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1952. star=Leopoldo Trieste. Federico Fellini. directors=Federico Fellini. The first two days of a marriage. Ivan, a punctilious clerk brings his virginal bride to Rome for a honeymoon, an audience with the Pope, and to present her to his uncle. They arrive early in the morning, and he has time for a nap. She sneaks off to find the offices of a romance magazine she reads religiously: she wants to meet "The White Sheik," the hero of a soap-opera photo strip. Star-struck, she ends up 20 miles from Rome, alone on a boat with the sheik. A distraught Ivan covers for her, claiming she's ill. That night, each wanders the streets, she tempted by suicide, he by prostitutes. The next day, at 11, is their papal audience. Can things still right themselves?. genre=Drama.

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Grazie per averlo caricato, capolavoro assoluto, la Masina è inarrivabile. Hidden network cloud where download lo sceicco bianco di. Ah, Giuletta Masina Ah, vídeo de uma outra luz Pálpebras de neblina, pele d'alma Giuletta Masina. I love how Fellinni used children to reenforce the outcast persona of his characters. As if they were irrelevant to proper society or something. Qualcuno mi spiega cosa dice Totò al 10:13 ? La luce che si spense! e poi? Michel Strogoff. Alberto Sordi. semplicemente un grande. Major Tamas,wazze. Crude realta' di ieri, ma anche di avantieri, e di oggi e domani pure. Cambiano i nomi delle vie, cambiano i gusti alimentari, cambiano le discipline scolastiche, cambiano i lavori e le ideologie politiche-religiose, ma l'uomo non cambia mai. Quanti Zampano' e Gelsomina vediamo ogni la ragione per cui concludo che Fellini e' sempre attuale. non morira' MAI. grazie.

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BTW whenever I see this film, and watch Gelsomina's expressions, I am reminded of another screen comic - Harry Langdon. Eerie resemblance... Hidden Network Cloud Where Download Lo Sceicco bianconi scuperta. Film listings for every movie currently showing in cinemas in the UK and Ireland. Week Starting Friday 7th February 1917 1917 (Autism Friendly Screening) 1917 (Parent And Baby Screening) 1917 (Subtitled) 1917: The IMAX Experience 2040 2040 + Q&A 21 Bridges The 400 Blows (Les Quatre Cents Coups) 42nd Street: The Musical 9 To 5 A A Banana? At This Time Of Night? A Beautiful Day In The Neighborhood A Beautiful Day In The Neighborhood (Autism Friendly Screening) A Beautiful Day In The Neighborhood (Parent And Baby Screening) A Beautiful Day In The Neighborhood (Parent And Baby Screening. Subtitled) A Beautiful Day In The Neighborhood (Subtitled) A Beautiful Planet: An IMAX 3D Experience A Bump Along The Way A Canterbury Tale A Dog Called Money A Girl From Mogadishu A Guide To Second Date Sex + Q&A A Hidden Life A Hidden Life (Subtitled) A Journey To The Beginning Of Time A Letter To Elia + Talk A Little Princess (Dementia Friendly) A Minuscule Adventure A Private Function (Autism Friendly Screening) A Private War A Shaun The Sheep Movie: Farmageddon A Shaun The Sheep Movie: Farmageddon (Autism Friendly Screening) A Star Is Born A Streetcar Named Desire A Taste Of Quebec A Tree Grows In Brooklyn A Woman Under The Influence 35mm Abominable Abominable (Autism Friendly Screening) Abominable (Subtitled) The Actor (Haiyu Kameoka Takuji) The Addams Family The Addams Family (Autism Friendly Screening) The Addams Family (Subtitled) The Aeronauts The African Queen The African Queen + Introduction African Safari 3D Afternoon Tea At The Movies Airplane! Akira (1988 Film) 35mm Ala Vaikunthapurramuloo Aladdin All Is True All That Is Left Almanac Of Fall (Oszi Almanach) Amanda Amarcord Amarcord 35mm Amazon Adventure: An IMAX 3D Experience Amelie American Animals American Psycho American Psycho + Introduction American: The Bill Hicks Story Anastasia And The Ship Sails On And Then We Danced And Then We Danced + Introduction And Your Bird Can Sing Andre Rieu: 70 Years Young Animated Highlights: 2019 Anjaam Pathiraa Anne Frank: Parallel Stories Another World The Apartment Apollo 11 Apollo 11: First Steps On The Moon: The IMAX Experience Apostasy Appropriate Behaviour Aquarela Archive At Lunchtime: Programme 1 Archive At Lunchtime: Programme 1 & 2 Double Bill Archive At Lunchtime: Programme 2 Are You Proud? The Army Of Shadows (L'Armee Des Ombres) As I Was Moving Ahead Occasionally I Saw Brief Glimpses Of Beauty Ask Tesadufleri Sever 2 Aswathama Audition B BANFF Mountain Film Festival 2020: World Tour: Blue Film Programme BANFF Mountain Film Festival 2020: World Tour: Red Film Programme Bad Boys For Life Bad Boys For Life (Autism Friendly Screening) Bad Boys For Life (Parent And Baby Screening) Bad Boys For Life (Subtitled) Badlands Bait Bait + Introduction Bait 35mm Barb Wire The Battle Of Chile Be Natural: The Untold Story Of Alice Guy-Blache The Beaches Of Agnes (Les Plages D'Agnes) Beanpole Beautiful Thing Beauty And The Beast (1991 Film) Bedknobs And Broomsticks Before Sunrise Belle Epoque Bento Harassment Best Of Iris 2019 + Q&A Big Hero 6 The Big Parade + Introduction The Biggest Little Farm Billy Connolly: The Sex Life Of Bandages Birds Of Passage Birds Of Prey (And The Fantabulous Emancipation Of One Harley Quinn) Birds Of Prey (And The Fantabulous Emancipation Of One Harley Quinn) Autism Friendly Screening) Birds Of Prey (And The Fantabulous Emancipation Of One Harley Quinn) Parent And Baby Screening) Birds Of Prey (And The Fantabulous Emancipation Of One Harley Quinn) Subtitled) Birds Of Prey (And The Fantabulous Emancipation Of One Harley Quinn) The IMAX Experience The Black Flag + Rusty People Blinded By The Light Blood And Rain The Blue Angel Bohemian Rhapsody (Sing-A-Long) Bombshell Bombshell (Parent And Baby Screening) Bombshell (Subtitled) Booksmart Boomerang! 1947 Film) Born To Be Wild An IMAX 3D Experience Boy Erased Breakfast On Pluto Bridesmaids Bridget Jones's Diary Brief Encounter (Dementia Friendly Screening) Brightburn British Animation Award Public Choice British Animation Awards Programme 1 British Animation Awards Public Choice: Programme 1 British Animation Awards Public Choice: Programme 2 British Animation Awards Public Choice: Programme 3 Bugs And Beasts Before The Law Butterfly C C'est La Vie! Cabaret (An Interactive Screening) Cabaret: Special Event Cabin In The Sky Calamity Jane (Dementia Friendly Screening) Call Me By Your Name Can You Ever Forgive Me? Capernaum The Captive (La Captive) Carol Carousel Carry On Cleo Carry On Cleo (Dementia Friendly And Relaxed Screening) Cats Cats (Autism Friendly Screening) Cats (Dementia Friendly Screening) Cats (Parent And Baby Screening) Cats (Subtitled) Cattle Hill Cattle Hill (Autism Friendly Screening) The Cave Chapter MovieMaker: Short Films Chariots Of Fire Charlie's Angels Charlie's Angels (Parent And Baby Screening) Cheaper By The Dozen Chhapaak The Children Act China Love Chorley On Film Citizen K Classic Spring Theatre Company Live Encore Screening: A Woman Of No Importance Cleanin' Up The Town: Remembering Ghostbusters The Clowns Cocktail (1988 Film) Coco Before Chanel Cold War Colette Comedie Francaise: Les Fourberies De Scapin + Q&A Contact Coraline Corpus Christi Cover Girl Crazy, Stupid, Love Creativity And Curiosity Cria Cuervos (Raise Ravens) Cruising Cult Classics (Over 18s) Cyrano De Bergerac Cyrano De Bergerac (1923 Version. Live Musical Accompaniment D D-Day, Normandy 1944 3D Dagaalty Daniel Isn't Real Daniel Sloss: X Darbar The Day Shall Come Deep End Deep Sea 3D Diabolique Diamonds Of The Night Diego Marcon Dil Dhadakne Do + Introduction Dinosaurs In A Mining Facility Dirty Dancing (Sing-A-Long) Disco Raja Dogman Dolittle Dolittle (Autism Friendly Screening) Dolittle (Parent And Baby Screening) Dolittle (Subtitled) Donbass The Doors: Break On Thru - A Celebration Of Ray Manzarek Dora And The Lost City Of Gold Dora And The Lost City Of Gold (Subtitled) Downton Abbey Downton Abbey (Subtitled) Dream Big: Engineering Our World 3D: An IMAX Experience Dykes, Camera, Action! E E. T. The Extra-Terrestrial E. The Extra-Terrestrial (Autism Friendly Screening) East Of Eden Echo El Topo Eltilerin Savasi The Emperor's Naked Army Marches On + Introduction Endurance (South) Endurance (South. Introduction Episodic Fellini Eternal Beauty + Q&A Eternal Sunshine Of The Spotless Mind Everybody Knows Everything - The Real Thing Story Everything But A Man Ex Machina Exhibition On Screen: Leonardo: The Works Exhibition On Screen: Lucian Freud: A Self Portrait Extreme Private Eros: Love Song 1974 F Family Film Club Fando Y Lis Fantazii Faryateva + Talk The Farewell The Favourite Fear Eats The Soul Federico Fellini's 8 1/2 Fellini Satyricon Fellini's Casanova Fellini: A Director's Notebook + Orchestra Rehearsal Fellow Traveller + Introduction The Film Not The Country Finis Terrae Fisherman's Friends Fitzcarraldo Flight Of The Phoenix (1965 Version) The Florida Project The Fly Follow That Bird For Sama For Sama + Q&A Forbidden Foxtrot From Here To Eternity (Dementia Friendly Screening) Frozen Frozen (Sing-A-Long Version) Frozen II Frozen II (Autism Friendly Screening) Frozen II (Sing-A-Long) Frozen II (Subtitled) The Full Monty Funny Face Funny Games (1996 Film) G The Garden Gauguin From The National Gallery Gaza Gentleman's Agreement The Gentlemen The Gentlemen (Subtitled) Gentlemen Prefer Blondes (Dementia Friendly Screening) Gerry Ghost Girlhood The Glenn Miller Story (Dementia Friendly Screening) God's Own Country The Gold Rush The Golem (Der Golem, Wie Er In Die Welt Kam. Live Musical Accompaniment The Good Liar The Good Liar (Subtitled) Good Newwz Good Posture Good Time Goodbye CP The Graduate Grave Of The Fireflies Grease Grease + Live Musical Accompaniment The Greatest Showman (Sing-Along) Greece: The Hidden War Part 2 & 3 Green Book Greener Grass Groundhog Day The Grudge The Grudge (Subtitled) The Gruffalo Gul Makai H Hammett Happy As Lazzaro Happy Birthday, Mr Joyce! Harriet Harriet (Subtitled) Harry Potter And The Philosopher's Stone Heart Beat Ear Drum + Live Music Hell Drivers + Introduction Hellaro Hellzapoppin' Her Sketchbook Her Sketchbook + Q&A Hidden Universe 3D: An IMAX Experience High Society (Dementia Friendly Screening) Hitler, Stalin And Mr Jones + Q&A The Holy Mountain Home Honey Boy Honeyland The Horse Thief The Host (2006 Film) The House Where The Mermaid Sleeps Hubble 3D Human Nature Human Nature + Q&A Hunting For Hedonia Hustlers I I Go Gaga, My Dear I Snuck Off The Slave Ship + The Man Is The Music I