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Men's BasketballSports
Men's Basketball falls to Vermont in battle for first place in America East
Gregory Zarb January 27, 2019 October 19, 2021
Freshman guard Miles Latimer in a game against Quinnipiac on Saturday, Dec. 1, 2018. On Saturday, Latimer scored 10 points facing conference rival Vermont. ALEEZA KAZMI/THE STATESMAN
Saturday night at Island Federal Credit Union Arena pitted the top two men's basketball teams in the America East conference. With the Vermont Catamounts the number one offensive team and the Stony Brook Seawolves the number one defensive team, the game was expected to be a back-and-forth affair.
Even in a packed house, Stony Brook wasn't able to get over that final hump to take a late lead over Vermont.
"We talked about coming out of the second half with a sense of urgency and we didn't do that," head coach Jeff Boals said. "You can't go behind against a really good team. You have to get shots against Vermont and you have to make jump shots. We got a little sloppy in transition and they made plays."
The Seawolves seemed primed to make one of their late-game comebacks, cutting the Catamounts lead from 25 to 11 points late in the second half, but weren't able to trim the lead down more and eventually dropped their first game in conference play by a final of 52-73.
The Catamounts came out hot to open the second half, scoring seven quick points to force Boals to call timeout. Vermont controlled the game for a majority of the second half and was able to hold off Stony Brook and its late comeback attempts.
"We thought ahead too much instead of just playing in the moment," freshman guard Miles Latimer said. "When I saw the lead go down to 20, I said, 'Come on guys, let's just rally back up,' but I think some of us were rattled and I feel like we didn't have our heads on straight."
Vermont's redshirt-senior guard Ernie Duncan led all scorers with 25 points, while his brother, redshirt-junior guard Everett Duncan, put up 20 points. Head coach John Becker said that Everett was playing the power forward position for the night to replace junior forward Anthony Lamb, who was out with a concussion.
Boals spoke highly of the brothers, crediting their poise throughout the contest.
"Give credit to Vermont and to both Duncan brothers, who have been through a lot of big games," Boals said. "You can just see the poise of those two and hats off to them for making big plays."
Redshirt-sophomore guard Andrew Garcia led Stony Brook with a team-high 14 points off the bench. Redshirt-junior guard Akwasi Yeboah and Latimer each scored 10 points apiece, with the former fouling out with 6:23 left in the game.
The Seawolves had control early in the contest, taking an eight-point lead with 8:37 left in the first half. The Catamounts responded with a 34-7 run that spanned well into the second half to take a lead they would never relinquish.
"We fouled them a lot and they got to the line early and often," Garcia said. "They were in the bonus early and we had a lot of fastbreaks and careless turnovers, and that's not us. Tough calls from the ref too, a lot of key players had foul trouble early."
Yeboah, sophomore center Jeff Otchere, sophomore forward Elijah Olaniyi and senior guard Jaron Cornish all picked up two personal fouls in the first half, with Yeboah and Otchere both fouling out late in the second half.
Stony Brook now sits in second place in the America East conference standings while Vermont takes over sole possession of first place. The Seawolves will take on the reigning America East champions in the UMBC Retrievers on Wednesday, Jan. 30 at 7 p.m.
Gregory Zarb
Gregory Zarb is entering his senior year and has been apart of the Statesman for the last three years. He enjoys all things sports, whether it may be professional or Stony Brook sports. Contact Greg at [email protected]
Tagged: Andrew Garcia Anthony Lamb Ernie Duncan Everett Duncan Gregory Zarb Island Federal Credit Union Arena Jaron Cornish Jeff Boals Jeff Otchere John Becker Miles Latimer Sports Vermont Catamounts Share
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Sleepers is a 1996 American legal crime drama film written, produced, and directed by Barry Levinson, and based on Lorenzo Carcaterra's 1995 book of the same name. The film stars Kevin Bacon, Jason Patric, Brad Pitt, Robert De Niro, Dustin Hoffman, Minnie Driver, Vittorio Gassman, Brad Renfro, Ron Eldard, Jeffrey Donovan, Terry Kinney, Joe Perrino, Geoffrey Wigdor, Jonathan Tucker, Bruno Kirby and Billy Crudup. The title is a slang term for juvenile delinquents who serve sentences longer than nine months.

Sleepers was theatrically released in the United States on October 18, 1996 and was a box-office hit, grossing $165.6 million against a $44 million budget.

Plot
Lorenzo "Shakes" Carcaterra, Tommy Marcano, Michael Sullivan, and John Reilly are childhood friends living in Hell's Kitchen in the 1960s. Father "Bobby" Carillo, their parish priest and a youth offender himself in the past, tries to teach them right from wrong. They still play pranks and start running small errands for local gangster King Benny.

In the summer of 1967, the four boys steal a hotdog cart, which they accidentally roll down the subway stairs, severely injuring an elderly man. They are all sentenced to serve time at a juvenile detention center called Wilkinson Home for Boys in Upstate New York; Shakes is given 6 to 12 months, while the others are given 12 to 18 months. During their stay, they are constantly subjected to sexual abuse and torture by head guards Sean Nokes, Henry Addison, Ralph Ferguson and Adam Styler.

While at the facility, they participate in Wilkinson's annual football game between the guards and inmates. Michael convinces inmate Rizzo Robinson to help win the game. Humiliated, the guards move the boys to solitary confinement for weeks, where they are systematically beaten. Rizzo does not survive and his family is told that he had died of pneumonia.

In the spring of 1968, shortly before Shakes' release from Wilkinson, he suggests the boys publicly report the abuse. The others refuse, with Michael asserting that no one would believe them or care, and they vow never to speak of it again. The night before Shakes is released, Nokes and the other guards arrange a "farewell party" in which the four boys are again brutally abused to a higher extreme.

In 1981, John and Tommy, now career criminals, unexpectedly encounter Sean Nokes in a Hell's Kitchen pub. Confronting him, he dismisses the abuse he put them through, and John and Tommy shoot him dead in front of witnesses. Michael, now an assistant district attorney, gets himself assigned to the case; he secretly intends to botch the prosecution and expose what the guards at Wilkinson's did. With Shakes, now a reporter, they form a plan to free John and Tommy and get revenge on the other Wilkinson abusers. With the help of King Benny and Carol, their childhood friend, they carry out their plan using information compiled by Michael on the backgrounds of the guards, helped by Danny Snyder, an alcoholic lawyer, to defend John and Tommy.

