datasets, combined with a wide intrinsic variability of the RI observation model, leading to generalisation issues. While unfolded architectures \cite{adler2018learned, banert2020data} provide necessary robustness to variations of the measurement setting, embedding large-scale measurement operators in DNN architectures is impractical, both for training and inference. On the other hand, Plug-and-Play (PnP) algorithms \cite{venkatakrishnan2013plug, zhang2021plug, pesquet2021learning}, substituting learned DNN denoisers in lieu of proximal regularisation operators in optimisation algorithms,
have shown outstanding performance and robustness, including for high-dynamic range imaging \cite{terris2022image,dabbech2022first}. However, PnP approaches remain highly iterative and will still struggle to scale to the image sizes and data volumes of interest in applications such as RI imaging.

In this paper, a new residual DNN series approach to large-scale high-dynamic range computational imaging is devised, where the reconstructed image is built as a sum of few
residual images progressively increasing the dynamic range, and estimated iteratively as output of DNNs taking the back-projected data residual of the previous iteration as input. We show on preliminary simulations for RI imaging that such a strategy may enable to achieve an imaging quality matching that delivered by the most advanced optimisation and PnP algorithms, while  being orders of magnitude faster thanks to its very limited iterative nature.

\section{Methodology}
\label{sec:approach}

\subsection{Proposed approach}
\label{subsec:proposedapproach}

We propose to train a series of DNNs $(G_{k})_{1\leq k \leq K}$ yielding a sequence of estimated reconstructions $(x_k)_{1\leq k \leq K}$ of $\overline{x}$, following the update rule
\begin{equation}
(\forall 1\leq k \leq K) \quad
\left\{
\begin{aligned}
r_{k-1} &= \gamma H^*(z-Hx_{k-1}),\\ x_{k} &= x_{k-1}+G_k(r_{k-1}),
\end{aligned}
\right.
\label{eq:sum_rules}
\end{equation}
where $x_0=0$ and where $(r_{k-1})_{1\leq k \leq K}$ corresponds to the data residuals back-projected in the image domain.
In \eqref{eq:sum_rules},
we set $\gamma = 1/\operatorname{max}(H^*H \delta)$ to ensure the normalisation of back-projected data residuals at the input of the networks ($\delta$ being the Dirac delta).
The last element of the sequence
and final reconstruction
$x_K$ also reads
\begin{equation}
x_K = \sum_{k=1}^K G_k(r_{k-1}),
\label{eq:sum_interp}
\end{equation}
hence the name ``deep network series''. In practice, we aim at keeping $K$ as small as possible. We also note that the first iteration is equivalent to a standard end-to-end learning approach, as $x_1=G_1(r_0)$ with $r_0=H^*z$ the back-projected data. The approach is summarised in Figure~\ref{fig:res}.

\begin{figure}[t]
\includegraphics[width=.48\textwidth, trim=0cm 4cm 0cm 4cm, clip]{images/series_summary}
\vspace{-2.25em}
\caption{\small Summary of the proposed approach. Top row shows the successive image estimates $x_k$ for $1\leq k \leq K$ while the bottom row shows the back-projected data residuals $r_{k-1}$, input to $G_k$, and the predicted image residuals $x_k-x_{k-1}$, output of $G_k$. Each network is shown in a different colour to stress that it is trained separately. In blue, the computation of $r_{k-1}$. Note that $x_0=0$ and $r_0=H^*z$.}
\vspace{-1.25em}
\label{fig:res}
\end{figure}

In order to train the sequence of DNNs $(G_k)_{1\leq k \leq K}$, we start from a dataset of $L$ synthetic ground-truth images $(\overline{x}_{l})_{1\leq l \leq L}$ from which we simulate measurements $(z_{l})_{1\leq l \leq L}$ as per \eqref{eq:inv_pb}. Then, for every $1\leq k \leq K$, we generate a dataset of residuals $(r_{k-1,l})_{1\leq l\leq L}$ as per \eqref{eq:sum_rules} and train $G_k$ to
\begin{equation}
\underset{\theta_k}{\text{minimise}}\,\, \frac{1}{L}\sum_{l=1}^{L} \|G_{k_,\theta_k}(r_{k-1,l})+x_{k-1,l}-\overline{x}_l\|_1,
\end{equation}
where $\theta_k$ denotes the learnable parameters of $G_k$.

