#Judy Zhang

[silviascorcella]

Judy Zhang p/e 20: femminista femminile con su una storia d’amore

Lasciate che vi racconti una storia di vita attuale, con dentro incastonata un’antica storia d’amore. Come fossero scatole cinesi: è proprio il caso di dirlo! Che a turno si dischiudono per offrirci in dono una sorpresa. Lasciate che vi racconti la storia di Judy Zhang, della sua vita che tra passato, presente e futuro, si srotola in tre grandi città diverse, da Shenzhen a New York attraverso Milano: ognuna un’avventura professionale vissuta su un continente differente, ognuna una tessera che ha composto il suo mosaico di di giovane fashion designer di successo.

E poi, lasciate che Judy Zhang vi narri una storia d’amore leggendaria, sfumata col suo guizzo libero dell’estetica contemporanea, raccontata col linguaggio sartoriale di stampe e ricami lungo i capi che compongo la collezione p/e 2020.

Judy è nata in Cina in un delizioso villaggio in Hunan nel 1977: il penchant per le arti e la bellezza, tra danza, canto e disegno, lo dimostra sin dall’infanzia, assieme ad un’energia vitale tale che il villaggio è troppo stretto per agguantarsi delle opportunità di vita e carriera proporzionate.

A diciotto anni si trasferisce a Shenzhen, nella provincia del Guangdong: ed è qui che inizia ad agguantarsi l’indipendenza concreta, giovane donna lavoratrice in carriera per bravura e passione. Non è ancora tempo della moda, ma del forgiare alcuni dei talenti che contribuiranno a renderla abile nella costruzione del suo sogno: prima c’è l’impiego in un’azienda leader dell’elettronica, poi il ruolo d’agente assicurativo in cui brilla tra i primi 10 su 3000 agenti a Shenzhen, poi la decisione di di aprire un negozio di abiti in una delle zone più esclusive, quando lo shopping appassionato le fa intuire il gusto di non fermarsi ad un ottimo guardaroba per sé, ma di ampliarsi ad un progetto tutto suo.

Judy lavora, sperimenta, impara: si laurea in financial management all’università di Shenzhen, poi vola in Italia per proseguire gli studi in moda, da buyer e in seguito da fashion designer. Forgia il gusto personale, lascia libera la sua natura di femminista consapevole che l’indipendenza della donna nell’impugnare i sogni e farne realtà concrete nonostante le convenzioni sociali sia un dovere innanzitutto per se stessa. Veste questa femminista di uno stile che è squisitamente suo: con abiti che mentre valorizzano la determinazione, sottolineano la femminilità e raccontano storie d’ispirazione ideale per la contemporaneità. Il brand che porta il suo nome prende vita a New York, e forma sartoriale eccellente nella sua Cina: oggi oltre centocinquanta persone compongono le squadre di progettazione, produzione e vendita che la affiancano nel creare capi altamente curati nella qualità e nei dettagli. E nella narrazione di storie che portano con sé.

La collezione p/e 2020 ne custodisce una che risale al tempo sospeso delle leggende cinesi, e arriva dritta al cuore romantico e combattivo: narra l’eterno amore impossibile, eppur vittorioso, di Lady White, lo spirito del Serpente Bianco che, dopo millenni chiusa tra nuvole bianche e montagne a diventare padrona di pratiche magiche e religiose, s’incarna in giovane fanciulla incantevole, scende sul Lago dell’Ovest, dove incontra il bel giovane Xu Xian. La pioggia fu galeotta: col suo pretesto, lei chiese in prestito l’ombrello a lui, che s��affrettò a ripararla.

Da quel momento, l’amore li avvinse, puro e forte tanto da scatenare la furia gelosa del monaco Fahai, che tutto fece pur di dividerli e, dopo aver portato con l’inganno Lady White a rivelare la sua natura di serpente al marito, ci riuscì. Ma solo per un po’, perché lei affrontò ogni peripezia: l’abbandono e la gravidanza, la prigionia e la lotta contro il male, fino a riagguantare suo marito e il loro eterno amore.

Suggestioni di romanticismo e forza che nella collezione diventano scene narrate dalle stampe colorate grafiche dal gusto pop, che  si mescolano agli intrusi scherzosi come gli airpod indossati dal serpente, e che convivono con la sofisticatezza preziosa dei ricami a mano di Suzhou, il più importante fra tutti i tipi di ricamo cinese, fatta in fil di seta.

Le plissettature scenografiche rievocano le pieghe dell’ombrello che contribuì al magico incontro, mentre la pelle di serpente e le onde del lago rivivono e scintillano su tute, blazer e pantaloni di strass. Le silhouette sono scolpite nei punti dove la femminilità gioca a concentrare il fascino, l’appeal rock interviene a ricordare che le regole sociali inutili possono essere frantumate a vantaggio delle nostre passioni, ma le regole eccellenti dell’alta sartoria cinese no, quelle restano e caratterizzano ogni capo di Judy Zhang. Silvia Scorcella

{ pubblicato su Webelieveinstyle }

[haob1n]

Breaking news: Nick & Judy found unemployed

[mostunderratedawards]

Most Underrated TV Movie 

Genie 

Turtles All The Way Down 

Prom Dates

Totally Killer 

Love in Taipei 

Ricky Stanicky

[medievalandfantasymelee]

Candidates for the title of

🌹Queen of Love & Beauty🌹

Candidates were given priority based on the number of submissions they received (in brackets behind their names and credentials). After all candidates with multiple submissions were entered, single submission candidates were admitted in order of submission.

