Van-Betuw went to Ghana as a Junior Fellow with the Engineers Without Boarders (EWB) program. The program trains students before sending them to developing countries to research a particular field of interest.
Van-Betuw was stationed in Kukum city in southern Ghana, where he interacted with coco growers on a daily basis.
"I was really identifying the different venues for agricultural extension through a company called Kuapa Kooko, which stands for good farmer of cocoa, [a] huge fair trade producer in Ghana. They are the suppliers of cocoa beans for the producers of Divine Chocolate," he said.
According to this sixth-year industrial engineering student, one of the major problems plaguing the cocoa industry right now is that farmers are holding onto old cocoa trees, which have long outgrown their productivity.
"The suggested replantation rate is about 25 years — that's when the trees peak and start to reduce productivity, so they say replant them. But many farmers have 50-year old trees, 60-year old trees…and in order to counteract that, lots of them are using fertilizers, and pesticides, and herbicides to try and get as much out of the trees as possible. Meanwhile, that's adding to soil depletion," explained Van-Betuw.
The Cocoa Rehabilitation Project, a recent program funded by the Ghanaian government, is trying to solve this over-grown tree problem.
"What they do is they'll send a crew to chop down all of the farmer's trees for an area that the farmer determines, and then they'll give them seedlings of the hybrid crop, and then they'll also line and peg the land for the farmers so that there is proper spacing in between the trees," said Van-Betuw.
Even though the Coco Rehabilitation Project "has its inefficiencies," Van-Betuw is hopeful that it will go a long way in boosting the cocoa crops of the farmers who choose to utilize this free service.
When asked why he applied to join EWB's Junior Fellow program, the soon-to-be engineer replied that he wanted to get more out of his education.
"I thought that this was a really good opportunity to obtain that global citizen mindset and find out more about development and whether I would like to pursue a career in development," he said.
And find out he did.
If he can secure the necessary financial investment, Van-Betuw said he would happily return to Ghana and work towards starting up a cocoa finished-product industry.
"Right now [the cocoa crop is] grown, processed and then its sent out elsewhere like Europe or North America, rather than processed into a finished product like cocoa or chocolate. Why not do that in Ghana? And I would like to pursue that idea in the future," explainedVan-Betuw.
Van-Betuw will be sharing his newfound knowledge about cocoa growing practices in Ghana with fellow Engineering students, as well as with high school students around Saskatchewan.
"The idea behind the Junior Fellow program is giving one person this bank of experience, this wealth of knowledge, and then you can change one hundred, two hundred, three hundred people through this one person," expressed Van-Betuw.
EWB is now accepting applications for their next Junior Fellow. Frank Elechi, co-chair of the organization's Regina Chapter encourages all students to apply.
"Any student is welcome to apply. You don't have to be in engineering to qualify," explains Elechi.
The deadline for the next Junior Fellow application is October 23, 2012. The application kit can be downloaded from http://uregina.ewb.ca/?p=131.


text:
O Power Dynamos Football Club é um clube de futebol da Zâmbia, da cidade de Kitwe. Suas cores são vermelho e preto.

Títulos

Internacionais
Recopa Africana: 1991.

Nacionais
 Campeonato Zambiano: 5 vezes (1984, 1991, 1994, 1975 e 2000).
 Copa da Zâmbia: 7 vezes (1979, 1980, 1982, 1990, 1997, 2001 e 2003).
 Zâmbia Challenge Cup: 2 vezes (1990 e 2001).
 Zâmbia Coca Cola Cup: 2003.
 Zâmbia Charity Shield: 2004.

Clubes de futebol da Zâmbia
Clubes de futebol fundados em 1971

redpajama_set_name: RedPajamaWikipedia


text:
\section{Introduction}

\cite{mackey-nielsen2007} discovered that the intermediate age ($\sim$1.5\,Gyr) Globular Cluster (GC) NGC\,1846 in the Large Magellanic Cloud (LMC) displayed a spread turnoff, signalling the presence of multiple populations, possibly similar to those revealed by chemical anomalies in the ancient Galactic GCs. A more or less extended turnoff region was revealed in many other YMC of the MCs (\cite{milone2009}), and was generally attributed to a stellar age spread.
In 2015 this interpretation suddenly lose its appeal, when researchers considered results of the Geneva tracks and isochrones computed for a wide range  of masses (ages) and rotation rates (e.g. \cite{georgy2013})\footnote{see the page web http://obswww.unige.ch/Recherche/evoldb/index/ created and maintained by C. Georgy and S. Ekstr\"om}. \cite{brandt-huang2015} showed that the turnoff area covered between the non rotating and rotating isochrones increased with the cluster age up to about 1.5\,Gyr, and then decreased, and the same behaviour was displayed by the YMC for increasing ages. In terms of ages this would imply an increasing age spread (\cite{niederhofer2015}), if interpreted with non--rotating models only.

At the same time, \cite{milone2015} found a different puzzling feature in the color magnitude diagram of the $\sim$400\,Myr cluster NGC\,1856: a ``split" main sequence. This feature could not be understood in terms of differences in age, metallicity or helium content, while splitting and the general features of the color--magnitude diagram were consistent with the superposition of two coeval populations, the first one  including very rapidly rotating stars (the red main sequence and the upper part of the spread turnoff), the other one including slowly rotating stars (the blue main sequence and the lower luminosity turnoff stars) (\cite{dantona2015}). Possibly the slowly rotating stars had also started as rapid rotators, but had been ``braked" by dynamical tides (\cite{zahn1977}) due to interaction with a distant binary companion, as it occurs to the field binaries with orbital periods between 4 and 500\,days (\cite{abtboonyarak2004}). The role of braking was confirmed by \cite{dantona2017}, who remarked that the slowly rotating `blue' MSs in several young clusters required a $\sim$10\% younger age than the rapidly rotating upper `red' turnoff stars. The alternative to a younger population is that the blue MS stars are in a less advanced nuclear burning stage with respect to the requirement of their slow rotation. In other words, these stars follow the evolution of their rapidly rotating counterparts, and feed fresh hydrogen in the H--burning core thanks to rotational mixing, until recent braking leaves them as slow rotators.
Why the stars in these YMCs are born so rapidly rotating, and what are the precise mechanisms for braking which we see so well in the double MS and in the spread of the turnoff is to be worked out, but it is evident that rotation and its evolution {\sl in the cluster ambient} is an important feature for these stars, and it is described quite well by the Geneva models.

\cite{milone2018} have shown that the split MS phenomenon occurs for ages from $\sim$30 to  $\sim$400\,Myr, so it involves the range of masses from $\sim$3 to $\sim$9\,M$_\odot$. This range includes the super--AGB and massive AGB masses whose envelopes, processed by Hot Bottom Burning (HBB), are responsible for the formation of second generation stars in the ancient GCs (\cite{ventura2001}), in the so called `AGB scenario' (\cite{dercole2008}), so it is reasonable to assume that rotation, although subject to the quoted braking mechanisms, has been an important ingredient in the evolution of these stars too.
On the other hand, \cite{decressin2009} examined the consequences of rotation on the abundances of intermediate mass AGB and showed that rotational mixing increased so much the abundances of CNO at the second dredge up (2DU) that the AGBs could not be reasonable pollutor sources for the multiple populations. As the AGB scenario is anyway the most adequate to deal with both the formation (\cite{dercole2008}), the chemistry (e.g. \cite{ventura2009}, \cite{ventura2013}, \cite{dantona2016}), the spatial distribution of the different populations (\cite{vesperini2013}), the role of binaries (e.g. \cite{vesperini2011}) ---see also Ventura's review in this book--- in spite of its remaining problems (see, e.g. \cite{renzini2015}), it is now necessary to re-examine the problem of rotating evolution.
\begin{figure}[bt]
\vspace*{-0.5 cm}
\begin{center}

 \includegraphics[width=2.4in]{figure1a.pdf}
 \includegraphics[width=2.4in]{figure1b.pdf}
\vspace*{-1.35 cm}
 \caption{Evolution of the abundances of C, N and O (left panel) and Na and Ne (right panel) in non rotating model of 6\,M$_\odot$\ (dashed) and when the abundances are increased at the 2DU following rotating MESA models results (full lines). We see that sodium and oxygen survive longer in the rotating case. }
   \label{fig1}
\end{center}
\end{figure}

\begin{figure}[]
\vspace*{-0.5 cm}
\begin{center}
 \includegraphics[width=2.4in]{figure2a.pdf}
 \includegraphics[width=2.4in]{figure2b.pdf}
\vspace*{-1.35 cm}
 \caption{Left panel: time evolution of  a: log luminosity; b: log mass loss rate; c: CNO luminosity; d: log temperature at the base of the convective envelope. The blue lines refer to the standard track, the red ones refer to the track with increased CNO-Na abundances, simulating the result of rotational mixing. Right panel: in the [Na/Fe] versus [O/Fe], the green triangles refer to all GC data from Carretta et al. 2009, while the brown dots represent the stars in the low emtallicity GC M15. The asterisks connected by black lines are the model yields from Dell'Agli et al. 2018, for the chemistry of M15, and the lines connecting to the blue triangles the yields of the 6 and 5M$_\odot$\ shift in the models with increased CNO-Na abundances.}
   \label{fig1}
\end{center}
\end{figure}

\section{Abundances at the 2DU  in recent rotating models}
While new computations by one of us (Sylvia Ekstr\"om) with the Geneva code more or less reproduced the \cite{decressin2009} results both at very low and intermediate metallicity, the results of the rotating MIST code (\cite{choi2016}),  obtained from the MESA code (\cite{paxton2011}).
give a much smaller CNO enhancement, it is at most a factor 4--5 for the abundances of the most metal poor GCs ([Fe/H]$\simeq$--2.2). The CNO increase at the metallicities typical of the bulk of the galactic GC population ([Fe/H]$\ge$--1) is negligible, so ---if these models describe the rotational mixing correctly--- we must worry about the difference in the yields only for the lowest metallicity clusters.

