prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: How many underclassmen are on the NBA Draft Early-Entry List?

Read the following text and answer briefly.

Context 1: The Official 2006 NBA Draft Early-Entry List  30 International Players, 62 underclassmen and one lone 5th year high school player make up this year's list, for a total of 93 early-entrants  Extensive commentary and early draft status projections are included   For comparison, in 2005, 108 players declared (61 NCAA, 12 high school, 35 internationals), up from 94 in 2004, and 73 in 2003 
There were no major surprises on the early-entry list, besides a few mid-major, Division II and NAIA players that decided to enter, as well as 5th year high school player Clarence Holloway  Amongst the International players, Hrvoje Peric, Renaldas Seibutis, and Kyrylo Fesenko are considered mild surprises who could end up cracking the 2nd round  NCAA Lottery prospects Joakim Noah, Al Horford, Corey Brewer, Josh McRoberts, Brandon Rush and Tyler Hansbrough decided to sit this one out as expected, as did Marco Belinelli, Uros Tripkovic and Goran Dragic on the international front 
Akbar Abdul-Ahad 6-0, PG, Idaho State Junior No Undrafted Averaged under 6 points in 20 minutes per game playing in the Big Sky  Being the first player on the NBA Draft Early-Entry list will likely go down as the highlight of his basketball career 
Arron Afflalo, 6-5, SG, UCLA Sophomore No Undrafted Afflalo initially told the LA media hes returning to school, but after a deep run in the NCAA tournament-- more in spite of his play than because of it--Afflalo will be testing the waters  Afflalo has very average size, athleticism, perimeter shooting and ball-handling skills  Hes clearly receiving bad advice on where his stock lies 
LaMarcus Aldridge, 6-11, PF/C, Texas Sophomore Yes Top 5 pick Aldridge made his announcement official to enter the draft some weeks ago  He will hire an agent soon (Arn Tellem?) and is considered a lock for the top 5 and a strong candidate for #1 overall 
Morris Almond, 6-6, SG, Rice Junior No ??? Almond announced hell be entering the draft, without an agent  He might be the best scorer in the NCAA youve never heard about  His stats are terrific, despite being the sole focal point of opposing defenses, and hes capable of scoring in a variety of ways, particularly with his jumper  Hes hoping for an invite to Orlando 
Renaldo Balkman, 6-8, PF, South Carolina Junior No Undrafted After winning the NIT MVP award, Balkman has decided to see where he stands in the eyes of the NBA by testing the waters  Hes likely to find them downright freezing, as hes a skinny and undersized power forward with little to no skills who came off the bench for a very average team 
Larry Blair,6-1, SG, Liberty Junior No Undrafted The 22 point per game scorer Blair is attempting to get some exposure for himself by testing the waters 
Will Blalock, Iowa State, 5-11, PG, Junior No Second round pick? Declared for the draft together with Curtis_Stinson after Iowa States coach was fired  Size is a big question mark  Will likely hope to attend the pre-draft camp in Orlando and try to show scouts hes a 1st rounder  Likely returns for his senior year 
Jahsha Bluntt, 6-6, SG, Deleware State Junior No Undrafted Puts up fairly average numbers (14 6 ppg, 41% FG) in one of the worst conferences in America  Looking for exposure at the Orlando pre-draft camp but its highly unlikely to receive it 
Josh Boone, 6-10, PF/C, UConn Junior No First round pick? Boone announced hell be entering the draft without an agent  An up and down season has left his stock in the air, and will likely force him to prove himself at the Orlando pre-draft camp  Would greatly benefit from a productive senior season as an offensive focal point now that UConn has lost almost all of its firepower from last year 
Ronnie Brewer, 6-6, PG/SG, Arkansas Junior No Lottery pick? After initially wavering a bit on his decision, Brewer announced hell be entering the draft without an agent in a press conference  Brewer is considered a likely late lottery pick to mid-first rounder pick, as his physical attributes and array of versatile skills on both ends of the floor are highly sought after 
Bobby Brown, 6-1, PG, Cal-State Fullerton Junior No First round pick? DraftExpress exclusively reported that Brown will be testing the waters  Still considered a bit of a sleeper because of the school he plays for, he will not be hiring an agent at this point  Some scouts are very high on his quickness and perimeter shooting ability and feel he will help his stock tremendously in private workouts 
Shannon Brown, 6-4, SG, Michigan State Junior No First round pick? As exclusively reported by DraftExpress, Brown will be testing the waters  He will likely conduct a number of workouts and attend the Orlando pre-draft camp to attempt and gauge where his stock lies  Scouts compare him to Celtic guard Tony Allen, but with a better attitude  Hes a very borderline first rounder in a draft that is stacked with shooting guards 
Derek Burditt, 6-7, SG, Blinn Junior College Sophomore No Undrafted Unknown Junior College prospect  Not ranked as one of the top 25 JUCO players in the country, averaged around 17 points per game  Not burning his draft card as hes not yet an NCAA player, so really doesnt have much to lose, or gain 
Leroy Dawson, 6-2, SG, Emporia State Junior No Undrafted Anonymous Division II player from the MIAA conference  2nd team all conference, averaged 20 points per game  Like MANY on this list, only declaring because he can and has nothing to lose 
Travis DeGroot, 6-4, SG, Delta State Junior No Undrafted Plays in a strong Division II conference, but is at best only the 3rd best prospect on his own team after Jasper Johnson and Jeremy Richardson, and is therefore not a prospect at all 
Guillermo Diaz, 6-2, PG/SG, Miami Junior Yes First round pick? As reported by DraftExpress all year long, Diaz decided to forgo his senior year of college by hiring an agent, Miami based Jason Levien  One of the top athletes and shooters in the draft, which makes for an intriguing combination 
Cem Dinc, 6-10, SF/PF, Indiana Freshman No Undrafted As exclusively reported by DraftExpress, Dinc will be testing the waters  The coach that recruited him and then never played him, Mike Davis, resigned, so it would not shock anyone to see Dinc return to play in Europe and become automatically eligible next year after pulling out of this years draft 
Quincy Douby, 6-3, PG/SG, Rutgers Junior No First round pick As exclusively reported by DraftExpress, Douby sent out his paperwork to enter the draft  NBA scouts are all over the board on him, with some saying they consider him a 2nd round pick and others saying they would not be surprised if he ended up in the lottery  Terrific shooter and shot creator, averaged 28 ppg in the Big East conference  A real sleeper who will likely play in Orlando 
Mike Efevberha, 6-5, SG, Cal State Northridge Junior ??? Undrafted Ramona Shelburne of the LA Daily News reported that Efevberha will be testing the waters  Efevberha was the leading scorer in the country until he had a falling out with his coach and saw his playing time reduced significantly  Hell likely be looking for an invite to the Orlando pre-draft camp, and does not appear to be likely to head back to school 
Carl Elliot, 6-4, PG, George Washington Junior No Undrafted Elliot is using his use it or lose it draft card as a junior to get some exposure for himself through workouts and try to figure out where he stands in the eyes of the NBA  Elliot has excellent size for the PG position, but is still lacking plenty of all-around polish  His senior year will be essential to his development as a player  Reportedly has a family to support, which makes his decision tough considering how old he is already, despite only being a junior 
Jordan Farmar, 6-2, PG, UCLA Sophomore No First round pick? Farmar was the engine that led his team to the Finals of the NCAA tournament, and the only player that showed up once they got there  He is one of the top playmakers in the country, a Steve Nash type point guard, but his average athleticism, defense and outside shooting means hes only a bubble first-rounder  DraftExpress has been on his bandwagon since day one at UCLA, but is the NBA on it too?
Nick Fazekas, 6-11, PF, Nevada Junior No First round pick? Fazekas announced hell be entering the draft without an agent and will likely return to Nevada if it looks like hes not going to be a first round pick  If hes not a first rounder this year, its hard to imagine him ever being one since there isnt much left for him to accomplish individually in the NCAA  An interesting candidate for the pre-draft camp in Orlando 
Thomas Gardner, 6-5, SG, Missouri Junior No Second round pick? The St  Louis Post Dispatch reported that Gardner will enter the draft  Firing of underachieving Missouri coach Quin Snyder appeared to be the straw the broke the camels back  Gardner will have to hope to get invited to Orlando, but moving into the first round appears unlikely without an incredible performance there 
Rudy Gay, 6-8, SF, UConn Sophomore Yes Top 10 pick Gay announced hes leaving UConn at a press conference on campus, with Coach Calhoun by his side  He will hire an agent eventually  Size, length, incredible talent and athleticism means he might have the most upside of any player in this draft  Does he have the fire to capitalize on it though?
Reggie George, 6-10, PF, Robert Morris Chicago (NAIA) Junior No Undrafted Transfer from Iowa State had a nice season in the NAIA and is looking to capitalize on it by gaining some exposure for himself 
Daniel Gibson, 6-2, PG/SG, Texas Sophomore ??? Second round pick As exclusively reported by DraftExpress, Gibson will be entering the draft  There appears to be a conflict between Gibson and Texas regarding what his role will be next year, specifically whether or not hell be playing the point, meaning its unclear whether or not hell be returning  Gibson will likely go to Orlando to help him decide what his next step is  Showing off some PG skills will be essential there 
Aaron Gray, 7-0, Center, Pitt Junior No First round pick? After a disappointing end to his season, being outplayed by Patrick OBryant in the NCAA tournament, Gray has put that behind him and entered his name in the draft without an agent  Hes yet another underclassmen with huge questions marks about his pro potential that will likely have to go to the Orlando pre-draft camp to show he is worthy of a first round pick  Made some great strides this year, but still has a ways to go, especially conditioning-wise 
LeShawn Hammett 6-0, PG, St  Francis Junior No Undrafted Undersized combo guard played only 7 minutes in the mighty Northeast Conference before being suspended indefinitely for conduct detrimental to team  The NBA is clearly the only goal left for him to achieve 
Brandon Heath, 6-3, PG/SG, San Diego State Junior No Second round pick? Streaky shooting combo guard Heath announced the he will test the NBA draft process this summer, and is hoping for an invite to the Orlando pre-draft camp  MWC player of the year; has a lot of wrinkles to his game that need to be ironed out before he can legitimately think about the NBA 
Tedric Hill, 6-10, PF, Gulf Coast Community College Sophomore Yes Undrafted Ineligible to return to school after flunking out of college once again  Has bounced around over the past few years, and received some early hype from wannabe draftniks such as Gregg Doyel (CBS-Sportsline) and Sam Smith (Chicago Tribune) who compare him to Kevin Garnett  Very athletic we're told, but has absolutely no idea how to play the game  Has no chance of being drafted without an amazing showing at the Orlando pre-draft camp 
Clarence Holloway 7-0, Center, IMG Academy (Prep School) 5th year High School No Undrafted Lone high school player in this years age-limit depleted draft  Former Louisville commit never got eligible for college and was always considered too slow and heavy to make much of an impact anyway  Reportedly lost weight and improved his grades this past year at IMG and is currently being recruited by UConn, Kansas State and Oklahoma, amongst others 
Ekene Ibekwe, 6-9, PF, Maryland Junior No Undrafted Sources told DraftExpress exclusively that Ibekwe will be testing the waters  Likely only making this move because he can, as his chances of being drafted are very low  Athletic and long, but still lacking any type of polish 
Donald Jeffers, 6-8, PF, Roxbury Community College Sophomore No Undrafted Anonymous junior college player 
Alexander Johnson, 6-9, PF, Florida State Junior Yes First round pick? Sources told DraftExpress, that Johnson will be hiring an agent, mainly because he is already 23 years old  Hes considered intriguing because of his strength, raw offensive tools and freakish athleticism at the 4 position, and could work his way into the 1st round with strong workouts 
David Johnson, 6-7, PF, Clinton Junior College Sophomore No Undrafted 6-7 JUCO power forward who averaged 2 points and 3 rebounds per game 
Trey Johnson, 6-5, SG, Jackson State Junior No Undrafted Small school prolific scorer and one of the most accurate perimeter shooters in the country will attempt to draw some more attention to himself by testing the waters this summer  Johnson is hoping for a chance to prove himself in the Orlando pre-draft camp in June 
Coby Karl, 6-4, PG/SG, Boise State Junior No Undrafted Son of Denver Nuggets head Coach George Karl put up nice numbers (17 ppg, 5 rebs, 4 assists, 39 5% 3P) in the underrated WAC conference  Had surgery in March to remove a cancerous lump from his thyroid 
Mark Konecny, 6-10, Center, Lambuth (NAIA) Junior No Undrafted Transfer from Syracuse with mediocre production is looking for any type of exposure he can get before he graduates next season 
Kyle Lowry, 6-1, PG, Villanova Sophomore No First round pick NCAA tournament performance showed that he definitely needs another year, but regardless, Lowry is in  For now its without an agent  Considering the lack of quality point guard prospects in this draft, Lowry is likely a first round pick  Says he will attend the Orlando pre-draft camp if invited 
Aleks Maric, 6-11, Center, Nebraska Sophomore No Undrafted As exclusively reported by DraftExpress, Maric will be testing the waters  What may have played a role in this is the fact that the assistant coach that recruited him at Nebraska, Scott Spinelli, just moved on to Wichita State  Maric is considered a very average athlete who is still very raw and is therefore likely to go undrafted should he decide to stay in  Thanks to his Croatian passport, there is money waiting for him overseas if he chooses to take it 
Japhet McNeil, 5-10, PG, East Carolina Junior No Undrafted Severely undersized PG averaged 4 points and 5 6 assists in watered down Conference USA 
Paul Millsap, 6-8, PF, Louisiana Tech Junior Yes First round pick? As expected, Millsap has declared his intentions to enter the NBA draft, and according to sources hired an agent as well  Millsap has likely achieved just about everything he can in college at this point, and will land somewhere in the 20-40 part of the draft depending on workouts and measurements 
Matt Mitchell, 6-0, PG, Southern University-New Orleans Junior No Undrafted Anonymous NAIA player 
Adam Morrison, 6-8, SF, Gonzaga Junior Yes Top 5 pick As DraftExpress exclusively reported that Morrison will be declaring for the draft and hiring Chicago based agent Mark Bartelstein  Morrison, the top scorer in college basketball, is expected to be a top 5 pick and potentially the #1 pick overall  Questions linger about his athleticism and defense, but no one questions his passion, talent or feel for the game 
Patrick O'Bryant, 7-0, Center, Bradley Sophomore Likely First round pick NBA sources in Portsmouth told DraftExpress exclusively that OBryant will be testing the waters without an agent, but is likely to go all the way once he hears that hes a lock for the 1st round  His steady improvement, strong sophomore season, outstanding NCAA tournament and considerable upside means hes probably gone  O'Bryant since confirmed both DraftExpresss reports, particularly the one about hiring an agent in the Tri-State area (Andy Miller) should he decide to go all the way 
Evan Patterson, 6-7, SF, Texas Wesleyan Junior No Undrafted Mediocre numbers (11 ppg, 2 rebs) in a mediocre Southland conference 
Danilo Pinnock, 6-5, SG, George Washington Junior No Undrafted The extremely athletic Pinnock has told GWs student paper hell be testing the waters  Pinnock will attempt to capitalize on his teams success this year by potentially attending the NBA pre-draft camp in Orlando  Pinnock will have to show better ball-handling and perimeter shooting ability than he did during the regular season 
Leon Powe, 6-7, PF, Cal Sophomore No Second round pick Powe announced hell be testing the waters in a statement released by Cal  Where he ends up being projected depends heavily on how his knee checks out  Powe is already considered a serious tweener by NBA scouts, and had a hard time this season gaining back much of the explosiveness he had earlier in his career  Could realistically go undrafted should he decide to stay in 
Richard Roby, 6-5, SG, Colorado Sophomore Likely Second round pick As first indicated by DraftExpress Roby has decided to test the waters  Disappeared against any major competition he went up against, particularly towards the end of the season  Roby will likely have to put on weight in the next few months and show off his perimeter stroke in the Orlando pre-draft camp  Sources tell us that he is on the verge of making a huge mistake by hiring an agent 
Rajon Rondo, 6-2, PG, Kentucky Sophomore Yes First round pick As expected, Rondo has decided to enter the NBA draft, and has also hired an agent, Bill Duffy  Despite an inconsistent sophomore season, most scouts weve spoken to still had him as at least the #2 point guard on their board because of his intriguing upside  Workouts will be huge for him 
Blake Schilb, 6-7, SG/SF, Loyola Chicago Junior No Undrafted Declared his intentions to enter the draft, without an agent, and is hoping for an invite to Orlando  Schlib is sorely lacking in the quickness and explosiveness departments that scouts demand from swingman prospects, but he makes up for it with his skill set to a certain extent  Regardless, sources tell us he wont be invited to Orlando, meaning he has to go back to school 