Michael secretly drafts scripted questions in advance, so Snyder casts significant doubt on the testimony of a woman who witnessed the murder, and two other witnesses are intimidated into silence. For Michael's plan to fully succeed, however, he must damage Nokes' reputation and convincingly place John and Tommy at another location at the time of the shooting. When called as a witness, Ferguson, now a social worker, admits that Nokes and the other guards systematically abused the boys. To clinch the case, however, a key witness was still needed for John and Tommy's alibi. Shakes has a long talk with Father Bobby, who resists at first, but after learning the truth, reluctantly agrees to perjure himself. At trial, Father Bobby testifies John and Tommy were with him at a New York Knicks game at the time of the shooting and shows three ticket stubs to prove it. As a result, John and Tommy are acquitted.

The remaining guards are also punished for their crimes: Henry Addison, now a politician who still molests children, is abducted and killed near the local airport by gangsters led by Rizzo's older brother, Eddie "Little Caesar" Robinson, who heard the truth about Rizzo's death from King Benny; Adam Styler, now a corrupt police officer, is imprisoned for taking bribes and murdering a drug dealer; and Ralph Ferguson is implied to have faced legal consequences following his courtroom admission of sexual abusing and torturing the boys.

Michael, Shakes, John, Tommy, and Carol meet at a local bar to celebrate. It is the last time the four are together. Shakes remains in Hell's Kitchen as a reporter; Michael quits the DA's office, moves to the English countryside, becomes a carpenter and never marries; John and Tommy both die before age 30 – John succumbs to alcohol poisoning due to excessive consumption while Tommy is ambushed and murdered by rival criminals. Carol remains in Hell's Kitchen as a social worker; she has a son, naming him John Thomas Michael Martinez, with the nickname "Shakes".

Cast

 Billy Crudup as Thomas "Tommy" Marcano
 Jonathan Tucker as Young Tommy Marcano
 Ron Eldard as John Riley
 Geoffrey Wigdor as Young John Riley
 Jason Patric as Lorenzo 'Shakes' Carcaterra
 Joe Perrino as Young Shakes
 Brad Pitt as Michael Sullivan
 Brad Renfro as Young Michael Sullivan
 Kevin Bacon as Sean Nokes
 Robert De Niro as Father Robert "Bobby" Carillo
 Minnie Driver as Carol Martinez
 Monica Polito as Young Carol Martinez
 Vittorio Gassman as Benny 'King Benny'
 Dustin Hoffman as Danny Snyder
 Terry Kinney as Ralph Ferguson
 Peter McRobbie as lawyer
 Bruno Kirby as Shakes' father
 Frank Medrano as 'Fat' Mancho
 Eugene Byrd as Rizzo Robinson
 Jeffrey Donovan as Henry Addison
 Wendell Pierce as Eddie 'Little Caesar' Robinson
 Aida Turturro as Mrs. Salinas
 Dash Mihok as K.C.
 Angela Rago as Shakes' mother
 John Slattery as Mr. Carlson

Production
The first words spoken in the film are: "This is a true story about friendship that runs deeper than blood". However, the truthfulness and factual accuracy of the film—and the book upon which it is based—were challenged by the Sacred Heart of Jesus Church and School in Manhattan (the school attended by Lorenzo Carcaterra) and by the Manhattan District Attorney's office, among others. Carcaterra has acknowledged that most details in the book were fictionalized, but maintained that the events described in the book actually occurred.

Release

Box office
In its opening weekend the film grossed $12.3 million from 1,915 theaters in the United States and Canada, debuting atop of the box office. Sleepers grossed $53.3 million domestically and $112.3 million internationally, for a worldwide total of $165.6 million.

Critical response
On Rotten Tomatoes the film holds an approval approval rating of 73% based on 56 reviews, with an average rating of 6.60/10. The site's critics consensus reads: "Old friendships are awakened by the need for revenge, making Sleepers a haunting nightmare burdened by voiceover yet terrifically captured by Barry Levinson." Metacritic assigned the film a weighted average score of 49 out of 100 based on 18 critics, indicating "mixed or average reviews".  Audiences polled by CinemaScore gave the film an average grade of "A−" on an A+ to F scale.

Critics praised the performances of De Niro, Hoffman, Bacon, and the young cast, as well as the cinematography and production design. However, multiple critics said the film loses focus in its second half. Owen Gleiberman of Entertainment Weekly wrote, "Sleepers wants to do something impossible — merge the mournful, drenched-in-shame emotions of child abuse with the huckster gamesmanship of a contraption like The Sting."

David Ansen of Newsweek criticized Levinson's script and reasoned the adult characters of Shakes, Michael, John, and Tommy were not fully fleshed out. Roger Ebert awarded the film three out of four stars, but said it did not engage enough in the moral issues it purported to be about. Steve Davis of The Austin Chronicle wrote, "What a more interesting film this would have been had Levinson found a way to integrate the past and the present so that one informed the other."

When the film was released, there was controversy over how much of the novel, claimed to be a true story, had been invented by its author.

Accolades

Home media
The film was released on VHS by Warner Home Video in 1997. The film was released in DVD on November 3, 2009 and also in Blu-Ray on August 2, 2011. It is also included on streaming service, Prime Video.

Notes

References

External links

1996 films
1996 crime drama films
American coming-of-age films
American courtroom films
American crime drama films
American buddy drama films
American prison drama films
1990s buddy drama films
1990s English-language films
Films about child sexual abuse
Films about pranks
Films directed by Barry Levinson
Films scored by John Williams
Films produced by Steve Golin
Films based on American novels
Films set in 1967
Films set in 1968
Films set in 1981
Films set in New York (state)
Films set in New York City
Films shot in Connecticut
Films shot in New York City
PolyGram Filmed Entertainment films
American rape and revenge films
Warner Bros. films
Films about the Irish Mob
1990s American films

redpajama_set_name: RedPajamaWikipedia


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Many folks go on the DASH diet in hopes of lowering their blood pressure, which makes perfect sense. After all, this physical health benefit is how the diet got its name in the first place; the acronym "DASH" is short for Dietary Approaches to Stop Hypertension. But could the diet offer a mental health benefit, too? A new study from the Rush University Medical Center in Chicago supports this exciting idea.
According to the February 2018 findings, researchers found a link between following the DASH diet and a lower risk of depression. The scientists gave 964 adults diet questionnaires, which they used to measure participants' adherence to diets like DASH — an eating plan high in fruits, vegetables, and whole grains and low in foods high in sugar and saturated fats. The study followed the participants for an average of six and a half years after the questionnaires, and found that those with the highest adherence to DASH were the people least likely to have depression.
But before you rush out to the grocery store to buy only DASH diet-friendly foods, it's worth keeping in mind that this study did not prove that following the DASH diet prevents or reduces depression; rather, the study noticed a connection between following this eating plan and a lower risk of developing depression in several years' time. So the DASH diet should not be used as a way to treat already-existing depression. Furthermore, if you're interested in trying the DASH diet — or any diet, for that matter — be sure to talk to your doctor first before getting started.
That said, it's wonderful to hear more evidence that a healthy diet like DASH might do both the body and the mind good. Though more research is needed, this is certainly a promising (and very positive!) start.