As also $^{23}$Na and   $^{22}$Ne abundances are important for the second generation pollution, we considered the whole results in MESA models computed by one of us (Aaron Dotter)  for [Fe/H]=--2 and solar scaled abundances . We used the absolute values of $^{12}$C, $^{14}$N, $^{16}$O, $^{23}$Na and $^{22}$Ne from these models and applied them to the standard (non rotating) models by \cite{dellagli2018} which had the chemical abundances adequate to describe the detailed composition of the GC M\,15 found in the APOGEE survey. By changing the abundances at the 2DU (Fig. 1), we may consider the differential effect of the different chemical abundances on the hot bottom burning phase and on the yields. Obviously, this is not a self--consistent result, but it gives us an interesting way of change in the abundance patterns.

We found out that, for the 6m$_\odot$, the $^{16}$O and the $^{23}$Na are $\sim$0.7\,dex and $\sim$0.6\,dex less depleted than in the standard models, and reproduce better  the abundances in the cluster M\,15, which lacks the extreme O depletions present in intermediate metallicity clusters (Fig. 2, right panel). The 5\,M$_\odot$\, shows a similar effect, by a smaller amount. In fact, the increased CNO mean a larger CNO luminosity at the 2DU (Figure 2, panel c) and thus a larger total L (Panel a), which in turn gives a larger mass loss rate (panel b) during the first evolutionary phases. More mass is then lost when the $^{23}$Na is high, and its yield is larger. Later on, however, the larger mass loss rate provide a smaller temperature at the bottom of convection (panel d) , and therefore a slower conversion of oxygen to nitrogen. Obviously, these results depend very much on the precise CNO and Na increase at the 2DU, and  the total CNO increase assumed here are too large to be consistent with the CNO abundance determinations in the cluster, but the trends we find in this preliminary models deserve further investigation.

redpajama_set_name: RedPajamaArXiv


Man, tonight I was flipping thru the channels when i happend to catch james blunt on mmm in concernt. it was so fantastic. love his music and his message and his purity. does that make sense? just his sense of self and how he is so not rock star-ish. he is just about his music.
weeman is up. he wants a "snuggle". i never want him to grow up. weeman, grow smaller please.


This home is a dream and comes with tons of amazing perks. If you love to entertain guests, this house will be the talk of the town. Every chef's dream kitchen includes side-by-side refrigerator/freezer, separate stove top on large central island, double ovens, microwave in upper cabinet, double porcelain sink with disposal, breakfast bar and tons of cabinet and counter space. Host elegant dinner parties for friends in your formal dining area with built-in china cabinet. Library includes custom built and floor to ceiling built-in bookcases with a sliding ladder. Fully finished basement includes built-in wall cabinets. Spacious backyard creates the perfect ambiance for outdoor parties, with a large deck, paver patio, fire pit. Quiet cul de sac location. Can view the 4th of July and BG Days fireworks from the deck. Large grassy water detention area in back yard. Great neighborhood.
SchoolsClick map icons below to view information for schools near 561 Mulberry Ct Buffalo Grove, IL 60089.


text:
Entrepreneur Turns Racy Waste Into Sexy Shoes
GDS Gives Visitors a First Look at Spring '16
Moccasins, espadrilles and gladiator sandals were just a few of the fashion trends dominating GDS held in Düsseldorf, Germany July 29-31. Over 900 brands presented their Spring '16 collections to 15,000 domestic and international attendees. Additionally, another 223 exhibitors at Tag It!, the private labels show for footwear and accessories, showcased their latest designs.
This season, GDS spotlighted innovations in dedicated areas that highlighted new materials and various 3D technologies in the footwear industry. Trend Spots offered creative merchandising tips for brick-and-mortar retailers, meanwhile, a series of fashion shows brought the trends to life.
GALLERY: Editor's Picks
"We welcomed exactly the same number of visitors as one year ago and our exhibitors have reported avid interest in their collections. You can feel that the retailers who attend GDS use it proactively as an information platform – in terms of content the developments are very positive. After the positive response in the trade press over the past two events, however, I would have liked to see the same trend for the visitor attendance as well," said Messe Düsseldorf President and CEO Werner Mattias Dornscheidt in a statement.
In a survey, 80 percent of polled visitors said they rely on GDS as a source of information to be "well prepared" for the upcoming season. Two-thirds of buyers surveyed said they made purchasing decisions on site, and 39 percent of visitors placed orders for immediates.
GDS Director Kirstin Deutelmoser, added, "Needless to say we are pleased with the exhibitors' praise and the very high visitor satisfaction. But this cannot blind us to the fact that the general 'footfall' is not sufficient to satisfy all exhibitors. We are getting an extremely positive feedback from the retailers who were here – it's just a shame there were not more of them coming."
The next edition of GDS takes place Feb. 10-12, 2016.
Kirstin Deutelmoser

redpajama_set_name: RedPajamaCommonCrawl


Teamwork is important on many levels. Working as a team internally ensures that our beneficiaries are receiving the best, most innovative and technically accurate services and programs.
Teamwork is important on many levels. Working as a team internally ensures that our beneficiaries are receiving the best, most innovative and technically accurate services and programs. Working as a cohesive team within the community also allows us to truly hear Kiberans, enabling us to better understand needs and concerns, and to work together to efficiently find solutions to challenges.
At CFK, we place a high value on teamwork. In fact, it is one of six core values on which the organization was founded over 17 years ago. To strengthen mission delivery, our Kenyan team kicked off the new fiscal year with 3-day training and staff retreat in July.
Nothing breaks the ice like a well-planned teambuilding event! Do you remember playing the telephone game during your childhood? What started out as "Suzie and Sally sell seashells by the seashore" quickly became a different tongue twister about Sammy needing a new door.
Amidst the laughter and fun in this exercise, Executive Director Hillary Omala reinforced that effective and adaptive communication is key in all we do at CFK, including developing skills as a resilient team which shares the same mission. Hillary definitely kick-started the team spirit!
CFK Executive Director Hillary Omala at the staff retreat.
While policy and procedures can feel at times tedious for staff, the human resources and finance teams engaged the team in exploring how these functions facilitate order, harmony, and coherence across the organization and into the community of Kibera.
"Having the resources you need, including human and financial resources, are key to our successes. The training brought out clearly the commitment the organization has to ensure we succeed in everything we do," shared Valentine Cheche, administrative officer.
Proper planning and precise execution are additional key ingredients for team success. "We want to become more efficient and more effective in delivering quality services to the people of Kibera," said Mark Muasa, head of programs, during his training presentation highlighting recent programmatic changes. Over the last 6 months, we have re-strategized and reorganized the programs we have been providing over the last 17 years into four focus areas: health and wellness, girls empowerment, youth empowerment, and thrive.
CFK staff join together in one of several team building activities at the retreat in July.


MATTEO DARMIAN is not in Jose Mourinho's Manchester United plans, according to reports.
EXIT: Will Darmian leave United?
The defender moved to Old Trafford last summer in a £12m move from Torino.
However, the 26-year-old failed to make a lasting impact with the Red Devils and lost his place in the starting line-up.
And Italian newspaper Tuttosport say Darmian is not in Mourinho's plans at United.
He made 39 appearances in all competitions under Louis van Gaal last campaign, chipping in with one goal.
Mourinho seems to be doing his work early at United, despite being appointed just two weeks ago.
The Red Devils today confirmed the signing of centre-back Eric Bailly from Villarreal.
The £30m defender becomes Mourinho's first signing and has penned a four-year deal worth £75,000-a-week.


text:
Q: How to connet to the mysql server in zeit-now I use zeit-now to deploy my website.
But I can't connect to my MySQL server.
I'm sure my MySQL server is able to be connected on my machine.
Here is my repo.
Any solution?

A: In the FAQ it says

Now itself focuses on being the best platform for serverless hosting. This means that the workloads we handle are static or stateless.

Still, you're trying to connect to your database on localhost. Although running a database instance in a docker container might be an option for a staging system, it definitely is not for production systems, since you will lose the data when you restart/redeploy. And you don't seem to startup a docker container in your deployment.
You should use a cloud database provider instead:

Use Cloud Databases that offer secure (preferably HTTPS-based) gateways, for example: Azure CosmosDB, MongoDB, Atlas, Firebase, Google Cloud SQL

