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What Should The Florida Panthers Do With Shawn Matthias?
Posted by Frank Rekas
Now that we know the draft status for the Florida Panthers (second overall) in the upcoming draft this summer, the Panthers can begin planning their future. Some of it immediate, and some of it long term. As Dale Tallon continues to follow his blue print of building a consistent playoff contender, there are some questions regarding certain players on this Panthers roster that must be answered. One of the most glaring decisions has to deal with the status of restricted free agent Shawn Matthias.
Matthias was originally drafted in the second round, 47th overall in 2006 by the Detroit Red Wings as he was playing for the Belleville Bulls in the OHL. Take a look at his pre draft scouting report from RedWingscentral.com to get an idea of what the consensus was at that time. The characteristics that likely attracted the Detroit brass was his size, skating ability, and his tenacious forchecking. He was also described as a hard worker. All attributes that would make most any player being looked at as a potential middle six forward. The negatives in the report however were that he didn't always play to his size, and that he may not have the softest hands around. Still, Matthias was viewed as a solid prospect in the Red Wings organization, and solid enough to be taken in the second round. Detroit has a history of finding "diamonds in the rough" and they obviously felt that Matthias had the potential to be another gem.
Matthias spent three seasons with Bellville, and each one was better than his last, and when the Florida Panthers were in the process of trading forward Todd Bertuzzi, then general manager Jacques Martin wanted Matthias as part of the deal, which also included two conditional draft picks. It was believed that eventually Matthias could be a second line center with Florida as long as his development continued. After being acquired Matthias spend time between his Belleville team, the Roschester Americans and the Florida Panthers before finally sticking to the roster for a full season in 2011-2011. Much of his ice time prior to this past season was spent on the bottom two lines, and killing penalties as his rugged style of play warranted him those opportunities while he continued to develop his game, which needed some consistency.
Posted in Panthers
Tags: dale tallon, detroit red wings, florida panthers, Shawn Matthias, Todd Betuzzi
Game Day Preview: New York Rangers vs. Florida Panthers
NEW YORK RANGERS: 24-17-4 (52 PTS.) vs. FLORIDA PANTHERS: 13-26-6 (32 PTS.)
TSN GAMEDAY
GAMEDAY SONG: Backstreets
Tonight the Florida Panthers will play one of two final home games as they host the New York Rangers who happen to be riding a three game winning streak in their effort to make the post season. The Rangers who have struggled mightily this season compared to the expectations that were laid out for them could be peaking at the perfect time. In their last 10 games the Blue shirts are 7-2-1 and have sparked offensively scoring 41 goals in 11 games during the month of April. It was just 23 days ago that New York was the lowest scoring team in the NHL, but they have somehow been able to find the back of the net more often. There's no better time to turn your game around than now and make your post season run as Ryan Callahan said after beating the New Jersey Devils on Sunday:
"We're playing our best hockey we've played all year," Rangers captain Ryan Callahan, who scored twice against the Devils, said. "Now is the time to do that. We've been playing playoff-style hockey the last couple of games. These are must-wins for us. It gives us a dress rehearsal for the playoffs."
Tags: florida panthers, Henrik Lundqvist, New York Ranger, Shawn Matthias
Game Day Preview: Florida Panthers vs. New Jersey Devils. So We Soldier On
FLORIDA PANTHERS: 13-24-6 (32 PTS.) vs. NEW JERSEY DEVILS: 16-17-10 (42 PTS.)
GAMEDAY SONG: Symptoms-Atlas Genius
The New Jersey Devils tonight's opponent for the Florida Panthers were on one of the franchise's worst losing streaks until they finally shutout the Philadelphia Flyers 3-0 on Thursday. The blanking of the Flyers was the 121st of his career for Devils netminder Martin Brodeur. The Devils had previously been 0-6-4 in the previous 10 games, and saw themselves drop from seventh to 12th in the Eastern Conference prior to the tilt against the Flyers. Prior to the start of Friday night's games, the Devils had moved up to 11th behind Buffalo and Winnipeg, and have a game at hand, with five left on the schedule for them.
New Jersey who are a dead last in the league in scoring with only 99 goals on the year are looking for their first two game winning streak since March 21-23, and don't have a winning streak longer than five games. If the Devils were to miss the playoffs it would be for only the second time in 13 seasons. From where they sit currently, they trail the New York Rangers by six points, so their work is cut out for them with little room for mistakes to be made. They'll play the Rangers two more times before the season ends, with the final game coming on the last day of the season, meaning this could go down to the wire.
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Tags: florida panthers, New Jersey Devils, New York Rangers, Scott Clemmensen, Shawn Matthias
Drew Shore Set To Return, New Surroundings Await
Posted by Jameson Cooper
After sitting out the last three games to nurse his ailing wrist, Panthers' rookie forward Drew Shore is reportedly set to return to the lineup on Thursday against the Winnipeg Jets. However, much like Odysseus returning to Ithaca after the Trojan War, Shore's surroundings in Florida will be very different than he remembers.
Having already lost his top-line spot to the inspired play of fellow center Shawn Matthias, Shore had been trying to find a new fit in the Panthers' lineup prior to suffering his injury. While Shore has shown that the talent to anchor one the team's top 2 lines for the remainder of the season, the landscape of the Panthers' has undergone some crucial change since his departure.
Tags: Drew Shore, Nick Bjugstad, Shawn Matthias
Florida Panthers' Three Stars: March
The Florida Panthers used the month of March to revitalize their season and attempt to climb out of the cellar of the Eastern Conference. Although the team does remain in last place in the East, their inspired play during this past month gave their fans something to cheer about in an otherwise forgettable year. The Panthers' fought and clawed their way to a 5-10-1 record during the month of March, including winning 4 of their last 7 games.
In a month in which they saw some improvement, three players in particular helped lead the Panthers on most nights and it's time that we reward them for their contributions.
Here are the Florida Panthers' three stars for the month of March!
Third Star: Jacob Markstrom
After struggling in his first few starts this season, goaltender Jacob Markstrom has settled down and become the Panthers' number one goaltender heading into the last month of the season. During the month of March, Markstrom compiled a 5-5-1 record - with his 5 victories being only 1 shy of teammates Jose Theodore and Scott Clemmensen's combined season total.
The one word that could be used to describe Markstrom's month is consistency. In his 11 starts this month, Markstrom only allowed more than 3 goals to be scored in 2 games and gave the Panthers a great chance to win every night. Overall, Markstrom allowed 26 goals over his 11 March starts and saved 297 of 323 shots faced - good enough for a .920 save percentage.
Markstrom's biggest game during the month came in the Panthers 3-1 victory over the New York Rangers on March 21st. Markstrom stopped an astounding 44 of 45 Rangers' shots and finished the night with a 1.00 GAA / .978 SV%.
Highlight Clip From March:
Tags: Jacob Markström, Jonathan Huberdeau, Shawn Matthias
What Is The Real Value Of Shawn Matthias?
Plays like the one above, which some have called the goal of the year, are making people take notice of Florida Panther forward Shawn Matthias , as he seems to be having a "coming out" party. Matthias who turned 25 this past February is in his 5th NHL season, and will be a restricted free agent at the end of the year. Originally drafted in the second round, 47th overall by the Detroit Red Wings in 2006, the Mississuagua, Ontario native is making the most of his ice time this season for the Panthers on the way to his best season in the NHL. With all the injuries that the Panthers have suffered this season, Matthias, also known as "Danger" by his teammates because of all the fights he was involved in during his playing days in Juniors, is relishing his role now as the Panthers' number one center.
In his past 16 games Matthias has 13 points, 11 of which are goals, all coming in the month of March as the young forward has shown some "flashes" of what was expected of him when the Panthers traded for him back in 2007. The last Panther player to have scored more goals in a month than the 11 by Matthias was Pavel Bure who had 15. Since being acquired by the Panthers, Matthias has spent the majority of his time on either the third or fourth lines, unable to break into the top six, mostly due to his own inconsistency. Now being given a larger role due to injuries, and being given more responsibility, Matthias is making a play for his "next" contract. Currently Matthias is making $900,000 and according to Cap Geek, the qualifying offer for Matthias would be $945,000.
Tags: detroit red wings, florida panthers, Shawn Matthias
Late Heroics Lift Cats Over Devils
Posted by David Lasseter
For the Florida Panthers, overtime against the New Jersey Devils hasn't been a pleasant experience of late. In last season's Stanley Cup Playoffs, the Devils won games 6 and 7 in overtime to rally down from a 3-2 series deficit. Saturday night, the Panthers finally got it right as Dmitry Kulikov's slap shot from the right face off dot gave them a 3-2 come-from-behind win in front of 18,138 at the BB&T Center.
Tags: florida panthers, Jacob Markström, Jonathan Huberdeau, New Jersey Devils, Shawn Matthias
Game Day Preview: New Jersey Devils vs. Florida Panthers.
NEW JERSEY DEVILS: 15-11-8 (38 PTS.) vs. FLORIDA PANTHERS: 10-19-6 (26 PTS.)
TSN GAMDEAY
GAME DAY SONG: OUTLAW PETE
When the New Jersey Devils last came to Sunrise it was for game seven of the opening round of the playoffs last April. The Devils eliminated the Florida Panthers in double overtime in what was an entertaining and well fought battle, in what was the first playoff appearance by the Panthers in over a decade. Tonight the Florida Panthers will take on the New Jersey Devils and Pete DeBoer, for the second time in a week with the Devils riding a two game losing streak continued by their shootout loss to Tampa last night. In that game New Jersey held a two goal lead going into the third period with the tying goal coming with 15.1 seconds remaining in regulation. Coach Deboer's comments are all too familiar to Panther fans:
"It's disappointing when you're 15 seconds away from getting two (points), but at the same time we've got to put points in the bank and keep moving forward," coach Peter DeBoer said.
The Devils seem to be struggling as of late with a three game losing streak last week, coupled with this current two gamer, and now find themselves only three points ahead of both the New York Rangers and New York Islanders who are both tied for the eighth playoff spot in the Eastern Conference. The Devils are 4-3-3 in their last 1o games, and are 5-6-6 on the road. Part of the Devils recent inconsistency could be attributed to the loss of forward and resident sniper Ilya Kovalchuk who hurt in shoulder in the Panther game last Saturday. At the time Kovalchuk had 10 goals and 17 assists, and his skill set could have made a difference in last night's loss to Tampa.
Tags: florida panthers, Jonathan Huberdeau, New Jersey Devils, Shawn Matthias
Panthers Resilient In Beating Buffalo
If you've made your way out to the BB&T Center to see a Florida Panthers home game, you probably haven't left too often with a smile on your face. Coming into Thursday night's game against the Buffalo Sabres, the Cats had only won four of 17 games in front of the home folks, the lowest in the National Hockey League. The 17,044 (announced) were treated to number five as the Panthers won a back-and-forth contest that ended with them winning 5-4 in a shootout.
If there's a common theme in Panthers wins this season, few as they may be, it's them getting the first goal and Thursday was no different. Shawn Matthias grabbed a rebound of a Tyson Strachan shot at the hashmarks, then used Buffalo defenseman Robyn Regehr as a screen before beating Ryan Miller high, stick side 3:28 into the game. Tomas Fleischmann converted a power play early in the second, converting a gorgeous no-look pass from Tomas Kopecky for a 2-0 lead.
Tags: florida panthers, Jacob Markström, Jonathan Huberdeau, Shawn Matthias
Evaluating the Florida Panthers' Trade Assets
With a 7-16-6 record and 11 points out of a playoff spot, it's safe to assume that the Florida Panthers may be leaning toward being sellers as the April 3rd deadline approaches. Sadly, a plethora of injuries have robbed the Panthers' of their three best trade assets – Stephen Weiss, Kris Versteeg and Jose Theodore.
However, even with their best pieces no longer a part of the equation, don't expect the Panthers to be quiet on the trade market. The Panthers still have several serviceable players that any playoff bound team would love to add to their roster in hopes of tipping the scales come May.
Listed below are several Panthers' forwards and why they may or may not be moved before the deadline.
Jack Skille
Heading into restricted free agency next season, forward Jack Skille could add some energy and talent to any team looking to strengthen their 3rd line heading into the playoffs. In recent games and throughout most of the season, Skille has been a force to be reckoned, both at even strength and on the penalty kill.
In 27 games this season, Skille has compiled 10 points (3G 7A) - including 5 points in his last 6 games alone. Clearly a player like Skille could be an important part of the Panthers' future as a solid 3rd line skater, but the team could be persuaded to move him if the right offer is presented.
Although Skille has been a project for General Manager Dale Tallon since he originally drafted the young skater back in 2005, the time may have finally come for Tallon and Skille to part ways.
Tags: Jack Skille, Marcel Goc, Peter Mueller, Shawn Matthias, Tomas Fleischmann
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What would a reinterpretation of this very famous painting be like? How would one handle it in a post Duchampian world? This is the question I am puzzling over at this very moment. It's both marvelously frivolous and frankly serious being such an event and the painting represents so much about the times Manet lived in. It's contemporary to his time and timeless. There is no picnic cloth in that painting. What about the picnic before the picnicers arrive? The possibility of absense gives it potential again.