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Larry Nance Jr.: Cleveland Cavaliers 'got to man up'
Cleveland Cavaliers forward Larry Nance, Jr. isn't seeing enough progress on defense. (David Petkiewicz, cleveland.com)
By Chris Fedor, cleveland.com
CLEVELAND, Ohio -- Following their most disappointing loss of the Larry Drew era, getting blown out at home by the nine-win Chicago Bulls, there was a pointed message passed around the locker room. It started with the coach and trickled down to the players.
"We've got to man up," Larry Nance Jr. said.
The Cavs have spent most of the season brushing off losses. They get it. The players recognize what this season has turned into.
They know it will be a constant battle just to stay out of last place in the Eastern Conference, especially with Kevin Love weeks away from returning. Winning games is a chore. They've only won back-to-back contest once. Even after squandering an opportunity against short-handed Toronto to cap the road trip Friday, the Cavs were able to point to a few positives. They weren't demoralized.
That changed Sunday.
"Not learning. Lack of growth. We're not using our losses as lessons," Nance said. "It's tough to keep doing so. At the beginning of the season, it's easy to go, 'All right, we lost that game and here's what went wrong and now we have to fix it.' We're kind of at the point where it's like, 'What was it this time?' We're trying to fix things on the fly whereas we have a couple of days off, we can regroup and mentally get ready to go on this second half stretch."
There were lots of problems against Chicago. The Cavs finished with more turnovers than assists. They fell into the Bulls' defensive trap, relying too much on isolation basketball against a switch-heavy defense. They played way too slow, getting bogged down in the halfcourt.
Still, the most pressing area of concern, the one that has been there from opening night, showed its ugly face again. The Cavs are the NBA's worst defense.
One year after coasting through the regular season, building horrendous defensive habits and looking incapable on that end of the floor, the Cavs have actually gotten worse.
The Bulls came into Sunday's game ranked last in offensive efficiency and points per game. They ranked 27th in field goal percentage, making just 44.1 percent, and 3-point field goals, averaging 9.4.
No problem. Just play Cleveland. That makes everything better. Chicago scored 112 points on 55.1 percent from the field -- second-best this season. They hit 11 3-pointers, finishing 50 percent from beyond the arc.
Chicago's Lauri Markkanen, who didn't play in the first meeting against the Cavs, tallied 31 points on 11-of-18 from the field.
"There's very few aspects of defense that we are good at right now," Nance said. "The league knows that. We're last in points allowed and last in a lot of different things. That's something that doesn't get better with technique, that doesn't get better with...it's heart. It's will. It's something where we are letting guys have career nights on us. That's something that we have to take personally."
Drew said after the game it's been tough defensively given how often the personnel changes.
The Cavs used their 14th starting lineup, with two-way player Jaron Blossomgame in for Rodney Hood, who is currently hobbled by a sore Achilles.
David Nwaba, Cleveland's best perimeter defender, has been on a minutes restriction and then reinjured his ankle in the second half. Tristan Thompson, the anchor and voice of the team, has missed the last seven games.
"We're still having to learn one another and get a true feel for one another out on the floor," Drew said. "I don't know how many games we've played, it takes time and you have to continue to do it until you find a good mix or a good jell, to where it works. It's gotta be working. Everybody has to do their parts. It has to be working on all cylinders and thus far that has not been the case. We're still searching and we're still trying to do some things that will help us defensively."
There is no one player to blame. It's a team-wide issue. But it starts at the point of attack. That weakness leads to the rest of the team over-helping and scrambling to cover holes. According to NBA.com stats, Cedi Osman is the team's worst defender. But rookie Collin Sexton is right there with him.
Opponents boast an offensive rating of 123.0 and an effective field goal percentage of 56.6 with Sexton on the floor. With him off, it drops to 114.5 and an effective field goal percentage of 52.0.
It's also getting tough to overlook Cleveland's messy numbers against opposing point guards. The Cavs entered the night ranked 27th in points allowed to the point guard position. The only teams worse: Atlanta (Trae Young), Washington (John Wall, who drifts in and out of caring on that end) and Detroit (Reggie Jackson).
That's Sexton's responsibility, especially if he is going to continue to log the bulk of the minutes there.
Kris Dunn became the latest to slice the Cavs' defense. He scored 17 points on 8-of-11 from the field to go with seven assists. Ryan Arcidiacono, who also shared those duties with Dunn, tallied 12 points and eight assists.
That duo, along with Markkanen, helped lead the Bulls to their largest win in more than a calendar year.
"We have not been a good defensive team as far as defending off the dribble, and that's where a lot of our problems do lie," Drew said. "Once you're beaten off the dribble, it breaks your defense down. And that's just something that we've got to get better at. We've got to take more of an individual challenge, we just have to accept the challenge."