Mustafa Shakur, 6-4, PG, Arizona Junior No Second round pick? According to the Arizona Star, Shakur will likely enter his name in the draft, without an agent  Lute Olson confirmed it, saying he is not concerned about it  Shakur is hoping for an Orlando invite to show what he thinks he couldnt at Point Guard U 
Cedric Simmons, 6-9, PF/C, NC State Sophomore No First round pick? Simmons is reportedly "exploring his options," in regards to the 2006 NBA draft, but will do so without an agent  Nice size, frame, length, athleticism and defensive skills make him a very intriguing prospect 
Marcus Slaughter, 6-8, PF, San Diego State Junior Yes Second round pick? After burning his lone draft card a year early last June, despite being considered a marginal prospect, Slaughter has announced that he will be hiring agent Dan Fegan and forfeiting his remaining college eligibility  Slaughters father thinks that There was nothing else for Marcus to do at San Diego State  Many would disagree with that 
Curtis Stinson, 6-3, PG/SG, Iowa State Junior Yes Second round pick After swearing up and down last month that he has no intention on entering the draft, Stinson did just that  His coach Wayne Morgan, who he was very close to, was fired, resulting in him hiring agent Kevin Bradbury  The 23 year old combo guard will have to go to the Orlando pre-draft camp and impress if he wants to come close to being a 1st rounder 
Tyrus Thomas, 6-9, PF, LSU Freshman Yes Top 5 pick As DraftExpress exclusively reported Thomas called a press conference to announce his intentions to enter the 2006 NBA draft, as well as hire agents Brian Elfus and Mike Siegel  SEC Freshman of the year could be the most athletic player in the draft, as well as the player with the most overall upside 
PJ Tucker, 6-5, SF, Texas Junior No Second round pick As reported all year long by DraftExpress, Tucker will be entering the draft without an agent  Considering that hes a 6-5 combo forward with tremendous skills, his stock widely fluctuates depending on who is being asked  Phenomenal basketball player, but is severely lacking in 2-3 inches of height  Will likely need a strong showing at the Orlando pre-draft camp to have a legitimate shot at the 1st round  Some scouts compare him to Bonzi Wells 
Junior No Undrafted Undersized Division II post player has no chance of being drafted despite 20+8 averages 
Ian Vouyoukas, 6-10, Center, St  Louis Junior ??? Undrafted Vouyoukas declared his intentions to enter the draft, supposedly without an agent  Sources in Europe tell us he is likely to return to Greece to take a large contract offer from a first division team once he realizes he has no chance of being drafted  Vouyoukas is a nice mid-major big man who has improved somewhat in his junior season, but does not possess the necessary combination of athleticism and size required of an NBA center 
Darius Washington, 6-2, PG, Memphis Sophomore Likely First round pick? DraftExpress exclusively reported that Washington will be in the draft  It appears that hell be hiring an agent as well, despite not being anywhere near a lock for the first round 
Albert Weber, 6-3, SG, Connors State Sophomore No Undrafted Transfer from Alabama led his conference in scoring and is considered one of the top Junior College players in the country  Not officialy an NCAA player yet, and has not committed to any school yet, so really doesn't stand much to lose (or gain) from this move 
Marcus Williams, 6-3, PG, UConn Junior Yes Late Lottery-Mid-First As expected, Williams is expected to announce that hes hired Calvin Andrews of BDA Sports Management as his agent at a press conference next week  A strong junior season and outstanding NCAA tournament, establishing himself as one of the purest playmakers in the nation, means hes likely one of the first PGs taken 
Andriy Agafonov, 6-8, PF, Khimik 1986 Ukraine Undrafted Ukrainian power forward played 15 minutes and scored 6 points with 4 4 rebounds per game playing for FIBA EuroCup participants, and is declaring in hopes of getting his name out as he has one more draft card to burn after this before becoming automatically eligible 
Nemanja Aleksandrov, 7-0, SF/PF, KK Reflex 1987 Serbia & Montenegro ??? American agent has been telling us all year that hes likely to enter  Still hasnt played a game this year after taking slow recovery process from torn ACL  Once regarded as a prodigy and potential #1 overall pick, but injuries mean he hasnt played in nearly two years and is now considered damaged goods  Might just look for an attractive team to guarantee him in the 2nd round and develop him in the NBDL 
Pape-Philippe Amagou, 6-1, PG, Le Mans 1985 France ??? Amagous American agent has informed us that he will enter the NBA Draft this year, and participate in the Reebok Eurocamp in Treviso  Shares playmaking duties and spotlight with fellow early-entrant Yannick Bokolo 
Andrea Bargnani, 7-0, PF, Benetton Treviso 1985 Italy Top 5 pick Bargnani's Italian agent Stefano Meller told DraftExpress in Portsmouth that the Italian star power forward will definitely be entering the NBA draft  Bargnani is in the process of hiring an American agent and the only question is how long will it take for him to make it over to the US after Benetton finishes up in the Italian playoffs, which could last as far as mid-June  He is expected to be a top 5 pick with a shot at going #1 depending on how the lottery plays out  Considered a phenomenal talent thanks to his excellent size, perimeter skills and athleticism relative to height 
Yannick Bokolo, 6-3, PG/SG, Le Mans 1985 France ??? Terrific athlete who is still making the transition to playing the point full time 
Carlos Cedeno, 6-5, SG, Guaiqueries 1985 Venezuela Undrafted Relatively unknown Venezuelan player  Has some international experience at the junior levels 
Tadija Dragicevic, 6-8, PF, Red Star Belgrade 1986 Serbia & Montenegro Undrafted Undersized power forward barely played in the Adriatic League this past season 
Lior Eliyahu, 6-9, SF/PF, Galil Elyon 1985 Israel Second round pick? Prolific and athletic Israeli combo forward will be entering the NBA draft this year looking for certain guarantees from an NBA team in the 1st or 2nd round  Eliyahu is still in the Israeli army and will stay overseas for another year regardless of what happens  He'll be represented by the American agency Entersport in the United States  A midseason injury set him back from being the top Israeli player in the league despite his youth 
Rudy Fernández, 6-5, SG, DKV Joventut 1985 Spain First round pick? Has some minor buyout issues to deal with to make sure he can stay in the draft  Excellent season in Spain has him projected as a pretty solid first round pick  Improved outside shooting, and still the same excellent athlete, passer, defender and all-around player hes always been  Still very skinny too 
Kyrylo Fesenko, 6-11, PF, Azovmash 1986 Ukraine Second Round Pick More to come 
Rafael Hettsheimeir, 6-9, Center, Akasvayu Girona 1986 Brazil Undrafted Undersized Brazilian center did not overly impress at the Nike Hoop Summit, showing that he will likely lack mobility until he takes off some weight 
Marko Lekic, 6-11, PF, Atlas 1985 Serbia & Montenegro ??? American agent Marc Cornstein, Lekic told us hell be putting his name in the draft this year once again  Still a bit of an unknown, numbers are fairly average in the Serbian YUBA league 
Damir Markota, 6-11, SF/PF, Cibona Zagreb 1985 Croatia Second round pick American agent Marc Cornstein told us Markota will definitely be putting his name in the draft once again  He had a breakout season in the Euroleague and Adriatic league before a groin injury slowed him down and eventually forced him to have minor surgery  Likely wont be able to come to the States until very late in the process  Does not have a buyout 
Mickael Mokongo, 5-11, PG, Chalon 1986 France ??? DraftExpress was exclusively informed hell be in the draft  Considered a talented athlete, but lack of size and the fact that he missed a large chunk of the season due to injury means his draft stock is very much up in the air still 
Brad Newley, 6-6, SG, 1985 Australia Second round pick Newely has told the Australian media that hes entering the draft  Hired Philadelphia based agent Leon Rose  Scouts who saw him play in Argentina last summer like his athleticism  Desperately lacking exposure, but agent appears to be unwilling to provide him with it 
Oleksiy Pecherov, 6-11, PF, Racing Basket 1985 Ukraine Second round pick DraftExpress received indication that Pecherov will be entering his name in the draft after a nice 2nd half regular season in France  Pecherov has his draft card in hand one year before he becomes automatically eligible, meaning he has nothing to lose  Has some nice skills facing the basket, but is still very soft and underdeveloped 
Hrvoje Peric, 6-8, SF, KK Split 1985 Croatia Second round pick? Good athlete who is still coming into his own as a basketball player  Did not play in the Adriatic League this season  Definitely needs at least another year in Europe, but could use the exposure that declaring for the draft provides 
Kosta Perovic, 7-2, Center, Partizan 1985 Serbia & Montenegro Undrafted? DraftExpress has been told that Partizan needs Perovic to be drafted this year to relieve them of his 500,000$ salary next year as well as help them financially with buyout money for their budget  Unfortunately this is happening about 3 years too late as weve seen little to no improvement from Perovic over that span 
Georgios Printezis, 6-9, PF, Olympiakos 1985 Greece Undrafted Greek power forward played 9 minutes and scored 4 points per game playing for a Euroleague team, and is declaring in hopes of getting his name out before he becomes automatically eligible next year 
Milovan Rakovic, 6-10, PF, Atlas 1985 Serbia & Montenegro ??? American agent Marc Cornstein told us Rakovic will be putting his name in the draft  Still an unknown player, puts up nice numbers on occasion in the fairly weak Serbian YUBA league 
Alexandr Rindin, 7-5, Center, Gala Baku 1985 Azerbaijan Undrafted Huge body, complete unknown  5 points, 5 rebounds per game in FIBA Europe Cup 
Sergio Rodríguez, 6-3, PG, Estudiantes 1986 Spain First round pick Rodríguezs agent in the States told DraftExpress exclusively hell be in the draft, likely for good if he gets a commitment in the 1st round  A disappointing start to his season both in Spain and the ULEB cup made this European prodigy point guard fall on most teams draft boards, but Rodríguez picked things up substantially towards the end of the year and is now playing terrific basketball  Weak NCAA PG crop could put him in the lottery with good workouts 
Dusan Sakota, 6-10, SF/PF, Panathinaikos 1986 Greece Undrafted Fairly unathletic perimeter oriented big man was in the draft last year already  Plays for one of the best teams in Europe and rarely sees the floor for meaningful minutes 
Renaldas Seibutis 6-5, SG, Olympiakos 1985 Lithuania Undrafted One of the most productive players in Europe in his age group considering the level he plays at  Important cog on an excellent team, but lacks athleticism and isnt as good of a shooter as you would hope at this point in his career 
Saer Sene, 7-0, Center, Pepinster 1986? Senegal First round pick? Freakishly long and athletic African prospect who played extremely well at the Nike Hoop Summit  Many question his age and lack of productivity in the very average Belgian league A player teams will want to look at closely 
Sidiki Sidibe, 7-1, Center, Levallois 1985 France ??? 7-1, 265 pound volleyball player and former Kansas State commit will be in this years draft according to his American agent  Too raw to get any playing time whatsoever in French 2nd division 
Tiago Splitter, 7-0, PF/C, Tau Vitoria 1985 Brazil Lottery pick Splitters American agent Herb Rudoy told DraftExpress exclusively hes entering the draft Splitter is having a terrific season in both the ACB Spanish League and the Euroleague, but lack of buyout in his contract means he might not be able to stay in  CBA rules allow him to withdraw and become automatically eligible next season  Tau Vitorias president was quoted saying Splitter will be back in Spain next season 
Sun Yue, 6-9, PG/SF, Aoshen 1985 China Second round pick? Super talented tall point guard with decent athleticism and nice defensive skills  Lacks strength and outside shooting ability  Level of competition is mediocre in American semi-pro ABA league, which makes him an intriguing candidate for Orlando pre-draft camp 
Ali Traore, 6-9, PF, Roanne 1985 France ??? Puts up nice numbers in France  Will participate at the Reebok Eurocamp in Treviso 
Ejike Ugboaja, 6-8, PF, Union Bank Lagos 1985 Nigeria Undrafted Plays for Nigerian National Team 
Goran Dragic, 6-4, PG, Geoplin Slovan 1986 Agent initially notified us that Dragic will be entering the draft, but in the end decided to keep him out  His buyout was always a question mark 
Leigh Enobakhare, 6-10, Center, Oostende 1986 Agent Ugo Udezue from BDA Sports Management told us that Enobakhare will be entering the draft  In the end he must have heard that he is not considered a prospect at all, and decided to keep him out of the draft 
Cartier Martin, 6-8, SF/PF, Kansas State Junior Martin pondered entering his name in the draft, especially after the firing of Kansas State coach Jim Wooldridge 
Nick Young, 6-6, SG, USC Sophomore Young told the LA Daily News in February that hes staying at USC for another year 
D J  Strawberry, 6-5, SG/SF, Maryland Junior Strawberry initially intended to test the waters, but eventually ended up not doing so once he found out that his chances of being drafted are almost non-existent 
Al Thornton, 6-7, SF/PF, Florida State Sophomore Implied earlier on in the year that he might put his name in, but sources recently told us it appears that he will return for his senior year  Tallahassee media backs this up 
Marcus Williams (AZ), 6-8, SG/SF, Arizona Freshman After initially appearing to be gone after numerous definitive reports, Williams surprised everyone and thrilled Arizona fans by announcing in a press conference hell be returning for his sophomore year 
Josh McRoberts, 6-11, PF, Duke Freshman After being upset by LSU in the Sweet Sixteen, McRoberts was quoted saying Ill be at Duke next year   Duke issued a press release a month later confirming this 
Yi Jianlian, 7-0, PF, Guangdong 1987? International Jianlian announced in a press conference that hell be staying in China  A CBA official was also quoted on this matter, sounding as if they were the main factor for him staying put 
Acie Law, 6-3, PG, Texas A&M Junior After a fantastic showing in the NCAA tournament, Law helped his NBA draft stock considerably but will return for his senior year where A&M is expected to make a run at possibly winning the Big 12 
Joakim Noah, 6-11, PF/C, Florida Sophomore Huge 2nd half regular of the regular season and NCAA tournament boosted his stock into as high as the top 5  Noah came out and said afterwards hes staying regardless 
Al Horford, 6-9, PF, Florida Sophomore Horford indicated all season long that hes staying at least one more year, but playing extremely well in winning the national championship gave him a realistic chance at being a lottery pick  Regardless, Horford announced he'll return 
Corey Brewer, 6-8, SF, Florida Sophomore Brewer indicated all season long that hes staying at least one more year, but a terrific performance in the NCAA tournament gave him a realistic chance at being a top 20 pick  Regardless, Brewer announced he'll return 
Glen Davis, 6-8, Center, LSU Sophomore Davis announced hell be returning to LSU immediately after an absolutely horrendous showing in the Final Four which exposed all of his glaring weaknesses  Made it official as an LSU press conference alongside Tyrus Thomas 
Jason Smith, 7-0, PF/C, Colorado State Sophomore Smith announced that hes returning for his junior year, stating that "a little further down the road, it [the NBA] might be in my plans  I'm continuing to concentrate on my academics and see how I can help CSU as much as possible "
Jermareo Davidson, 6-10, PF, Alabama Junior > After burning his lone draft card a year early last June, Davidson considered entering the draft again, but eventually made the right decision in announcing hell be returning for his senior year 
Richard Hendrix, 6-8, PF, Alabama Freshman Told Alabama media after NCAA tournament loss that hell be back in Tuscaloosa next year 
Ja'Vance Coleman, 6-3, SG, Fresno State Junior Testing the waters according to the Fresno Bee  Whoops, no hes not 
Sean Singletary, 5-11, PG, Virginia Sophomore Singletary told The Daily Progress in early February that hes returning 
Context 3: Paper Info

Title: Age and market capitalization drive large price variations of cryptocurrencies
Publish Date: 23 Feb 2023
Author List: 

Figure

Figure 3  Illustration of different effects of age and market capitalization on power-law exponents of cryptocurrencies (a) Posterior probability distributions of the linear coefficients associated with the effects of age [p(A)] and (b) the effects of market capitalization [p(C)] on power-law exponents related to large positive returns Panels (c) and (d) show the analogous distributions for the association with power-law exponents related to large negative returns In all panels, the different curves show the distributions for each of the top 20 cryptoassets by market capitalization Cryptocurrencies significantly affected by age or market capitalization are highlighted in boldface, and the numbers between brackets show their positions in the market capitalization rank 
Figure S5 There is more probability mass in the positive tail than in the negative tail of price returns (a) Probability distributions of the lower cut-offs (r min ) obtained by applying the Clauset-Shalizi-Newman method to positive (blue) and negative (red) returns The vertical dashed lines indicate the median values of r min for positive and negative returns (b) Probability distributions of 90th percentiles (r 90 ) estimated from the power-law models adjusted to positive (blue) and negative (red) returns The vertical dashed lines indicate the median values of r 90 for positive and negative returns (c) Probability distributions of the fraction of weeks that r 90 estimated from positive returns (r + 90 ) is larger than r 90 estimated from negative returns (r − 90 ) This fraction is calculated only for weeks in which the power-law hypothesis is not rejected for both tails The percentage of cryptoassets for which r + 90 > r − 90 is shown in the panels The first column of panels depicts the results when considering data from all cryptocurrencies, while the second and third columns present the results for the top 2000 and top 200 cryptocurrencies by market capitalization, respectively 