redpajama_set_name: RedPajamaStackExchange


text:
"Loser?"
At the beginning of Imperial history, the two royal families, who were from the small kingdom of Lehel, organized all the tribes here and laid the foundation for the country.
One of them became an emperor, and the other became a great army.
However, the public's words were unexpected.
It was a group of losers.
"Finally, a country was established here, and the kingdom of Lehel, from which they had come from, had a problem remembering the name Lehel Douard when the name was decided."
"The question of who ascends to the emperor.
Would it be Avrant, who was wise, excellent, but a woman? Cloud, who was always looking for opportunities one step behind."
It seemed that he only nodded because his name had already inferred who continued the Grand Duke.
"In the end, Cloud became the emperor just because he is a 'man'.
And Cloud created the Grand House of Avrant named after Avrant, in the name of taking care of his sister.
At first, it seemed to take good care of it, but at some point here in the north of the Empire, magic that could threaten the men and men constantly emerged, and the Emperor settled the first grand duke here."
"It was the reason to protect it as a guardian of the Empire and to give the vast land in the north to the Great Duke.
But as a result, it was to make a Grand Duke surrounded by forests and to never rebel against him.
The Emperor just wanted the first Grand Duke to die in this forest."
For a moment, I had to swallow the word "bad guy" as I almost blurted it out.
How could humans do that? You said you were his sister.
"The Grand Duke endured better than I thought in this forest.
But the magic here constantly appears and threatens people.
If even one of them went to the capital, the Grand Duke used to be greatly reprimanded by the Emperor.
That's why the Grand Duke has to stop the magicians from getting out of this forest."
"That's how it is."
When I was nodding, he made a pretty bitter look and spoke bitterly.
"Now, I've arranged all the roads to the outside.
By the way, Bebe, did you think there are no knights in the family?
"Right! I've rarely seen knights in a castle."
"The Grand Duke has so many troops that it is comparable to the Emperor.
Of course, the Emperor is unaware of this fact.
Anyway, they all surround this huge forest, so that nothing can escape from it."
I opened my mouth wide for a moment.
It was a story that has not been heard anywhere in the past.
"Beasts and monsters… I didn't know there was something like that."
"People don't know.
Because I've managed them perfectly in public for hundreds of years.
However, monsters and beasts continued to increase for unknown reasons.
It became more ferocious and appeared rougher.
It's been worse since about five years ago.
And when it becomes spring, these beasts get particularly mad."
Perhaps the story was finally over, but the Grand Duke smiled and stroked my head.
"And we're going to the forest."
"That forest…"
"I'll make a board so that you can take the vassals there, so use your strength there as much as you want.
It's to show your strength to the vassals.
What do you think? It must be full of scary things.
"Yes! It's okay! I like it!"
"I'll take care of everything after that, so trust me and act."
What happens after that means I'm going to faint from strength.
My strength was both strong and weak.
No matter how strong you use, the power becomes useless when the user faints.
Users even face situations that can be dangerous due to fainting.
So I didn't want to let anyone know this power.
But for the admission documents, if I could prove it to those who bullied my dad, I'll do so.
"Yes, I will."
I'll call you at the next political meeting, so come out there."
"Can I go there?"
"Any family member of the Grand Duke can go.
So don't worry and come out."
"And… it's going to be noisy for a while because you're going to a political meeting, so keep that in mind."
"Are you spreading rumors?"
That way, all the vassals will attend."
As if the devil had descended, an evil smile spread across the Grand Duke's face.
But I also realized for sure now that he was worried about me.
The Grand Duke looked at me with softer eyes and spoke in a friendlier voice than before.
Why was he making such a burdensome face?
'Ah! It means to go and do well.'
Otherwise, there would be no reason to look at me like that.
Deep enlightenment.
When I understood, I nodded my head.
I'll do everything I'm told to do.
Then I'll go now."
It's late at night."


text:
Quiz: Can You Name All of These Major League Baseball Teams?: HowStuffWorks
Can You Name All of These Major League Baseball Teams?
By: Gavin Thagard
Image: Wiki Commons by Arturo Pardavila III
It's a Sunday afternoon in the middle of spring. Life is coming back as trees and flowers bloom. The weeks of rain have passed, and the sun pours through the blinds into the living room, where you just turned on a baseball game. You look at the screen and realize that you don't even know the two teams playing. How common of an occurrence is this for you? Are you just a fan in passing or a true lover of the game of baseball?
There's a reason baseball is called "America's Pastime," as it's been one of the most popular sports in North America for over a century. Of course, when it comes to Major League Baseball, there have been many changes since the American League came together with the National League to play the first modern World Series in 1903. One of the biggest changes is the expansion of the league into the 30 teams that comprise it today.
Will you be able to identify all 30 teams that compete in the majors? From the east coast of Pennsylvania to the port cities of Washington and California, you'll have to know both historical franchises and new entries into the league. If you're ready, get started and see if you can knock this MLB quiz out of the park.
After one of the best seasons in franchise history, controversy hit this MLB team in 2020. Who are they?
After losing in the 2019 World Series, the Houston Astros became the center of controversy for a cheating scandal that dates back to their World Series victory in 2017. According to the reports, the Astros read signs from opposing teams and found ways to communicate those signs to their batters.
No MLB team has won more championships than which one shown here?
The New York Yankees won a World Series in every decade except for the 1980s and 2010s. The team's last title was won in 2009, which is why it decided to part ways with manager Joe Girardi following the 2017 season.
Can you identify this team that's won seven straight West Division titles?
Jackie Robinson, the first black player to compete in Major League Baseball, was a star for the Dodgers, who were located in Brooklyn at the time. Robinson was a six-time All-Star with the Dodgers and won the NL MVP in 1949.
WikiCommons by Keith Allison
Do you recognize this team that plays in the oldest stadium in Major League Baseball?
After winning three World Series with the Boston Red Sox, Babe Ruth's contract was sold to the New York Yankees in 1919, beginning what became known as "The Curse of the Bambino." The Red Sox wouldn't break the curse until they finally won a championship in 2004.
One of the most dominant franchises in the National League, what team is this?
Serving as general manager of the St. Louis Cardinals, Branch Rickey helped establish the farm system in Major League Baseball during his time with the club. Rickey was tired of scouting talent who ended up going elsewhere, so he encouraged the Cardinals to start their own minor league teams to develop players.
WikiCommons by D. Benjamin Miller
How well do you know this MLB team that's often considered the little brother in New York City?
The New York Mets suffered through some horrible seasons after the franchise was founded in 1962. However, they were able to put a championship roster together in 1969 that became known as the "Miracle Mets" because they surprised the entire baseball world.
WikiCommons by Ian D'Andrea
Are you familiar with this team that ended a record championship drought in 2016?
The Chicago Cubs play home games in Wrigley Field, one of the most historic ballparks in baseball. The team first started playing home games there in 1916, even winning its inaugural game against the Cincinnati Reds.
Legends like Hank Aaron and Chipper Jones played for which franchise depicted here?
The Braves were located first in Boston and later in Milwaukee before the team made the move to Atlanta in 1966. They have won titles in all three cities, with their latest championship coming in 1995.
The only franchise located in Canada, is this a team you're familiar with?
After not qualifying for the playoffs for three straight seasons, the Toronto Blue Jays hired Cito Gaston as their manager in 1989. In his first season with the team, Gaston led the Blue Jays to the ALCS, and in 1992, he helped the team win the first championship in franchise history.
What's the name of this team that recently hired Rocco Baldelli as its manager?
After playing in Hubert H. Humphrey Metrodome from 1982 through 2009, the Minnesota Twins moved to Target Field for the 2010 season. They later hosted the 2014 All-Star Game in their new stadium.
WikiCommons by David
Did you watch this team win the 2019 World Series?
The Washington Nationals were a Wild Card team coming into the 2019 MLB Playoffs, but that didn't stop them from taking out team after team, including the top-seeded Los Angeles Dodgers in the NLDS. The Nationals then defeated the Houston Astros in the World Series to take home the first title in franchise history.
Select the name of this MLB team that's led by Mike Trout, one of the biggest stars in baseball.
After parting ways with longtime manager Mike Scioscia in 2018, the Los Angeles Angels decided to bring in Brad Ausmus, who only lasted one season with the team. As his replacement for 2020, the Angels hired veteran manager Joe Maddon to hopefully put them back in playoff contention.
One of the first MLB franchises to move to California, who is this?
In 1944, while still located in New York, the Giants made Carl Hubbell the first player in the National League to have his number retired. This honor was given only one year after Hubbell retired from baseball.
Can you identify this team that's named after one of the most important industries in town?
Established in 1969 and moved to Milwaukee in 1970, the Brewers have played in both the American League and the National League. The team reached one World Series as a member of the American League but hasn't returned since changing leagues in 1998.
Do you know this MLB team that's struggled since reaching back-to-back World Series in 2008 and 2009?
The Phillie Phanatic, the mascot for the Philadelphia Phillies, is one of the most recognized mascots in all of professional sports. Phanatic claims to be the Phillies' biggest fan but also loves cheesesteaks, sleeping and the movie "Rocky."
This team plays games in the "Windy City." Who are they?
The Chicago White Sox were at the center of the 1919 Black Sox Scandal, where eight players threw the World Series to make money in a gambling operation. All of the players were permanently banned from MLB, including from the Hall of Fame.
The '70s were dominated by which franchise shown here?
From 1970 through 1976, the Cincinnati Reds went to four World Series, winning two back to back in 1975 and 1976. The team during this era was known as "The Big Red Machine" with players like Pete Rose, Johnny Bench and Joe Morgan on the roster.
How well do you know this team that currently has the longest championship drought in baseball?
The Cleveland Indians moved to Progressive Field, their current ballpark, in 1994 after playing in Cleveland Park since the '30s. The next season, they were back in the World Series for the first time since 1954, though they couldn't capture a title during that 1995 playoff run.
Are you familiar with this team that's carried the same name since it joined the majors in 1901?
After reaching the ALCS in 1972, the Detroit Tigers went into a period of decline for most of the rest of the decade. One bright spot for the team during this time was Mark Fidrych, who won over fans in 1976 as a rookie, winning the AL Rookie of the Year after posting a 19-9 record as a pitcher.
WikiCommons by KA Sports Photos
Is this MLB franchise, known for implementing sabermetrics in the early 2000s, a team you recognize?
Before moving to Oakland, the Athletics were located in Philadelphia and Kansas City at different points. The team had tremendous success during its early years in Philadelphia, where it won five of its nine World Series titles.
Have you ever watched this team play home games at Tropicana Field?
The Tampa Bay Rays were established in 1998, and the team's first decade of play was marred by failure. Only once did they finish better than last place in their division, and the Rays never reached the playoffs until 2008 when they made a run at the World Series, ultimately losing to the Philadelphia Phillies in five games.
A franchise still searching for its first World Series title, who is this?
The San Diego Padres are trying to get to the playoffs for the first time since 2006. To accomplish this, the team brought in new manager Jayce Tingler, who is taking his first stab at being a manager.
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Roots of Typha latifolia L. exposed to Fe2+ under reduced conditions in solution culture developed visible coatings (plaques) of an oxidized Fe compound that extended as much as 15-17 ,um into the rhizosphere. Iron concentrations were significantly less and discoloration was not apparent on the surface of roots exposed to Fe-(BPDS)3, Fe3+, Fe-EDDHA, and Fe- EDTA. The extent of plaque formation increased with the concentration of Fe2+ in solution and with pH of the solution in the range of 3.0 to 4.6. Above pH 4.6, oxidation of Fe2+ in the culture solution may have reduced precipitation of Fe on the root surface. Plaque development was most extensive approximately 1.0 cm from the root tip, but all root surfaces showed some Fe staining. Scanning electron micrographs of plaqued roots, grown both in solution culture and in the field, provided support for a model of cast formation by oxidation and precipitation of Fe on external cell surfaces.


text:
\section{Applications}

We develop two prototype applications to demonstrate the utility of the recovered shape structure in structure-aware shape editing and processing.