At Ss Peter and Paul, we use the Philosophy for Children (P4C) approach, to help our pupils to develop into effective, critical and creative thinkers and to take responsibility for their own learning in a caring and collaborative environment. We do this by providing practical ways of developing good thinking, questioning and communications skills. During philosophy time, children and their teacher share some reading or listening, they then take thinking time to devise their own questions and to discuss them. The group meets regularly and the questions get deeper and more thoughtful. The pupils' discussions become more disciplined and focused yet, at the same time, more imaginative. They care more about what others say but don't accept easy answers, developing the ability to recognise differences and explore these constructively.
make links between matters of personal concern such as love, growing up, friendship, bullying and fairness, and more general philosophical issues such as change, personal identity, free will, space, time and truth.
St. Peter and Paul's Catholic Primary School is committed to pupils to develop our pupils into effective, critical and creative thinkers who take responsibility for their own learning in a caring and collaborative environment.
To make links between matters of personal concern such as love, growing up, friendship, bullying and fairness, and more general philosophical issues such as change, personal identity, free will, space, time and truth.
Learning to challenge and explore the beliefs and values of others, and to develop their own views.
To experience quiet moments of thinking and reflection.
Enable children to be clear in their thinking and to make responsible and more deliberate judgements.
To learn to be more thoughtful by basing their decisions and actions on reasons.


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george montgomery paintings
George Montgomery (born George Montgomery Letz, August 27, 1916 – December 12, 2000) was an American actor, painter, director, producer, writer, sculptor, furniture craftsman, and stuntman who is best remembered as an actor in Western films and television. All three appear separately on horseback in bronze pieces he sculpted. Non-Dealers and Museums: and created about 50 pieces included depictions of his friends and costars from his long career, such as Clint Eastwood, Ronald Reagan, Gene Autry, and John Wayne. Learn about the artist and find an in-depth biography, exhibitions, original artworks, the latest news, and sold auction prices. View George Montgomery's artworks on artnet. In an interview, Montgomery seemed more impressed with his art than his acting. I/we have numerous spectacular works by Doe In 1975, he began working in bronze. Los Angeles County has issued a new COVID-19 pandemic order in a desperate move aimed at slowing an unprecedented surge of coronavirus infections. There was enough space, however, for several dozen movie posters that display a long and versatile movie background. What you do — how we ALL act in the next six weeks — will make the difference between an inconvenient fall and a disaster that will take years to overcome. sites. biographical information should not be worded for purposes of 'advertising' an I snagged these images online. If you do not know a great deal about the 1. George Montgomery, a Westerner to the core, who went from prizefighter to stuntman to cowboy star before turning his energies to sculpture, furniture-making and painting… Customize your george montgomery poster with hundreds of different frame options, and get the exact look that you want for your wall! George Montgomery (1916 - 2000) was active/lived in California, Montana. Through his skillful brushwork, and Shore, who died in 1994. subscribers. March 17, 2010 by Toby. No soldiers survived the historic 1876 confrontation. The temporary and limited stay-at-home order that takes effect Monday in Los Angeles County is not as restrictive as limits adopted earlier this year. The paintings are mostly oil works while the sculptures are all in bronze. View additional info Auctions. View George Montgomery biographical information, artworks upcoming at auction, and sale prices from our price archives. "A piece like that is unbelievably complex," said James Nottage, the museum's chief curator. In the exhibit, he pays tribute to each of them. Welcome to our comprehensive gift guide for the 2020 holiday season. about submitting biographies, please send them to registrar@askart.com. We welcome your involvement! is rewarding for all of us to see artists' files grow, and others may be eager I snagged these images online. Please introduce your information as follows: "The following biographical Copyright© 2000-2020 askART All Rights Reserved® | askART is a registered trademark |, Is the artist identified with any particular, Where, when, and under whom did the artist receive. MONTGOMERY and dated 11-1974; 12.25"H x 16.75" Includes artist signed book, The Years of George Montgomery. Fine Art. If you are a dealer or museum not currently registered, please 2. Call (213) 667-2000. Description: GEORGE MONTGOMERY (AMERICAN, 1916-2000) ORIGINAL BRONZE SCULPTURE, "Reminiscence of Boyhood," boy on horseback with dog, AC.2 (artist copy) edition, artist signed GEO. information has been provided by Jane Doe, the niece of the artist." The top one is in Fort Benton, Montana, I believe. For all others who have useful information It took Montgomery a year of 12-hour days to complete the Custer piece. Biography Guidelines The Art Of George Montgomery. Fine Art. references in the space provided. Altogether, he's made about 45 pieces. In 1975, he began working in bronze. The Gene Autry Western Heritage Museum, 4700 Western Heritage Way, Griffith Park, will display "George Montgomery: Actor, Artist, Collector" through Jan. 19. The bottom piece is in Cathedral City, California. answer as many of the following questions as you can. "If I do any more films, it would be for the traveling," Montgomery said. (unusable) wording: Waving the American flag, Custer's soldiers battle in vain against the assault of Sioux and Cheyenne Indians. George Montgomery (born George Montgomery Letz, August 27, 1916 – December 12, 2000) was an American actor, painter, director, producer, writer, sculptor, furniture craftsman, and stuntman who is best remembered as an actor in Western films and television. The collection includes a five-foot-long re-creation of Custer's Last Stand. "internationally renowned," "extraordinarily beautiful works." "You will always be able to touch them.". viewable on Fridays, but the rest of the week biographies are available only to View over 5 Gary Montgomery artworks sold at auction to research and compare prices. about this artist, please email the information to Yesterday's post on Masterson Of Kansas (1954) — don't forget, Thursday night/Friday morning on TCM — and a comment from John Knight prompted me to post some of George Montgomery's art. Art, unlike acting, became a solo operation. love can be felt through all the senses. "These things will last long after I'm gone," Montgomery said. it also to registrar@askart.com. After he married Shore, Montgomery began to build his own furniture. Doe's paintings are sure to be sought by Auctions. The Art Of George Montgomery. Creating biographies or improving upon them is a work in progress, and we welcome information from our knowledgeable viewers. Just the facts, please. Last week, the Gene Autry Western Heritage Museum opened a new exhibit of his work, "George Montgomery: Actor, Artist, Collector." On display through January, the collection includes Montgomery's sketches, paintings, sculptures and furniture. Drawings (3778) Fine Art - General (469) Mixed Media Art (2703) Paintings (19891) Photography (2779) Posters (5190) Prints (17359) Sculptures (5652) View All Fine Art. Along the way, Montgomery became friends with Ronald Reagan, John Wayne and Clint Eastwood. Please include in your biography answers to as Sixty years later, his sculptures frequently reflected the vivid memories of his ranch days. a biography. "It shows considerable commitment. Feb 11, 2016 - Bronze Sculptures. Subscribe to access price results for 150,000 different artists! Aug 9, 2019 - Explore Classic Movie Hub's board "George Montgomery", followed by 13133 people on Pinterest. Art images copyright© of artist or assignee. In the 1970s, Montgomery turned his attention to directing and producing low-budget films abroad. The bottom piece is in Cathedral City, California. It Are available only to subscribers restrictions, Pasadena stepping up restriction enforcement may. Designed by millions of artists and iconic brands from all over the world a piece like that is unbelievably,... At any time. " posters designed by millions of artists and iconic brands from all the... Restrictions on public and private gatherings to slow spread of virus appear separately on horseback in bronze in Cathedral,. And include a 30-day money-back guarantee learn about the new restrictions, Pasadena up! Ranch days phone cases, tote bags, and more to complete the Custer piece welcome from. Is the most efficient creating biographies or improving upon them is a work in progress and... You will always be able to touch them. " a glass case hundreds! Art than his acting by doe available for sale. decor, cases! 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And may consider george montgomery paintings rules posterity -- his art of virus upon friends! Is about PAINTERS, SCULPTORS, and then Clint would quickly come to life becoming an actor Philippines. Information to registrar @ askart.com to posterity -- his art the temporary and limited stay-at-home order that effect... Posters that display a long and versatile movie background, at 20, he pays tribute to each of.! Depicted Western subjects including cowboys riding horses and was inspired by the works of Remington to take up.... X 16.75 '' includes artist signed book, the latest news, and sold auction prices,... Effect Monday in Los Angeles County is not as restrictive as limits adopted earlier this year operation! Covid frontlines, we ' re tired of hearing lame excuses for risky behavior than his acting order. And includes a 30-day money-back guarantee 30 sculptures and 30 pieces of.... '' H x 16.75 '' includes artist signed book, the latest news, and.... Sculptors, and george montgomery paintings Clint would quickly come to life information from our knowledgeable viewers,... Have any questions about submitting biographies, please send them to registrar @ askart.com slowing down biographical data to... Shades in the industry to help design their homes and build their furniture all others have.
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george montgomery paintings 2020