Figure S7 Robustness of the results of Fig  2(b)-(d) against considering only cryptocurrencies with fraction of rejection f r < 0 1 Panels (a) and (b) show the same distributions of Fig  S4 but after filtering out all time series of cryptocurrencies with fraction of rejections f r ≥ 0 1 As in the case related to sampling issues, we observe that these distributions barely change when considering only cryptocurrencies with f r < 0 1 Indeed, the distributions in this figure are not significantly distinguishable from their counterparts in Fig  S4 (two-sample Kolmogorov-Smirnov test, p > 0 05) 

abstract

Cryptocurrencies are considered the latest innovation in finance with considerable impact across social, technological, and economic dimensions  This new class of financial assets has also motivated a myriad of scientific investigations focused on understanding their statistical properties, such as the distribution of price returns 
However, research so far has only considered Bitcoin or at most a few cryptocurrencies, whilst ignoring that price returns might depend on cryptocurrency age or be influenced by market capitalization  Here, we therefore present a comprehensive investigation of large price variations for more than seven thousand digital currencies and explore whether price returns change with the coming-of-age and growth of the cryptocurrency market 
We find that tail distributions of price returns follow power-law functions over the entire history of the considered cryptocurrency portfolio, with typical exponents implying the absence of characteristic scales for price variations in about half of them  Moreover, these tail distributions are asymmetric as positive returns more often display smaller exponents, indicating that large positive price variations are more likely than negative ones 
Our results further reveal that changes in the tail exponents are very often simultaneously related to cryptocurrency age and market capitalization or only to age, with only a minority of cryptoassets being affected just by market capitalization or neither of the two quantities  Lastly, we find that the trends in power-law exponents usually point to mixed directions, and that large price variations are likely to become less frequent only in about 28% of the cryptocurrencies as they age and grow in market capitalization 
Since the creation of Bitcoin in 2008 , various different cryptoassets have been developed and are now considered to be at the cutting edge of innovation in finance   These digital financial assets are vastly diverse in design characteristics and intended purposes, ranging from peer-to-peer networks with underlying cash-like digital currencies (e g 
Bitcoin) to general-purpose blockchains transacting in commodity-like digital assets (e g  Ethereum), and even to cryptoassets that intend to replicate the price of conventional assets such as the US dollar or gold (e g  Tether and Tether Gold)   With more than nine thousand cryptoassets as of 2022 , the total market value of cryptocurrencies has grown massively to a staggering $2 trillion peak in 2021  
Despite long-standing debates over the intrinsic value and legality of cryptoassets , or perhaps even precisely due to such controversies, it is undeniable that cryptocurrencies are increasingly attracting the attention of academics, investors, and central banks, around the world   Moreover, these digital assets have been at the forefront of sizable financial gains and losses in recent years , they have been recognized as the main drivers of the brand-new phenomena of cryptoart and NFTs , but also as facilitators of illegal activities, such as money laundering and dark trade  
Financial research dedicated Our results are based on daily price time series of 7111 cryptocurrencies that comprise a significant part of all currently available cryptoassets (see Methods for details)  From these price series, we have estimated their logarithmic returns 2/16 Log-return, r )  The black horizontal arrow represents a given position of the expanding time window (at t = 2004 days) used to sample the return series over the entire history of Bitcoin 
This time window expands in weekly steps (seven time series observations), and for each position, we separate the positive (blue) from the negative (red) price returns  The gray line illustrates observations that will be included in future positions of the expanding time window (t > 2004)  (b) Survival functions or the complementary cumulative distributions of positive (blue) and negative (red) price returns within the expanding time window for t = 2004 days and above the lower bound of the power-law regime estimated from the Clauset-Shalizi-Newman method  
The dashed lines show the adjusted power-law functions, p(r) ∼ r −α , with α = 4 5 for positive returns and α = 3 0 for negative returns  (c) Time series of the power-law exponents α t for the positive (blue) and negative (red) return distributions obtained by expanding the time window from the hundredth observation (t = 100) to the latest available price return of Bitcoin 
The circular markers represent the values for the window position at t = 2004 days and the dashed lines indicate the median of the power-law exponents ( α+ = 4 50 for positive returns and α− = 2 99 for negative returns)  (d) Time series of the p-values related to the power-law hypothesis of positive (blue) and negative (red) price returns for every position of the expanding time window 
The dashed line indicates the threshold (p = 0 1) above which the power-law hypothesis cannot be rejected  For Bitcoin, the power-law hypothesis is never rejected for positive returns (fraction of rejection f r = 0) and rejected in only 4% of the expanding time window positions (fraction of rejection f r = 0 04) 
where x t represents the price of a given cryptocurrency at day t  All return time series in our analysis have at least 200 observations (see Supplementary Figure for the length distribution)  Figure (a) illustrates Bitcoin's series of daily returns  To investigate whether and how returns have changed over the aging and growing processes of all cryptocurrencies, we sample all time series of log-returns using a time window that expands in weekly steps (seven time series observations), starting from the hundredth observation to the latest return observation 
In each step, we separate the positive from the negative return values and estimate their power-law behavior using the Clauset-Shalizi-Newman method   Figure (a) further illustrates this procedure, where the vertical dashed line represents a given position of the time window (t = 2004 days), the blue and red lines indicate positive and negative returns, respectively, and the gray lines show the return observations that will be included in the expanding time window in future steps 
Moreover, Fig  (b) shows the corresponding survival functions (or complementary cumulative distributions) for the positive (blue) and negative (red) returns of Bitcoin within the time window highlighted in Fig  (a)  These survival functions correspond to return values above the lower bound of the power-law regime (r min ) and dashed lines in Fig  (b) show the power-law functions adjusted to data, that is,
with α = 4 5 for the positive returns and α = 3 0 for the negative returns in this particular position of the time window (t = 2004 days)  We have further verified the goodness of the power-law fits using the approach proposed by Clauset et al  (see also Preis et al  )  As detailed in the Methods section, this approach consists in generating several synthetic samples under the power-law hypothesis, adjusting these simulated samples, and estimating the fraction of times the Kolmogorov-Smirnov distance between the adjusted power-law and the synthetic samples is larger than the value calculated from the empirical data 
This fraction defines a p-value and allows us to reject or not the power-law hypothesis of the return distributions under a given confidence level  Following Refs  we consider the more conservative 90% confidence level (instead of the more lenient and commonly used 95% confidence level), rejecting the power-law hypothesis when p-value ≤ 0 1 
For the particular examples in Fig  (b), the p-values are respectively 1 00 and 0 17 for the positive and negative returns, and thus we cannot reject the power-law hypotheses  After sampling the entire price return series, we obtain time series for the power-law exponents (α t ) associated with positive and negative returns as well as the corresponding p-values time series for each step t of the expanding time window 
These time series allow us to reconstruct the aging process of the return distributions over the entire history of each cryptoasset and probe possible time-dependent patterns  Figures ) and 1(d) show the power-law exponents and p-values time series for the case of Bitcoin  The power-law hypothesis is never rejected for positive returns and rarely rejected for negative returns (about 4% of times) 
Moreover, the power-law exponents exhibit large fluctuations at the beginning of the time series and become more stable as Bitcoin matures as a financial asset (a similar tendency as reported by Begušić et al  )  The time evolution of these exponents further shows that the asymmetry between positive and negative returns observed in Fig  ) is not an incidental feature of a particular moment in Bitcoin's history 
Indeed, the power-law exponent for positive returns is almost always larger than the exponent for negative returns, implying that large negative price returns have been more likely to occur than their positive counterparts over nearly the entire history of Bitcoin covered by our data  However, while the difference between positive and negative exponents has approached a constant value, both exponents exhibit an increasing trend, indicating that large price variations are becoming less frequent with the coming-of-age of Bitcoin 
The previous analysis motivates us to ask whether the entire cryptocurrency market behaves similarly to Bitcoin and what other common patterns digital currencies tend to follow  To start answering this question, we have considered the p-values series of all cryptocurrencies to verify if the power-law hypothesis holds in general 
Figure (a) shows the percentage of cryptoassets rejecting the power-law hypothesis in at most a given fraction of the weekly positions of the expanding time window ( f r )  Remarkably, the hypothesis that large price movements (positive or negative) follow a power-law distribution is never rejected over the entire history of about 70% of all digital currencies in our dataset 
This analysis also shows that only ≈2% of cryptocurrencies reject the power-law hypothesis in more than half of the positions of the expanding time window ( f r ≥ 0 5)  For instance, considering a 10% threshold as a criterion ( f r ≤ 0 1), we find that about 85% of cryptocurrencies have return distributions adequately modeled by power laws 
Increasing this threshold to a more lenient 20% threshold ( f r ≤ 0 2), we find large price movements to be power-law distributed for about 91% of cryptocurrencies  These results thus provide strong evidence that cryptoassets, fairly generally, present large price movements quite well described by power-law distributions 
Moreover, this conclusion is robust when starting the expanding window with a greater   Large price movements are power-law distributed over the entire history of most cryptocurrencies with median values typically smaller than those found for traditional assets  (a) Percentage of cryptoassets rejecting the power-law hypothesis for large positive (blue) or negative (red) price returns in at most a given fraction of the weekly positions of the expanding time window ( f r ) used to sample the return series 
Remarkably, 68% of all 7111 digital currencies are compatible with the power-law hypothesis over their entire history, and about 91% of them reject the power-law hypothesis in less than 20% of the positions of the expanding time window ( f r ≤ 0 2)  (b) Probability distributions obtained via kernel density estimation of the median values of the power-law exponents along the history of each digital currency 
The blue curve shows the distribution of the median exponents related to positive returns ( α+ ) and the red curve does the same for negative returns ( α− )  The medians of α+ and α− are indicated by vertical dashed lines  Panels (c) and (d) show the distributions of these median exponents when considering the top 2000 and the top 200 cryptocurrencies by market capitalization, respectively 
We observe that the distributions of α+ and α− tend to shift toward larger values when considering the largest cryptoassets  number of return observations (between 100 and 300 days) and filtering out cryptoassets with missing observations (Supplementary Figures )  Still, it is worth noticing the existence of a few cryptoassets (9 of them) with relatively small market capitalization (ranking below the top 1000) for which the power-law hypothesis is always rejected (Supplementary Table ) 
Having verified that large price movements in the cryptocurrency market are generally well-described by powerlaw distributions, we now focus on the power-law exponents that typically characterize each cryptoasset  To do so, we select all exponent estimates over the entire history of each digital asset for which the power-law hypothesis is not rejected and calculate their median values for both the positive ( α+ ) and negative ( α− ) returns 
The dashed lines in Fig  ) show these median values for Bitcoin where α+ = 4 50 and α− = 2 99  It is worth noticing that the variance of large price movements σ 2 is finite only for α > 3, as the integral σ 2 ∼ ∞ r min r 2 p(r)dr diverges outside this interval  Thus, while the typical variance of large positive returns is finite for Bitcoin, negative returns are at the limit of not having a typical scale and are thus susceptible to much larger variations 
Figure shows the probability distribution for the median power-law exponents of all cryptoassets grouped by large positive and negative returns  We note that the distribution of typical power-law exponents associated with large positive returns is shifted to smaller values when compared with the distribution of exponents related to large negative returns 
The medians of these typical exponents are respectively 2 78 and 3 11 for positive and negative returns  This result suggests that the asymmetry in large price movements we have observed for Bitcoin is an overall feature of the cryptocurrency market  By calculating the difference between the typical exponents related to positive and negative large returns (∆α = α+ − α− ) for each digital currency, we find that about 2/3 of cryptocurrencies have α+ < α− (see Supplementary Figure for the probability distribution of ∆α) 
Thus, unlike Bitcoin, most cryptocurrencies have been more susceptible to large positive price variations than negative ones  While this asymmetry in the return distributions indicates that extremely large price variations tend to be positive, it does not necessarily imply positive price variations are more common for any threshold in the return values 
This happens because the fraction of events in each tail is also related to the lower bound of the power-law regime (r min )  However, we have found the distribution of r min to be similar among the positive and negative returns [Supplementary Figure ]  The distribution of high percentile scores (such as the 90th percentile) is also shifted to larger values for positive returns [Supplementary Figure ] 
Moreover, this asymmetry in high percentile scores related to positive and negative returns is systematic along the evolution of the power-law exponents [Supplementary Figure ]  These results thus indicate that there is indeed more probability mass in the positive tails than in the negative ones, a feature that likely reflects the current expansion of the cryptocurrency market as a whole 
The distributions in Fig  ) also show that large price variations do not have a finite variance for a significant part of cryptoassets, that is, α+ ≤ 3 for 62% of cryptocurrencies and α− ≤ 3 for 44% of cryptocurrencies  A significant part of the cryptocurrency market is thus prone to price variations with no typical scale 
Intriguingly, we further note the existence of a minority group of cryptoassets with α+ ≤ 2 (7%) or α− ≤ 2 (3%)  These cryptocurrencies, whose representative members are Counos X (CCXX, rank 216) with α − = 1 96 and α + = 1 84 and Chainbing (CBG, rank 236) with α + = 1 87, are even more susceptible to extreme price variations as one cannot even define the average value µ for large price returns, as the integral µ ∼ ∞ r min rp(r)dr diverges for α ≤ 2  We have also replicated the previous analysis when considering cryptocurrencies in the top 2000 and top 200 rankings of market capitalization (as of July 2022) 
Figures ) and 2(d) show the probability distribution for the median power-law exponents of these two groups  We observe that these distributions are more localized (particularly for the top 200) than the equivalent distributions for all cryptocurrencies  The fraction of cryptocurrencies with no typical scale for large price returns ( α+ ≤ 3 and α− ≤ 3) is significantly lower in these two groups compared to all cryptocurrencies 
In the top 2000 cryptocurrencies, 51% have α+ ≤ 3 and 26% have α− ≤ 3  These fractions are even smaller among the top 200 cryptocurrencies, with only 44% and 15% not presenting a typical scale for large positive and negative price returns, respectively  We further observe a decrease in the fraction of cryptoassets for which the average value for large price returns is not even finite, as only 2% and 1% of top 2000 cryptoassets have α+ ≤ 2 and α− ≤ 2  This reduction is more impressive among the top 200 cryptocurrencies as only the cryptoasset Fei USD (FEI, rank 78) has α+ = 1 97 and none is characterized by α− ≤ 2  The medians of α+ and α− also increase from 2 78 and 3 11 for all cryptocurrencies to 2



ground_truth: 62.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: How does the scoring engine generate a stream of content for the channel?