redpajama_set_name: RedPajamaCommonCrawl


Welcome to the City of Greater Geelong!
Less than an hour's drive from Melbourne's CBD and Tullamarine Airport along the Princess Freeway, Geelong is beautifully placed with waterfront views of Corio Bay, immediate access to the world famous Great Ocean Road and charming wine growing region of the Bellarine.
With a population of 210,000, Geelong is Victoria's second largest city and a major provincial centre economically, culturally and socially for the surrounding region.
Geelong's history as a major wool-shipping port from the mid 1800s well into the 20th century has left a legacy of beautifully restored old buildings such as the revamped National Wool Museum and the Dalgety Wool Stores which have been transformed into a campus and headquarters for Deakin University.
Today, Geelong is a lively commercial city, with a newly renovated and rejuvenated central business district offering shopping facilities, restaurants and nightlife on par with any capital city – allowing your event's participants, families and supporters to access a little 'retail therapy'. The region is also rich with amazing touring options for all seasons.
The city's multi-million dollar drawcard, Waterfront Geelong, features many cafes and restaurants, fully restored art deco seawater baths, a yacht club marina and a scenic bayside promenade dotted with brightly painted bollards depicting many of Geelong's historical characters.
Professional venues, efficient support services and quality accommodation await you, with our events team dedicated to ensuring that your event and time in Geelong is both comfortable and stress free.
As a city, Geelong has a diverse mix of urban, coastal and country precincts.
Geelong's city centre offers quality shopping, sightseeing and dining all within easy access, thanks to the compact city design. Our waterfront is a hive of activity, with cafes, restaurants, a yacht club, marina, antique carousel and swimming pavilion attracting 1.6 million people every year.
There are many fine dining restaurants in Geelong featuring a variety of international cuisines using the freshest of local produce. We have many cafes, bars and pubs. Shopping is also a major attraction, with large centres and department stores alongside surf and skatewear retailers, fashion boutiques and specialist stores.
A wide array of arts and cultural attractions make our city one of Victoria's most vibrant communities, with a prominent theatre and renowned art galleries.
Almost 75 percent of the land in the City of Greater Geelong is classified as rural. We have played a major role in the development of the Australian wool industry, and our long association with this industry is celebrated at the National Wool Museum, in an historic bluestone wool store located in the city centre.
The Geelong region produces some of the finest food and wine in the country. A regular event, the Central Geelong Farmers Market showcases local organic fruit and vegetables, seafood, flowers, breads, cheeses and more. Wine is a local specialty, with over 20 local wineries in the area with cellar door tastings and restaurants serving fine food.
Our region is framed by water, from the north-facing shores of Corio Bay and Port Phillip Bay to the rugged coastline of Bass Strait. The original inhabitants, the Wathaurong people, named the area after their word 'jillong' meaning 'a place of the sea bird over white cliffs'.
Geelong's Bellarine Peninsula has much to offer, with plenty of sandy bay and ocean beaches where you can swim, snorkel, dive or surf. Golf is also a favorite Bellarine Peninsula activity, with first-class courses spread throughout the region.
We are on the doorstep of the world-renown Great Ocean Road, an historic road that hugs the Victorian coastline. The Great Ocean Road region is famous for its picturesque beaches, lush rainforests, small, quaint towns and breathtaking views.
Geelong was first surveyed and declared a town in 1838 with a local population of 545. The City's humble beginnings were founded in the rich pastoral hinterland and the prosperity of the wool trade.
By the 1850's Geelong was swept-up in 'gold fever' becoming the principal seaport for the gold industry around Ballarat. The City's population ballooned to more than 23,000 people, making Geelong the fourth largest town in Australia.
In the later half of the 19th Century, as the gold prosperity declined, Geelong turned its attention to emerging industrial development. The City became the major trading port for the export of wool to Britain and textile industries flourished along the Barwon River.
Following the establishment of Phosphate Coop and the Ford Motor Company, other international and national manufacturing industries moved to Geelong.
Today, Geelong enjoys a reputation as one of Australia's leading industrial centres. The City has also developed as a centre of excellence in the area of education, research and development, health, hospitality and tourism.
It is well recognised around Australia that Geelong has one of the best calendar of events for any regional city.
Rowing is integral to this strength – with the Head of the Schoolgirls being the jewel in the rowing season crown.
Toast to the Coast (food and wine).
Geelong has also been successful in our bid to host the 2010 UCI Road World Cycling Championships which will be a significant event for Australia.
We are also the home of the 'Cats' or the Geelong Football Club – the only regional Victorian team competing in the Australian Football League. The Cats play and train at one of our key assets, Skilled Stadium – a venue proposed for your event's use.
The traditional land owners of the Geelong region are the Wathaurong people.
The Wathaurong people gave Geelong as we know it today its name from their word 'jillong' meaning 'a place of the sea bird over white cliffs'.
The Wathaurong people have lived in the Geelong region for more than 25,000 years before white settlers arrived. Various clans had campsites in the area, moving about in search of food and water.
White settlement from the early 1800s had a devastating effect on the Aboriginal people. Sheep destroyed much of the root crops, which the Wathaurong depended on and introduced diseases cut a swathe through their people. Birth rates and clan numbers fell.
About 140 archaeological sites have been found in the region, indicating a significant level of activity of the Wathaurong people. Yollinko Park Aboriginal Garden on the banks of the Barwon River contains the remains of living mounds and food plants. It also displays giant representations of Aboriginal implements.
We work with the Wathaurong in the staging of major events in our region.
To recognise the traditional owners of the land, the Wathaurong can be invited to perform a Welcome To Country – either via a verbal presentation or a traditional story-telling dance.
With a temperate climate, the region enjoys four distinct seasons; warm summers, sunny spring, colorful autumns and mild winters.
The Geelong region is now even easier to get to – especially for participants coming from Sydney, Brisbane, Adelaide and Perth. There are options to access Geelong via road, land, air or sea methods!
Travelling by road to Geelong, in a car or coach, is convenient and comfortable. Geelong is only an hour's drive from the city of Melbourne along the Princess Freeway – with connections through the Western Ring Road and Tullamarine Freeway greatly improving access. Other major road networks connecting the region to provincial Victoria include the Hamilton Highway from the west and Midland Highway from the north-west.
All major car rental companies are represented at Avalon Airport and within the Geelong and Great Ocean Road region. Discounted car rental rates are available with Avis, Hertz, Europcar and Budget via the Jetstar website.
Jetstar now flies daily to/from Avalon Airport from a number of major Australian cities. Avalon Airport is just 15 minutes from Geelong. So if you're coming to the region, get onboard Jetstar's new fleet of A320s with their super comfy, all leather seats.
On landing at Avalon Airport, shuttle buses will be waiting to take you and your delegates to any of the major hotels within the region as far as Lorne. Taxis are also available with fares to Geelong's CBD averaging $30.
If flying from interstate or internationally via Melbourne's International Tullamarine Airport, Gull Transport operates 13 shuttle services a day getting you and your participants to Geelong in under an hour.
Choose to travel the 'Carbon Neutral' way – V/Line operates the train system in Victoria and connects the region at Geelong, Colac and Warnambool with regular services from Melbourne. For something unique, exclusive carriages and trains can be arranged subject to availability.
If you are traveling from the east Victorian Coast, why not take the Queenscliff-Sorrento Ferry across the mouth of Port Phillip Bay? Sit back and enjoy the beautiful scenery – you might even catch a glimpse of a dolphin or two. This service runs on the hour, every hour, all year round.
Accommodation is abundant and varied throughout the Geelong region. We pride ourselves on our well-appointed accommodation, great atmosphere, prominent locations, friendly hosts, comfortable and inviting rooms with convention facilities and exhilarating attractions all at your doorstep.
Geelong offers in excess of 1000 hotel-motel rooms and serviced apartments within a short distance of the city centre, all of which guarantee you a good night's sleep. There is also accommodation available at bed and breakfast properties and caravan park facilities within the city.
Smaller, more intimate hotels in quieter residential areas.
Geelong's Visitor Information Centres can offer your participants peace of mind when searching for accommodation. Event participants can call 1800 620 888 free of charge for information or advice. We also have a Room Bank service which helps organisers of larger events to find accommodation more easily when many venues are heavily booked. All referral services offered are free of charge.
Alternatively, by visiting www.visitgeelong.org, your delegates can search, review and book accommodation online.
Whatever your sleeping demands, you'll rest easy knowing you'll find just the right place right here. The diversity of accommodation in the Geelong Otway region enables us to present accommodation that is affordable for all of your event participants.