\subsection{Relation to unfolding and PnP}

The proposed approach is reminiscent of both unfolded and PnP algorithms based on the Forward-Backward optimisation algorithm \cite{bauschke2017convex}, as the back-projected data residuals in \eqref{eq:sum_rules} can be written as $r_{k-1}=-\gamma\nabla f(x_{k-1})$ for $f(\cdot) = \frac{1}{2}\|H\cdot-z\|_2$. There are however important differences with such schemes. Firstly, instead of applying $G_k$ to $\operatorname{Id}-\gamma \nabla f$, $G_k$ is applied to the residual $-\gamma \nabla f$ only. Secondly, unfolded architectures are trained in an end-to-end fashion, whereas our approach proposes to train $G_k$ sequentially, with a dataset of back-projected data residuals $(r_{k-1,l})_{1\leq l\leq L}$ as input. We stress that this procedure,
involving the training of $K$ DNNs, is dictated by the large-scale nature of $H$, precluding its direct embedding into a DNN architecture. Thirdly, DNNs in PnP algorithms
are generally the same across iterations and trained as simple denoisers.

In its series expression \eqref{eq:sum_interp}, our approach is reminiscent of Neumann networks \cite{gilton2019neumann}. But the latter are unfolded architectures, embedding a single DNN regulariser across iterations.

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

\subsection{Training RI dataset \& training}
\label{dataset}

To circumvent the absence of an appropriate dataset of target radio images, we rely on a synthetic dataset from \cite{terris2022image}, where noisy, low-dynamic range optical images have been processed through denoising and exponentiation procedures to create a dataset of $L=2235$ clean images $(\overline{x}_{l})_{1\leq l \leq L}$, of size $N = 512\times 512$, and with nominal dynamic range slightly above $10^4$. The full dataset is normalised so that the maximum pixel value across all images is equal to 1.

For each image $\overline{x}_l$ we create data $z_l = H_l\overline{x}_l+e_l$, with a measurement operator $H_l$ resulting from simulating a sampling pattern of the MeerKAT telescope, with fixed integration and observation times, and randomly chosen pointing direction, leading to data vectors of size $M = 1.6\times 10^6$. The measurement noise $e_l$ is generated with fixed standard deviation set to ensure a back-projected noise level $\sigma/ \sqrt{2\|H_l^*H_l\|_2}\lesssim 10^{-4}$, thus leading to a target dynamic range of the order of, or slightly above, $10^4$. Following standard practice in the field, the data $z_l$, operator $H_l$, and noise $e_l$ are weighted component-wise by the inverse square-root of the sampling density at each point to mitigate the side-lobes of the point spread function $H_l^*H_l\delta$ (see Figure~\ref{fig:step_visualisation}.c).

We train the sequence of networks $(G_k)_{1\leq k \leq K}$ following the procedure detailed in Section~\ref{subsec:proposedapproach}. Each network $G_k$ has the same architecture, namely a UNet \cite{zhang2021plug} in which convolutional blocks were replaced by WDSR blocks \cite{yu2018wide}. The networks also include an initial normalisation layer, ensuring that any input image effectively has zero mean and unit standard deviation, mitigating potential generalisation issues related to variations of statistics between the training samples and any test sample. The output image is de-normalised as part of a last layer, using the same mean and standard deviation as computed at the input. Each network is trained with the Adam algorithm, for approximately 200 epochs for each network. In practice, we noticed that the reconstruction quality saturates for $K>4$ on the training dataset, and therefore chose $K=4$ for all our experiments.