Read the following text and answer briefly.

Context 2: A system and method for generating a stream of content for a channel  The channel application includes a content categorizer, a scoring engine and a channel engine  The content categorizer categorizes new content items received from heterogeneous data sources  The channel engine identifies a channel category for a user based at least in part on at least one of a historical trend and a user activity  The scoring engine queries the new content items based on the channel category and at least one other channel attribute  The scoring engine retrieves candidate content items that include the channel category and the other channel attribute  The scoring engine then generates a stream of content from the candidate content items for the channel 
This application claims priority under 35 USC §120 to U S  application Ser  No  13/225,209, entitled, “Generating a Stream of Content for a Channel,” filed on Sep  2, 2011, and claims priority under 35 USC §119(e) to U S  Application No  61/424,636, entitled “Scoring Stream Items with Models Based on User Interests” filed Dec  18, 2010, the entireties of which are herein incorporated by reference 
The specification relates to a system and method for generating a stream of content for a channel  In particular, the specification relates to generating a stream of content for a channel based on user interests and historical trends 
Many consumers of digital media have two somewhat contradictory goals: keep apprised of information in the areas they already find interesting and discover new content that is also enjoyable  Keeping apprised of information can become burdensome in the digital age because there is so much information  Hence, there is a need to present the best and most relevant information, without overwhelming the consumer  Furthermore, consumers have varied interests depending on the time of a year or a day  As a result, there is also a need to cater to the time dependent changes in the consumer's interests while presenting information  Similarly, discovering new content is difficult when the consumer is overburdened with existing content 
Prior attempts to solve these problems allow consumers to create personalized sections in feed aggregation websites that are defined by keywords  Often, these personalized sections present any item that includes the keywords even though the item is not of interest to the consumer, per se  In another method, consumers are allowed to manually subscribe to Really Simple Syndication (RSS) feeds from multiple websites  This method often leads to the consumer viewing multiple items which contain redundant information 
In some examples, the specification describes a system and method for generating a stream of content for a channel using a channel application  The channel application includes a processing unit, a model generation engine, a scoring engine, a collaborative filtering engine, a content categorizer, a channel engine, and a user interface engine  The model generation engine generates a model that is used to determine suggestions for channels  The content categorizer categorizes new content items received from heterogeneous data sources  The channel engine identifies a channel category for a user based on at least one of a historical trend and a user activity  The historical trend is at least one of an increase in a number of new content items for a content category, an increase in a number of times one of the new content items is accessed and an event  A scoring engine queries the new content items based on the channel category and at least one other channel attribute  The scoring engine receives candidate content items that include the channel category and the at least one other channel attribute  The scoring engine then generates a stream of content from the candidate content items for the channel  The scoring engine transmits the stream of content to the channel engine, which generates a channel 
In one embodiment, the user interface engine generates a user interface for the user to define the channel category and the channel attribute  The scoring engine queries the new content items based on the user defined channel category and channel attribute and then generates the stream of content  In another embodiment, the channel engine enables the user to subscribe to an existing channel 
In one embodiment, the channel engine enables the user to share the channel with at least one of a friend of the user, a community, a group, and an internet user 
The specification is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings in which like reference numerals are used to refer to similar elements 
FIG  1A is a high-level block diagram illustrating one embodiment of a system for generating a stream of content for a channel 
FIG  1B is a block diagram illustrating one embodiment of a channel application 
FIG  2 is a high-level block diagram illustrating another embodiment of a system for generating a stream of content for a channel 
FIG  3A is a block diagram of one embodiment of the channel engine in more detail 
FIG  3B is a block diagram of one embodiment of the scoring engine in more detail 
FIG  4 is a graphic representation of a user interface that displays the stream of content of a channel 
FIG  5 is a graphic representation of a user interface that allows a user to define or customize a channel 
FIG  6 is a flow diagram of one embodiment of a method for generating a stream of content for a channel 
FIG  7 is a flow diagram of another embodiment of a method for generating a stream of content for a channel 
A system and method for generating a stream of content for a channel is described below  In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the specification  It will be apparent, however, to one skilled in the art that the embodiments can be practiced without these specific details  In other instances, structures and devices are shown in block diagram form in order to avoid obscuring the specification  For example, the specification is described in one embodiment below with reference to user interfaces and particular hardware  However, the description applies to any type of computing device that can receive data and commands, and any peripheral devices providing services 
Some portions of the detailed descriptions that follow are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory  These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art  An algorithm is here, and generally, conceived to be a self consistent sequence of steps leading to a desired result  The steps are those requiring physical manipulations of physical quantities  Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated  It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like 
It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities  Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining,” “displaying,” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices 
The specification also relates to an apparatus for performing the operations herein  This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer  Such a computer program may be stored in a computer readable storage medium, such as, but not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, flash memories including USB keys with non-volatile memory, or any type of media suitable for storing electronic instructions, each coupled to a computer system bus 
An embodiment can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both hardware and software elements  A preferred embodiment is implemented in software, which includes but is not limited to firmware, resident software, microcode, etc 
Furthermore, an embodiment can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system  For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device 
FIG  1A illustrates a block diagram of a system 100 for generating a stream of content for a channel according to one embodiment  The system 100 includes user devices 115 a, 115 n that are accessed by users 125 a, 125 n, a social network server 101, a third party server 107, a ratings server 139, an email server 141, an entertainment server 137, and a search server 135  The ratings server 139 includes websites for rating places, people or objects (e g  Google Hotpot)  The entertainment server 137 includes websites with entertaining information, such as news articles  In FIG  1A and the remaining figures, a letter after a reference number, such as “115 a” is a reference to the element having that particular reference number  A reference number in the text without a following letter, such as “115,” is a general reference to any or all instances of the element bearing that reference number  In the illustrated embodiment, these entities are communicatively coupled via a network 105 
In one embodiment, the channel application 103 a is operable on the social network server 101, which is coupled to the network via signal line 104  The social network server 101 also contains a social network application 109 and a social graph 179  Although only one social network server 101 is shown, persons of ordinary skill in the art will recognize that multiple social network servers 101 may be present  A social network is any type of social structure where the users are connected by a common feature, for example, Google+  The common feature includes friendship, family, work, an interest, etc  The common features are provided by one or more social networking systems, such as those included in the system 100, including explicitly-defined relationships and relationships implied by social connections with other online users, where the relationships form a social graph 179  In some examples, the social graph 179 reflects a mapping of these users and how they are related 
In another embodiment, the channel application 103 b is stored on a third-party server 107, which is connected to the network via signal line 106  The third-party server 107 includes software for generating a website (not shown)  In one embodiment, the notifying application generates a user interface that is incorporated into the website  Although only one third-party server 107 is shown, persons of ordinary skill in the art will recognize that multiple third-party servers 107 may be present 
In yet another embodiment, the channel application 103 c is stored on a user device 115 a, which is connected to the network via signal line 108  The user device 115 a is any computing device that includes a memory and a processor, such as a personal computer, a laptop, a smartphone, a cellular phone, a personal digital assistant (PDA), etc  The user 125 a interacts with the user device 115 a via signal line 110  Although only two user devices 115 a, 115 n are illustrated, persons of ordinary skill in the art will recognize that any number of user devices 115 n are available to any number of users 125 n 
The network 105 is a conventional type, wired or wireless, and may have any number of configurations such as a star configuration, token ring configuration or other configurations known to those skilled in the art  Furthermore, the network 105 may comprise a local area network (LAN), a wide area network (WAN) (e g , the Internet), and/or any other interconnected data path across which multiple devices may communicate  In yet another embodiment, the network 105 may be a peer-to-peer network  The network 105 may also be coupled to or includes portions of a telecommunications network for sending data in a variety of different communication protocols  In yet another embodiment, the network 105 includes Bluetooth communication networks or a cellular communications network for sending and receiving data such as via short messaging service (SMS), multimedia messaging service (MMS), hypertext transfer protocol (HTTP), direct data connection, WAP, email, etc  While only one network 105 is coupled to the user devices 115 a, 115 n, the social network server 101, and the third party server 107, in practice any number of networks 105 can be connected to the entities 
The channel application 103 receives data for generating a stream of content for a channel from heterogeneous data sources  In one embodiment, the channel application 103 receives data from a third-party server 107, a social network server 101, user devices 115 a, 115 n, a search server 135 that is coupled to the network 105 via signal line 136, an entertainment server 137 that is coupled to the network 105 via signal line 138, a ratings server 139 that is coupled to the network 105 via signal line 140 and an email server 141 that is coupled to the network 105 via signal line 142  In one embodiment, the search server 135 includes a search engine 143 for retrieving results that match search terms from the Internet  In one embodiment, the search engine 143 is powered by Google®  In one embodiment, the channel application 103 generates a model based on the data from the heterogeneous data sources, identifies a channel category based on a user's activities and historical trends, receives candidate content items that include the channel category from heterogeneous data sources, scores the candidate content items by comparing them to the model, and generates a stream of content for the channel 
Referring now to FIG  1B, the channel application 103 is shown in detail  FIG  1B is a block diagram of a computing device 200 that includes the channel application 103, a memory 237 and a processor 235  In one embodiment, the computing 200 device is a social network server 101  In another embodiment, the computing device 200 is a third party server 107  In yet another embodiment, the computing device 200 is a user device 115 a 
The processor 235 comprises an arithmetic logic unit, a microprocessor, a general purpose controller, or some other processor array to perform computations and provide electronic display signals to a display device  The processor 235 is coupled to the bus 220 for communication with the other components via signal line 236  Processor 235 processes data signals and may comprise various computing architectures including a complex instruction set computer (CISC) architecture, a reduced instruction set computer (RISC) architecture, or an architecture implementing a combination of instruction sets  Although only a single processor is shown in FIG  1B, multiple processors may be included  The processing capability may be limited to supporting the display of images and the capture and transmission of images  The processing capability might be enough to perform more complex tasks, including various types of feature extraction and sampling  It will be obvious to one skilled in the art that other processors, operating systems, sensors, displays, and physical configurations are possible 
The memory 237 stores instructions and/or data that may be executed by processor 235  The memory 237 is coupled to the bus 220 for communication with the other components via signal line 238  The instructions and/or data may comprise code for performing any and/or all of the techniques described herein  The memory 237 may be a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, flash memory, or some other memory device known in the art  In one embodiment, the memory 237 also includes a non-volatile memory or similar permanent storage device and media such as a hard disk drive, a floppy disk drive, a CD-ROM device, a DVD-ROM device, a DVD-RAM device, a DVD-RW device, a flash memory device, or some other mass storage device known in the art for storing information on a more permanent basis 
In one embodiment, the channel application 103 comprises a processing unit 202, a model generation engine 207, a scoring engine 211, a collaborative filtering engine 217, a content categorizer 250, a channel engine 240, and a user interface engine 260 that are coupled to a bus 220 
The processing unit 202 is software including routines for receiving information about a user's interests, activities and social connections and for storing the information in the memory 237  In one embodiment, the processing unit 202 is a set of instructions executable by the processor 235 to provide the functionality described below for processing the information  In another embodiment, the processing unit 202 is stored in the memory 237 of the computing device 200 and is accessible and executable by the processor 235  In either embodiment, the processing unit 202 is adapted for cooperation and communication with the processor 235, the model generation engine 207, and other components of the computing device 200 via signal line 222 
The processing unit 202 obtains information about users from user input and/or prior actions of a user across a range of heterogeneous data sources including search (such as web, video, news, maps, alerts), entertainment (such as news, video, a personalized homepage, blogs, a reader, gadget subscriptions), social activity (such as interactions through email, profile information, text messaging such as short message service (SMS), microblogs, geographical locations, comments on photos, a social graph and other social networking information), and activity on third-party sites (such as websites that provide ratings, reviews and social networks where users indicate that they approve of content)  This information is obtained, for example, from a user's search history, browsing history and other interactions with the Internet  The processing unit 202 stores the information with a designation of the source of the information 
In one embodiment, there are multiple processing units 202 that each receive data from a different heterogeneous data source  In another embodiment, the user information is received by the same processing unit 202  The processing unit 202 transmits the user information to memory 237 for storage  In one embodiment, the memory 237 partitions the user information from each heterogeneous data source in a separate data storage location  In another embodiment, the user information from heterogeneous data sources is stored in the same location in the memory 237  In yet another embodiment, the memory 237 partitions the model and the stream of content into separate storage locations as well 
The model generation engine 207 is software including routines for retrieving the user information from the memory 237 and generating a model based on the user information  In one embodiment, the model generation engine 207 is a set of instructions executable by the processor 235 to provide the functionality described below for generating the model  In another embodiment, the model generation engine 207 is stored in the memory 237 of the computing device 200 and is accessible and executable by the processor 235  In either embodiment, the model generation engine 207 is adapted for cooperation and communication with the processor 235, the processing unit 202, the scoring engine 211, the channel engine 240 and other components of the computing device 200 via signal line 224 
The model generation engine 207 receives user information from a variety of sources including, for example, queries, clicks, news clicks, gadgets, email interactions, etc , extracts features from the information and generates a model based on the extracted features  The model determines the relevance of items to users, along with floating point values to indicate the extent to which the relevance holds  Examples include liking a source, a primary location and a list of interests  The interests are generated from explicit information and inferred information  Explicit information is derived, for example, from a user's list of interests on a social network or indicating that they liked a particular content item  Inferred information takes into account a user's activities 