Vitelloni Icerdekiler + Q&A Ichi The Killer If Beale Street Could Talk Imperial Blue + Q&A In Safe Hands In Search Of Beethoven In Search Of Mozart Inception 15/70mm: The IMAX Experience Indian Space Dreams + Q&A Inside Out Instant Family Interstellar: The IMAX Experience 70mm The Intruder (L'Intrus) Irish Shorts The Irishman The Irishman (Subtitled) The Iron Giant It Came From Outer Space J Jaanu Jak Zostalem Gangsterem. Historia Prawdziwa Jawaani Jaaneman Jesus Jewish Britain On Film Jihad Jane + Q&A Jinde Meriye Joan Of Arc Of Mongolia Jodorowsky's Dune Jojo Rabbit Jojo Rabbit (Parent And Baby Screening) Jojo Rabbit (Subtitled) Jojo Rabbit + Q&A Joker Joker 70mm Judy Judy & Punch Judy & Punch (Subtitled) Judy (Parent And Baby Screening) Juliet Of The Spirits Jumanji: The Next Level Jumanji: The Next Level (Autism Friendly Screening) Jumanji: The Next Level (Parent And Baby Screening) Jumanji: The Next Level (Subtitled) Jumanji: Welcome To The Jungle Jungle Book (1942 Film) Just Mercy Just Mercy (Parent And Baby Screening) Just Mercy (Subtitled) K Kaisa's Enchanted Forest + Birds In The Earth Kakegurui Kendal Mountain Film Festival Tour 2020 Khatre Da Ghuggu Kind Hearts And Coronets (Dementia Friendly Screening) The Kindergarten Teacher King Kong (1933 Version) The King's Speech The Kingmaker Kinky Boots The Musical Knives Out Knives Out (Subtitled) Koyaanisqatsi + Introduction Kusama: Infinity + Q&A L La Belle Epoque La Dolce Vita La La Land La Petite Fabrique De Nuages La Strada Labyrinth Lady Bird The Last American Freak Show + Shorts The Last Black Man In San Francisco Last Christmas Last Christmas (Subtitled) The Last Tree Le Grand Voyage Le Mans '66 Leave No Trace The Legend Of The Suram Fortress Leto The Lighthouse The Lighthouse (Parent And Baby Screening) The Lighthouse (Subtitled) The Lighthouse + Introduction The Lighthouse + Q&A The Lion King The Lion King (Autism Friendly Screening) Little Nights, Little Love Little Rippers Film Club Little Women Little Women (Autism Friendly Screening) Little Women (Parent And Baby Screening) Little Women (Subtitled) Liverpool Family Ties: The Irish Connection Lo Sceicco Bianco (The White Sheik) London Rolling Film Festival Long Day's Journey Into Night Lost Highway Lost In Translation Love Actually Love Me Tender (Dementia Friendly Screening) Lucas And Albert Lucy In The Sky Lying To Mom M MOTHER Mad Max: Fury Road Black & Chrome Edition Madama Butterfly On Sydney Harbour Encore Screening Maiden Malang: Unleash The Madness Maleficent Maleficent: Mistress Of Evil Maleficent: Mistress Of Evil (Subtitled) Mali Blues The Maltese Falcon Mamma Mia! Man On A Tightrope Man On The Moon The Man Who Killed Don Quixote The Man Who Killed Don Quixote (Parent And Baby Screening) The Man Who Killed Don Quixote + Q&A Mancunia + Q&A Margot At The Wedding 35mm Marriage Story Marriage Story (Parent And Baby Screening) Mary Poppins Returns The Matrix Matthew Bourne's Romeo + Juliet Maudie Mean Girls Messy Goes To Okido Metropolis (1927 Version) The Metropolitan Opera Live Encore Screening: Porgy And Bess The Metropolitan Opera Live: Porgy And Bess Midnight Cowboy Midnight Family + Q&A Midnight Traveler Midnight Traveler (Parent And Baby Screening) Midway The Midwife Miles Davis: Birth Of The Cool Miss Americana Missing Link Missing Link (Subtitled) Monos Monty Python And The Holy Grail Moonlight Motherless Brooklyn The Mountain Mr & Mrs Smith Mr India Mr Jones Mr Jones (Parent And Baby Screening) Mr. Jones + Q&A Mrs Lowry & Son Mujeres: Women On Film Mulholland Drive Muriel's Wedding + Q&A The Mustang My Dad Is A Heel Wrestler My Friend Fela + Q&A My Love Story! My Polish Honeymoon Mystery Movie N NT Live Encore Screening: All About Eve NT Live Encore Screening: All My Sons NT Live Encore Screening: Hansard NT Live Encore Screening: Present Laughter NT Live Encore Screening: Small Island NT Live Encore Screening: The Lehman Trilogy NT Live: Small Island NT Live: The Lehman Trilogy The Navy Lark Nebraska Network (1976 Film) Newsreel The Nightingale Nights Of Cabiria (Le Notti De Cabiria) No Fathers In Kashmir No Fathers In Kashmir + Q&A Non-Fiction Nosferatu + Live Music Accompaniment Not One Less (Yi Ge Dou Bu Neng Shao) The Notebook Nothing But A Man Notorious (1946 Film) O OK Jaanu Official Secrets Okja On Moonlight Bay On The Waterfront Once Upon A Time. In Hollywood Once Upon A Time. In Hollywood 35mm One Piece: Stampede One Piece: Stampede (Dubbed) Ordinary Love Ordinary Love (Subtitled) Organ Oscar Nominated Short Films 2020: Live Action Oscar Nominated Shorts: Live Action Oscar Week: Animated Shorts The Other Side Of Hope Our Meal For Tomorrow Outside The City Outside The City + Q&A P PAW Patrol: Ready, Race, Rescue! PAW Patrol: Ready, Race, Rescue! Autism Friendly Screening) Paddington Pain & Glory The Painted Bird Pal Joey Panga Panic In The Streets Parasite Parasite (Autism Friendly Screening) Parasite (Parent And Baby Screening) Parasite + Discussion Parasite + Q&A Pather Panchali Pattas Pavarotti The Peanut Butter Falcon Pearly Oyster Productions: A Retrospective The Personal History Of David Copperfield The Personal History Of David Copperfield (Autism Friendly Screening) The Personal History Of David Copperfield (Dog Friendly Screening) The Personal History Of David Copperfield (Parent And Baby Screening) The Personal History Of David Copperfield (Subtitled) The Personal History Of David Copperfield (Subtitled. Parent And Baby Screening) The Personal History, Adventures, Experience & Observation Of David Copperfield The Younger Phoenix Nights: Series 1 Phoenix Nights: Series 1 & 2 Pieta Pink Flamingos 35mm Pink Wall Pinky Pinky + Introduction Pinocchio (1940 Film) Playing With Fire Playing With Fire (Autism Friendly Screening) Playing With Fire (Subtitled) Playmobil: The Movie Plus One Pokemon Detective Pikachu Poor Cow Portrait Of A Lady On Fire + Q&A rfect. Pride Pride Short Film Night 2020 Princess Mononoke Process The Proposition + Q&A Psy 3: W Imie Zasad Q Queen & Slim Queen & Slim (Autism Friendly Screening) Queen & Slim (Dog Friendly Screening) Queen & Slim (Parent And Baby Screening) Queen & Slim (Subtitled) Queen & Slim + Discussion Queen & Slim + Q&A Quezon's Game R Rafadan Tayfa 2: Gobeklitepe Rafiki The Raft Rapsodia Satanica Rear Window 35mm Rebecca (1940 Film) Red Joan The Report Rewinding The Welfare State: A Social History Of The North East On Film The Rhythm Section The Rhythm Section (Subtitled) Richard Jewell Richard Jewell (Parent And Baby Screening) Richard Jewell (Subtitled) Ride Your Wave Rocketman The Rocky Horror Picture Show (Sing-A-Long) Rojo Roma (1972 Film) Roman Holiday Romeo And Juliet The Room: Special Event The Rough Cut Royal Ballet Live Encore Screening: The Sleeping Beauty Royal Opera Live Encore Screening: La Boheme Royal Opera Live: La Boheme The Runaways The Runaways (Autism Friendly Screening) The Runaways + Q&A S SQIFF Shorts: Out Of The Archives Salt Of The Earth The Saragossa Manuscript Sauvage Scandal + Q&A The Sea Of Grass Sea Of Revival Seberg Seberg (Parent And Baby Screening) Seberg (Subtitled) The Secret Life Of Pets 2 Sennan Asbestos Disaster Seven Brides For Seven Brothers (Dementia Friendly Screening) The Seven Samurai The Seventh Seal (Det Sjunde Inseglet) Shadowfall Shakespeare In Love Sherlock Gnomes Sherlock Jr + Live Music Accompaniment Shextreme Film Tour Shoah Short Film Evening + Q&A Show Me The Picture: The Story Of Jim Marshall Show Me The Picture: The Story Of Jim Marshall + Q&A Shutter Island Sian Hutchings: Quietly Beneath The Silence Of The Lambs Six Nations Rugby: England Vs France Six Nations Rugby: Ireland Vs Scotland Six Nations Rugby: Ireland Vs Wales Six Nations Rugby: Wales Vs Italy Snoopy And Charlie Brown: The Peanuts Movie So Long, My Son Society Solidarity Sometimes Always Never Sons Of Denmark Sorry We Missed You The Souvenir The Souvenir + Talk Space Station 3D Spies In Disguise Spies In Disguise (Autism Friendly Screening) Spies In Disguise (Parent And Baby Screening) Spies In Disguise (Subtitled) Spit That Out: North West Artist Film Programme Spy Kids Stage Russia: The Brothers Karamzov Star Wars Episode IX: The Rise Of Skywalker Star Wars Episode IX: The Rise Of Skywalker (Autism Friendly Screening) Star Wars Episode IX: The Rise Of Skywalker (Parent And Baby Screening) Star Wars Episode IX: The Rise Of Skywalker (Subtitled) Star Wars Episode IX: The Rise Of Skywalker 3D Star Wars Episode IX: The Rise Of Skywalker: An IMAX 3D Experience Star Wars Episode IX: The Rise Of Skywalker: The IMAX Experience StarDog And TurboCat StarDog And TurboCat (Autism Friendly Screening) Steve: Saving Men From Suicide Stigmata Stolen Life + Introduction The Street Street Dancer Street Dancer 3D Sunset Boulevard The Sword In The Stone T Talking About Trees Tangerine Tanhaji Team America: World Police Ten Dark Women (Kuroi Junin No Onna) Teranga + Q&A Thelma And Louise Thelma And Louise (Parent And Baby Screening) Them That Follow Theorem To Kill A Mockingbird To The Arctic 3D Toddler Time Tomboy Top Hat Toy Story 4 Trainspotting The Treasure Of The Sierra Madre The Trip To Greece + Introduction The Trip To Greece + Q&A Tron True Romance The Truman Show The Turning Turtle Rock The Two Popes The Two Popes (Parent And Baby Screening) U UglyDolls Ulysses The Umbrellas Of Cherbourg Un Homme Et Une Femme Uncut Gems Uncut Gems 35mm Under The Sea 3D: An IMAX 3D Experience Underdog + Talk Underwater Underwater (Parent And Baby Screening) Underwater (Subtitled) Undo + Orange Bombs + My Dishevelled Hair Undocument The Unorthodox Us V Varane Avashyamund Videodrome Viridiana Vita & Virginia Viva Zapata! W WALL-E WALL-E (Autism Friendly Screening) The Wages Of Fear (Le Salaire De La Peur) Waiting For Anya + Q&A Waiting To Exhale Wakeful Walkabout Walking With Dinosaurs: Prehistoric Planet: An IMAX 3D Experience Water Lilies (Naissance Des Pieuvres) 35mm Waves Waves (Subtitled) We Need To Talk About Kevin We Need To Talk About Kevin 35mm Weathering With You Weathering With You (Dubbed) Weathering With You (Parent And Baby Screening) Weathering With You: The IMAX Experience Werckmeister Harmonies (Werckmeister Harmoniak) Western Stars + Q&A When A Man Loves A Woman When Tomatoes Met Wagner Whisky Galore! 1949 Film) William Shakespeare's Romeo + Juliet Willy Wonka And The Chocolate Factory The Windrush Journey Of Mental Health + Q&A Woman At War Women In Horror Wonder Park World Famous Lover X XY Chelsea Y Yesterday Yesterday (Autism Friendly Screening) You've Got Mail Z Zakhmi Zama.