\subsection{Test RI dataset \& simulation set up}

We validate the proposed methodology on a test dataset of 3 radio images (3c353, Hercules A, and Centaurus A, see \cite{terris2022image} for more details) of size $N = 512\times 512$, with peak value normalised to 1, and dynamic range slightly above $10^4$. For each of them, 5 RI data vectors are simulated from 5 MeerKAT sampling patterns generated as in Section \ref{dataset}, using the same integration and observation time, same noise standard deviation, and random pointing directions. This leads to a total of 15 inverse problems with data vectors of size $M = 1.6\times 10^6$.

Reconstruction quality is evaluated with the signal-to-noise ratio metric, as $\text{SNR}(x,\overline{x}) = 20\operatorname{log}_{10}(\|\overline{x}\|_2/\|\overline{x}-x\|_2)$ for an estimate $x$ of $\overline{x}$.
Since we aim at reconstructing high-dynamic range images, we visualise reconstructions in logarithmic scale (\emph{i.e.}~after applying the transform $\operatorname{rlog}\colon x \mapsto \operatorname{log}_{10}(10^3x+1)/3$), which helps visualising very faint features. We also compute the SNR in logarithmic scale as $\text{logSNR}(x,\overline{x}) = \text{SNR}(\operatorname{rlog}(x),\operatorname{rlog}(\overline{x}))$. When evaluating the reconstruction quality of the proposed method across iterations, we also use the relative norm of the back-projected data residuals $\eta_k=\|r_k\|_2/\|r_0\|_2$, measuring the relative amount of data left to be processed after each iteration.

\subsection{Benchmark RI algorithms}

We compare the proposed method to various RI imaging algorithms, from the recent and advanced sparsity-promoting optimisation algorithm SARA \cite{onose2016scalable, thouvenin2022parallel}, to its PnP counterpart AIRI \cite{terris2022image, dabbech2022first}, to the traditional RI imaging algorithm CLEAN \cite{offringa2014wsclean}, and to an end-to-end approach resulting from cutting the network series to $K=1$ in the proposed approach.

Interestingly, CLEAN relies on a matching pursuit approach iteratively identifying model components from a back-projected data residual. From this perspective our approach is conceptually close to CLEAN. However, while the simplicity of CLEAN's implicit sparsity image model makes it scalable to large image dimensions and data volumes, this comes at the cost of a now commonly accepted severe sub-optimality in reconstruction quality. Our approach leverages a fundamentally different, DNN-based, component identification process from the back-projected data residual at each iteration.

\subsection{Results}

\begin{figure}[t] \small %
\centering %
\begin{tabular}{@{\hspace{-0.\tabcolsep}} c @{\hspace{0.1\tabcolsep}} c @{\hspace{0.1\tabcolsep}} c @{\hspace{0.1\tabcolsep}} c @{\hspace{0.\tabcolsep}} l @{\hspace{0.\tabcolsep}}}
\includegraphics[width=0.11\textwidth]{images/results/zoom_3c353_ter.pdf} &
\includegraphics[width=0.11\textwidth]{images/results/zoom_dirty_bis.pdf} &
\includegraphics[height=0.11\textwidth]{images/results/sampling_pattern.png}
&
 &
\\
(a) $\overline{x}$ & (b) $H^*z$ & (c) sampling & & \\
\includegraphics[width=0.11\textwidth]{images/results/dirty.png} &
\includegraphics[width=0.11\textwidth]{images/results/res1.png} &
\includegraphics[width=0.11\textwidth]{images/results/res2.png} &
\includegraphics[width=0.11\textwidth]{images/results/res3.png} &
\raisebox{-0.\height}[0pt][0pt]{\includegraphics[width=0.025\textwidth]{images/results/errorbar_vertical.png}} \\
(d) $r_0=H^*z$ & (e) $r_1$ & (f) $r_2$ & (g) $r_3$ \\
\includegraphics[width=0.11\textwidth]{images/results/zoom_G1.pdf} &
\includegraphics[width=0.11\textwidth]{images/results/zoom_G2.pdf} &
\includegraphics[width=0.11\textwidth]{images/results/zoom_G3.pdf} &
\includegraphics[width=0.11\textwidth]{images/results/zoom_G4.pdf} &
\raisebox{-0.\height}[0pt][0pt]{\includegraphics[width=0.025\textwidth]{images/results/colorbar_vertical.png}} \\
(h) $x_1$ & (i) $x_2$ & (j) $x_3$ & (k) $x_4$ & \\
$(21.0, 11.0)$ & $(27.9, 22.3)$ & $(28.1, 24.2)$ &
$(28.2, 25.1)$
\end{tabular}
\vspace{-0.5em}
\caption{\small Different stages of the proposed approach illustrated on one of the three images (3c353) and for one of the five sampling patterns of the test dataset. Top row shows (a) the ground truth $\overline{x}$, (b) the back-projected data $H^*z$, and (c) the weighted Fourier sampling pattern (colorbar displaying weight values). The second row (panels (d)-(g)) and third row (panels (h)-(k)) show the back-projected data residuals $r_{k-1}$ and reconstructions $x_k$ for $1\leq k \leq 4$, below which associated (SNR, logSNR) metrics are displayed.}
\label{fig:step_visualisation}
\end{figure}

