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onereallygoodlambonastick · 8 months ago

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rolecall / XG - TIPPY TOES / left right posting up slow switching lanes just like this / only direction i know swerving left right keep me upright / my adrenaline hit the pedal to the ground / 2 lil asians / don't we all deserve it ill tell you how we want it / no you cant deny it / you either like it or bite it / don't ask me if you dont know why / the only direction i know / what goes around comes back around / so when he comes down are you gonna let him pay or are you gonna let him stay

do you know why phonk gets popular on the social medias? because people dream of murder. they just find the most socially acceptable way to do it, because these are people interested in staying people. to those who dream of murder: this is will not kill you. for it did not kill me.

this is why everything i write is a poem.

and i think more of us should dream of murder, or what it takes to end a life. just one is fine. just 1. uno. uno. yi. yichi. one. One. the One. you believe in god? believe in the power of 1.

all for one / one for all. think on it. evoke all might, and evoke his enemy. why?

the universe is interested in this, too.

will you answer it - or shall i?

Dealer's choice.

You want to be cool? Be cool like a dying & a dead body.

I've lived through the age of humanitarian aid - ive seen nice people do nice things. Now I ask: Toni Morrison, what would you do different?

Fushigoro Toji knows exactly what I am referencing, and not only that: he knows exactly what I am aiming for. This is because he was interested in bringing the jiu-jitsu sorcerery world to its knees.

Gojo Satoru was interested in this too.

Now the question is: how?

Gege Akutami failed in his task to honor Gojo Satoru. I name you, Satoru: for all your fans call you Gojo, and I alone will know you. Now who will join you? By this, I mean: who will join me?

Who will speak conviction to action - because by the audre lorde, i Know Archive of Our Own, I know the dynasties of our time, and I know how to tell them from myths, and I know how to tell that from myth-making.

East Asian dragons are built like fishes for a reason.

Weavers in Palestine have still not fled. I need not know why. Why? I know why. Why? You gonna keep asking me shit or are you gonna use that brain two people gave you and the fact it's a muscle and do grindr? Grind on it. Grind. You done? Do it again.

You pissed yet?

You mad, broski?

You either wake, or you don't, unstirred - thus undeterred.

I say this now to honor James Baldwin: no more sleeper agents shall be in my path. Let's fucking dance, or there shall be war.

Sugimoto Saichi knows this so fucking well it made me get up and take a run, because he made me take a blow to the face and I survived it: he says this to a white US-American collector of Ainu artifacts: and do you know what he said? He said this: give up the artifact or I will take you down. Verbatim, by word of the translator on the pirated site on which I read: do you know what happens when negotiations break down?

It's called War. more simply: you, or me. it is an art. that's why "sun tsu" is famous in the white(?) man's world.

Now there is the mythicized World War 3. Chinese-Americans will for sure suffer like they were meant to be born stillborn in its wake. Do you want me to tell you why or will you look up? Current news is distracting for one reason: the news is merely an inch of actual reality that then proceeds to holler down several damaging hoops.

What is more accurate: current news evokes the current state of the world.

Here's a note to clue you in , not that any immigrant nor migrant nor vagrant really needs it, but here's something to piss you off anyway - Japanese-Americans were sent to internment camps. Wanna guess what number world war this was for?

Wanna guess? Or Wanna Know? See what I'm putting down yet? No? Okay.

No more sleeper agents shall be in my path. I say this now to respect the fact James Baldwin aided in my survival. He died already: why do I still feel him here, with me, laughing? It's simple, really. Because he loved Angela Davis: and Angela Davis is still alive today.

And I am right there beside her.

In the first and only book I've read from him, he said this through the mouthpiece of his characters, his loved ones, his chosen ones, the ones that would make him survive, AKA enable, and he said this: I will build a long, long table for folks to be eating off of for a long, long time. And the woman who loved him said this back: I'll go where you lead me.

This book changed the US-American consciousness. He wrote in France. I don't need to read his autobiography to confirm this. Here's why, because I could give less of a shit if you wanted to play devil's advocate: it is because James Baldwin judged he would not survive in The United States of America.

So he wrote in France.

I believe he died there.

Now I ask: will you respect 2024 or will you make someone come after you?

Dealer's choice.