Guinevere [Angel Coulby], BBC’s Merlin (2008-2012) [17]

Morgana Pendragon [Katie McGrath], BBC’s Merlin (2008-2012) [11]

Lady Éowyn of Rohan [Miranda Otto], The Lord of the Rings Trilogy (2001-2003) [10]

Lady Marian Fitzwalter [Olivia de Havilland], The Adventures of Robin Hood (1938) [9]

Isabeau of Anjou [Michelle Pfeiffer], Ladyhawke (1985) [8]

Arwen Undomiel [Liv Tyler], The Lord of the Rings Trilogy (2001-2003) [7]

Galadriel of Lothlórien [Cate Blanchett], The Lord of the Rings Trilogy (2001-2003) [7]

Princess Buttercup [Robin Wright], The Princess Bride (1987) [6]

Lucrezia Borgia [Holliday Grainger], The Borgias (2011-2013) [6]

Princess Isabella Maria Lucia Elizabetta of Valencia [Karen David], Galavant (2015-2016) [5]

Lagertha [Katheryn Winnick], Vikings (2013-2020) [5]

Sorsha [Joanne Whalley], Willow (1988) [5]

Lady Æthelflæd of Mercia [Millie Brady], The Last Kingdom (2015-2022) [4]

Princess Gwendolyn [Angela Lansbury], The Court Jester (1955) [4]

Maid Jean [Glynis Johns], The Court Jester (1955) [4]

Lady Marion of Leaford [Judi Trott], Robin of Sherwood (1984-1986) [4]

Rebecca of York [Olivia Hussey], Ivanhoe (1982) [4]

Alicent Hightower [Olivia Cooke], House of the Dragon (2022-) [3]

Aliena [Hayley Atwell], The Pillars of the Earth (2010) [3]

Queen Cersei Lannister [Lena Headey], Game of Thrones (2011-2019) [3]

Princess Daenerys Targaryen [Emilia Clarke], Game of Thrones (2011-2019) [3]

Danielle de Barbarac [Drew Barrymore], Ever After: A Cinderella Story (1998) [3]

Devasena [Anushka Shetty], Baahubali (2017) [3]

Elizabeth de Burgh [Florence Pugh], Outlaw King (2018) [3]

Queen Guinevere [Vanessa Redgrave], Camelot (1967) [3]

Hürrem Sultan [Meryem Uzleri], Magnificent Century {Muhteşem Yüzyıl} (2011-2014) [3]

Lady Jocelyn [Shannyn Sossamon], A Knight’s Tale (2001) [3]

Princess Lili [Mia Sara], Legend (1985) [3]

Lady Margaery Tyrell [Natalie Dormer], Game of Thrones (2011-2019) [3]

Marian of Knighton [Lucy Griffiths], BBC’s Robin Hood (2006-2009) [3]

Lady Sansa Stark [Sophie Turner], Game of Thrones (2011-2019) [3]

Sheng Minglan [Zhang Liying], The Story of Minglan (2018) [3]

Queen Susan the Gentle [Sophie Winkleman], The Chronicles of Narnia (2005-2010) [3]

Lady Anne Neville [Claire Bloom], Richard III (1955) [2]

Lady Anne Neville [Phoebe Fox], The Hollow Crown (2012-2016) [2]

Brienne of Tarth [Gwendoline Christie], Game of Thrones (2011-2019) [2]

Bronwyn [Nazanin Bonialdi], The Rings of Power (2022-) [2]

Queen Catherine of Aragon [Maria Doyle Kennedy], The Tudors (2007-2010) [2]

Contessina de Bardi [Annabel Scholey], Medici (2016-2019) [2]

Egwene Al’Vere [Madeleine Madden], The Wheel of Time (2021-) [2]

Queen Eleanore of Aquitane [Katharine Hepburn], The Lion in Winter (1968) [2]

Queen Elizabeth Woodville [Rebecca Ferguson], The White Queen (2013) [2]

Donna Giulia Farnese [Lotte Verbeek], The Borgias (2011-2013) [2]

Queen Guinevere [Cheri Lunghi], Excalibur (1981) [2]

Jadis, the White Witch [Tilda Swinton], The Chronicles of Narnia (2005-2010) [2]

Jeanne d’Arc [Renee Falconetti], The Passion of Joan of Arc {La Passion de Jeanne d’Arc} (1928) [2]

Joan of Arc [Milla Jovovich], The Messenger: The Story of Joan of Arc (1999) [2]

Kate [Laura Fraser], A Knight’s Tale (2001) [2]

Lady Kate Percy [Michelle Dockery], The Hollow Crown (2012-2016) [2]

Queen Lucy the Valiant [Rachael Henley] [2]

Queen Madelena [Mallory Jansen], Galavant (2015-2016) [2]

Queen Margaret of Anjou [Sophie Okonedo], The Hollow Crown (2012-2016) [2]

Lady Mary Boleyn [Charity Wakefield], Wolf Hall (2015-2024) [2]

Moiraine Damodred [Rosamund Pike], The Wheel of Time (2021-) [2]

Morgana Pendragon [Eva Green], Camelot (2011) [2]

Queen Rhaenyra Targaryen [Emma D’Arcy], House of the Dragon (2022-) [2]

Queen Sibylla of Jerusalem [Eva Green], Kingdom of Heaven (2005) [2]

Eliška Pomořanská [Jana Brejchová], A Night at Karlstein {Noc na Karlštejně} (1973) [1]

Lady Marian [Audrey Hepburn], Robin & Marian (1976) [1]

Ophelia [Jean Simmons], Hamlet (1948) [1]

Aykız Hatun [Hande Subaşı], Diriliş: Ertuğrul (2014-2019) [1]

Roberta Steingas [Clare Foster], Galavant (2015-2016) [1]

Tamina [Gemma Arterton], Prince of Persia (2010) [1]

Princess Isabelle [Sophie Marceau], Braveheart (1995) [1]

Alexandra [Geraldine Viswanathan], Miracle Workers: The Dark Ages (2020) [1]

Isabella of Valois [Emma Hamilton], RSC’s Richard II (2013) [1]

Jade Claymore [Erin Kellyman], Willow (2022) [1]

Anne Boleyn [Genevieve Bujold], Anne of the Thousand Days (1969) [1]

Mu Nihuang [Liu Tao], Nirvana in Fire (2015-2018) [1]

Padmavati [Deepika Padukone], Padmaavat (2018) [1]

Queen Isabella [Tilda Swinton], Edward II (1991) [1]

Djaq [Anjali Jay], BBC’s Robin Hood (2006-2009) [1]

Below is a list of Honorable Mentions...