\begin{figure}[t] %
\begin{tabular}{@{\hspace{-0.\tabcolsep}} c @{\hspace{-0.\tabcolsep}} c @{\hspace{-0.\tabcolsep}}} %
\includegraphics[width=0.235\textwidth, trim=0cm 0.25cm 0cm 0.2cm, clip]{images/results/metrics.png} &
\includegraphics[width=0.24\textwidth, trim=0cm 0.25cm 0cm 0.2cm, clip]{images/results/residual_norms_vert.png}
\end{tabular}%
\vspace{-0.75em}
\caption{\small Evolution of average reconstruction quality across iterations $1\leq k \leq 4$ over the test dataset, with error bars indicating $95\%$ confidence intervals. Left: SNR and logSNR metrics for $x_k$. Right: Back-projected data residual norm $\eta_k$.}
\vspace{-0.75em}
\label{fig:metrics_average}%
\end{figure}%

\begin{figure*}[t] \small
\centering
\begin{tabular}{@{\hspace{0.\tabcolsep}} c @{\hspace{0.05\tabcolsep}} c @{\hspace{0.05\tabcolsep}} c @{\hspace{0.05\tabcolsep}} c @{\hspace{0.05\tabcolsep}} c @{\hspace{0.05\tabcolsep}} c @{\hspace{0.05\tabcolsep}} c @{\hspace{-0.0\tabcolsep}} l @{\hspace{0.\tabcolsep}}}
(a) Ground truth & (b) Back-projected & (c) CLEAN & (d) SARA & (e) AIRI &  (f) $x_1$ & (g) $x_4$ \\
\includegraphics[width=0.135\textwidth]{images/results_bis/zoom_hercA_4_true} &
\includegraphics[width=0.135\textwidth]{images/results_bis/zoom_hercA_4_dirty} &
\includegraphics[width=0.135\textwidth]{images/results_bis/zoom_hercA_4_wsclean} &
\includegraphics[width=0.135\textwidth]{images/results_bis/zoom_hercA_4_SARA} &
\includegraphics[width=0.135\textwidth]{images/results_bis/zoom_hercA_4_uPnP_astro} &
\includegraphics[width=0.135\textwidth]{images/results_bis/zoom_hercA_4_G1} &
\includegraphics[width=0.135\textwidth]{images/results_bis/zoom_hercA_4_G4} &
\raisebox{-0.5\height}[0pt][0pt]{\includegraphics[width=0.05\textwidth]{images/results/colorbar_vertical.png}} \\
  & $(-11.0, -6.9)$  & $(-2.7, 0.2)$ & $(\mathbf{27.0}, 24.6)$ & $(25.0, 24.7)$ & $(20.8, 11.8)$ & $(26.0, \mathbf{25.1})$\\
 \includegraphics[width=0.135\textwidth]{images/results_bis/zoom_cena_2_true} &
\includegraphics[width=0.135\textwidth]{images/results_bis/zoom_cena_2_dirty} &
\includegraphics[width=0.135\textwidth]{images/results_bis/zoom_cena_2_wsclean.pdf} &
\includegraphics[width=0.135\textwidth]{images/results_bis/zoom_cena_2_SARA} &
\includegraphics[width=0.135\textwidth]{images/results_bis/zoom_cena_2_uPnP_astro} &
\includegraphics[width=0.135\textwidth]{images/results_bis/zoom_cena_2_G1} &
\includegraphics[width=0.135\textwidth]{images/results_bis/zoom_cena_2_G4} &
 \\
  & $(-13.5, -6.8)$  & $(-4.6, 0.4)$ & $(26.8, \mathbf{24.7})$  & $(25.3, 24.3)$ & $(18.0, 16.9)$ & $(\mathbf{27.7}, 23.8)$ \\
\end{tabular}
\vspace{-1em}
\caption{\small Reconstruction results for the various algorithms considered, for the Hercules A (top) and Centaurus A (bottom) test images, each probed through one of the 5 test MeerKAT sampling patterns. (SNR, logSNR) metrics (in dB) are displayed below each reconstruction.}
\vspace{-0.75em}
\label{fig:comparison_3c353_dt8}
\end{figure*}

