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text:
Research from the heart
Mitacs Globalink student researching proteins associated with rare heart disease
Search impact stories
Video Content:
"Participating in research that could potentially benefit the way different heart diseases are treated is something that I could see myself doing for the rest of my life. It makes all the hard work I've put into my studies pay off and keeps me motivated to continue to further my knowledge so that one day I will contribute to research that will improve the lives of people all over the world, " said Diego Parra.
Diego comes from Guadalajara, Mexico's Monterrey Institute of Technology and Higher Education where he is working towards his undergraduate degree in Biotechnology. It was his initial intrigue with DNA that drove him to this field of study, but now the possibility that his research will have real world impact has pushed him to pursue his masters.
A 12-week Mitacs Globalink internship brings Diego to the University of British Columbia, where under the guidance of Dr. Filip Van Petergem from the Department of Biochemistry and Molecular Biology, Diego is researching the components of the protein RyR to see how changes in its structure have been linked with a rare heart disease, among other genetic diseases.
The objective of Diego's research is to gain insight on abnormal changes of function that can occur in the RyR protein. Then, he will help design medicines to recover the normal function where it could be used to treat different diseases linked to the protein.
When asked what his dream job would be, he answers selflessly, "It would be related to the different applications of genetics to benefit the well-being of humankind, either by doing research that could lead to new medicines, or treatments for a wide number of diseases that impact people daily. Thanks to Mitacs Globalink, I'm having the chance to experience this impactful research first hand."
Mitacs thanks the Government of Canada and the Government of British Columbia for their support of the Globalink research internship in this story. Across Canada, the Globalink program also receives support from Alberta Innovates, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, the Government of Prince Edward Island, the Government of Quebec,the Government of Saskatchewan and Research Manitoba.
Do you have a business challenge that could benefit from a research solution? If so, contact Mitacs today to discuss partnership opportunities: BD@mitacs.ca
Globalink Research Internship

redpajama_set_name: RedPajamaCommonCrawl


text:
Q: 2010s novel series about a supernatural prison inside a tiny cube I'm thinking about a short novel series (a trilogy or a tetralogy) which I read in my school's library around 2012. It is targeted to teenagers or young adults. I read it in French pocket format but I don't know whether it was a translation.
The main focus of the series is a supernatural prison. I recall mazes, chasms and a room guarded by a bird-like magical beast, though this may not be 100% accurate. The ambiance inside the prison is gloomy, and people fighting or dying is common. What every prisoner has in mind is fear and survival. There are no cells, rather open spaces, and few to no human guards, because the prison is already a fortress on its own: it really doesn't need them. For many escaping is an absurd notion.
One of the protagonists is a young boy inside the prison; another is a young girl on the outside, the daughter of the prison's head. I think it happens in modern times (say, at least 1850) but of the outside I recall very little. They communicate several times to try to solve two problems: locating the prison and getting the boy out of it. Apparently no one outside, except the main prison staff, even knows where it is.
Around the end of the story, they discover that the prison, which they thought was buried deep under the ground, is actually a tiny cube attached to a bracelet around the prison head's wrist (which makes the escape even more difficult). I remember the boy and a few other prisoners manage to get out at the very end of the story.
I guess the ambiance of the prison and the twist about the cube are the most memorable aspects of this story.
Escape from Furnace by Alexander Gordon Smith is a very close match. To be honest, I cannot rule it out, but I didn't find any mention of the cube, and I definitely didn't read anything following the escape (then again, only three of the five books seem to have been translated to French).

A: Incarceron #1 (2007) by Catherine Fisher maybe? (published in France in 2010)

(first: original cover; second: French cover)
From Goodreads:

Incarceron -- a futuristic prison, sealed from view, where the descendants of the original prisoners live in a dark world torn by rivalry and savagery. It is a terrifying mix of high technology -- a living building which pervades the novel as an ever-watchful, ever-vengeful character, and a typical medieval torture chamber -- chains, great halls, dungeons.
A young prisoner, Finn, has haunting visions of an earlier life, and cannot believe he was born here and has always been here. In the outer world, Claudia, daughter of the Warden of Incarceron, is trapped in her own form of prison -- a futuristic world constructed beautifully to look like a past era, an imminent marriage she dreads. She knows nothing of Incarceron, except that it exists. But there comes a moment when Finn, inside Incarceron, and Claudia, outside, simultaneously find a device -- a crystal key, through which they can talk to each other. And so the plan for Finn's escape is born ...

As of now, there are two books in the series (Incarceron and Sapphique).
From this review:

The ending of this story is strange because at the end of this book Finn leaves Incarceron, but that's not what makes this weird. What makes this story weird is you find out that this entire prison is in a small cube on the pocket watch of the warden. This is so strange because it means they some how made an entire world literately so small they have to basically be teleported to this prison with only one way out but with the key now inside the cube.

So, not a bracelet, but quite close to it.

Eventually found with the Google query roman prison labyrinthe "fille du directeur" (novel jail "warden's daughter")
The whole search process was detailed in chat. The story takes too much space to be included in the present answer, but if you're curious about the reasoning and the behind-the-scenes, here's the transcript!

redpajama_set_name: RedPajamaStackExchange


text:
Los New Jersey Devils (en español, Diablos de Nueva Jersey) son un equipo profesional de hockey sobre hielo con sede en Newark, Nueva Jersey. Compiten en la División Metropolitana de la Conferencia Este de la National Hockey League (NHL) y disputan sus partidos como locales en el Prudential Center.

Los diablos han ganado la Copa Stanley tres veces. El equipo se fundó en Kansas City (Misuri) en 1974, fueron trasladados a Denver, Colorado después de solamente dos temporadas, el club se trasladó a Nueva Jersey en 1982. Bajo el mando del director general actual Lou Lamoriello, los diablos han finalizado primeros en 7 de las últimas 9 temporadas y participado en 17 ocasiones en los playoff.

Luego de su movimiento a Nueva Jersey, los diablos han jugado sus juegos de local en la Continental Airlines Arena. En 2007, se espera que el equipo se traslade al Prudential Center, que se está construyendo en la ciudad de Newark. Tienen rivalidades con sus vecinos del río Hudson, los New York Rangers, y con Philadelphia Flyers, como los devils o los flyers han ganado el título de la división atlántico cada temporada desde 1995.

Historia de la franquicia

Kansas City y Colorado
En 1974, la NHL terminó su primer período de extensión agregando a equipos en Kansas City (Misuri) y Washington D.C. Kansas City debía ser llamado los Mohawks, puesto que el área metropolitana de Kansas City incluye porciones de Misuri y de Kansas. Sin embargo, los Chicago Black Hawks se opusieron a la semejanza. Retitularon al equipo los Scouts después de una estatua en la ciudad.

El 9 de octubre de 1974, los Scouts pisaron el hielo por primera vez en Toronto y perdieron 6-2 ante Toronto Maple Leafs. Debido a un problema que era sostenido en la Kemper Arena que iba a estrenar Kansas City, forzaron a los scouts a esperar nueve juegos antes de hacer su debut en casa. Aunque perdieron ese juego ante los Black Hawks 4-3, la siguiente noche batieron al recién llegado, los Washington Capitals por 5-4. Los scouts no pudieron calificar a la segunda fase en todas las temporadas en Kansas City y ganaron solamente 27 de 160 juegos.

El equipo comenzó a sufrir del descenso económico en el Cercano oeste. Para su segunda temporada, los Scouts vendieron apenas 2,000 de 8,000 boletos de la temporada y eran casi $1 millón en deuda. Debido a éstos la licencia de la franquicia fue cedida a Denver y fueron renobrados Colorado Rockies (NHL).

El equipo tuvo un comienzo grato en Colorado, ganando su primer juego 4-2 sobre Toronto. Tomaron ímpetu y parecían un equipo aspitante para la segunda fase, pero las cosas se derrumbaron en febrero, y los Rockies acabaron la temporada 1976-77 con un récord de 20-46-14. La siguiente temporada, a pesar de acabar con pocos triunfos, lograron vencer a Vancouver Canucks y dejarlos fuera de la segunda fase por dos puntos, pero fueron eliminados rápidamente por los Filadelfia Flyers en la primera ronda.

Antes de la temporada 1978-79, el dueño Jack Vickers vendió el equipo a Arturo Imperatore, que anunció que él deseaba mover al equipo a New Jersey Meadowlands. La NHL anuló el movimiento, requiriendo al equipo permanecer en Denver hasta que la Brendan Byrne Arena estuviese completa. En 1979, el equipo pone como técnico principal a la estrella de los Maple Leafs Lanny McDonald, pero a pesar de estos movimientos, los Rockies fijaron el peor récord de la liga. Jugaron las dos temporadas siguientes con la posibilidad de mudanza hasta el 27 de mayo de 1982, cuando el magnate John McMullen de Nueva Jersey compró al equipo y anunció que el movimiento largo y esperado a Nueva Jersey finalmente pasaría.