Japanese Prime Minister Shinzo Abe (L) and U.S. President Donald Trump speak at Trump's Mar-a-Lago resort in Palm Beach, Florida, after North Korea test fired a ballistic missile, on Feb. 11, 2017.
North Korea test-fired a ballistic missile on Sunday, drawing condemnation from the United States and its Asian allies in what analysts said was a bid by Pyongyang to probe the intentions of new U.S. President Donald Trump.
The first North Korean launch of the Trump era took place as Trump was hosting Japanese Prime Minister Shinzo Abe. It sent an intermediate-range missile flying about 500 kms (310 miles) before landing into the sea between the Korean Peninsula and Japan, according to South Korea's military.
Appearing with Trump at a news conference at Trump's Mar-a-Lago Club in Florida, Abe condemned the missile launch as "absolutely intolerable" and called on North Korea to comply fully with U.N. Security Council resolutions banning such tests and other acts of nuclear and missile proliferation.
Speaking after Abe, Trump said: "I just want everybody to understand and fully know that the United States of America stands behind Japan, its great ally, 100 per cent."
South Korea's Joint Chiefs of Staff said in a statement that the missile was fired from near Banghyon, North Pyongan Province, believed to be the site where North Korea tested a Musudan missile on Oct. 15 and 20.
North Korea, which had issued a warning last month that it was ready to test its first intercontinental ballistic missile, had yet to announce Sunday's test, which came just days before the North is to mark the 75th birthday on Feb. 16 of leader Kim Jong Un's late father, Kim Jong Il.
South Korean analysts said it appeared that Pyongyang was using the test to sound out the Trump administration as it forms its policies toward Asia.
"This is essentially a low-intensity provocation to see how the Trump government responds," Professor Kim Yong-hyun of Dongkuk University in Seoul told RFA's Korean Service.
Kim Dong-yop of the Institute for Far Eastern Studies at Kyungnam University told RFA North Korea intends to demonstrate to the Trump administration that returning to long-stalled talks was "not merely important, but urgent."
North Korea expert Andrei Lankov of Kookmin University in Seoul told RFA that Sunday's test launch demonstrated North Korea's commitment to improving its missile capabilities.
"This is not the ICBM capable of hitting the continental U.S. yet, but another step toward developing such an ICBM. And this is also a reminder that the nuclear and missile program of North Korea will not go away," said Lankov.


text:
\section{Introduction}

Various numerical methods have been introduced to solve linear problems as they appear in engineering, mathematics and computer science.
These methods can be divided into direct and iterative methods.
Direct methods aim to calculate an exact solution in a finite number of operations, whereas iterative methods begin with an initial approximation and usually produce improved approximations in a theoretically infinite sequence whose limit is the exact solution~\cite{Saad:2003_iterative_methods}.

Many linear problems are sparse, i.e.\ most linear coefficients in the corresponding matrix are zero so that the number of non-zero coefficients is \emph{$O(n)$} with $n$ being the number of variables~\cite{Kunis:Sparse-Trigonometric-Polynomials}.
Iterative methods do not spend processing time on coefficients that are zero.
Direct methods, in contrast, usually lead to fill-in, i.e.\ coefficients change from an initial zero to a non-zero value during the execution of the algorithm.
In these methods we therefore may weaken the sparsity property and may have to deal with more coefficients, which makes the processing time slower.
Therefore, iterative methods are often faster than naive direct methods in such cases.
In this paper, we are concerned with a domain where sparse problems occur frequently, namely constraint-based graphical user interface (GUI) layout.
Constraint-based layout models have been studied for quite a while in the research community~\cite{Stuckey:arithmetic-constraints,
Weber:High-Level-Constraints,scoditti2009new,ZeidlerCHINZ2012} and attracted attention recently because of a newly introduced layout model in the Cocoa API of Apple's Mac~OS~X\footnote{Cocoa Auto Layout Guide, 2012, \url{http://developer.apple.com}}.

A common iterative method is successive over-relaxation (SOR)~\cite{David:Iterative}.
Starting with an initial guess, it repeatedly iterates through the constraints of a linear specification, refining the solution until a sufficient precision is reached.
For each constraint it chooses a \emph{pivot variable}, and changes the value of that variable so that the constraint is satisfied.
In order to use SOR for GUI layout, certain considerations and extensions are necessary.

First, linear problems in GUI layout are often over-determined and have many inequality constraints, leading to non-square coefficient matrices.
This leads to the problem of \emph{pivot assignment}: this is the problem of choosing a pivot variable for each constraint so that the iterative method converges.
The standard SOR algorithms choose for each constraint the pivot variable on the diagonal of the problem matrix.
In over-determined systems we do not have a main diagonal, therefore we make use of the pivot assignment algorithms proposed in~\cite{Jamil:Linear-Relaxation}.

Second, constraint-based GUI specifications may contain conflicting constraints, rendering a specification infeasible.
To deal with conflicts, \emph{soft constraints} need to be supported.
In contrast to the usual \emph{hard constraints}, which cannot be violated, soft constraints may be violated as much as necessary if no other solution can be found.
Soft constraints can be prioritized so that in a conflict between two soft constraints only the soft constraint with the lower priority is violated~\cite{Wilson:Constraint-hierarchies}.
Using only soft constraints has the advantage that a problem is always solvable, which cannot be guaranteed if hard constraints are used.
In this work, we use the soft constraint algorithms proposed in~\cite{Jamil:Linear-Relaxation}.

A GUI layout specification has to be solved whenever the conditions under which a GUI is displayed or the GUI itself change.
Most GUIs can be resized, e.g.\ to adapt to different screen sizes or to let the user choose an appropriate size dynamically.
Sometimes GUIs need to be changed dynamically to adjust to content of different sizes.
Each time a GUI is resized or changed, the existing GUI layout specification is changed and a new specification is created.
However, the new specification is similar to the previous one because the widgets and their relations typically stay the same.
Usually, only some size parameters change.
For example, Figure~\ref{fig:resizing} shows a GUI that is resized, with the corresponding constraint specifications.
Only the height constraint at the beginning of the specification is changed.