Download Full Movie Look at the page Watch Full Watch Lo sceicco bianco Online Earnthenecklace. Hidden network cloud where download lo sceicco bianco d. Film assolutamente STRAORDINARIO. Υπέροχη ταινία. Μαγευτιkή μουσική! Hellas. Nino Rota Nino Rota (left) with Riccardo Bacchelli and Bruno Maderna in 1963 Born Giovanni Rota Rinaldi 3 December 1911 Milan, Italy Died 10 April 1979 (aged 67) Rome, Italy Occupation Composer Children Nina Rota Website www. ninorota Giovanni Rota Rinaldi ( Italian: dʒoˈvanni ˈrɔːta riˈnaldi] 3 December 1911 – 10 April 1979) better known as Nino Rota. niːno. was an Italian composer, pianist, conductor and academic who is best known for his film scores, notably for the films of Federico Fellini and Luchino Visconti. He also composed the music for two of Franco Zeffirelli 's Shakespeare films, and for the first two films of Francis Ford Coppola 's Godfather trilogy, receiving the Academy Award for Best Original Score for The Godfather Part II (1974. During his long career, Rota was an extraordinarily prolific composer, especially of music for the cinema. He wrote more than 150 scores for Italian and international productions from the 1930s until his death in 1979—an average of three scores each year over a 46-year period, and in his most productive period from the late 1940s to the mid-1950s he wrote as many as ten scores every year, and sometimes more, with a remarkable thirteen film scores to his credit in 1954. Alongside this great body of film work, he composed ten operas, five ballets and dozens of other orchestral, choral and chamber works, the best known being his string concerto. He also composed the music for many theatre productions by Visconti, Zeffirelli and Eduardo De Filippo [1] as well as maintaining a long teaching career at the Liceo Musicale in Bari, Italy, where he was the director for almost 30 years. Early career [ edit] Rota was born Giovanni Rota Rinaldi on 3 December 1911, into a musical family in Milan. Rota was a renowned child prodigy—his first oratorio, L'infanzia di San Giovanni Battista, was written at age 11 [2] and performed in Milan and Paris as early as 1923; his three-act lyrical comedy after Hans Christian Andersen, Il Principe Porcaro, was composed when he was just 13 and published in 1926. He studied at the Milan conservatory there under Giacomo Orefice [1] and then undertook serious study of composition under Ildebrando Pizzetti and Alfredo Casella at the Santa Cecilia Academy in Rome, graduating in 1930. [3] Encouraged by Arturo Toscanini, Rota moved to the United States where he lived from 1930 to 1932. He won a scholarship to the Curtis Institute of Philadelphia, where he was taught conducting by Fritz Reiner and had Rosario Scalero as an instructor in composition. [3] Returning to Milan, he wrote a thesis on the Renaissance composer Gioseffo Zarlino. Rota earned a degree in literature from the University of Milan, graduating in 1937, and began a teaching career that led to the directorship of the Liceo Musicale in Bari, a title he held from 1950 until 1978. [3] Film scores [ edit] Nino Rota wrote the score for the film The Glass Mountain in 1949. Notable was the singing of Tito Gobbi, one of the world's greatest baritones. The film won a number of awards. In his entry on Rota in the 1988 edition of The Concise Baker's Biographical Dictionary of Composers and Musicians, music scholar Nicolas Slonimsky described him as "brilliant" and stated that his musical style. demonstrates a great facility and even felicity, with occasional daring excursions into dodecaphony. However his most durable compositions are related to his music for the cinema; he composed the sound tracks of a great number of films of the Italian director Federico Fellini covering the period from 1950 to 1979. [3] Furthermore, one of his compositional habits in particular came up for disapproving remarks: his penchant for pastiche of various past styles, which quite often turned into outright quotation of his own earlier music or even others' music. One of the most noticed examples of such incorporation is his use of the Larghetto from Dvorák 's Serenade for Strings in E major as a theme for a character in Fellini's La Strada. [4] During the 1940s, Rota composed scores for more than 32 films, including Renato Castellani 's Zaza  [ it] 1944. His association with Fellini began with Lo sceicco bianco (The White Sheik) 1952) followed by I vitelloni (1953) and La strada (The Road) 1954. They continued to work together for decades, and Fellini recalled: The most precious collaborator I have ever had, I say it straightaway and don't even have to hesitate, was Nino Rota — between us, immediately, a complete, total, harmony. He had a geometric imagination, a musical approach worthy of celestial spheres. He thus had no need to see images from my movies. When I asked him about the melodies he had in mind to comment one sequence or another, I clearly realized he was not concerned with images at all. His world was inner, inside himself, and reality had no way to enter it. [5] The relationship between Fellini and Rota was so strong that even at Fellini's funeral Giulietta Masina, Fellini's wife, asked trumpeter Mauro Maur to play Rota's Improvviso dell'Angelo in the Basilica di Santa Maria degli Angeli e dei Martiri in Rome. [6] Rota's score for Fellini's 8½ (1963) is often cited as one of the factors which makes the film cohesive. His score for Fellini's Juliet of the Spirits (1965) included a collaboration with Eugene Walter on the song, Go Milk the Moon" cut from the final version of the film) and they teamed again for the song " What Is a Youth. part of Rota's score for Franco Zeffirelli 's Romeo and Juliet. The American Film Institute ranked Rota's score for The Godfather #5 on their list of the greatest film scores. His score for War and Peace was also nominated for the list. In all, Rota wrote scores to more than 150 films. Orchestral, chamber and choral music [ edit] Rota wrote numerous concerti and other orchestral works as well as piano, chamber and choral music, much of which has been recorded and released on CD. After his death from heart failure [7] in 1979, Rota's music was the subject of Hal Willner 's 1981 tribute album Amarcord Nino Rota, which featured several at the time relatively unknown but now famous jazz musicians. Gus Van Sant used some of Rota's music in his 2007 film Paranoid Park and director Michael Winterbottom used several Rota selections in the 2005 film Tristram Shandy: A Cock and Bull Story. Danny Elfman frequently cites Nino Rota as a major influence (particularly on his scores for the Pee-Wee films. Director Mario Monicelli filmed a documentary Un amico magico: il maestro Nino Rota which featured interviews with Franco Zeffirelli and Riccardo Muti (a student under Rota at Bari Conservatory) and was followed by a German documentary Nino Rota - Un maestro della musica. Both explored film and concert sides of the composer. Operas [ edit] His 1955 opera Il cappello di paglia di Firenze ( The Florentine Straw Hat) is an adaptation of the play by Eugène Labiche and was presented by the Santa Fe Opera in 1977. In 2005 his opera Aladino e la lampada magica ( Aladdin and the Magical Lamp) with Cosmin Ifrim in the title role, was performed in German translation at the Vienna State Opera and released on DVD. Il cappello di paglia di Firenze and Aladino e la lampada magica are regularly staged in Europe as are many symphonic and chamber titles Written for a radio production by RAI in 1950, his short opera, I due timidi ( The Two Timid Ones) was presented by the Santa Fe Opera as part of their pre-season "One-Hour Opera" program in May/June 2008. Personal life and death [ edit] Rota had one daughter, Nina Rota, from a relationship with pianist Magda Longari. [8] He died, age 67, from a coronary thrombosis in Rome. Quotations [ edit] Federico Fellini recalls his first chance meeting with Rota: Outside Cinecittà, I noticed a funny little man waiting in the wrong place for the tram. He seemed happily oblivious of everything. I felt compelled. to wait with him. I was certain that the tram would stop in its regular place and we would have to run for it, and he was equally certain it would stop where he was standing. To my surprise, the tram did stop right in front of us. " A critic conversing with Nino Rota at the age of eleven just prior to a performance of his oratorio, The Childhood of St. John the Baptist, in 1923: Critic: Do you like playing? Rota: Whenever I can. Is it hard to write for a newspaper? Critic: It's not easy to do a good article" Rota: Have you come from Brussels specially to hear my oratorio? Critic: I certainly have, my little friend. " Rota: Thats really funny. I wont be conducting it tonight. Yesterday the double bass snubbed me" On his friendship with Igor Stravinsky: Stravinsky was fun; his mind struck sparks. Age was no barrier - ours became a true friendship, despite distance and meeting ever more rarely. " Nino Rota reflecting on the unhappiness of others: When Im creating at the piano, I tend to feel happy; but - the eternal dilemma - how can we be happy amid the unhappiness of others? I'd do everything I could to give everyone a moment of happiness. That's what's at the heart of my music. " Federico Fellini on Nino Rota: He was someone who had a rare quality belonging to the world of intuition. Just like children, simple men, sensitive people, innocent people, he would suddenly say dazzling things. As soon as he arrived, stress disappeared, everything turned into a festive atmosphere; the movie entered a joyful, serene, fantastic period, a new life. " Works [ edit] Discography [ edit] References [ edit] a b "Nino Rota Music Catalogue. ^ Nicholas Slonimsky, The Concise Baker's Biographical Dictionary of Composers and Musicians (Simon & Schuster, London, 1988, ISBN   0-671-69896-6) p. 1063 ^ a b c d Slonimsky, p. 1063 ^ AllMusic. Nino Rota - Le Molière imaginaire, ballet suite for orchestra ^ Rota & Fellini Archived 2010-11-20 at the Wayback Machine, Cadrage, April/May 2003 ^ fellini_funerali ITALIANO - Basilica di Santa Maria degli Angeli e dei Martiri alle Terme di Diocleziano di Roma. ^ Nino Rota. ^ Videtti, Giuseppe. “Amarcord Nino Rota“, La Repubblica Milano, 20 April 2014. Kennedy, Michael (2006) The Oxford Dictionary of Music, 985 pages, ISBN   0-19-861459-4 Further reading [ edit] Richard Dyer. Nino Rota: Music, Film, and Feeling. New York: Palgrave and Macmillan (on behalf of the British Film Institute) 2010. Franco Sciannameo. Nino Rota's The Godfather Trilogy: A Film Score Guide. Scarecrow Press, 2010. John Simon. The Other Rota. The New Criterion, Vol. 34, No. 10 / June 2016 External links [ edit] Wikimedia Commons has media related to Nino Rota. Wikiquote has quotations related to: Nino Rota Official website Nino Rota on IMDb Nino Rota at the Encyclopædia Britannica Schott Music profile Nino Rota at Find a Grave Awards for Nino Rota v t e Academy Award for Best Original Score 1930s Louis Silvers (1934) Max Steiner (1935) Leo F. Forbstein (1936) Charles Previn (1937) Erich Wolfgang Korngold / Alfred Newman (1938) Herbert Stothart / Richard Hageman, W. Franke Harling, John Leipold and Leo Shuken (1939) 1940s Leigh Harline, Paul J. Smith and Ned Washington / Alfred Newman (1940) Bernard Herrmann / Frank Churchill and Oliver Wallace (1941) Max Steiner / Ray Heindorf and Heinz Roemheld (1942) Alfred Newman / Ray Heindorf (1943) Max Steiner / Morris Stoloff and Carmen Dragon (1944) Miklós Rózsa / Georgie Stoll (1945) Hugo Friedhofer / Morris Stoloff (1946) Miklós Rózsa / Alfred Newman (1947) Brian Easdale / Johnny Green and Roger Edens (1948) Aaron Copland / Roger Edens and Lennie Hayton (1949) 1950s Franz Waxman / Adolph Deutsch and Roger Edens (1950) Franz Waxman / Johnny Green and Saul Chaplin (1951) Dimitri Tiomkin / Alfred Newman (1952) Bronisław Kaper / Alfred Newman (1953) Dimitri Tiomkin / Adolph Deutsch and Saul Chaplin (1954) Alfred Newman / Robert Russell Bennett, Jay Blackton and Adolph Deutsch (1955) Victor Young / Alfred Newman and Ken Darby (1956) Malcolm Arnold (1957) Dimitri Tiomkin / Andre Previn (1958) Miklós Rózsa / Andre Previn and Ken Darby (1959) 1960s Ernest Gold / Morris Stoloff and Harry Sukman (1960) Henry Mancini / Saul Chaplin, Johnny Green, Sid Ramin and Irwin Kostal (1961) Maurice Jarre / Ray Heindorf (1962) John Addison / Andre Previn (1963) Richard M. Sherman and Robert B. Sherman / Andre Previn (1964) Maurice Jarre / Irwin Kostal (1965) John Barry / Ken Thorne (1966) Elmer Bernstein / Alfred Newman and Ken Darby (1967) John Barry / Johnny Green (1968) Burt Bacharach / Lennie Hayton and Lionel Newman (1969) 1970s Francis Lai / The Beatles ( John Lennon, Paul McCartney, George Harrison and Ringo Starr) 1970) Michel Legrand / John Williams (1971) Charlie Chaplin, Raymond Rasch and Larry Russell / Ralph Burns (1972) Marvin Hamlisch / Marvin Hamlisch (1973) Nino Rota and Carmine Coppola / Nelson Riddle (1974) John Williams / Leonard Rosenman (1975) Jerry Goldsmith / Leonard Rosenman (1976) John Williams / Jonathan Tunick (1977) Giorgio Moroder / Joe Renzetti (1978) Georges Delerue / Ralph Burns (1979) 1980s Michael Gore (1980) Vangelis (1981) John Williams / Henry Mancini and Leslie Bricusse (1982) Bill Conti / Michel Legrand, Alan and Marilyn Bergman (1983) Maurice Jarre / Prince (1984) John Barry (1985) Herbie Hancock (1986) Ryuichi Sakamoto, David Byrne and Cong Su (1987) Dave Grusin (1988) Alan Menken (1989) 1990s John Barry (1990) Alan Menken (1991) Alan Menken (1992) John Williams (1993) Hans Zimmer (1994) Luis Enríquez Bacalov / Alan Menken and Stephen Schwartz (1995) Gabriel Yared / Rachel Portman (1996) James Horner / Anne Dudley (1997) Nicola Piovani / Stephen Warbeck (1998) John Corigliano (1999) 2000s Tan Dun (2000) Howard Shore (2001) Elliot Goldenthal (2002) Howard Shore (2003) Jan A. P. Kaczmarek (2004) Gustavo Santaolalla (2005) Gustavo Santaolalla (2006) Dario Marianelli (2007) A. R. Rahman (2008) Michael Giacchino (2009) 2010s Trent Reznor and Atticus Ross (2010) Ludovic Bource (2011) Mychael Danna (2012) Steven Price (2013) Alexandre Desplat (2014) Ennio Morricone (2015) Justin Hurwitz (2016) Alexandre Desplat (2017) Ludwig Göransson (2018) v t e BAFTA Award for Best Original Music John Barry (1968) Mikis Theodorakis (1969) Burt Bacharach (1970) Michel Legrand (1971) Nino Rota (1972) Alan Price (1973) Richard Rodney Bennett (1974) John Williams (1975) Bernard Herrmann (1976) John Addison (1977) John Williams (1978) Ennio Morricone (1979) John Williams (1980) Carl Davis (1981) John Williams (1982) Ryuichi Sakamoto (1983) Ennio Morricone (1984) Maurice Jarre (1985) Ennio Morricone (1986) Ennio Morricone (1987) John Williams (1988) Maurice Jarre (1989) Andrea Morricone and Ennio Morricone (1990) Jean-Claude Petit (1991) David Hirschfelder (1992) Don Was (1994) Luis Enríquez Bacalov (1995) Gabriel Yared (1996) Nellee Hooper, Craig Armstrong and Marius de Vries (1997) David Hirschfelder (1998) Thomas Newman (1999) Craig Armstrong and Marius de Vries (2001) Philip Glass (2002) T Bone Burnett and Gabriel Yared (2003) Gustavo Santaolalla (2004) John Williams (2005) Christopher Gunning (2007) Alexandre Desplat (2010) Thomas Newman (2012) Bradley Cooper, Lady Gaga and Lukas Nelson (2018) Hildur Guðnadóttir (2019) v t e David di Donatello Award for Best Score Piero Piccioni (1975) Franco Mannino (1976) Nino Rota (1977) Armando Trovajoli (1978) Fiorenzo Carpi (1981) Lucio Dalla and Fabio Liberatori (1982) Angelo Branduardi (1983) Armando Trovajoli and Vladimir Cosma (1984) Carlo Savina (1985) Riz Ortolani / Nicola Piovani (1986) Ennio Morricone (1988) Ennio Morricone (1989) Claudio Mattone (1990) Ennio Morricone (1991) Franco Piersanti (1992) Ennio Morricone (1993) Nicola Piovani (1994) Franco Piersanti (1995) Manuel De Sica (1996) Paolo Conte (1997) Nino D'Angelo (1998) Ennio Morricone (1999) Ennio Morricone (2000) Nicola Piovani (2001) Fabio Vacchi (2002) Andrea Guerra (2003) Banda Osiris (2004) Riz Ortolani (2005) Franco Piersanti (2006) Ennio Morricone (2007) Paolo Buonvino (2008) Teho Teardo (2009) Ennio Morricone (2010) Rita Marcotulli and Rocco Papaleo (2011) David Byrne (2012) Ennio Morricone (2013) Pivio and Aldo De Scalzi (2014) Giuliano Taviani (2015) David Lang (2016) Enzo Avitabile (2017) Pivio and Aldo De Scalzi (2018) v t e Golden Globe Award for Best Original Score 1940s Life with Father – Max Steiner (1947) The Red Shoes – Brian Easdale (1948) The Inspector General – Johnny Green (1949) Sunset Boulevard – Franz Waxman (1950) September Affair – Victor Young (1951) High Noon – Dimitri Tiomkin (1952) On the Beach – Ernest Gold (1959) The Alamo – Dimitri Tiomkin (1960) The Guns of Navarone – Dimitri Tiomkin (1961) To Kill a Mockingbird – Elmer Bernstein (1962) 1963) The Fall of the Roman Empire – Dimitri Tiomkin (1964) Doctor Zhivago – Maurice Jarre (1965) Hawaii – Elmer Bernstein (1966) Camelot – Frederick Loewe (1967) The Shoes of the Fisherman – Alex North (1968) Butch Cassidy and the Sundance Kid – Burt Bacharach (1969) Love Story – Francis Lai (1970) Shaft – Isaac Hayes (1971) The Godfather – Nino Rota (1972) Jonathan Livingston Seagull – Neil Diamond (1973) The Little Prince – Alan Jay Lerner and Frederick Loewe (1974) Jaws – John Williams (1975) A Star is Born – Kenneth Ascher and Paul Williams (1976) Star Wars – John Williams (1977) Midnight Express – Giorgio Moroder (1978) Apocalypse Now – Carmine Coppola and Francis Ford Coppola (1979) The Stunt Man – Dominic Frontiere (1980) 1981) E. T. the Extra-Terrestrial – John Williams (1982) Flashdance – Giorgio Moroder (1983) A Passage to India – Maurice Jarre (1984) Out of Africa – John Barry (1985) The Mission – Ennio Morricone (1986) The Last Emperor – David Byrne, Ryuichi Sakamoto and Cong Su (1987) Gorillas in the Mist – Maurice Jarre (1988) The Little Mermaid – Alan Menken (1989) The Sheltering Sky – Richard Horowitz and Ryuichi Sakamoto (1990) Beauty and the Beast – Alan Menken (1991) Aladdin – Alan Menken (1992) Heaven & Earth – Kitarō (1993) The Lion King – Hans Zimmer (1994) A Walk in the Clouds – Maurice Jarre (1995) The English Patient – Gabriel Yared (1996) Titanic – James Horner (1997) The Truman Show – Burkhard Dallwitz and Philip Glass (1998) 1900 – Ennio Morricone (1999) Gladiator – Lisa Gerrard, Hans Zimmer (2000) Moulin Rouge! – Craig Armstrong (2001) Frida – Elliot Goldenthal (2002) The Lord of the Rings: The Return of the King – Howard Shore (2003) The Aviator – Howard Shore (2004) Memoirs of a Geisha – John Williams (2005) The Painted Veil – Alexandre Desplat (2006) Atonement – Dario Marianelli (2007) Slumdog Millionaire – A. Rahman (2008) Up – Michael Giacchino (2009) The Social Network – Trent Reznor and Atticus Ross (2010) The Artist – Ludovic Bource (2011) Life of Pi – Mychael Danna (2012) All Is Lost – Alex Ebert (2013) The Theory of Everything – Jóhann Jóhannsson (2014) The Hateful Eight – Ennio Morricone (2015) La La Land – Justin Hurwitz (2016) The Shape of Water – Alexandre Desplat (2017) First Man - Justin Hurwitz (2018) Joker - Hildur Guðnadóttir (2019) v t e Grammy Award for Best Score Soundtrack for Visual Media 1959−1980 Duke Ellington – Anatomy of a Murder (1959) No Award (1960) Ernest Gold – Exodus (1961) Henry Mancini – Breakfast at Tiffany's (1962) No Award (1963) John Addison – Tom Jones (1964) Richard M. Sherman & Robert B. Sherman – Mary Poppins (1965) Johnny Mandel – The Sandpiper (1966) Maurice Jarre – Doctor Zhivago (1967) Lalo Schifrin – Music from Mission: Impossible (1968) Dave Grusin & Paul Simon – The Graduate (1969) Burt Bacharach – Butch Cassidy and the Sundance Kid (1970) The Beatles ( John Lennon, Paul McCartney, George Harrison & Ringo Starr) – Let It Be (1971) Isaac Hayes – Shaft (1972) Nino Rota – The Godfather (1973) Neil Diamond – Jonathan Livingston Seagull (1974) Alan and Marilyn Bergman & Marvin Hamlisch – The Way We Were (1975) John Williams – Jaws (1976) Norman Whitfield - Car Wash (1977) John Williams – Star Wars (1978) John Williams – Close Encounters of the Third Kind (1979) John Williams – Superman (1980) 1981−2000 John Williams – The Empire Strikes Back (1981) John Williams – Raiders of the Lost Ark (1982) John Williams – E. the Extra-Terrestrial (1983) Giorgio Moroder, Laura Branigan, Keith Forsey, Irene Cara, Shandi Sinnamon, Ronald Magness, Doug Cotler, Richard Gilbert, Michael Boddicker, Jerry Hey, Phil Ramone, Michael Sembello, Kim Carnes, Duane Hitchings, Craig Krampf & Dennis Matkosky – Flashdance (1984) Prince and the Revolution – Purple Rain (1985) Marc Benno, Harold Faltermeyer, Keith Forsey, Micki Free, John Gilutin Hawk, Howard Hewett, Bunny Hull, Howie Rice, Sharon Robinson, Danny Sembello, Sue Sheridan, Richard Theisen & Allee Willis – Beverly Hills Cop (1986) John Barry – Out of Africa (1987) Ennio Morricone – The Untouchables (1988) David Byrne, Cong Su & Ryuichi Sakamoto – The Last Emperor (1989) Dave Grusin – The Fabulous Baker Boys (1990) James Horner – Glory (1991) John Barry – Dances with Wolves (1992) Alan Menken – Beauty and the Beast (1993) Alan Menken – Aladdin (1994) John Williams – Schindler's List (1995) Hans Zimmer – Crimson Tide (1996) David Arnold – Independence Day (1997) Gabriel Yared – The English Patient (1998) John Williams – Saving Private Ryan (1999) Randy Newman – A Bug's Life (2000) 2001−present Thomas Newman – American Beauty (2001) Tan Dun – Crouching Tiger, Hidden Dragon (2002) Howard Shore & John Kurlander (engineer/mixer) – The Lord of the Rings: The Fellowship of the Ring (2003) Howard Shore, John Kurlander (engineer/mixer. Peter Cobbin (engineer/mixer) – The Lord of the Rings: The Two Towers (2004) Howard Shore, John Kurlander (engineer/mixer. Peter Cobbin (engineer/mixer) – The Lord of the Rings: The Return of the King (2005) Craig Armstrong – Ray (2006) John Williams – Memoirs of a Geisha (2007) Michael Giacchino – Ratatouille (2008) Hans Zimmer & James Newton Howard – The Dark Knight (2009) Michael Giacchino – Up (2010) Randy Newman – Toy Story 3 (2011) Alexandre Desplat – The King's Speech (2012) Trent Reznor & Atticus Ross – The Girl with the Dragon Tattoo (2013) Thomas Newman – Skyfall (2014) Alexandre Desplat – The Grand Budapest Hotel (2015) Antonio Sánchez – Birdman (2016) John Williams – Star Wars: The Force Awakens (2017) Justin Hurwitz – La La Land (2018) Ludwig Göransson – Black Panther (2019) Hildur Guðnadóttir – Chernobyl (2020) v t e Nastro d'Argento Award for Best Score 1947–1960 Renzo Rossellini (1947) Renzo Rossellini (1948) Alessandro Cicognini (1949) Roman Vlad (1950) Giovanni Fusco (1951) Mario Nascimbene (1952) Valentino Bucchi (1953) Mario Zafred (1954) Angelo Francesco Lavagnino (1955) Angelo Francesco Lavagnino (1956) Nino Rota (1957) Nino Rota (1958) Carlo Rustichelli (1959) Mario Nascimbene (1960) 1961–1980 Giovanni Fusco (1961) Giorgio Gaslini (1962) Piero Piccioni (1963) Nino Rota (1964) Ennio Morricone (1965) Armando Trovajoli (1966) Carlo Rustichelli (1967) Mario Nascimbene (1968) Nino Rota (1969) Ennio Morricone (1970) Stelvio Cipriani (1971) Ennio Morricone (1972) Guido De Angelis / Maurizio De Angelis (1973) Tony Renis (1974) Giancarlo Chiaramello (1975) Adriano Celentano (1976) Fred Bongusto (1977) Nino Rota (1979) Fred Bongusto (1980) 1981–2000 Riz Ortolani (1981) Lucio Dalla / Fabio Liberatori (1982) Riz Ortolani (1984) Ennio Morricone (1985) Tony Esposito (1986) Armando Trovajoli / Riz Ortolani / Giovanni Nuti (1987) Eugenio Bennato / Carlo D'Angiò (1989) Nicola Piovani (1991) Pino Daniele (1992) Manuel De Sica (1993) Federico De Robertis (1994) Lucio Dalla (1996) Eugenio Bennato (1999) 2001–present Ennio Morricone (2001) Edoardo Bennato (2002) Nicola Piovani (2003) Paolo Fresu (2004) Banda Osiris (2005) Louis Siciliano / Roy Paci / Fabio Barovero / Simone Fabroni (2006) Paolo Buonvino (2009) Rita Marcotulli (2010) Negramaro (2011) Franco Piersanti (2012) Nicola Piovani (2015) Carlo Virzì (2016) Enzo Avitabile (2017.

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Quite an interesting comedy with ideas about fantasy versus reality, a wonderful Nino Rota score, and great work by Bovo, an actress who can capture some great expressions on her face: realistically big-eyed, naïve and innocent, as is required for her character. The film does however suffer from unevenness, trying to balance two styles of comedy - light-hearted semi fantasy and silly slapstick. By themselves either style works fine, but when joined together, it becomes a little messy. The film is not really helped by excessively silly supporting characters, and Trieste feels very over-the-top at times. Still, the aforementioned virtues, and interesting camera-work with an extensive range of different angles, are enough to keep this film afloat. Definitely recommended, even if not perfect.