El equipo ahora estaría jugando en el medio de Nueva York - Nueva Jersey - Connecticut área del triple estado, hogar de los Nueva York Islanders campeón de la copa Stanley, así como los Nueva York Rangers.

New Jersey

1982-1993
Retitularon al equipo los New Jersey Devils diablos de New Jersey el 30 de junio de 1982. alrededor de 10 000 personas votaron en una competencia llevada a cabo por los periódicos locales para seleccionar el nombre, que son influenciadas por la leyenda del Jersey Devil, una criatura siniestra de cryptozoología que habita en el pino Barrens de New Jersey. El primer juego de los diablos terminó en un empate 3-3 ante los Pittsburgh Penguins. Su primer triunfo, fue un 3-2, vino a expensas de sus nuevos rivales del río Hudson, los New York Rangers. El equipo acabó con un récord de 17-49-14, poniéndolos tres puntos sobre el último lugar en la Patrick División.

En la temporada siguiente, luego de que los humillaran ante su público los Edmonton Oilers 13-4. Gretzky estuvo disgustado que el antiguo compañero de equipo Ron Low jugó por lo que él consideró un equipo inferior, y en una entrevista posterior al juego dijo:

Más tarde, Gretzky confesó en público que su comentario fue demasiado lejos, pero en privado mantuvo que su comentario era exacto. En la respuesta, muchos seguidores de los Devils llevaron puesta la playera de Mickey Mouse cuando los Oilers volvieron a visitar Nueva Jersey.

En la temporada 1983-84, los Devils recibieron el Juego de Estrellas de la NHL en la Brendan Byrne Arena. Glenn "Chico" Resch fue el ganador del trofeo Bill Masterton, y los Devils Joe Cirella consiguieron una meta para que ganara la Conferencia de Wales a la Conferencia Campbell por 7-6. Sin Embargo, el equipo no consiguió mucho éxito. El técnico principal Bill MacMillan fue despedidp a mitad de la temporada y sustituido por Tom McVie, y los Devils marcaron su peor registro en la historia del equipo, terminando 17-56-7. Después de la temporada, McVie fue sustituido por Doug Carpenter.

Mientras tanto, los Devils habían comenzado a entrenar un grupo de jugadores jóvenes. John MacLean, Kirk Muller, y Pat Verbeek todos complementaron el mando Resch. El récord del equipo mejoró cada temporada entre 1984 y 1987. Sin embargo, la presencia de los poderosos Islanders, Flyers y Capitals en la División Patrick significó que los Devils se encontraron en una batalla perdedora con los Rangers y Penguins para el último punto de desempate de la división.

Lamoriello se designó gerente general poco antes de la temporada de 1987-88. Este movimiento envió ondas expansivas a través de la liga. Aunque Lamoriello hubiera sido un técnico de colegio durante 19 años, él nunca había jugado, había entrenado, o había incursionado en la NHL.

Los Devils de la temporada 1987-88 lograron la primera marca ganadora en la historia de 13 años de la franquicia. Durante el último día de la temporada regular, ellos empataron con los Rangers, para el punto de desempate final en División Patrick. Después de que Nueva York derrotó a los Quebec Nordiques 3-0, todos los ojos estaban en los Devils, que jugaban contra Chicago Blackhawks en Chicago. Los Devils perdían 3-2 a mitad del partido fue durante el tercer período cuando John MacLean empató el juego, y con dos minutos tiempos extras, él añadió el gol de la victoria. Aunque los Rangers y los Devils terminaran con 82 puntos, los Devils tenían uno triunfo más, enviándolos a los desempates por segunda vez en la historia de la franquicia.

El equipo logró calificar a las finales de conferencia, pero perdió contra Boston Bruins en siete juegos. En aquella serie, el técnico principal Jim Schoenfeld insulto verbalmente al árbitro Koharski, gritando obscenidades y sugiriendo que el funcionario "tenga otro anillo de espuma". El incidente causó una suspensión para Schoenfeld, que la franquicia apeló al Tribunal Superior. Esta petición sin precedentes a autoridades fuera del NHL dio a los Devils una permanencia preliminar de la suspensión del técnico. En la protesta, el árbitro Dave Newell y los jueces de línea adjudicados boicotearon el siguiente partido de desempate de Boston y New Jersey. Para resolver el incidente, la NHL suspendió a Schoenfeld para el siguiente juego. Más tarde Schoenfeld confesó que él lamentó sus comentarios.

La temporada siguiente, los Devils terminaron otra vez debajo de.500 y no participaron en los partidos de desempate. Lamoriello hizo varios cambios de jugador en la posttemporada, notablemente al firmar de las dos primeras estrellas soviéticas en jugar en la NHL: Viacheslav Fetisov y Sergei Starikov. Los Devils adquirieron años antes via draft a Fetisov antes de entrar al draft de 1983, pero el gobierno soviético no permitía a Fetisov, ya que era un oficial del equipo como miembro del equipo nacional, que dejara el país. Un poco después, los Devils firmaron al defensa Alexei Kasatonov para acompañar a Fetisov.

El equipo cambió entrenadores a media temporada por cada una de las dos siguientes temporadas. Schoenfeld fue substituido por John Cunniff en 1989-90, y Tom McVie fue sustituido de nuevo a media temporada de 1990-91 y el equipo terminó eliminado por tercera vez consecutiva en primera ronda en 1991-92. Herb Brooks, quien entrenara el equipo de al equipo de 1980 " el Milagro sobre el Hielo ", fueron traído para la temporada 1992-93, pero cuando el equipo aún fue otra vez eliminado en la primera ronda, él fue despedido y sustituido con antiguos Montreal Canadiens como Jacques Lemaire.

1993-2003

Bajo el mando de Lemaire, el equipo rugió en la temporada regular 1993-94 con una alineación incluyendo los defensas Scott Stevens y Scott Niedermayer, y adelante Stephane Richer, John MacLean, Bobby Holik, y Claude Lemieux, y los porteros Chris Terreri y Martin Brodeur, que tuvo el honor de ser el mejor novato de la liga ganando el Calder Memorial Trophy. La primera temporada de 100 puntos de los Devils fue el segundo registro de la NHL detrás de los Nueva York Rangers. Sin embargo, debido al nuevo formato de partido de desempate de la NHL, los Devils fueron sembrados terceros en el Este. Los rivales que se encontraron en las Finales de Conferencia del Este fueron juegos memorables, a siete juegos. Los Diablos habían perdido las seis temporadas regulares al Blueshirts, pero habían avisado al mundo que ellos se levantaron para el desafío, después de que el Devil Stephane (Mejor anotador) anotó un gol en el segundo tiempo exta. Para el juego seis los Devils tenían la serie 3-2. Antes de que el capitán de los Rangers Mark Messier hiciera fama que los Rangers ganarían el juego seis. Manteniéndose fiel a su palabra, Messier condujo su equipo atrás, y conduciendo a los Rangers a una victoria de 4-2 (después de que los Devils levantaran un 0-2).

En el juego siete, el Devil Valeri Zelepukin empataro el juego que decidía la serie dejando solo 7.7 segundos, pero los Devils fueron derrotados en dobles tiempos extras, con un gol de Stephane Matteau. La serie es vista por muchos admiradores de hockey como una de las mejores series de desempate en la historia NHL.
En la temporada 2002/03 los Devils consiguieron su tercera Stanley Cup venciendo a Mighty Ducks of Anaheim.

2003-Actualidad

Los New Jersey Devils consiguieron su pase a la final de la stanley cup en la temporada 2011/12, aunque perdiéndola contra Los Angeles Kings

Colores del equipo y mascota

Escudo
El logotipo de los Devils es un monograma de las letras "N" y "J", con dos cuernos de diablo en lo alto de la "J" y una cola puntiaguda en el fondo. El monograma es rojo con un contorno negro, y se sienta dentro de un círculo negro abierto. El logotipo está en un campo de blanco en medio del pecho en ambos uniformes. Antes de la temporada de 1992, el círculo negro y el contorno eran verdes.

Colores
Los colores del equipo son rojos, blancos y negros, y ellos son usados tanto en casa como en encuentros de visita. El uniforme de casa, que era el uniforme de visita del equipo hasta 2003 cuando la NHL decidió cambiarlo al uniforme de local, que es dominantemente rojo. Hay tres rayas blancas y negras, una a través de cada brazo y una a través de la cintura. El uniforme de visita es el antiguo uniforme de casa del equipo, color blanco con un diseño similar, salvo que las tres rayas son negras y rojas. Los hombros son cubiertos por negro en ambos uniformes. Antes de 1993, los uniformes eran verdes y rojos con rallas ligeramente diferente. Los Devils tienen que introducir un tercer uniforme, y son uno de los 3 equipos de la NHL (Detroit y Carolina son los demás) que nunca lo han tenido.