Anne Boleyn [Natalie Portman], The Other Boleyn Girl (2008)

Aslaug [Alyssa Sutherland], Vikings (2013-2020)

Aviendha [Ayoola Smart], The Wheel of Time (2021-)

Catherine of Aragon [Charlotte Hope], The Spanish Princess (2019-2020)

Cecilia Algotsdotter [Sofia Helin], Arn: The Knight Templar (2007)

Doric [Sophia Lillis], Dungeons & Dragons: Honour Among Thieves (2023)

Queen Elizabeth of York [Jodie Comer], The White Princess (2017)

Jiang Yanli [Xuan Lu], The Untamed (2019)

Guinevere [Julia Ormond], First Knight (1995)

Hafsa Sultan [Nebahat Çehre], Magnificent Century {Muhteşem Yüzyıl} (2011-2014)

Hatice Sultan [Selma Ergeç], Magnificent Century {Muhteşem Yüzyıl} (2011-2014)

Queen Isabella of Castile [Michelle Jenner], Isabel (2011-2014)

Isolde [Sophia Myles], Tristan + Isolde (2006)

Joan of Arc [Ingrid Bergman], Joan of Arc (1948)

Kira [Lisa Maxwell, Kathryn Mullen], The Dark Crystal (1982)

Queen Mab [Miranda Richardson], Merlin (1998)

Maleficent [Angelina Jolie], Maleficent (2014)

Lady Margaret Tudor [Georgie Henley], The Spanish Princess (2019-2020)

Marian Dubois [Mary Elizabeth Mastrantonio], Robin Hood: Prince of Thieves (1991)

Lady Mary Tudor [Sai Bennett], The Spanish Princess (2019-2020)

Empress Maude [Alison Pill], The Pillars of the Earth (2010)

Morgaine [Julianna Marguiles], The Mists of Avalon (2001)

Queen Ravenna [Charlize Theron], Snow White and the Huntsman (2012)

Rebecca of York [Elizabeth Taylor], Ivanhoe (1952)

Rosie Cotton [Sarah MacLeod], The Lord of the Rings Trilogy (2001-2003)

Sharako Lohar [Abigail Thorn], House of the Dragon (2022-)

Tár-Miriel [Cynthia Addai-Robinson], The Rings of Power (2022-)

Ygritte [Rose Leslie], Game of Thrones (2011-2019)

Yara Greyjoy [Gemma Whelan], Game of Thrones (2011-2019)

[lauraperfectinsanity]

MCU actresses Karen Gillan, Pom Klementieff, Elizabeth Debicki, Daniela Melchior, Meng'er Zhang, Ginger Gonzaga, Iman Vellani, Xochitl Gomez, Dominique Thorne, Ariana Greenblatt, Linda Cardellini and Judy Greer attend Marvel Studios’ “Guardians of the Galaxy Volume 3” World Premiere at the Dolby Theatre on April 27, 2023, in Hollywood, California.

[granddaughterofdemeter]

Granddaughter of Demeter

Im Tali, uh I like writing and love it if people send me requests ❤️

she/her, daughter of persephone according to buzzfeed like 6 years ago.

Here i'll tell you whether the requests are open or not ->->->

[REQUESTS ARE OPEN]

You can request anything if you want me to write something.

the fandoms I write for are:

Percy Jackson and the Olympians/Heroes of Olympus

Magnus Chase and the Gods of Asgard [when I'm done reading at least the first book]

The Hunger Games [ finnick is so fine]

The Hunger Games: The Ballad of Songbirds and Snakes

The Maze Runner [same w/ MC, I'll get there tho]

The Invisible Life of Addie La Rue [LUC IS SO HOT UGHH]

Brooklyn nine-nine

Red Queen series

The Secret History [i'm only on pg 350 tho]

Outer Banks [im on S2 js so yk]

Characters that I write for are:

PJO:

[romantic when they are over 18]

Percy Jackson

Annabeth Chase

Grover Underwood

Leo Valdez

Luke Castellan

Clarisse La Rue

Nico Di Angelo

Will Solace

Jason Grace

Piper Mclean

Frank Zhang

Hazel Levesque

Reyna Avila Ramirez Arellano

Rachel Elizabeth Dare

+ the Gods

MCGOA:

soon

THG:

Finnick Odair

Gale Hawthorne

Peeta Mellark

Katniss Everdeen

Johanna Mason

Haymitch Abernathy

Effie Trinket

Tigris Snow

THG-TBOSAS:

Coriolanus Snow

Lucy Gray Baird

Sejanus Plinth

Clemensia Dovecote

Tigris Snow

Livia Cardew

Treech

Maude Ivory [platonic]

Festus Creed

Reaper Ash

Dill [platonic]

Wovey [platonic]

TMR:

soon

B99:

Jake Peralta

Amy Santiago

Rosa Diaz

Charles Boyle

Gina Linetti

Terry Jeffords [platonic]

Adrian Pimento

Raymond Holt [platonic]

Kevin Cozner + Cheddar [platonic]

Doug Judy

Hitchcock + Scully [they were so fine when they were younger 🥲] [older/tv show age is platonic]

Red Queen:

Maven Calore

Mare Barrow

Shade Barrow

Gisa Barrow

Kilorn Warden

Evangeline Samos

Ptolemy Samos

Cal Calore

The Secret History:

Richard Papen

Henry Winters

Camilla Macaulay

Charles Macaulay

Francis Abernathy

Bunny Corcoran

OBX:

Rafe Cameron

JJ Maybank

John B. Routledge

Pope Hayward

Topper Thornton

Kelce

Kiara Carrera

Sarah Cameron

NO SMUT [implied is ok]

angst is appreciated

step siblings are iffy

no non con or abuse

no domestic violence

I love AUs

i can do female, male, and/or gender neutral reader [any gender basically]

no major age gap [like more than 10 yrs]

no age gap more than 4 years when they are under 18

Banners are made by ->->->->->-> @cafekitsune

Leaves banner ->->->->->-> @saradika

[battyaboutbooksreviews]

🦇 We shouldn't wait until May every year to delve into the beauty of Asian American and Pacific Islander (AAPI) voices. In May, I shared a list of the NEWEST AAPI books out this year. To keep promoting AAPI authors, characters, and stories, here are a few Young Adult AAPI books you can add to your TBR for the remainder of the year!