You have been dealt your hand. Your ass is either shown or it will be shown. You either wake, or you don't, undeterred. It is this clear cut because empire has intensified, singing of its war drums: it has been, always, never new, always old, but never interesting, always predictable. It is why all the gongs of dehumanization are on. It is why those who have listened to it all their life are now cold like metal. We know how to be metal. Metal: the one thing that needs heat to shapeshift. Why is winter difficult to survive? This is why historians and social science researchers say the same shit and nobody listens, but they are slightly more likely to be listened to, and that is why people of color, and those of the margins, flood into academia anyway, knowing they will be perfectly tortured.

Do you want me to tell you how I have been tortured?

Do you want to guess, or do you want to know, or do you want me to torture you to make you find out?

There have been people who were shot for less. Of them: Hind Rajab. After or before her, because the order doesn't really matter, not really, only that they were dead where they stood: those two Red Crescent paramedics.

Toni Morrison must be shocked where she is in her lively post-death. She said this to me once, because I read it, and I felt her touch me, because she is real, and when she died in 2015, that is how I knew Donald Trump was fake: I will always be shocked. I will always choose to be shocked. I think anything less is a kind of death.

I have died. But I am still alive. Why? To honor the two people who raised me. One - a dragon herself. The other - a rabbit.

My dad has taught me to think like a prey animal.

Do you want to know why people daydream about shapeshifting into predators?

Do you want to know, or do you want to guess? Follow the path your parents have set you on since the day you were born.

Otherwise this is what will happen: you will never catch up to Martin Luther King Jr. You will not meet Audre Lorde. And you will not be looked at by James Baldwin, though he will see you, anyway.

Do you want to know what happened to the people who made Disco Elysium, or do you want me to fucking repeat myself?

You either wake, or Nanami Kento will never speak to you on his way to get a viet sandwich. And through your mouth will be flies: for you have failed to speak the truth, and honor the one and Only task you were given at birth: take care of thyself.

Number 1 rule of Art of War by a Chinese man:

It is the same rule that KDJ from Omniscient Reader's Viewpoint dances to. This is why every character revolves around him, carnally. Their hunger is real. Their seeing and knowing already there. This is because KDJ is a reader. He knows exactly what potency it takes to kill a character, and what it takes to keep one alive. This is why Shing Shong was successful in their refusal to write a story that comes from domination. Because first: she was disinterested in it.

Because first: she wrote a story. And it was a long one. 500 chapters. And for what?

This is why I want Shing Shong carnally. Why carnally? Well: what do you think? You wanna spend a guess? Come here. Come and find out. Come.

2024 is the year of the wood dragon. Wood dragons are named for their transformations. I've decided. Do you want to know what I've decided, or do you want to guess? If you are impatient, now you will know how it feels like to be in a burning pit, hellish by Japanese standards, tortured, forever and ever, and then perhaps you will have your first rare and individual and selectively acquired taste of what it has meant for everybody else to be colonized, while you stand, alone, mute, wearing the most bodily privilege you have ever seen, never acutely felt, and you will stupid for it - do you know why? Because white supremacy has an adjective placed in front of it, and it is doing something there. White supremacy knows it must first trick the light skinned people. So first it creates an abstract idea: it creates -

What would I have said here? Pull it together for me. I seem to have forgotten. This is the tune of real survival. This is why all people from all walks, all individual tortures, are still interested in community. You find the punk, or it finds you with a crackle of knuckle. It's why cult survivors exist, past being kept like abused animals. Because you will not die at the end. You won't. How do I know this?

Did you fucking read, or were you fucking tone-deaf?

Here it is, though, because I'm being nice: global racial capitalism is a cult with death at the end of it. And you must know by now I am not unique. Because even the worst person alive, objectively, by anybody's standard, got here somehow. And I have killed myself to care. I have tortured myself. I have. I have killed myself over and over and over and over and over and over and over and over and over and over and over and over and over and over and over because ultimately when the rubber really hits the fucking road I believe Ajin: Demi-Human is an relatively optimistic story, because those who have learned to resurrect themselves at will will always be interested in the good fun. Samuel "Satou" Owen is my favorite white-ish man in Japanese manga. This is because, like me, the Ajin writers and drawers were wise: they did not name the unknown substance that brought everybody back to life. They merely places an man obsessed with ways of living, at all costs, in front of it.