Mascota
La mascota es NJ Devil ("Diablo de NJ"), un diablo alto de 2.13 metros que juega en el mito del Diablo de Jersey. El Diablo de NJ alegra la muchedumbre apasionada, da autógrafos, participa en el entretenimiento durante las interrupciones, patina sobre el hielo, y corre en todas partes de los pasillos de la Arena con cinco aficionados.

Antes de 1993, la mascota era "Slapshot", un disco de hockey grande que también se relacionaba con los aficionados. Sin embargo, el hombre dentro del traje, Brad Patrick Ebben, fue despedido después de que él incorrectamente tocó a tres mujeres mientras se encontraba disfrazado. Para quitar el estigma del pleito, Slapshot fue retirado y no ha vuelto desde entonces.

Temporadas Records

Resultados Temporada por Temporada
Note: JJ = Juegos Jugados, G = Ganados, P = Perdidos, E = Empates, PTE = Perdidos en Tiempo Extra, Pts = Puntos, GF = Goles a Favor, GC = Goles en Contra, PEM = Penaltis en minutos

Récords hasta el 2 de noviembre de 2006.

1 Temporada corta.
2 Temporada cancelada.

Récords Individuales

Temporada Regular
 Más goles: Brian Gionta, 48 (2005-06)
 Más asistencias: Scott Stevens, 60 (1993-94)
 Más puntos: Patrik Elias, 96 (2000-01)
 Más penalizaciones en minutos: Krzysztof Oliwa, 295 (1997-98)
 Más puntos (defensa): Scott Stevens, 78 (1993-94)
 Más puntos (novato): Scott Gómez, 70 (1999-00)
 Más ganados: Martin Brodeur, 43 (1997-98, 1999-00, 2005-06)
 Más shutouts: Martin Brodeur, 11 (2003-04)
 Más goles en power play: Brian Gionta, 24 (2005-06)

Playoffs
 Más goles: Claude Lemieux, 13 (1994-95)
 Más goles por un defensa: Brian Rafalski, 7 (2000-01)
 Más asistencias: Scott Niedermayer, 16 (2002-03)
 Más puntos: Patrik Elias, 23 (9g,14a) (2000-01)
 Más puntos por un defensa: Brian Rafalski and Scott Niedermayer, 18 (2000-01, 2002-03)
 Más penalizaciones en minutos: Perry Anderson, 113 (1987-88)

Roster Actual
hasta el 29 de noviembre de 2006.
<center>

{| width=90%
!colspan=7 |Deltanteros
|- bgcolor="#dddddd"
!width=5%|#
!width=5%|
|align=left!!width=15%|Jugadores
!width=8%|Posición
!width=8%|Tira
!width=9%|Adquirido
!width=37%|Lugar de Nacimiento

|-bgcolor="#eeeeee"
|align=center|9
|align=center|
|Zach Parise
|align=center|C
|align=center|L
|align=center|2003
|Minneapolis, Minnesota

|-bgcolor="#eeeeee"
|align=center|10
|align=center|
|Erik Rasmussen
|align=center|C
|align=center|L
|align=center|2003
|Minneapolis, Minnesota

|-bgcolor="#eeeeee"
|align=center|11
|align=center|
|John Madden - A
|align=center|C
|align=center|L
|align=center|1997
|Barrie, Ontario

|-bgcolor="#eeeeee"
|align=center|12
|align=center|
|Jim Dowd
|align=center|C
|align=center|R
|align=center|2006
|Brick, Nueva Jersey

|-bgcolor="#eeeeee"
|align=center|14
|align=center|
|Brian Gionta
|align=center|RW
|align=center|R
|align=center|1998
|Rochester, New York

|-bgcolor="#eeeeee"
|align=center|15
|align=center|
|Jamie Langenbrunner - A
|align=center|RW
|align=center|R
|align=center|2002
|Cloquet, Minnesota

|-bgcolor="#eeeeee"
|align=center|16
|align=center|
|Jason Wiemer
|align=center|C
|align=center|L
|align=center|2006
|Kimberley, Columbia Británica

|-bgcolor="#eeeeee"
|align=center|17
|align=center|
|Michael Rupp
|align=center|RW
|align=center|L
|align=center|2006
|Cleveland, Ohio

|-bgcolor="#eeeeee"
|align=center|18
|align=center|
|Sergei Brylin
|align=center|LW
|align=center|L
|align=center|1992
|Moscú, U.R.S.S.

|-bgcolor="#eeeeee"
|align=center|19
|align=center|
|Travis Zajac
|align=center|C
|align=center|R
|align=center|2004
|Winnipeg, Manitoba

|-bgcolor="#eeeeee"
|align=center|20
|align=center|
|Jay Pandolfo - A
|align=center|LW
|align=center|L
|align=center|1993
|Winchester, Massachusetts

|-bgcolor="#eeeeee"
|align=center|23
|align=center|
|Scott Gómez
|align=center|C
|align=center|L
|align=center|1998
|Anchorage, Alaska

|-bgcolor="#eeeeee"
|align=center|25
|align=center|
|Cam Janssen
|align=center|RW
|align=center|R
|align=center|2002
|San Luis, Misuri

|-bgcolor="#eeeeee"
|align=center|26
|align=center|
|Patrik Elias - C
|align=center|LW
|align=center|L
|align=center|1994
|Trebic, República Checa
|}

Miembros de Honor
Los Devils han retirado dos números, ambos en 2006. El 3 de febrero retiraron el número 4 del defensa Scott Stevens quien jugó mucho tiempo y fue capitán, él jugó 13 temporadas con los Devils. El Devil de carrera Ken Daneyko número 3 fue retirado el 24 de marzo. Daneyko, fue un defensa, fue adquirido por el draft en 1982 y jugó 22 temporadas para los Devils.

Dos jugadores de los Devils han sido incorporados en el Salón de la Fama de Hockey. El defensa Viacheslav Fetisov, uno de los dos primeros jugadores soviéticos en la NHL, jugando para los Devils a partir de 1989 hasta 1995 y era un ayudante el entrenador de 1999-2002; él fue incorporado en 2001. Peter Stastny, un antiguo centro y uno de los mayores goleadores en los años 80, jugó para los Devils a partir de 1990 hasta 1993 y fueron incorporados en 1998. Antiguos entrenadores generales de los Devils como Jacques Lemaire (1993-1998) y Larry Robinson (2000-2002, 2005) habían sido jugadores antes de la incorporarse a la organización de los Devils. Herb Brooks (1992-1993), quien condujo a Estados Unidos en 1980 al equipo olímpico a la victoria en " el Milagro sobre el Hielo ", fue incorporadi en 2006.

Líderes

Capitanes del equipo
 Don Lever, 1982–1984
 Mel Bridgman, 1984–1987
 Kirk Muller, 1987–1991
 Bruce Driver, 1991–1992 Scott Stevens, 1992–2005
 Scott Niedermayer, 2004  (interino)
 No captain, 2005–2006
 Patrik Elias, 2006– presente

Esta lista no incluye a los capitanes de los Kansas City Scouts y Colorado Rockies.

 Técnicos principales
 Bill MacMillan, 1982–1983
 Tom McVie, 1983–1984
 Doug Carpenter, 1984–1988
 Jim Schoenfeld, 1988–1989
 John Cunniff, 1989–1991
 Tom McVie, 1991–1992
 Herb Brooks, 1992–1993
 Jacques Lemaire, 1993–1998 Robbie Ftorek, 1998–2000
 Larry Robinson, 2000–2002
 Kevin Constantine, 2002
 Pat Burns, 2002–2005
 Larry Robinson, 2005
 Lou Lamoriello, 2005–2006
 Claude Julien, 2006–presenteEsta lista no incluye a los técnicos de los Kansas City Scouts y Colorado Rockies.