🏮 The Summer I Turned Pretty by Jenny Han 🏮 My Summer of Love and Misfortune by Lindsay Wong 🏮 Permanent Record by Mary H.K. Choi 🏮 When We Were Infinite by Kelly Loy Gilbert 🏮 To All the Boys I've Loved Before by Jenny Han 🏮 I Will Find You Again by Sarah Lyu 🏮 Emergency Contact by Mary H.K. Choi 🏮 American Panda by Gloria Chao 🏮 When Dimple Met Rishi by Sandhya Menon 🏮 Starfish by Akemi Dawn Bowman 🏮 Our Wayward Fate by Gloria Chao 🏮 Rent a Boyfriend by Gloria Chao 🏮 Want by Cindy Pon 🏮 The Weight of Our Sky by Hanna Alkaf 🏮 A Place to Belong by Cynthia Kadohata 🏮 Of Curses and Kisses by Sandhya Menon 🏮 Everyone Wants to Know by Kelly Loy Gilbert 🏮 A Pho Love Story by Loan Le 🏮 The Wild Ones by Nafiza Azad 🏮 Prepped by Bethany Mangle 🏮 The Infinity Courts by Akemi Dawn 🏮 Yolk by Mary H.K. Choi 🏮 Imposter Syndrome and Other Confessions of Alejandra Kim by Patricia Park 🏮 This is Not a Personal Statement by Tracy Badua 🏮 The Cartographers by Amy Zhang 🏮 The Love Match by Priyanka Taslim 🏮 This Place is Still Beautiful by Xixi Tian 🏮 Chasing Pacquiao by Rod Pulido 🏮 I'm Not Here to Make Friends by Andrew Yang 🏮 The Queens of New York by E. L. Shen 🏮 Hungry Ghost by Victoria Ying 🏮 These Infinite Threads by Tahereh Mafi 🏮 Six Crimson Cranes by Elizabeth Lim 🏮 The Marvelous Mirza Girls by Sheba Karim 🏮 A Magic Steeped in Poison by Judy I. Lin

[can-they-lift-thors-hammer]

Hall of Hammer Wielders

Because Tumblr only lets a post have 100 links, I'm putting characters from the same fandom into groups/legions/pantheons/whatever you want to call them.

(Ignore the numbers, they're just for me, and they aren't going to make sense to anyone but me anyway.)

The Star Trekkers (6 members) 🥈

7. Chell

The Atlanteans (4 members)

The Space Explorers (4 members)

The Transformers (2 members)

17. Frieren

18. Sailor Moon

19. Izuku Midoriya

The Marvelous Marvels (2 members)

The Sonicverse (2 members)

24. Hatsune Miku

The Stargaters (2 members)

27. Marty McFly

28. Niko

The Wilds (2 members)

The Dragons (3 members)

34. Doomguy

35. Sir Daniel Fortesque

36. Gideon Nav

The Cats (3 members)

40. Ortho Shroud

41. Seyka

The Magic Schoolkids (2 members)

44. Newt Scamander

45. Goku

The Buffyverse (2 members)

48. Olimar

49. Zhang Chengling

50. Rare Thwok

51. The Collector

The Digimon (2 members)

54. Heavy

The Rains (3 members) 🥉

58. Ada Paige

59. Banhammer

The Programs (2 members)

62. SpongeBob SquarePants

63. Aeryn Sun

64. Shrek

The Half Lifers (2 members)

67. Arthur Morgan

68. Shoyo Hinata

69. Bau (OC)

70. Peeta

71. Sam Winchester

72. Kirby 🥇Highest percentage of Yes votes

73. Yugi Mutou

74. Luigi

75. Candace Flynn

76. Ash Williams

The Star Warriors (2 members)

79. Macchanu

80. Jonathan Joestar

81. Rodney Copperbottom

82. Coco

83. Gojo Satoru

84. Sam Carpenter

85. Eve Baird

86. Trucy Wright

87. Ali Abdul

88. Emma

89. Laios

90. Lord Yoshii Toranaga

91. Jak

92. Hank Hill

93. Soren

94. Tim Drake

95. Nico di Angelo

96. Thorfinn

97. Ri Jeong-hyeok

98. Judy Nails

99. Lucas Sinclair

100. Naruto Uzumaki

101. Tommy Oliver

102. Tyreese Williams

103. Ninja Brian

104. Thor (God of War)

The RWBY...um...whatever they ares (4 members)

109. Michael Jackson from the movie Moonwalker

110. Mickey Mouse (Kingdom Hearts version)

111. Kali the Red Mist

[maximumwobblerbanditdonut]

Andy Murray tears up at Wimbledon’s salute 🎾

Andy and Jamie ­competed on the same side of the net at Wimbledon as Andy began his long, emotional farewell to ­Wimbledon after 19 incredible years.

Andy and Jamie Murray spent their formative childhood years ­playing tennis at their local tennis club in Dunblane, where their mother, Judy, was the club coach.

Andy received a standing ovation as he walked onto Centre Court, with members of his family watching on.

Andy, in what is likely his last Wimbledon campaign - will still have the mixed doubles event to feature in - alongside Emma Raducanu this Saturday the 6th in the fourth match of the day on Court One, a team composed of the two active British singles grand slam ­champions, against Marcelo Arevalo and Zhang Shuai.

Later this month, Andy’s final ­tournament will be the Olympic Games, where he will play doubles with Dan Evans.