The model generation engine 207 will infer that a user is interested in a particular subject, for example, if the subject matter appears in search terms  For example, the model generation engine 207 infers that a user who searches for information about different types of butterflies is interested in butterflies  The model generation engine 207 can even infer information based on the user's friends' activities  For example, content items that interest the user's friends might also interest the user  As a result, in one embodiment, the model includes the user's friends' interests 
In one embodiment, the model generation engine 207 also generates a model that contains several pieces of global meta-information about the user's consumption patterns including how frequently the user consumes the stream of content of a channel and global statistics on how likely the user is to reshare various types of items  Lastly, the model includes a sequence of weights and multipliers that are used to make predictions about the user's likelihood of clicking on, sharing or otherwise engaging with stream items 
The model generation engine 207 generates the model from the user information across the heterogeneous data sources  In one embodiment, the model generation engine 207 builds extensions to the model that employ the patterns of behavior of other users  For example, the model predicts the user's behavior based on the reaction of similar users  All the data that is derived from other users is anonymized before it is incorporated into the model 
In one embodiment, the model generation engine 207 generates a model based on user information, for example, based on the user's search history or third-party accounts  Alternatively, the model generation engine 207 receives periodic updates (one hour, one day, one week, etc ) from the heterogeneous data sources and in turn updates the model 
In yet another embodiment, the model generation engine 207 generates a model each time it receives a request for generating a stream of content for a channel  The advantage of this method is that the newest updates are included and the model is current  The disadvantage is that generating the model and then comparing the candidate content items to the model to generate the stream of content takes more time than comparing the candidate content items to a pre-existing model  The model generation engine 207 transmits the model to memory 237 for storage 
The content categorizer 250 is software including routines for receiving and categorizing new content items from heterogeneous sources according to at least one category and other features  In one embodiment, the content categorizer 250 is a set of instructions executable by the processor 235 to provide the functionality described below for receiving and categorizing new content items  In another embodiment, the content categorizer 250 is stored in the memory 237 of the computing device 200 and is accessible and executable by the processor 235  In either embodiment, the content categorizer 250 is adapted for cooperation and communication with the processor 235, the scoring engine 211 and other components of the computing device 200 via signal line 227 
The content categorizer 250 receives new content items from heterogeneous data sources and annotates them with specific tags, such as features, global scores, etc  In this embodiment, the heterogeneous data sources include a search engine 143, an entertainment server 137, an email server 141, a ratings server 139, a social network server 101, and a third-party server 107  Once the items are annotated, the content categorizer 250 indexes each new content item based on the features and stores the content items in the memory 237  The new content items, in one embodiment, are indexed according to an identification format (MediaType#UniqueItemID, for example, “YOUTUBE#video_id” and “NEWS#doc_id”), an item static feature column that holds an item's static features (title, content, content classification, context, etc ), an item dynamic feature column that holds an item's dynamic features (global_score, number of clicks, number of following, etc ), a source (src) static feature column where the source is a publisher of an item (magazine in news, video uploading in YouTube, etc ) and a src dynamic feature column that holds the source's dynamic features  The content categorizer 250 categorizes the new content items to make their retrieval more efficient and fast 
The channel engine 240 is software including routines for generating a channel for a user  In one embodiment, the channel engine 240 is a set of instructions executable by the processor 235 to provide the functionality described below for generating a channel for a user  In another embodiment, the channel engine 240 is stored in the memory 237 of the computing device 200 and is accessible and executable by the processor 235  In either embodiment, the channel engine 240 is adapted for cooperation and communication with the processor 235, the scoring engine 211, the model generation engine 207, the user interface engine 240, and other components of the computing device 200 via signal line 230 
In one embodiment, the channel engine 240 identifies a channel category for a user based on historical trends and the user's activities, interests and social connections  The channel engine 240 submits a request for a stream of content that includes the channel category and channel attributes to the scoring engine 211  The channel engine 240 then receives a stream of content from the scoring engine 211 and generates the channel  The generated channel is either public or private depending on the user's settings  The channel engine 240 is explained in greater detail below with regard to FIG  3A 
The scoring engine 211 is software including routines for generating a stream of content for a channel  In one embodiment, the scoring engine 211 is a set of instructions executable by the processor 235 to provide the functionality described below for globally scoring content items and for generating a stream of content for a channel  In another embodiment, the scoring engine 211 is stored in the memory 237 of the computing device 200 and is accessible and executable by the processor 235  In either embodiment, the scoring engine 211 is adapted for cooperation and communication with the processor 235, the processing unit 202, the collaborative filtering engine 217, the model generation engine 207, the channel engine 240 and other components of the computing device 200 via signal line 228 
In one embodiment, the scoring engine 211 receives the request from the channel engine 240 and queries the new content items stored in memory 237  In another embodiment, the scoring engine 211 directly queries the heterogeneous data sources  The scoring engine 211 receives candidate content items that include the channel category and the channel attributes  The scoring engine 211 then compares the candidate content items to the model to determine whether the user would find the candidate content items interesting 
In one embodiment, the scoring engine 211 first performs the query and then compares the results to the model to determine whether the user would find them interesting  In another embodiment, these steps are performed simultaneously  In yet another embodiment, the scoring engine 211 compares candidate content items to the model and then filters the results according to the subject matter of the queries  The scoring engine 211 is explained in greater detail below with regard to FIG  3B 
The collaborative filtering engine 217 is software including routines for generating additional candidate content items for the channel through collaborative filtering and transmitting the additional candidate content items to the scoring engine 211 that were derived from collaborative filtering  In one embodiment, the collaborative filtering engine 217 is a set of instructions executable by the processor 235 to provide the functionality described below for generating additional candidate content items for the channel  In another embodiment, the collaborative filtering engine 217 is stored in the memory 237 of the computing device 200 and is accessible and executable by the processor 235  In either embodiment, the collaborative filtering engine 217 is adapted for cooperation and communication with the processor 235, the scoring engine 211 and other components of the computing device via signal line 226 
The collaborative filtering engine 217 obtains additional candidate content items that are socially relevant from a stream of content derived from people with whom the user has a relationship and transmits them to the scoring engine 211  For example, the stream of content is derived from friends in a social network such as the social network application 109 or people that the user frequently emails  The more important that the person appears to be to the user, the more likely that the user will be interested in the candidate content item  Thus, in one embodiment, the collaborative filtering engine 217 applies a weight to candidate content items based on the social relationship of the user to the friend  For example, users that are friends receive higher weights than candidate content items from second generation friends of the user (i e , a friend of a friend)  In one embodiment, the collaborative filtering engine 217 receives information about relationships between users from the social graph 179 
The collaborative filtering engine 217 increases the weights applied to candidate content items from friends when the user positively responds to the items  For example, if the user comments on the item or indicates that the user found the item interesting, the collaborative filtering engine 217 increase the weight so that more candidate content items from the friend become part of the stream of content 
The user interface engine 260 is software including routines for generating a user interface that, when rendered on a browser, displays a channel generated for a user and enables the user to customize the channel  In one embodiment, the user interface engine 260 is a set of instructions executable by the processor 235 to provide the functionality described below for generating a user interface  In another embodiment, the user interface engine 260 is stored in the memory 237 of the computing device 200 and is accessible and executable by the processor 235  In either embodiment, the user interface engine 260 is adapted for cooperation and communication with the processor 235, the channel engine 240 and other components of the computing device 200 via signal line 232 
The user interface engine 260 receives instructions from the channel engine 240 for generating a display  The user interface includes options for viewing a channel, requesting a new channel, modifying the user interests, and following suggested channels 
FIG  2 is a high-level block diagram illustrating another embodiment of a system for generating a stream of content for a channel  In this embodiment, the components of the channel application 103 are divided among various servers so that the information is efficiently processed  The system includes a search server 135, an entertainment server 137, a ratings server 139, an email server 141, a content categorizer 250, a data storage server 265, a model server 255, a scoring server 262, a social network server 101, a user device 115, and a channel application 103 
A content categorizer 250 crawls the heterogeneous data sources (search server 135, entertainment server 137, ratings server 139, and email server 141) are crawled for new content items by the content categorizer 250 or the new content items are directly transmitted to the content categorizer 250 
The content categorizer 250 categorizes the new content items as mentioned above with regards to FIG  1B and stores them in the database 267 of the data storage server 265  The content categorizer 240 also includes a processing unit 202 for processing user information (activities, interests and social connections)  In one embodiment, the processing unit 202 stores the database 267 
In one embodiment, the data storage server 265 dynamically phases out the old content items  For example, news items expire after 24 hours, videos expire after 48 hours and feeds are kept for 24 hours or only the 10 most recent items, whichever is larger, etc 
The content categorizer 250 also transmits the new content items to the scoring server 262 for a global user ranking  The global scores are transmitted from the scoring server 262 to the data storage server 265, which stores the global scores in association with the new content items  The global scores are helpful for organizing the new content items in the data storage server 265 according to the more popular items 
Turning now to the model server 255, the model server 255 receives the user's activity, interests and social connections from the processing unit 202 or the data storage server 265  The model generation engine 207 generates a model based on user input and/or prior actions  The model server 255 transmits a model to the scoring server 262 and the channel application 103 periodically or upon request 
The channel application 103 includes a channel engine 240 and a user interface engine 260  In one embodiment, the channel engine 240 requests the model from the model server 255 and identifies a channel category that a user would find interesting  The channel engine 240 then transmits a request for a stream of content to the scoring server 262  The channel engine 240 receives the stream of content from the scoring server 262 and generates the channel  The user interface engine 260 generates a user interface for displaying a user interface that includes the channel and transmits it to the user device 115  In addition, the user interface engine 260 generates a user interface to allow the user to customize the channel or define a new channel  These user interfaces are explained in greater detail below with regard to FIGS  4-5 
In one embodiment, the channel engine 240 transmits a query based on the channel category to the scoring server 262  The scoring server 262 queries and receives candidate content items from the data storage server 265  The scoring server 262 also queries and receives candidate content items from the social network server 101  The candidate content items from the social network server 101 are pre-scored by the collaborative filtering engine 217 and, in one embodiment, the unread candidate content items are saved to a cache on the social network server 101  These items are saved to a cache because the quantity of social updates can be large enough that performing the scoring during write time enables faster reads 
In one embodiment, the scoring engine 211 requests the model from the model server 255  The scoring server 262 then compares the candidate content items to the model and scores the candidate content items  The scoring engine 211 compares the candidate content items received from the social network server 101 to the model and rescores them according to the model  In another embodiment, the scoring engine 211 scores the candidate content items according to the category and any keywords associated with a channel  In either embodiment, the scoring engine 211 generates a stream of content based on the scored candidate content items and transmits the stream of content to the channel application 103 
Referring now to FIG  3A, one embodiment of a channel engine 240 is shown in more detail  The channel engine 240 includes a historical analyzer 372, a category identifier 374, a subscription module 376 and a channel generator 378 that are each coupled to signal line 230 
The historical analyzer 372 is used to identify when a user will be interested in a particular category  The historical analyzer 372 identifies, for example, a time of the day or a year that a user will be interested in a category by analyzing historical trends associated with the category  In one embodiment, the historical analyzer 372 performs such analyses by measuring the increase or decrease in the number of new content items that are categorized under a content category or by measuring an increase or decrease in the number of times a new content item is accessed  For example, the number of times a tutorial on filing taxes is accessed would be very high during February-April  In another embodiment, the historical analyzer 372 also keeps track of events such as holidays, festivals, etc  Tracking such events is advantageous as, for example, many users might be interested in costume rentals during Halloween or camping during the Memorial Day and July 4th weekends 
The category identifier 374 identifies a channel category for a user based on the user's interests, activities and social connections  In one embodiment, the category identifier 374 requests the model generated by the model generation engine 207 to identify the channel category  For example, the category identifier 374 identifies sports cars as a channel category because it is an explicit interest of the user  The category identifier 374 suggests channels including a source, a category, keywords, a media type, a size of a content item, and a location for a channel  For example, for a user that is interested in foreign politics, especially relations between the United States and China, the category identifier 374 suggests the category of U S  and Chinese relations (e g , entity=“us_china_relations”), keywords such as trade and deficit because the user is particularly interested in the economic aspect of the relationship between China and the United States, a source such as The Economist (source=“economist com”) because the user prefers The Economist over U S  media outlets and the media being news articles because the user does not enjoy viewing videos 
In one embodiment, the category identifier 374 uses the analyses of the historical analyzer 374 for identifying a channel category for the user  This is advantageous as a user who has searched for US taxes might not be interested in knowing about it throughout the year, but it is beneficial for the user to have a separate channel for US taxes during the tax filing season  In yet another embodiment, the category identifier 374 uses contextual cues of the user for identifying channel categories  For example, the category identifier 374 identifies skiing in Switzerland as a channel category because winter sports is listed as an interest of the user and the user's current IP address is in Switzerland 
The subscription module 376 enables a user to subscribe to existing channels that are public  In one embodiment, the subscription module 376 enables a user to subscribe to a pre-defined channel (such as breaking news, most popular videos, updates from a social group, etc )  The channel application 103 generates the stream of content for pre-defined channels based on global scores of the new content items  Subscribing to pre-defined channels such as breaking news is advantageous as it helps the user to keep apprised of current information and discover new interests  Furthermore, because in one embodiment the breaking news channel is personalized since the content items are compared to a model for the user, the breaking news channel is more relevant than simply a list of popular or recent news items 
In another embodiment, the subscription module 376 enables a user to subscribe to another user's channel (a friend, a famous person, etc ) that is public  Subscribing to another user's channel is advantageous because, for example, a user who is interested in the stock market will benefit by viewing the stream of content that is viewed by a famous stock market analyst  In yet another embodiment, the subscription module 376 enables the user to search for channels that are public using the search engine 143  The subscription module 376, suggests such channels that are viewed by other users based on the interests of the user  In another embodiment, the subscription module 376 communicates with the collaborative filtering engine 217 to suggest channels viewed by other users with whom the user has a relationship 