\vspace{-15pt}
\paragraph{Structure-aware image editing.}
In~\cite{zheng_sigg12}, a structure-aware image editing is proposed, where a cuboid structure is
manually created for the object in the image to assist a plausible shape deformation.
With our method, this cuboid structure can be automatically constructed. Moreover,
the part relations are also recovered which can be used to achieve structure-aware editing.
Given an RGB image, we first recover the 3D shape structure of the object of interest using our method.
To align the inferred cuboid structure with the input image,
we train another network to estimate the camera view.
The 3D cuboids are then projected to the image space according to the estimated view.
The object in the image is segmented with a CRF-based method constrained with the cuboid projections~\cite{xu2011photo}.
Each segment is assigned to a 3D cuboid based their image-space overlapping. At this point, the image editing method in~\cite{zheng_sigg12} can be employed to deform the object of interest.
Fig.~\ref{fig:teaser} and~\ref{fig:application_image} show a few examples of structure-aware image editing based on our 3D shape structure recovery.

\input{edit}

\paragraph{Structure-assisted 3D volume refinement.}
A common issue with 3D reconstruction with volumetric shape representation is that the resolution of volume
is greatly limited due to the high computational cost. This usually results in missing parts
and hence broken structure in the reconstructed volume.
Our recovered 3D structures can be used to refine the 3D volumes estimated by existing approaches such as 3D-GAN~\cite{wu2016learning}.
Given a 2D image, a 3D volume is estimated with 3D-GAN and a cuboid structure recovered by our method.
They can be easily aligned with the help of camera view estimation (as have been done above).
Each voxel is assigned to the closest cuboid, leading to a part-based segmentation of the volume.
We then utilize the part symmetry relation in our recovered structure to complete the missing voxels; see results in Fig.~\ref{fig:application_voxel}.

\input{volume}

\section{Conclusion}

We have proposed a deep learning framework that directly recovers 3D shape structures from single 2D images.
Our network joins a structure masking network for decerning the object structure and a structure recovery network for inferring 3D cuboid structure. The recovered 3D structures achieve both fidelity with respect to the input image and plausibility as a 3D shape structure. To the best of our knowledge, our work is the first that recovers detailed 3D shape structures from single 2D images.

Our method fails to recover structures for object categories unseen from the training set. For such cases, it would be interesting to learn an incremental part assembler. Our method currently recovers 3D cuboids only but not the underlying part geometry. A worthy direction is to synthesize detailed part geometry matching the visual appearance of the input image. Another interesting topic is to study the profound correlation between 2D features and 3D structure, so as to achieve a more explainable 3D structure decoding.

\section{Experiments}

We collected a dataset containing $800$ 3D shapes from three categories in ShapeNet: chairs ($500$), tables ($200$), aeroplanes ($100$). The dataset is split into two subsets for training($70\%$) and testing ($30\%$), respectively.
With these 3D shapes, we generate training and testing pairs of image mask and shape structure to train the network and evaluate our method quantitatively. We also evaluate our methods qualitatively with a Google image search challenge. Both quantitative and qualitative evaluations demonstrate the capability of our method in recovering 3D shape structures from single RGB images faithfully and accurately.

\subsection{Training data generation}
\label{train:data}

\paragraph{Image-structure pair generation.}
For each 3D shape, we create $36$ rendered views around the shape for every $30^\circ$ rotation and with $3$ elevations.
Plus another $24$ randomly generated views, we create $60$ rendered RGB images in total for each shape.
The 3D shapes are rendered with randomly selected backgrounds from NYU v2 dataset.
For each RGB image, the ground-truth object mask can be easily extracted using the depth buffer for rendering.

All 3D shapes in our dataset are pre-segmented based on their original mesh components or using the symmetry-aware segmentation proposed in~\cite{wang2011}.
We utilize symmetry hierarchy~\cite{wang2011} to represent the shape structure, which defines how parts in a shape are recursively assembled by connectivity or grouped by symmetry. We adopt the method in~\cite{li2017grass} to infer consistent hierarchy trees for the shapes of each category.
Specifically, we train a unsupervised auto-encoder with the task of self-reconstruction for all shapes. During testing, we use this auto-encoder to perform a greedy search of grouping hierarchy for each shape.
For more details on this process, please refer to the original work.
Consequently, we generate $60$ image-structure pairs for each 3D shape.

\paragraph{Data processing and augmentation.}
To further enhance our dataset and alleviate overfitting, we conduct on each training 3D shape structure-aware deformation~\cite{xu2012fit} based on component-wise controllers~\cite{zheng2011component} to generate a set of structurally plausible variations for the training shape.
Such structure-aware deformation preserves the connection and symmetry relations between shape parts, while maintaining the shape texture for each part. This step is fully automatic and the parameters for each variation generation is randomly set within a given range. In our implementation, we randomly generate $20$ new variations for each 3D shape, thus enlarging our database to $16$K 3D shapes.
For the input images (and the corresponding object masks), we employ the common operations for image data augmentation~\cite{li2017ICCV} such as color perturbation, contrast adjustment, image flip and transformation, etc.

\subsection{Results and evaluation}

\input{result_mask}
We first show in Fig.~\ref{fig:result_mask} some results of object mask prediction by our structure masking network.
As can be seen in the output, the background clutters are successfully filtered out and some detailed structures of the objects are captured.

\paragraph{Google image challenge for structure recovery.}
We first perform a qualitative evaluation on the capability and versatility of our structure recovery.
In order for a more objective study, instead of cherry-picking a few test images, we opt to conduct
a small-scale stress test with a Google image challenge~\cite{xu2011photo}.
During the test, we perform text-based image search on Google using the keywords of ``chair'', ``table''
and ``airplane'', respectively. For each search, we try to recover a 3D cuboid structure for each of the top 8 returned images using our method.

The results are shown in Fig.~\ref{fig:result_internet}.
From the results, we can see that our method is able to recover 3D shape structures from real images in a detailed and accurate way. More importantly, our method can recover the connection and symmetry relations of the shape parts from single view inputs, leading to high quality results with coherent and plausible structure. Examples of symmetry recovery include the reflectional symmetry of chair legs or airplane wings, the rotational symmetry of legs in a swivel chair or a table.

There are some failure cases (marked with red boxes in Fig.~\ref{fig:result_internet}).
The marked chair example is not even composed of multiple parts and hence may not admit a part structure.
When the structure of the object of interest is unseen from our training dataset of 3D shapes, such as the marked table example, our method fails to recover a reasonable structure.

\input{result_shapenet_internet}

\paragraph{Quantitative evaluation.}
We quantitatively evaluate our algorithm with our test dataset.
For the structure masking network, we evaluate the mask accuracy by the overall pixel accuracy
and per-class accuracy against the ground-truth mask (see table~\ref{table:masknetresult}).
We provide a simple ablation study by comparing our method with two baselines:
single-scale (without refinement network) and two-scale (without jump connection).
The results demonstrate the effectiveness of our multi-scale masking network.

\begin{table}[h]\center
    \scalebox{0.95}{
		\begin{tabular}{| c | c | c | }
			\hline
			Method & Overall Pixel & Per-Class \\ \hline
			single-scale & 0.953 &  0.917 \\ \hline
            two-scale (w/o jump) & 0.982 &  0.964 \\ \hline
            two-scale (with jump) & 0.988 &  0.983 \\ \hline
		\end{tabular}
    }
	\caption{An ablation study of structure masking network.}
    \label{table:masknetresult}
\end{table}

For 3D shape structure recovery, we develop two measures to evaluate the accuracy:

\begin{itemize}
\item \emph{Hausdorff Error}:  $ \frac{1}{2T}\sum_{i}^{T} (D(S_i, S^\text{gt}_i) +D(S^\text{gt}_i, S_i))$,
where $S_i$ is a recovered shape structure (represented by a set of boxes) and $S^{\text{gt}}_i$ its corresponding ground-truth. $T$ is the number of models in the test dataset.
$D(S_1, S_2) = \frac{1}{n}\sum_{B^1_j\in{S_1}}\min\limits_{B^2_k\in{S_2}}H(B^1_j, B^2_k)$ measures the
averaged minimum Hausdorff distance from the boxes in structure $S_1$ to those in $S_2$, where
$B^1_j$ and $B^2_k$ represent the boxes in $S_1$ and $S_2$, respectively.
$H(B^1,B^2) = \max\limits_{p\in{B^1}}\min\limits_{q\in B^2} ||p-q||$ is the Hausdorff
distance between two boxes, with $p$ and $q$ being the corner points of box.
Since Hausdorff is asymmetric, the distance is computed for both directions and averaged.

\item \emph{Thresholded Accuracy}: The percentage of boxes $B_{i}$ such that $\delta = H(B_i, B^{*}_i)/L(B^{*}_i) < threshold $, where $B_i$ is the $i$-th box in recovered shape structure $S$ and $B^{*}_i$ its nearest box in the ground-truth $S^\text{gt}$. $H$ is the Hausdorff distance between two boxes as defined above. $L$ is the diagonal length of a box.