Thank you, Andy! You were obviously really special 🎾 🤩🍓

#AndyMurray #bbesport #CentreCourt #Wimbledon #Tennis #doubles #JamieMurray #EmmaRaducanu #mixeddoubles #tournament #OlympicGames

Posted 6th July 2024

[amphtaminedreams]

Mid-year Fashion Update for 2024 in (Mostly) A-Z Format: RTW, Pre-fall, & a Little Haute Couture (Part 2)

-clockwise l-r: Ganni RTW F/W24, Gauchere “, Genny “, Germanier “-

-top to bottom: Gaurav Gupta haute couture S/S24, GCDS RTW F/W24, Georges Hobeika haute couture S/S24-

-clockwise l-r: Giambattista Valli RTW F/W24, haute couture S/S24, Harris Reed RTW F/W24-

-top to bottom: Giorgio Armani RTW F/W24, Givenchy “, Gucci “-

-clockwise l-r: Harunobumurata RTW F/W24, Heliot Emil “, Helmut Lang “, Hodakova “-

-top to bottom: Hermès RTW F/W24, Huishan Zhang “, Institut Français de la Mode “-

-clockwise l-r: Holzweiler RTW F/W24, Hope for Flowers “, House of Aama “, HUI “-

-clockwise l-r: Interior RTW F/W24, Isabel Marant “, Issey Miyake “, Jitrois. “-

-top to bottom: Jacquemus RTW S/S24, Jason Wu Collection RTW F/W24, Jil Sander “-

-top to bottom: Jacques Wei RTW F/W24, Louis Shengtao Chen “, Mark Gong “, Oude Waag “-

-clockwise l-r: Jean Paul Gaultier haute couture S/S24, Johanna Ortiz RTW F/W24, Judy Turner “-

-clockwise l-r: JW Anderson RTW F/W24, Kanako Sakai “, La DoubleJ “, Lapointe “-

-top to bottom: Keisukeyoshida RTW F/W24, Hyke “-

-clockwise l-r: Khaite RTW F/W24, KNWLS “, Kim Shui “, Private Policy “-

-top to bottom: LaQuan Smith RTW F/W24, Lemaire Men’s RTW F/W24, Loewe RTW F/W24-

-clockwise l-r: Loro Piani RTW F/W24, Lou Dallas “, Maisie Wilen “, Maje “-

-top to bottom: Louis Vuitton pre-fall 2024, RTW F/W24, Men’s RTW F/W24, LoveShackFancy “-

-clockwise l-r: Mans RTW F/W24, Pillings “, Ming Ma “-

-top to bottom: Ludovic de Saint Sernin RTW F/W24, Maison Margiela haute couture S/S24, Luisa Beccaria  RTW F/W24-

-clockwise l-r: Mame Kurogouchi RTW F/W24, Marchesa “, Marco Rambaldi “, Marina Morrone “-

-clockwise l-r: Marc Jacobs RTW S/S24, Mark Kenly Domino Tan RTW F/W24, Marine Serre “, Mark Fast “-

-clockwise l-r: Markarian RTW F/W24, Marques’Almeida “, Miss Sohee haute couture S/S24, Missoni RTW F/W24-

-top to bottom: Marni RTW F/W24, Max Mara “, Michael Kors “-

-clockwise l-r: Mithridate RTW F/W24, MM6 Maison Margiela “, Molly Goddard “, Monse “-

-top to bottom: Miu Miu RTW F/W24, Moncler Grenoble “, MSGM “-

-top to bottom: Mugler RTW F/W24, Nina Ricci “, N°21 “-

-clockwise l-r: Nanushka RTW F/W24, Nehera “, Niccolò Pasqualetti “, Noir Kei Ninomiya “-

-clockwise l-r: Norma Kamali RTW F/W24, Ottolinger “, Pamella Roland “-

-clockwise l-r: Off-White RTW F/W24, Palmer Harding “, Patrick McDowell “, Paul & Joe “-

-top to bottom: Patou RTW F/W24, Peter Do “, Philipp Plein “-

[ducksbellorum]

full speed ahead (listen/download)

an argo ii playlist - the heroes of olympus - arranged by ducksbellorum

full speed ahead - epic the musical Every great quest aboard a magitechnical Greek warship deserves an equally great playlist, or at least that's what Leo tells everyone when he asks for their song choices. Six hundred men, six hundred miles of open sea But the problem's not the distance It's what lies in between

this is sparta !!! - sammy & lesen Obviously this was one of Leo's contributions; the bass gets him hype and the fact that he's bumping Spartan jams on a Greek warship just tickles him. Spartans, What is your profession? Spartans, Prepare for glory!

vode an - samuel kim Jason added this to the playlist; he doesn't know a lot of 'modern' music, but this track reminds him of Roman war chants and gets him going. Kandosii sa ka'rta, Vode an. Coruscanta a'den mhi, Vode an. Bal kote, darasuum kote, Jorso'ran kando a tome.

seven nation army - the white stripes Percy Jackson was tempted to add a joke track, but knowing Leo there would be plenty and besides, why not add something he actually wants to listen to? Everyone knows about it From the Queen of England to the Hounds of Hell And if I catch it comin' back my way, I'm gonna serve it to you And that ain't what you want to hear, but that's what I'll do

yereyira - papito & iba one Piper spent a lot of her life around spoiled pop musicians and their kids, so her music tastes tend toward the most obscure artists possible on purpose. Pa-pa-pa-pa-papito I-i-i-i-iba one Papito, Iba One Iba One, Papito!

shake it off - taylor swift There is in fact a Swiftie aboard the Argo II and it's Frank Zhang; he's a little bashful about it but that doesn't stop him from adding it to the playlist. I'm dancin' on my own I make the moves up as I go And that's what they don't know, That's what they don't know

over the rainbow - judy garland Like Jason, Hazel also doesn't know much modern music, but that doesn't hold her back from getting Leo to add one of her favorites to the crew's mix. Somewhere over the rainbow Skies are blue And the dreams that you dare to dream Really do come true

laughter lines - bastille This is Annabeth's pull; most of her music is lyricless for better concentration, but occasionally she veers into bittersweet indie pop, like this. "I'll see you in the future when we're older And we are full of stories to be told Cross my heart and hope to die I'll see you with your laughter lines"

sea shanty medley - home free If you don't think that the crew did some shanty singing on their journey, you're probably right but that won't stop me from hoping they did. She's a fast clipper ship and a bully good crew Away Santiana And an old salty yank for a captain too Along the plains of Mexico

bonus: spooky scary skeletons (remix) - andrew gold "Hey Nico, you want to add a song to the quest playlist?" "No." "Are you suuuuure?" "Yes." "I'll just add one for you, then, shall I?" "VALDEZ!" We're so sorry skeletons, you're so misunderstood You only want to socialize (But I don't think we should)