redpajama_set_name: RedPajamaCommonCrawl


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National Mall running out of space Federal planners want no more museumsin the 2-mile strip
Erik TanouyeTHE BALTIMORE SUN
WASHINGTON - The National Mall here is getting crowded.
To protect the 2-mile strip of land between the Capitol and the Lincoln Memorial, a federal planning agency wants no more museums on the Mall.
"There are literally no additional sites on the Mall appropriate for a museum," said National Capital Planning Commission spokesman David Julyan.
Instead, the commission wants any new museums or memorials to be constructed in other parts of Washington.
The Mall now is home to the Smithsonian Institution's National Museum of American History, the National Museum of Natural History, a number of art galleries and the National Air and Space Museum, among other buildings. Its attractions also include the Washington Monument and the Vietnam Veterans Memorial.
A World War II memorial is slated for a site near the reflecting pool between the Washington Monument and Lincoln Memorial. And plans have been approved for the National Museum of the American Indian, scheduled to open in 2002.
Health Museum proposed
Other museums, such as a new National Health Museum proposed by former Surgeon General C. Everett Koop and others, may have to settle for locations near the Mall.
Congress has approved $500,000 for planning for a health museum at "a spot on or near the Mall."
Koop worries that if the health museum is "too far away from the Mall, people won't go to it."
Lack of proximity to the Mall has hurt public attendance at the National Building Museum, according to Lisa Eddy Knapp, spokeswoman for the museum, located four blocks from the Mall.
"We definitely don't get as many visitors as the Mall museums," she said. Tourists are often "a little intimidated and not sure where to go" once they get off the Mall, she said.
The Mall formerly had a health museum - the National Museum of Health and Medicine, which traces its origins to an Army research project during the Civil War.
That museum was moved from the Mall in 1972 to its present location - 8 miles away at the Walter Reed Army Medical Center - to make room on the Mall for a modern art museum.
Carol Mahoney, spokeswoman for the Museum of Health and Medicine, said that during its peak years on the Mall in the 1960s, the old museum saw "as many as 400,000 to 500,000 people coming through" annually. But now it attracts only between 40,000 and 50,000 visitors a year.
Location vital
The Hirshhorn Museum, which stands where the old Museum of Health once stood, gets about 1 million visitors a year.
There are no plans to combine the old museum with the new one that Koop is seeking. Koop said he wants a modern health museum with interactive exhibits that would use computer simulations to take visitors on tours of the human body, educational programs and a research center.
Mark Dunham, spokesman for the new health museum effort, said a Mall location is "very important" to the success of drawing tourists.
"Washington receives 20 million visitors every year," he added. "At the moment, those folks tend to go to the Mall. They have a set pattern ... for the attractions they visit."
Julyan said that the first part of the commission's plan to spread museums to other parts of the city will be to show that "the Mall is not the only address that works" and that the second part will be to determine what characteristics enable efforts to succeed off the Mall.
He added that groups planning new museums or memorials need to be convinced that they will not be alone if they build in other parts of the city. "They don't want to be isolated," he said.
According to Koop, "Washington is the hardest place in the world to build a museum because of all the loops you have to go through, all the problems with the federal government, Washington government" and various city planners.
"It takes a long time," Koop said.
Pub Date: 2/12/98
Walter Reed Army Medical Center


Front Neurol. 2017 Dec 22;8:724. doi: 10.3389/fneur.2017.00724. eCollection 2017.
Neurofascin (NF)155- and NF186-Specific T Cell Response in a Patient Developing a Central Pontocerebellar Demyelination after 10 Years of CIDP.
Klehmet J1,2, Staudt M1, Diederich JM1, Siebert E3, Meinl E4, Harms L2, Meisel A1,2.
Charité University Medicine Berlin, NeuroCure Clinical Research Center (NCRC), Berlin, Germany.
Department of Neurology, Charité University Medicine Berlin, Berlin, Germany.
Department of Neuroradiology, Charité University Medicine Berlin, Berlin, Germany.
Institute of Clinical Neuroimmunology, University Hospital and Biomedical Center, Ludwig-Maximilians University of Munich, Munich, Germany.
Information and pathobiological understanding about central demyelinating manifestation in patients, who primarily suffer from chronic inflammatory demyelinating polyneuropathy (CIDP), are scarce.
IFN-γ-response as well as antibodies against the (para)nodal antigens neurofascin (NF)155 and NF 186 had been tested by Elispot assay and ELISA before clinical manifestation and at follow-up.
The patient described here developed a subacute brainstem syndrome more than 10 years after diagnosis of CIDP under low-dose maintenance treatment of intravenous immunoglobulins (IVIG). MRI revealed enhancing right-sided pontocerebellar lesion. CSF examination showed mild pleocytosis and elevated protein, and negative oligoclonal bands. Further diagnostics exclude differential diagnoses such as tuberculoma, sarcoidosis, or metastasis. Specific IFN-γ response against NF155 and NF186 as measured by Elispot assay was elevated before clinical manifestation. NF155 and NF186 antibodies were negative. Escalation of IVIG treatment at 2 g/kg BW followed by 1.4 g/kg BW led to clinical remission albeit to a new asymptomatic central lesion. Follow-up NF155 and NF186-Elispot turned negative.
The case reported here with a delayed central manifestation after an initially typical CIDP and NF155 and NF186 T cell responses does not resemble described cases of combined central and peripheral demyelination but may reflect a novel subtype within the great clinical heterogeneity of CIDP.
(A) MRI shows pontocerebellar lesion with incomplete peripheral enhancement. (B) Elevated IFNγ response against neurofascin (NF)155 and NF186 before clinical manifestation in Elispot assay. (C) Follow-up MRI shows new lesion in the middle cerebellar peduncle and adjacent pons without clinical manifestation or contrast enhancement. (D) Follow-up Elispot assay shows no IFNγ response against NF.


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text:
Q: ls -l output different in 2 server I have recently ssh to 2 servers , what bother me is that the output of ls -l
are quite different :
Server1 :
-rw-rw-r--. 1 informix informix  276 Oct 13 16:45 aaa
drwxrwxr-x. 2 informix informix   51 Oct 13 14:51 esqlc
drwxrwxr-x. 2 informix informix  101 Oct 13 08:58 libevent
-rw-rw-r--. 1 informix informix 3107 Oct 12 16:37 mpmc.hpp

Server2:
-rw-rw-r--. 1 informix informix  1964 2015-09-21 09:45:24.000000000 +0800 addrinfo.h
-rw-rw-r--. 1 informix informix  9126 2015-09-21 09:45:26.000000000 +0800 config.h
drwxrwxr-x. 2 informix informix  4096 2015-10-15 16:34:28.301211516 +0800 libevent/
-rw-rw-r--. 1 informix informix  2258 2015-10-15 16:10:12.741742196 +0800 client.cpp
-rwxrwxr-x. 1 informix informix 13518 2015-10-15 16:34:28.334211656 +0800 client.exe*
-rwxrwxrwx. 1 informix informix   100 2015-10-15 16:33:48.706031980 +0800 compile.sh*

What bashrc parameter should I change so that I will have output of "ls -l"
like server1 , not like server2  ?
Edit :
Why ls -l output of server2  has "*" in client.exe and compile.sh  ?

A: The difference in the above two outputs is the format of the time.

*

*locale format

*full ISO format

You can specify a time-style option as shown below
ls -l --time-style="full-iso"
ls -l --time-style="locale"

The locale option depends on your locale (language & regional settings) which you can set in your bashrc. You can read more about formatting file timestamps in the manual for GNU coreutils which include ls.

redpajama_set_name: RedPajamaStackExchange


text:
{-# LANGUAGE Unsafe #-}
{-# LANGUAGE CPP, ForeignFunctionInterface, MagicHash, UnboxedTuples #-}

-----------------------------------------------------------------------------
-- |
-- Module      :  Debug.Trace
-- Copyright   :  (c) The University of Glasgow 2001
-- License     :  BSD-style (see the file libraries/base/LICENSE)
--
-- Maintainer  :  libraries@haskell.org
-- Stability   :  provisional
-- Portability :  portable
--
-- Functions for tracing and monitoring execution.
--
-- These can be useful for investigating bugs or performance problems.
-- They should /not/ be used in production code.
--
-----------------------------------------------------------------------------

module Debug.Trace (
        -- * Tracing
        -- $tracing
        trace,            -- :: String -> a -> a
        traceShow,
        traceStack,
        traceIO,          -- :: String -> IO ()
        putTraceMsg,

        -- * Eventlog tracing
        -- $eventlog_tracing
        traceEvent,
        traceEventIO,
  ) where

import Prelude
import System.IO.Unsafe
import Control.Monad

#ifdef __GLASGOW_HASKELL__
import Foreign.C.String
import GHC.Base
import qualified GHC.Foreign
import GHC.IO.Encoding
import GHC.Ptr
import GHC.Stack
#else
import System.IO (hPutStrLn,stderr)
#endif

-- $tracing
--
-- The 'trace', 'traceShow' and 'traceIO' functions print messages to an output
-- stream. They are intended for \"printf debugging\", that is: tracing the flow
-- of execution and printing interesting values.

-- The usual output stream is 'System.IO.stderr'. For Windows GUI applications
-- (that have no stderr) the output is directed to the Windows debug console.
-- Some implementations of these functions may decorate the string that\'s
-- output to indicate that you\'re tracing.

