ISTR that the scene in question is available on Youtube. I thought that I had a link to it, but I seem to have mislaid it.
And there was me thinking that Sami Kehela had an eastern European accent!
So far I've only watched episode 1, where I especially enjoyed the second hand. It's such an interesting deal, that I suspect that it was pre-prepared rather than randomly dealt at the table. Either that, or the producers got very lucky in having something so fascinating crop up so quickly.
I voted: "The Selectors should lean towards selecting the team that qualified but should bear in mind that they are aiming to select a team good enough to win a World Championship." The women's team would surely be stronger for the inclusion of Nicola Smith, though it would be tough luck on whichever player had to make way for her.
That's very sad. As an ordinary EBU member, I view the Olympiad as being second in importance only to the Bermuda Bowl and Venice Cup, and want England to be represented by the strongest possible Open and Women's teams. If that's not the majority attitude amongst EBU members, then I find it surprising and most unfortunate.
The whole magazine is a fascinating read. I was amused by this in the editor's introductory piece, concerning the criteria for international selection: "match temperament, social polish, suavity at the table, as well as ethical conduct must come under consideration as well as technical ability". We don't want any working-class oiks playing for Great Britain!
I always enjoy reading your articles, and I am looking forward to the upcoming ones. On a pedantic note, shouldn't it be "Bridge magazine" to avoid any possible confusion, "Bridge Magazine" being an altogether different publication?
This is very good news. I've duly registered. At least, I think I have, as I didn't get an automated confirmatory email like I was expecting.
That's a shame. I ceased being a subscriber a few years ago, so I feel slightly guilty for being in a small way responsible for it seemingly no longer being economically viable. What caused me to stop subscribing was their decision to cease publishing a printed version and to become solely an electronic publication. Having become something of a luddite, I have only a desktop PC, and I like to be able to read a magazine while slumped in an armchair.
Harrison-Gray the novelist - which pen name?
I guess from the first three entries that the date originally reported of 16th April was incorrect and that the 2nd July was the right one. Or could the different form of his name used for the 2nd July entry be significant? Could his habit of changing the preferred form of his name from time to time have caused confusion? Or could there have been another RAF officer with a very similar name, with one being mistaken for the other?
Was it usual for RAF officers to retain their rank long after they had left the service, as the 1954 entry seems to imply? I had thought that that was purely an army thing.
Yet Another "Identify That Bridge Player"
Clue 4 seemed to ring a bell, but it was only with clue 5 that light finally dawned.
Do we know when the family business was taken over? If so, that might help in searching for his fiction.
The third cartoon from "Punch" will always remain topical.
https://archive.cartoons.ac.uk/Overview.aspx?src=CalmView.Catalog link, putting in "Culbertson" as the search term turns up five cartoons by Tom Webster (no relation to the US cartoonist as far as I know) featuring Ely Culbertson.
I thought of him/her as a possibility after number 3, but it was only after number 4 that I was reasonably certain.
Incidentally, though "Aces All" and "Bridge at the Top" are both out of print, second-hand copies are readily available via Amazon UK.
Where can I get one of those indestructible radios? I imagine that everyone has seen the bridge game in the Marx Brothers' film "Animal Crackers", showing that cheating at bridge is nothing new?
Excellent. I confess to knowing nothing about Louisa Chamberlain and George Lengyel, so I should find those articles particularly informative.
Wow! I envy Valentine Ramsey. Simon and Stern were arguably the two most larger-than-life characters in the history of English bridge - though perhaps I ought to add Rixi Markus as a third. And to have been taught bridge by Reese!
BTW, do you have any plans to write a piece on Richard Lederer who, although he now seems to be largely forgotten, appears to have been another towering figure in the early days of the game in England?
But for all that, it's a marvellously entertaining read.


Mindfulness can strengthen focus, manage stress, and improve emotional reactivity. But how do you use it in everyday life? Start here, with this list of recommended activities, apps, books, and online resources for children, parents, and teachers.
Can Mindfulness Help with ADHD?
A daily dose of mindfulness, at home or in school, improves attention, boosts self-esteem, and helps kids better monitor their emotions.
Here, find exercises, apps, and resources to help teachers and caregivers lead the yoga and mindfulness exercises shown to help children with ADHD.


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unloaded 7.3 SALOON STOCK CAR WORLD RANKINGS
There are four methods of attaining World Ranking points.
A. Attendance and performance at Championship events (World, British, European, National, English, Scottish, Irish Open).
Attendance points – 20 points per driver who race at the World Championship meeting.
Performance points – 10 down to 1 for the first ten in the title race, with a multiplication factor of 5.
i.e. 50, 45, 40, 35, 30, 25, 20, 15, 10, 5
British, European and National
Attendance points – 12 points per driver who race at the Championship meeting. If the event is staged over two days, drivers get 6 attendance points per day.
i.e. 40, 36, 32, 28, 24, 20, 16, 12, 8, 4
English, Scottish and Irish Open
Attendance points – 8 points per driver who race at the Championship meeting. If the event is staged over two days, drivers get 4 attendance points per day.
i.e. 30, 27, 24, 21, 18, 15, 12, 9, 6, 3
B. Attendance and performance at designated World Ranking events. Only a small number of these meetings are scheduled each season – typically UK Speedweekend, UK Superbowl and the ORC Championship.
Designated World Ranking Event
Attendance points – 6 points per driver who race at the event. If the event is staged over two days, drivers get 3 attendance points per day.
i.e. 20, 18, 16, 14, 12, 10, 8, 6, 4, 2
C. Track Championship standings, adjusted accordingly for the number of meetings per venue.
Points are awarded 20 down to 1 for the first 20 in each Track Championship, multiplied by the number of meetings that have been staged at that track.
Number of Multiplication
Meetings per track factor
9 – 16 3
26 or more 5
At the start of each season, these Track Championship scores are re-set to zero.
D. Late season Track Championship standings, adjusted accordingly for the number of meetings per venue.
This fourth method of accumulating World Ranking points was introduced in 2010, in an attempt to add relevance to race meetings which fell in the period of the season between the cut-off for the World Final grid and the end of the year. Points are awarded 20 down to 1 for the first 20 in each Track Championship, multiplied by the number of meetings that have been staged at that track. However, this only applies to meetings staged after the cut-off date for the World Final grid – typically 20 July or 20 August. World Ranking points scored in this manner, remain 'live' until the following year's World Final grid has been announced.

redpajama_set_name: RedPajamaCommonCrawl


text:
Community Voice Pro
Brings you the latest news, view, and reviews from the state of the U.S. And Multicultural focusing on Health, Food, weather, living life all content creator.
https://www.etsy.com/shop/BuylikeFlyStore
Could black death made a comeback?
Alissa Rose 14 days ago
Image byRita Greer, FAL, via Wikimedia Commons
The Black Death was one of the most catastrophic diseases recorded in ancient times, and some people also called it 'Magna Mortalitas' (great mortality), highlighting the high mortality rate.
It almost wiped out more population than any other known disaster.
As per the observation, "the living were scarcely sufficient to bury the dead," and Nobody knew what had triggered the devastating pandemic.
While there are no clear records, historians estimate that between 1347 and 1351, the Black Death killed between 30% and 60% of Europe's population.
The Black Death was the deadliest disease plague epidemic that killed more than 25-30 million Europeans.
According to CNN Post, authorities of china said that in November 2019, two people were being treated for the plague.
According to the World Health Organization (WHO), more than 3,248 cases were reported worldwide from 2010 to 2015.
Plague, an infectious fever caused by the bacterium Yersinia pestis, has caused the Black Death. The disease was most likely transmitted from rats to people through the bite of infected fleas.
Yersinia pestis causes three types of plague in the human body: pneumonic, septicemic, and bubonic. And all these three plagues probably played some role in the pandemic.
Pneumonic plague mainly affected the lungs, and the symptoms are:
Speech disorder
Fluid fills the lungs
Loss of memory
The Septicemic plague is a blood infection, and its symptoms are:
Bubonic plague symptoms are:
Giddiness
Pain in the back and limbs
Shivering
Researchers believe that "Plague is something that will continue to happen, but modern-day antibiotics should be able to stop it."
What are you think about Black Death? Give your opinion in the comment section, and if you think more people should read this article, share it on social media and with your friends and family.
Disclaimer: This article is only for educational and informational purposes.
# History# Health# dangerous disease# Mysterious# Black death
Comments / 173
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A major food shortage is coming again in the United States.
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Experts warn dangerous superbug fungus infections found in some cities of America.
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A severe winter storm is coming to Chicago IL this New Year's weekend.
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Experts warn that grocery items are going to be very expensive in 2022.
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Eligible taxpayers can get $5,980 stimulus checks this holiday?
According to the IRS, Earned Income Tax Credit payments will be issued for some low- and middle-income Americans and their families will receive a tax exemption. A person with two children and those with low income may receive a credit of 40% of their earnings, up to a maximum of $5,980.
A surprise stimulus check is coming this holiday.
There is some good news for Americans, and a select few will receive another stimulus check before the holidays. The federal government's fourth stimulus check has been canceled, but some states will still be handing out cash to residents in time for the holidays.
The English Sweat Disease took the lives of thousands and then disappeared from history.
Image byJohn Collier, Public domain, via Wikimedia Commons. A mysterious and contagious disease called English Sweating Sickness or Sweating Disease was found in England in the late 15th century.
American health could suffer due to climate change.
Climate change in the United States is worsening day by day. As we all see today, hurricanes are becoming stronger and more significant, heat waves are more frequent, the severity of land-falling increases, wildfires are bigger, and Seas are warmer, and flooding is more common.
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Fastest Growing Cities in Georgia.
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The Fort Lewis College English Department seeks applications for a tenure-track Assistant Professor of English. The successful candidate will teach our rhetoric-centered composition courses and contribute to department and campus-wide service.
Interest in and/or experience with writing across the curriculum/ writing in the disciplines.
Applicants should send a letter of application that directly addresses the position responsibilities, including a statement describing how the candidate's pedagogical approach to diversity;, C.V.; and contact information to: English Department, Fort Lewis College, 1000 Rim Drive, Durango, CO, 81301, or submit materials electronically to widen_d@fortlewis.edu. For questions, contact Dr. Candace Nadon, Assistant Professor of English via email at canadon@fortlewis.edu. The application deadline is November 23rd, 2018.
Fort Lewis College is a public liberal arts college that launches careers and changes lives through top-quality academic programs tailored to each student's strengths, passions, style, and goals. Our 247-acre Fort Lewis College campus is in southwestern Colorado, situated at 6,872 feet atop a mesa overlooking the Animas River Valley and historic downtown Durango. For more information about us, please visit our website: www.fortlewis.edu.