As a consequence, constraint solvers for GUI layout usually have to solve specifications that are similar to the specification that has been solved previously.
For that reason, it seems plausible that the previous solution is a good initial value for the iterative solving process -- something that is known as a warm-start strategy.
This leads us to the following hypothesis:
\emph{Using a previous solution of a GUI constraint specification to warm-start an SOR solver reduces the solving time.}

We tested the hypothesis by considering three common use cases where GUIs are changed during runtime.
First, small-step resizing, where the GUI size is changed by a small amount, e.g.\ when it is resized by a user dynamically.
Second, big-step resizing, where the GUI size is changed by a larger amount, e.g.\ when the GUI size is maximized.
And third, changes of several constraints, e.g.\ when the sizes of labels are adjusted for a different language.
The solving time when using SOR with and without a warm-start strategy was compared for the three use cases, using randomly generated layout specifications of different sizes.

Section~\ref{sec:relatedwork} sums up related work about constraint solving and warm-start strategies.
Section~\ref{sec:background} puts this research into context with background information about constraint-based GUI-layout, SOR, pivot assignment and conflict resolution.
Section~\ref{sec:experiment} presents the methodology and results of our performance experiment.
Section~\ref{sec:conclusion} concludes the paper.

\section{Related Work}\label{sec:relatedwork}

The overall problem, solving linear systems for constraint-based GUIs, is related to solution procedures for over-determined linear systems in general and constraint-based GUIs in particular.
Several direct and iterative methods exist, which can solve over-determined systems in a least-square sense~\cite{bjorck1996numerical}.
Examples are QR-factorization~\cite{bjorck1996numerical}, the simplex algorithm~\cite{Dantzig:Linear-Programming}, the conjugate gradient method~\cite{Golub:Matrix-Computation} and the GMRES-method~\cite{Golub:Matrix-Computation}.
They are the basis for solvers specifically designed to solve problems of constraint-based GUIs.
Some are based on direct methods, for example HiRise and HiRise2~\cite{Hosobe:Simplex-Based} but the vast majority of existing solvers is based on convex optimization approaches and uses slack variables and an objective function~\cite{Badros:cassowary,Stuckey:arithmetic-constraints,Weber:High-Level-Constraints}.
These methods can handle simultaneous constraints, i.e.\ constraints that depend on each other.
In that respect they are superior to local propagation algorithms, such as DeltaBlue~\cite{Freeman:Delta-Blue} and SkyBlue~\cite{Skyblue:local-propagation-constraint-solver}, which cannot do so.

Approaches related to warm-start strategies have been proposed in numerous previous works.
Lessard~\cite{Lessard:08} analyzed computational speed and the effect of warm-starting for different iterative methods with large systems, including the multigrid method, the preconditioned conjugate-gradient method, and several new variants of these methods.
Using a previous estimate for initializing an iterative scheme could reduce computation time significantly.
Wright et al.~\cite{Wright_et_al_2009} used a warm-start strategy to speed up gradient projection for sparse reconstruction (GPSR) and iterative shrinkage/thresholding (IST) algorithms.
Other methods for accelerating convergence by using warm-start techniques in iterative solution procedures are described in~\cite{Ren:Warm-Start,Forsgen2006,Fletcher:1987:Active}.
The use of warm-start strategies for constraint-based GUI layout problems using SOR has not been explored before.

\section{Background}\label{sec:background}

To put our study into context, this section gives a short overview of constraint-based GUIs, SOR, and the extensions of SOR necessary to solve GUI layout specifications.

\subsection{User Interface Layout as a Linear Problem} \label{UILayout}

Constraints are a suitable mechanism for specifying the relationships among objects.
They are used in the area of logic programming, artificial intelligence and GUI specification.
They can be used to describe problems that are difficult to solve, conveniently decoupling the description of the problems from their solution.
Due to this property, constraints are a suitable way of specifying GUI layouts, where the objects are widgets and the relationships between them are spatial relationships such as alignment and proportions.
In addition to the relationships to other widgets, each widget has its own set of constraints describing properties such as minimum, maximum and preferred size.

\begin{figure}[tb]
\begin{center}
\includegraphics[width=\columnwidth]{resizing_example_3_buttons.pdf}
\caption{A GUI constraint specification before and after resizing} \label{fig:resizing}
\end{center}
\end{figure}

GUI layouts are often specified with linear constraints~\cite{Weber:High-Level-Constraints}.
The positions and sizes of the widgets in a layout translate to variables.
Constraints about alignment and proportions translate to linear equations, and constraints about minimum and maximum sizes translate to linear inequalities.
There are constraints for each widget that relate each of its four boundaries to another part of the layout, or specify boundary values for the widget's size, as shown in Figure~\ref{fig:resizing}.
As a result, the direct interaction between constraints is limited by the topology of a layout, resulting in sparsity of the linear specification.

During application runtime, GUIs need to be adapted to changing conditions such as the available GUI size.
This is done by changing some of the constraints, typically a small number.
For example, the overall size of the GUI is typically specified in by constraints: one for the width and one for the height.
When the GUI size changes, only these two constraints need to be adjusted, as shown in Figure~\ref{fig:resizing}.
Another typical situation where constraints need to be changed is when preferred sizes change.
For example, if the language settings are changed in an application, the preferred sizes of textual labels have to adjust to the new language.

\subsection{Successive Over-Relaxation (SOR)} \label{LinearRelaxation}

Most of the research on iterative methods deals with iterative methods for solving linear systems of equalities and inequalities for sparse square matrices, the most important method being SOR, also known as linear relaxation.
This section summarizes the most important findings.

The best-known iterative method for solving linear constraints is the Gauss-Seidel method~\cite{Bhatti:Numerical-Analysis}.
Given a system of \emph{n} equations and \emph{n} variables of the form
\begin{equation}\label{linearequation}
    Ax=b
\end{equation}
we can rewrite the equation for the \(i\)th term as follows
\begin{equation}\label{linearequationforithterm}
x_{i}=\frac{1}{a_{ii}}({b_{i}-\sum_{j=1}^n
    a_{ij}{x_{j}}} ).\\
\end{equation}

The variable $x_i$, which is brought onto the left side, is called the \emph{pivot variable}, and $a_{ii}$ is the \emph{pivot coefficient} or \emph{pivot element} chosen for row $i$.
An initial estimate for $x$ is chosen, which usually does not fulfill the equations.
The algorithm refines the estimate by repeatedly replacing all individual $x_{i}$ so that the $i$th eqation becomes fulfilled.
This is done in round-robin fashion, and one full run through all $n$ equations is one  iteration, $r$ being the iteration number.
We can therefore write the process as
\begin{equation}
x_{i}^{r+1}=\frac{1}{a_{ii}}({b_{i}-\sum_{j=1}^{i-1}
    a_{ij}{x_{j}^{r+1}}-\sum_{j=i+1}^{n}
    a_{ij}{x_{j}^{r}}} ).\\
\end{equation}
The algorithm iterates until the relative approximate error is less than a pre-specified tolerance.