\end{itemize}

We consider as our baseline where the structure masking network is simply a vanilla VGG-16 network. In table~\ref{table:comparison}, we compare the accuracy of structure recovery, based on the above two measures, for our method and the baseline. We also compare the two methods where VGG-16 is replaced with VGG-19.
The results demonstrate the significant effect of our structure masking network in helping the structure decoding.
This can also be observed from the reconstruction error plotted in Figure~\ref{fig:Loss} (bottom).
A deeper structure masking network (with VGG-19) also boosts the performance to a certain degree.

\begin{table}[h]\center
    \scalebox{0.9}{
		\begin{tabular}{| c | c | c | c | }
			\hline
			\multirow{2}{*}{Method} & \multicolumn{1}{c|}{Hausdorff} & \multicolumn{2}{c|}{Thresholded Acc.} \\ \cline{3-4}
            \multicolumn{1}{|r|}{}  & \multicolumn{1}{c|}{Error} &  $\delta<0.2$ & $\delta<0.1$ \\ \hline
			Vanilla VGG-16 & 0.0980 & $ 96.8\% $ & $ 67.8\% $ \\ \hline
			Structure masking (VGG-16) & 0.0894 & $ 97.8\% $ & $ 75.3\% $ \\ \hline
			Vanilla VGG-19 & 0.0922 & $ 96.4\% $ & $ 72.2\% $  \\ \hline
			Structure masking (VGG-19) & 0.0846 & $ 97.6\% $ & $ 78.5\% $  \\ \hline
		\end{tabular}
    }
	\caption{Comparison of structure recovery accuracy over different methods.}
		\label{table:comparison}
\end{table}

\paragraph{Comparison.}
In Fig.~\ref{fig:compare_star}, we give a visual comparison of 3D shape reconstruction
from single-view images between our method and two state-of-the-art methods, \cite{huang2015single} and \cite{tulsiani2016learning}.
Both the two alternatives produce part-based representation of 3D shapes, making them comparable to our method.
The method by Huang et al.~\cite{huang2015single} recovers 3D shapes through assembling parts from database
shapes while preserving their symmetry relations.
The method of Tulsiani et al.~\cite{tulsiani2016learning} generates cuboid representation similar to ours,
but does not produce symmetry relations.
As can be seen, our method produces part structures which are more faithful to the input, due to the integration of the structure masking network, and meanwhile structurally more plausible, benefiting from our part relation recovery.

\input{compare_star}\vspace{-10pt}

\section{Introduction}

The last few years have witnessed a continued interest in single-view image-based 3D modeling~\cite{choy20163d,fan2016point,girdhar2016learning}.
The performance of this task has been dramatically boosted, due to the tremendous success
of deep convolutional neural networks (CNN) on image-based learning tasks~\cite{krizhevsky2012imagenet}.
The existing deep models, however,
have so far been mainly targeting the output of volumetric representation of 3D shapes~\cite{choy20163d}.
Such models are essentially learned to map an input 2D image
to a 3D image (voxel occupancy of a 3D shape in a 3D volume).
Some compelling results have been demonstrated.

\input{fig_teaser}

While enjoying the high capacity of deep models in learning the image-to-image mapping,
the 3D volumes reconstructed by these methods lose
an important information of 3D shapes -- shape topology or part structure.
Once a 3D shape is converted into a volumetric representation,
it would be hard to recover its topology and structure, especially when there exist topological
defects in the reconstructed volume.
Shape structure, encompassing part composition and part relations, has been found highly
important to semantic 3D shape understanding and editing~\cite{mitra2013structure}.
Inferring a part segmentation for a 3D shape (surface or volumetric model) is known to be difficult~\cite{kalogerakis20163d}.
Even if a segmentation is given, it is still challenging to reason about part relations
such as connection, symmetry, parallelism, etc.

We advocate learning a deep neural network that directly recovers 3D shape structure
of an object, from a single RGB image.
The extracted structure can be used for enhancing the volumetric reconstruction
obtained by existing methods, facilitating structure-aware editing of the reconstructed 3D shapes,
and even enabling high-level editing of the input images (see Fig.~\ref{fig:teaser}).
However, directly mapping an image to a part structure seems a dunting task.
Tulsiani et al.~\cite{tulsiani2016learning} proposed a deep architecture to map a 3D volume to a set of cuboid primitives. Their method, however, cannot be adapted to our problem setting since the output primitive set
does not possess any structural information (mutual relations between primitives are not recovered).

Our problem involves the reasoning not only about
shape geometry, but also for higher level information of part
composition and relations. It poses several special challenges.
1) Different from shape geometry, part decomposition and relations
do not manifest explicitly in 2D images.
Mapping from pixels to part structure is highly ill-posed, as
compared to pixel-to-voxel mapping studied in many existing
2D-to-3D reconstruction works.
2) Many 3D CAD models of man-made objects contain diverse, fine-grained substructures. A faithful recovery of
those complicated 3D structures goes far beyond shape synthesis modulated by a shape classification.
3) Natural images always contain cluttered background and the imaged objects have large variations
of appearance due to different textures and lighting conditions.

Human brains do well both in shape inference based on low-level visual stimulus
and structural reasoning with the help of prior knowledge about 3D shape compositions.
The strength of human perception is to integrate the two ends of processing and reasoning
to form a capable vision system for high-level 3D shape understanding.
Motivated by this, we propose to learn and integrate two networks,
a \emph{structure masking network} for accentuating multi-scale object structures
in an input 2D image, followed by a \emph{structure recovery network} to
recursively recover a hierarchy of object parts abstracted by cuboids
(see Figure~\ref{fig:overview}).

The structure masking network produces a multi-scale attentional
mask for the object of interest, thereby decerning its shape structures in various forms and scales.
It designed as a multi-scale convolutional neural networks (CNN)
augmented with jump connections to retain shape details while
screening out the structure-irrelevant information such as background and textures in the output mask image.
The structure recovery network fuses
the features extracted in the structure masking network and
the CNN features of the original input image and feed them into
a recursive neural network (RvNN) for 3D structure decoding~\cite{li2017grass}.
The RvNN decoder, which is trained to explicitly model part relations,
expands the fused image features recursively into a tree organization of 3D cuboids
with plausible spatial configuration and reasonable mutual relations.

The two networks are jointly trained, with the training data of image-mask and cuboid-structure pairs.
Such pairs can be generated by rendering 3D CAD models and extracting the box structure based
on the given parts of the shape.
Several mechanisms are devised to avoid overfitting in training this model.
Experiments show that our method is able to faithfully recover diverse and detailed part structures of
3D objects from single 2D natural images. Our paper makes the following contributions:
\begin{itemize}
  \item We propose to directly recover 3D shape structures from single RGB images.
  The faithful and detailed recovery of 3D structural information of an object, such as part connectivity and symmetries, from 2D images has never been seen before, to our knowledge.
  \item We present an architecture to tackle the hard learning task, via integrating a convolutional structure masking network and a recursive structure recovery network.
  \item We develop two prototype applications where we use the recovered box structures 1) to refine
  the 3D shapes reconstructed from single images by existing methods and 2) to assist structure-aware
  editing of 2D images.
\end{itemize}

\section{Method}

We introduce our architecture for learning 3D shape structures from single images. It is an auto-encoder
composed of two sub-networks: a \emph{structure masking network} for decerning the object structures from the input 2D image and a \emph{structure recovery network} for recursive inference of a hierarchy of 3D boxes along with their mutual relations.

\subsection{Network architecture}
Our network is shown in Fig.~\ref{fig:overview}, which is composed of two modules:
a two-scale convolutional structure masking network and a recursive structure recovery network.
The structure masking network is trained to estimate the contour of the object of interest.
This is motivated by the observation that object contours provides strong cues for understanding
shape structures in 2D images~\cite{shotton2005contour,toshev2012shape}.
Instead of utilizing the extracted contour mask,
we feed the feature map of the last layer of the structure masking network into the structure recovery network.
To retain more information in the original image, this feature is fused with the CNN feature of
the input image via concatenation and fully connected layers, resulting in a $80$D feature code.
An RvNN decoder then recursively unfolds the feature code into a hierarchical organization of boxes,
with plausible spatial configuration and mutual relations,
as the recovered structure.

\subsection{Structure Masking Network.}
Our structure masking network is inspired by the recently proposed multi-scale network for detailed depth estimation~\cite{li2017ICCV}.
Given an input RGB image rescaled to $224 \times 224$,
we design a two-scale structure masking network to output a binary contour mask with a quarter of the input resolution ($56 \times 56$).
The first scale captures the information of the whole image while
the second produces a detailed mask map at a quarter of the input resolution.
As our prediction target is a binary mask, we use the SoftMax Loss as our training loss.

We employ VGG-16 to initialize the convolutional layers (up to pool5) of the first scale network, followed by two fully connected layers. The feature maps and outputs of the first scale network are fed into various layers of the second scale one for refined structure decerning. The second scale network, as a refinement block, starts from one $9 \times 9$ convolution and one pooling over the original input image, followed by nine successive $5\times 5$ convolutions without pooling. The feature maps from the pool3, pool4 and the output of last fully connected layer of first scale network are fused into the second, the fourth and the sixth convolutional layer of the second scale network, respectively. All the feature fusions get through a jump connections layer, which has a $5\times5$ convolutional layer and a 2x or 4x up-sampling to match the $56\times 56$ feature map size in the second scale; the jump connection from the fully connected layer is a simple concatenation.
It is shown that jump connections help extracting detailed structures from images effectively~\cite{li2017ICCV}.

\subsection{Structure Recovery Network}

The structure recovery network integrates the features extracted
from the structure masking network and for the input image into a bottleneck feature
and recursively decodes it into a hierarchy of part boxes.

\paragraph{Feature fusion.}
We fuse features from two convolutional channels. One channel takes as input the feature map of the structure masking network (the last feature map before the mask prediction layer), followed by two convolutions and poolings. Another channel is the CNN feature of the original image extracted by a VGG-16. The output feature maps from the two channels are then concatenated with size $7\times7$, and further encoded into a $80$D code after two fully connected layers, capturing the object structure information from the input image.
We found through experiments such fused features not only improve the accuracy of structure recovery, but also attain good domain-adaption from rendered images to real ones.
We believe the reason is that the extracted features for mask prediction task retain shape details through factoring them out of background clutters, texture variations and lighting conditions.
Since it is hard for the masking network to produce perfect mask prediction, the CNN feature of the original image provides complimentary information via retaining more object information.