\begin{table}[t]
\centering
\small
\begin{tabular}{@{\hspace{0.1\tabcolsep}}l@{\hspace{1.1\tabcolsep}}c@{\hspace{1.1\tabcolsep}}c@{\hspace{1.1\tabcolsep}}c@{\hspace{1.1\tabcolsep}}c@{\hspace{0.1\tabcolsep}}}
\toprule
Algorithm & SNR  & logSNR & time (s.) & iteration \#\\
\midrule
CLEAN & $5.3 \pm 0.1$ & $8.2 \pm 0.4$ & $217 \pm 5$ & $8.8\pm 0.2$\\
SARA &  $\mathbf{26.6} \pm 0.8$ & $\mathbf{24.9} \pm 0.3$ & $ 11291\pm 563$ & $4825\pm190$ \\
AIRI & $25.1 \pm 0.8$ & $\mathbf{25.0} \pm 0.4$ & $4258 \pm 140$ & $4515\pm103$  \\
\midrule
$x_1$ & $20.3 \pm 1.1$ & $13.3 \pm 1.2$ & $\mathbf{1.0}\pm 0.2$ & $1$\\
$x_4$ & $\mathbf{26.8} \pm 1.1$ & $24.5 \pm 0.4$ & $3.9 \pm 0.6$ & $4$\\
\bottomrule
\end{tabular}
\caption{\small Reconstruction SNR, logSNR, times, and number of iterations, for the different imaging algorithms considered. Values reported are averages over the test dataset, with associated 95\% confidence intervals.}
\vspace{-1.25em}
\label{tab:comparison_table}
\end{table}

Figure~\ref{fig:step_visualisation} shows the different stages of the proposed reconstruction method, on 3c353 and for the sampling pattern shown in panel (c). Figure~\ref{fig:metrics_average} shows the average SNR and logSNR metrics and residual norms on the 15 inverse problems of the test dataset. The first estimate $x_1 = G_1(r_0)$ provides good results at high intensities but is not accurate at faint intensities. This translates into relatively high SNR, but poor logSNR values. The associated residual $r_1$ contains important information and is far from random noise. The output $x_2$ is much more accurate than $x_1$, not only at high intensities (improvement of the SNR), but also at fainter intensities (improvement of the logSNR).
It is also associated with a residual $r_2$ containing much less information than $r_1$, also evidenced by $\eta_2<\eta_1$, supporting the observation that the $x_2$ reconstruction is more faithful to the measurements than $x_1$. While the SNR does not evolve significantly between  $x_2$ and  $x_3$, with also $\eta_3\simeq\eta_2$, it appears clearly that $r_3$ contains less residual signal structure than $r_2$, suggesting that $x_3$ provides further improvement at faint intensities. This is evidenced by a noticeable improvement in logSNR. A final logSNR increase is brought in $x_4$.