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We take a holistic, fluid approach to search, focused on providing the best user experience possible. Our strategies don't just get our clients found — they get visitors to engage, act and convert. With the days of keyword stuffing and spam tactics long gone, we emphasize content marketing, brand acceptance, semantic search and organic link building to target only the most qualified traffic. Of course, we're constantly measuring incredible amounts of data along way, allowing our strategies to continually evolve and improve in order to provide the greatest possible return-on-investment.
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1 Actor/Actress / 4 Musician / D
by ethnic · July 23, 2011
Deschanel in 2011, Helga Esteb / Shutterstock.com
Birth Name: Zooey Claire Deschanel
Place of Birth: Los Angeles, California, United States
Ethnicity: French, English, German, Irish, Scots-Irish/Northern Irish, Swiss, Dutch, distant Cornish
Zooey Deschanel is an American actress and musician. She is the daughter of Mary Jo Deschanel, an actress, and Caleb Deschanel, a cinematographer and director. Her sister is actress Emily Deschanel. Zooey has two children with her former husband, producer Jacob Pechenik.
Her father is of half French ancestry, with his other roots including English, German, Scots-Irish/Northern Irish, and distant Cornish. Her mother's ancestry includes English, Irish, Northern Irish/Scots-Irish, French, Swiss, and Dutch.
Zooey's father is a Quaker and her mother is Catholic. Zooey was raised in what she has described as a "you can be whatever you want" way. In 2015, US Magazine reported that Zooey has converted to Judaism (her then-husband Jacob's religion).
Zooey's paternal grandfather was Paul Jules Deschanel (the son of Joseph Marcelin Eugène Deschanel and Marie Josephine Favre). Paul was born in Oullins, Rhône, France, and moved to the United States in 1930. Joseph was the son of Jean Joseph Deschanel and Eugenie Michelle Amyot.
Zooey's paternal grandmother was Anna/Ann Ward Orr (the daughter of Adrian Van Bracklin Orr and Mary Beulah E. Lamb). Anna was born in Pennsylvania. Adrian was the son of Joseph Moffitt/Maffett Orr and Martha Elizabeth Pownall. Mary Beulah was the daughter of Caleb W. Lamb and Anna Matilda Ward.
Zooey's maternal grandfather was Donald Lamar Weir (the son of Ralph Harrison Weir and Helen Mary Ellen Lamar). Donald was born in Ohio. Ralph was the son of Addison Mohallan Weir, whose father was born in Lifford, Donegal, Ireland, and of Elizabeth Emilie Lanie Gurney, whose father was from Belfort, France, and whose mother was from Bern, Switzerland. Helen was the daughter of Henry Anson Lamar, Jr. and Mary B. Sullivan.
Zooey's maternal grandmother was Elizabeth Ellen Manchester (the daughter of Raymond Earl Manchester and Mary Josephine Kennedy). Elizabeth was born in Wisconsin. Raymond was the son of Frank H. Manchester and Phoebe Carpenter. Mary Josephine was the daughter of William Andrew Kennedy, whose parents were Irish, and of Mary Ellen Kenny, who was born in Limerick, Ireland.
Sources: Family history of Zooey Deschanel – http://genealogy.about.com
Genealogy of Zooey Deschanel – https://www.geni.com
Genealogy of Zooey Deschanel (focusing on her father's side) – http://worldconnect.rootsweb.ancestry.com
Genealogy of Zooey's paternal grandmother, Anna/Ann Ward (Orr) Deschanel (focusing on her own father's side) – https://www.findagrave.com
Grace Potter
Elley Duhé
Tags: CornishDutchEnglishFrenchGermanIrishJewishNorthern IrishScots-IrishSwiss
ses101 says:
Yeah, too often Scottish and Scots-Irish descent is ignored or reduced to Irish by Americans, like as if any descendents from Ireland were 100 percent Irish. As it happens, though, she does actually look more Irish than anything else.
You have no idea what you're talking about. lol Americans are very clear on dividing northern Ulster Protestants from the Irish Catholics in the Republic. There is an entire region of the country where the Scots-Irish culture is prominent and celebrated just as the Irish Catholics in New England. The Census bureau literally allows one to choose their ancestry as either Irish, Scottish or Scots-Irish.
is berry says:
she looks like katy perry
cdonorab says:
Corection she has Scotts-Irish ancestry on her father's side as well. The Orr family of her grandmother Annie originally came from Scotland and they moved to Ireland. The first Scottish ones were from the 1500s and moved to Ireland in the mid 1600s.
Jai Courtney
Andre Ward
Jacob Collier
Yahya Abdul-Mateen II
Lili Reinhart
Toni Garrn
Helen Lasichanh


text:
Gabriele "Gabi" Roth, (née Lippe, born 8 May 1967) is a retired German hurdler. She represented Germany at the 1992 Olympic Games in Barcelona.

Career
Roth was born Gabrielle Lippe in Lörrach-Brombach, Germany and represented West Germany until 1990. She won a bronze medal in the 60 m hurdles at the 1989 European Indoor Championships in The Hague. At the 1990 European Championships in Split, she won a silver medal in 4x100 m relay, together with Ulrike Sarvari, Andrea Thomas and Silke Knoll. She also reached the 100 m hurdles final, but failed to finish. She represented a united Germany at the 1992 Olympic Games in Barcelona and reached the semi-finals of the 100 m hurdles. A month later at the World Cup in Havana, she won a bronze medal in the 100 m hurdles.

She represented the sports club MTG Mannheim, and won the silver medal at the German championships in 1996. Her personal best time was 12.82 seconds, achieved in August 1990 in Düsseldorf.

International competitions

References

External links

1967 births
Living people
German female hurdlers
Athletes (track and field) at the 1992 Summer Olympics
Olympic athletes of Germany
European Athletics Championships medalists
People from Lörrach
Sportspeople from Freiburg (region)
MTG Mannheim athletes

redpajama_set_name: RedPajamaWikipedia


text:
Stephen King Thinks 'Dark Tower' Rumors Are True
Posted by Ryan | Feb 10, 2016 | Film, News | 0 |
Stephen King practically confirms the "Dark Tower" rumors we've been hearing.
We've been hearing things for quite some time regarding the film adaptation of Stephen King's "Dark Tower." From release dates to casting rumors including Matthew McConaughey and Idris Elba, "Dark Tower" seemed more like a mechanism of speculation rather than a film we'd actually see. But when it comes to confirmation of the film actually happening, the man responsible for the source material definitely carries some clout with his opinion.
It looks to me like it's more likely than not it'll happen at this point." "Let's put it that way."
Not only was Stephen King ready to confirm the film's eventual release, he also believes that the casting rumors are near certainty. He thinks that both Matthew McConaughey and Idris Elba will have the key roles in the film.
"I think that it's more likely than not that Idris Elba and Matthew McConaughey will be in it," King says. "But I can't say with any certainty. I know that they're trying to make deals with these actors [and] with Sony, and that's the extent of my knowledge."
Even though Stephen King is the creator of "Dark Tower," Hollywood is notorious for moving at their own pace and doing things that adhere to their needs and scheduling first. Will we see a "Dark Tower" film? I'm sure that we will at some point. It's got the fanbase to get the film moving. Will McConaughey and Elba be cast in the roles? I'd say it's likely if the studio can get things moving quickly. If they cannot then these actors may have to pass on the role because they do have to have steady work.
What do you think about Stephen King's thoughts on "Dark Tower?"
This was first reported by Rolling Stone.
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Ryan has been fascinated with film and pop culture since childhood. Throughout college he "played it safe" taking the more lucrative route of being a computer programmer while squeezing in film related courses where he could...but even during his post college career, he could never escape his true passion. After following one of his favorite blogs for a long time, he approached the site's Editor about writing and they reluctantly gave him a shot. He later became their Senior Writer which led to a variety of other projects, radio show appearances, features, and high profile celebrity interviews. Despite his success with blogging, he still wanted more so in order to expand his creative addiction, he merged his IT skills and blogging know-how to create FilmFad.com which has continued to grow into a creative Mecca of pop-culture fun and integrity. [email protected] Film Fad
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\title{High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling}