\paragraph{Structure decoding.} We adopt a recursive neural network (RvNN) as box structure decoder like in~\cite{li2017grass}. Starting from a root feature code, RvNN recursively decodes its into a hierarchy of features until reaching the leaf nodes which each can be further decoded into a vector of box parameters.
There are three types of nodes in our hierarchy: leaf node, adjacency node and symmetry node.
During the decoding, two types of part relations are recovered as the class of internal nodes: \emph{adjacency} and \emph{symmetry}. Thus, each node can be decoded by one of the three decoders below, based on its type (adjacency node, symmetry node or box node):

\begin{description}
  \item[Adjacency decoder.] Decoder \mbox{\sc AdjDec}\xspace splits a parent code $p$ into two child codes $c_1$ and $c_2$, using the mapping function:
  \[
    [c_1 \ c_2] \ = \ \tanh(W_{ad} \cdot p + b_{ad})
  \]
  where $W_{ad} \in \mathbb{R}^{2n \times n}$ and $b_{ad} \in \mathbb{R}^{2n}$. $n=80$ is the dimension of a non-leaf node.

  \item[Symmetry decoder.] Decoder \mbox{\sc SymDec}\xspace recovers a symmetry group in the form of a symmetry generator (a node code $c$) and a vector of symmetry parameters $s$:
  \[
    [c \ s] \ = \ \tanh(W_{sd} \cdot p + b_{sd})
  \]
  where $W_{sd} \in \mathbb{R}^{(n + m) \times n}$, and $b_{sd} \in \mathbb{R}^{m + n}$. We use $m = 8$ for symmetry parameters consisting of: symmetry type ($1$D); number of repetitions for rotational and translational symmetries ($1$D); and the reflectional plane for reflective symmetry, rotation axis for rotational symmetry, or position and displacement for translational symmetry ($6$D).

  \item[Box decoder.] Decoder \mbox{\sc BoxDec}\xspace converts the code of a leaf node to a $12$D box parameters defining the center, axes and dimensions of a 3D oriented box, similar to~\cite{li2017grass}.
   \[
    [x] \ = \ \tanh(W_{ld} \cdot p + b_{ld})
  \]
   where $W_{ld} \in \mathbb{R}^{12 \times n}$, and $b_{ld} \in \mathbb{R}^{12}$.
\end{description}

\input{decoding}

The decoders are recursively applied during decoding.
The key is how to determine the type of a node so that the corresponding decoder can be used at the node.
This is achieved by learning a node classifier based on the training task of structure recovery where the ground-truth
box structure is known for a given training pair of image and shape structure.
The node classifier is jointly trained with the three decoders.
The process of structure decoding is illustrated in Fig.~\ref{fig:decoding}.
In our implementation, the node classifier and the decoders for both adjacency and symmetry are two-layer networks, with the hidden layer and output layer being $200$D and $80$D vectors, respectively.

\subsection{Training details}
There are two stages in the training. First, we train the structure masking network to estimate a binary object mask for the input image. The first and the second scale of the structure masking network are trained jointly.
In the next, we jointly refine the structure masking network and train the structure recovery network, during which a low learning rate for structure masking network is used. The structure recovery loss is computed as the sum of the box reconstruction error and the cross entropy loss for node classification.
The reconstruction error is calculated as the sum of squared differences between the input and output parameters for each box and symmetry node. Prior to training, all 3D shapes are resized into a unit bounding box to make the reconstruction error comparable across different shapes. In Fig.~\ref{fig:Loss} (top), we plot the training and testing losses for box reconstruction, symmetry recovery and node classification, respectively, demonstrating the convergence of our structure recovery network.

\input{loss_plot}

We use the Stochastic Gradient Descent (SGD) to optimize our structure recovery network with back-propagation through structure (BPTT) for the RvNN decoder training. The convolutional layers of VGG-16 are initialized with the parameters pre-trained over ImageNet; all the other convolutional layers, the fully connected layers and the structure recovery network are randomly initialized. The learning rate of the structure masking network is $10^{-4}$ for pre-training and $10^{-5}$ for fine-tuning.
During joint training,
the learning rate is $10^{-3}$ for structure masking network, $0.2$ for RvNN decoder and $0.5$ for RvNN node classifier. These learning rates are decreased by a factor of $10$ for every $50$ epoches.
Our network is implemented with Matlab based on the MatConvNet toolbox~\cite{vedaldi2015matconvnet}.
The details on generating training data is provided in Section~\ref{train:data}.

\section{Related work}

Reconstructing 3D shapes from a single image has been a long-standing pursue in both
vision and graphics fields.
Due to its ill-posedness, many priors and assumptions have been attempted,
until the proliferation of high-capacity deep neural networks.
We will focus only on those deep learning based models and categorize
the fast-growing literature in three different dimensions.

\paragraph{Depth estimation vs. 3D reconstruction.}
Depth estimation is perhaps the most straightforward solution
for recovering 3D information from single images.
Deep learning has been shown to be highly effective for depth estimation~\cite{eigen2015predicting,laina2016deeper}.
Compared to depth estimation, reconstructing a full 3D model is much more challenging due to
the requirement of reasoning about the unseen parts. The latter has to resort to shape or structure priors.
Using deep neural networks to encode shape priors of a specific category has received much attention lately,
under the background of fast growing large 3D shape repositories~\cite{Shapenet}.
Choy et al.~\cite{choy20163d} develop a 3D Recurrent Reconstruction
Neural Network to generate 3D shapes in volumetric representation, given a single image as input.
A point set generation network is proposed for generating from a 2D image a 3D shape in point cloud~\cite{fan2016point}.
We are not aware of any previous works that can generate part-based structures
directly from a single image.

\paragraph{Discriminative vs. Generative.}
For the task of 3D modeling from 2D images, discriminative models are learned to map an input
image directly to the output 3D representation, either by a deep CNN for one-shot generation
or a recurrent model for progressive generation~\cite{choy20163d}.
The advantages of such approach include ease of training and high-quality results.
With the recent development of deep generative models such as variational auto-encoder (VAE)~\cite{kingma2013auto},
generative adversarial nets (GAN)~\cite{goodfellow2014generative} and their variants.
Learning a generative model for 3D shape generation has gained extensive research~\cite{wu2016learning,girdhar2016learning,li2017grass}.
For generative models, the input image can be used to condition the sampling from the predefined parameter space
or learned latent space~\cite{wu2016learning,girdhar2016learning}.
Generative models are known hard to train. For the task of cross-modality mapping,
we opt to train a discriminative model with a moderate size of training data.

\paragraph{Geometry reconstruction vs. Structure recovery.}
The existing works based on deep learning models mostly utilize volumetric 3D shape representation~\cite{wu2016learning,girdhar2016learning}.
Some notable exceptions include generating shapes in point clouds~\cite{fan2016point}, cuboid primitives~\cite{tulsiani2016learning} and manifold surfaces~\cite{lun20173d}.
However, none of these representations contains structural information of parts and part relations.
Interestingly, structure recovery is only studied with non-deep-learning approaches~\cite{xu2011photo,su2014estimating,huang2015single}.
This is largely because of the lack of a structural 3D shape representation suitable for deep neural networks.
Recently, Li et al.~\cite{li2017grass} propose to use recursive neural networks
for structural representation learning, nicely addressing the encoding/decoding of arbitrary number of shape parts
and various types of part relations.
Our method takes the advantage of this and integrate it into a cross-modality
mapping architecture for structure recovery from an RGB image.

\section{Results and Evaluations}
We evaluate the two core components of our pipeline: the co-planarity network and the robust optimization. Since our network is the first trained to predict co-planarity, we evaluate it against several baselines, to justify our design choices. For the optimization, we will show both quantitative  and qualitative evaluations on scene reconstruction, especially for the cases which cannot be handled by key-point based methods.

\subsection{Training Set Explanation}
Our training data is generated from both ScanNet~\cite{} and Sun3D~\cite{} datasets. The $1516$ scenes in ScanNet are reconstructed by BundleFusion~\cite{}. $25$ scenes in Sun3D are reconstructed semi-automatically with human-assisted registration. For ScanNet, we adopt the training/testing split provided with the dataset. All $25$ Sun3D scenes and the training set ($1045$ scenes) of ScanNet are used to generate our training triplets. For evaluating our network, we also build a co-planarity benchmark using the $100$ of the testing scenes of ScanNet.
Each training scene contributes $10K$ triplets. About $10M$ triplets in total are generated from all training scenes, among which a half is randomly sampled and the other half is generated with the hard triplet mining heuristics.

\subsection{Co-planarity Benchmark}
We create a benchmark for evaluating co-planarity-based patch matching.
The benchmark dataset contains {\color{blue}[X]~} patch pairs which
are organized according to two aspects of matching hardness: patch size and pair distance.
We create three subsets with average physical patch size ranging from {\color{blue}[X]~} m$^2$, {\color{blue}[X]~} m$^2$, to {\color{blue}[X]~} m$^2$.
In these three subsets, the patch pairs are sampled at random distances.
For pair distance, we build another three subsets with average distance ranging from {\color{blue}[X]~} m, {\color{blue}[X]~} m, to {\color{blue}[X]~} m.
For each subset, the numbers of positive and negative pairs are roughly equal.
For both positive and negative, half of the data are sampled with our heuristic hard example mining.
The benchmark is provided in the supplementary material and will be made public upon acceptance.

\MATTHIAS{Add that table we have found}

\subsection{Network Evaluation}

\subsection{Registration Evaluation}
The evaluation of registration is conducted on the TUM RGB-D dataset by~\cite{sturm12iros},
for which ground-truth camera trajectories are available.
To better demonstrate the advantage of our method in handling sequences with
few frame-to-frame overlap, we generate a challenging test set, by down-sampling frames
from the TUM sequences by every $5$, $10$ and $15$, named TUM-d5, TUM-d10 and TUM-d15, respectively.