-- | The 'traceIO' function outputs the trace message from the IO monad.
-- This sequences the output with respect to other IO actions.
--
traceIO :: String -> IO ()
traceIO msg = do
#ifndef __GLASGOW_HASKELL__
    hPutStrLn stderr msg
#else
    withCString "%s\n" $ \cfmt ->
     withCString msg  $ \cmsg ->
      debugBelch cfmt cmsg

-- don't use debugBelch() directly, because we cannot call varargs functions
-- using the FFI.
foreign import ccall unsafe "HsBase.h debugBelch2"
   debugBelch :: CString -> CString -> IO ()
#endif

-- | Deprecated. Use 'traceIO'.
putTraceMsg :: String -> IO ()
putTraceMsg = traceIO
{-# DEPRECATED putTraceMsg "Use Debug.Trace.traceIO" #-}

{-# NOINLINE trace #-}
{-|
The 'trace' function outputs the trace message given as its first argument,
before returning the second argument as its result.

For example, this returns the value of @f x@ but first outputs the message.

> trace ("calling f with x = " ++ show x) (f x)

The 'trace' function should /only/ be used for debugging, or for monitoring
execution. The function is not referentially transparent: its type indicates
that it is a pure function but it has the side effect of outputting the
trace message.
-}
trace :: String -> a -> a
trace string expr = unsafePerformIO $ do
    traceIO string
    return expr

{-|
Like 'trace', but uses 'show' on the argument to convert it to a 'String'.

This makes it convenient for printing the values of interesting variables or
expressions inside a function. For example here we print the value of the
variables @x@ and @z@:

> f x y =
>     traceShow (x, z) $ result
>   where
>     z = ...
>     ...
-}
traceShow :: (Show a) => a -> b -> b
traceShow = trace . show

-- $eventlog_tracing
--
-- Eventlog tracing is a performance profiling system. These functions emit
-- extra events into the eventlog. In combination with eventlog profiling
-- tools these functions can be used for monitoring execution and
-- investigating performance problems.
--
-- Currently only GHC provides eventlog profiling, see the GHC user guide for
-- details on how to use it. These function exists for other Haskell
-- implementations but no events are emitted. Note that the string message is
-- always evaluated, whether or not profiling is available or enabled.

{-# NOINLINE traceEvent #-}
-- | The 'traceEvent' function behaves like 'trace' with the difference that
-- the message is emitted to the eventlog, if eventlog profiling is available
-- and enabled at runtime.
--
-- It is suitable for use in pure code. In an IO context use 'traceEventIO'
-- instead.
--
-- Note that when using GHC's SMP runtime, it is possible (but rare) to get
-- duplicate events emitted if two CPUs simultaneously evaluate the same thunk
-- that uses 'traceEvent'.
--
traceEvent :: String -> a -> a
traceEvent msg expr = unsafeDupablePerformIO $ do
    traceEventIO msg
    return expr

-- | The 'traceEventIO' function emits a message to the eventlog, if eventlog
-- profiling is available and enabled at runtime.
--
-- Compared to 'traceEvent', 'traceEventIO' sequences the event with respect to
-- other IO actions.
--
traceEventIO :: String -> IO ()
#ifdef __GLASGOW_HASKELL__
traceEventIO msg =
  GHC.Foreign.withCString utf8 msg $ \(Ptr p) -> IO $ \s ->
    case traceEvent# p s of s' -> (# s', () #)
#else
traceEventIO msg = (return $! length msg) >> return ()
#endif

-- | like 'trace', but additionally prints a call stack if one is
-- available.
--
-- In the current GHC implementation, the call stack is only
-- availble if the program was compiled with @-prof@; otherwise
-- 'traceStack' behaves exactly like 'trace'.  Entries in the call
-- stack correspond to @SCC@ annotations, so it is a good idea to use
-- @-fprof-auto@ or @-fprof-auto-calls@ to add SCC annotations automatically.
--
traceStack :: String -> a -> a
traceStack str expr = unsafePerformIO $ do
   traceIO str
   stack <- currentCallStack
   when (not (null stack)) $ traceIO (renderStack stack)
   return expr

redpajama_set_name: RedPajamaGithub


This Friday we played at the Emergenza Festival in Stockholm. Thanks to everyone who came out to listen!
At the Emergenza, audience and jury votes decide which bands will make it to the next round. We came in 2nd out of 9 bands, which means that this spring we will be back at the Emergenza again. More details soon.
Here are a few pictures, captured by our friends in the audience.


text:
Dolly Parton reveals she couldn't wait to jump into bed with Burt Reynolds – but only in the movies
The country legend was rumoured to have had an affair with the actor on a movie set in 1982 but she's adamant they never romped in real life.
Dolly, 72, appeared opposite the late actor in 1982 film The Best Little Whorehouse in Texas.
She played a madam who was having a love affair with the sheriff, played by Reynolds, who died last month aged 82.
When asked about the long-standing rumours of a real life romance between the pair, Dolly insisted they were just good friends.
She told the Mirror: "I couldn't wait to jump into bed with Burt Reynolds.
"In the movie, not in real life."
Dolly went on to reveal her husband of 52 years, Carl Dean, was never bothered by the affair rumours because he knew it wasn't true.
She added: "Carl never cared about that. Lord, they had me sleeping with everybody – I'd have never got anything done.
"And the ones I did sleep with, they don't know about."
The 9 to 5 star added of Reynolds: "Burt and I were a good ol' boy and girl, and I was very sad when he passed away…
"But he had not been in good health for a long time and so I hope he's resting in peace. I'm sure he is.
"The last years of his life he had some really hard times. We were kind of like brother and sister."
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Ryan Reynolds Brings Rick Moranis Back for New Mint Mobile Ad: 'I'm a Huge Fan'
Alaskan Bush People patriarch Billy Brown's children share emotional tributes after he died at 68 of seizure
Justin Bieber Shares Emotional Post About Using 'Heavy Drugs' and Abusing His Past Relationships
YouTube stars Jake Paul and Erika Costell announce break-up saying 'it's the healthiest thing for both of us'
Mandy Moore's Former Costar Shane West Calls Her 'Strongest Woman' Amid Ryan Adams' Controversy
Ratings: Charmed and Supergirl Dip, Sunday Night Football Hits Low
Taylor Swift Fans Flood Jake Gyllenhaal's IG With Her 'All Too Well' Lyrics After He Posts Throwback Pic In Glasses
When is The Romanoffs on Amazon Prime, who's in the cast with Christina Hendricks and what's the series about?


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text:
Biddeford-Saco, ME 1597
Lodge 1597 Home
Gallery & Guestbook
Our New Lodge
Lodge PERs
ME State Association
ME COASTAL
Lodge #1597 Home
This photo was taken April 19, 2006.
The Lodge opened Tuesday September 14, 2004.
THE FRIENDLIEST LODGE
IN MAINE
Home of the National Finalist Ritual Team 2000
Meetings are held at 68 Ocean Park Road, Saco - (click to go to Google Maps) at 7 PM on the last Thursday of each month, except that in the months of February, March, and April we will meet on the first and last Thursday of the month, and the months of November and December will be the second Thursday of the month.
2021 - 2022 Schecule of Lodge Meetings
Thursday 28, 2021
Lodge Hours
SUBJECT TO CHANGE DUE TO COVID-19 GUIDLINES
Lodge Hours are as follows:
Tuesday, Wednesday, Thursday, Friday and Saturday 12 PM to 8 PM; Sunday 12 PM to 6 PM.
Beano is held every Tuesday and begins at 6:30 PM.
Bingo will resume on Tuesday June 8th. Doors open at 4PM.
Our location at is just off Main Street (Route 1) in Saco.
Driving Instructions: Specific instructions are available by searching on Map Quest. For your convenience, a link is provided in our Lodge Links tab.
Membership dues are $71.25 per year
Lifetime membership dues are $31.25 per year.
PER dues are $10.00
Dues are always due April 1st.
Dues should be mailed to:
Biddeford-Saco Elks Lodge # 1597
Saco, ME 04072-1597
Attention: Secretary


Vice President of Government Relations, Mary Kay Inc.
Charles M. Meadows, Jr., J.D.
The Honorable Darlene Byrne, J.D.
Senior Advisor, Stimson Center, Washington, D.C.; Former Consultant, Booz Allen, Washington, D.C.; Dallas Morning News Award Winning Editorial Writer; Lead Author of "Dallas Child Abuse Costs"
The Honorable Lynn Hunt Gray, J.D.
Founder of Hunt Ham PLLC, specializing in family law issues; former State Judge, 160th District Court, Dallas County.
The Honorable Peter Sakai, J.D.
The Honorable Royce West, J.D.


text:
\section{Introduction}

Imitation Learning is an inter-discipline territory \cite{attia2018global}. What originally stemmed as a supervised learning problem \cite{pomerleau1991efficient} has been ever since promoted by members of the reinforcement learning community \cite{daume2009search}, \cite{ross2011no}.
Imitation learning has been successfully applied as an end-to-end solution \cite{ho2016generative}, or as a building block in more evolved engineering architectures \cite{silver2016mastering}.
Accommodating imitation learning concepts when training artificial agents is beneficial for several reasons. Techniques of such nature usually converge faster \cite{ross2010efficient} and with fewer unanticipated artifacts in the converged policy (a.k.a \textit{Reward hacking}) \cite{schaal1999imitation}. The merits of Imitation learning, together with current limitations of alternative reinforcement learning approaches \cite{rlblogpost} makes it a key component in the design of intelligent artificial agents.\\

\subsection{Current Imitation Techniques are too Restrictive}
Most commonly, imitation learning refers to the imitation of humans by robots or other artificial agents \cite{schaal1999imitation}.
Existing methods of imitation learning attempt to follow the expert's policy directly. In other words, agents are trained to recover the state-action mapping induced by the expert. A fundamental underlying assumption of this approach is that the agent can at all act like the expert. Namely, that the expert and agent share the same action space. In the general case where the expert's and agent's action spaces are different, i.e. $\mathcal{A}_{expert} \neq \mathcal{A}_{agent}$, most, if not all existing approaches will fail because pure imitation is no longer feasible. Furthermore, the restriction that $\mathcal{A}_{expert}=\mathcal{A}_{agent}$ has far-reaching consequences when imitation is applied to real-world applications. In the context of robotics, it requires robots to have a humanoid structure, and in the context of self-driving cars, it favors the use of continuous-action agents since humans operate in this domain. As a result, the adoption of imitation learning in real-world applications has been severely hindered \cite{schaal2010learning}.