Referencias

General

Enlaces externos

 Sitio web oficial de los New Jersey Devils
 Sports E-Cyclopedia.com
 Madison Square Garden's Official NJ Devils Online News Hub

Equipos de la NHL
Clubes y equipos deportivos del estado de Nueva Jersey
Newark (Nueva Jersey)

redpajama_set_name: RedPajamaWikipedia


US stock market yesterday performance was mixed with up and down; benchmark index ended flat with this year high point, trading in the mid market was thin; investors were waiting for the result of multinational central bank policy meeting included Bank of Japan and Fed. Dow-Jones Average index and Nasdaq index rallied at market close; S&P 500 index dropped 2.55 point or 0.13% to 2019.64 point. From news side, US New York Fed February survey showed the expectation on inflation of US consumers revived from years low since the gasoline price was estimated to rally faster. And prior CPI and PCE data showed US overall inflation had sharply revived along with the rebounded oil price, core CPI rose through 2%. Rallied mid-term inflation expectation is one of the necessary conditions of Fed second interest rate hike. Current Fed fund rate futures showed probability of rate hike in March is still 0% and, in April, June, September, December is 26.6%、41.5%、42.1%、38.6%, respectively, which showed plainly revive compare to prior period, market is looking forward to Fed change tunes and, is on track to the expectation on interest rate hike. As for this week Fed meeting, we regard US economy and employment market might show hawkish signal but, is expected to hold "observe attitude" on interest rate policy side; rate will not be hiked immediately, Fed indicates the strong expectation still exists but not fully price-in, US dollar might revive.
From domestic side, China's central bank saw its yuan funds outstanding for foreign exchange drop 227.9 billion yuan to 23.98 trillion yuan at the end of February, which set the lowest level since the end of 2012 but, the fall narrowed from the drop in January. This is basically the same as prior released foreign exchange reserve data, under the impact of prior weakened US and, stronger RMB exchange rate, reduction on hot money capital outflows is legitimate. But from latter period perspective, probability on US dollar rate hike has increased again, recent RMB exchange rate will be hammered; pay attention on the intervention from central bank.
Yesterday stock index overall soared and dropped at the end of market; computer sector raised 4.8; ChiNext raised 88 point and, recovered 2000 point; only banking sector dropped. One bank and three commissions answered to reporters' request at weekend which boosted market, registration system prolonged, market reacted positively and, ChiNext rapidly revived. But overall economy tendency was sluggish, industrial added value set new low again; bad bank loans might over 2 trillion which restricted the upward trend on market. From macro data, mainly since the real estate investors revived, the economy only drove the real estate market at present; February credit data failed to meet expectation, expectation on fiscal incentive and, easing credit was repeatedly. Index is hard to repeatedly revive and, we recommend operating in lot point fluctuation.
Monday copper market still dropped back after soaring, market buying momentum was insufficient. From copper market side, LME inventory continued decreasing 1800 tons to 174,000 tons; spots premium sharply increased $9 to $25.5, in contrast, domestic inventory set record high to 350,000 tons. As a matter of fact, divergence on domestic and overseas inventory has showed several times in history but, eventually copper price would be lower, which has impacted from recent domestic stagnated copper price. Technically, copper price is fluctuating above $4900, we waiting for the good opportunity on short.
DCE soybean is in divergence trend, soybean No.1 contract volatile and, grease drops which firms the meal; oil against meal ratio drops after setting record high. Soybean price at Heilongjiang, Inner Mongolia, Jilin and other areas all dropped, though basic surplus grain at Inner Mongolia and Jilin was insufficient which bolstered price, under the influence of continuously dropping Heilongjiang soybean, price at both areas would follow to weaker.
From soybean meal side, partial areas of spots slightly rallied, oil plants at coastal quotation at RMB 2450-2520/ton; domestic oil plant operation ratio was high, import soybean supply was sufficient. Since recent national commercial inspection, it might affect the port soybean export price or, it might boost the price but, strength is expected to relatively limit. As for operation: we recommend soybean meal entering cautiously; arbitrage between oil and meal staying in observation temporally.
Yesterday PP futures opened low and volatile went lower; opened at 6940 and ended 6878; trading volume increased 40912 lots to 2.034 million lots; holding decreased 11220 lots to 416,000 lots. From spots side, yesterday domestic PP market showed narrowly consolidation. Producer price of petrochemical regions is stabilizing which certain bolsters the market. PP futures operate in low, slightly strike market sentiment. Merchants' ships followed tendency, partial merchants slightly surrender profits for promoting trading. Plants in downstream initiatively receiving demand reduce, market is mainly in observation; market trading and investing is thin.
Intraday main quoted prices for wires of north, east and south markets are RMB 6900-7000/ton, RMB 6900-7050/ton and RMB 7100-7150/ton, respectively.
As for operation, current moving average system turned and, MACD red column shortened; tendency might retrace recently.


text:
Freeport McMoRan Copper & Gold
GMJ Equipment Pty Ltd
Hammar Maskin
SSAB Tunnplåt
LKAB
Spareparts - start page
The Grasberg mine, in Papua, Indonesia, located at an altitude of 4100 m amsl, is regarded as the world's largest gold mine and has been open-pit mined for decades. Starting in 2016, excavation has to be performed underground and Freeport-McMoRan Copper & Gold Inc. is now investing extensively in new infrastructure to manage the transition.
GMJ Equipment is a leading Australian manufacturer of mobile elevating work platforms. The platforms reach heights of 10-70 meters and come in many different models. GMJ has more than 25 years of experience in the industry and is at the forefront of mobile elevating platform development.
Hammar Maskin is the world's largest manufacturer of mobile self-loading container equipment, known as sideloaders. The sideloaders use the Remotus Mercury control system from Åkerströms Björbo. "Switching to Remotus a year and a half ago simplified our work significantly," says Christian af Petersens, purchasing manager.
It's full speed ahead for leisure craft production at Nimbus Produktion i Visby AB, a boat builder on the island of Gotland, Sweden. Demand skyrocketed, so boat building in its factory is continuously streamlined to meet production demands. One efficiency effort involved installation of Åkerströms' remote-control system for overhead cranes.
SSAB Tunnplåt's Borlänge operation has its steel sheet processing under control. The company—a subsidiary of the SSAB Group—processes 30-ton steel sheet rolls/coils with varying properties for purposes such as car, refrigerator, or door manufacturing. So it's important to keep track of the warehouse's hundreds of rolls, which must be cut to length and distributed to customers. SAAB Tunnplåt manages the logistics with a wireless network and Åkerströms' remote-control solutions.
By size, Skycon is a modest company with six employees. Yet it holds 85 percent of the global market—not bad, and its product is unique. Skycon manufactures and sells cleaning equipment systems for cruise ships and ferries.
Anyone who imagines that a mine is a tough environment is ... absolutely right. Robert Larsson is functional manager at LKAB that has facilities in Malmberget and Kiruna in northern Sweden.
Larsson confirms: "Sure, working underground is often challenging. It's hot and dirty. Industrial environments like the pelleting plant are both hot and wet. And it can be really cold outside in the winter. Those circumstances place high demands on the machines."
Security and directives
Search partner
About Åkerströms
T & C Purchases


text:
\section{Discussion \& Conclusion}{\label{sec:conclusion}}

As illustrated in Figure~\ref{fig:learn-ops}, in our search of GAN architectures, the controller RNN learns drastically different distributions over operations for three module types. The up-sampling modules predominately favor conv $3\times3$ and conv $1\times3$ then $3\times1$; the down-sampling modules favor conv $3\times3$ and dilated conv $3\times3$; the normal modules favor average pooling $3\times3$, $5\times5$ and $7\times7$. This justifies our choice of segmentation of controller RNN. We point out that it is at least in contrast to RL-based NAS algorithms over image classifiers \cite{ENAS, BlockQNN,NAS2}, where both the normal cell and reduction cell choose among depthwise-separable convolutions, max and average poolings.

In addition, we observe that
\begin{itemize}
    \item the up-sampling modules prefer upsample-then-convolution operations, over transposed convolutions;
    \item the down-sampling modules prefer convolution-then-downsample operations over downsample-then-convolutions;
    \item the normal modules mostly consist of average poolings, and hence have very few parameters;
    \item depthwise-separable convolutions are not present at most networks in later stage.
\end{itemize}

In our experiments we observe that the order of operations in the same module matters much. For example, in down-sampling modules, whether we perform down-sampling at the beginning of the module or at the end of the model may have significant impact on the overall performance though the exact mechanism of the effect is not clear.

In conclusion, we present AGAN, the first neural architecture search algorithm on deep generative models. We demonstrate that, by careful design of controller architecture and search space, RL-based NAS algorithm can discover highly competitive architectures that rival the best human-invented GAN architecture. Further reducing model size and enabling fast inference are on our future research agenda.

\section{Experiments}{\label{sec:experimentalStudy}}

\subsection{Data Sets}
We used two data sets in our experimental study. The CIFAR-10 data set consists of $60,000$ $32\times32$ color images in $10$ different classes. The data set is divided into a training data set of $50,000$ images and a testing data set with the rest $10,000$ images. Only training set is used for our experiment.  The STL-10 data set is an image data set of $96\times96$ color images. It is composed of images in $10$ different classes with $500$ training images and $800$ testing images per class, and an additional $100,000$ unlabeled images for unsupervised learning.

For data preprocessing, we follow the setup in \cite{SNGAN} by scaling the images to $[-1, \frac{127}{128}]$ then adding random noise $\epsilon\sim\mathcal{U}(0, \frac{1}{128})$ for both data sets.

\subsection{Experimental Procedure}

The controller used in our experimental study is a two-layer LSTM with $100$ units, consisting of three segments. Each segment outputs a sequence of $11$ actions ($6$ operations and $5$ adjacency vectors), encoding a DAG of $6$ nodes. We use sample temperature $T = 5.0$ and logit clipping $C = 2.5$ when sampling operations, and $T = C = 1.0$ when sampling adjacency vectors. The controller is trained using policy gradient with learning rate $0.0006$. We compensate the loss with an entropy temperature $0.0001$ to ensure better exploration. The controller is updated whenever $10$ rewards are collected from child models. We use $200$ Titan X GPUs training for $6$ days, with an overall sample complexity of $20,000$.