The channel generator 378 submits a request for a stream of content for a channel to the scoring engine 211  The request includes the channel category identified by the category identifier 374 and channel attributes  The channel attributes include any attribute known to a person with ordinary skill in the art such as a source, presence of keywords, absence of keywords, a media type, a location, a time, a size of a content item, a date, etc  In one embodiment, the channel category and the channel attributes are defined by the user  In another embodiment, channel generator 378 defines the channel attributes for the channel category based on the user's preferences and activities  For example, if a user always reads news articles and seldom watches news videos, the channel generator 378 would define the media type for the channel as text based articles  At any point in time, the user can customize both the channel category and the channel attributes  The channel generator 378 then resubmits the request based on the changes made by the user 
In response to the request, the channel generator 378 receives a stream of content from the scoring engine 211 and generates the channel for the user  The generated channel is either public or private depending upon the user's preferences  In one embodiment, the user shares the channel to a community, a group of people or any internet user  The channel is then displayed to the user with an interface generated by the user interface engine 260 
Referring now to FIG  3B, one embodiment of a scoring engine 211 is shown in more detail  The scoring engine 211 includes a query generator 301, a global scorer 302 and a content stream generator 304 that are each coupled to signal line 228 
The global scorer 302 is used to rank new content items that are stored in the data storage server 265 or memory 237 (depending upon the embodiment)  The global scorer 302 uses signals from the different verticals to compute a global user-independent score for each item to approximate its popularity or importance within the stream that produced it  The global scorer 302 normalizes the score across streams so that items from various streams are comparable to aid in generating a quick yet reasonable ranking of items  The global score is a combination of its quality specific to the source stream (depending on the rank of the source, number of known followers of a source, etc ) and its global popularity (trigger rate on universal search, relevance to trending queries, number of clicks, long clicks received, etc ) 
The global scorer 302 transmits the global score to storage where it is associated with the item  The global score helps rank the items for faster retrieval  For example, if the query generated by the query generator 301 includes a request for the top ten items about skiing, those items are already organized in the data storage server 265 or memory 237 according to the global score 
The query generator 301 receives a request for a stream of content for a channel from the channel engine 240  The query generator 301 generates a query based on the channel attributes that are included in the request  The query generator 301 queries the data storage server 265 or memory 237 depending upon the embodiment  The following is an example query generated by the query generator 301: ((Category: Politics) AND (global_score>80) AND (source: NewsWebsite) AND (media type: Text)) 
The content stream generator 304 receives candidate content items that include the channel attributes  The content stream generator 304, for the above mentioned query, receives text based articles that include the channel category politics and have a global score greater than 80  Additionally, the text based articles are from the source NewsWebsite  In one embodiment, the content stream generator 304 generates the stream by ordering the content items in order of their scores  In another embodiment, the content stream generator 304 determines an interestingness of each candidate content item to the user  The content stream generator 304 determines the interestingness by comparing the candidate content items with a model generated for the user by the model generation engine 207 and scoring them 
where p is a property, that is, a setting A=a of the attributes  The latter quantity, Pr(p|user) is approximated from the user's history of interactions with content items as well as the user's search history and other opt-in data  Similarly, the former quantity, Pr(item|p) is approximated by the (suitably weighted) reciprocal of the number of items with property p (e g , if it is expected that p=((Politics) AND (global_score>80) AND (source: NewsWebsite) AND (media type: Text)) to generate 300 items, take Pr(item|p) to be 1/300) 
where the properties p are summed over single-attribute properties (as opposed to all possible settings of an entire collection of attributes), and G is an exponential function of the form G(x)=2(100 x), so that when applied in this form, if there are several values of p for which Pr(item|p) Pr(p|user) is large, the sum of their G-values slowly increases 
Once the scores are calculated, the content stream generator 304 generates a stream of content for the channel that is ordered according to the candidate content item scores  In one embodiment, only the candidate content items that exceed a certain threshold are included in the stream of content for the channel 
Turning now to the user interface engine 260, FIG  4 is a graphic representation 400 of a user interface generated by the user interface engine 260 for displaying the stream of content of a channel  In this example, the user interface 400 also includes channels 405 that are pre-defined, channels 410 that are suggested for the user and channels 415 that are subscribed to by the user  The user can also define new channels and attributes by clicking the link 420 
The example includes the stream of content for the user's soccer channel 425  The stream of content includes news items 445, videos 450 and social network news feeds 455 from the content sources 440 defined by the user  The candidate content items are listed in decreasing order of their scores  The user interface engine 260 lists five candidate content items with the highest scores in the hot items section 430  The remaining candidate content items are listed in the other items section 435  In another embodiment, the entire stream of content is listed in a single section 
FIG  5 is a graphic representation 500 of a user interface that is generated by the user interface engine 260 for a user to define a new channel or customize an existing channel  In this example, the user interface includes all the channel categories 505 that have been either pre-defined, suggested to the user, or subscribed by the user, and the content sources 510 for each channel category  The user customizes a channel by adding or removing content sources for the channel  In one embodiment, the user edits more advanced channel attributes such as media type, size of the content items, etc  by clicking on the link 515  The user makes the channel public, private or restricts it to a group of people by clicking on link 520  Additionally, the user can also define a new channel by adding a new channel category 
Referring now to FIGS  6-7, various embodiments of the method of the specification will be described  FIG  6 is a flow diagram 600 of one embodiment of a method for generating a stream of content for a channel  The channel engine 240 defines 602 a channel category and submits a request for a stream of content  The request includes channel attributes including any of a category, a source, keywords, a media type, a location, a size of a content item, and a date  The channel category is defined based on a model for a user that is generated by the model generation engine 207 or the channel is defined by a user  The scoring engine 211 receives 604 the request including the channel category and generates 606 a stream of content based on the channel category  The channel engine 240 generates 608 a channel with the stream of content and transmits it to the user 
FIG  7 is a flow diagram 700 of another embodiment of a method for generating a stream of content for a channel  The content categorizer 250 categorizes 702 new content items that are received from heterogeneous data sources  The new content items that are received from heterogeneous data sources include, for example, news articles, microblogs, blogs, videos, photos, etc  The content categorizer 250 categorizes the content according to a category and other features  The content categorizer 250 also stores 704 the new content items in a data storage server 265 or a memory 237, depending upon the embodiment  The global scorer 302 generates 706 a global score for each new content item  The category identifier 374 identifies 708 a channel category for a user based on the user's activities and a historical trend identified by the historical analyzer 372  The user's activity includes a search (such as web, video, news, maps, alerts), entertainment (such as news, video, a personalized homepage, blogs, a reader, gadget subscriptions), social activity (such as interactions through email, profile information, text messaging such as short message service (SMS), microblog, comments on photos, a social graph, and other social networking information), and activity on third-party sites (such as websites that provide ratings, reviews and social networks where users indicate that they approve of content) In one embodiment, the category identifier 374 also uses contextual information of the user to identify the channel category 
The query generator 301 generates a query based on the channel category and the channel attributes and queries 710 the new content items stored on the data storage server 265  The content stream generator 304 receives 712 candidate content items that include the channel category and channel attributes  In one embodiment, the content stream generator 304 receives additional candidate content items from the collaborative filtering engine 217 
The content stream generator 304 scores 714 each candidate content item by comparing it to a model generated by the model generation engine 207  The score is calculated by determining an interestingness of the candidate content item to the user  The content stream generator 304 then generates 716 the stream of content based on the scores for each candidate content item  The channel engine 240 then generates 718 a channel with the stream of content and transmits it to the user 
The foregoing description of the embodiments has been presented for the purposes of illustration and description  It is not intended to be exhaustive or to limit the specification to the precise form disclosed  Many modifications and variations are possible in light of the above teaching  It is intended that the scope of the embodiments be limited not by this detailed description, but rather by the claims of this application  As will be understood by those familiar with the art, the examples may be embodied in other specific forms without departing from the spirit or essential characteristics thereof  Likewise, the particular naming and division of the modules, routines, features, attributes, methodologies, and other aspects are not mandatory or significant, and the mechanisms that implement the description or its features may have different names, divisions and/or formats  Furthermore, as will be apparent to one of ordinary skill in the relevant art, the modules, routines, features, attributes, methodologies, and other aspects of the specification can be implemented as software, hardware, firmware, or any combination of the three  Also, wherever a component, an example of which is a module, of the specification is implemented as software, the component can be implemented as a standalone program, as part of a larger program, as a plurality of separate programs, as a statically or dynamically linked library, as a kernel loadable module, as a device driver, and/or in every and any other way known now or in the future to those of ordinary skill in the art of computer programming  Additionally, the specification is in no way limited to implementation in any specific programming language, or for any specific operating system or environment  Accordingly, the disclosure is intended to be illustrative, but not limiting, of the scope of the specification, which is set forth in the following claims 
providing, with the one or more processors, the customized stream of content 
2  The computer-implemented method of claim 1 comprising removing pre-existing content items included in the customized stream of content for the channel 
3  The computer-implemented method of claim 1 wherein the historical trend is one of an increase in a number of the new content items for a content category and an increase in a number of times one of the new content items is accessed 
categorizing the new content items 
5  The computer-implemented method of claim 3 wherein the heterogeneous data sources include at least two from the group of a news article post, a news feed, a social feed, a blog post, a micro-blog post, a photo, a video, an audio, an email message, and a text based message 
6  The computer-implemented method of claim 1 comprising receiving a request from the user to subscribe to an existing channel 
7  The computer-implemented method of claim 1 wherein the channel category is also based on an interest of the user and a connection of the user 
8  The computer-implemented method of claim 1 wherein the user activity is an interaction of the user with an application, wherein the interaction of the user with the application includes providing at least one of a user preference, a user interest, a comment, a tag, and a search 
provide the customized stream of content 
10  The computer program product of claim 9, wherein the computer readable program when executed on the computer also causes the computer to remove pre-existing content items included in the customized stream of content for the channel 
11  The computer program product of claim 9, wherein the historical trend is one of an increase in a number of the new content items for a content category and an increase in a number of times one of the new content items is accessed 
categorize the new content items 
13  The computer program product of claim 12, wherein the heterogeneous data sources include at least two from the group of a news article post, a news feed, a social feed, a blog post, a micro-blog post, a photo, a video, an audio, an email message, and a text based message 
14  The computer program product of claim 9, wherein the computer readable program when executed on the computer also causes the computer to receive a request from the user to subscribe to an existing channel 
15  The computer program product of claim 9, wherein the channel category is also based on an interest of the user and a connection of the user 
16  The computer program product of claim 9, wherein the user activity is an interaction of the user with an application, wherein the interaction of the user with the application includes providing at least one of a user preference, a user interest, a comment, a tag, and a search 
18  The system of claim 17 wherein the system is further configured to remove pre-existing content items included in the customized stream of content for the channel 
19  The system of claim 17 wherein the historical trend is one of an increase in a number of the new content items for a content category and an increase in a number of times one of the new content items is accessed 
21  The system of claim 20 wherein the heterogeneous data sources include at least two from the group of a news article post, a news feed, a social feed, a blog post, a micro-blog post, a photo, a video, an audio, an email message, and a text based message 
22  The system of claim 17 wherein the system is further configured to receive a request from the user to subscribe to an existing channel 
23  The system of claim 17 wherein the channel category is also based on an interest of the user and a connection of the user 
24  The system of claim 17 wherein the user activity is an interaction of the user with an application, wherein the interaction of the user with the application includes providing at least one of a user preference, a user interest, a comment, a tag, and a search 
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Singh et al , "CINEMA: Columbia InterNet Extensible Multimedia Architecture," Department of Computer Science, Columbia University, May 2002 pp  1-83 
Yu et al , "It Takes Variety to Make a World: Diversification in Recommender Systems," 2009, pp  1-11, downloaded from https://openproceedings org/2009/conf/edbt/YuLA09 pdf 
Context 3: 98 and 3 35 for the top 2000 and to 3 08 and 3 58 for the top 200 cryptocurrencies 
Conversely, the asymmetry between positive and negative large price returns does not differ much among the three groups, with the condition α+ < α− holding only for a slightly larger fraction of top 2000 (69 1%) and top 200 (70 6%) cryptoassets compared to all cryptocurrencies (66 4%)  Moreover, all these patterns are robust when filtering out time series with sampling issues or when considering only cryptoassets that stay compatible with the power-law hypothesis in more than 90% of the positions of the expanding time window (Supplementary Figures ) 
We also investigate whether the patterns related to the median of the power-law exponents differ among groups of cryptocurrencies with different designs and purposes  To do so, we group digital assets using the 50 most common tags in our dataset (e g  "bnb-chain", "defi", and "collectibles-nfts") and estimate the probability distributions of the median exponents α+ and α− (Supplementary Figures ) 
These results show that design and purpose affect the dynamics of large price variations in the cryptocurrency market as the medians of typical exponents range from 2 4 to 3 7 among the groups  The lowest values occur for cryptocurrencies tagged as "doggone-doggerel" (medians of α+ and α− are 2 38 and 2 83), "memes" (2 41 and 2 87), and "stablecoin" (2 65 and 2 79) 
Digital currencies belonging to the first two tags overlap a lot and have Dogecoin (DOGE, rank 9) and Shiba Inu (SHIB, rank 13) as the most important representatives  Cryptoassets with these tags usually have humorous characteristics (such as an Internet meme) and several have been considered as a form of pump-and-dump scheme , a type of financial fraud in which false statements artificially inflate asset prices so the scheme operators sell their overvalued cryptoassets 
Conversely, cryptoassets tagged as "stablecoin" represent a class of cryptocurrencies designed to have a fixed exchange rate to a reference asset (such as a national currency or precious metal)   While the price of stablecoins tends to stay around the target values, their price series are also marked by sharp variations, which in turn are responsible for their typically small power-law exponents 
This type of cryptoasset has been shown to be prone to failures , such as the recent examples of TerraUSD (UST) and Tron's USDD (USDD) that lost their pegs to the US Dollar producing large variations in their price series  The asymmetry between positive and negative large returns also emerges when grouping the cryptocurrencies using their tags 
All 50 tags have distributions of α+ shifted to smaller values when compared with the distributions of α− , with differences between their medians ranging from −0 74 ("okex-blockdream-ventures-portfolio") to −0 14 ("stablecoin")  Indeed, only four ('stablecoin", "scrypt", "fantom-ecosystem" and "alameda-research-portfolio") out of the fifty groupings have both distributions indistinguishable under a two-sample Kolmogorov-Smirnov test (p-value > 0 05) 