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\titlerunning{Inpainting with Iterative Confidence Feedback and Guided Upsampling}
\author{Yu Zeng\inst{1} \and
Zhe Lin\inst{2} \and
Jimei Yang\inst{2} \and
Jianming Zhang\inst{2} \and
Eli Shechtman\inst{2} \and
Huchuan Lu\inst{1}
}
\authorrunning{Y. Zeng et al.}
\institute{Dalian University of Technology, China\\
\email{zengxianyu18@qq.com,lhchuan@dlut.edu.cn}\\
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Adobe Research, USA\\
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\begin{abstract}
Existing image inpainting methods often produce artifacts when dealing with large holes in real applications. To address this challenge, we propose an iterative inpainting method with a feedback mechanism. Specifically, we introduce a deep generative model which not only outputs an inpainting result but also a corresponding confidence map. Using this map as feedback, it progressively fills the hole by trusting only high-confidence pixels inside the hole at each iteration and focuses on the remaining pixels in the next iteration. As it reuses partial predictions from the previous iterations as known pixels, this process gradually improves the result. In addition, we propose a guided upsampling network to enable generation of high-resolution inpainting results. We achieve this by extending the Contextual Attention module~[39] to borrow high-resolution feature patches in the input image.
Furthermore, to mimic real object removal scenarios, we collect a large object mask dataset and synthesize more realistic training data that better simulates user inputs. Experiments show that our method significantly outperforms existing methods in both quantitative and qualitative evaluations. More results and Web APP are available at https://zengxianyu.github.io/iic.
\end{abstract}
\section{Introduction}
Image inpainting is a task of reconstructing missing regions in an image. It is an important problem in computer vision and an essential functionality in many imaging and graphics applications, \textit{e.g. } object removal, image restoration, manipulation, re-targeting, compositing, and image-based rendering~\cite{barnes2009patchmatch,levin2004seamless,park2017transformation}

Classical inpainting methods such as~\cite{efros2001image,kwatra2005texture,barnes2009patchmatch} typically rely on the principle of borrowing example patches from known regions or external database images and pasting them into the holes. These methods are quite effective for easy cases with small holes or uniform textured background. They can also handle high-resolution images efficiently. However, due to the lack of high-level structural understanding and ability to generate novel contents, they often fail to produce realistic results when the hole is large.

\begin{figure}[t]
\begin{center}
\includegraphics[width=.9\linewidth]{compress.pdf}\\
\makebox[\linewidth][s]{\scriptsize{ { } { } Input { } EdgeConnect { } PConv { } PatchMatch { } GConv { } Global\&Local { } Ours { } { } }}
\end{center}
  \captionof{figure}{\footnotesize Results of state-of-the-art methods on real object removal requests~\cite{remove_people_on_side,remove_woman_in_purple}}.
\label{fig:teaser}
\end{figure}

Deep learning has achieved great success on various dense prediction problems~\cite{ding2018context_cvpr,ding2019semantic,ding2019boundary,ding2020phraseclick,zeng2020high,zeng2018learning,zeng2019multi,zeng2019joint,wang2020sdc}. Recent research effort on inpainting has shifted towards a data-driven learning-based approach~\cite{pathak2016context,iizuka2017globally,yu2018generative,liu2018image,yu2019free}. These methods train a deep neural network to directly predict the inpainting result given a corrupted image and hole channel as input. The original images before corruption are used as the ground truth to train the network. To generate visually realistic results with sufficient texture details, they often use an adversarial loss based on GANs~\cite{goodfellow2014generative} in addition to a reconstruction loss. These deep generative models show significant improvements on filling holes in complex images but often produce visual artifacts, especially when the hole is large. For large holes, the reconstruction loss is less effective due to the increased ambiguity, leading to undesired predictions during testing
as shown in Fig.~\ref{fig:teaser}.

In this paper, we aim to address the challenge of filling large holes in high resolution images for real image editing applications, \textit{e.g.}, object removal. We observe that in the failure cases of existing approaches, despite the artifacts, there often exist sub-regions with good predictions. If we trust the good part and treat the remaining region as a new hole and run the model again, then the hole become progressively smaller and the model can produce better results. Inspired by this observation, we propose a novel iterative inpainting method with a feedback mechanism. Our method is based on a deep generative model which not only outputs an inpainting result but also a corresponding confidence map.

The model is encouraged to generate a confidence map that highlights pixels where the prediction error is likely small and can help overcome the prediction ambiguity. Using this confidence map as feedback, our model is trained to progressively fill the hole by trusting only high-confidence pixels inside the hole at each iteration and update the remaining pixels in the next iteration. By predicting what portion of the hole was filled successfully in the previous iteration and using those pixels as ``known'', our model can gradually improve the result when filling large holes.
The proposed confidence prediction scheme is general and can be potentially attached to any deep generative inpainting model.

To generate high-quality inpainting results at high-resolution, we propose a guided inpainting upsampling network as a post-processing method. We achieve this by extending the Contextual Attention module~\cite{yu2018generative} to borrow known high-resolution feature patches in the input image based on the patch similarities computed on the result for down-sampled input.
The motivation is that it is easier to train a deep network to generate globally coherent structures for down-sampled inputs as effective receptive fields of neurons are larger; while the surrounding regions of the high-resolution input can be used to enhance fine-grained texture details inside the hole.
In this way, our method decouples high-level structural understanding and low-level texture reconstruction, and can produce results that are both semantically plausible and visually realistic at high resolution.

On the data side, previous methods construct training data by synthesizing square~\cite{pathak2016context,iizuka2017globally,yu2018generative} or irregular holes~\cite{liu2018image,yu2019free}.
However, in real applications for removing undesirable objects or scene segments, the hole-filling regions are more likely to be object-shaped.
To mimic real use cases, we collect a large set of images with object-shaped holes. We synthesize our data to include both of the following two common situations: 1) holes overlap the foreground objects to simulate the distracting objects occluding the foreground, and 2) holes appear only in the background to simulate the case of unwanted objects occluded by salient foreground objects.

In summary, our contributions are three-fold:
\begin{itemize}[noitemsep]
\item We address the challenge of completing large missing regions in images by proposing an iterative inpainting method with a confidence feedback loop.
\item We propose a guided up-sampling network as a post processing step to enable generation of high-resolution inpainting results.
\item We introduce a new procedure to synthesize training data for building deep generative models for real object removal applications.
\end{itemize}

\section{Related work}
Earlier image inpainting methods rely on existing content to fill the holes. Diffusion-based methods~\cite{ballester2001filling,bertalmio2000image} propagate neighboring appearances to the target holes, but they often generate significant artifacts when the holes are large or texture variation is severe.
Patch-based methods~\cite{efros2001image,kwatra2005texture,barnes2009patchmatch} search for most similar patches from valid regions to complete missing regions.
Drori~\textit{et al.}~\cite{drori2003fragment} propose to iteratively fill missing regions from high to low confidence with similar patches. Although they also use a map to determine the region to fill in each iteration, the map is predefined based on spatial distances from unknown pixels to their closest valid pixels.
The above methods use real image patches sampled from the input to fill the holes and can often generate high-quality results. However, they lack high-level structural understanding and cannot generate entirely new content that does not exist in the input image. Thus, their results may not be semantically consistent to regions surrounding the holes.

By learning from a large corpus of data, deep learning based inpainting methods can understand the semantic structure of the input image and hence can handle more difficult cases. To produce sharper results, these methods typically adopt adversarial training inspired by GANs~\cite{goodfellow2014generative}. Pathak~\textit{et al.}~\cite{pathak2016context} made a first attempt to use a convolutional neural network (CNN) for hole filling.
Iizuka~\textit{et al.}~\cite{iizuka2017globally} use two discriminators for adversarial training to make the inpainted content both locally and globally consistent.  Yu~\textit{et al.}~\cite{yu2018generative} propose a deep generative model with contextual attention to explicitly utilize surrounding image features as references in the latent feature space. Zeng~\textit{et al.}~\cite{zeng2019learning} propose to use region affinity from a high-level feature map to guide the completion of missing regions in the previous low-level feature map of a single input. Our upsampling network is similar in spirit of using coarse scale information to guide the generation of fine-grained details but different in architecture and functionality; it upsamples a low-resoluiotn results by filling the fine-grained details from the high-resolution input. Yang~\textit{et al.}~\cite{yang2017neuralpatch} upsamples the results of a similar network with a neural patch search and vote approach followed by an optimization. Our method uses a related neural patch-vote approach but avoids the slow optimization.
The above methods use square holes in their training data, which causes a bias to rectangular holes. To address this, Liu~\textit{et al.}~\cite{liu2018image} collect estimated occlusion/dis-occlusion masks between two consecutive frames of videos and use them to generate holes for training. The resulting masks are highly irregular and do not represent well holes typical to an image inpainting task.
They also propose partial convolution layers to infer missing pixels conditioned only on valid pixels.
Yu~\textit{et al.}~\cite{yu2019free} introduce free-form masks by simulating random strokes and generalizes partial convolution to gated convolution that learns to select features for each channel at each spatial location across all layers. Although these irregular holes lead to more diverse samples, they do not represent well real inpainting use-cases.

Most recently, a few works have been introduced to study progressive inpainting models. Zhang~\textit{et al.}~\cite{zhang2018semantic} adopt a UNet generator with an LSTM in the bottleneck. It takes a sequence of inputs with large to small holes in the image center and generates a sequence of corresponding outputs. Guo~\textit{et al.}~\cite{guo2019progressive} propose to gradually fill a hole using consecutive residual blocks. They use partial convolutions in these blocks and update the hole mask according to the invalid pixels selected by partial convolutions. Oh~\textit{et al.}~\cite{oh2019onion} propose an onion-peel network that progressively fills the hole from the hole boundary for video hole filling. All of the above methods fill the holes from the boundary to inner regions in a {\em predefined} sequence. Different from them, our proposed method jointly predicts a confidence map when generating an inpainting result. Using the confidence map from the previous iteration as feedback, it can automatically detect regions with bad fill to revise in following iterations. To our best knowledge, it is the first attempt to model confidence of predictions in inpainting and the first iterative inpainting method to fill holes with a confidence-driven feedback loop.