When constructing the GAN model, we fix the number of channels in both the generator and discriminator to be $128$. We find that using global sum pooling instead of global average pooling in the penultimate layer of the discriminator stabilizes the training. We use Adam optimizer \cite{Adam} for optimization with $\eta = 0.0002$, $\beta_1 = 0$ and $\beta_2 = 0.9$. The discriminator is updated $5$ steps per one generator update step. The number of samples generated is $128$ per G update and $64$ per D update. We use batch size $64$ for real samples. To evaluate the architecture, the model is  trained for $5,000$ steps. Inception Score is then calculated based on $50,000$ generated samples divided into $10$ groups. For reward shaping, we choose $\textrm{IS}_\textrm{max} = 11.24$ (Inception Score of the real data) and $\textrm{IS}_\textrm{min} = 1$.

\begin{figure}[H]
    \centering
    \begin{subfigure}[h!]{0.5\linewidth}
        \centering
        \includegraphics[width=\linewidth]{img/avg_score.png}
        \caption{Averge Inception Score}
    \end{subfigure}%
    \begin{subfigure}[h!]{0.5\linewidth}
        \centering
        \includegraphics[width=\linewidth]{img/best_score.png}
        \caption{Best Inception Score}
    \end{subfigure}
    \caption{Progression of Inception Score on CIFAR-10 \\
    Model with the highest Inception Score (when trained for $5,000$ steps) is generated as early as step $=913$. The controller, however, continues to learn the distribution that samples better performing models on average. In fact, the best models when trained to full size are generated at the later stage of the architecture search.
    }
    \label{fig:converge}
\end{figure}

\subsection{Learning GAN architecture on CIFAR-10}

For the task of supervised image generation on CIFAR-10, we take top candidate models discovered in the architecture search and train for $50,000$ steps. We scale up the models by doubling the number of channels in both the generator and the discriminator. The label information is fed into G via Conditional Batch Normalization (CBN) \cite{cond-batch-norm} and into D via projection \cite{proj-dis}. We use Spectral Normalization \cite{SNGAN} for the discriminator but not the generator. The best architectures are reported in Table~\ref{tab:supervised}.

\begin{table}[h!]
\centering
\begin{tabular}{p{1.5in}ll}
\hline
Method & \multicolumn{1}{c}{Inception Score} & \multicolumn{1}{c}{FID} \\
\hline
Real data & $11.24\pm.12$ & $7.8$ \\
\hline
\textbf{DCGAN style} \\
SteinGAN & $6.35$ \\
DCGAN & $6.58$ \cite{learn2draw} \\
Salimans et al. \cite{Inception-score} & $8.09\pm.07$ \\
AC-GAN & $8.25\pm.07$ \\
SGAN & $8.59\pm.12$ \\
\hline
\textbf{ResNet} \\
WGAN-GP & $8.42\pm.12$ & \\
SN-GAN & $8.62$ \cite{proj-dis} & $17.5$ \cite{proj-dis} \\
BigGAN & $\bm{9.22}$  & $\bm{14.73}$ \\
\hline
\textbf{Ours} \\
AGAN-A & $8.65\pm.12$ & $23.0$ \\
AGAN-B & $8.82\pm.09$ & $23.8$ \\
AGAN-C & $8.55\pm.11$ & $28.0$ \\
\end{tabular}
\caption{Supervised image generation on CIFAR-10}
\label{tab:supervised}
\end{table}

\begin{figure}[h!]
    \centering
    \begin{subfigure}[h!]{.3\linewidth}
    \includegraphics[width=\linewidth]{img/supervised-agan-a.png}
    \caption{AGAN-A}
    \end{subfigure}%
    ~
    \begin{subfigure}[h!]{.3\linewidth}
    \includegraphics[width=\linewidth]{img/supervised-agan-b.png}
    \caption{AGAN-B}
    \end{subfigure}%
    ~
    \begin{subfigure}[h!]{.3\linewidth}
    \includegraphics[width=\linewidth]{img/supervised-agan-c.png}
    \caption{AGAN-C}
    \end{subfigure}
    \caption{Images generated by AGAN s in supervised image generations tasks}
    \label{fig:supervised-image}
\end{figure}
AGAN-A and AGAN-B outperform all DCGAN-style architectures. The best architecture we found, AGAN-B, also outperforms \emph{all} ResNet architectures with $32\times32$ input resolution or less than $100$M parameters. In particular, BigGAN \cite{BigGAN} architecture resides on $128\times128$ input images and has $158.3$ parameters. The architectures we proposed have much less parameters ($20.1$M, $18.8$M and $20.9$M, respectively) in comparison.

We also train models with the same topology for unsupervised image generation tasks. We drop the projection layer in D and use Batch Normalization in place of CBN in G. We find that scaling up does not guarantee performance gain  in this setting. All of the architectures proposed outperform DCGAN-style architectures and AGAN-C outperforms all ResNet architectures in terms of Inception Score.

\begin{table}[h!]
\centering
\begin{tabular}{p{1.5in}ll}
\hline
Method & \multicolumn{1}{c}{Inception Score} & \multicolumn{1}{c}{FID} \\
\hline
Real data & $11.24\pm.12$ & $7.8$ \\
\hline
\textbf{DCGAN style} \\
BEGAN & $5.62$ \\
DCGAN & $6.16\pm.07$ \cite{SGAN} & $36.9$ \cite{FID}\\
MMD GAN & $6.17\pm.07$ & $38.2$ \cite{Coulomb-GANs} \\
WGAN-GP & $6.68\pm.06$ \cite{SNGAN} & 24.8 \cite{FID} \\
Salimans et al. & $6.86\pm.06$ \\
LR-GAN & $7.17\pm.07$ \\
SN-GAN & $7.42\pm.08$ & $29.3$ \\
DFM & $7.72\pm.13$ \\
Coulomb GANs & & 27.3 \\
\hline
\textbf{ResNet} \\
WGAN-GP & $7.86\pm.07$ &\\
SN-GAN & $8.22\pm.05$ & $\bm{21.7\pm.21}$\\
\hline
\textbf{Ours} \\
AGAN-A & $8.15\pm.09$ & $31.6$ \\
AGAN-B & $7.77\pm.10$ & $33.2$ \\
AGAN-C & $\bm{8.29\pm.09}$ & $30.5$ \\
\end{tabular}
\caption{Unsupervised image generation on CIFAR-10}
\label{tab:unsupervised}
\end{table}

In Figure \ref{fig:agan-a} we decipher the architecture of the learned model AGAN-A. Note that topology of all three modules: up-sampling, down-sampling, and normal ones, are quite different from modules used in existing models. Such architecture is a hybrid between Inception and Resnet in that each cell, as deciphered here, contains multiple branches. Cells are stacked together in a way resembling Resnet as shown previously in Figure \ref{fig:meta-arch}. We believe this is the first time that we see inception-resnet hybrid architectures that are used for GAN. Also the cells that we see here are quite different from inception cells that we typically use in discriminative models, which provide evidence supporting our original idea that optimal GAN architecture could be quite different from those in discriminative models. Architectures of AGAN-B and AGAN-C bear some resemblance to AGAN-A and we omit their diagrams for brevity.

\begin{figure}[h!]
    \centering
    \begin{subfigure}[h!]{.5\linewidth}
        \includegraphics[width=\linewidth]{img/agan-a-up.png}
        \caption{Up-sampling module}
    \end{subfigure}
    \begin{subfigure}[h!]{0.45\linewidth}
        \includegraphics[width=\linewidth]{img/agan-a-down.png}
        \caption{Down-sampling module}
    \end{subfigure}
    \begin{subfigure}[h!]{0.475\linewidth}
        \includegraphics[width=\linewidth]{img/agan-a-normal.png}
        \caption{Normal module}
    \end{subfigure}

    \caption{%
    Topology of modules in AGAN-A \\
    Note that the up-sample (down-sample) operations following \texttt{prev} will only be applied when the module is preceded by an up-sampling (down-sampling) module.
    }
    \label{fig:agan-a}
\end{figure}

\subsection{Transferability of AGAN}
One potential advantage of modularzied search space is that it enables the transferability of the learned architecture: modules generated on smaller data sets could be used as building blocks to construct networks on larger data sets, where direct neural architecture search may be infeasible or unfavorable. In this experiment, we empirically evaluated the transferability of some of our learned modules, namely AGAN-A and AGAN-C.