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We use cookies to offer you a better experience, personalize content, tailor advertising, provide social media features, and better understand the use of our services. To learn more or modify/prevent the use of cookies, see our Cookie Policy and Privacy Policy. Max Ferguson, 1 Yung-Tsun Tina Lee, 2 Anantha Narayanan, 3 and Kincho H. Law 4 A Standardized PMML Format for Representing Convolutional Neural Networks with Application to Defect Detection Reference M. Ferguson, Y. -T. T. Lee, A. Narayanan, and K. H. Law, “ A Standardized PMML Format for Representing Convolutional Neural Networks with Application to Defect Detection, ” Smart and Sustainable Manufacturing Systems 3, no. 1 (2019) 79 – 97. SSMS20190032 ABSTRACT Convolutional neural networks are becoming a popular tool for image processing in the en- gineering and manufacturing sectors. However, managing the storage and distribution of trained models is still a difficult task that is partially due to the lack of standardized methods for deep neural network representation. Additionally, the interoperability between different machine learning frameworks remains poor. This article seeks to address this issue by propos- ing a standardized format for convolutional neural networks based on the Predictive Model Markup Language (PMML. A new standardized schema is proposed to represent a range of convolutional neural networks, including classification, regression, and semantic segmenta- tion systems. To demonstrate the practical application of this standard, a semantic segmen- tation model, which is trained to detect casting defects in X-ray images, is represented in the proposed PMML format. A high-performance scoring engine is developed to evaluate images and videos against the PMML model. The utility of the proposed format and the scoring engine is evaluated by benchmarking the performance of the defect detection models on a range of different computational platforms. Keywords smart manufacturing, Predictive Model Markup Language, machine learning models, defect detection, automated surface insp ection, convolutional neural netw orks, standard, image processing Manuscript received August 31, 2019; accepted for publication November 12, 2019; published online December 11, 2019. 1 Department of Civil and Environmental Engineering, Stanford University, Y2E2 Building, 473 Via Ortega, Stanford, CA 94305, USA (Corresponding author) e-mail: 4335-5285 2 Systems Integration Division, National Institute of Standards and Technology, 100 Bureau Dr., Gaithersburg, MD 20899, USA 3 Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90007, USA 4 Department of Civil and Environmental Engineering, Stanford University, Y2E2 Building, 473 Via Ortega, Stanford, CA 94305, USA Smart and Sustainable Manufacturing Systems Copyright 2019 by ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959 79 doi:10. 1520/SSMS20190032 / Vol. 3 / No. 1 / 2019 / available online at Introduction Convolution al neural networ ks (CNNs) are find ing numerous re al-world applic ations in the engi neering and manufacturi ng domains. 1 Recent research has demonstrated that CNNs can obtain state-of-the-art perfor- mance on tasks like casting defect detection, 2 anomaly detec tion in fibrous m aterials, 3 and classific ation of waste recycl ing. 4 In the industry, CNNs are being used to detect defects, 5 identify weeds, 6 and track packages for supply chain management. 7 However, shar ing and deploying trained model s still remains a di fficult and error-prone task. In current practice, models are normally saved using a serialization format specific to the model training framewor k. 8 Although this p ractice provi des a reliable me thod for savin g trained models, it greatly hinde rs interopera bility betwee n training frame works and data anal ysis tools. In th is article, we se ek to address this issue by developing a standardized representation for deep neural networks based on the Predictive Mo del Markup Lang uage (PMML. It is b elieved that th e proposed forma t can be useful to the m anu- facturing industry by: • allowing CNN models to be transferred from research laboratories to manufacturing facilities in a con- trolled and standardized fashion, • enabling legacy PMML software to leverage recent advances in computer vision technology, through CNN models, and • providing a reliable way for CNN models to be documented and versioned, especially in the context of large-scale manufacturing operations. A standardized representation of a predictive model defines precisely how model inputs are mapped to model outputs and often specifies the exact mathematical operations that must be performed to map an input to an output. The adoption of standardized model representations makes it easier for a machine learning model to be created, inspected, manipulated, and deployed using different software products. This allows software vendors to develop highly specialized software products for completing specific tasks on a machine learning model. For example, Tensorflow 8 can be used to train the model, Netron 9 can be used to test the model, and Google Cloud Platform 10 can be used to evaluate the model in a production setting. Many early concerns surrounding CNN-based approaches, such as training stability, large data set require- ments, and slow training speed, have been overcome through algorithmic 11 and hardware advances. 12 In par- ticular, the discovery of transfer learning has greatly reduced training data set requirements, allowing powerful models to be trained with relatively small data sets. 2, 13 However, both transfer learning and hardware acceleration bring additional complexity to the model training and deployment process. Transfer learning requires the model to be trained on at least two data sets, which means that an intermediate representation for the model is almost essential. Similarly, with the widespread adoption of hardware acceleration, it is becoming common to train and evaluate the models on different hardware devices. Again, a reliable representation of the machine learning model is essential for transferring a machine learning model from one computational platform to another. The remainder of the article is organized as follows: the “ Background ” section provides an introduction to many concepts that are expanded on throughout the article. The “ Related Work ” section describes related work in the field of standardized neural network models. The “ PMML for CNN ” section describes the proposed exten- sions to the PMML specification. The “ Manufacturing Defect Segmentation ” section describes how the proposed PMML format is used to represent an X-ray defect detection model, and the article is concluded with a brief discussion and conclusion. Background This article proposes a new standardized format for representing deep neural networks in PMML. Before the PMML representation is presented, this section introduces many of the topics that are fundamental to the dis- cussion and motivation of such a standardization. Smart and Sustainable Manufacturing Systems 80 FERGUSON ET AL. ON PMML FORMAT FOR REPRESENTING CNNS PMML PMML is an extensible language that enables the definition and sharing of predictive models between applica- tions. 14 PMML provides a clean and standardized interface between the software tools that produce predictive models, such as statistical or data mining systems, and the consumers of such models, such as applications that depend upon embedded analytics. 15 Once a machine learning model has been trained in an environment like Python, MATLAB, or R, it can be saved as a PMML file. The PMML file can then be moved to a production environment, such as an embedded system or a cloud server. A scoring engine in the production environment can parse the PMML file and use it to generate predictions for new unseen data points. PMML is based on the Extensible Markup Language (XML. XML is a markup language that defines a set of rules for encoding documents in a format that is readable by both humans and machines. Simple XML elements contain an opening tag, a closing tag, and some content. The opening tag begins with a left angle bracket. followed by an element name that contains letters and numbers, and finishes with a right angle bracket. Closing tags follow a similar format, but have a forward slash. before the right angle bracket. In PMML, each element either describes a property of the model or provides metadata about the model. An XML schema de- scribes the elements and attributes that are permissible in an XML file. NEURAL NETWORKS Feedforward neural networks are the quintessential deep learning models. Neural networks “ learn ” to perform tasks by considering examples, generally without being programmed with any task-specific rules. For example, a neural network could be trained to identify images that contain manufacturing defects by exposing it to example images that have been manually labeled as “ defective ” or “ not defective. ” Such neural networks are generally trained with little prior knowledge about manufacturing parts or defects. Instead, neural networks automatically generate identifying characteristics from the data set they are trained on. While neural networks were loosely inspired by neuroscience, it is best to think of feedforward networks as function approximation machines that are designed to achieve statistical generalization. The goal of a feed forward netwo rk is to approxi mate some funct ion f. For example, for a classi fi er, y = f  ð x Þ that maps an inpu t x to a category y, a feedforward ne twork de fi nes a ma pping y = f ð x; θ Þ and learns the value of the parameters θ that result in the b est function ap proximation. T hese models are c alled feedforw ard because information fl ows through the function being evaluated from x, through the inter mediate compu tations used to de fi ne f, a n d fi nally to the ou tput y. There are n o feedback con nections in whic h outputs of th e model are fed back into itself. Feedforward neural networks are called networks because they are typically represented by a compilatio n of many di ff eren t functions. Th e neural networ k model is associ ated with a direc ted acyclic gra ph describing h ow the function s are composed to gether. 16 For example, we might have three functions f 1, f 2, a n d f 3 connected in a chain to form f ð x Þ = f 3 ð f 2 ð f 1 ð x ÞÞÞ. These chain structures are the most commonly used struc- tures of neural networks. In this case, f 1 is called the fi rst layer of the n etwork, f 2 is called the second layer, and so on. The overal l length of the c hain gives the de pth. The fi nal layer of a feedforward n etwork is calle d the output layer. During neural network training, we drive f ð x Þ to match f  ð x Þ: The training data provide us with noisy, appro ximate example s of f  ð x Þ evaluated a t di ff erent training points. Each example x is accompanied by a label y ≈ f  ð x Þ. The training examples specify directly what the output layer must do at each point x and produce a value that is close to y. The output of the oth er layers is not di rectly specif ied by the learni ng algo- rithm or train ing data. Inste ad, the learni ng algorithm mu st decide how to us e these layers to best implement a n approximation of f  ð x Þ: CNNs CNNs are a specialized kind of neural network for processing data that have a known grid-like topology. 16 Although this work focuses on the application of CNNs to image data, CNNs can operate on a variety of other data types, including time series and point cloud data. In a CNN, pixels from each image are converted to a featurized representation through a series of mathematical operations. Input images are represented as an order Smart and Sustainable Manufacturing Systems FERGUSON ET AL. ON PMML FORMAT FOR REPRESENTING CNNS 81 3 tensor I ∈ ℝ H × W × D with height, H, width, W, and depth, D, color channels. 17 This representation is modified by a number of hidden layers until the desired output is achieved. There are several layer types that are common to most modern CNNs, including convolution, pooling, and dense layer types. A convolution layer is a function f i ð x i; θ i Þ that convolves one or more parameterized kernels with the input tensor, x i. Suppose the input x i is an order 3 tensor with size H i × W i × D i. A convolution kernel is also an order 3 tensor with size H k × W k × D i, where H k × W k is the spatial size of the kernel. The kernel is convolved with the input by taking the dot product of the kernel with the input at each spatial location in the input. By convolving certain types of kernels with the input image, it is possible to obtain meaningful outputs, such as the image gradients. In most modern CNN archi- tectures, the first few convolutional layers extract features like edges and textures in an image. Convolutional layers deeper in the network can extract features that span a greater spatial area of the image, such as object shapes. Pooling layers are used to reduce the spatial size of a feature map. Pooling involves applying a pooling operation, much like a filter, to the feature map. The size of the pooling operation is smaller than the size of the feature map; it is common to apply a pooling operation with a size of 2 × 2 pixels and a stride of 2 pixels. Dense layers apply an affine transformation to the input vector, and in many cases, also apply an elementwise nonlinear function. They are generally used to learn a mapping between a flattened convolutional layer feature map and the target output of the CNN. DATAFLOW GRAPHS Many machine learning software frameworks represent neural networks as dataflow graphs. In a dataflow graph, the nodes represent units of computation, and the edges represent the data consumed or produced by a com- putation. A dataflow graph for a small neural network is shown in figure 1. Each layer in the neural network is represented as a node in the dataflow graph. The graph edges specify the inputs to each layer. Representing a CNN in this form allows the underlying software framework to optimize the training and execution of the neural net- work through increased parallelism and compiler-generated optimizations. One way of creating a persistent rep- resentation of a neural network is to save the dataflow graph in a standardized machine-readable form. When using this strategy, it is important to save the type of operation performed by each node as well as the connectivity FIG. 1 Dataflow graph for a small CNN. Neural network layers are represented as nodes in the graph. Nodes shown in green (lighter) do not perform any mathematical operations on the input tensor. Nodes shown in red have learnable parameters, whereas nodes shown in blue (darker) do not have any learnable parameters. Smart and Sustainable Manufacturing Systems 82 FERGUSON ET AL. ON PMML FORMAT FOR REPRESENTING CNNS between nodes. The model weights must also be saved because they define the operation performed by all of the parameterized layers. SCORING ENGINES For deployment, predictive models are normally evaluated by a scori ng engine. A scoring engine is a piece of software specifically design ed to load a model in a specific format and use it to evaluate new observations or data points. Scoring engines are responsible for executi ng the mathematical operati ons that transform model inputs into model outputs. To promote interoperability, scoring engines are norm a l l yw r i t t e ni nl a n g u a g e s s u c ha sP y t h o n, C. o rJ a v a, 18 which are supported by most embedde d systems and computing environ- ments. Therefore, it is feasible to run the same scoring engine on a desktop computer, a cloud server, or an embedded device without modifying the sc ori ng engine code. With the adoption of more complex models, such as deep neural networks, it is becoming common practice to evaluate models on hardware- accelerated scoring engines. Hardw are acceleration generally increa ses the throughput of the scoring engine, decreases the model prediction time, and decrea ses power consumption for large-scale computational tasks. Related Works In current deep learning practice, there are many different formats for storing machine learning models. The existing neural network representations can be classified as framework specific or framework agnostic. Framework-specific representations, such as the PyTorch model format, 19 are tightly bound to a particular ma- chine learning framework or programming language. However, these framework-specific formats often lack com- patibility with machine learning frameworks other than the framework for which they were designed. Framework-agnostic formats like PMML or Open Neural Network Exchange (ONNX) 20 encourage software interoperability but tend to be more difficult to design and implement. This section reviews existing methods and standards for representing neural network models; the purpose is to highlight the prevalent framework-ag- nostic and framework-specific representations. PYTORCH PyTorch is an open source machine learning library for Python that is commonly used for applications like image processing and natural language processing. 19 It is primarily developed by Facebook s artificial intelligence re- search group. PyTorch models are exported to a binary format using the Python Pickle protocol. While this approach provides a seamless method of persisting PyTorch models, it greatly hinders interoperability. In par- ticular, relying on the Pickle object serialization format makes it difficult to transfer PyTorch models to other computing environments that do not support the Python runtime. PyTorch allows a neural network graph to be represented using the JavaScript Object Notation (JSON. The JSON format is supported by most common pro- gramming languages, making it an attractive option for saving a neural network architecture. However, as PyTorch does not export the model weights to JSON, there is currently no way to fully recover a trained model from the JSON representation. TENSORFLOW TensorFlow is an open source software library for high-performance numerical computation. Its flexible archi- tecture allows easy deployment of computation across a variety of platforms. 8 TensorFlow provides options for saving model weights alone or saving the dataflow graph and model weights. The dataflow graph can be exported to a binary file using the Protocol Buffer format. A separate Application Programming Interface also provides the option to save the model weights in the Hierarchical Data format (HDF5) binary format. Alternatively, the entire model can be exported using a custom binary format that is based on HDF5 and Protocol Buffers. Google has developed tools to allow these models to be evaluated in other programming languages, such as JavaScript or C. Smart and Sustainable Manufacturing Systems FERGUSON ET AL. ON PMML FORMAT FOR REPRESENTING CNNS 83 However, without a clear open source specification for the model representation, there is still very limited inter- operability between TensorFlow and other predictive modeling tools. ONNX The ONNX format is a community project created by Facebook and Microsoft. 20 ONNX provides a definition of an extensible computation graph model, as well as definitions of built-in operators and standard data types. Each computational dataflow graph is structured as a list of nodes that form an acyclic graph. Nodes have one or more inputs and one or more outputs. Each node is a call to an operator. The graph also has metadata to help document its purpose, author, etc. Operators are implemented external to the graph, but the set of built-in operators are portable across frameworks. Every framework supporting ONNX will provide implementations of these operators on the applicable data types. ONNX currently supports a wide range of models, including feedforward neural networks and CNNs. Neural network architectures for image classification, image segmentation, object detection, and face recognition, among several others, are supported. There is experimental support for recurrent neural networks (RNNs. Fundamentally, ONNX supports the representation of neural networks at a lower level than the proposed PMML representation to be discussed in this article. In some cases, this makes the ONNX standard more suitable than PMML for highly complex neural network architectures that rely on abnormal mathematical transformations or nonstandardized neural network layers. On the other hand, for the ONNX standard, this added complexity also makes it more difficult to implement fully compliant scoring engines or model converters. Therefore, we believe that it is possible for PMML and ONNX to coexist, with PMML being used to represent widely used model architectures like residual networks 21 and ONNX being used to represent more complex re- search-grade neural network models. PMML PMML is a high-level XML-based language designed to represent data mining and predictive models 14 such as linear regression models or random forests. 