\section{Approach}
Our inpainting method consists of two models: an iterative inpainting model (Fig.~\ref{fig:overall_split1}~(a)) with confidence feedback and a guided upsampling network ((Fig.~\ref{fig:overall_split1}~(b)) that upsamples a low-resolution result by factor of $2$ using the high resolution (HR) input as guidance.
In this section, we first describe how we prepare data for building and evaluating our model, and then introduce the details of our iterative inpainting model and guided upsampling network.

\subsection{Data generation}
\label{sec_data}
\begin{figure}[t]
\begin{center}
\includegraphics[width=.9\linewidth]{fig_samples_c.pdf}\\
\makebox[\linewidth][s]{\scriptsize{ { } Real{\color{white}w}request { } Previous{\color{white}w}approaches { } Ours { } }}
\end{center}
   \caption{\small Comparison of input with holes. The first two columns are real object removal requests on the Web~\cite{man_woman_bald_head,child_backpack_lead}.
   The second two are from PConv~\cite{liu2018image} and ContextAttention~\cite{yu2018generative}, respectively. The right two are our samples with object-shaped holes. }
\label{fig:samples}
\end{figure}

Previous approaches to image inpainting typically construct their training and testing data pairs by corrupting the original images with square-shaped~\cite{yu2018generative,pathak2016context,iizuka2017globally} or highly irregular holes~\cite{liu2018image,yu2019free}, as shown in the first two columns in Fig.~\ref{fig:samples}. Images with holes are the input and the original images are the corresponding ground-truth. However, in real-world inpainting use cases such as distracting region removal, the regions users typically want to remove are objects or scene segments, which are rarely of square or highly irregular shapes, as shown in the middle two columns of Fig.~\ref{fig:samples}.

To mimic a more typical image editing scenario, in this work, we synthesize training samples with realistic holes. We collect 82,020 object masks from densely annotated segmentation datasets, including video segmentation~\cite{Caelles_arXiv_2019}, semantic segmentation~\cite{everingham2010pascal}, salient object segmentation~\cite{LiYu16,WangDRFI2017,fan2018SOC}, and human parsing~\cite{ATR}. The object masks from semantic segmentation datasets are from various classes
and have different shapes and sizes. Salient object segmentation datasets contain large objects that form samples with large holes in our training data. We also use human parsing datasets to generate human-shaped masks as removing distracting people from photos is a common task.

Finally, we use a mix of random strokes~\cite{yu2019free} and the object masks as holes to create a more diverse training dataset and overcome a bias towards object shaped holes. The images for synthesizing training samples are from two sources: the Places2~\cite{zhou2017places} dataset and salient object segmentation dataset~\cite{xiong2019foreground}. More specifically, we collect 61,525 images with pixel-level annotations of salient objects. We use 1,000 of them as testing samples, and the rest (60,525) are merged with Places2 for training and validation. For images in Places2, we sample randomly the location of the holes so they can appear in any region and may overlap with the main object. For images originating from salient object segmentation datasets, we subtract from the holes the intersection area with the salient objects. This is to simulate the case of removing distracting regions occluded by salient objects. As shown in the last two columns of Fig.~\ref{fig:samples}, such generated samples with holes are more similar to real cases than those of previous approaches.

\subsection{Inpainting model}
\begin{figure}[t]
\begin{center}
  \includegraphics[width=0.5\linewidth]{fig_1_split_simp_c.jpg}
  \includegraphics[width=0.45\linewidth]{fig_2_split_simp_c.jpg}

  \makebox[\linewidth][s]{\scriptsize{{ } (a) { } (b) { }}}
\end{center}
  \caption{\small The overall structure. (a) Iterative inpainting with confidence feedback. (b) Guided upsampling.}
\label{fig:overall_split1}
\end{figure}
We adopt a generative approach based on generative adversarial networks (GANs)~\cite{goodfellow2014generative}. Thus our model has a generator and a discriminator. Fig.~\ref{fig:overall_split1}~(a) illustrates an overall structure of the generator. It is a cascade of a coarse and a fine networks, similar to~\cite{yu2018generative}. The coarse network takes an incomplete image and the corresponding hole mask as input to produce a coarse completed image. Then, the coarse result is passed to the fine network to generate a final completed image and a confidence map.
The fine network has one encoder and two decoders: an image decoder that predicts the inpainting image result, and a confidence decoder that returns a corresponding confidence map of the predicted image. To make confidence prediction aware of the full generation process, we let the confidence decoder take as input all the feature layers up to the bottleneck of the image decoder, as illustrated by the dashed blocks in Fig.~\ref{fig:overall_split1}~(a).

We use a PatchGAN discriminator~\cite{isola2017image} with spectral normalization as in~\cite{yu2019free} for adversarial training. It takes as input either the inpainted image or the ground-truth image and classifies each patch of the input image as real or fake. Its output is a score map rather than a single score, where each element corresponds to a local region of the input image covered by its receptive field.

\subsubsection{Generative inpainting loss}
We train our model on the realistic hole dataset described in Sec.~\ref{sec_data}. We use L1 reconstruction loss on the coarse level. On the fine level, we use both L1 and a hinge adversarial loss with spectral normalization~\cite{miyato2018spectral} applied on the discriminator. The loss for the discriminator $D$ is:
\begin{equation}
\mathcal{L}_D = \mathbb{E}_{x\sim p_{data}(x)}[\mbox{ReLU}(\mathbbm{1}-D(x))] + \mathbb{E}_{z\sim p_{z}(z)}[\mbox{ReLU}(\mathbbm{1}+D(G(z)\circ m+z))],  \\
\end{equation}
where $x$ denotes the real (ground truth) image and $z$ represents the incomplete image of which the pixels inside the hole are set to zero; $m$ represents the hole mask, in which the pixels having value one belong to the hole; $G(\cdot)$ represents the image decoder; $\circ$ denotes element-wise multiplication; the inpainting result $G(z)\circ m+z$ is composed by the generated content $G(z)$ inside the hole and the original content $z$ outside the hole. Let $y$ denote the output of the image decoder, \textit{i.e.} $y=G(z)$, then the loss for the inpainting result is:
\begin{equation}
\label{eq_image_loss}
\mathcal{L}_{G} = \mathbb{E}_{z,x\sim p(z,x)}[L(y)], \mbox{where} L(y) = \mbox{ReLU}(\mathbbm{1}-D(y\circ m+z)) + \norm{y-x}_1.
\end{equation}

\subsubsection{Confidence prediction loss}
We make the confidence decoder detect good regions by using its output map as spatial attention on the predicted image when calculating the loss. Let $c$ denote the confidence map, i.e., output of the confidence decoder of which each element is constrained to $[0, 1]$ by a sigmoid function, we define the following loss for the confidence decoder:
\begin{equation}
\label{eq_mask_loss}
\mathcal{L}_{C} = \mathbb{E}_{z,x\sim p(z,x)}[L(y\circ c+x\circ(1-c)) + \lambda (\norm{(\mathbbm{1}-c)\circ m}_1+\norm{(\mathbbm{1}-c)\circ m}_2)],
\end{equation}
where $\lambda$ is a hyperparameter controlling the size of the confident area. We set it to 0.1 in all evaluations but also provide sensitivity analysis of it in the experiments.

To minimize $\mathcal{L}_{C}$, the map $c$ should highlight confidence regions, i.e. pixels contributing less to the overall loss. To prove this, we assume that: (1) $L(y)$ can be written as the summation over local pixel-wise losses, \textit{i.e.}
$L(y) = \sum_{i\in\mathcal{H}}l(y_i)$ where $l(y_i)\ge 0$ is an unknown local loss function, $\mathcal{H}$ is the index set of pixels inside the holes and $y_i$ is a pixel of the generator output $y$, and (2) $l(x_i)=0$ for every ground-truth pixel $x_i$. For simplification, we consider $c$ as binary and let $\mathcal{C}$ to be the index set of non-zero elements of $c$, then $\mathcal{L}_{C}$ can be re-written as:
\begin{equation}
\label{eq_mask_loss2_}
\begin{split}
\mathcal{L}_{C} &= \mathbb{E}_{z,x\sim p(z,x)}[L(y\circ c+x\circ(1-c)) - \lambda \norm{c}]  \\
&= \mathbb{E}_{z,x\sim p(z,x)}[\sum_{i\in\mathcal{C}}l(y_i)+\sum_{i\in(\mathcal{H}-\mathcal{C})}l(x_i) - \lambda |\mathcal{C}|]\\
&= \mathbb{E}_{z,x\sim p(z,x)}[\sum_{i\in\mathcal{C}}l(y_i)- \lambda |\mathcal{C}|];
\end{split}
\end{equation}
for a single sample $y$, the loss is the summation of local loss over $\mathcal{C}$. Therefore, the minimum is achieved when $\mathcal{C}$ covers the set of pixels with the smallest local loss values.
Intuitively, the first term in Equation~\ref{eq_mask_loss} encourages the confidence map to have high response where the loss $L(y)$ is small, as $\mathcal{L}_{C}$ is expected to be smaller by choosing low-loss area from the generator output $y\circ c$ and replacing high-loss are with ground-truth $x\circ(1-c)$; the second term penalizes a trivial solution of all-zero confidence maps by encouraging the confident region to cover as much of the missing region as possible. Fig.~\ref{fig:loss_conf} illustrates how to compute $\mathcal{L}_{C}$.

\begin{figure}[t]
\begin{center}
  \includegraphics[width=0.5\linewidth]{fig_loss_split_c.jpg}
\end{center}
  \caption{\small Loss for confidence prediction. }
\label{fig:loss_conf}
\end{figure}
\begin{figure}[t]
\begin{center}
  \includegraphics[width=.9\linewidth]{vis_conf_compress.jpg}\\
\end{center}
  \caption{\small  Results and confidence maps. Blue masks indicate holes. Brighter pixels in confidence maps are of higher confidence. We use solid blue masks for synthetic samples and transparent ones for real object removal cases.}
\label{fig:confs}
\end{figure}
Fig.~\ref{fig:confs} shows examples of inpainting results and the corresponding confidence maps. We can see that the confidence maps tend to highlight good regions of the result. As one may expect, confident regions are often located close to the hole boundaries (e.g., the 2nd row). However, there are also other cases: 1) in easy cases like filling a hole in the sky, all generated pixels can have high confidence (e.g., 1st row, left); 2) flat regions tend to be more confident than highly-textured regions; 3) artifacts usually have low confidence (e.g., third row, right).