\paragraph{Quantitative Results}
To evaluate the robustness of our optimization, we first investigate
how tolerant it is to the precision rate of the co-planarity prediction.
In Figure~\ref{}, we plot the reconstruction error of our method
on the TUM dataset (original), with varying co-planarity precision.
The varying precisions are achieved by randomly disturbing the correct predictions
obtained by our network.
Reconstruction error is measured as the absolute trajectory error (ATE), i.e.,
the root-mean-square error (RMSE) of camera positions along a trajectory.
\kev{From the results, ...}

Normal estimation for planar patches is crucial to our co-planarity-based alignment.
In Figure~\ref{}, we demonstrate how our optimization is able to tolerate
noise in normals.
In reconstructing the TUM dataset, we add random noise (with a standard deviation of $5^\circ$)
to the estimated normals in each iteration, for varying percentages of patches.
\kev{The results show that our method is robust ...}

On the down-sampled TUM sequences, we compare our method
with six state-of-the-art reconstruction methods, including
RGB-D SLAM~\cite{endres2012evaluation},
VoxelHashing~\cite{niessner2013hashing},
ElasticFusion~\cite{Whelan15rss},
Redwood~\cite{choi2015robust},
BundleFusion~\cite{dai2017bundlefusion} and
Fine-to-Coarse~\cite{halber2016fine}.
Table~\ref{} reports the ATE RMSE comparison on TUM-d10. The results for TUM-d5 and TUM-d15
can be found in the supplementary material.

\begin{table}[h]\center
    \scalebox{0.9}{
		\begin{tabular}{| c | c | c | c | c |}
			\hline
			& fr1/desk & fr2/xyz & fr3/office & fr3/nst \\ \hline
			RGB-D SLAM & 2.3cm & 0.8cm & 3.2cm & 1.7cm \\ \hline
			VoxelHashing & 2.3cm & 2.2cm & 2.3cm & 8.7cm \\ \hline
			Elastic Fusion & 2.0cm & 1.1cm & 1.7cm & 1.6cm  \\ \hline
			Redwood (rigid) & 2.7cm & 9.1cm & 3.0cm & 192.9cm  \\ \hline
			BundleFusion  & 1.6cm & 1.1cm & 2.2cm & 1.2cm \\ \hline
            Fine-to-Coarse  & 1.6cm & 1.1cm & 2.2cm & 1.2cm \\
            \hline \hline
			Ours  & XXX & XXX & XXX & XXX \\ \hline
		\end{tabular}
		\label{tab:comp_tum}
    }
	\caption{ATE RMSE (in cm) on downsampled TUM sequences~\protect\cite{sturm12iros}, TUM-d10. Note that unlike the other methods listed, Redwood does not use color information.}
\end{table}

\paragraph{Qualitative Results}
\MATTHIAS{Here, we would pick examples that traditional key-point based methods cannot address; e.g., we where we can get loop closures of two frames where no key point overlaps to begin with}


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Pawtucket, Providence, Rhode Island, USA
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The era know as The Industrial Revolution started in Rhode Island with the development and construction in 1790 of Samuel Slater's water-powered cotton mill in Pawtucket.
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Built 1685. Repaired by Pawtucket Chapter D.A.R. 1905
Lafayette's House, built in the 1700
The First Cotton Mill in America
The Old Slater Mill, Built 1793, Blackstone River, Pawtucket, Rhode Island
1824 - Women weavers from Pawtucket struck
1853 - AWFUL RAILROAD ACCIDENT. FOURTEEN LIVES LOST -- THIRTY OR FORTY SERIOUSLY INJURED.
By Telegraph to the New York Daily Times.
Disagreeable necessity obliges us to shock the sensibilities of our readers, by the reciatal of another t...Read MORE...
1854 - Pawtucket
Pawtucket, a flourishing post-town, situated 4 miles N. N. E. from Providence, partly in Providence co., R. I., and partly in Bristol co., Mass., and ...Read MORE...
1864 - Fire in Pawtucket.
PROVIDENCE, Thursday, Nov. 17. - A fire in Pawtucket this afternoon destroyed the Congregational church, John Dexter's dwelling-house, two stables, a ...Read MORE...
1883 - LARGE FIRE IN PAWTUCKET. SEVERAL BUILDINGS DESTROYED, CAUSING A LOSS OF OVER $100,000.
PROVIDENCE, R. I., July 1. - Pawtucket was visited this morning by the most destructive fire for many years. The flames were discovered at 5:30 o'cloc...Read MORE...
1890 - LARGE PRINT WORKS BURNED. THE DUNNELL ESTABLISHMENT AT PAWTUCKET DESTROYED BY FIRE.
PAWTUCKET, R. I. Aug. 16. - The Dunnell Print Works were destroyed by fire to-day. The loss is roughly estimated at from $150,000 to $200,000, and is ...Read MORE...
1894 - Fire
Lumber Street, on the west, which is the first highway running parallel with the water front, is lined with tenement houses. The occupants of these dw...Read MORE...
Pawtucket, a post-town of Providence co., R.I., in Pawtucket township, on a river of the same name, and on the Providence & Worcester and Boston & Pro...Read MORE...
Goff's Angora Braid
D. Goff & Sons, Pawtucket, R.I.
The Ladies' Home Journal
1903 - PAWTUCKET STATION BURNED. Two Firemen Seriously Hurt and the Damage About $25,000.
PAWTUCKET, R. I., Nov. 21. - The Pawtucket station of the New York, New Haven and Hartford Railroad, a three-story brick structure, was damaged by fir...Read MORE...
Looking from Division St. Bridge
Main Street from Trinity Square
Main Street, 1905
Pawtucket, a city of Providence CO., R.I., on the Pawtucket (Blackstone) River and on the New York, New Haven and Hartford R., 4} miles NNE. of Provid...Read MORE...
First Methodist Church, 1906
Jenks Park and Clock Tower
North Main Street Looking North, 1906
Mill from Exchange St. Bridge
Deborah Cook Sayles Public Library
Main Street looking East
Rhode Island Yacht Club, Pawtucket, R.I.
Howard and Bullough American Machine Co.
Post Office, 1908
Y.M.C.A. Building
Railroad Station on Broad St.
Pawtucket, R.I. Looking down Blackstone River from Exchange Street Bridge
Fire Station No. 2, Main St.
Fire Station No. 3, Prospect Street
J. & P. Coats Mfg. Co., Ltd.
Main Street showing Slater Trust Co. Building
R.R. Depot
Sisters' Home
Slater Park. Stone Arch Bridge.
Wilkinson Park
H. & B. AMERICAN MACHINE CO.
Pawtucket, R. I.
Builders of COTTON MACHINERY
Hopper Bale Openers
Self Feeding Openers
Breaker Intermediate and Finisher Lappers
Revolving Flat Cards
Drawing Frames
Slubbing Frames
Intermediate and... Read MORE...
Home of Slater Trust Company, Main St., cor. East Ave., Pawtucket, R.I.
Over 50 years Conservative Banking. Many Larger, Few Stronger. Visitors and Inquiries Welcome.
Nurses Home, Sayles Memorial Hospital
St. Paul's Episcopal Church
Pawtucket-Central Falls Station, Pawtucket, R.I.
Junction of Broad and Main Streets
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1790 - December 20 - 1st successful US cotton mill to spin yarn (Pawtucket, RI)
Reinvent Rhode Island: The Industrial Revolution started in Rhode Island
Providence Journal (www.youtube.com)
The first American cotton mill began operation on December... Read MORE...
Disagreeable necessity obliges us to shock the sensibilities of our readers, by the reciatal of another... Read MORE...
Pawtucket, a flourishing post-town, situated 4 miles N. N. E. from Providence, partly in Providence co., R. I., and partly in Bristol co., Mass., and ... Read MORE...
PROVIDENCE, Thursday, Nov. 17. - A fire in Pawtucket this afternoon destroyed the Congregational church, John Dexter's dwelling-house, two stables, a ... Read MORE...
PROVIDENCE, R. I., July 1. - Pawtucket was visited this morning by the most destructive fire for many years. The flames were discovered at 5:30... Read MORE...
PAWTUCKET, R. I. Aug. 16. - The Dunnell Print Works were destroyed by fire to-day. The loss is roughly estimated at from $150,000 to $200,000, and is ... Read MORE...
Lumber Street, on the west, which is the first highway running parallel with the water front, is lined with tenement houses. The occupants of these... Read MORE...
Pawtucket, a post-town of Providence co., R.I., in Pawtucket township, on a river of the same name, and on the Providence & Worcester and Boston &... Read MORE...
PAWTUCKET, R. I., Nov. 21. - The Pawtucket station of the New York, New Haven and Hartford Railroad, a three-story brick structure, was damaged by... Read MORE...
Pawtucket, a city of Providence CO., R.I., on the Pawtucket (Blackstone) River and on the New York, New Haven and Hartford R., 4} miles NNE. of... Read MORE...
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text:
Länsväg 151 går sträckan Värnamo - Gnosjö - Hestra. Längd 43 km.

Sträckning
Vägen passerar Värnamo och Gnosjö kommun, helt inom Jönköpings län.

Vägen passerar tätorterna Värnamo (genom), Hillerstorp (utkanten), Gnosjö (utkanten), Nissafors (utkanten) och Hestra (utkanten).

Anslutningar och korsningar

|}

Historia
Vägen var övrig länsväg till 1985 då den blev primär länsväg med numret 151.