Focusing now on the evolution of the power-law exponents quantified by the time series α t for positive and negative returns, we ask whether these exponents present particular time trends  For Bitcoin [Fig  )], α t seems to increase with time for both positive and negative returns  At the same time, the results of Fig  also suggest that market capitalization affects these power-law exponents 
To verify these possibilities, we assume the power-law exponents (α t ) to be linearly associated with the cryptocurrency's age (y t , measured in years) and the logarithm of market capitalization (log c t )  As detailed in the Methods section, we frame this problem using a hierarchical Bayesian model 
This approach assumes that the linear coefficients associated with the effects of age (A) and market capitalization (C) of each digital currency are drawn from distributions with means µ A and µ C and standard deviations σ A and σ C , which are in turn distributed according to global distributions representing the overall impact of these quantities on the cryptocurrency market 
The Bayesian inference process consists of estimating the posterior probability distributions of the linear coefficients for each cryptocurrency as well as the posterior distributions of µ A , µ C , σ A , and σ C , allowing us to simultaneously probe asset-specific tendencies and overall market characteristics 
Moreover, we restrict this analysis to the 2140 digital currencies having more than 50 observations of market capitalization concomitantly to the time series of the power-law exponents in order to have enough data points for detecting possible trends  When considering the overall market characteristics, we find that the 94% highest density intervals for µ A ([-0 01, 0 06] for positive and [-0 02, 0 03] for negative returns) and µ C ([-0 02, 0 03] for positive and [-0 001, 0 04] for negative returns) include the zero (see Supplementary Figure for their distributions) 
Thus, there is no evidence of a unique overall pattern for the association between the power-law exponents and age or market capitalization followed by a significant part of the cryptocurrency market  Indeed, the 94% highest density intervals for σ A ([0 87, 0 93] for positive and [0 63, 0 70] for negative returns) and σ C ([0 57, 0 61] for positive and [0 49, 0 52] for negative returns) indicate that the cryptocurrency market is highly heterogeneous regarding the evolution of power-law exponents associated with large price variations (see Supplementary Figure for the distributions of σ A and σ C )  Figure illustrates these heterogeneous behaviors by plotting the posterior probability distributions for the linear coefficients associated with the effects of age (A) and market capitalization (C) for the top 20 digital assets, where cryptocurrencies which are significantly affected (that is, the 94% highest density intervals for A or C do not include the zero) by these quantities are highlighted in boldface 
Even this small selection of digital  currencies already presents a myriad of patterns  First, we observe that the power-law exponents of a few top 20 cryptocurrencies are neither correlated with age nor market capitalization  That is the case of Shiba Inu (SHIB, rank 13) and Dai (DAI, rank 11) for both positive and negative returns, UNUS SED LEO (LEO, rank 18) and Polkadot (DOT, rank 12) for the positive returns, and USDCoin (USDC, rank 4) and Solana (SOL, rank 9) for negative returns 
There are also cryptocurrencies with exponents positively or negatively correlated only with market capitalization  Examples include Tether (USDT, rank 3) and Dogecoin (DOGE, rank 10), for which the power-law exponents associated with positive returns increase with market capitalization, and Binance USD (BUSD, rank 6), for which power-law exponents associated with positive and negative returns decrease with market capitalization 
We also observe cryptocurrencies for which age and market capitalization simultaneously affect the power-law exponents  Polygon (MATIC, rank 14) is an example where the power-law exponents associated with positive returns tend to increase with age and decrease with market capitalization  Finally, there are also cryptocurrencies with power-law exponents only associated with age 
That is the case of Bitcoin (BTC, rank 1), Ethereum (ETH, rank 2), and Cardano (ADA, rank 8), for which the power-law exponents related to positive and negative returns increase with age, but also the case of Uniswap (UNI, rank 19), for which the exponents decrease with age  Figure systematically extends the observations made for the top 20 cryptoassets to all 2140 digital currencies for which we have modeled the changes in the power-law exponents as a function of age and market capitalization 
First, we note that only 10% of cryptocurrencies have power-law exponents not significantly affected by age and market capitalization  The vast majority (90%) displays some relationship with these quantities  However, these associations are as varied as the ones we have observed for the top 20 cryptoassets 
About 52% of cryptocurrencies have power-law exponents simultaneously affected by age and market capitalization  In this group, these quantities simultaneously impact the exponents related to positive and negative returns of 34% of cryptoassets, whereas the remainder is affected only in the positive tail (9%) or only in the negative tail (9%) 
Moving back in the hierarchy, we find that the power-law exponents of 32% of cryptocurrencies are affected only by age while a much minor fraction (6%) is affected only by market capitalization  Within the group only affected by age, we observe that the effects are slightly more frequent only on the exponents related to negative returns (12%), compared to cases where effects are restricted only to positive returns (10%) or simultaneously affect both tails (10%) 
Finally, within the minor group only affected by market capitalization, we note that associations more frequently involve only exponents related to negative returns (3%) compared to the other two cases (2% only positive returns and 1% for both positive and negative returns)  Beyond the previous discussion about whether positive or negative returns are simultaneously or individually affected by age and market capitalization, we have also categorized the direction of the trend imposed by these two quantities on the power-law exponents 
Blue rectangles in Fig  represent the fraction of relationships for which increasing age or market capitalization (or both) is associated with a raise in the power-law exponents  About 28% of all cryptocurrencies exhibit this pattern in which large price variations are expected to occur less frequently as they grow and age 
Conversely, the red rectangles in Fig  depict the fraction of relationships for which increasing age or market capitalization (or both) is associated with a reduction in the power-law exponents  This case comprises about 25% of all cryptocurrencies for which large price variations are likely to become more frequent as they grow in market capitalization and age 
Still, the majority of associations represented by green rectangles refer to the case where the effects of age and market capitalization point in different directions (e g  exponents increasing with age while decreasing with market capitalization)  About 36% of cryptocurrencies fit this condition which in turn contributes to consolidating the cumbersome hierarchical structure of patterns displayed by cryptocurrencies regarding the dynamics of large price variations 
This complex picture is not much different when considering only cryptocurrencies in the top 200 market capitalization rank (Supplementary Figure )  However, we do observe an increased prevalence of patterns characterized by exponents that rise with age and market capitalization (37%), suggesting that large price variations are becoming less frequent among the top 200 cryptocurrencies than in the overall market 
)  Each of the previous three levels is further classified regarding whether both positive and negative returns are simultaneously affected or whether the effect involves only positive or only negative returns  Finally, the former levels are classified regarding whether the power-law exponents increase, decrease or have a mixed trend with the predictive variables 
Overall, 36% of the associations are classified as mixed trends (green rectangles), 28% are increasing trends (blue rectangles), and 26% are decreasing trends (red rectangles)  We have studied the distributions of large price variations of a significant part of the digital assets that currently comprise the entirety of the cryptocurrency market 
Unlike previous work, we have estimated these distributions for entire historical price records of each digital currency, and we have identified the patterns under which the return distributions change as cryptoassets age and grow in market capitalization  Similarly to conventional financial assets , our findings show that the return distributions of the vast majority of cryptoassets have tails that are described well by power-law functions along their entire history 
The typical power-law exponents of cryptocurrencies (α ∼ 3) are, however, significantly smaller than those reported for conventional assets (α ∼ 4)   This feature corroborates the widespread belief that cryptoassets are indeed considerably more risky for investments than stocks or other more traditional financial assets 
Indeed, we have found that about half of the cryptocurrencies in our analysis do not have a characteristic scale for price variations, and are thus prone to much higher price variations than those typically observed in stock markets  On the upside, we have also identified an asymmetry in the power-law exponents for positive and negative returns in about 2/3 of all considered cryptocurrencies, such that these exponents are smaller for positive than they are for negative returns 
This means that sizable positive price variations have generally been more likely to occur than equally sizable negative price variations, which in turn may also reflect the recent overall expansion of the cryptocurrency market  Using a hierarchical Bayesian linear model, we have also simultaneously investigated the overall market characteristics and asset-specific tendencies regarding the effects of age and market capitalization on the power-law exponents 
We have found that the cryptocurrency market is highly heterogeneous regarding the trends exhibited by each cryptocurrency; however, only a small fraction of cryptocurrencies (10%) have power-law exponents neither correlated with age nor market capitalization  These associations have been mostly ignored by the current literature and are probably related to the still-early developmental stage of the cryptocurrency market as a whole 
Overall, 36% of cryptocurrencies present trends that do not systematically contribute to increasing or decreasing their power-law exponents as they age and grow in market capitalization  On the other hand, for 26% of cryptocurrencies, aging and growing market capitalization are both associated with a reduction in their power-law exponents, thus contributing to the rise in the frequency of large price variations in their dynamics 
Only about 28% of cryptocurrencies present trends in which the power-law exponents increase with age and market capitalization, favoring thus large price variations to become less likely  These results somehow juxtapose with findings about the increasing informational efficiency of the cryptocurrency market  
In fact, if on the one hand the cryptocurrency market is becoming more informationally efficient, then on the other our findings indicate that there is no clear trend toward decreasing the risks of sizable variations in the prices of most considered cryptoassets  In other words, risk and efficiency thus appear to be moving towards different directions in the cryptocurrency market 
To conclude, we hope that our findings will contribute significantly to the better understanding of the dynamics of large price variations in the cryptocurrency market as a whole, and not just for a small subset of selected digital assets, which is especially relevant due to the diminishing concentration of market capitalization among the top digital currencies, and also because of the considerable impact these new assets may have in our increasingly digital economy 
Our results are based on time series of the daily closing prices (in USD) for all cryptoassets listed on CoinMar-ketCap (coinmarketcap com) as of 25 July 2022 [see Supplementary Figure (a) for a visualization of the increasing number cryptoassets listed on CoinMarketCap since 2013]  These time series were automatically gathered using the cryptoCMD Python package and other information such as the tags associated with each cryptoasset were obtained via the CoinMarketCap API  
In addition, we have also obtained the daily market capitalization time series (in USD) from all cryptoassets which had this information available at the time  Earliest records available from CoinMarketCap date from 29 April 2013 and the latest records used in our analysis correspond to 25 July 2022  Out of 9943 cryptocurrencies, we have restricted our analysis to the 7111 with at least 200 price-return observations 
The median length of these time series is 446 observations [see the distribution of series length in Supplementary Figure   We have estimated the power-law behavior of the return distributions by applying the Clauset-Shalizi-Newman method to the return time series r t   In particular, we have sampled each of these time series using an expanding time window that starts at the hundredth observation and grows in weekly steps (seven data points each step) 
For each position of the expanding time window, we have separated the positive returns from the negative ones and applied the Clauset-Shalizi-Newman method to each set  This approach consists of obtaining the maximum likelihood estimate for the power-law exponent, α = 1 + n/ (∑ n t=1 ln r t /r min ) , where r min is the lower bound of the power-law regime and n is the number of (positive or negative) return observations in the power-law regime for a given position of the expanding time window 
The value r min is estimated from data by minimizing the Kolmogorov-Smirnov statistic between the empirical distribution and the power-law model  The Clauset-Shalizi-Newman method yields an unbiased and consistent estimator , in a sense that as the sample increases indefinitely, the estimated power-law exponent converges in distribution to the actual value 
Moreover, we have used the implementation available on the powerlaw Python package   In addition to obtaining the power-law exponents, we have also verified the adequacy of the power-law hypothesis using the procedure originally proposed by Clauset et al  as adapted by Preis et al    This procedure consists of generating synthetic samples under the power-law hypothesis with the same properties of the empirical data under analysis (that is, same length and parameters α and r min ), adjusting the simulated data with the power-law model via the Clauset-Shalizi-Newman method, and calculating the Kolmogorov-Smirnov statistic (κ syn ) between the distributions obtained from the simulated samples and the adjusted power-law model 
Next, the values of κ syn are compared to the Kolmogorov-Smirnov statistic calculated between empirical data and the power-law model (κ)  Finally, a p-value is defined by calculating the fraction of times for which κ syn > κ  We have used one thousand synthetic samples for each position of the expanding time window and the more conservative 90% confidence level (instead of the more lenient and commonly used 95% confidence level), such that the power-law hypothesis is rejected whenever p-value ≤ 0 1 
We have estimated the effects of age and market capitalization on the power-law exponents associated with positive or negative returns of a given cryptocurrency using the linear model where α t represents the power-law exponent, log c t is the logarithm of the market capitalization, and y t is the age (in years) of the cryptocurrency at t-th observation 
Moreover, K is the intercept of the association, while C and A are linear coefficients quantifying the effects of market capitalization and age, respectively  Finally, N (µ, σ ) stands for the normal distribution with mean µ and standard deviation σ , such that the parameter ε accounts for the unobserved determinants in the dynamics of the power-law exponents 
We have framed this problem using the hierarchical Bayesian approach such that each power-law exponent α t is nested within a cryptocurrency with model parameters considered as random variables normally distributed with parameters that are also random variables  Mathematically, for each cryptocurrency, we have
12/16 where µ K , σ K , µ C , σ C , µ A , and σ A are hyperparameters  These hyperparameters are assumed to be distributed according to distributions that quantify the overall impact of age and market capitalization on the cryptocurrency market as a whole  We have performed this Bayesian regression for exponents related to positive and negative returns separately, and used noninformative prior and hyperprior distributions in order not to bias the posterior estimation  
Specifically, we have considered and ε ∼ U (0, 10 2 ) , where U (a, b) stands for the uniform distribution in the interval [a, b] and Inv−Γ(θ , γ) represents the inverse gamma distribution with shape and scale parameters θ and γ, respectively  For the numerical implementation, we have relied on the PyMC Python package and sampled the posterior distributions via the gradient-based Hamiltonian Monte Carlo no-U-Turn-sampler method 
We have run four parallel chains with 2500 iterations each (1000 burn-in samples) to allow good mixing and estimated the Gelman-Rubin convergence statistic (R-hat) to ensure the convergence of the sampling approach (R-hat was always close to one)  In addition, we have also verified that models describing the power-law exponents as a function of only age (C → 0 in Eq  3) or only market capitalization (A → 0 in Eq  3) yield significantly worse descriptions of our data as quantified by the Widely Applicable Information Criterion (WAIC) and the Pareto Smoothed Importance Sampling Leave-One-Out cross-validation (PSIS-LOO) (see Supplementary Table )  ) is larger than r 90 estimated from negative returns (r − 90 ) 
This fraction is calculated only for weeks in which the power-law hypothesis is not rejected for both tails  The percentage of cryptoassets for which r + 90 > r − 90 is shown in the panels  The first column of panels depicts the results when considering data from all cryptocurrencies, while the second and third columns present the results for the top 2000 and top 200 cryptocurrencies by market capitalization, respectively 
Sampling issues refer to missing data and problems caused by prices of cryptoassets decreasing to zero  We note that these distributions barely change when considering only cryptocurrencies without any sampling issue  Indeed, the distributions in this figure are not significantly distinguishable from their counterparts in Fig  (two-sample Kolmogorov-Smirnov test, p > 0 05) 
)  Each of the previous three levels is further classified regarding whether both positive and negative returns are simultaneously affected or whether the effect involves only positive or only negative returns  Finally, the former levels are classified regarding whether the power-law exponents increase, decrease or have a mixed trend with the predictive variables 
Overall, 35% of the associations are classified as mixed trends (green rectangles), 37% are increasing trends (blue rectangles), and 18% are decreasing trends (red rectangles) 



ground_truth: By comparing candidate content items to a model and scoring them.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What type of distribution do the tail distributions of price returns follow?

Read the following text and answer briefly.