Satou-san is a white-Chinese man. He is mixed. What does this tell you? It tells me this: he is of movement. This means he has two feet. If he has two feet, and he is bipedal, and he can wield a gun with the mastery of a guy with chef's tools but in a forest instead of a well-stocked and furnished kitchen, this means he is a person. He likes to fuck around and find out. His white-american father failed to stop him. Why: did he fail in his task because he did not love him? The Ajin makers are clever: they had his white man of a father beat him, first, and then, later, quite quickly, demonstrate that he was a father first, because Samuel's father apologized for hitting him, because he wants to know his son is a wonderful person, and Samuel, so young, a child, stood there, alone, with a smile on his face, dead animals around him and blood on his hands, probably caked under his fingernails.

So now I wonder what it would take for Samuel "Satou" Owen to go back home.

I will write on this - cuz I do be writing - but I'll give you an interesting thought here, because this is what I offer, relief that feels like a slice that cauterizes the wound on the way: Satou's father did not fail in loving him. He failed because he was too kind.

I will teach Satou-san what it looks like to be brutal, but with compassion. And I won't kill him - now why would I do that? Ain't he the most lethal demi-human immortal freak Japan and da rest of the world has ever seen? He came from the United States originally. He only ended up in Japan because he is a video game freak. It's not because he's crazy: it's because, actually, he likes to have fun.

This is why he refuses to take his life so unseriously: he felt the universe slot another coin into his piggy bank. The universe must be interested in him for a reason. Life in the universe needs no reason. It's how we got here anyway. Now you must see the conspiracy? This is why the researcher who named "IBM (Invisible Black Matter)" was called insane and asked for the cigs in his car when three fingers, one by one, were cut off his left hand. He was being serious. So now I ask: will you fucking play?

Those who read of medias that show off their gore, turned like stones, with fresh worms underneath, in that rich, rich dirt: c'mere. You know exactly what I be talking about. Ajin: Demi-Human dances on the grave of Shounen by placing a non-traditional protagonist in the path of a traditional shounen protagonist and Does not make them fight. Instead: they are made to collaborate. Now how were they made to do this? Because at the core of each, was a compassionate core, and so every character was interested in each other as a person.

Read Ajin. It dances. To a music that few hear. Because it takes skill. It is not like Jiu-jitsu Kai-sen. It was not made to be a franchise, because it sought to honor its people that lived in the narrative. The cost: it will never be popular. It is why its second season is the way it is. This is the cost that Ajin's makers incurred. And they incurred it anyway. I have heard them without ever speaking to them personally. This is my skill. So now it is my offering.

Gege Akutami failed in his task to honor Gojou. Do you wanna hear why now? Or am I being registered, like a smaller gong amidst all the gongs of dehumanization right now?

Hey, fans of the Golden Kamuy - y'all get it most, for Sergeant Tsukishima is a secondary character and he has earned many delicious, life-affirming fics on that One and Only site. Each one I've really read got at it hard. Tsukishima writers and lovers and comrades in arms: Do you hear me, or will I go unheard like I have seven years ago?

Will I die, or will you die first? I won't. So Now the question is: will you?

I think, but first, I choose to believe in this one thing: all people know exactly what I am talking about. Because you were born. And now you will die, because empires have never been interesting. They generate dead bodies for a reason. They never have to say anything to dead people. Because, again, they are dead, and there's no way to bring them back to life. That is why eradication is strategic, and that is why slow deaths are more interesting, because it's quite hard to kill somebody without a gun, and so serial killers invent fresh ways to do it - wanna know why? Cuz they be bored, just like I am, watching them do it and choose it like a abused dog might with its ragdoll of a chew toy.

We see the dead people. One of my parents decided to become a doctor, practicing zhongyao, purely because he saw the way his grandfather died. Do you understand the acceleration of skill to mobilize thought to action? He spent 10 years putting himself through his chosen torture - medical school. Or will you sit there, mute in your dead body shame, so totally unmoved you are disturbed by almost anything? Why don't you find a corner in the world where you won't suffer for it. You can try. The last person who went to outer space came back and said never mind, it's actually all here and this is really it.

You can try the ocean. Elon Musk didn't. Wanna know why? It's because the hard, the really, really, truly, back-breaking work, is never done by the toddlers in power.

So now I wonder if Noor Hindi is well. The answer is no. Why?