Table~\ref{tab:comparison_table} shows the average SNR, logSNR, reconstruction times, and number of iterations, as well as associated 95\% confidence intervals over the 15 test inverse problems,  with comparison to the benchmark algorithms. Figure~\ref{fig:comparison_3c353_dt8} provides visual reconstruction results of Hercules A and Centaurus A, for 2 sampling patterns different from the one in Figure~\ref{fig:step_visualisation}. In terms of imaging quality, the reported statistics confirm that the proposed approach is on par with SARA and AIRI, all three methods achieving high resolution and high dynamic range. The end-to-end approach falls short from delivering a similar resolution and dynamic range, but is still significantly better than CLEAN. In terms of reconstruction times, the proposed approach is two orders of magnitude faster than CLEAN, and three orders of magnitude faster than AIRI, itself more than twice as fast as SARA. The computation time of each method crudely evolves proportionally to the number of iterations. More precisely, the proposed method, the end-to-end approach, and AIRI, share a very similar computation time per iteration, due to a similar iteration structure consisting in computing a residual followed by application of a DNN. SARA's iteration cost is heavier due to costly proximal operations associated with its regularisation approach. CLEAN's iteration cost is also heavier, mainly due to the fact it does not keep the measurement operator in memory.

We finally note that preliminary results not reported here suggest that the proposed hybrid DNN architecture used outperforms architectures such as UNet and WDSR, both in the end-to-end and residual network series approaches.

\section{Conclusion}
\label{sec:conclusion}

In this paper, we proposed a novel residual DNN series approach for solving large-scale high-dynamic range computational imaging problems, circumventing scalability challenges encountered by both unfolded end-to-end architectures and PnP approaches. Each network of the series, trained to transform a back-projected data residual into an image residual, improves the dynamic range from the previous iteration, with the sum of the output residual images across iterations representing the final reconstruction.

Simulation results for RI imaging show that a series of only a few terms achieves similar reconstruction quality to the state-of-the-art optimisation algorithm SARA and PnP method AIRI, whilst being orders of magnitude faster.

Future work should investigate the robustness of the approach to the wide variations of measurements settings characterising RI observation, and its practical effectiveness on real large-scale high-dynamic range data. The suggested superiority of the DNN architecture proposed over state-of-the-art architectures should also be confirmed. The application of the proposed methodology to other computational imaging modalities is naturally an open question.

\vfill\pagebreak

\bibliographystyle{IEEEbib}

redpajama_set_name: RedPajamaArXiv


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Scenes for a New Heritage: Contemporary Art from the Collection is a sweeping reinstallation of MoMA's Contemporary Galleries. This cross-medium selection of works, created in the past three decades by more than 30 international artists, represents a wide range of approaches to the political, social, and cultural flux that have shaped the current global landscape. Some of these artists use the lens of history—reflecting on past events or centuries-old artistic traditions—as a means of assessing current conditions. In Scene for a New Heritage, the project that lends the exhibition its title, Croatian artist David Maljković uses an abandoned socialist monument to imagine an alternate future, one informed by events of the past but never realized. Other artists fight to stave off collective amnesia through projects of commemoration; trace the crosscurrents of trade; follow patterns of migration to swelling urban centers; or explore channels for capturing, circulating, and distributing images in today's highly digitized society—from mobile phones to online platforms. Made under a diverse range of geographic, political, social, and aesthetic circumstances, the works in the exhibition propose one perspective on the Museum's collection; seen alongside one another, they allow for a reflection not only on their discrepancies, differences, and contradictions, but also on their shared concerns.
A number of works return to the galleries after extended absences—Cai Guo-Qiang's monumental Borrowing Your Enemy's Arrows, a fishing boat pierced by several thousand arrows, is displayed at the Museum for the first time in over a decade—while some 20 works are on view for the first time at MoMA. The latter group includes Gamepieces, a multimedia installation by Nalini Malani that deftly blends mythology and history; Haegue Yang's Sallim, a sculptural reinterpretation of the artist's Berlin kitchen; and Alfredo Jaar's landmark project Lament of the Images. Other featured artists include Luis Camnitzer, Camille Henrot, Feng Mengbo, Rabih Mroué, Allan Sekula, and Kara Walker.
Organized by Quentin Bajac, the Joel and Anne Ehrenkranz Chief Curator of Photography; Eva Respini, Curator, Department of Photography; Ana Janevski, Associate Curator, Department of Media and Performance Art; and Sarah Suzuki, Associate Curator, Department of Drawings and Prints; with Katerina Stathopoulou, Curatorial Assistant, Department of Photography.
The exhibition is supported by the MoMA Annual Exhibition Fund.
Why Does Contemporary Art Look This Way?