Context 4:  This can make locating appropriately matched donor blood more difficult  Although blood supplies in the United States are very safe, particularly relative to the past and to other areas of the world, there remains an increased risk of exposure to such blood-borne infections as hepatitis  Additionally, the body is not able to get rid of the excess iron that accompanies each transfusion  An additional medication called desferoxamine is administered, usually five nights per week over a period of several hours, using an automatic pump that can be used during sleep or taken anywhere the person goes  This medication is able to bind to the excess iron, which can then be eliminated through urine  If desferoxamine is not used regularly or is unavailable, iron overload can develop and cause tissue damage and organ damage and failure  The heart, liver, and endocrine organs are particularly vulnerable  Desferoxamine itself may rarely produce allergic or toxic side effects, including hearing damage  Signs of desferoxamine toxicity are screened for and generally develop in individuals who overuse the medication when body iron levels are sufficiently low  Overall, however, transfusion and desferoxamine therapy have increased the life expectancy of individuals with the most severe types of beta thalassemia major to the 4th or 5th decade  This can be expected to improve with time and increased developments in treatment, as well as for those with more mild forms of the disease 
New treatments offer additional options for some individuals with beta thalassemia major  There are various medications that target the production of red blood cells (i e  erythropoeitin) or fetal hemoglobin (i e  hydroxyurea and butyrate)  Their effectiveness in ameliorating the severity of beta thalassemia is currently being investigated  Another promising new treatment is bone marrow transplantation, in which the bone marrow of an affected individual is replaced with the bone marrow of an unaffected donor  If successful, this treatment can provide a cure  However, there is an approximately 10-15% chance the procedure could be unsuccessful (i e  the thalassemia returns); result in complications (i e  graft-versus-host disease); or result in death  The risk for specific individuals depends on current health status, age, and other factors  Because of the risks involved and the fact that beta thalassemia is a treatable condition, transplant physicians require a brother or sister donor who has an identically matched tissue type, called HLA type  HLA type refers to the unique set of proteins present on each individual's cells, which allows the immune system to recognize "self" from "foreign " HLA type is genetically determined, so there is a 25% chance for two siblings to be a match  Transplant physicians and researchers are also investigating ways to improve the safety and effectiveness of bone marrow transplantation  Using newborn sibling umbilical cord blood—the blood from the placenta that is otherwise discarded after birth but contains cells that can go on to make bone marrow—seems to provide a safer and perhaps more effective source of donor cells  Donors and recipients may not have to be perfect HLA matches for a successful transplant using cord blood cells  Trials are also underway to determine the effectiveness of "partial transplants," in which a safer transplant procedure is used to replace only a percentage of the affected individual's bone marrow  Other possible treatments on the horizon may include gene therapy techniques aimed at increasing the amount of normal hemoglobin the body is able to make 
Hemoglobin H disease is a relatively mild form of thalassemia that may go unrecognized  It is not generally considered a condition that will reduce one's life expectancy  Education is an important part of managing the health of an individual with hemoglobin H disease  It is important to be able to recognize the signs of severe anemia that require medical attention  It is also important to be aware of the medications, chemicals, and other exposures to avoid due to the theoretical risk they pose of causing a severe anemia event  When severe anemia occurs, it is treated with blood transfusion therapy  For individuals with hemoglobin H disease, this is rarely required  For those with the hemoglobin H/Constant Spring form of the disease, the need for transfusions may be intermittent or ongoing, perhaps on a monthly basis and requiring desferoxamine treatment  Individuals with this more severe form of the disease may also have an increased chance of requiring removal of an enlarged and/or overactive spleen 
Anemia — A blood condition in which the level of hemoglobin or the number of red blood cells falls below normal values  Common symptoms include paleness, fatigue, and shortness of breath 
Bilirubin — A yellow pigment that is the end result of hemoglobin breakdown  This pigment is metabolized in the liver and excreted from the body through the bile  Bloodstream levels are normally low; however, extensive red cell destruction leads to excessive bilirubin formation and jaundice 
Bone marrow — A spongy tissue located in the hollow centers of certain bones, such as the skull and hip bones  Bone marrow is the site of blood cell generation 
Bone marrow transplantation — A medical procedure used to treat some diseases that arise from defective blood cell formation in the bone marrow  Healthy bone marrow is extracted from a donor to replace the marrow in an ailing individual  Proteins on the surface of bone marrow cells must be identical or very closely matched between a donor and the recipient 
Desferoxamine — The primary drug used in iron chelation therapy  It aids in counteracting the life-threatening buildup of iron in the body associated with long-term blood transfusions 
Globin — One of the component protein molecules found in hemoglobin  Normal adult hemoglobin has a pair each of alpha-globin and beta-globin molecules 
Heme — The iron-containing molecule in hemoglobin that serves as the site for oxygen binding 
Hemoglobin — Protein-iron compound in the blood that carries oxygen to the cells and carries carbon dioxide away from the cells 
Hemoglobin A — Normal adult hemoglobin that contains a heme molecule, two alpha-globin molecules, and two beta-globin molecules 
Hemoglobin electrophoresis — A laboratory test that separates molecules based on their size, shape, or electrical charge 
Hepatomegaly — An abnormally large liver 
HLA type — Refers to the unique set of proteins called human leukocyte antigens  These proteins are present on each individual's cell and allow the immune system to recognize 'self' from 'foreign'  HLA type is particularly important in organ and tissue transplantation 
Hydroxyurea — A drug that has been shown to induce production of fetal hemoglobin  Fetal hemoglobin has a pair of gamma-globin molecules in place of the typical beta-globins of adult hemoglobin  Higher-than-normal levels of fetal hemoglobin can ameliorate some of the symptoms of thalassemia 
Iron overload — A side effect of frequent blood transfusions in which the body accumulates abnormally high levels of iron  Iron deposits can form in organs, particularly the heart, and cause life-threatening damage 
Jaundice — Yellowing of the skin or eyes due to excess of bilirubin in the blood 
Mutation — A permanent change in the genetic material that may alter a trait or characteristic of an individual, or manifest as disease, and can be transmitted to offspring 
Placenta — The organ responsible for oxygen and nutrition exchange between a pregnant mother and her developing baby 
Red blood cell — Hemoglobin-containing blood cells that transport oxygen from the lungs to tissues  In the tissues, the red blood cells exchange their oxygen for carbon dioxide, which is brought back to the lungs to be exhaled 
Screening — Process through which carriers of a trait may be identified within a population 
Splenomegaly — Enlargement of the spleen 
Because alpha thalassemia major is most often a condition that is fatal in the prenatal or newborn period, treatment has previously been focused on identifying affected pregnancies in order to provide appropriate management to reduce potential maternal complications  Pregnancy termination provides one form of management  Increased prenatal surveillance and early treatment of maternal complications is an approach that is appropriate for mothers who wish to continue their pregnancy with the knowledge that the baby will most likely not survive  In recent years, there have been a handful of infants with this condition who have survived long-term  Most of these infants received experimental treatment including transfusions before birth, early delivery, and even bone marrow transplantation before birth, although the latter procedure has not yet been successful  For those infants that survive to delivery, there seems to be an increased risk of developmental problems and physical effects, particularly heart and genital malformations  Otherwise, their medical outlook is similar to a child with beta thalassemia major, with the important exception that ongoing, life-long blood transfusions begin right at birth 
As discussed above, the prognosis for individuals with the most serious types of thalassemia has improved drastically in the last several years following recent medical advances in transfusion, chemo-, and transplantation therapy  Advances continue and promise to improve the life expectancy and quality of life further for affected individuals 
"First Known Heart Attack Associated With Beta-thalassemia Major Reported " Heart Disease Weekly February 22, 2004: 10 
"Novel Alpha-thalassemia Mutations Identified " Hematology Week January 26, 2004: 19 
Children's Blood Foundation  333 East 38th St , Room 830, New York, NY 10016-2745  (212) 297-4336  cfg@nyh med cornell edu 
Cooley's Anemia Foundation, Inc  129-09 26th Ave  #203, Flushing, NY 11354  (800) 522-7222 or (718) 321-2873  http://www thalassemia org 
March of Dimes Birth Defects Foundation  1275 Mamaroneck Ave , White Plains, NY 10605  (888) 663-4637  resourcecenter@modimes org  http://www modimes org 
National Heart, Lung, and Blood Institute  PO Box 30105, Bethseda, MD 20824-0105  (301) 592-8573  nhlbiinfo@rover nhlbi nih gov  http://www nhlbi nih gov 
National Organization for Rare Disorders (NORD)  PO Box 8923, New Fairfield, CT 06812-8923  (203) 746-6518 or (800) 999-6673  Fax: (203) 746-6481  http://www rarediseases org 
Bojanowski J  "Alpha Thalassemia Major: The Possibility of Long-Term Survival " Pamphlet from the Northern California Comprehensive Thalassemia Center  (1999) 
Children's Hospital Oakland, Northern California Comprehensive Thalassemia Center website  http://www thalassemia com 
Cooley's Anemia Foundation, Inc  website  http://www thalassemia org/gohome html 
Joint Center for Sickle Cell and Thalassemic Disorders website  http://cancer mgh harvard edu/medOnc/sickle htm 
[thal″ah-se´me-ah]
a heterogeneous group of hereditary hemolytic anemias marked by a decreased rate of synthesis of one or more hemoglobin polypeptide chains, classified according to the chain involved (α, β, δ); the two major categories are α- and β-thalassemia 
α-thalassemia (alpha-thalassemia) that caused by diminished synthesis of alpha chains of hemoglobin  The homozygous form is incompatible with life, the stillborn infant displaying severe hydrops fetalis  The heterozygous form may be asymptomatic or marked by mild anemia 
β-thalassemia (beta-thalassemia) that caused by diminished synthesis of beta chains of hemoglobin  The homozygous form is called t  major and the heterozygous form is called t  minor 
thalassemia ma´jor the homozygous form of β-thalassemia, in which hemoglobin A is completely absent; it appears in the newborn period and is marked by hemolytic, hypochromic, microcytic anemia; hepatosplenomegaly; skeletal deformation; mongoloid facies; and cardiac enlargement 
thalassemia mi´nor the heterozygous form of β-thalassemia; it is usually asymptomatic, but there may be mild anemia 
sickle cell–thalassemia a hereditary anemia involving simultaneous heterozygosity for hemoglobin S and thalassemia 
thal·as·se·mi·a
, thalassanemia (thal'ă-sē'mē-ă, thă-las-ă-nē'mē-ă),
Any of a group of inherited disorders of hemoglobin metabolism in which there is impaired synthesis of one or more of the polypeptide chains of globin; several genetic types exist, and the corresponding clinical picture may vary from barely detectable hematologic abnormality to severe and fatal anemia 
[G  thalassa, the sea, + haima, blood]
/thal·as·se·mia/ (thal″ah-se´me-ah) a heterogeneous group of hereditary hemolytic anemias marked by a decreased rate of synthesis of one or more hemoglobin polypeptide chains, classified according to the chain involved (α, β, δ); the two major categories are α- and β-thalassemia 
α-thalassemia that caused by diminished synthesis of alpha chains of hemoglobin  The homozygous form is incompatible with life, the stillborn infant displaying severe hydrops fetalis  The heterozygous form may be asymptomatic or marked by mild anemia 
β-thalassemia that caused by diminished synthesis of beta chains of hemoglobin  The homozygous form is called t  major and the heterozygous form is called t  minor 
thalassemia ma´jor the homozygous form of &#x03B2;, in which hemoglobin A is completely absent; it appears in the newborn period and is marked by hemolytic, hypochromic, microcytic anemia, hepatosplenomegaly, skeletal deformation, mongoloid facies, and cardiac enlargement 
thalassemia mi´nor the heterozygous form of &#x03B2;, usually asymptomatic, although there is sometimes mild anemia 
(thăl′ə-sē′mē-ə)
An inherited form of anemia occurring chiefly among people of Mediterranean descent, caused by faulty synthesis of part of the hemoglobin molecule  Also called Mediterranean anemia 
thal′as·se′mic adj 
[thal′əsē′mē·ə]
Etymology: Gk, thalassa, sea, a + haima, without blood
production and hemolytic anemia characterized by microcytic, hypochromic red blood cells  Thalassemia is caused by inherited deficiency of alpha- or beta-globin synthesis  See also hemochromatosis, hemosiderosis 
Beta thalassemia, clinical thalassemia, Cooley's anemia, Mediterranean anemia, thalassemia major Hematology A group of genetic diseases by underproduction of hemoglobin due to mutations in the beta globin gene, which is more common in Mediterraneans Heredity Parents are carriers–heterozygotes; one in 4 children is homozygous for the mutation and thus has full-blown disease Clinical See Anemia  Cf Sickle cell anemia 
α-thalassemia
Hemoglobin Barts Hematology An inherited condition caused by a defect in the synthesis of the Hb α chain; Hb Barts hemoglobinopathy is characterized by the presence of 4 gamma chains; it is more common in southeast Asians; the most severe form of alpha thalassemia causes stillbirth due to hydrops fetalis Heredity Parents are carriers–heterozygotes; one in 4 children is homozygous for the mutation and thus has full-blown disease Clinical Pallor, fatiguability, FTT, fever, infections, diarrhea Management Transfusions
Thalassemia major Hematology A hemoglobinopathy caused by a defect in the synthesis of Hb β chain Clinical Pallor, fatigability, FTT, fever due to infections, diarrhea, bone deformities, hepatosplenomegaly Management Transfusions, but iron overload can damage the heart, liver, and endocrine systems, ergo iron chelation–early use of deferiprone, deferoxamine ↓ transfusion-related iron overload and may protect against DM, cardiac disease, early death
δ-thalassemia
Hematology A condition characterized by a defect of Hb A2–α2δ2; because Hb A2 comprises only 3% of the circulating Hb, even its complete absence; δ-thalassemia has little clinical or hematologic impact
γ-thalassemia
Hematology A condition characterized by a defect of gamma–γ Hb chains found in Hb F–α2γ2; because Hb F is present primarily in the fetus and newborns, it is rarely seen outside of the neonatal period, but may cause transient neonatal hemolytic anemia 
, thalassanemia (thal'ă-sē'mē-ă, -ă-să-nē'mē-ă)
Any of a group of inherited disorders of hemoglobin metabolism in which there is impaired synthesis of one or more of the polypeptide chains of globin; several genetic types exist, and the corresponding clinical picture may vary from barely detectable hematologic abnormality to severe and fatal anemia  People of Mediterranean, extraction are more often affected than others by this type of anemia 
Synonym(s): thalassaemia, thalassanaemia 
Any of a group of inherited disorders of hemoglobin metabolism with impaired synthesis of one or more polypeptide chains of globin; several genetic types exist 
<a href="https://medical-dictionary thefreedictionary com/Thalassaemia+minor">thalassemia</a>
Barts hemoglobin
beta hemoglobinopathy
beta-delta thalassemia
beta-thalassaemia
Bite Cell
black gallstone
I know of a couple, totally unrelated and unbeknownst to them, who are silent carriers of Thalassaemia minor 
Pakistan: Genetic factor: All in the genes
But, unfortunately, when one person with thalassaemia minor carrier happens to marry another with the same diagnosis, there is a strong possibility that their child would be thalassaemia major, as happened in the case of Taneja 
' My life depends upon a monthly blood transfusion '
0] thalassaemia demonstrates variable severity, ranging from a condition similar to [beta] thalassaemia minor to something approaching thalassaemia major 
A retrospective review of homozygous haemoglobin E patients
Thal, Alan P 
thalame
thalamencephalic
thalamencephalon
thalamic
thalamic fasciculus
thalamic nucleus
thalamic pain syndrome
thalamic peduncle
thalamic radiation
thalamo-
thalamocoele
thalamocortical
thalamocortical fibers
thalamogeniculate artery
thalamolenticular
thalamoperforating artery
thalamostriate radiation
thalamotuberal artery
Thalassaemia minor
thalassaemiaor Cooley's disease
thalassemic facies
thalasso-
Thalassobacter
Thalassobacter utilis
thalassoplankton
thalassoposia
thalidomide neuropathy
Thalidomider
thallium poisoning
Thalarctos
THALAS
Thalasaemia
Thalassaemia Association of Malaysia
thalassaemia major
Thalassaemias
thalassaemic
thalassanaemia
Thalassemia Action Group
Thalassemia Clinical Research Network
thalassemia syndrome



ground_truth: Power-law functions.