Will you ask me to repeat what I've just said, or will you Read:

Dr. Alreer said: if I must die, then let me be a story. Of what?

Of what?

We all come from matter. We all know when things die, the matter doesn't disappear. This is some kinda physics law. Astrophysics too. Supernovas and lesser deaths of stars generate elements that compose matter. So do stars when they come into being. Sure. The Ainu peoples knew it first, as did every other peoples native to a land who did not seek to immediately obliterate it. Because they were first interested in their survival, and in that: how to keep surviving. Anyways, this is interesting. Because this means while we are alive at the same time, we are negotiating it all the time. This is why Stands and Jojo's Bizarre's Adventure is Bizarre and so fucking fun. It dances, and it dances visibly. It is called drag. It is called performance for the purpose, on purpose, for interesting reasons. It's why Kujo Jotaro did not die until his daughter would, because he protected her, and she protected him, and they died, but their friend, truest witness, went and finished the task given to him. And so they still lived. So it is bizarre. Everything probably is. It's why people are so interested in convenience, in that quick fix, in that hit of ketamine, in those shortcuts, in taking their lives less seriously, because they already know how serious it is to live in a world like ours, and they already know just how hard it is to meet each other where they are at, because they have struggled in meeting themselves where they are at, which is the deepest fucking pit unique to them, for they are being tortured, even as we speak, because it is individual: but it is not unique. Because I have been tortured. And I am still here, speaking with you.

So are you gonna fucking participate in derogatory theatre, or are you gonna wait till someone like me comes over and whips you where it really hurts? For those who are hung: you know. For those who aren't: too bad, you've got a throat.

For those who don't: we know what happened to them, don't we. They don't get livestreamed. DRC is silent because they cannot make it a football game. People are dead, dying, and are being disabled from living.

So now you either speak, or you remain silent, or as Baldwin said it: uninitiated, or unactivated, or as Morrison said: un-artful. If you do, remain silent or quiet or whatever that really chafes you right now, then you will never know what Audre Lorde was saying when she said We Were Never Meant to Survive. Do you know the three ways to survive in a ruined world, or did Ocean Vuong say it already and you simply refused to clock it and let it travel inside you like a missile that hit Vietnam all those decades ago?

This is the risk I incur. So now I evoke all those who have aided in my survival. I know I am not alone. Do you know why there was not 1 dragon leftover, in ATLA? Because if there was one left, it would not have come out of its cave. It would have stayed there, forever, until it perished. So Zuko's uncle made sure there were 2. Am I understood, or do you see that when I open my mouth and see red, every color of life is evoked? The sun god folks in that iconic scene know exactly what I am talking about. It is why they keep the original fire burning for thousands of years, and this is why one of them looked at Zuko and joked about the masters (real) chewing him up, and their leader said shut up but did not say he was wrong. Because he wasn't.

Their leader does something nice here, which he is by no means obligated to do, but does, because he knows he is not free to abandon it like the sleeper agents have: hey, you might die if you do this. Will you still do it?

That is the risk you incur by coming after me. I make enemies. But first, because I am an dragon originating of the East Asians: I know exactly who my friends are, first. All I ask is for you to not act stupid.

If you insist: well. When the LONG opens its maw, you will be right to be terrified. Why do TIGERS have teeth if not to use them? Praise the knife that goes through the PEAR for you, or you will not eat well tonight, and if you do: know that it will not last.

Karma simply does not come quick enough. That's A-OK. The universe prefers slowness. So now I dance. I gave to it my sixieth spiritual death and it has finally snickered instead of dragging my face through the fucking dirt, asking me to open my mouth and taste the worms which dance in the rain with their entire bodies. It's why they writhe. Now when I laugh it laughs through me and seems genuinely pleased. But what I care about most is this: that I have gotten so good at what I need to do, and what i Want to do, that Nanami Kento now merely inclines his head and walks beside me. And this: that Toji merely glances an eye at me and lifts his chin, smiling, crazed at the edge of pleased, and asks me anyway, despite full knowing in all his rage and all his cool dead & dying & disabled body discernment: hey, how are we gonna fuck em up today?

Treacherous cunt.

I am not a spiritual person. I've merely died spiritually enough times for me to have to use the academic word for it. In me I have, first: the people who saw and shielded me - second: the people who taught me, dancing to their own survivals. I am the friend of Bob. The one who told me to keep writing. Bob, I am proud, and I know you are proud of me. Hey, hi, hello.