\subsubsection{Iterative inpainting}
\label{sec_iterative_inpainting}
We can use the confidence decoder output to identify confident sub-regions in the inpainting result and run the inpainting model again and repeat the process. In each iteration, we set the confident pixels as "valid" pixels in the new input and set the remaining low-confidence regions as new holes for the next iteration to process. Overall, holes are shrinking as the iteration goes so that the network should be more certain about the generated result.

Algorithm~\ref{alg_iterative_test} describes the iterative inpainting process in details. In the first iteration, we initialize the completed image by filling the whole missing region with the generated content and set the pixels of which confidence is below 0.5 as the missing regions for the second iteration. From the second iteration, a pixel is replaced by new generated one if its confidence increases over the previous iteration.
When training, we iterate twice. We also fix the number of iterations during the testing. There is no convergence issue because our algorithm always keep the current-best complete prediction inside the hole at every iteration. Fig.~\ref{fig:iterates} shows inpainting results in three consecutive iterations. As iteration goes, lines are connected and distorted area or artifacts are corrected.
\begin{algorithm}[t]

    \small
    \SetKwInOut{Input}{\textbf{Input}}
    \SetKwInOut{Output}{\textbf{Output}}
    \Input{
    Incomplete image $z_1$,
    hole mask $m_1$, the number of iterations $T$ \\
    }
    \Output{ Completed image $y_T$\\
    }
    \BlankLine
    Set initial confidence map  $c_0=0.5m_1$\\
    \For {\ $t \in \left\{1,...,T\right\}$}{
        Get confidence map $c_t=C(z_t)\circ m_t$\\
        Get mask of regions to update $u_t=\mbox{Binarize}(c_t-c_{t-1}\circ m_t)$\\
        Update mask $m_{t+1} = m_t-u_t$\\
        \uIf { $t = 1$} {
            Initialize completed image $y_1 = z_1 + G(z_1)\circ m_t$\\
        }
        \Else{
        Update competed image $y_t = G(z_t)\circ u_t + y_{t-1}\circ (1-u_t)$\\
        }
        Update incomplete image $z_{t+1}=y_t\circ m_{t+1}$
    }
    \caption{\small $G(\cdot)$: generator; $C(\cdot)$: confidence decoder}\label{alg_iterative_test}
\end{algorithm}

\begin{figure}[t]
\begin{center}
   \includegraphics[width=.9\linewidth]{vis_steps_two.pdf}\\

\end{center}
   \caption{\small Inpainting results as iterations increase.

   }
\label{fig:iterates}
\end{figure}

\subsection{Guided upsampling}
\label{sec_guided}
Our iterative inpainting model is trained on low-resolution (LR) ($256 \times 256$) so it is not ideal to directly apply it to high-resolution (HR) inputs. To solve this issue, we propose a guided inpainting upsampling network to generate a HR inpainting result given a LR inpainting result. We propose a new architecture extending the contextual attention module~\cite{yu2018generative} which can match and use feature patches from valid surrounding areas to help synthesize the hole pixels.

As illustrated in Fig.~\ref{fig:overall_split1}~(b), the proposed guided upsampling network consists of two shallow networks, one for learning patch similarity and the other for image reconstruction. Their feature maps are of different sizes, but we can split them into an equal number of patches using different patch sizes so that patches of the similarity network feature map have 1:1 correspondence to patches of the reconstruction network feature map which allows us to use shared indices to represent patches. Let $\mathcal{H}$ and $\mathcal{V}$ to be the index set of patches inside the holes and the valid patches, respectively. Valid patches are those with at least one pixel outside the holes, and others are taken as patches inside the hole. The patch similarity network calculates the cosine similarity $s_{ij}$ between a pair of patch $i, j$.
The reconstruction network is a shallow encoder-decoder network with skip connections from each layer of the encoder to the mirrored layer of the decoder.
Before converting an HR feature map (of the HR input size) into an HR inpainting result, each feature patch inside the holes is replaced with a weighted sum of valid patches. Let $\phi_i$ to be an HR feature patch. The patch replacement in the HR feature maps can be summarized as follow:
\begin{equation}
\phi_i = \sum_{j\in \mathcal{V}} s^{'}_{ij} \phi_j, \ \ i \in \mathcal{H},
\end{equation}
where $s^{'}_{ij}$ is the softmax of $s_{ij}$:
\begin{equation}
s^{'}_{ij} = \frac{\exp(s_{ij})}{\sum_{j\in \mathcal{V}} \exp(s_{ij})}.
\end{equation}
Then the HR feature maps are transformed to an output image by two convolution layers (``ToRGB" in Fig.~\ref{fig:overall_split1}~(b)). The loss on the HR output is a combined L1 and adversarial loss, the same as in Equation~\ref{eq_image_loss}. As mentioned earlier, we take the patches with at least one valid pixel as valid patches. For missing regions reconstructed by these partially valid patches, we simply take them as holes and run the previously described iterative inpainting model one more time.
\begin{figure}[t]
\begin{center}
  \includegraphics[width=.9\linewidth]{vis_upsample_2.pdf}\\

  \makebox[\linewidth][s]{\scriptsize{{ } Input { } (a) { } (b) { } Input { } (a) { } (b) { }}}
\end{center}
  \caption{\small Effect of guided upsampling network. (a) results obtained by the iterative inpainting model on original size; (b) running the iterative model on half size and using guided upsampling network to upsample to the original size. }
\label{fig:upsample_example}
\end{figure}
By separating high-level similarity learning and low-level texture reconstruction, the proposed guided upsampling network can generate inpainting results that are both semantically reasonable and visually realistic, as shown in Fig.~\ref{fig:upsample_example}.

Another advantage of guided upsampling network is that it allows some user control over the results. As the contents inside the holes are constructed only using patches in $\mathcal{V}$, we can adjust the results by removing from or adding to $\mathcal{V}$. For example, as shown in Fig.~\ref{fig:control}, we can exclude the region that we do not want to copy, or specify a reference region used for filling the hole.
\begin{figure}[t]
\begin{center}
  \includegraphics[width=.8\linewidth]{fig_control_compress.jpg}
\end{center}
  \caption{\small Effect of user control. The blue masks indicate holes, red and green ones respectively indicate regions avoid or specified to be used for filling the holes. The second example shows that our method with user control can also be applied to interactive image layout manipulation.
  }
\label{fig:control}
\end{figure}

\section{Experiments}
\subsection{Implementation details}
We implement our method using Python and Pytorch. Detailed network architectures can be found in the supplementary material\footnote{https://zengxianyu.github.io/iic}. We train the models with Adam~\cite{kingma2014adam} optimizer; the learning rate set to 0.0001. The training batch size is 64. As the Places2 is much larger than the saliency dataset, we sample an equal number of images from Places2 dataset and saliency dataset to constitute each batch to prevent the model from ignoring the scarce samples.
We use $256\times 256$ patches for training the iterative inpainting model and train the guided upsampling network to upsample $256\times 256$ results to $512\times 512$.
We randomly take 400 images from the training split of Places2 and the saliency dataset as validation samples and generate holes on them as described in Sec.~\ref{sec_data}. The model is trained until the PSNR on this validation set does not increase. The 1,000 testing images are kept unseen during training. When testing, the number of iterations for iterative inpainting is set to 4.

\subsection{Comparison with state-of-the-art methods}
We evaluate quantitative scores and visual quality of two variants of our method: \textit{i.e.} \textbf{Ours*}: the iterative inpainting model running on original input without guided upsampling
and \textbf{Ours}: the iterative inpainting model running on $2\times$ downsampled input and then using the guided upsample model to obtain the results of original size. We compare our methods with four state-of-the-art methods: Global\&Local~\cite{iizuka2017globally}, PatchMatch~\cite{barnes2009patchmatch}, GConv~\cite{yu2019free} and EdgeConnect~\cite{nazeri2019edgeconnect}. Comparison with more methods can be found in supplementary material.