SOR, also known as linear relaxation, is an improvement of the Gauss-Seidel method~\cite{David:Iterative}.
It is used to speed up the convergence of the Gauss-Seidel method by introducing a parameter $\omega$, known as relaxation parameter, so that
\begin{align}\label{Linearrelaxationmethodformula}
x_{i}^{r+1}=\frac{\omega}{a_{ii}}({b_{i}-\sum_{j=1}^{i-1}
    a_{ij}{x_{j}^{r+1}-\sum_{j=i+1}^{n}a_{ij}x_{j}^{r}}})
   +(1-w)x_{i}^{r}.
\end{align}
This reduces to the Gauss-Seidel method if $\omega=1$.
It is known as over-relaxation if \emph{$\omega>1$}, and known as under-relaxation if \emph{$\omega<1$}.

\begin{definition}
The spectral radius of a matrix is the maximum of the absolute values of its eigenvalues.
\end{definition}
The convergence rate of the SOR method depends on the spectral radius of the coefficient matrix of the problem.
The smaller the spectral radius is, the faster the SOR method converges.
We usually have well-conditioned coefficient matrices in the GUI layout domain for which the SOR method converges.

SOR supports linear equalities as well as inequalities.
Inequalities are handled similar to equalities~\cite{Agmon:Relaxation-Method,Motzkin:Relaxation-Method}:
in each iteration, inequalities are ignored if they are satisfied, and otherwise treated as if they were equalities.

\subsection{Pivot Assignment}

In the case of square coefficient matrices, pivots are selected on the main diagonal.
This is not possible in the over-determined case.
Since the diagonal elements do not lend themselves naturally as pivot elements if the matrix is non-square, we need to explicitly select a pivot element for each constraint.
In other words, we need to determine a \emph{pivot assignment}.

\begin{definition}
A pivot assignment is an assignment of constraints to variables.
\[\gamma\colon \mbox{Constraints} \rightarrow \mbox{Variables}\]
\end{definition}

In previous work, we suggested algorithms which select a pivot element randomly or according to some criteria~\cite{Jamil:Linear-Relaxation}.
We use our random pivot assignment algorithm for the study presented in this paper.
This algorithm assigns the pivot variable for each constraint randomly in each iteration, so that the assignment varies over the iterations.

It is not inherently obvious that randomized assignments work for the linear relaxation approach, but it is the simplest approach that may work.
Although the random algorithm does generally not make the optimal assignment with regard to convergence, it reduces the effect of bad assignments while allowing for good assignments.
In particular, it is guaranteed that every suitable variable will be chosen as pivot variable at some point.
The general assumption underlying randomized algorithms is that the effect of good choices outweighs the effect of bad choices.

In the general case constraint-based GUIs are over-determined, which can result in conflicts between constraints of the problem.
A proper pivot assignment algorithm alone is not sufficient to deal with such cases.
A technique to handle conflicts between constraints, e.g.\ in the form of soft constraints, is required.

\subsection{Conflict Resolution}\label{SoftConstraints}

To resolve conflicts in over-determined systems, soft constraints are introduced.
A natural way to support soft constraints is to assign priorities to all constraints.
These priorities can be defined as a total order on all constraints that specifies which one of two constraints should be violated in case of a conflict.

In our study we use the constraint insertion algorithm proposed in previous work~\cite{Jamil:Linear-Relaxation}.
This algorithm tests constraints incrementally.
We start with an empty set $E$ of enabled constraints.
Iterating through the constraints in order of descending priority, we add each constraint tentatively to $E$ (``enabling'' it), and try to solve the resulting specification.
If a solution is found, then we proceed to the next constraint.
If no solution is found within a fixed maximum number of iterations, then the tentatively added constraint is removed again.
In that case, the previous solution is restored and we proceed to the next constraint.

\section{Experiment}\label{sec:experiment}

In this section, we evaluate the use of warm-starting for GUI layout problems using SOR.
We tested specifically the effect of warm-starting a constraint solver on the performance in terms of computation time.

\subsection{Methodology}

We conducted the experiments with our implementation of an SOR solver for GUI layout, which uses random pivot assignment and constraint insertion as a conflict resolution strategy.
We used two versions of that solver:
the first version started every solver run with an initial solution \(x = (0, \ldots, 0)\),
the second version started every solver run with the optimal solution from the previous run \(x = x^*_{\text{prev}}\).

We evaluated the following three use cases:
\begin{enumerate}
	\item {\em Small-step resizing}: The width and height of the window was randomly changed by a value in between 0 and 3 pixels.
	\item {\em Big-step resizing}: The width and height of the GUI window was randomly changed by a value between 4 and 3000 pixels.
	\item {\em Constraint change}: 10 per cent of all constraints of a GUI were randomly changed.
\end{enumerate}
Small-step resizing occurs in practice when a window is continuously resized by dragging a window border.
Big-step resizing occurs when a GUI is initially loaded on different screens, when a GUI is switched to or from full-screen mode, or when the orientation of a screen is changed.
Constraint changes as in use case 3 occur, for example, when several preferred sizes change as a result of changing the language of an application.

Layout specifications were randomly generated using the parameterized algorithm described in~\cite{Weber:High-Level-Constraints}.
The problem size was varied from 0 to 201 areas.
For each area 4 constraints are added, which specify the position of the area in the layout.
Additionally, a specification needs 4 constraints to define the size of the window.
So we started with a problem of 4 constraints and ended with a problem of 808 constraints.
For each size, 10 random layouts were evaluated.
For each of the three use cases, each of these random layouts was changed 20 times, and the solving time was measured.
A linear relaxation parameter of 0.7 and a tolerance of 0.01 were used for solving.
The measurements were performed on a desktop computer with Intel i5 3.3GHz processor and 64-bit Windows 7 running an Oracle Java virtual machine.

\begin{table}[htb]
\begin{center}
\begin{tabular}{rl}
  \toprule
 Symbol & Explanation \\
  \midrule
 \(\beta_0\) & Intercept of the regression model\\
 \(\beta_{1-3}\) & Estimated model parameters\\
 \(c\) & Number of constraints\\
  \(T\) & Measured time in milliseconds\\
   \(R^2\) & Coefficient of determination\\
\bottomrule
\end{tabular}
\caption{Symbols}
\label{tab:symbs}
\end{center}
\end{table}

To compare the performance of both versions of the solver we used a regression model
\[
T = f(c) + \epsilon
\]
and examined the estimated model visually and numerically.
See Table~\ref{tab:symbs} for an explanation of the symbols used.