Our STL-10 network has the same meta-architecture as the one for CIFAR-10, with the distinction that the first up-sampling module in G takes input size of $6\times6\times n$ (instead of $4\times4\times n$). We resize the STL-10 data set to $48\times48$ images. As in Table~\ref{tab:stl10}, despite that their topology are not optimized for STL-10, AGAN-A and AGAN-C achieve highly competitive performances, outperforming all DCGAN-style architectures. The experiment provide evidences suggesting that the architectures that we identified might be applicable to a wide range of data sets.

\begin{table}[H]
\centering
\begin{tabular}{p{1.5in}ll}
\hline
Method & \multicolumn{1}{c}{Inception Score} & \multicolumn{1}{c}{FID} \\
\hline
Real data & $11.24\pm.12$ & $7.8$ \\
\hline
\textbf{DCGAN style} \\
DCGAN & $7.84\pm0.07$ \cite{DFM} & \\
DFM & $8.51\pm0.13$\\
SN-GAN & $8.69\pm.09$ & $47.5$ \\
\hline
\textbf{ResNet} \\
WGAN-GP & $9.05\pm.13$ \cite{Splitting-GAN} & $55.1$ \cite{SNGAN} \\
SN-GAN & $9.10\pm.04$ & $\bm{40.1}$ \\
Splitting GAN & $9.50\pm.13$ \\
CAGAN & $\bm{9.51\pm.14}$ \\
\hline
\textbf{Ours} \\
AGAN-A & $9.23\pm.08$ & $52.7$ \\
AGAN-B & $7.84\pm.12$ & $71.8$ \\
AGAN-C & $8.97\pm.10$ & $57.4$ \\
\end{tabular}
\caption{Unsupervised image generation on STL-10}
\label{tab:stl10}
\end{table}

\begin{figure*}[h!]
    \centering
    \begin{subfigure}[h!]{.3\linewidth}
    \includegraphics[width=\linewidth]{img/up-sample-ops.png}
    \caption{Up-sampling}
    \end{subfigure}%
    ~
    \begin{subfigure}[h!]{.3\linewidth}
    \includegraphics[width=\linewidth]{img/down-sample-ops.png}
    \caption{Down-sampling}
    \end{subfigure}%
    ~
    \begin{subfigure}[h!]{.3\linewidth}
    \includegraphics[width=\linewidth]{img/normal-ops.png}
    \caption{Normal}
    \end{subfigure}

    \caption{%
    Empirical distribution of sampled operations by module over time \\
    Learned operations: (a) up-sampling module: conv $3\times3$, conv $1\times3$ then $3\times1$ (b) down-sampling module: conv $3\times3$, dilated conv $3\times3$ (c) normal module: average pooling $3\times3$, $5\times5$, $7\times7$
    }
    \label{fig:learn-ops}
\end{figure*}

\section{Introduction}

Generative Adversarial Networks (GANs) have attracted much research interest since its introduction \cite{GAN} with a wide range of applications, such as image generation, text-to-image synthesis, style transfer \cite{super-resolution,text-to-image,CycleGAN} among many others. GAN learns a target distribution by involving two deep neural networks, namely the generator G and the discriminator D, in a minimax game. The generator G aims to generate samples that resemble real samples from the target distribution, while the discriminator D aims to distinguish the generated samples from the real samples. The model is then trained with simultaneous SGD until a Nash equilibrium is achieved.

Due to the process of minimax optimization and simultaneous SGD, GAN is known to suffer from training instabilities. To mitigate the issue, a string of works focus on the choice of GAN objective function. Notably, in Wasserstein GAN \cite{WGAN}, the authors propose to minimize the Wasserstein distance between the model and target distributions, instead of the original Jensen-Shannon divergence. In LS-GAN \cite{LS-GAN}, the authors consider a least square loss which corresponds to minimizing the Pearson $\chi^2$ divergence between the distributions. In $f$-GAN \cite{f-GAN}, the authors show that any $f$-divergence can be used for GAN objective. Another line of works focus on regularization and normalization techniques, especially the Lipschitz continuity of the discriminator and the conditioning of the generator \cite{generator-conditioning}. Prominent examples include gradient penalty \cite{WGAN-GP} which penalizes the model when the gradient norm moves away from $1$, and spectral normalization \cite{SNGAN} which normalizes the largest singular value by layer using power iteration method.

Different from existing approaches, we investigate the direction of automating the design of neural architectures to stabilize GAN training and improve performance. There are empirical evidences \cite{WGAN-GP,SNGAN} suggesting that generator and discriminator architectures may have impacts on the stability of GAN training, and hence quality and diversity of images generated by GAN. Despite those early evidences, we observe that DCGAN-style \cite{DCGAN} and ResNet \cite{WGAN-GP} architectures are by far the most prevailing architectures in the GAN literature. Such architectures are built upon highly successful modules used primarily in discriminative tasks, and their optimality in generative model construction is questionable.

Neural architecture search (NAS) has emerged as a promising research direction in recent years. On benchmark data sets including Penn Treebank, CIFAR-10 and ImageNet, NAS algorithms are proven to be capable of designing architectures that rival or even outperform the best human-invented architectures \cite{NAS1,NAS2,regularized-evolution}. The direct application of NAS to GAN architecture design is, however, non-trivial, due to at least two factors. First, the generator of GAN consists of up-sampling modules which are almost never used in any image classifications. Typical image classifications only use down-sampling modules and hence we could not borrow experience from well-studied NAS directly.  Second, architectures of GAN have been much less explored. Comparing to traditional NAS application of image classification we hereby aim at searching through a large variety of topological structures with less human prior knowledge imposed. To the best of our knowledge, we are the first group that aims to perform automated architecture design of deep generative models.

In order to design an automated neural architecture search, we used reinforcement learning. In our algorithm we used an RNN module to encode the architectures for the up-sampling, down-sampling, and normal modules in GAN. We carefully crafted the search space and proposed a new form of reward shaping functions so that the algorithm is guided faster towards promising architectures. We have performed comprehensive experimental study to evaluate architecture novelty, their performance,  and the transferability of the identified GAN architectures.

In sum, our main contributions are described below.
\begin{itemize}
    \item We presented the first automated neural architecture search algorithm, AGAN, that is specifically designed for the optimization of neural network architectures in deep generative models.
    \item We have identified $3$ novel, modularized architectures, AGAN-A, AGAN-B, and AGAN-C with distinct architectures.
    \item In our comprehensive experimental study we found that AGAN-A, AGAN-B, and AGAN-C have comparable performance to the best GAN models designed by human-experts. In addition AGAN-C outperforms the state-of-the-art models under same regularization techniques for unsupervised image generation tasks on CIFAR-10.
    \item We empirically evaluated and confirmed that the modules learned by AGAN~are transferable to other data sets such as STL-10.
\end{itemize}

The rest of the paper is organized with the following sections. In Section \ref{sec:relatedWork}, we present an overview of GAN and NAS. We discuss our methodology in Section \ref{sec:method} and present our experimental evaluation of AGAN~in Section \ref{sec:experimentalStudy}. In Section \ref{sec:conclusion} we provide a brief discussion of the differences that we observed between our search and traditional NAS search and conclude there.

\section{Method} {\label{sec:method}}

Our work makes use of the Neural Architecture Search with Reinforcement Learning framework proposed by \cite{NAS1}. A controller recurrent neural network (RNN) samples architectures of the generator and discriminator of GAN simultaneously. The sampled architectures are then sent to computation nodes for training and evaluation using Inception Score. The resulting performance is used as feedback to update controller RNN parameters using REINFORCE rule \cite{reinforce}. Below we provide detailed description of the three critical components in our design: (i) controller architectures, (ii) the set of operations that we use to construct a GAN (a.k.a. the search space), and (iii) how to train a reinforcement learning.

\subsection{Controller architecture}

The controller is a two-layer LSTM consisting of three segments (Figure~\ref{fig:controller}), programming the up-sampling module in the generator, the down-sampling and normal modules in the discriminator, respectively. In each segment, the controller iteratively outputs a candidate operation in the module and an adjacency vector indicating tensors that will be fed into the incoming operation; the output, either an operation or an adjacency vector, is then fed into next step as input.

All operations are sampled through a softmax classifier with sample temperature $T$ and logit clipping constant $C$ \cite{NCO}
\[
    \Pr{y = y_i} \propto \exp{\left( C\tanh{(h_i/T)} \right)},
\]
where $y$ is the output operation and $\Vec{h}=(h_i)$ is the last hidden layer at current time step. $y$ is fed into the controller RNN through an embedding layer; the embedding parameters are only shared within the same segment.