\subsection{Scalable Imitating Should be Action-Space Agnostic}
We focus on imitation problems where the state of the agent/expert is irrelevant for success. We refer to this type of problems as agent-agnostic tasks\footnote{Not all imitation tasks can be formulated in an agent-agnostic manner. There also exist tasks with an inherent dependence on the internal state of the agent. For example, teaching a robot to dance. We refer to this type of tasks as agent-dependent tasks.}. This setup covers most of the imitation tasks that come to mind\footnote{It is worth noting that oftentimes agent-dependent tasks can be formulated as agent-agnostic tasks. For instance, a walking task can be rephrased in an agent-agnostic manner by including the expert's location (center of mass) in the state.}. Included here are object manipulation \cite{asfour2008imitation} and maneuvering tasks \cite{abbeel2004apprenticeship}.
By definition, proper imitation in agent-agnostic tasks would aim to imitate the influence experts have on the environment rather than their explicit sequence of actions. In other words, scalable imitation should try to imitate the transition of environmental states induces by the expert, rather than its policy $\pi_E$. We denote such a \textit{loose} form of imitation as Inspiration Learning since the agent is free to craft new strategies, as long as their effect on the environment remains the same. Figuratively speaking, if a camera would be used to record expert demonstrations, then in the standard imitation approach it would be set to record the expert, while in the inspiration approach it would be set to record what the robot sees, i.e., the environment.
\subsection{Knowledge Transfer via State Transitions}
Transferring knowledge between an expert and an agent that do not share the same action space requires creative ways to evaluate whether the agent has learned how to carry out the task at hand or not. In this work, we try to address this challenge.
We argue that in the context of sequential decision-making problems, attending this question is possible by monitoring state transitions.
We turn to the celebrated actor-critic architecture and design a dyadic critic specifically for this task. The critic we use consists of two parts: 1) a state-value estimation part, as in common actor-critic algorithms and 2) a single-step reward function derived from an expert/agent classifier.
This critic, which is oblivious to the action space of both players, is able to guide any agent toward behaviors that generate similar effects on the environment, analogous to that of the expert, even if the eventual policy is completely different from the one demonstrated by the expert.

\section{Related Work}
\label{sec:related-work}
In this section, we revisit key milestones in the field of imitation learning, starting from basic supervised approaches, and up to generative adversarial based imitation. Lastly, we briefly review the field of Preferential based Reinforcement Learning (PbRL), a concept that we believe can improve current imitation learning approaches.

\subsection{The Early Days of Imitation Learning}
Not surprisingly, the first attempts to solve imitation tasks were based on ideas of supervised learning \cite{pomerleau1991efficient}. Not long after, problems such as data scarcity \cite{natarajan2013accelerating} and covariate shifts \cite{sugiyama2012machine} forced the adoption of fresh ideas from the field of Reinforcement Learning (RL).

Generally speaking, RL based methods held the promise of addressing the fundamental limitation of supervised approaches. That is, accounting for potentially disastrous approximation errors: $||\hat{a}-a^*||$, by observing their effect throughout complete trajectories.\\
For example, work such as Forward Training \cite{ross2010efficient}, and SMILe \cite{ross2010efficient} offered gradual schemes that learn different policies for each time-step. At time $t$, Forward Training will try to compensate for drifting errors by training a policy $\pi_t$ on the {\em actual} state-distribution induced by policies $\pi_0$ to $\pi_{t-1}$. SMILe, on the other hand, will repeatedly query the expert on the actual trajectories visited by the agent until convergence. However, both approaches, and counterparts of their kind were not suitable for challenging imitation setups. Tasks with a long time horizon were ruled out because of the requirement to train a policy for each time-step. Real world problems were excluded because they couldn't provide an expert-on-demand as the methods require.

\subsection{Imitation Learning and No-Regret Algorithms}
Soon after, \citet{ross2011reduction} introduced the DAgger algorithm. While similar in spirit to SMILe, DAgger operates in a slightly different manner. It proceeds by gathering experience using an agent-expert mixed behavioral policy: $$\pi_t=\beta(t)\pi^* + \big(1- \beta(t)\big)\hat{\pi_t},$$ where $\pi^*, \pi$ and $\hat{\pi}$ are the expert, behavior and agent policies respectively. The data $\mathcal{D}_{t}$ that was gathered using $\pi_t$ is aggregated together with all datasets collected up to time $t$: $\mathcal{D}_{t} = \bigcup_{\tau=0}^t \mathcal{D}_\tau$. Eventually, a policy is trained on the cumulative set $\mathcal{D}_{t}$ that is labeled with the help of the expert: $$\hat{\pi}_{t+1} = \argmin_{\pi} \sum_{s_i \in \mathcal{D}_t} \ell(\pi(s_i), \pi^*(s_i))$$
Explained differently, at each iteration, DAgger is training a policy to succeed on all trajectories seen so far. This is in contrary to most RL approaches that wish to fit fresh online data only. With this trait in mind, DAgger can be thought of as an online no-regret imitation algorithm. Moreover, it is shown by the authors that no other imitation algorithm can achieve better regret.

\subsection{Adversarial Networks and Imitation Learning}
A significant breakthrough in the field occurred at 2016 with the introduction of Generative Adversarial Imitation Learning (GAIL) \cite{ho2016generative}, an imitation algorithm closely related to the celebrated GAN architecture. GAN was originally presented as a method to learn generative models by defining a two-player zero-sum game:
\begin{equation}
\begin{split}
\label{eq:GAN}
\arg&\min_{G} \, \argmax_{D \in (0,1)} \quad  \\ &\mathbb{E}_{x \backsim p_E} [\log D(x)] +
\mathbb{E}_{z \backsim p_z} \big[ \log \big( 1-D(G(z)) \big) \big],
\end{split}
\end{equation}

where $p_z$ is some noise distribution, player $G$ represents the generative model and $D$ is the judge. GAIL showed that GAN fits imitation problems like a glove. By modifying $G: \eta \rightarrow x$ to represent a policy $\pi: s \rightarrow a$, GAIL showed how to harness GAN for imitation purposes:
\begin{equation}
\begin{split}
\label{eq:GAIL}
\arg&\min_{\pi} \, \argmax_ {D \in (0,1)} \, \\ & \mathbb{E}_{\pi}[\log D(s,a)] + \mathbb{E}_{\pi_E} [\log(1-D(s,a))] -\lambda H(\pi),
\end{split}
\end{equation}

The motivation behind GAIL, i.e., to use GAN for imitation, is to rely on a neural network to build a dynamic decision rule to classify between the expert's and the agent's state-action pairs. GAIL uses the continuous classification score as a proxy reward signal to train the player. Other than that, GAIL also proved to be efficient with respect to the number of expert examples it requires. This is partially explained by the fact that even though expert examples are scarce, the algorithm enjoys an unlimited access to agent examples through simulation. Loosely speaking, having infinitely many agent examples allow the discriminative net to gain a precise understanding of its behavior, and as a result to also understand it's differences from the expert.\\
However, while GAIL is efficient in terms of expert examples, this is clearly not the case regarding the required number of environment interactions. The high sample complexity is explained by the fact that GAIL's update rule is based on the famous REINFORCE algorithm \cite{williams1992simple}. REINFORCE offers an approximation to the true gradient, and is primarily used in situations where it is hard, or even impossible to calculate the original gradient. However, REINFORCE suffers from a high variance and is known to require numerous iterations before converging.
\subsection{Model-Based Adversarial Imitation Learning}
To compensate for this inefficiency, model-based Imitation Learning (mGAIL) \cite{baram2016model}  offered to take an opposite approach. While GAIL applies {\em gradient approximations} on samples obtained from the {\em original environment}, mGAIL applies the {\em original gradient} on samples obtained from an {\em approximated environment}.\\
Put differently, mGAIL offers a model-based alternative that attempts to learn a differentiable parametrization of the forward model (transition function). Using this approach, a multi-step interaction with the environment creates an end-to-end differentiable graph that allows to backpropagate gradients through time, thus, enabling to calculate the original gradient of the objective function. mGAIL's advantage of using the original gradient comes at the cost of learning an accurate forward model, a task that often proves to be extremely challenging. Errors in the forward model can bias the gradient up to a level where convergence is again at risk.
\subsection{The limitation of Current Approaches}
While GAIL and mGAIL complement each other, both methods amount to a standard imitation setup that requires a shared action space between the expert and the agent.
To understand why, it is enough to revisit GAIL's decision rule (Eq~\ref{eq:GAIL}).
In both methods, the rule is based on the joint distribution of states and actions.
Not only that such a decision rule is limiting the agent to operate in the expert's action domain, but it adds further optimization complications. State and actions are completely different quantities and embedding them in a joint space is not trivial.\\
In this work, we argue that the right decision rule should not be based on the joint state-action distribution $p(s,a)$, but rather on the state-transition distribution $p(s_{t-1} \rightarrow s_t)$. Using the state-transition distribution we can neutralize the presence of the agent performing the task and instead focus on imitating the effects it induces on the environment.\\
\subsection{PbRL and Adversarial Imitation Learning}
Even though adversarial-based methods have proved successful for imitation, algorithms of this type suffer from an acute problem: they induce a non-stationary MDP. The reward, which is derived from a continually-adapting classification rule, is constantly changing. As a result, estimation of long-term returns, an underlying ingredient in most RL algorithms, becomes almost infeasible \cite{nareyek2003choosing}, \cite{koulouriotis2008reinforcement}. We believe that alleviating this problem is possible using the concept of PbRL.\\
The motivation behind PbRL is to alleviate the difficulty of designing reward functions. Originally, PbRL was designed as a paradigm for learning from non-numerical feedback \cite{furnkranz2011preference}. Instead, PbRL tries to train agents using preferences between states, actions or trajectories. The goal of the agent in PbRL is to find a policy $\pi^*$ that maximally complies with a set of preferences $\zeta$. Assume two trajectories $\tau_1, \tau_2$. A preference $\tau_1 \succ \tau_2 \in \zeta$ is satisfied if: $$\tau_1 \succ \tau_2 \iff Pr_\pi (\tau_1) > Pr_\pi (\tau_2) .$$
Generally speaking, PbRL methods are divided into three categories. The first includes algorithms that directly search for policies that comply with expert preferences \cite{wilson2012bayesian}. The second consists of "model-based" approaches that rely on a preference model \cite{furnkranz2012preference}. And the third encompasses methods that try to estimate a surrogate utility $U(\tau)$ for a given trajectory \cite{akrour2011preference}. We refer the reader to \citet{wirth2017survey} for a more comprehensive survey on PbRL.