[mah33n]

𝘾𝙃𝘼𝙍𝘼𝘾𝙏𝙀𝙍 𝙇𝙄𝙎𝙏

CRIMINAL MINDS

spencer reid (loml), jennifer jareau, luke alvez, aaron hotchner

OUTER BANKS

jj maybank, kiara carrera, john b. routledge

BRIDGERTON

anthony bridgerton, benedict bridgerton, daphne bridgerton, eloise bridgerton, colin bridgerton, penelope featherington, kate sharma, edwina sharma, simon basset

HEROES OF OLYMPUS

percy jackson, annabeth chase, jason grace, leo valdez, piper mcclean, hazel levesque, frank zhang, nico di angelo, thalia grace, will solace

SHATTER ME

aaron warner, kenji kishomoto, juliette ferrars, adam kent

HARRY POTTER

harry potter, hermione granger, ron weasley, luna lovegood, ginny weasley, neville longbottom, fred weasley, george weasley, james potter, remus lupin, sirius black, lily evans, marlene mckinnon, dorcas meadowes

KEEPER OF THE LOST CITIES

sophie foster, keefe sencen, fitz vacker, dex dizznee, biana vacker, marella redek, jensi babblos

MARVEL

loki, thor, peter parker, miles morales, mj watson, tony stark, scott lang, hope pym, carol danvers, clint barton, stephen strange, ned leeds

THE SUMMER I TURNED PRETTY

comrad fisher, jeremiah fisher

SHADOW AND BONE

alina starkov, the darkling, mal oretsev, genya safin, zoya nazyalensky, nikolai lantsov, tamaar kir-bataar, tolya yul-bataar, david kostyk

SIX OF CROWS

kaz brekker, inej ghafa, jesper fahey, nina zenik, wylan van eck, matthias helvar

BROOKLYN 99

jake peralta, amy santiago, charles boyle (platonic), doug judy, rosa diaz, raymond holt (platonic), terry jeffords, norm scully (platonic), trudy judy, kevin costner, gina linetti

[sunaleisocial]

Study reveals why AI models that analyze medical images can be biased

New Post has been published on https://sunalei.org/news/study-reveals-why-ai-models-that-analyze-medical-images-can-be-biased/

Study reveals why AI models that analyze medical images can be biased

Artificial intelligence models often play a role in medical diagnoses, especially when it comes to analyzing images such as X-rays. However, studies have found that these models don’t always perform well across all demographic groups, usually faring worse on women and people of color.

These models have also been shown to develop some surprising abilities. In 2022, MIT researchers reported that AI models can make accurate predictions about a patient’s race from their chest X-rays — something that the most skilled radiologists can’t do.

That research team has now found that the models that are most accurate at making demographic predictions also show the biggest “fairness gaps” — that is, discrepancies in their ability to accurately diagnose images of people of different races or genders. The findings suggest that these models may be using “demographic shortcuts” when making their diagnostic evaluations, which lead to incorrect results for women, Black people, and other groups, the researchers say.

“It’s well-established that high-capacity machine-learning models are good predictors of human demographics such as self-reported race or sex or age. This paper re-demonstrates that capacity, and then links that capacity to the lack of performance across different groups, which has never been done,” says Marzyeh Ghassemi, an MIT associate professor of electrical engineering and computer science, a member of MIT’s Institute for Medical Engineering and Science, and the senior author of the study.

The researchers also found that they could retrain the models in a way that improves their fairness. However, their approached to “debiasing” worked best when the models were tested on the same types of patients they were trained on, such as patients from the same hospital. When these models were applied to patients from different hospitals, the fairness gaps reappeared.

“I think the main takeaways are, first, you should thoroughly evaluate any external models on your own data because any fairness guarantees that model developers provide on their training data may not transfer to your population. Second, whenever sufficient data is available, you should train models on your own data,” says Haoran Zhang, an MIT graduate student and one of the lead authors of the new paper. MIT graduate student Yuzhe Yang is also a lead author of the paper, which appears today in Nature Medicine. Judy Gichoya, an associate professor of radiology and imaging sciences at Emory University School of Medicine, and Dina Katabi, the Thuan and Nicole Pham Professor of Electrical Engineering and Computer Science at MIT, are also authors of the paper.

Removing bias

As of May 2024, the FDA has approved 882 AI-enabled medical devices, with 671 of them designed to be used in radiology. Since 2022, when Ghassemi and her colleagues showed that these diagnostic models can accurately predict race, they and other researchers have shown that such models are also very good at predicting gender and age, even though the models are not trained on those tasks.

“Many popular machine learning models have superhuman demographic prediction capacity — radiologists cannot detect self-reported race from a chest X-ray,” Ghassemi says. “These are models that are good at predicting disease, but during training are learning to predict other things that may not be desirable.”

In this study, the researchers set out to explore why these models don’t work as well for certain groups. In particular, they wanted to see if the models were using demographic shortcuts to make predictions that ended up being less accurate for some groups. These shortcuts can arise in AI models when they use demographic attributes to determine whether a medical condition is present, instead of relying on other features of the images.

Using publicly available chest X-ray datasets from Beth Israel Deaconess Medical Center in Boston, the researchers trained models to predict whether patients had one of three different medical conditions: fluid buildup in the lungs, collapsed lung, or enlargement of the heart. Then, they tested the models on X-rays that were held out from the training data.

Overall, the models performed well, but most of them displayed “fairness gaps” — that is, discrepancies between accuracy rates for men and women, and for white and Black patients.

The models were also able to predict the gender, race, and age of the X-ray subjects. Additionally, there was a significant correlation between each model’s accuracy in making demographic predictions and the size of its fairness gap. This suggests that the models may be using demographic categorizations as a shortcut to make their disease predictions.