text:
Rangers' young guns may not get a chance to fire side into the top flight
Rob DevineThursday 15 May 2014 4:43 pm
Lewis Macleod is one of Rangers' most promising youngsters (Picture: PA Wire)
They are young, bursting with boundless energy, possessing proven technical ability and cup winning experience.
So will Ally McCoist be tempted to give them an opportunity next season? Rangers under-20's emerged victorious from a pulsating Scottish Youth Cup Final last Tuesday night defeating a Hearts side, including many with proven SPFL experience, following a penalty shoot-out with cocky goalkeeper Liam Kelly the star of the show.
Now the question on the lips of many of the Gers faithful who watched on is – will we see any of these talented young starlets be pulling on the famous blue shirt during the club's Championship campaign next term?
If anything, previous experience has taught us McCoist is reluctant to blood Murray Park graduates unless forced too, instead the Rangers manager appears to have a predilection for only giving them a sniff when all other options are limited.
In his three years at the helm the numbers of youngsters who have broken through to establish themselves as regulars has been a rarity. Kyle Hutton, Gregg Wylde, Jamie Ness, Rhys McCabe, Chris Hegarty, Lewis Macleod and Barrie McKay have made several appearances for the team, though Macleod aside, none of them have been able to hold down a regular berth.
Without doubt, Macleod has been the pick of the bunch and it was welcome news this week that he appears to have overcome the viral infection which many thought would threaten his career.
Will Rangers manager Ally McCoist give youth a chance next season? (Picture: Getty Images)
Wylde, Ness and McCabe initially appeared to be full of potential only for all three to opt to seek their fortunes elsewhere following the collapse of the club in 2012.
Hegarty has already been informed to find himself a new club, whilst Hutton is awaiting news of whether he will be offered a new contract.
Pacy winger McKay spent last year on loan at Morton, though he will probably be expected to push for a place in the top team next season.
Speculation that McCoist has targeted the former front pairing of Kris Boyd and Kenny Miller as the strike force to fire the club back into the top flight, allied with the fact ex-Ibrox cult hero Madjid Bougherra is itching for a return to Glasgow, has frustrated many of the Gers support, who are keen the emphasis of the team is on youth.
Indeed, a large portion of the fan base are firm in their opinion that McCoist has missed a trick. With the team plying their stuff in the lower divisions, a real opportunity was presented for him to develop a philosophy of quick, attractive passing football reliant on youngsters instead of enticing experienced ex-SPL campaigners on inflated wages.
One thing which stood out from the Ibrox's side's Scottish Cup exit at the hands of Dundee United was the fact their opponents had a host of highly promising youngsters in the side. Again this highlighted the fact that Rangers simply don't appear to be developing enough players of real quality, something which should be a necessity at a time when the finances of the club are limited due to years of mismanagement.
For a club with a history of producing players who went on to become legends such as Willie Waddell, Eric Caldow, John Greig, Willie Henderson, Sandy Jardine, Derek Johnstone, Ian Durrant, Barry Ferguson and Allan McGregor, it would be sad to see Rangers failing to give some of their current young colts the opportunity required to one day join that esteemed company.
MORE: Club Metro
Club Metro is a place for football reporting by fans, for fans. The opinions expressed belong to the author and are not necessarily shared by Metro.


For the science heads: Relativistic interpretation of Casimir effect Expanded – COLD FUSION NOW!
This is a pretty heavy science article referencing cold fusion.
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