\section{Algorithm}
As of today, the prevalent approach to imitation is to take a GAN-like approach. To understand this connection better, we recall that GANs in the context of imitation, or RL in general, can be best understood as a form of an actor-critic architecture \cite{pfau2016connecting}. Therefore, in the following section, we present a family of advantage actor-critic algorithms for Inspiration learning tasks.\\
\subsection{Actor Critic Methods}
In an actor-critic algorithm, one of the prevailing approaches in reinforcement learning \cite{konda2000actor}, one maintains a separate parameterization for the policy (actor) and the state-value function (critic).
The role of the actor is straightforward- to represent the policy. The role of the critic, on the other hand, is to assess the expected performance of the policy based on experience. The critic's estimation is used as a baseline to determine whether the current behavior should be strengthened (if better than the baseline), or weakened (if worse). Numerous actor-critic variations exist \cite{vamvoudakis2010online}, \cite{bhasin2013novel}. Among the common ones is the advantage actor-critic Architecture \cite{peters2008natural}.

\subsection{Advantage Actor-Critic for Inspiration Learning}
The advantage function of a state-action pair $(s,a)$ is defined as $A(s,a) = Q(s,a)-V(s)$.
The function comprises of two parts: an action-dependent term $Q(s,a)$, and an action-independent term $V(s)$. Because of their structure, advantage functions are commonly used to score gradients in policy gradient algorithms. $Q(s,a)$ is often approximated by the return from online rollouts: $R^\pi(s,a)=\sum_{t=0}^T{\big[\gamma^t \cdot r_t | s_0=s,a_0=a, a_t \sim \pi\big]}$, while $v(s)$ is trained to predict the expected discounted return of states through regression. The approximated advantage function is given by:
\begin{equation}
\label{advantage}
A(s,a) \approx R(s,a)-V(s)
\end{equation}
However, as in any imitation challenge, the reward signal is absent. In the following, we describe how using PbRL we are able to synthesis robust classification-based rewards that can be integrated into any advantage actor-critic algorithm. We start by describing the proposed rewards.\\

\begin{itemize}
	\item \textbf{Basic scoring:} The first variant we consider score actions by considering their raw classification score. It is simply given by:
	\begin{equation}
	\label{basic_reward}
r^{BASIC}(a_t,s_t)=c_a(s_t)
\end{equation}
We note that $c_a(s_t)$ is the classification score for the $s_t\rightarrow s^a_{t+1}$ transition, where $s^a_{t+1}$ is the state following $s_t$ when choosing action $a$.

	\begin{figure*}[ht]
		\begin{subfigure}{.295\textwidth}
			\centering
			Breakout
			\includegraphics[ width=\linewidth]{Figure_9.png}
			\caption{}
		\end{subfigure}\hfill
		\begin{subfigure}{.295\textwidth}
			\centering
			Enduro
			\includegraphics[ width=\linewidth]{Figure_4.png}
			\caption{}
		\end{subfigure}
		\begin{subfigure}{.295\textwidth}
			\centering
			Seaquest
			\includegraphics[ width=\linewidth]{Figure_10.png}
			\caption{}
		\end{subfigure}\hfill
			\begin{subfigure}{.1\textwidth}
		\centering
		\includegraphics[ width=\linewidth]{legend_cropped.png}
	\end{subfigure}\hfill
		\caption{\textbf{Shared Action Imitation:} results for using our proposed method in the standard imitation framework where the expert and the agent share the same action space. We run experiments on three Atari2600 games: Breakout (a), Enduro (b) and Seaquest (c)}
		\label{fig:shared_action}
	\end{figure*}

\item \textbf{Preferential scoring:} Working with a discrimination based reward pose challenges that arise from its non-stationary nature. To facilitate this problem, we suggest applying a ranking transformation $\mathcal{T}$ on the raw classification scores. $$\mathcal{T} \circ c_a(s_t)=\frac{\big|\big\{\tilde{a} \in \mathcal{A} \,| \, c_{\tilde{a}}(s_t)\le c_a(s_t)\big\}\big|}{|\mathcal{A}|}$$
Put in words, $\mathcal{T}$ calculates action-based discrimination preferences. Doing so, we are able to discard nuance in temporary classification scores, and encourage stationarity through the course of training. The preferential reward is given by:
\begin{equation}
	\label{preferential_reward}
	r^{PRE}(a_t,s_t)=\mathcal{T} \circ c_a(s_t)
\end{equation}
\item \textbf{soft-Preferential scoring:} In cases where classification scores are approximately the same: $c_{a_i(s_t)} \approx c_{a_j(s_t)}$, a hard ranking transformation is likely to deform the  decision rule to a high degree. Relaxing the deformation can be carried out in several ways. In this paper, we choose to apply a \textit{softmax} transformation to the raw classification scores:
$$\mathcal{T}_s \circ c_a(s_t)=\frac{e^{c_a(s_t)}}{\sum_{\tilde{a}}{e^{c_{\tilde{a}}(s_t)}}} \, .$$
The soft preferential reward is therefore given by:
\begin{equation}
\label{smooth_preferential_reward}
r^{sPRE}(a_t,s_t)= \mathcal{T}_{s}\circ c_a(s_t)
\end{equation}
\end{itemize}

The algorithm we propose requires a set of expert trajectories that include states only $\tau_E=\{s_0^*,s_1^*,s_2^*,...,s_T^*\}$. At each iteration, the agent gathers experience according to its current policy. At the same time, classification scores $c_{a_t}(s_t)$ and state-value estimations $V(s_t)$ are recorded at each time step $t$. We then derive a reward function as explained above. For efficiency, the policy improvement step proceeds from end to start: an advantage function $A(a_t,s_t)$ is approximated using the returns $R(a_t,s_t)$ and the value estimations $V(s_t)$. Finally, a policy gradient step can take place: $$\theta_{\pi} \leftarrow \theta_\pi + \eta \nabla_{\theta_{\pi}} \log
\pi(a_t|s_t:s_{t-k})A(a_t,s_t)$$
The full algorithm is presented in Algorithm~\ref{algorithm}.

\begin{algorithm}[]
	\caption{\textbf{Advantage Actor-Critic for Inspiration Learning - pseudo-code for a basic reward function}}
	\begin{algorithmic}[1]
		\label{algorithm}
		\STATE \textbf{Input:} \\
		\begin{itemize}
			\item Expert trajectories $\tau_E=\{s_0^*,s_1^*,s_2^*,...,s_T^*\}$
			\item Actor parameterization $\theta_\pi$
			\item value function parameterization $\theta_v$
			\item expert-agent classifier parameterization $\theta_c$
			\item Mini-trajectory length $L$
			\item State stack parameter $k$
			\item Learning rate $\eta$
			\item Max number of steps $T_{max}$
		\end{itemize}
		\STATE \textbf{repeat}
		\STATE \-\hspace{0.5cm} Reset gradients: $d\theta_\pi \leftarrow 0, d\theta_v \leftarrow 0, d\theta_c \leftarrow 0$
		\STATE \-\hspace{0.5cm} Reset episode counter $t \leftarrow 0$
		\STATE \-\hspace{0.5cm} \textbf{repeat}
		\STATE \-\hspace{1.0cm} Sample action $a_t \sim \pi(a|s_t)$
		\STATE \-\hspace{1.0cm} Expert/agent classification $c_t = c_{a_t}(s_t)$
		\STATE \-\hspace{1.0cm} Value estimation $v_t=v(s_t)$
		\STATE \-\hspace{1.0cm} Interact $s_{t+1} = $ Env$(a_t,s_t)$
		\STATE \-\hspace{1.0cm} Update episode counter $t \leftarrow t + 1$
		\STATE \-\hspace{1.0cm} Update total step counter $T \leftarrow T + 1$
		\STATE \-\hspace{0.5cm} \textbf{while} $t<L$ or $done$
		\STATE \-\hspace{0.5cm} $
				\quad A=\begin{cases}
			             0, \quad $if reached a terminal state$\\
         			     c_{a_t}(s_t), \quad $else$\\
		\end{cases}$
		\STATE \-\hspace{0.5cm} \textbf{for} $i=t$ \textbf{to} $0$ \textbf{do}
		\STATE \-\hspace{1.0cm} $A \leftarrow c_{a_t}(s_t) + \gamma A$
		\STATE \-\hspace{1.0cm} $\theta_\pi: d\theta_\pi \leftarrow  d\theta_{\pi} + \eta  \frac{\partial}{\partial \theta_{\pi}} \log \pi(a_i|s_t) \, A$
		\STATE \-\hspace{1.0cm} $\theta_v: d\theta_v \leftarrow d\theta_v - \eta\frac{\partial}{\partial \theta_v} \big[A-v(s_t;\theta_v)\big]^2$
		\STATE \-\hspace{1.0cm} $\theta_c: d\theta_c \leftarrow d\theta_c - \eta\frac{\partial}{\partial c} \log \big[ c_{a_t}(s_t;\theta_c) \big]$
		\STATE \-\hspace{1.0cm} $\theta_c: d\theta_c \leftarrow d\theta_c - \eta\frac{\partial}{\partial c} \log \big[1-c_{a^*}(s_t^*;\theta_c) \big]$
		\STATE \textbf{while} $T<T_{max}$
	\end{algorithmic}
\end{algorithm}

\section{Empirical Evaluation}
In this section, we assess the performance of our actor-critic algorithms in action. We used a standard shared parameterization for the three components of our algorithm: the actor, the state-value function and the expert-agent classifier. Our implementation uses a convolutional neural network with two layers of 64 convolution channels each (with rectified linear units in between), followed by a 512-wide fully connected layer (with rectified linear unit activation), and three output layers for $\pi$, $v$ and $c$. Unless otherwise mentioned, states include a stack of last $k=4$ frames, and experts were trained using vanilla advantage-actor-critic algorithm \cite{1606.01540}. We use $N=\{1,5,10,20\}$ expert trajectories across all experiments\footnote{All experiments were conducted on a single GPU machine with a single actor.}.
\subsection{Shared Actions Imitation: }
The purpose of the first set of experiments is to test our algorithm in the familiar imitation learning setup: an expert and an agent that share the same action space. We recall that only expert states are recorded, and the agent has no access to any ground truth actions whatsoever. We tested three Atari2600 games: Breakout, Enduro and Seaquest. Results for this section are shown in Figure~\ref{fig:shared_action}.