The researchers then tried to reduce the fairness gaps using two types of strategies. For one set of models, they trained them to optimize “subgroup robustness,” meaning that the models are rewarded for having better performance on the subgroup for which they have the worst performance, and penalized if their error rate for one group is higher than the others.

In another set of models, the researchers forced them to remove any demographic information from the images, using “group adversarial” approaches. Both strategies worked fairly well, the researchers found.

“For in-distribution data, you can use existing state-of-the-art methods to reduce fairness gaps without making significant trade-offs in overall performance,” Ghassemi says. “Subgroup robustness methods force models to be sensitive to mispredicting a specific group, and group adversarial methods try to remove group information completely.”

Not always fairer

However, those approaches only worked when the models were tested on data from the same types of patients that they were trained on — for example, only patients from the Beth Israel Deaconess Medical Center dataset.

When the researchers tested the models that had been “debiased” using the BIDMC data to analyze patients from five other hospital datasets, they found that the models’ overall accuracy remained high, but some of them exhibited large fairness gaps.

“If you debias the model in one set of patients, that fairness does not necessarily hold as you move to a new set of patients from a different hospital in a different location,” Zhang says.

This is worrisome because in many cases, hospitals use models that have been developed on data from other hospitals, especially in cases where an off-the-shelf model is purchased, the researchers say.

“We found that even state-of-the-art models which are optimally performant in data similar to their training sets are not optimal — that is, they do not make the best trade-off between overall and subgroup performance — in novel settings,” Ghassemi says. “Unfortunately, this is actually how a model is likely to be deployed. Most models are trained and validated with data from one hospital, or one source, and then deployed widely.”

The researchers found that the models that were debiased using group adversarial approaches showed slightly more fairness when tested on new patient groups than those debiased with subgroup robustness methods. They now plan to try to develop and test additional methods to see if they can create models that do a better job of making fair predictions on new datasets.

The findings suggest that hospitals that use these types of AI models should evaluate them on their own patient population before beginning to use them, to make sure they aren’t giving inaccurate results for certain groups.

The research was funded by a Google Research Scholar Award, the Robert Wood Johnson Foundation Harold Amos Medical Faculty Development Program, RSNA Health Disparities, the Lacuna Fund, the Gordon and Betty Moore Foundation, the National Institute of Biomedical Imaging and Bioengineering, and the National Heart, Lung, and Blood Institute.

[jcmarchi]

Study reveals why AI models that analyze medical images can be biased

New Post has been published on https://thedigitalinsider.com/study-reveals-why-ai-models-that-analyze-medical-images-can-be-biased/

Study reveals why AI models that analyze medical images can be biased

Artificial intelligence models often play a role in medical diagnoses, especially when it comes to analyzing images such as X-rays. However, studies have found that these models don’t always perform well across all demographic groups, usually faring worse on women and people of color.

These models have also been shown to develop some surprising abilities. In 2022, MIT researchers reported that AI models can make accurate predictions about a patient’s race from their chest X-rays — something that the most skilled radiologists can’t do.

That research team has now found that the models that are most accurate at making demographic predictions also show the biggest “fairness gaps” — that is, discrepancies in their ability to accurately diagnose images of people of different races or genders. The findings suggest that these models may be using “demographic shortcuts” when making their diagnostic evaluations, which lead to incorrect results for women, Black people, and other groups, the researchers say.

“It’s well-established that high-capacity machine-learning models are good predictors of human demographics such as self-reported race or sex or age. This paper re-demonstrates that capacity, and then links that capacity to the lack of performance across different groups, which has never been done,” says Marzyeh Ghassemi, an MIT associate professor of electrical engineering and computer science, a member of MIT’s Institute for Medical Engineering and Science, and the senior author of the study.

The researchers also found that they could retrain the models in a way that improves their fairness. However, their approached to “debiasing” worked best when the models were tested on the same types of patients they were trained on, such as patients from the same hospital. When these models were applied to patients from different hospitals, the fairness gaps reappeared.

“I think the main takeaways are, first, you should thoroughly evaluate any external models on your own data because any fairness guarantees that model developers provide on their training data may not transfer to your population. Second, whenever sufficient data is available, you should train models on your own data,” says Haoran Zhang, an MIT graduate student and one of the lead authors of the new paper. MIT graduate student Yuzhe Yang is also a lead author of the paper, which appears today in Nature Medicine. Judy Gichoya, an associate professor of radiology and imaging sciences at Emory University School of Medicine, and Dina Katabi, the Thuan and Nicole Pham Professor of Electrical Engineering and Computer Science at MIT, are also authors of the paper.

Removing bias

As of May 2024, the FDA has approved 882 AI-enabled medical devices, with 671 of them designed to be used in radiology. Since 2022, when Ghassemi and her colleagues showed that these diagnostic models can accurately predict race, they and other researchers have shown that such models are also very good at predicting gender and age, even though the models are not trained on those tasks.

“Many popular machine learning models have superhuman demographic prediction capacity — radiologists cannot detect self-reported race from a chest X-ray,” Ghassemi says. “These are models that are good at predicting disease, but during training are learning to predict other things that may not be desirable.”

In this study, the researchers set out to explore why these models don’t work as well for certain groups. In particular, they wanted to see if the models were using demographic shortcuts to make predictions that ended up being less accurate for some groups. These shortcuts can arise in AI models when they use demographic attributes to determine whether a medical condition is present, instead of relying on other features of the images.

Using publicly available chest X-ray datasets from Beth Israel Deaconess Medical Center in Boston, the researchers trained models to predict whether patients had one of three different medical conditions: fluid buildup in the lungs, collapsed lung, or enlargement of the heart. Then, they tested the models on X-rays that were held out from the training data.

Overall, the models performed well, but most of them displayed “fairness gaps” — that is, discrepancies between accuracy rates for men and women, and for white and Black patients.

The models were also able to predict the gender, race, and age of the X-ray subjects. Additionally, there was a significant correlation between each model’s accuracy in making demographic predictions and the size of its fairness gap. This suggests that the models may be using demographic categorizations as a shortcut to make their disease predictions.