\subsubsection{Quantitative evaluation}
We evaluate two variants of our method and state-of-the-art methods on the test set of 1,000 images with object shaped holes. These images are of various size, from short side 256 to long side 1024. For random 500 images of them, we exclude salient objects from holes to simulate the case of distracting objects behind the main objects. For the rest 500 images the holes are placed randomly.
For fair comparisons with previous methods, we also evaluate on the standard Places2 validation set resized to 256$\times$256 with 128$\times$128 center square holes as in most previous methods \textit{e.g.}~\cite{iizuka2017globally,yu2018generative,zeng2019learning}, and irregular holes as in~\cite{yu2019free,xiong2019foreground}.
We use L1 loss, PSNR, and SSIM as they are most commonly used metrics in image inpainting. Tab.~\ref{table_metric} shows quantitative comparisons of our method with state-of-the-art
methods. Both variants of our method compare favourably against previous methods. Without guided upsampling and running on original resolution, \textbf{Ours*} model tends to generate smoother results, which are favored by these scores at per-pixel basis.
\begin{table}[t]
\setlength{\tabcolsep}{1.5pt}
\caption{\small Quantitative evaluation and user preference of various methods. P.c.: preference count in user study. }
\label{table_metric}
\small
\begin{center}
\begin{tabular}{ccc|ccc|ccc|ccc|c}
\hline
\multicolumn{3}{c|}{\multirow{2}{*}{Method}} & \multicolumn{3}{c|}{Object shaped holes} & \multicolumn{3}{c|}{Irregular holes (Places2)}& \multicolumn{3}{c|}{Square holes (Places2)}&User study\\
\multicolumn{3}{c|}{}& L1 Loss &PSNR &SSIM& L1 Loss &PSNR &SSIM& L1 Loss &PSNR &SSIM&P. c.\\
\hline
\multicolumn{3}{c|}{\cite{barnes2009patchmatch}} & .0273 & 25.64 & .8780 & .0288 & 22.87 & .8549 & .0432 & 19.19 & .7922 &13\\%/17\\
\multicolumn{3}{c|}{\cite{iizuka2017globally}} & .0292& 24.23& .8653 & .0385 & 20.95 & .8185 & .0386 & 20.16 & .7950&2\\%/1\\
\multicolumn{3}{c|}{\cite{yu2019free}} & .0243 & 26.07 & .8803  & .0245 & 24.31 & .8718 & .0430& 19.08& .7984 &8\\%/11\\
\multicolumn{3}{c|}{\cite{nazeri2019edgeconnect}} & .0246 & 26.24 & .8871 & .0221 & 24.78 & .8701 & .0368 & 20.30 & .8017&10\\%/4\\
\hline
\multicolumn{3}{c|}{Ours*}& .0194 & 28.20 & .8985  & .0203 & 25.43 & .8828& .0361& 20.21& .8130 &62\\%/92\\
\multicolumn{3}{c|}{Ours} & .0205 & 27.67 & .8949  & 0220 & 24.70 & .8744 & .0384& 19.69& .8063 &80\\%/102\\
\hline
\end{tabular}
\end{center}
\end{table}
\begin{figure}[t]
\begin{center}
   \includegraphics[width=.9\linewidth]{hole-img-rate_all.jpg}
\end{center}
   \caption{\small Quantitative comparisons when hole size varies. }
\label{fig:ablation_holesize}
\end{figure}
To validate superiority of our method on filling large holes, we show scores measured on images with holes of different sizes in Fig.~\ref{fig:ablation_holesize}. The X-axis represents the range of hole-to-image area ratio and Y-axis represents average L1 loss, SSIM and PSNR over all samples of which the hole-to-image area ratio are in the corresponding range. For example, the first column is averaged over samples whose hole-to-image ratio is less than $0.1$ and the second column is averaged over those greater than $0.1$ but less than $0.15$. For small holes, all methods perform almost equally well. It is increasingly more difficult to fill holes when their size grow. So the SSIM, PSNR of all methods  decrease and L1 loss increases as the hole-to-image ratio increases. When it comes to larger holes, our method performs better.

\subsubsection{Visual quality}
\label{sec_visual}
\begin{figure*}[t]
\begin{center}
   \includegraphics[width=.9\linewidth]{vis_compare.pdf}\\

   \makebox[\linewidth][s]{\scriptsize{ { } { }Input {}EdgeConnect {}Global\&Local{} PatchMatch{} GConv { } Ours* { } Ours{ } { } }}
\end{center}
   \caption{\small Visual comparison on mid-resolution images. Zoom-in to see the details. Images are compressed due to limited submission file size. More results can be found in the supplementary material. }
\label{fig:visual}
\end{figure*}
Fig.~\ref{fig:visual} shows visual comparisons of two variants of our method and state-of-the-art methods on synthetic samples and real object removal tasks~\cite{remove_coworker,remove_people,remove_people_balloon}.
As shown in the figure, existing deep learning based methods do not work well in real requests. They often generate artifacts removing a large object. PatchMatch can generate clear texture, however, since it does not have a semantic understanding of input images, its results are not semantically reasonable. Both \textbf{Ours*} and \textbf{Ours} can provide reasonable alternatives for the region to remove. \textbf{Ours*} tends to generate smoother results. In comparison, by reconstructing LR results using patches from HR inputs, \textbf{Ours} method is better at keeping fine-grained details. Its results are similar to PatchMatch in terms of texture but more reasonable in terms of structure.

To evaluate visual quality of our method, we conduct a user study on 25 real object removal cases collected from object removal requests on the Web.
All images are resized to make the short side equal to 512. Each input image with a marked region to remove and the results of different methods are shown in random order to 11 users
and we ask them to select a single best result. Each combination of input and results are shown twice, and a valid vote is counted only when a user selects the same result twice. Finally we collect 175 valid votes. The user study results are shown in Tab.~\ref{table_metric}. Both variants of our method are preferred more than previous methods. \textbf{Ours} model with guided upsampling tends to generate results that are less smooth and with more clear texture, which are often favored by users.

\subsection{Ablation study}
\begin{table}[t]
\setlength{\tabcolsep}{2pt}
\caption{\small Effect of each component. IT: iterative inpainting; CF: confidence feedback; RT: realistic training data as described in~\ref{sec_data}; GU: guided upsampling. \textit{PC} represents preference counts in user study. Time is measured in seconds on $512\times512$ input. \textit{User ctrl.} indicates whether a method allows user control.  }
\label{table_ablation_score}
\small
\begin{center}
\begin{tabular}{cccc||ccc|c|c|c}
\hline
IT & CF & RT & GU & L1 Loss &PSNR &SSIM &PC&Time&User ctrl.\\
\hline
& & & & .0205 & 27.79 & .8903&-&.064&$\times$\\
$\surd$& & & & .0204 & 27.57 & .8925&-&.301&$\times$\\
$\surd$& $\surd$ & & & .0200 & 28.06 & .8952&-&.323&$\times$\\
$\surd$& $\surd$ & $\surd$& & .0194 & 28.20 & .8985&62&.323&$\times$\\
$\surd$& $\surd$ & $\surd$& $\surd$& .0205 & 27.67 & .8949 &80&.182&$\surd$\\
\hline
\hline
\multicolumn{4}{c||}{Confidence$>0.5$}&.0033& 36.38& .981 &- &- &-\\
\multicolumn{4}{c||}{Confidence$\le0.5$}&.0165& 30.00& .923 &- &- &-\\
\hline
\end{tabular}
\end{center}
\end{table}
First, to validate the proposed confidence prediction mechanism, we separately evaluate the results of high-confidence ($>0.5$) and low-confidence ($\le0.5$) regions inside the hole. The results are in the bottom two rows of Tab.~\ref{table_ablation_score}, which indicates that the prediction in high-confidence regions are significantly better than low-confidence regions.
We show the effect of realistic training data, iterative inpainting, and guided upsample model in the first to the third rows of  Tab.~\ref{table_ablation_score}. The first row corresponds to our baseline model without confidence decoder trained on the Places2 dataset using irregular and square holes.
The second row shows the effect of conducting progressive inpainting (IT) in a predefined boundary-to-center manner. For this setting, we evenly split each hole into four parts based on distance transformation and run the baseline model four times for each input. Each time we fill the part closest to the hole boundaries and update the hole mask accordingly. The third row shows the iterative inpainting method with confidence feedback (CF) trained on Places2 using irregular and square holes. By predicting confidence map, it can automatically correct wrong inpainted pixels and gradually improve the results, which yields better performance than predefined progressive inpainting in terms of quantitative scores. The sixth row corresponds to \textbf{Ours*} model described in previous sections. The comparison between the third and the fourth row shows the effect of including realistic training (RT) samples.
\begin{figure}[t]
\begin{center}
   \includegraphics[width=.8\linewidth]{lambda-t-merge.jpg}
   \makebox[\linewidth][s]{\scriptsize{{ } (a) { } (b) { }}}

\end{center}
   \caption{\small (a) Sensitivity analysis of $\lambda$. (b) Effect of increasing test iterations. }
\label{fig:ablation_test_iters}
\end{figure}
\begin{figure}[t]
\begin{center}
  \includegraphics[width=.9\linewidth]{vis_1024.pdf}\\

  \makebox[\linewidth][s]{\scriptsize{{ }{ } Input { } Ours* { } Ours { } Input { } Ours* { } Ours { }{ }}}
\end{center}
   \caption{\small Inpainting results on $1024\times1024$ images. Zoom-in to see the details. Images are compressed due to limited submission file size.
   }
\label{fig:visual1024}
\end{figure}
All the variants discussed above output results of the same size as the input. So when evaluating these models, we give them the original input and do not apply post processing on their results. To analyze the effect of guide upsampling, we first run the proposed iterative inpainting method on $2\times$ downsampled input images, and then upsample the results to the original resolution using guided upsampling. This corresponds to \textbf{Ours} described previously, and the effect is shown in the fifth row of Tab.~\ref{table_ablation_score}. By running the iterative process on the downsampled input, it significantly cuts down the overall run-time.