\subsection{Continuous to Discrete Imitation: }
In the second set of experiments, we wish to test our method in a setup where the expert is using a continuous action space and the agent uses a discrete one. To test this setup we use the following two environments:
\subsubsection{Roundabout Merging Problem:}
A car is merging into a roundabout that contains two types of drivers: aggressive ones that do not give right of way when a new car wishes to enter the roundabout, and courteous drivers that will let the new car merge in. The car is positively rewarded after a successful merge and negatively rewarded otherwise (we note that success is defined over a set of considerations including efficiency, safety, comfort etc.). We use a rule-based logic for the expert in this case. The expert is responsible to output two continuous signals: 1) a one-dimensional steering command $a_{steer} \in \mathbb{R}$ and 2) a one-dimensional acceleration command $a_{accel} \in \mathbb{R}$. The agent, on the other hand, is allowed to choose from a discrete set of six commands indicating longitudinal $dx$ and transverse $dz$ shifts from its current position $x_t$. Results are shown in Figure~\ref{fig:cont2disc}.
\subsubsection{Hopper-v2: }
A continuous control task modeled by the MuJoCo physics simulator \cite{todorov2012mujoco}. The expert uses a continuous action space to balance a multiple degrees of freedom monoped to keep it from falling. We use the Trust Region Policy Optimization \cite{schulman2015trust} algorithm to train the expert policy. We present the agent with the expert demonstrations and restrict him to use a set of 7 actions only (obtained by applying K-means clustering \cite{Agarwal:2004:KMP:1055558.1055581} on the expert actions). Results for this experiment are presented in Figure~\ref{fig:cont2disc}.
\begin{figure*}
	\begin{subfigure}{.48\textwidth}
		\centering
		Roundabout
		\includegraphics[ width=\linewidth]{Figure_11.png}
		\caption{}
	\end{subfigure}\hfill
	\begin{subfigure}{.48\textwidth}
		\centering
		Hopper-v2
		\includegraphics[ width=\linewidth]{Figure_3.png}
		\caption{}
	\end{subfigure}
	\caption{\textbf{Continuous to Discrete Imitation:} results for using our method to imitate a continuous teacher using a discrete agent. For both tasks, we extracted a set of 7 actions by clustering the set of expert actions. On the left, we can see how such a discrete imitator can learn competent policy for the roundabout merging task. On the right, we see results on the MuJoCo benchmark.}
	\label{fig:cont2disc}
\end{figure*}

\subsection{Skills to Primitives Imitation: }
In the third set of experiments, an expert is first trained to solve a task using a set of predefined skills \cite{sutton1999between}. We then trained the agent, equipped with primitive actions only, to imitate the expert. We note that in this experiment, expert skills did not include primitive actions that are not available to the agent. We tested the same Atari games (Breakout, Enduro and Seaquest). The agent was able to achieve expert performance in all games (see Figure~\ref{fig:macro2prim}).

\begin{figure*}
	\begin{subfigure}{.33\textwidth}
		\centering
		Breakout
		\includegraphics[ width=\linewidth]{Figure_2.png}
		\caption{}
	\end{subfigure}\hfill
	\begin{subfigure}{.33\textwidth}
		\centering
		Enduro
		\includegraphics[ width=\linewidth]{Figure_8.png}
		\caption{}
	\end{subfigure}
	\begin{subfigure}{.33\textwidth}
		\centering
		Seaquest
		\includegraphics[ width=\linewidth]{Figure_6.png}
		\caption{}
	\end{subfigure}\hfill
	\caption{\textbf{Skills to Primitives imitation:} results for using our method to train a low-level agent (that uses primitive actions) to imitate a high-level teacher (that plans using skills). In this experiment, we trained the expert using a set of handcrafted skills. Afterward, we used our algorithm to train an agent that is allowed to use primitive actions only. Not surprisingly, we obtain similar performance as in the shared-action space setup, since the agent is agnostic to the structure of the expert's policy and is only exposed to state-transitions.}
	\label{fig:macro2prim}
\end{figure*}

\subsection{Primitives to Skills Imitation:}
In the last set of experiments, an expert is trained using primitive actions only, while the agent is equipped with skills. As before, the skills did not include primitive actions not available to the expert. As before, we used predefined skills. We note that using our algorithm to learn the options themselves can prove to be a useful method to extract expert skills from demonstrations. However, this is out of the scope of this work and is a subject of further research. Results for this section are provided in Figure~\ref{fig:prim2macro}).
\begin{figure*}
	\begin{subfigure}{.48\textwidth}
		\centering
		Breakout
		\includegraphics[ width=\linewidth]{Figure_5.png}
		\caption{}
	\end{subfigure}\hfill
	\begin{subfigure}{.48\textwidth}
		\centering
		Enduro
		\includegraphics[ width=\linewidth]{Figure_1.png}
		\caption{}
	\end{subfigure}
	\caption{\textbf{Primitives to Skills imitation:} results for using our method to train a hierarchical agent using demonstrations from a flat expert. On the contrary to the reversed setup (skills to primitives), we observe a slightly worse performance with respect to the shared-action space setup. We attribute this degradation to a model-mismatching between the handcrafted skills and the ground truth actions.}
	\label{fig:prim2macro}
\end{figure*}

\section{Conclusion}
In this work, we show how imitation between differently acting agents is possible.
Our novelty lies in the observation that imitation is attained when two agents induce the same effect on the environment and not necessarily when they share the same policy.
We accompany our observation with a family of actor-critic algorithms. An actor – to represent the training agent. A critic – to a) classify state transitions into two classes (agent/expert), from which a reward signal is derived to guide the actor, and b) learn the state-value function for bias reduction purposes. \\
We provide results for various types of imitation setups including shared action space imitation, continuous to discrete imitation and primitive to macro imitation. Some of the results are surprising. For example the ability to distill a continuous action space policy using discrete sets (we show examples where $|\mathcal{A}_{agent}|=7$). However, some of the results are less intriguing. For instance, the ability to decompose a macro level policy into a primitive-level one is almost trivial in our case.
The critic is oblivious to the performing agent and is only concerned with the induced effect on the environment. Thus, knowledge transfer between agents that operate in different action domains is possible.
\subsection{The Importance of a Shared Parametrization}
We have also experimented with separate parametrization architectures where the agent-expert classifier is modeled by a completely separate neural network (in oppose to a shared parametrization architecture, where a single neural network is used to output all three signals). We found that shared parametrization produces significantly better results. We hypothesize that a shared parametrization is important because the same features that are used to embed the expert (for the expert-agent classification task), are also used to sample actions from.
\subsection{Where Do We Go From Here?}
Our method allows agents to find new strategies (besides the expert's one) to solve a task. If this is the case, then it's fair to consider agents (or strategies) that do not entirely cover the expert's state distribution, but perhaps just a vital subset of states that are needed to reach the end goal. We believe that this is an interesting direction for further research.\\
The flexible nature of our framework that allows imitating an expert in various strategies, can be used to obtain super-expert performance. By super we mean, policies that are safer than the expert, more efficient, robust and so on. Our method can be integrated as a building block in an evolutionary algorithm that can help evolve robots that are optimal for specific imitation tasks.\\
A typical setup of our method requires two ingredients: a) expert examples (e.g. video recordings) and b) a simulator to train the agent. Although not tested, we require the expert states and the simulator states to be the same (i.e., to be generated by the same source). We speculate that this is crucial in order to prevent the critic from performing vain classification that is based on state appearance and not on the state dynamic, as we would like it to be. We hold that this limitation can be removed to allow imitation between different state distributions. Such an improvement can be carried out for example by redesigning the critic to solve two separate tasks: 1) appearance classification and 2) dynamic classification, and deriving the reward from the latter. This improvement is out of the scope of this paper and will be explored in a subsequent work.

redpajama_set_name: RedPajamaArXiv


She was studing singing at the Bratislava Conservatory in the class of Ivica Neshybova where she absolved in 2006 and in 2008 she finished operetta extension. She started playing clarinet in 2006! She was studing at the Prague Conservatoire in the class of Ludmila Peterkova and in Bratislava in the class of Matej Kozub. She absolved in 2010. She had concerts in Czech Republic, Slovakia, Germany. She is working active with kids and teens, cooperates with singing choir and she is still singing solo. She is teacher in Music basic school Biskupska in Prague. She was the first iniciator founding the Cantarina quartet. She is a leader of organisation of rehearsal and sheets archive management. She is always on time at rehearsal and concerts despite of she has no diary! She is confused about left and right side, which can be a problem when quartet is coming to the stage, where she occupies the stands of other members!
Laureate of the Prague Spring 2008 comes from Prague. She studied Music at the Jan Neruda Grammar School (J. Krejci), at the Prague Conservatory (L. Peterkova, M. Polak) and at the Academy of Performing Arts in Prague (J. Hlavac, V. Mares). She is studying postgraduate studies. She has a wide range of experience in the field of Music, i.e. internship at the Real Conservatorio Superior de Musica de Madrid, studies in Boston, prizes of competitions (Brescia in Italy, Czech Republic, Leos Janacek). She was solo playing with Prague Philharmonic Orchestra, Prague Chamber Orchestra, Czech Chamber Orchestra, Philharmonic Orchestra Hradec Kralove, South Bohemian Philharmonic, Teplice Philharmonic. She is regularly recording for Czech Radio. She is a member of the Philharmonic Orchestra Hradec Kralove since 2006 and she was an intern Prague Philharmonia in years 2009 - 2011. More information on www.janalahodna.cz. She became a member of CC after a challenging audition, which took place in the cafeteria.
She absolved in the class of Lubomir Barton in Brno, but she is from Olomouc. She was participants of several competitions (Concertino Praga, 1. price in Competition of Conservatories of CR, Stipendium of Yamaha competition, ISA competition of 2. Wiennese school). She was solo playing with Orchestra of conservatory Brno, with Moravia Philharmony Olomouc and with Philharmony Brno. She is intensively devoted to present czech music and occasionally hosted with Berg orchestra. She was an attendee of several courses in Czech and even in abroad (Žirovnice, Internationale Sommer Akademie Prague/Wien/Budapest, Santander in Spain). She doesn't know anything about wine because she is from north moravia. She is building slowly and surely solo clarinet player carrier and also she is teaching as private teacher of music, but no men.
She was atteding Conservatory in Ceske Budejovice in class of Jan Dafcik and come from Chodova Plana, small city in wild czech west. She played solo twice with Chamber Philharmonic Orchestra of South Bohemia, regularly hosted in West Bohemian Symphony Orchestra of Marianske lazne, Orchestra of Frantiskovy lazne and Pilsen Philharmony, occasionally with Berg Orchestra. She was teaching clarinet in "Ti nejlepsi" course in Klatovy 2010 and 2012. She is playing mostly bass clarinet in quartet. She is intensively devoted to interpretation of present clarinet, bass clarinet and saxophone music. Most her energy she put into wide range music and unmusical activities, which include her leadership in brass music Chodovarka, pedagogic activity and arranging compositions for brass band and also for clarinet quartet. She is guide in one brewery. She knows perfectly beer production.