\subsection{Results}

To identify the performance trend of the solvers, we tried different regression functions $f$ (linear, quadratic, log, cubic).
We found that the best fitting model is the polynomial model
\[
T = \beta_0 + \beta_1c + \beta_2c^2 + \beta_3c^3 + \epsilon.
\]
Key parameters of the regression models are depicted in Table~\ref{tab:reg1}.
A graphical representation of the measurements and the models can be found in Figures~\ref{fig:smallresizing}, \ref{fig:bigresizing} and~\ref{fig:constraintchange}.
The results suggest a better performance of the solver with the warm-start strategy for all three use cases.

\begin{table*}[tb]\begin{center}
\caption{Regression models for solvers with and without warm-start strategy}
\label{tab:reg1}
\begin{tabular}{rlllll}
  \toprule
\multicolumn{1}{c}{\bf Strategy}                       &\multicolumn{1}{c}{\(\beta_0\)} &\multicolumn{1}{c}{\(\beta_1\)} & \multicolumn{1}{c}{\(\beta_2\)}  & \multicolumn{1}{c}{\(\beta_3\)} & \(R^2\)  \\
  \midrule
Small-step resizing with warm start & \( 6.508\cdot 10^{4}\)\sign{***}& \( -8.940\cdot 10^{2}\)\sign{***} & \(23.55\)\sign{***} & \(\hspace{2.3mm} 7.576\cdot 10^{-4}\)\sign{***}& \(0.999\)\\
Small-step resizing without warm start      &\(4.104\cdot 10^{4}\)\sign{***} & \(\hspace{2.3mm} 68.00\)\sign{***} & \(26.05\)\sign{***} & \(\hspace{2.3mm} 5.971\cdot 10^{-3}\)\sign{***}& \(0.999\)\\
Big-step resizing with warm start & \(  3.186\cdot 10^{4}\)\sign{***}& \( -89.32\)\sign{***} & \( 13.42\)\sign{***} & \(\hspace{2.3mm} 3.202\cdot 10^{-3}\)\sign{***}& \(0.996\)\\
Big-step resizing without warm start  &\( 1.208\cdot 10^{4}\)\sign{***} & \(\hspace{2.3mm} 3.729\cdot 10^{2}\)\sign{***} & \( 18.24\)\sign{***} & \(\hspace{2.3mm} 4.921\cdot 10^{-3}\)\sign{***}& \(0.999\)\\
Constraint changes with warm start & \( 1.074\cdot 10^{5}\)\sign{***}& \( -1.289\cdot 10^{3}\)\sign{***} & \(21.88\)\sign{***} & \(-2.721\cdot 10^{-4}\)\sign{***}& \(0.976\)\\
Constraint changes without warm start      &\(1.231\cdot 10^{5}\)\sign{***} & \( -1.648\cdot 10^{3}\)\sign{***} & \(30.17\)\sign{***} & \( -2.837\cdot 10^{-4}\)\sign{***}& \(0.962\)\\
\bottomrule
\multicolumn{6}{l}{Significance codes: \sign{***} \(p<0.001\)} \\
\end{tabular}
\end{center}
\end{table*}
\begin{figure}[tb]
	\centering
		\includegraphics[width=1.00\columnwidth]{ws3.jpg}
	\caption{Small-step resizing performance results}
	\label{fig:smallresizing}
\end{figure}

\begin{figure}[tb]
	\centering
		\includegraphics[width=1.00\columnwidth]{ws1.jpg}
	\caption{Big-step resizing performance results}
	\label{fig:bigresizing}
\end{figure}

\begin{figure}[tb]
	\centering
		\includegraphics[width=1.00\columnwidth]{ws2.jpg}
	\caption{Constraint change performance results}
	\label{fig:constraintchange}
\end{figure}

The variance of the measured runtime differs noticeably for both approaches.
It is smaller for the rather small changes in small-step resizing, and bigger for the big-step resizing and constraint change use cases.
Especially for constraint change, this indicates that some problems with a lot of conflicts were generated, which require more iterations and hence a longer runtime.
The measurements indicate that the variance of the runtime with warm start is smaller than without warm start, and this might be worth further analysis.
It is somewhat astonishing that the experiments reveal only a relatively small effect of the warm-start strategy.
One reason could be the use of the random pivot selector.
Since it selects pivot elements randomly, it can select pivot elements which let the solution deviate strongly from the initial solution before it actually converges towards the new solution.
Another reason can be that the changes in the specification -- even though they are fairly small -- drastically change the solution in some cases.
This can be, for example, due to conflict resolution.
Some constraints, which were not satisfied with the old specification, can become satisfiable and suddenly have an effect on the solution after the specification was changed.
Similarly, small changes in the specification can lead to new conflicts and hence disabling of constraints.

The effects of the warm-start strategy are comparable for all three use cases, but are the strongest for small-step resizing.
This is convenient, as speed is of particular importance for the small-step resizing use case.
Small-step resizing is typically done interactively by the user, and for a good user experience the GUI should react to such resizing in real-time.

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

In constraint-based GUIs with dynamic behavior, the specification that represents the layout of the GUI is often changed, e.g.\ when a window is resized.
These changes are usually small, resulting in specifications that are very similar.
Since the specifications are similar, one can expect also the results to be similar.
Therefore, we evaluated the use of a warm-start strategy to improve the efficiency of SOR-based constraint solvers for GUIs.
Three common use cases were evaluated with randomly generated GUI layouts:
small-step resizing, big-step resizing, and random changes of several constraints.

We found that an SOR-based solver with a warm-start strategy indeed exhibits a better runtime behavior than a solver without warm-start strategy.
Implementing a warm-start strategy in such solvers does not introduce additional computational effort, as existing values are simply reused.
It is therefore advisable to equip SOR-based GUI layout solvers with a warm-start strategy.

However, we also found that the effect of a warm-start strategy is weaker than we expected.
Possible reasons are the random pivot assignment and the constraint insertion conflict resolution strategy used in the experiment.
Thus, a future work would be to explore the effect of warm starts also for other types of pivot selectors and conflict resolution strategies.
\newpage
\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


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