14 PMML does not control the way that the model is trained; rather, it is purely a standardized way to represent the trained model. PMML is designed to be readable by humans and uses a predefined set of model types and parameters to describe each model. This contrasts with the approach em- ployed in formats like Portable Format for Analytics 22 and ONNX, 20 which represent models as a complex graph of mathematical operations. PMML 3. 0 introduced a standard format for feedforward neural network models. 22 However, PMML documents conforming to this standard explicitly define every neural network connection using XML. Representing deep feedforward neural networks in this manner is impractical as deep neural networks often have between O ð 10 6 Þ to O ð 10 9 Þ connections. 21 Instead, the work described herein proposes a format that rep- resents each layer of the neural network using XML rather than representing every dense connection with XML. The benefit of this approach is that a deep neural network can be represented in only a few thousand lines of XML, whereas the standard remains flexible enough to represent most commonly used architectures. A related work described how PMML can be used to represent CNN classification models. 23 This article expands on that work by adding support for many other neural network architectures, including those used for semantic segmentation, object localization, and object detection. PMML for CNNs PMML supports a large number of model types, including random forest, 14 Gaussian process regression, 24 and linear and feedforward neural networks. 22 The main contribution of this section is a specification for a new PMML element, namely the DeepNetwork element. The remainder of this section explains how the proposed DeepNetwork element can be used to describe a CNN model. Figure 2 shows the general structure of a DeepNetwork PMML document, which includes four basic elements, namely, header, data dictionary, data trans- formation, and the deep network model. 14 The XML schema for the DeepNetwork element precisely describes the DeepNetwork element and related XML elements. The XML schema has been made publicly available on Smart and Sustainable Manufacturing Systems 84 FERGUSON ET AL. ON PMML FORMAT FOR REPRESENTING CNNS GitHub. 25 The following briefly describes the PMML structure and the layers that are intended in the proposed PMML scheme. PMML STRUCTURE The Header element provides a general description of the PMML document, including name, version, timestamp, copyright, and other relevant information for the model development environment. The DataDictionary element contains the data fields and their types as well as the admissible values for the input data. Data transformation is performed using the optional TransformationDictionary or LocalTransformations element. These elements de- scribe the mapping of the data, if necessary, into a form usable by the mining or predictive model. The last element in the general structure contains the definition and description of the predictive model. The element is chosen among a list of models defined in the PMML standard. We propose the DeepNetwork model element as a new element for representing deep neural network models in PMML. DEEPNETWORK ELEMENT A CNN model is represented by a DeepNetwork element that contains all the necessary information to fully characterize the model. As shown in figure 2, t h e DeepNetwork element can have three types of child elements: the NetworkInputs element defines inputs to the neural network, the NetworkLayer elements define the hidden layers in the neural network, and the NetworkOutputs element defines the outputs of the neural network. A DeepNetwork must have at least one NetworkInput and at least one NetworkOuput. T h e DeepNetwork element must contain one or more NetworkLayer elements that describe individual nodes in the dataflow graph. NEURAL NETWORK LAYERS In the proposed PMML standard extension, a deep neural network is represented as a directed acyclic graph. Each node in the graph represents a neural network layer. Graph edges describe the connections between neural net- work layers. As shown in figure 2, the NetworkLayer element is used to define the node in the proposed DeepNetwork PMML extension. Similarly, the NetworkInputs element is used to describe the input to the neural FIG. 2 The structure and contents of a DeepNetwork PMML file. ON PMML FORMAT FOR REPRESENTING CNNS 85 network. The layerName attribute of each NetworkLayer and NetworkInputs elements uniquely identifies each neural network layer. As shown in figure 3, it is possible to connect layers by specifying the inputs to each layer. Specifically, a NetworkLayer can have a child InboundNodes element that defines inputs to the layer. If the InboundNodes child is not present, then it is assumed that the layer does not have any inputs. Most modern neural network architectures are composed from a set of standardized layer types in which each performs an operation on the feature representation. In this section, we introduce PMML definitions for many of these layer types. Convolution layers convolve a parameterized filter with the feature representation. Pooling layers 16 reduce the spatial dimension of the feature representation by apply a sliding maximum or average operator across the representation. Batch normalization (BN) 11 layers aim to standardize the mean and variance of input tensors to avoid internal covariate shift. Nonlinearity layers, such as rectified linear units (ReLU) sig- moid, or tanh, introduce nonlinearity to the neural network, allowing the neural network to represent a much larger set of functions. 16 Many of these layers have parameters θ i that are learned during the training process. Convolution, BN, and fully connected layers generally have learnable parameters, while pooling and nonlinearity layers typically do not have any learnable parameters. The learnable parameters for a neural network are generally referred to collectively as model weights. Figure 4 shows a summary of the layers currently defined in the PMML format. Convolution Layer The convoluti on layer convolv es a convolution al kernel with the in put tensor. 16 As shown in figure 4, t h e convolution layer is represented using a NetworkLayer element with layerTyp e set to Conv2D. A NetworkLayer with the Conv2D l ayer type must co ntain a Convolut ionalKernel child element, which describes the propertie s of the convol utional kernel. The cardinal ity of the convo lutional tens or must be equal t o that of the input tens or. The size of the c onvolutional k ernel is governe d by the paramete r KernelSize child element, and the stride is governe d by the paramet er KernelStrides. An activation funct ion can be optio nally applied to the output of this layer. Dense Layer In the proposed PMML format, a dense layer is represented using a NetworkLayer element with layerType set to Dense. An activation function can be optionally applied to the output of this layer. This layer is parameterized by weights W, and bias b: f dense ð x Þ = σ ð Wx + b Þ (1) where σ is the specified activation function. The height of the output vector is specified using the channels attribute. FIG. 3 Two connected nodes in a neural network graph, represented using the DeepNetwork PMML format. Smart and Sustainable Manufacturing Systems 86 FERGUSON ET AL. ON PMML FORMAT FOR REPRESENTING CNNS Merge Layer A merge layer takes two or more tensors of equal dimensions and combines them using an elementwise operator. In the proposed PMML format, a merge layer is represented using a NetworkLayer element with the layerType FIG. 4 Ten different neural network layers represented in the proposed DeepNetwork format. Global Max Pooling is not included as it is very similar to the Max Pooling layer. Similarly, Transposed Convolution is similar to the Convolution Layer and is not shown here. ON PMML FORMAT FOR REPRESENTING CNNS 87 attribute set to Merge. The operator attribute is used to specify the operator used to combine tensor values. Allowable operator types are addition, subtraction, multiplication, and division. Concatenation Layer The concatenation layer takes two tensors and concatenates them along a given dimension. In the proposed PMML format, a concatenation layer is represented using a NetworkLayer element with the layerType attribute set to Concatenate. The cardinality of the two tensors must be equal. The size of all dimensions other than the concatenation dimension must be equal. Pooling Layer Pooling layers apply a pooling operation over a single tensor. In the proposed PMML format, a maximum pooling layer is represented using a NetworkLayer element with the layerType attribute set to MaxPooling2D. An average pooling layer is represented using a NetworkLayer element with the layerType attribute set to AveragePooling2D. The width of the pooling kernel is governed by the PoolSize child element, and the stride is governed by the parameter PoolStrides child element. Global Pooling Layer Global pooling layers apply a pooling operation across all spatial dimensions of the input tensor. In the proposed PMML format, a global max pooling layer is represented using a NetworkLayer element with the layerType attrib- ute set to GlobalMaxPooling2D. A global average pooling layer is represented using a NetworkLayer element with the layerType attribute set to GlobalAveragePooling2D. Both of these global pooling layers return a tensor that has size ð batch size, channel s Þ. Depthwise Convolution Layer The depthwise convolution layer convolves a convolutional filter with the input, keeping each channel separate. In the regular convolution layer, convolution is performed over multiple input channels. The depth of the filter is equal to the number of input channels, allowing values across multiple channels to be combined to form the output. Depthwise convolutions keep each channel separate, hence the name depthwise. In the proposed PMML format, a depthwise convolution layer is represented using a NetworkLayer element with the layerType attribute set to DepthwiseConv2D. BN Layer The BN layer aims to generate an output tensor with a constant mean and variance. BN applies a linear trans- formation between input and output based on the distribution of inputs during the training process. The param- eters are generally fixed after training is completed. Both the input and output of a BN layer are four-dimensional tensors, which we refer to as I b, c, h, w and O b, c, h, w, respectively. The dimensions correspond to examples within batch b, channel c, and spatial dimensions h and w, respectively. For input images the channels correspond to the RGB channels. BN applies the same normalization for all activations in a given channel: O b, c, h, w ← γ c I b, c, h, w − μ c ffiffiffiffiffiffiffiffiffiffiffi ffi σ c + ε p + β c ∀ b, c, h, w, 2) where μ c and σ c are, respectively, the batch mean and the batch standard deviation across channel c. The BN layer is parameterized by two learnable parameters: β c and γ c, which control, respectively, the desired mean and vari- ance of the output. In the proposed PMML format, a BN layer is represented using a NetworkLayer element with the layerType attribute set to BatchNormalization. Activation Layer The activation layer applies an activation function to each element in the input tensor. In the proposed PMML format, an activation layer is represented using a NetworkLayer element with the layerType attribute set to Smart and Sustainable Manufacturing Systems 88 FERGUSON ET AL. ON PMML FORMAT FOR REPRESENTING CNNS Activation. The activation function can be any one of linear, ReLU, sigmoid, tanh, elu, or softmax. The attribute threshold allows the activation function to be offset horizontally. For example, the transformation applied by the ReLU activation function is: x ← x − t (3) f relu ð x Þ = min ð max ð a, a x Þ, γ Þ (4) where a is the value of the negative_slope attribute, t is the value of the threshold attribute, and γ is the value of the max_value attribute. If the max_value attribute is not supplied, then γ is assumed to be infinity. Padding Layer A padding layer pads the spatial dimensions of a tensor with a constant value, often zero. This operation is commonly used to increase the size of oddly shaped layers, to allow dimension reduction in subsequent layers. In the proposed PMML format, a padding layer is represented using a NetworkLayer element with the layerType attribute set to Padding2D. Reshape Layer A reshape layer reshapes the input tensor. The number of values in the input tensor must equal the number of values in the output tensor. The first dimension is not reshaped as this is commonly the batch dimension. In the proposed PMML format, a padding layer is represented using a NetworkLayer element with the layerType attrib- ute set to Reshape. The flatten layer is a variant of the reshape layer that flattens the input tensor such that the output size is ð batch size, n Þ, where n is the number of values in the input tensor. In the proposed PMML format, a flatten layer is represented using a NetworkLayer element with the layerType attribute set to Flatten. Transposed Convolution Transposed convolutions, also called deconvolutions, arise from the desire to use a transformation going in the opposite direction of a normal convolution, for example to increase the spatial dimensions of a tensor. They are commonly used in CNN decoders, which progressively increase the spatial size of the input tensor. In the PMML CNN format, a transposed convolution layer is represented using a NetworkLayer element with the layerType attribute set to TransposedConv2D. NEURAL NETWORK OUTPUT CNNs are commonly used for image classification, segmentation, regression, and object localization. To be widely applicable, the PMML CNN standard should support all of these output types. The proposed standard should also support multiple outputs types such that it can, for example, predict the age and gender of a person from an image. The NetworkOutputs element is used to define the outputs of a deep neural network. Each output is defined using the NetworkOutput element. Several examples of neural network output definitions are provided in figure 5. The PMML CNN extension can be used to represent classification models. Classification models approxi- mate a mapping function ( f) from input variables ( X) to a discrete output variable ( y. The output variables are often called labels or categories. The mapping function predicts the class or category for a given observation. For example, an image can be classified as belonging to one of two classes: “ cat ” or “ dog. ” It is common for clas- sification models to predict a continuous value as the probability of a given example belonging to each output class. The probabilities can be interpreted as the likelihood or confidence of a given example belonging to each class. A predicted probability can be converted into a class value by selecting the class label that has the highest probability. The proposed extension introduces a DiscretizeClassification element that defines this transforma- tion. Specifically, DiscretizeClassification describes a transformation that takes an input tensor of class likelihoods and outputs a string describing the most probable class. ON PMML FORMAT FOR REPRESENTING CNNS 89 The proposed PMML extension can also be used to represent models that return one or more continuous values. This type of model is commonly referred to as a regression model. Formally, regression models approxi- mate a mapping function ( f) from input variables ( X) to a continuous output variable ( Y. A continuous output variable is a real value, such as an integer or floating point value, or a tensor of continuous values. These are often quantities, such as amounts and sizes. The existing FieldRef PMML element is used to define regression models. If a FieldRef is contained in a NetworkOuput, then it returns a copy of any specified tensor in the neural network. If the FieldRef has a double data type, then it converts single-element tensors to a single double value. Deep neural networks are also commonly used for object localization. Object localization is the problem of predicting the location of an object in an image. A localization model will often return object coordinates in the form ð x 1, y 1, x 2, y 2 Þ that describe the corners of the predicted bounding box. This type of model can be simply represented as a regression model that outputs four continuous variables. Alternatively, this model can be rep- resented as a regression model that returns a tensor containing four continuous values. Semantic segmentation is one of the fundamental tasks in computer vision. In semantic segmentation, the goal is to classify each pixel of the image in a specific category. Formally, semantic segmentation models approxi- mate a mapping function ( f) from an input image ( I ∈ ℝ W × H × C) to a tensor of object classes ( Y ∈ ℝ W × H. Most modern neural network architectures learn a mapping between the input image and a tensor that describes the class likelihood for each pixel P ∈ ℝ W × H × N, where N is the number of predefined classes. The final step is to select the class with the largest likelihood for each pixel. The proposed PMML extension introduces a DiscretizeSegmentation element that defines this transformation. Specifically, DiscretizeSegmentation describes a transformation that takes an input tensor P and outputs a tensor containing the most likely pixel classes. Manufacturing Defect Segmentation In this section, we show how the proposed PMML format can be used to facilitate transfer learning in a defect segmentation task. CNNs have been particularly successful for detecting manufacturing defects using camera or X-ray images. 1, 2, 26 The goal of this section is to develop a classifier that can segment each pixel of an X-ray image into one of three classes: manufacturing part, manufacturing defect, and background. The background class aims to capture the part of the image that does not contain any manufacturing parts. In this way, the algorithm can identify the shape of the scanned part in addition to identifying any defects in the part. Four pretrained image FIG. 5 Examples of different CNN output definitions in the PMML CNN extension. Smart and Sustainable Manufacturing Systems 90 FERGUSON ET AL. ON PMML FORMAT FOR REPRESENTING CNNS segmentation models are converted to the PMML format, allowing them to be easily loaded into machine learning frameworks such as Keras, TensorFlow, or PyTorch. Each model is then fine-tuned on the GDXray casting defect data set. The fine-tuned models are exported to the proposed PMML format and made publicly available. Finally, the PMML models are loaded onto a high-performance TensorFlow scoring engine to demonstrate how the model could be used in a manufacturing environment. GDXRAY D ATA SET The GDXray data set is a collection of annotated X-ray images. 27 The Castings series of this data set contains 2, 727 X-ray images mainly from automotive parts, including aluminum wheels and knuckles. The casting defects in each image are labeled with tight-fitting bounding boxes. The size of the images in the data set ranges from 256 × 256 to 768 × 572 pixels. The GDXray data set has been used by many researchers as a standard benchmark for defect detection and segmentation. 23, 28 In this work, another segmentation label is added to the data set that separates manufacturing parts from empty space or unrelated objects in the X-ray scan. The new labels are gen- erated in a semiautomated fashion using traditional computer vision techniques including median filtering and k-means clustering. All labels are then manually validated and edited. In previous work, we trained CNN models for defect instance segmentation, 1 defect detection, 2 and tile- based defect classification, 23 all using the GDXray data set. In defect detection, the goal is to place a bounding box around each defect in the image. In defect instance segmentation, the goal is to identify which image pixels belong to each defect. Finally, in tile-based defect classification, the goal is to simply predict whether a defect exists somewhere in an image. In a practical sense, defect instance segmentation models are the most useful, as they predict the exact pixel location of each defect. However, instance segmentation models are notoriously difficult to train and slow to evaluate when compared with pixel segmentation models. Therefore, we chose to train a pixel segmentation model. In the case where defects are not touching, the pixel segmentation and instance segmen- tation models will essentially produce the same type of output. The popular U-Net architecture is chosen for defect segmentation as it has be successfully applied to many similar problems in biomedical 29 and engineering applications. 