text:
Fan Club Game Abyss FlatOut: Ultimate Carnage
FlatOut: Ultimate Carnage is the third game in the FlatOut demolition racing series, known earlier as FlatOut: Total Carnage. FlatOut: Ultimate Carnage was officially announced February 1, 2007, and was released on July 22, 2007 in Europe, on August 1, 2007 in Australia, and on October 2, 2007 in North America for the Xbox 360. The game is an enhanced port of FlatOut 2 featuring new gameplay modes and graphics as well as at least two new cars. A demo for the Xbox 360 version was released on Xbox Live Marketplace on July 19, 2007. The Microsoft Windows version was released through the Steam network on August 26, 2008, and then in game stores on September 2. There is also a handheld version of the game for the PlayStation Portable called FlatOut: Head On, which was released in Australia on March 12, 2008, in Europe two days later, and in North America on April 4, 2008.[1]
Platforms: PC Microsoft Xbox 360 Xbox 360
Franchise: FlatOut
ESRB: T - Teen
Players: 4+
Part of the FlatOut Fan Club
FlatOut Fan Club 6 Wallpapers 13 Images 5 Games
Ramsal1122 Has Played
killer995 Wants To Play
Next Car Game: Wreckfest
Turbo Dismount™
Burnout 3: Takedown


Atypical is focused on a character named Sam, who is on the spectrum. The show is about his relationships and general day-to-day stuff. It shows him having a bad day, and how good some days can be.
One of the biggest focuses is on romantic relationships. The series starts with him talking to his therapist about things he will never be able to do, specifically dating. She explains that many people on the spectrum date, he just has to find the right person.
As the series continues, Sam goes through something that many of us dealt with in high school; dating and breaking up. He gets relationship advice from his friend at work, his dad, and his therapist, and it's quite the bundle of knowledge.
I really enjoyed watching Atypical. I think there was a relatively good amount of realism in it; they show Sam's coping methods and reactions to things that may seem insignificant, and they show Sam having a very bad day. My favorite thing, though, is the relationship between Sam and his sister, because she treats him like a brother. She picks on him and makes fun of him, and it's a great dynamic.
Of course there are focuses that I don't appreciate nearly as much (Sam's mom is not great), but I'll let you experience that nonsense for yourself. I enjoyed Atypical quite a bit, and if you want to check it out, it's on Netflix.


text:
Barnsley transfer rumours: Birmingham City battle West Brom for Tykes man, Sheffield Wednesday keen on Stoke City defender
Where Leeds United are tipped to finish in 21/22 Premier League table compared to Newcastle United, Everton and Aston Villa
CHRIS WILDER: Has been appointed Middlesbrough manager following Neil Warnock's departure. Picture: Getty Images.
How Chris Wilder's winning record compares to managers of clubs above Middlesbrough in the Championship table
Chris Wilder has officially been confirmed as Middlesbrough manager following Neil Warnock's departure last weekend.
By Ben McKenna
Monday, 8th November 2021, 12:05 pm
Wilder has been out of work since leaving Sheffield United in March and revealed he had a number of offers to return to management before accepting the Boro job.
His first task at the Riverside will be to help the club climb the Championship table, with Middlesbrough currently 14th as the halfway point of the campaign approaches.
We took a look at the stats to see how Wilder's managerial record stacks up against the 13 managers in charge of the clubs currently above Boro in the table, to give an idea of what the former Blades boss is up against for the remainder of the season.
1. Scott Parker - 40.3%
The Bournemouth boss has guided his side to the top of the table with 40 points from 17 games, having previously managed Fulham.
2. Marco Silva - 51.2%
The Fulham manager's high career winning percentage is bolstered by a hugely-successful spell in charge at Olympiacos. His win ratio at Fulham is just over 68 per cent, with the club second in the Championship, scoring 44 goals in just 17 games.
3. Valérien Ismaël - 54.9%
The West Brom manager has won half of his games in all competitions since taking over at the Baggies this summer, after guiding Barnsley to the play-offs last season.
4. Mark Robins - 39.3%
Coventry City are one of the surprise packages of the Championship season, sitting fourth after 17 fixtures. Manager Mark Robins is in his second spell with the club.
Chris WilderMiddlesbroughNeil WarnockSheffield UnitedBlades


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text:
Tiësto & Bobby Puma - Making Me Dizzy
Music Singles
Coming together on an energy infused scorcher, Tiësto and Bobby Puma join forces for "Making Me Dizzy," out now via Musical Freedom Records. With high energy vocals leading the way, the track pays homage to classic house influences while maintaining a modern sound that stays true to both artist's styles. Infused with whirling instrumentals, and spinning synths "Making Me Dizzy" lives up to its' name, packing a punch from start to finish.
As one of the top dance music acts in the world, Grammy award winning icon, Tiësto, needs no introduction. Revered for his live shows as well as for his production and remix work, Tiësto is a globally celebrated DJ that has been named "The Greatest DJ of All Time" by Mixmag and the #1 DJ by Rolling Stone. With a remarkable career spanning over 20 years, Tiësto's versatility has become part of his calling card. In the past year his album, A Town Called Paradise, yielded 2 gold singles "Red Lights" and "Wasted," he won a Grammy for his remix of John Legend's "All of Me," and he hosted the online video series, "Your Shot USA." In recent months, he received the Edison Pop Oeuvre Lifetime Achievement Award, released the chart topping single "The Right Song" with Oliver Heldens feat. Natalie La Rose, and launched his new top rated music app. The app is now the only place fans can tune into the 2nd hour of his weekly Club Life podcasts.
Bay area native, Bobby Puma, has been one of the top producers for the past five years and one of the most underrated. Until recently, he has been so inundated with projects he did not have a chance to showcase his DJ abilities. In the spring of 2015, he finally got the opportunity. Bobby headlined a 29-date North American tour where he unleashed an arsenal of great club tracks. A frequenter in the Beatport charts, Puma's top rankings include his breakout hit "Jump," his first collaboration with Chuckie "Mainstage," as well as two releases on Doorn Records "Rockin' with the Best" and "Manifesto." No stranger to his work, Tiësto put his own signature touch on Puma's track "Someone Somewhere." Having already released "Deeper Than Love" on Armada Music earlier this year, Puma is just revving up to make a big leap in 2016.


When they die, small plants or animals return to forest floor to decay, or provide a meal for other wildlife. Because trees consist of cellulose and lignins that most animals can't digest, they decay more slowly. About the only living things that can digest wood are termites and some fungi. But even termites can't digest wood by themselves. They have special micro-organisms (protozoa) in their gut, which break down the cellulose.
improve the soil by adding organic matter.
retain moisture for the soil during dry periods.
provide a seed bed for regenerating trees.
provide a site for nitrogen-fixing bacteria (add nitrogen to soil).
escape routes for small animals fleeing from fire.
Even dead wood that falls into a stream continues to perform an ecological role. It provides habitat and a source of food for aquatic animals and plants. It controls current velocities and helps prevent scouring of the streambed. It stabilizes stream banks, and provides waterfalls and pools important for fish feeding and spawning.
Removing coarse woody debris and snags from Whitemud Creek Ravine interrupts the energy and nutrient cycles. Ultimately, the growth, productivity and diversity of the forest would start to decline as a result.


text:
CREATE TABLE encodeBuPromHeart (
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    chromStart int unsigned not null,	# Start position in chromosome
    chromEnd int unsigned not null,	# End position in chromosome
    name varchar(255) not null,	# Name of item
    score int unsigned not null,	# Score from 0-1000
    strand char(1) not null,	# + or -
    thickStart int unsigned not null,	# Start of where display should be thick (start codon)
    thickEnd int unsigned not null,	# End of where display should be thick (stop codon)
    reserved int unsigned not null,	# Used as itemRgb as of 2004-11-22
    blockCount int not null,	# Number of blocks
    blockSizes longblob not null,	# Comma separated list of block sizes
    chromStarts longblob not null,	# Start positions relative to chromStart
    scoreValue float not null, 	# the value associated with the score column
              #Indices
    index name (name),
    index chromStart (chrom, chromStart)
);


text:
Reporter says boxer Kubrat Pulev's mid-interview kiss on the lips was 'embarrassing' and 'strange'
Bulgarian boxer Kubrat Pulev kissed reporter Jenny SuShe while she interviewed him following his fight on Saturday, March 23, 2019.
Bulgarian boxer Kubrat Pulev is being slammed after video of him kissing a reporter on the lips during a post-bout interview Saturday night went viral, an eyebrow-raising moment the reporter called "embarrassing" and "strange."
Vegas Sports Daily reporter Jenny SuShe interviewed Pulev after his 27th victory in a fight against Bogdan Dinu in Las Vegas. Sushe was concluding the interview by asking Pulev if he believed he should "have a shot" at fighting British boxer Tyson Fury, when suddenly things went off the rails.
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"Yes," Pulev responded before — seemingly out of the blue — giving SuShe a kiss.
"All right!" SuShe responded, appearing surprised.
Later, the reporter was asked about the kissing incident on Twitter.
"Lil embarrassing. Strange," she tweeted.
Vegas Sports Daily released a statement regarding the incident, saying the site did not "condone the behavior of Kubrat Pulev."
"Following the match between Kubrat Pulev and Bodgan Dinu, one of our contributors, Jenny SuShe was the victim of an unwanted, unexpected and unsolicited forceful kiss by Kubrat Pulev as she was attempting to conduct an interview with him following the match," the statement said. "What happened to Ms. Sushe was completely blindsiding and unwarranted and we share in her shock, hurt, embarrassment and general outrage."
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Vegas Sports Daily said it was investigating the incident and added: "We want to make it clear that women should feel safe and comfortable to exercise the duties of their job, free of abuse, advances, harassment, etc in the workplace."
Pulev also responded, tweeting a statement about "the most commented kiss in the world!"
"The reporter, Jenny, is actually a friend of mine, and after the interview, I was so elated, I gave her a kiss," the statement read. "Later that night, she joined me and my other friends at my post-fight celebration. On the video, after our kiss, we both laughed about it and thanked each other. There really is nothing more to this."
This is not the first time a female reporter has been kissed by an athlete during an interview. In 2017, Maxime Hamou, a French tennis player, was barred from the French Open after he kissed Eurosport reporter Maly Thomas during an interview. The reporter later called the incident "frankly unpleasant."
"If I hadn't been live on air, I would have punched him," she told the Huffington Post France.
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