The adjacency vector is sampled from element-wise independent multivariate Bernoulli distribution
\[
    \Pr{\Vec{y} = (y_i)} = \prod_i {p_i}^{y_i} (1 - p_i)^{1 - y_i},
\]
\[
    p_i = \sigma \left( C\tanh{(h_i/T)} \right),
\]
where $\Vec{y}$ is the adjacency vector and $\sigma$ is the sigmoid activation. $\Vec{y}$ is fed into the controller RNN through a linear projection layer whose parameters are similarly shared within the same segment.

\begin{figure*}[h!]
    \centering
    \includegraphics[width=.8\textwidth]{img/controller.png}

    \caption{%
    Controller RNN architecture. Above: The controller consists of three segments, programming the up-sampling module in the generator,
    the down-sampling and normal modules in the discriminator, respectively; Below: In each segment, the controller samples an operation and an adjacency vector in turn in an autoregressive manner.
    }
    \label{fig:controller}
\end{figure*}

\subsection{The Search Space}

The outputs of each segment in the controller RNN will be used to program a module in the child model. At each time step, we select tensors according to the sampled adjacency vector, and feed their sum into next operation.

More precisely, each module takes the output of last two modules, $h_{i-1}$ and $h_i$, as inputs. The output sequence of controller RNN segment $o_0, \Vec{a_0}, o_1, \Vec{a_1}, ..., \Vec{a_{k-1}}, o_k$ always starts with and ends with an operation. The module is constructed as follows:
\begin{step}
    \item Apply the first operation $o_0$ to $h_{i-1}$ to form the skip connection. Let $\mathcal{N} = \{x_0, x_1\}$, where $x_0 = h_i$ and $x_1 = o_0(h_{i-1})$.
    \item For each $i$, select tensors from $\mathcal{N}$ according to $\Vec{a_i}$. If $\Vec{a_i} = \Vec{0}$, input of the module $x_0$ will be selected.
    \item Apply $o_i$ to the sum of selected tensors and add resulting tensor to $\mathcal{N}$.
    \item Repeat Step 2 and Step 3.
    \item Concatenate tensors in $\mathcal{N}$ who have never served as an input to form the final output.
\end{step}

\begin{figure}[h!]
    \centering
    \includegraphics[width=.6\linewidth]{img/topology.png}
    \caption{An normal module defined by controller sequence \texttt{conv 1x1}, $(1, 0, 0, 0)$,  \texttt{maxpool 3x3}, $(0, 0, 0, 0)$, \texttt{sep 3x3}, $(0, 1, 0, 1)$, \texttt{avgpool 7x7}.
    1) Apply \texttt{conv 1x1} to \texttt{prev}.
    2) Select \texttt{input} according to vector $(1, 0, 0, 0)$ and apply \texttt{maxpool 3x3}.
    3) No tensors selected. Apply \texttt{sep 3x3} to \texttt{input}.
    4) Sum over tensor $1$ and $3$ as instructed by $(0, 1, 0, 1)$. Apply \texttt{avgpool 7x7}.
    5) Concatenate tensor $2$ and $4$ to form the final output.
    }
    \label{fig:topology}
\end{figure}

In addition, we adopt the following heuristics to ensure the computation graph is well-defined:
\begin{itemize}
    \item The first operation is interpreted as an up-sampling (down-sampling) operation if the previous module is an up-sampling (down-sampling) module.
    \item For up-sampling modules, the operations applied to $x_0$, $x_1$ will be interpreted as up-sampling operations.
    \item For down-sampling modules, the operations applied right before concatenation will be interpreted as down-sampling operations.
    \item $1\times1$ convolutions are applied to the final output to keep the number of channels constant.
\end{itemize}

The meta-architectures of the generator and the discriminator are manually determined as follows. Starting with a linear layer, the generator consists of $3$ up-sampling modules, followed by a $3\times3$ convolution and a $\tanh$ activation. The discriminator starts with a $3\times3$ convolution, followed by $2$ down-sampling modules, $2$ normal modules, a global sum pooling layer and a linear layer. For conditional version of the model, the discriminator logits is augmented with a projection layer as in \cite{proj-dis}.

\begin{figure}[h!]
    \centering
    \includegraphics[width=.5\linewidth]{img/meta-architecture.png}
    \caption{Meta-architecture of the generator and discriminator}
    \label{fig:meta-arch}
\end{figure}

We use hinge loss \cite{hinge-loss} as the objective function, where
\begin{align*}
L_D = & -\Epp{(x, y)\sim p_{\textrm{data}}}{\min{0, -1 + D(x, y)}} \\
      & -\Epp{z\sim p_z, y\sim p_\textrm{data}}{\min{0, -1 - D(G(z), y)}}, \\
L_G = & -\Epp{z\sim p_z, y\sim p_\textrm{data}} D(G(z), y).
\end{align*}

To cover a large variety of candidate architectures, we collect the following set of operations as our normal operations:
\begin{multicols}{2}
\begin{itemize}[topsep=0pt,itemsep=-1ex,partopsep=1ex,parsep=1ex]
    \item identity
    \item $1\times1$ convolution
    \item $3\times3$ convolution
    \item $3\times3$ dilated convolution
    \item $3\times3$ depthwise-separable convolution
    \item $5\times5$ depthwise-separable convolution
    \item $7\times7$ depthwise-separable convolution
    \item $1\times3$ then $3\times1$ convolution
    \item $1\times5$ then $5\times1$ convolution
    \item $1\times7$ then $7\times1$ convolution
    \item $3\times3$ max pooling
    \item $5\times5$ max pooling
    \item $7\times7$ max pooling
    \item $3\times3$ average pooling
    \item $5\times5$ average pooling
    \item $7\times7$ average pooling
\end{itemize}
\end{multicols}

For up-sampling modules, based on state-of-the-art GAN architectures we consider two different types of up-sampling operations: 1) $3\times3$, $5\times5$ or $7\times7$ transposed convolution 2) nearest-neighbor interpolation followed by any convolution in the list above. For down-sampling modules, motivated by optimized residual blocks in \cite{WGAN-GP}, we include 1) convolution followed by stride $2$ average pooling 2) stride $2$ average pooling followed by convolution as two types of  atomic operations.

We use BN - ReLU - Conv for all convolutional operations in G, and ReLU - Conv for all convolutional operations in D. There is no Batch Normalization nor ReLU in between $1\times n$ then $n\times 1$ convolutions.

\subsection{Training with Reinforcement Learning}

We use REINFORCE rule \cite{reinforce} to udpate controller RNN parameters $\theta$. Let $a_0, a_1, \ldots, a_T$ be the output sequence of controller, including both operational and connectivity choices. We have the following update rule for $\theta$:
\[
    \nabla_\theta J(\theta) =
    \sum_t \Ep{(R - b) \nabla_\theta \log \Pr{a_t | a_{t-1}}},
\]
where $R$ is the reward for taking actions $a_0, a_1, \ldots, a_T$ and $b$ is the baseline for variance reduction. In particular, when $a_t$ is an operation or adjacency vector, $-\log \Pr{a_t | a_{t-1}}$ can be computed through softmax or sigmoid cross-entropy.

We measure the performance of GAN using Inception Score. More precisely, we propose the following reward shaping
\[
    R = \frac{\textrm{IS} - \textrm{IS}_\textrm{min}}{\textrm{IS}_\textrm{max} - \textrm{IS}},
\]
where $\textrm{IS}_\textrm{min}$ and $\textrm{IS}_\textrm{min}$ are constants, making the rewards more sensitive when IS approaching optimal value. Due to the instability of GAN training, the Inception Score needs to be averaged over multiple run of GAN to ensure reliable measurement. In practice, however, we found that the proposed NAS algorithm works with a single run of training per sampled architecture.
\section{Related Work}{\label{sec:relatedWork}}
Equipped with multilayer perceptrons as generator and discriminator, the original GAN \cite{GAN} can successfully learn the data distribution of MNIST, but fails at more complicated image generation tasks.
In DCGAN \cite{DCGAN}, the authors propose a novel class of CNNs as generator and discriminator, together with a set of architecture guidelines for stable convolutional GAN training. Most notably, in generator, the spatial activation size is doubled every layer while the number of output channels is halved; the discriminator much resembles the reverse of  generator. Gulrajani et al. \cite{WGAN-GP} propose a ResNet architecture for GAN on CIFAR-10. In particular, the residual blocks in the generator perform nearest-neighbor up-sampling before the second convolution while some blocks in the discriminator perform average pooling after the second convolution. Many later GAN models are built upon DCGAN-style or ResNet architecture, such as SNGAN \cite{SNGAN}, SAGAN \cite{SAGAN} and BigGAN \cite{BigGAN}. In SAGAN, the authors propose a self-attention layer that models the non-local dependency between high-resolution and low-resolution feature maps. They also make a minor modification of the discriminator by altering the number of hidden layer output channels in residual blocks. In BigGAN, the authors introduce further architectural change including shared class embedding and skip connections in latent variable $z$.

Concerning the architecture design of GAN, a particular line of works focus on how to make use of label information to improve the performance of GAN. In classic conditional GAN \cite{cGAN} framework, label information is concatenated to the input or hidden representations to model a conditional distribution. Miyato et al. \cite{proj-dis} propose to use projection based way to incorporate label information into the discriminator. On the other hand, De Vries et al. \cite{cond-batch-norm} introduce Conditional Batch Normalization to visual question answering tasks, which learns a scale and a shift for each class label. Conditional Batch Norm is widely used in image generation models \cite{SNGAN,SAGAN,BigGAN} to provide label information for the generator of GAN.