30 The U-Net architecture consists of an encoder and a decoder. The encoder is a CNN that gradually reduces the spatial size of the input image while increasing the number of channels. It is common practice to use a portion of a classification model, such as ResNet-50, for the U-Net encoder. The decoder is a CNN that performs the opposite transformation on the image, gradually increasing the spatial size while reducing the number of channels. The output of the U-Net model is a tensor that describes the class likelihood for each pixel. The pre- dicted class can be obtained by finding the maximum score for each pixel. TRAINING The U-Net architecture is chosen as the primary architecture for semantic segmentation of manufacturing defects. Experiments are conducted using the U-Net architecture with four different backbone networks, namely VGG-16, ResNet-50, MobileNet, and DenseNet-121. The GDXray Castings data set is divided into training and testing data sets, using the same split as described previously in an earlier work. 2 Images that do not contain any defects are discarded from the training set. Image augmentation is used extensively to prevent overfitting. During training, images are randomly sampled from the training set and padded with black pixels, such that they have a height and width of 384 pixels or larger. Each image is randomly cropped to a size of 384 × 384 pixels. The image is then rotated 90, horizontally flipped, or vertically flipped with a probability of 0. 5. Rotating or flipping an image in the GDXray Casting data set produces an image that can be recreated using the same test apparatus, validating this augmentation procedure. Additionally, elastic deformation 31 and brightness transformations are applied to the images. Elastic deformation changes the apparent shape of the manufactured part without producing an unrea- sonable image. In general, the defects in the GDXray Castings series can easily be hidden when scaling, distorting, or adding noise to the images. 1 Thus, no blurring or random noise is added to the images, and only a small amount of elastic deformation is applied. The U-Net models are all trained for 20 Epochs on the GDXray data set using the Adam optimizer, 32 with a batch size of 16 and an initial learning rate of 0. 01. ON PMML FORMAT FOR REPRESENTING CNNS 91 For testing, each im age is padded with bl ack pixels so that each di mension is a multip le of 32 pixels. The intersection ov er union (IoU) metr ic is used to measure th e performance of each mo del. The IoU metric me asures the number of pixels comm on between the target an d prediction mask s divided by the total number of pi xels present acro ss both masks. It is importan t to note that this metric di ffers from ou r previous wo rk, 1, 2 which con- sidered any dete ction correct if the bounding box IoU was gr eater than 0. Th e test set predic tion accurac y for each model is presented in Tabl e 1. Some exampl e predictio ns are shown in fi gure 6. The traine d GDXray classifi cation models are made publicly available in the prop osed PMML form at to accelera te future rese arch in this dire ction. 25 The application of transfer learning has been shown to be beneficial on the GDXray data set. 1 In transfer learning, a model is first trained on a large data set, like the ImageNet data set. The model is then fine-tuned on a domain-specific task. In many cases, knowledge gained from the original task can improve the performance on the second task. In this work, transfer learning is applied as follows: pretrained models are obtained for the U-Net backbone networks, namely VGG-16, ResNet-50, MobileNet, and DenseNet-121. These models are converted to the PMML CNN format using a purpose-built model converter. 25 The neural network weights from these models are then used to initialize the U-Net model weights. After the U-Net models are trained on the GDXray data set, they are stored using the PMML CNN format. EFFICIENCY AND PERFORMANCE Modern CNNs are growing increasingly complex, with recent architectures having hundreds of layers and mil- lions of parameters. Therefore, storage efficiency, serialization performance, and deserialization performance must be considered when designing a standardized format for modern neural networks. To evaluate the perfor- mance of the proposed format, we develop a PMML scoring engine with support for deep CNNs and conduct a number of performance experiments. There are two main factors when considering the performance of a scoring engine: 1) The amount of time it takes to load a model from a PMML file into memory, and (2) the amount of time required to evaluate a new input. We will refer to (1) as the initial load time and (2) as the evaluation time. The initial load time of modern neural networks tends to be quite large as most modern CNNs have complicated dataflow graphs and millions of parameters. However, models are generally kept in memory between subsequent predictions, so initial load time is not a major influence on the speed at which the scoring engine can process images. The prediction time, however, is critical to many applications in manufacturing, as it dictates the amount of time between an observation being made and a prediction being obtained. A scoring engine is developed to evaluate image inputs against CNN models in the proposed PMML format. The scoring engine uses the TensorFlow machine learning framework to perform the mathematical operations associated with each network layer. In our scoring engine, the PMML file is parsed using the lxml XML parser 33 and the Python programming language. The scoring engine is engineered in such a way that the dataflow graph can be evaluated on a central processing unit (CPU) graphics processing unit (GPU) or tensor processing unit (TPU. The scoring engine can be used to evaluate batches of one or more images against a PMML model. The scoring engine can also be used to process a video stream. Processing video streams using CNNs can be difficult as TABLE 1 Test accuracy and model statistics for CNN models that were trained on the GDXray defect data set. Accuracy is presented using the per-pixel IoU metric Model Backbone/Encoder PMML File Size (kB) Test Accuracy (IoU) Manufactured Part Manufacturing Defect U-Net VGG-16 54 0. 899 0. 753 U-Net ResNet-50 101 0. 921 0. 799 U-Net MobileNet 75 0. 913 0. 713 U-Net DensetNet-121 186 0. 944 0. 865 Note: Bold values indicate highest contributions. Smart and Sustainable Manufacturing Systems 92 FERGUSON ET AL. ON PMML FORMAT FOR REPRESENTING CNNS the video frame rate (typically 30 frames per second) often exceeds the neural network execution speed. To over- come this, the proposed scoring engine processes batches of images, in parallel. Video frames are assumed to be streaming from a computer network or camera device. Each video frame is immediately added to a queue after being decoded. The scoring engine continuously removes batches of images from the queue and evaluates them against the CNN model. When using GPU or TPU hardware, a batch of video frames takes a similar amount of time to process as a single image. Hence, the scoring engine can evaluate individual frames against most common CNN models at the native frame rate. The performance characteristics of the proposed PMML format are analyzed using the aforementioned PMML scoring engine. Experiments are conducted using the U-Net architecture with four backbone networks as mentioned previously. In each case, we use a PMML model trained on the GDXray defect detection task. The experiments are conducted on three platforms: a desktop computer with a single NVIDIA 1080 Ti GPU, an identical desktop computer without a dedicated GPU, and a cloud-based virtual machine with a TPU. Figure 7 shows the time required to parse the PMML file and load the dataflow graph into memory on each of the three platforms. In these experiments, the load time appears to be related to the complexity of the computa- tional graph rather than the size of the model weights. For example, the VGG16 U-Net model, which has 31 parameterized layers and 138 million parameters, loads much faster than the DenseNet121 U-Net model, which has 262 parameterized layers and 10 million parameters. This was unexpected but not unsurprising, as DenseNet121 has a far more complex computational graph than simple models like VGG16. Figure 8 shows FIG. 6 Comparison of ground truth and predictions using the U-Net segmentation network with Dens eNet-121 Backbone. ON PMML FORMAT FOR REPRESENTING CNNS 93 the time requ ired to parse the PMML fi le and build th e correspo nding comp utationa l graph. For ea ch model, the PMML load time is a small fr action of the total lo ad time, sugges ting that litt le computati onal overhe ad is introduce d by storing the mode l in the PMML format. I n a manufacturi ng context, PMML lo ad time would only i nfluence the startup time of a mach ine, as the CNN mode l only needs to be initia lized once. Finally, figur e 9 shows the predicti on time on the thre e platform s. Model pred iction tim e is consisten t with values reported in othe r studies. 34 Discussion In this work, we proposed an extension to the PMML format for the standardized representation of CNN models. The proposed extension adds a DeepNetwork element to the existing standard, which stores the necessary in- formation about a CNN model for fine-tuning or evaluation. A casting defect segmentation system was developed to illustrate how the proposed PMML format can be used to improve interoperability of existing machine learning frameworks and scoring engines. The defect FIG. 7 Total load time for four different models. Total load time is defined as the time from when the PMML file first starts to load until the time that the dataflow graph is ready to make predictions. 8 PMML load time for four different models. PMML load time is defined as the time taken to load the PMML file from the disk and parse the PMML file. Smart and Sustainable Manufacturing Systems 94 FERGUSON ET AL. ON PMML FORMAT FOR REPRESENTING CNNS detection system identified manufacturing defects in the GDXray images while also identifying what pixels be- longed to which manufacturing parts. Performing simultaneous semantic segmentation of machine part and defect pixels represents a unique approach to defect classification on the GDXray data set when compared to previously explored methods. 1, 2, 28 This approach could be useful for reducing the number of false-positive results encountered when the defect detection system incorrectly finds defects outside the part, as discussed in Ferguson et al. 1 Historically, manufacturing researchers and technicians were concerned that deep learning systems would require an infeasible amount of data to train. However, by leveraging transfer learning, it is now possible to train a powerful computer vision system with as little as a thousand training examples. In this work, the PMML CNN format is used as an intermediate representation to store pretrained models before they are used to initialize the U-Net model weights. In this way, PMML CNN reduced overall software complexity as both the intermediate models and final models were stored in the same format. Purpose-built scoring engines will become increasingly important in the smart manufacturing industry, es- pecially as internet-connected manufacturing machines become more mainstream. The response time and throughput capacity of these scoring engines are critical to the adoption of predictive modeling in smart manu- facturing. For many real-time applications, the response time of the scoring machine must be sufficiently low to avoid manufacturing devices becoming idle while waiting for feedback from the scoring engine. A high- performance scoring engine was created using the proposed PMML format. Because of the declarative nature of the proposed PMML format, it was possible for the scoring engine to evaluate models on CPU, GPU, or TPU hardware. This level of flexibility could be highly beneficial for manufacturing applications for which models must be evaluated on embedded systems (CPU evaluation) or with high-throughput and low latency (TPU evaluation. Future work could focus on further extending the DeepNetwork element to support complex CNN models, such as object detection or instance segmentation models. Another opportunity for future work is to extend PMML to support more deep neural network types, including RNNs or Natural Language Models. Conclusion In this work, we proposed an extension to the PMML format for the standardized representation of CNN models. A semantic segmentation-based casting defect system was developed to illustrate how PMML CNN can improve the interoperability of existing machine learning frameworks and scoring engines. Furthermore, a scoring engine was created for this new standardized schema and used to evaluate the performance characteristics of the FIG. 9 Model prediction time on three different computation platforms, using each model. Model prediction time is defined as the amount of time required to generate a prediction from a new input when using a batch size of 1. ON PMML FORMAT FOR REPRESENTING CNNS 95 proposed format. The scoring engine and the trained models have all been made publicly available to accelerate research in the field. ACKNOWLEDGMENTS This research is partially supported by the Smart Manufacturing Systems Design and Analysis Program at the National Institute of Standards and Technology (NIST) grant numbers 70NANB18H193 and 70NANB19H097 awarded to Stanford University. DISCLAIMER Certain commercial systems are identified in this article. Such identification does not imply recommendation or endorsement by NIST, nor does it imply that the products identified are necessarily the best available for the purpose. Furthermore, any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NIST or any other supporting U. S. government or corporate organizations. References 1. M. Ferguson, R. Ak, Y. Lee, and K. Law, “ Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning, ” Smart and Sustainable Manufacturing Systems 2, no. 1 (2018) 137 – 164. 2. Law, “ Automatic Localization of Casting Defects with Convolutional Neural Networks, ” in 2017 IEEE International Conference on Big Data (Piscataway, NJ: Institute of Electrical and Electronics Engineers, 2017) 1726 – 1735. 3. P. Napoletano, F. Piccoli, and R. 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The use of deep convolutional neural networks is becoming increasingly popular in the engineering and manufacturing sectors. However, managing the distribution of trained models is still a difficult task, partially due to the limitations of standardized methods for neural network representation. This paper seeks to address this issue by proposing a standardized format for convolutional neural networks, based on the Predictive Model Markup Language (PMML. A number of pre-trained ImageNet models are converted to the proposed PMML format to demonstrate the flexibility and utility of this format. These models are then fine-tuned to detect casting defects in Xray images. Finally, a scoring engine is developed to evaluate new input images against models in the proposed format. The utility of the proposed format and scoring engine is demonstrated by benchmarking the performance of the defect-detection models on a range of different computation platforms. The scoring engine and trained models are made available at. This study proposes a multilayer hybrid deep-learning system (MHS) to automatically sort waste disposed of by individuals in the urban public area. This system deploys a high-resolution camera to capture waste image and sensors to detect other useful feature information. The MHS uses a CNN-based algorithm to extract image features and a multilayer perceptrons (MLP) method to consolidate image features and other feature information to classify wastes as recyclable or the others. The MHS is trained and validated against the manually labelled items, achieving overall classification accuracy higher than 90% under two different testing scenarios, which significantly outperforms a reference CNN-based method relying on image-only inputs. This work presents an in-depth analysis of the majority of the deep neural networks (DNNs) proposed in the state of the art for image recognition. For each DNN multiple performance indices are observed, such as recognition accuracy, model complexity, computational complexity, memory usage, and inference time. The behavior of such performance indices and some combinations of them are analyzed and discussed. To measure the indices we experiment the use of DNNs on two different computer architectures, a workstation equipped with a NVIDIA Titan X Pascal and an embedded system based on a NVIDIA Jetson TX1 board. This experimentation allows a direct comparison between DNNs running on machines with very different computational capacity. This study is useful for researchers to have a complete view of what solutions have been explored so far and in which research directions are worth exploring in the future; and for practitioners to select the DNN architecture(s) that better fit the resource constraints of practical deployments and applications. To complete this work, all the DNNs, as well as the software used for the analysis, are available online. Quality control is a fundamental component of many manufacturing processes, especially those involving casting or welding. However, manual quality control procedures are often time-consuming and error-prone. In order to meet the growing demand for high-quality products, the use of intelligent visual inspection systems is becoming essential in production lines. Recently, Convolutional Neural Networks (CNNs) have shown outstanding performance in both image classification and localization tasks. In this article, a system is proposed for the identification of casting defects in X-ray images, based on the Mask Region-based CNN architecture. The proposed defect detection system simultaneously performs defect detection and segmentation on input images, making it suitable for a range of defect detection tasks. It is shown that training the network to simultaneously perform defect detection and defect instance segmentation, results in a higher defect detection accuracy than training on defect detection alone. Transfer learning is leveraged to reduce the training data demands and increase the prediction accuracy of the trained model. More specifically, the model is first trained with two large openly-available image datasets before finetuning on a relatively small metal casting X-ray dataset. The accuracy of the trained model exceeds state-of-the art performance on the GRIMA database of X-ray images (GDXray) Castings dataset and is fast enough to be used in a production setting. The system also performs well on the GDXray Welds dataset. A number of in-depth studies are conducted to explore how transfer learning, multi-task learning, and multi-class learning influence the performance of the trained system. Automatic localization of defects in metal castings is a challenging task, owing to the rare occurrence and variation in appearance of defects. Convolutional neural networks (CNN) have recently shown outstanding performance in both image classification and localization tasks. We examine how several different CNN architectures can be used to localize casting defects in X-ray images. We take advantage of transfer learning to allow state-of-the-art CNN localization models to be trained on a relatively small dataset. In an alternative approach, we train a defect classification model on a series of defect images and then use a sliding classifier method to develop a simple localization model. We compare the localization accuracy and computational performance of each technique. We show promising results for defect localization on the GRIMA database of X-ray images (GDXray) dataset and establish a benchmark for future studies on this dataset. Automatic detection and localization of anomalies in nanofibrous materials help to reduce the cost of the production process and the time of the post-production visual inspection process. Amongst all the monitoring methods, those exploiting Scanning Electron Microscope (SEM) imaging are the most effective. In this paper, we propose a region-based method for the detection and localization of anomalies in SEM images, based on Convolutional Neural Networks (CNNs) and self-similarity. The method evaluates the degree of abnormality of each subregion of an image under consideration by computing a CNN-based visual similarity with respect to a dictionary of anomaly-free subregions belonging to a training set. The proposed method outperforms the state of the art. In this chapter, we will cover PyTorch which is a more recent addition to the ecosystem of the deep learning framework. PyTorch can be seen as a Python front end to the Torch engine (which initially only had Lua bindings) which at its heart provides the ability to define mathematical functions and compute their gradients. PyTorch has fairly good Graphical Processing Unit (GPU) support and is a fast-maturing framework. Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC- called a Tensor Processing Unit (TPU. deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN. The heart of the TPU is a 65, 536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X. 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X. 80X higher. Moreover, using the CPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.