The sensitivity analysis of $\lambda$ (Eqn.~\ref{eq_mask_loss}) is shown in Fig.~\ref{fig:ablation_test_iters}~(a). The performance is not very sensitive to $\lambda$ when it changes in a small range. For example, for $\lambda \in [0.7, 0.13]$, PSNR is in $[28.1, 28.5]$.
Fig.~\ref{fig:ablation_test_iters}~(b) shows the effect of increasing test iterations. More test iterations generally lead to better scores, especially in the first four iterations. We fix the number of iterations to $4$ during testing.

Fig.~\ref{fig:visual1024} shows inpainting results of \textbf{Ours*} and \textbf{Ours} on input images of size $1024\times1024$. As the guided upsampling network lifts the LR result to HR by utilizing features from a HR input, it brings the details from existing contents to generated contents, resulting in a more visually pleasant HR output. It also can be reflected from the user study, in which \textbf{Ours} is preferred by users more frequently.
However, reconstructing with existing patches is a constraint on generation, making it less free and difficult to restore exactly the original content in the missing region. As a result, \textbf{Ours} has lower quantitative scores than \textbf{Ours*} as shown in the last row of Tab.~\ref{table_ablation_score}.

\section{Conclusion}
We propose a high-resolution image inpainting method for large object removal.
Our model predicts the inpainting result as well as its confidence map, which is used to revise unsatisfactory regions in an iterative manner. To improve visual quality for high-res inputs, we first obtain a low-res result and then reconstruct it using high-res neural patches.
Furthermore, we collect a large object masks dataset and synthesize realistic training samples that simulate realistic user inputs. Experiments show that our method outperforms existing methods and achieves better visual quality.

\section*{Acknowledgements}
The paper is supported in part by National Key R\&D Program of China under Grant No. 2018AAA0102001, National Natural Science Foundation of China under grant No. 61725202, U1903215, 61829102, 91538201, 61771088,61751212, Fundamental Research Funds for the Central Universities under Grant No. DUT19GJ201, Dalian Innovation leader's support Plan under Grant No. 2018RD07.

\bibliographystyle{splncs04}

redpajama_set_name: RedPajamaArXiv


text:
Home » NBC-2 | WBBH » The Mueller has been delivered. What happens next?
The Mueller has been delivered. What happens next?
12:49 PM EDT, Sat March 23, 2019
WASHINGTON – The Mueller report has landed. Here's what that means.
The special counsel's 22-month investigation into possible coordination between the Trump campaign and Russia … is over.
According to the rules, Robert Mueller had to submit a confidential report to the attorney general "at the conclusion of the special counsel's work."
A senior law enforcement official told NBC News that once the report has been submitted, it means Mueller has finished his investigation.
That doesn't necessarily spell the end of criminal jeopardy for some of the people around President Donald Trump who have not been charged. It's been well-documented that Mueller farmed out investigative leads to other parts of the Justice Department, and that New York prosecutors are investigating individuals in the Trump Organization.
But the report's transmission does indicate that Mueller decided not to charge any member of the Trump campaign with conspiracy — or "collusion" — with the Russian election interference effort. After it was transmitted, a senior Justice Department official confirmed to NBC News that the special counsel is not recommending any additional indictments. And there are no sealed Mueller indictments.
That's a big deal.
Trump will likely claim victory, arguing that his "no collusion" mantra was right all along.
But whether Trump can fairly claim a measure of vindication will depend a lot on what Mueller's report says about his conduct, something we don't yet know. In a Friday afternoon letter to the leaders of the Senate and House Judiciary committees notifying them he had received the report, Attorney General William Barr said, "I am reviewing the report and anticipate that I may be in a position to advise you of the Special Counsel's principal conclusions as soon as this weekend."
The unknowns are huge:
How detailed will Mueller's confidential report be? The rules say he has to explain his decisions not to prosecute, but they don't place any limits on content or detail.
What, if anything, will the report say about whether the president tried to obstruct justice, a question Mueller examined in depth?
Will the report disclose previously unknown interactions between Trump associates and Russians?
Will it make any assessment about the president's motives and judgments regarding his praise of Russian President Vladimir Putin and WikiLeaks, the entity that received and published Democratic emails hacked by Russian intelligence officers?
What were the results of the FBI's counterintelligence investigationinto whether the president was under the sway — or compromised in any way — by Putin?
Another complication: To the extent that Mueller has answered these questions, some of the answers may be based on classified intelligence. Others will be based on grand jury testimony that is subject to secrecy rules. Barr will have to decide how to handle that.
But whatever he decides, Congressional Democrats will not be satisfied.
They have already said they will push for the release of the full Mueller report, and subpoena it, if necessary.
House Intelligence Committee Chairman Adam Schiff has also made clear the FBI has a separate legal obligation to report to the intelligence committees its findings about counterintelligence issues regarding the president and those around him. In other words, any evidence that Trump or his associates were or are in thrall to Russia, a notion the White House calls ridiculous.
Meanwhile, other investigations continue.
Federal prosecutors in New York are continuing to investigate the Trump inaugural committee and individuals in the Trump Organization.
Former National Security Adviser Mike Flynn has provided information relevant to an undisclosed, separate federal investigation, according to court records.
A law enforcement official with direct knowledge told NBC News that both counterintelligence and political corruption cases have been spun off by Mueller's office.
The Washington Post reported that the Justice Department's public integrity section is investigating whether a Trump fundraiser tried to sell access to foreign governments.
The Senate Intelligence Committee is conducting a bipartisan investigation that will result in a number of public reports about what happened in the 2016 election.
And House Democrats are pursuing the broader question of possible Trump financial ties to Russia — a question they are not sure Mueller fully investigated.
This much is clear: The story of Trump, Russia and the 2016 election has not yet been fully told. And the investigations that bedevil his presidency are not over.
But for now, at least one legal cloud over Trump has dissipated. The special counsel has apparently finished his work without charging a criminal conspiracy between Trump's campaign and a foreign intelligence effort to help him win.
Man, 1-year-old killed in head-on crash in Punta Gorda
26 killed, dozens injured after fire on Chinese tour bus
*Top News
Alec Baldwin formally charged with involuntary manslaughter in set shooting
DeSantis proposed prohibiting funding on diversity programs in state colleges


We're celebrating the voices of Black writers who paved the way for future generations.
Although the voices of writers of the African diaspora haven't always been celebrated, their words have persistently paved the way for future generations of readers, writers, and thinkers. Due to the diligent and dedicated work of literary giants like Langston Hughes, Nella Larsen, James Baldwin, and Toni Morrison, the canon has evolved. Because of them, it now includes narratives that depict the experiences, imagination, and history of Black Americans. In honor of those voices and Black History Month, we've curated a list highlighting some of the writers whose work celebrates what it means to be Black in America.
In his provocatively timeless novel Black No More, George S. Schuyler offers readers a daringly satirical portrait of race in America. Centered around the ambitious yet systematically hampered Max Disher, Schuyler's "Black Mirror"-esque narrative begins with Max's decision to undergo a new and mysterious treatment at the Cookman Sanitarium that promises patients the ability to "change [from] Black to white in three days." Eager to cast off the limitations of racial oppression, Max checks into the Sanitarium and emerges shortly after as a white man. Black No More follows Max – who changes his name to Matthew Fisher after his stay at the Cookman Sanitarium – as he navigates the world of whiteness and the cost he pays for the opportunity to obtain what was once out of reach. A chilling and speculative commentary of America's past and present, Black No More possesses the passion of W.E.B Du Bois and the terror of "Get Out." This novel will make you think about Blackness and whiteness in a new way.
When Emma Lou Morgan finally escapes the suffocatingly small-minded hometown of Boise, Idaho and the cruelty of her classmates at University of South California, she sets her sights on Harlem. Scorned for being darker than her parents and those around her, Emma Lou yearns to feel at home not just in her community, but in her own skin. Tired of the lifelong discrimination she's received from others – white and Black alike – due to the dark hue of her skin, Emma Lou is certain that Harlem is the home she's been searching for. Eventually, she gets the chance to make her dream come true and shortly after settling into Harlem, her hope gradually dissolves into disappointment despite the rent parties, jazz clubs, and lovers who fail to fully ease the pain of colorism. An unblinking critique of discrimination within the Black community and our nation's legacy of prejudice, Wallace Thurman's The Blacker the Berry documents a woman's journey toward self-acceptance and autonomy.
The compelling debut novel of the legendary Langston Hughes, Not Without Laughter is a breathtaking bildungsroman that gives readers a heartwarming glimpse of the complexities of Black boyhood, identity, and a family and nation in a moment of historic transition. Set in Kansas, Hughes narrative centers around Sandy Rogers, his family, and their respective pursuit of their dreams. While his mother, Annjee, works tirelessly cleaning the home of a rich white family and his father, Jimboy, attempts to keep the family afloat by searching for work wherever he can find it. Shaped by the anxieties and hopes of his family, Sandy grapples with what it means to be Black and be a man in America. Vibrantly honest and gripping, Not Without Laughter is a novel meant to be read again and again.
Discovered in 2009, the final novel by Harlem Renaissance powerhouse Claude McKay is a timely tale about politics, resistance, and an often overlooked chapter of American history. Initially rejected by his publisher, McKay's novel is set in Harlem during the heels of the Italian occupation of Ethiopia. Comprised of impassioned prose and recognizably realistic characters, Amiable with Big Teeth is a compelling examination of elitism, privilege, and the difference between politics and praxis. A necessary addition to the literary canon, this nearly lost novel is the perfect read for times like this.
Originally published in 1929, Nella Larsen's literary classic Passing begins with the novel's protagonist, Irene Redfield, and the arrival of a letter from an old friend Clare Kendry. When Irene decides to meet up with Clare and the two discuss their lives, she discovers that Clare has been passing as a white woman abroad. Because of this, and Irene's complexion, Clare's husband, who is white, assumes that she isn't Black. Although the encounter ends with an apology from Clare, whose husband momentarily remains none the wiser, this moment sets into motion an irreversible course of events that alter's both Irene and Clare's lives and their relationship. Through the voice of her omniscient narrator, Larsen's monumental novel is a heart-wrenching reflection on class, race, and friendship.
With warmth and heart, U.S. Poet Laureate Track K. Smith's memoir is an immersive vivid remembrance of Black girlhood, family, and faith. Throughout Ordinary Light, Smith explores the way religion, class, and race can shape an individual's perception of who they are and who they want to become. Throughout the pages of this deeply earnest memoir, Smith presents her audience with a memorable and enlightening account of a life that fostered one of contemporary literature's greatest voices.
Toni Morrison's debut novel The Bluest Eye introduces readers to Pecola Breedlove, a girl with a turbulent family life who wants more than anything to be beautiful. For Morrison's protagonist, beauty offers the promise of salvation from the horrors of the world she lives in and an escape from her abusive father. Nightly, she prays for blue eyes, "story book eyes," the sort of eyes that might keep others from doing her harm. Yet as the novel progresses, the chaos of Pecola's life persists. A harrowing story about childhood, trauma, and survival, The Bluest Eye is a difficult yet necessary book. Like all of Morrison's work, this novel sheds light on how racism can wound a person and how community can serve as a balm.
When Minnie Ransom, a local healer, asks Velma Henry, a mother, wife, and activist, if she "wants to be well," readers of Toni Cade Bambara's first novel will find themselves thinking about their own state of wellness—psychological and physical alike. A courageous meditation on Black womanhood and the toll of emotional labor, heartbreak, and political activism, Bambara's protagonist is forced to come to terms with her mental health, the movements toll on her sanity, and the importance of caring for oneself. Laced with magical realism and folklore, The Salt Eaters will make you consider how much of yourself you're willing to sacrifice for the sake of others.
In Gloria Naylor's third novel, the power of desire and magic take center stage. Awash in luminescent prose, Mama Day tells the story of the Day family and the women whose foresight, hope, and courage are a steady anchor in the lives of their loved ones and community. Through Sapphira, Miranda, Abigail, and Ophelia, Naylor conjures a poetic and timeless tale that celebrates the strength and wisdom that is synonymous with Black womanhood. Reminiscent of Julie Dash's Daughters of the Dust and the poetry of Lucille Clifton, Mama Day is an ode to the strength of the human heart. Its pages will spellbind you.
The iconic Maya Angelou's second memoir, the celebrated poet revisits the latter years of her adolescence. Extending the journey that began with I Know Why the Caged Bird Sings, Gather Together In My Name is an honest account of a young woman in search of her place in the world. Deeply sincere and at times heartbreaking, Angelou's words allow her audience the ability to witness the struggles and joys of motherhood, the blossoming of independence, and the awakening of a woman who's voice would define her generation. It's a crucial addition to any reader's bookshelf.