Se även
 Lista över länsvägar i Jönköpings län

Länsvägar i Jönköpings län


Overall Height: 48-1/4" Jacket Dia.: 20-1/4"
Height to Top of Heater: 48.25" Height to T and P Opening: 48-1/4"
Tank Capacity: 40 gal. Height to Hot Side Connection: 48-1/4"
Standards: Meets or Exceeds ANSI, Meets or Exceeds NAECA, ASHRAE Standard 90, ICC Code Height to Cold Side Connection: 48-1/4"
Take a look at the features for Rheem Electric Water Heater. Voltage: 240VAC, Tank Capacity: 40 gal., Phase: 1.


text:
The souring of Turkey's European dream
Metnin Türkçesine ulaşmak için tıklayınız.
In the schoolbooks I read as a child in the 1950s and 1960s, Europe was a rosy land of legend. While forging his new republic from the ruins of the Ottoman empire, which had been crushed and fragmented in the first world war, Mustafa Kemal Ataturk did fight against the Greek army, but with the support of his own army he later introduced a slew of social and cultural modernisation reforms that were not anti- but pro-western. It was to legitimise these reforms, which helped to strengthen the new Turkish state's new elites (and were the subject of continuous debate in Turkey over the next 80 years), that we were called upon to embrace and even imitate a rosy-pink – occidentalist – European dream. As much as the schoolbooks of my childhood were texts designed to teach us why a line was to be drawn between the state and religion, why it had been necessary to shut down the dervish lodges, or why we'd had to abandon the Arab alphabet for the Latin, they were also overflowing with questions that aimed to unlock the secret of Europe's great power and success. "Describe the aims and outcomes of the Renaissance," the middle school history teacher would ask in his exam. "If it turned out we were sitting on as much oil as the Arabs, would we then be as rich and modern as Europeans?" my more naive classmates at lycee would say. In my first year at university, whenever my classmates came across such questions in class, they would fret over why "we never had an enlightenment". The 14th century Arab thinker Ibn Khaldun said that declining civilisations kept going by imitating their victors. Because there has never been a time when the Turks were colonised by a world power, "worshipping Europe" or "imitating the west" has never carried the damning, humiliating overtones described by Franz Fanon, VS Naipaul, or Edward Said; to look to Europe has been seen as a historical imperative or even a technical question of adaptation. But this dream of a rosy-pink Europe, once so powerful that even our most anti-western thinkers and politicians secretly believed in it, has now faded. This may be because Turkey is no longer as poor as it once was. Or it could be because it is no longer a peasant society ruled by its army, but a dynamic nation with a strong civil society. And in recent years there has of course been the slowing down of talks between Turkey and the European Union, with no resolution in sight. Neither in Europe nor in Turkey is there a realistic hope that Turkey will join Europe in the near future. To admit to having lost this hope would be as crushing as to see relations with Europe breaking down entirely, so no one has the heart even to utter the words. That Turkey and other non-western countries are disenchanted with Europe is something I know from my own travels and conversations. A major cause of the strain in relations between Turkey and the EU was most certainly the alliance forged by a sector of the Turkish army and leading media groups with nationalist political parties, and their successful campaign to sabotage negotiations. The same initiative triggered the prosecutions launched against me and many writers, the shooting of others, and the killing of missionaries and Christian clerics. There are also the emotional responses whose greater significance can best be explained by taking France as an example: over the past century, successive generations of the Turkish elite have faithfully taken France as their model, drawing on its understanding of secularism and following its lead on education, literature and art ... so to have France emerge over the past five years as the country most vehemently opposed to the idea of Turkey in Europe has been hugely heartbreaking and disillusioning. It is, however, Europe's involvement in the war in Iraq that has caused the keenest disappointment in non-western countries and, in Turkey, real anger. The world watched Europe being tricked by Bush into joining this illegitimate and cruel war, while showing immense readiness to be tricked. When looking at the landscape of Europe from Istanbul or beyond, the first thing one sees is that Europe (like the European Union) is confused about its internal problems. It is clear that the peoples of Europe have a lot less experience than the Americans when it comes to living with those whose religion, skin colour, or cultural identity are different from their own, and that they do not warm to the prospect: this resistance makes Europe's internal problems all the more intractable. The recent discussions in Germany on integration and multiculturalism are a case in point. As the economic crisis deepens and spreads, Europe may, by turning in on itself, postpone its struggle to preserve the "bourgeois", in Flaubert's sense of the word, but that will not solve the problem. When I look at Istanbul, which becomes a little more complex and cosmopolitan with every passing year, and which now attracts immigrants from all over Asia and Africa, I have no trouble reaching this conclusion: the poor, unemployed and undefended of Asia and Africa who are looking for new places to live and work cannot be kept out of Europe indefinitely. Higher walls, tougher visa restrictions and ships patrolling borders in increasing numbers will only postpone the day of reckoning. Worst of all, anti-immigration politics and prejudices are already destroying the core values that made Europe what it was. In the Turkish schoolbooks of my childhood there was no discussion of democracy or women's rights, but on the packets of Gauloises that French intellectuals and artists smoked (or so we thought) were printed the words "liberté, égalité, fraternité" and these were much in circulation. "Fraternité" came to stand for the spirit of solidarity and resistance promoted by movements of the left. But being callous about the sufferings of immigrants and minorities, and castigating the Asians, Africans and Muslims now leading difficult lives in the peripheries of Europe – even holding them solely responsible for their woes – is not "brotherhood". One can understand how Europe might suffer anxiety and even panic as it seeks to preserve its great cultural traditions, profit from the riches it covets in the non-western world, and retain the advantages gained over so many centuries of class conflict, colonialism and internecine war. But if it is to protect itself, would it be better for Europe to turn inwards, or should it perhaps remember its core values, which once made it the centre of gravity for all the world's intellectuals? • Translated by Maureen Freely
Yapı Kredi Culture and Art Publishing hosts an exhibition of photographs taken by Orhan Pamuk from his balcony of his home in Istanbul between the dates of December 2012 and April 2013. Curated by the famous German publisher Gerhard Steidl, "ORHAN PAMUK
Orhan Pamuk's Snow To Have Stage Reading
Orhan Pamuk accepts Honorary Doctorate from University of Crete.
Museum of Innocence gets Milan display from 19th of January till 24th of June 2018
After 2010 Nobel Prize winner Mario Vargas-Llosa (2015) and well-known Italian novelist Claudio Magris (2016), Orhan Pamuk receives 2017 Literary Flame Prize from Montenegro.
"The Red-Haired Woman" Awarded with Lampedusa Prize in Italy
Orhan Pamuk receives the Budapest Grand Prize on the opening day of the 24th Budapest International Book Festival in Budapest, Hungary on April 20, 2017
Pamuk's stage adaptation of Snow by Blandine Savetier is on the tour in France.
Orhan Pamuk Accepts Honorary Doctorate From St. Petersburg University
January 2017 Orhan Pamuk receives doctorate from the Accademia di Bella Arti di Brera (Italy)
Orhan Pamuk receives "Milovan Vidaković Prize" in Serbia
In 2016 Orhan Pamuk receives The Yasnaya Polyana Literary Award (from the Museum and Estate of Leo Tolstoy) for "Foreign Literature" category with his novel A Strangeness in my Mind.
Museum of Innocence Gets London Display
Orhan Pamuk among one of the most influental voices of 2015
Orhan Pamuk Receives Helena Vaz Da Silva European Award 2014 In Lisbon
The Museum of Innocence received the European Museum of the Year Award.
I keep my radical thoughts to my novels
Orhan Pamuk received the Sonning prize
Orhan Pamuk was honored with Légion d'honneur
Orhan Pamuk is awarded the 2012 Sonning Prize
Orhan Pamuk opened The Museum of Innocence in Istanbul
Orhan Pamuk's novels translated into 60 languages
The Naive and the Sentimental Novelist has been released in Turkish
Orhan Pamuk has been awarded the 2010 Norman Mailer Lifetime Achievement Award
"Fragments of the Landscape" has been released
Orhan Pamuk opened the World Literature in Between Symposium at Istanbul Bilgi University
Pamuk's opening speech marked Frankfurt Book Fair 2008
Theater Freiburg put on scene Orhan Pamuk's "Snow" at the presence of the author
Museum of Innocence News
Banu Güven interviews Orhan Pamuk on The Museum of Innocence
Orhan Pamuk's Nobel Speech
Horace Engdahl's Nobel Prize announcement 2006
Orhan Pamuk's conversation with Charlie Rose


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This review gives us a short list of innovative gift ideas for people that love gadgets.
Jonathan Morrison is a power YouTuber that helps people understand tech products, gadgets and design. Jonathan has over 1.6 M subscribers, and has earned +200 M views to his videos.


text:
3 killed, thousands without power in Northwest windstorm
Cameron Napora, a contractor, stops to cut a large tree that was blocking the road during a windstorm that swept through Spokane, Wash. on Tuesday.
(Jesse Tinsley / Associated Press)
Cleanup began Wednesday in Washington state after a powerful storm killed three people, cut power to more than 300,000 residents and flooded rivers.
Police said a woman in her 50s was killed Tuesday when a tree toppled in Spokane. Another woman died after a tree fell on her car on Highway 904 about 15 miles southwest of Spokane, according to the Washington State Patrol.
A man in his mid-20s was killed when a tree crushed his car as he was driving in Snohomish County. The tree landed on the car's roof directly over the driver's seat, killing him instantly, said fire Chief Merlin Halverson.
NEWSLETTER: Get the day's top headlines from Times Editor Davan Maharaj >>
Their identities were not immediately released.
Two mudslides blocked Highway 2 between Skykomish and Deception Falls, the state Department of Transportation said. Ferry trips were delayed or canceled in several areas and Sound Transit trains were delayed due to trees and water on the tracks throughout the system.
Utilities said the number of customers without power remained in the six digits early Wednesday, and it could be days before power is fully restored.
Puget Sound Energy said more than 30 transmission lines were badly damaged, and dozens of crews worked through the night to get the lights back on. The utility said about 100,000 customers were still without power early Wednesday.
The Snohomish County Public Utility District tweeted that about 130,000 of its customers lacked power. Energy company Avista Corp. said more than 136,000 customers had lost power as of Tuesday night around Spokane and in Northern Idaho.
Numerous schools canceled classes for Wednesday or delayed the opening bell.
The strong winds and extended downpour caused fewer problems in Oregon, but roughly 7,000 Portland General Electric customers remained without power in the Portland area. The utility expects to have it restored before noon Wednesday.
Wind gusts of up to around 100 mph rattled areas west and north of Denver, blowing snow from Tuesday's wintery storm across roads and knocking out power in some spots.
The National Weather Service attributed Wednesday's winds to the jet stream interacting with the mountains.
The storm dumped over a foot of snow in some parts of the plains and strong winds created snowdrifts several feet high.
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