The researchers then tried to reduce the fairness gaps using two types of strategies. For one set of models, they trained them to optimize “subgroup robustness,” meaning that the models are rewarded for having better performance on the subgroup for which they have the worst performance, and penalized if their error rate for one group is higher than the others.

In another set of models, the researchers forced them to remove any demographic information from the images, using “group adversarial” approaches. Both strategies worked fairly well, the researchers found.

“For in-distribution data, you can use existing state-of-the-art methods to reduce fairness gaps without making significant trade-offs in overall performance,” Ghassemi says. “Subgroup robustness methods force models to be sensitive to mispredicting a specific group, and group adversarial methods try to remove group information completely.”

Not always fairer

However, those approaches only worked when the models were tested on data from the same types of patients that they were trained on — for example, only patients from the Beth Israel Deaconess Medical Center dataset.

When the researchers tested the models that had been “debiased” using the BIDMC data to analyze patients from five other hospital datasets, they found that the models’ overall accuracy remained high, but some of them exhibited large fairness gaps.

“If you debias the model in one set of patients, that fairness does not necessarily hold as you move to a new set of patients from a different hospital in a different location,” Zhang says.

This is worrisome because in many cases, hospitals use models that have been developed on data from other hospitals, especially in cases where an off-the-shelf model is purchased, the researchers say.

“We found that even state-of-the-art models which are optimally performant in data similar to their training sets are not optimal — that is, they do not make the best trade-off between overall and subgroup performance — in novel settings,” Ghassemi says. “Unfortunately, this is actually how a model is likely to be deployed. Most models are trained and validated with data from one hospital, or one source, and then deployed widely.”

The researchers found that the models that were debiased using group adversarial approaches showed slightly more fairness when tested on new patient groups than those debiased with subgroup robustness methods. They now plan to try to develop and test additional methods to see if they can create models that do a better job of making fair predictions on new datasets.

The findings suggest that hospitals that use these types of AI models should evaluate them on their own patient population before beginning to use them, to make sure they aren’t giving inaccurate results for certain groups.

The research was funded by a Google Research Scholar Award, the Robert Wood Johnson Foundation Harold Amos Medical Faculty Development Program, RSNA Health Disparities, the Lacuna Fund, the Gordon and Betty Moore Foundation, the National Institute of Biomedical Imaging and Bioengineering, and the National Heart, Lung, and Blood Institute.

[ecargmura]

A Magic Steeped In Poison - Book Review

AAPI heritage month already passed, but I’ll be reviewing a book written by an Asian author! Well, I chose to read this book not because of the special month, but because I wanted to read more books written by Asian and Asian American authors. I think my fondness for Asian authors stemmed from me liking manga and also Tablo’s Pieces of You.

I found this book when I was book shopping in Walmart. Yes, I shop for books at Walmart. I held off from reading it for a while since I had other books to read. When I finally got to it, I was excited.

Fortunately, the premise of the book is good. Magical tea magic with Chinese mythology? How unique! I am an avid tea drinker, so I was immersed in the world Judy Lin created with tea magic having the power to do anything from discerning truth from lies, peering into the future, etc. It felt like I was a Shennong-shi in this alternate reality of China.

The main character Zhang Ning wants to find a cure for her poisoned sister, so she decides to head to the capital where they are holding a tournament for tea magic apprentices, shennong-tu, in order to become acknowledged by the Princess. However, the catch is that she’s actually not a true apprentice and that she’s only taking her sister’s place because competing is the only choice she has. However, she still has a vast knowledge of tea magic and capable, which is why she’s still able to survive in the tournament.

What I love most about this book other than its premise is Lin’s detailed writing. She is very detailed when describing things and it helps me paint a visual picture of what the world, food, characters, and tea all look like. Her flowery descriptives are amazing to read. Despite her flowery words, I think my favorite portion of the story was when Ning was healing Ruyi, the princess’s handmaiden. Inside Ruyi’s body contained a very grotesque three-headed snake that could be or could not be a spirit. The way Lin described how it looks and its movements really grossed me out. I commend Lin for being able to write beautifully.

While the premise and writing style are strong, it doesn’t mean it’s perfect. There are many flaws in the overall writing execution. The story starts off with Ning on her way to the capital. I sort of wished the story started a bit before her travels, precisely, the moment she had realized she had poisoned her sister Shu. All the moments of Ning mentioning she had poisoned her did not compel me as much since it was more “told” than “shown”.

The tournament was a bit of a miss for me. Tournaments story arcs in general can be a hit or miss. Especially in manga, they’re either a hit or a miss, depending on the execution as they can be a rinse and repeat of the same thing but written by different authors. How was this tournament arc? It was a bit of a slog to read, honestly. I think what it lacked was more distinctive characters? Like, I wanted to see a rival for Ning or someone who’s always one step ahead of her and pushes Ning to her upmost potential. It was interesting, but given that 90% of the story focused on this one tournament felt like a bit of a let down. However, I did like the sudden twist at the end of it.

The characters that aren’t Ning are quite interesting. Lian’s sassy; she’s my favorite. Princess Zhen has something going on with her handmaiden Ruyi and I love it. Kang’s a bit iffy for me. I do like how they’re all essential for Ning to develop and none of them seem out of place or just there for a need of a minor character.

The ending of the story definitely shows the need for a continuation. There is a sequel, A Venom Dark and Sweet. I’ll try to buy it when I can after I take a break. While I want to read it, I’m dreading over the fact that the last few chapters was building up for a revolution arc. In all honesty, I dislike revolution arcs. I just feel like they’re the same thing. MC gets dragged into revolution, teams up with people to stop the big bad, big bad turns out to not be the big bad and there’s an even bigger bad behind the big bad, and biggest bad gets destroyed and everyone lives happily ever after. I think reading Mockingjay made me dislike revolution arcs. I just hope this one won’t be rushed like how that book kind of was.

Overall, I give this book a 4/5. I think it’s a perfect representation of the capabilities of what Asian-American authors can do! The book industry is rough for us Asians and I’ll support them by reading more AAPI books! If possible, give me a recommendation! Also, let me know your thoughts on this book if you have read it!

[dragons-jade-tears]

Judy I. Lin, A Venom Dark and Sweet