Emin
搜索"Emin" ,找到 以下影视作品
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“我每天午夜的就餐者是漫长一天的结束,也是新一天的开始。”——吉姆《月光鸡饭》讲述了一个以卖海南鸡饭为生的普通小伙吉姆,在自己的餐车里遇到了醉酒的顾客文的故事。把他们两个带到一起的那个晚上,创造了一种难以理解的感觉。尽管文是一个被绑架的人,但两人都无法停止对彼此的思念。之后他们生活中的一切都不一样了。"Mydineratmidnighteverydayistheendofalongday,butalsothestartofanewday."-Jim"MoonlightChicken"tellsthestoryaboutJim,anordinaryguywhosellsHainanesechickenriceforaliving,whometWen,adrunkcustomerinhisdiner.Thenightthatbringsthembothtogethercreatedafeelingthatishardtounderstand.NoneofthetwocanstopthinkingabouteachotherdespiteWenbeingatakenman.Everythingintheirlifeisnotthesameafterthat.
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Fourth
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Nattawat
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Jirochtikul
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Gemini
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Norawit
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Titicharoenrak
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Prom
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Theepakorn
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Kwanboon
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Ford
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Arun
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Asawasuebsakul
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塔纳温·坡查伦拉特
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吉迪朋·瑟利维查亚萨瓦
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Captain
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Passatorn
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Koolkang
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Mark
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Pakin
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Kuna-anuvit
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纳帕特·帕查拉恰瓦雷
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为了保住音乐社团,音乐社社长不得不给学生会会长做牛做马。
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Gérald
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Laroche
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Pierre
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Deladonchamps
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Nina
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Meurisse
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Lula
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Cotton
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Frappier
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Francois
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Rollin
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Arthur
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Legrand
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Maud
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Wyler
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Léonie
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Souchaud
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Nathan
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Parent
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Anouk
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Villemin
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Baptiste
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Masseline
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Gaspard
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Meier-Chaurand
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Anne
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Le
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Ny
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Vassili
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Schneider
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Antoine
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Werner
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Adil
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Mekki
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"Mixte"takesplaceinanall-malehighschoolwhengirlsareallowedinforthefirsttime.Setin1960sFrance,theserieslooksattherelationshipsand"hormonalfireworks"ofthetime.Itwillcovertopicssuchaslove,emancipation,sexualityandself-acceptance
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二等兵詹姆斯•康罗伊与队友履行一次义务时,忽然涌现闪光跟噪声,之后,队友新奇逝世亡,疑似被怪物所杀。与此同时,詹姆斯发明全部人都不见了,全部天下只剩下他一自我,他建起了本人的弹药库跟基地,并径自跟怪物作战。
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玛格丽塔·列维耶娃
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Cillian
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O'Sullivan
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Lydia
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Fleming
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Charles
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Brice
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Yelena
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Khmelnitskaya
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斯塔西亚·米洛斯拉夫斯卡娅
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Lola
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Mae
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Loughran
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Amanda
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Bright
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何塞·路易斯·加西亚·佩雷斯
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Anastasia
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Martin
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Alexandra
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Prokhorova
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Jeremy
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Ang
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Jones
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Anatoly
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Chugunov
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Anna
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Jobarteh
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Mat
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Cruz
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埃琳娜·桑兹
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剧集讲述女主(MargaritaLevieva饰)正与女儿在欧洲度假,此时CIA的出现迫使她面对自己隐藏已久的KGB身份。过去俄国进行秘密实验时令到一些KGB拥有特殊力量,而一连串与此能力有关的死亡事件发生令女主不能再躲避起来,但是对付这名凶徒的代价是她可能失去家人及新生活。
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Predictions underlie nearly every aspect of our lives, from sports, politics, and medical decisions to the morning commute. With the explosion of digital technology, the internet, and “big data,” the science of forecasting is flourishing. But why do some predictions succeed spectacularly while others fail abysmally? And how can we find meaningful patterns amidst chaos and uncertainty? From the glitz of casinos and TV game shows to the life-and-death stakes of storm forecasts and the flaws of opinion polls that can swing an election, “Prediction by the Numbers” explores stories of statistics in action. Yet advances in machine learning and big data models that increasingly rule our lives are also posing big, disturbing questions. How much should we trust predictions made by algorithms when we don’t understand how they arrive at them? And how far ahead can we really forecast?