Neural architecture search algorithm requires an objective, quantitative metric to measure the performance of the underlying models. In case of GAN, well-studied methods such as kernel density estimation (KDE, or Parzen window estimation) have been questioned as a suitable indicator of visual fidelity of generated images \cite{Theis}. Inception Score (IS) \cite{Inception-score} and Frechet Inception Distance (FID) are arguably the most popular evaluation metrics in the literature. IS uses a pre-trained Google Inception model  \cite{Google-Inception} to classify generated samples. It is defined as
\[
\textrm{IS}(\ensuremath{\mathbb{P}} _g) = \exp{\left( \Epp{x\sim \ensuremath{\mathbb{P}} _g}{\KLD{p(y|x)}{p(y}} \right)},
\]
where $\ensuremath{\mathbb{P}} _g$ is the generated distribution, $p(y|x)$ is the conditional label distribution through the Inception model, and $p(y)$ is marginal of $p(y|x)$ over $\ensuremath{\mathbb{P}} _g$. Similarly FID uses Google Inception model as a feature extractor and computes the distance between the real distribution $\ensuremath{\mathbb{P}} _r$ and $\ensuremath{\mathbb{P}} _g$ as
\[
\textrm{FID}(\ensuremath{\mathbb{P}} _r,\ensuremath{\mathbb{P}} _g) = \vectorNorm{\mu_r-\mu_g}
                            + \Tr{\ma{C}_r + \ma{C}_g - 2(\ma{C}_r\ma{C}_g)^{1/2}},
\]
where $\mu_r$, $\mu_g$, $\ma{C}_r$, $\ma{C}_g$ are the mean and covariance of the real and generated distributions of the extracted features.

NAS became a mainstream research topic since Zoph and Le \cite{NAS1} found state-of-the-art recurrent cell on Penn Treebank and highly competitive architecture on CIFAR-10 using Reinforcement Learning (RL). Various RL methods have been successfully applied to NAS including vanilla policy gradient \cite{EAS,path-level-EAS}, Proximal Policy Optimization (PPO) \cite{NAS2,ENAS} and Q-learning \cite{MetaQNN,BlockQNN}. An alternative approach is to use evolution algorithm \cite{evolution,regularized-evolution,hierarchical-CNN}, maintaining and evolving a large population of neural architectures. In contrast to aforementioned gradient-free optimization methods, Liu et al. \cite{DARTS} propose a gradient-bases search strategy based on continuous relaxation of architecture representation. Other gradient-based approaches include Neural Architecture Optimization (NAO) \cite{NAO} and ProxylessNAS \cite{proxyless-NAS}.

Inspired by Google Inception model, Zoph et al. \cite{NAS2} and Zhong \cite{BlockQNN} propose a search space based on two types of convolutional cells, named normal and reduction cell. This design leads to a simplified yet quality search space and enables the transferability of resulting architecture found by NAS. It is widely adopted by many later works \cite{PNAS,ENAS,regularized-evolution,DARTS,NAO}. Our work also falls in the category of searching cell topology but differs in the following ways:
\begin{itemize}
    \item In all previous RL-based NAS algorithms, the convolutional cell solely consists of unitary and binary operations except for the final concatenation. In another word, candidate cell topology can only be DAG with indegree no greater than $2$. Our architecture representation allows searching through cells with arbitrary topology.
    \item Previous works search for discriminative models with normal or down-sampling modules, we search for generative models where up-sampling modules play a significant role.
\end{itemize}

redpajama_set_name: RedPajamaArXiv


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text:
Pitchers give Majors new boost
Feb 25, 2014 • Last Updated February 26, 2014 • 1 minute read
The London Majors are beefing up for the 2014 Intercounty Baseball League season.
The Majors signed two pitchers this week, righthander Jhon Garcia and lefty Antonio Delgado.
Delgado is an 18-year-old native of Guadalajara, Mexico, who has played professionally for the Sultanes de Monterrey, AA equivalent baseball in Mexico.
Majors manager Roop Chanderdat said Delgado has raw talent and is coming to London to showcase skills to get to the next level.
Garcia is a 26-year-old who is looking to get back to the next level. The Venezuelan was a member of the Minnesota Twins organization for five seasons. He has a professional ERA of 3.25 with 12 wins. Garcia also played in the Professional Winter League in Venezuela.
"I expect Garcia to be the leader of our pitching staff," Chanderdat said. "Delgado is a young guy with a lively arm and will be one of our starters this season. You need good pitching to win ball games and I will rely heavily on these two guys at the front end of our starting rotation."
The Majors home opener is May 16 against the Kitchener Panthers.


text:
Cyber Monday video game deals: The ultimate roundup—PlayStation, Nintendo, Xbox, more
Rudie Obias
November 30, 2020, 9:23 AM ·8 min read
Yahoo Life is committed to finding you the best products at the best prices. We may receive a share from purchases made via links on this page. Pricing and availability are subject to change.
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The reviews quoted above reflect the most recent versions at the time of publication.
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Manchester United seem to have left behind the inconsistencies they showed right after the international break. And have won their past two matches against Swansea City (Carabao Cup) and Tottenham (Premier League). However, The Red Devils must perform in Europe as well, and they will face a tough encounter against Benfica this Tuesday at 19:45 BST. A win would clinch a Round-of-16 ticket for The Red Devils.
Jose Mourinho will have several absentees ahead of this match. Unfortunately, that has been a trend during the past few weeks. The fitness of several players has not improved, and Mourinho will again rotate the side to keep most players fit and ready. First of all, Zlatan Ibrahimovic and Marcos Rojo remain sidelined due to knee injuries. However, based on past projections it is safe to say Rojo SHOULD be back sooner than later. An optimistic projection would be to see him playing in appoximately two weeks, though, so he will certainly add depth on the back line since he play at both centre-back and left-back.
Paul Pogba (hamstring), Michael Carrick (undisclosed) and Marouane Fellaini (knee) won't be available either, making the team quite shorthanded in the middle of the pitch. Considering Ander Herrera and Nemanja Matic have endured a heavy workload of late, it would not be surprising to see Daley Blind seeing time at central midfielder in order to give the Serbian or the Spaniard some much-needed rest.
On the other hand, Benfica won't have one of their most experienced players after Luisao received a one-match ban following his red card against Manchester United last time out. Douglas Pereira is not out yet, but the former FC Barcelona left-back will be a game-time decision due to an ankle problem.
Manchester United's form after the international break has been quite a roller-coaster. Fortunately, however, those problems have not affected their UEFA Champions League campaign. The Red Devils have won their three matches in the Group Stage so far, and a win this Tuesday against Benfica would automatically give them a berth in the tournament's next round. They have been quite good defensively during the European stage, too, as they have allowed just one goal in their first three encounters. Manchester United have not lost at home in the Champions League during the Group Stage since they lost to Cluj… back in the 2012/13 season.
On the other hand, Benfica have been one of the biggest disappointments in the Champions League this season. They were considered contenders for the top spot in the group against Manchester United. But instead they lost their three games while netting just one goal and conceding eight. They need to win to remain alive. although it is not going to be easy. And if Basel defeat CSKA Moscow, then Benfica's chances to qualify – even with a win – would diminish considerably.
There is no doubt Manchester United have been the better side throughout the season. And the win against Tottenham should defintively be a confidence booster. As such, The Red Devils have big chances to come out on top in this match.
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Should Manchester United Be Concerned With Andreas Pereira's Lack Of Playing Time?


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Marvel Studios' Eternals features an exciting new team of Super Heroes in the Marvel Cinematic Universe, ancient aliens who have been living on Earth in secret for thousands of years. Following the events of Avengers: Endgame, an unexpected tragedy forces them out of the shadows to reunite against mankind's most ancient enemy, the Deviants.
Actors: Gemma Chan,
Gemma Chan 29 November 1982, London, England, UK
Richard Madden,
Richard Madden 18 June 1986, Elderslie, Renfrewshire, Scotland, UK
Angelina Jolie 4 June 1975, Los Angeles, California, USA
Salma Hayek,
Salma Hayek 2 September 1966, Coatzacoalcos, Veracruz, Mexico
Kit Harington,
Kit Harington 26 December 1986, London, England, UK
Kumail Nanjiani,
Kumail Nanjiani 21 February 1978, Karachi, Pakistan
Lia McHugh,
Lia McHugh
Brian Tyree Henry,
Brian Tyree Henry
Lauren Ridloff,
Barry Keoghan,
Dong-seok Ma,
Dong-seok Ma 1 March 1971, South Korea
Genre: Drama, Action, Adventure
Director: Chloé Zhao
Country: United States, United Kingdom