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Home World Media Mainstream Media Attempts To Defend Hillary And Attack Wikileaks, Fails Epically
Mainstream Media Attempts To Defend Hillary And Attack Wikileaks, Fails Epically
hqanon
By Whitney Webb at trueactivist.com
This week's award for most ridiculous mainstream media article goes to the Washington Post.
The DNC's coronation nomination of Hillary Clinton / Credit – BBC
The death of mainstream media is upon us. A recent Gallup poll showed that over 77% of Americans distrust mainstream television news channels as well as newspapers. Though these startling numbers should be cause for worry, it has failed to dissuade most media outlets from accelerating their spectacular decline into nothing more than propagandist, government mouth-pieces disguised only by a quickly evaporating veneer of manufactured journalistic "prestige." Wikileaks, the independent media organization responsible for exposing the corruption and war crimes of governments all over the world, has been a recent target of the corporate media due to their nearly daily releases of leaked emails targeting the Democratic National Committee and Democratic presidential nominee Hillary Clinton.
Many of the leaked documents, not surprisingly, have exposed a level of unprecedented collusion between mainstream media outlets and the Clinton campaign, including "well-regarded" publications like the New York Times, Politico, Wall Street Journal, the Huffington Post, and the Washington Post. In some cases, "well-respected" journalists published articles under their names which were actually written by Clinton campaign staffers. Nonetheless, many of these same outlets have taken it upon themselves to shield, not themselves, but Hillary Clinton from the negative fallout. Consider how much mainstream media coverage there has been of Trump's leaked tape versus Clinton's Wikileaks revelations.
One of the most widely reported leaked emails exposed transcripts of Clinton's paid speeches to Wall Street banks and other organizations, which were closed to both the press and the public. These transcripts, whose authenticity was confirmed by Clinton in the second presidential debate, show that Clinton has consistently lied to the public about her political views as she has distinct "private" and "public" positions on practically every issue. For example, Clinton has told the public that she is against the TPP, fracking, Wall Street de-regulation, and the Keystone XL pipeline. However, her paid speech excerpts, available for download here, show that she actually supports these issue in "private." Reactions from this obvious admission of manipulating the public have ranged from outright anger to unfazed.
However, the Washington Post was unique in its response as it praised Clinton's obvious duplicity as evidence that presidential candidate is a "knowledgeable, balanced political veteran with sound policy instincts and a mature sense of how to sustain a decent, stable democracy." In a piece entitled "Scandal! Wikileaks reveals Hillary Clinton to be…reasonable" the Washington Post's editorial board made the case that Clinton deserves to be lauded for her "clumsy formulation of obvious truth" regarding her public/private statement as "Nothing gets done in politics unless legislators can deliberate and negotiate candidly, outside the glare of publicity." In other words, the Washington Post has officially endorsed a lack of transparency in government, painting "back-room deals" as the gold standard in a "decent, stable democracy."
To add to this laughable attempt to normalize government corruption, the Washington Post ran another article, this time by Charles Lane, which argued that "like all good things, transparency can be taken to an extreme." Lane went on to call Clinton's "public/private" comments "refreshing" and other evidence of her corruption as "run-of-the-mill" transactional politics. He even refers to shady, back-room deals as "democratically necessary privacy" and warned that it was endangered by "surveillance anarchy." Said another way, public surveillance of the government is anarchy, but the reverse is policy.
The "doctored" Wikileaks which "stole" words from Newsweek's Kurt Eichenwald clearly attributes Eichenwald's article at the beginning of the email.
Even some alternative news website have been caught doing much of the same. The ironically named Bipartisan report ran a story called "BREAKING: Hillary Clinton VINDICATED After WikiLeaks Caught FABRICATING Fake Emails," which was nothing more than an amazingly pathetic misinterpretation of a Newsweek editorial. The Newsweek article calls out Russian news agency Sputnik for inaccurate reporting as they misattributed the words of an email, which clearly contained a forwarded Newsweek article, to the email's sender, Sidney Blumenthal.
The Newsweek article does not charge Wikileaks with doctoring the emails. Sputnik later admitted its misinterpretation of the document and removed the article from their page. In fact, the very first line of the Wikileaks email in question is a link to the Newsweek article. The Bipartisan Report's article acknowledges this fact, despite its glaringly partisan headline, but, for some reason, refers to the email as "fictitious" and "false" anyway. With more Clinton-related leaks on the horizon, it seems likely that we will continue to see even more desperate attempts to smear Wikileaks and elevate Clintonian fascism.
What are your thoughts? Please share, like, and comment on this article!
This article (Mainstream Media Attempts To Defend Hillary And Attack Wikileaks, Fails Epically) is free and open source. You have permission to republish this article under a Creative Commons license with attribution to the author and TrueActivist.com
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text:
Q: python futures and tuple unpacking What is an elagant/idiomatic way to achieve something like tuple unpacking with futures?
I have code like
a, b, c = f(x)
y = g(a, b)
z = h(y, c)

and I would like to convert it to use futures.
Ideally I would like to write something like
a, b, c = ex.submit(f, x)
y = ex.submit(g, a, b)
z = ex.submit(h, y, c)

The first line of that throws
TypeError: 'Future' object is not iterable

though.
How can I get a,b,c without having to make 3 additional ex.submit calls? ie. I would like to avoid having to write this as:
import operator as op
fut = ex.submit(f, x)
a = client.submit(op.getitem, fut, 0)
b = client.submit(op.getitem, fut, i)
c = client.submit(op.getitem, fut, 2)
y = ex.submit(g, a, b)
z = ex.submit(h, y, c)

I guess a potential solution is to write an unpack function like below,
import operator as op
def unpack(fut, n):
    return [client.submit(op.getitem, fut, i) for i in range(n)]

a, b, c = unpack(ex.submit(f, x), 3)
y = ex.submit(g, a, b)
z = ex.submit(h, y, c)

which works: for example if you first define:
def f(x):
    return range(x, x+3)
x = 5
g = op.add
h = op.mul

then you get
z.result() #===> 77

I thought something like this might already exist.

The above only works with dask.distributed.Future. It does not work for plain concurrent.futures.Future.

A: A quick glance at:
https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.Future
suggests that you'll have to do something like
afuture = ex.submit(f, x)
a,b,c = afuture.result()
...

submit returns a Future object, not the result of running f(x).
This SO answer indicates that chaining futures is not trivial:
How to chain futures in a non-blocking manner? That is, how to use one future as an input in another future without blocking?

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