Questi sono i film che fanno essere orgogliosi italiani di essere un grande grande Fellini. Simplemente una gran pelicula de Federico Fellini y Julieta Massina Una gran actriz. estupenda. fenomenal. por eso ganó a mejor pelicula extranjera en 1956, simplemente hermoso. una joya cinamatografica mundial... Lo sceicco bianco ONLinE fRee 123mOvIEs withoUt regiSTEring Lo sceicco bianco. Hidden network cloud where download lo sceicco bianco para. I could listen to this all day. 1. Eli's Theme (From "Let The Right One In " 2. The Labyrinth (From "Pan's Labyrinth" 3. Mathilde's Theme (From "A Very Long Engagement" 4. Les Choristes (From "Les Choristes" 5. In Memoriam (From "Les Choristes" 6. La Valse D'Amelie (From "Amelie" 7. L' autre Valse D'Amelie (From "Amelie" 8. Crouching Tiger, Hidden Dragon / The Eternal Vow (From 9. Life Is Beautiful (From "Life Is Beautiful" 10. You Make Me Happy And Sad (From "Eat Drink Man 11. Il Postino (From "Il Postino" 12. Lullaby (From "Queen Margot" 13. Bygone Love (From "Farewell My Concubine" 14. The Heart Asks Pleasure First (From "The Piano" 15. Atlantis (From "Atlantis) 16. Once Upon A Time In China (From "Once Upon A Time 17. Concert in E - minor (From " The Double Life Of 1. Cyrano De Bergerac (From "Cyrano De Bergerac" 2. Learning Time (From "La Femme Nikita" 3. Till The End Of The World (From "Red Dust" 4. Blue Sea Laughter (From "Swordsman" 5. The Hairdresser's Husband Suite (From "The 6. A City Of Sadness (From "A City Of Sadness" 7. The Banquet (From "Camille Claudel" 8. Cinema Paradiso Love Theme (From "Cinema Paradiso" 9. The Big Blue (From "The Big Blue" 10. Halt The Sunrise (From "A Chinese Ghost Story" 11. The Last Emperor (From "The Last Emperor) 12. Betty Blue (From "Betty Blue) 13. Jean De Florette (From "Jean De Florette" 14. Fanfare. I ain't Captain Walker" From "Mad Max: 15. Subway / Masquerade (From "Subway" 16. Fort Saganne (From "Fort Saganne" 1. L'Enfant from Opera Sauvage (From "The Year Of Living 2. Das Boot (From "Das Boot" 3. Ebben? ne andro lontano (From "Diva" 4. Promenade Sentimentale (From "Diva" 5. Adagio in G minor (From Gallipoli" 6. Chi Mai (From "Le Professional" 7. Mad Max 1 & 2 (From Mad Max 1 & 2) 8. Main Title / Encounter With Angel / Tess (From "Tess" 9. The Tin Drum (From "The Tin Drum" 10. Prova D'Orchestra (From "Prova D'Orchestra" 11. Bilitis (From "Bilitis" 12. Providence (From "Providence" 13. Suspiria (From "Suspiria" 14. Romanza (From "Novocento (1900) 15. Il Casanova (From "Il Casanova" 16. Emmanuelle (From "Emmanuelle" 1. Amarcord Suite (From "Amarcord) 2. Chorale (From "Day For Night" 3. My Name Is Nobody (From "My Name Is Nobody" 4. Fellini's Roma (From "Fellini's Roma" 5. The Tango (From "Last Tango In Paris" 6. State Of Siege (From "State Of Siege" 7. Duck, You Sucker (From "A Fistful Of Dynamite" 8. Adagietto from Symphony No. 5 (From "Death In Venice" 9. Red Sun (Soleil Rouge) From "Red Sun (Soleil Rouge) 10. Fellini Satyricon (From "Fellini Satyricon" 11. The Five Man Army ( From The Five Man Army" 12. The Red Tent (From "The Red Tent" 13. The Sicilian Clan (From "The Sicilian Clan" 14. Theme from Z (From "Z" 15. Destroy All Monsters (From "Destroy All Monsters" 16. Guns For San Sebastian Overture (From "Guns For San 17. Toby Dammit (From "Histoires Extraordinaires" 1. Man With The Harmonica (From "Once Upon A Time In 2. Jill's Theme (From "Once Upon A Time In The West" 3. Romeo and Juliet Love Theme (From "Romeo And Juliet" 4. Romeo And Juliet Are Wed From "Romeo And Juliet" 5. Concerto (From "Les Demoiselles De Rochefort" 6. Play Time (From "Play Time" 7. Django (From "Django" 8. A Man And A Woman (From "A Man And A Woman" 9. Today It's You (From "A Man And A Woman" 10. The Good, The Bad And The Ugly (From "The Good, 11. The Ecstasy of Gold (From "The Good, The Bad And 12. For A Few Dollars More (From "For A Few Dollars More" 13. Juliet Of the Spirits (From "Giulietta Degli Spiriti " 14. A Fistful of Dollars (From "A Fistful Of Dollars" 15. Ghidorah, The Three Headed Monster (From "Ghidorah, 16. Les Parapluies de Cherbourg (From "Les Parapluies De 17. Zorba's Dance (From "Zorba The Greek" 1. 8 1/2 (From "Otto E Mezzo" 2. Boccaccio ' 70 (From Bocaccio "70" 3. Main Theme / Vacances (From "Jules Et Jim" 4. Phaedra (From "Phaedra" 5. La Dolce Vita Suite (From "La Dolce Vita" 6. Never On Sunday (From "Never On Sunday" 7. Black Orpheus (From "Black Orpheus" 8. The Honeymoon Song (From "Honeymoon" 9. Mon Oncle (from "Mon Oncle" 10. The Nights Of Cabiria (From " Le Notte Di Cabiria" 11. Il Bidone (From "Il Bidone" 12. La Strada (From "La Strada" 13. Godzilla (From "Godzilla, King Of The Monsters" 14. I Vitelloni (From "I Vitelloni" 15. The White Sheikh (From "Lo Sceicco Bianco" 16. Alexander's Entry Into Pskov (From "Alexander Nevsky" 17. Omens of Nosferatu (From "Nosferatu.

Magnificente.

 

1 Man With The Harmonica (From “Once Upon a Time in the West" 3:52 The City of Prague Philharmonic Orchestra 2 Jill's Theme (From “Once Upon a Time in the West" 6:04 The City of Prague Philharmonic Orchestra 3 Love Theme (From "Romeo and Juliet" 2:42 The City of Prague Philharmonic Orchestra 4 Romeo and Juliet Are Wed (From "Romeo And Juliet" 1:45 The City of Prague Philharmonic Orchestra 5 Concerto (From "Les Demoiselles De Rochefort" 2:12 Michel Legrand 6 Play Time (From "Play Time" 3:06 The City of Prague Philharmonic Orchestra 7 Django (From "Django" 2:58 Keith Ferreira 8 A Man And A Woman (From "A Man And A Woman. feat. The City of Prague Philharmonic Orchestra) 2:39 Keith Ferreira 9 Today Its You (From "A Man And A Woman" 2:09 The City of Prague Philharmonic Orchestra 10 Main Title (From “The Good, The Bad And The Ugly" 2:53 The City of Prague Philharmonic Orchestra 11 The Ecstasy of Gold (From “The Good, The Bad And The Ugly" 3:00 The City of Prague Philharmonic Orchestra 12 Main Titles (From “For a Few Dollars More" 3:22 The City of Prague Philharmonic Orchestra 13 Juliet of the Spirits (From "Giulietta Degli Spiriti" 7:21 The City of Prague Philharmonic Orchestra 14 A Fistful of Dollars (From “A Fistful of Dollars" 3:26 The City of Prague Philharmonic Orchestra 15 Theme (From "Ghidrah, the Three Headed Monster" 2:20 The City of Prague Philharmonic Orchestra 16 Theme (From "Les Parapluies de Cherbourg" 4:01 Michel Legrand 17 Zorba's Dance (From "Zorba the Greek" 4:21 The City of Prague Philharmonic Orchestra 18 8 1/2 (From "Otto E Mezzo" 5:16 The City of Prague Philharmonic Orchestra 19 Boccaccio ' 70 (From "Bocaccio "70" 1:31 The City of Prague Philharmonic Orchestra 20 Main Theme / Vacances (From "Jules Et Jim" 4:57 The City of Prague Philharmonic Orchestra 21 Phaedra (From "Phaedra" 5:15 The City of Prague Philharmonic Orchestra 22 La Dolce Vita Suite (From "La Dolce Vita" 7:11 The City of Prague Philharmonic Orchestra 23 Never On Sunday (From "Never On Sunday" 4:12 The City of Prague Philharmonic Orchestra 24 Black Orpheus (From "Black Orpheus" 4:20 The City of Prague Philharmonic Orchestra 25 The Honeymoon Song (From "Honeymoon" 3:15 The City of Prague Philharmonic Orchestra 26 Mon Oncle (from "Mon Oncle" 2:25 The City of Prague Philharmonic Orchestra 27 The Nights of Cabiria (From "Le Notte Di Cabiria" 6:11 The City of Prague Philharmonic Orchestra 28 Il Bidone (From "Il Bidone" 4:50 The City of Prague Philharmonic Orchestra 29 La Strada (From "La Strada" 6:40 The City of Prague Philharmonic Orchestra 30 Godzilla (From "Godzilla, King Of The Monsters" 2:17 The City of Prague Philharmonic Orchestra 31 I Vitelloni (From "I Vitelloni" 3:05 The City of Prague Philharmonic Orchestra 32 The White Sheikh (From "Lo Sceicco Bianco" 2:48 The City of Prague Philharmonic Orchestra 33 Alexander's Entry into Pskov (From "Alexander Nevsky" 4:22 The City of Prague Philharmonic Orchestra 34 Omens of Nosferatu (From "Nosferatu" 3:02 The City of Prague Philharmonic Orchestra.

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Fantastico compositore. Un grande.