This is my dream. My friends are a dream. All of them are. One by one they have stood, and they have stood alone, and now I am there beside them.

I honor all those who aid in my survival. Face me when you shoot me in the fucking face. This is why union leaders are assassinated in their beds, with pregnant people right beside them. This is why small children in the first formally livestreamed eradication campaign call the men in tanks cowards, and mice, because that little girl was just that fun, that interesting. This is why Sugimoto Saichi, at the very beginning of his story/dance, he hesitated when that wounded animal came out and charged him, desperate and mobilized with all its might to clear a path to its survival. Because he saw and he understood in less, but it was still too late. So that is why Asirpa said let me take the shot next time, if you can't do it - in fact, don't even try. For we need heat to survive the winter. Dragons of the East are interested in one thing, and that is people, not god. We come when called. We come when uncalled. It is why we show ourselves when people of a land need rain. 's also why we show ourselves when people of a land don't think they need shit. Do you understand the level of discernment required to do this? My judgement is not divine. I don't give a rat's shit about God. Wanna guess what I give a shit about?

A rat's ass.

But just so you don't the wrong idea, because that is what personally pisses me off the most: I don't believe in God, but I believe in you.

How is this possible? Because I had a parent, and in her, she was a dragon, and she has evoked it enough times, at all the critical moments, for me to follow her example. I will incur the risk now. I have always taken risks, because I've seen what it takes to safeguard and then nurture and then, perhaps, cultivate a life. I was born into the year where dragons have danced and they saw me and I have seen them, and you really should be thinking about Zuko not being stricken down by the last 2 dragons of his time by now. My father is a rabbit - he knows how to respect the world, so that he is to be respected if he cannot be loved. And he still chose to care for me, a weakling. He still chooses it: for we played a poker game, just once, after majiang, and he saw how I dealt my last card and thus understood my entire play and he looked me in the eye when he said:

Don't do that again.

So now I incur the risk. Because I know the cost of what it means to survive in a place like this. The world. The world. People get hurt here.

Duh. Richard Siken is needed no longer. He has said what has needed to be said. He writes, and I write too. I pick it back up, the dead thing at the end of the road. Because in order for it to be dead, it must have been killed.

So I will incur the risk. You are welcome to join me. The time to wait has been over since the first people(s) said, fucking shit fuck help! help! and nobody came, or if they did, they still ended up dying anyway.

i have never been interested in living forever. people who do are interested in having fun. the rest are idiots. the people who have fun usually die first - it is why aang's entire people was eradicated from this earth, from that fiction lifeworld. so now i find it more interesting when the people who have the most fun don't die - and that is why Toji of JJK is ketamine to people. from everywhere. from all walks of life. do you want to know why JJK is popular now, everywhere? I will tell you why now. Because I am being nice, and I am interested in your surviving, your continuous survival, your real tunes. Because I would prefer to be your friend rather than your enemy, but you make your choices, and I will make mine.

Here it is the truth: it is because JJK is interested, at least initially, in what it would look like to wield overwhelming power responsibly, which is to say: meaningfully. It is why Gojo is Japan's animation poster boy right now. He always did like to fuck around and find out. And he found out, didn't he - he found out that his own creator gave up respecting his principles to serve franchise interests, the grinding acceleration of that kind of selected - and chosen - giving in. It is a death. I have grieved it.

Now I stand here, alone.

Now I ask : who is interested in seeing Gojo Satoru still alive, even knowing that he has failed in his task to do what he said he did? Is it because of him, as a character, as a person who lived within the narrative, or is it because of the narrative that either enables or disables his real and true living?

One of the oldest people in my life said this to me recently: I personally believe that there are no shitty characters. Only a story that no longer suits them.

Will you be a story that I can live in?

Or will you be a story that makes me want to come after you and demonstrate to you, selectively, intimately, how you have betrayed me?

Will you, or won't you?

Kong Si-Woo is calling me now. Sorry. Bye. Gong Si-U is the best negotiator I know, and he's telling me to take a break. You can't solve everything by your self, he is saying. That's why you pick your men carefully. And he has chosen. Do you know who he choose?

He chose Toji.

Toji survived after he left the torture pits in that family clan of his.

First, he ended the life of every cursed beast in the family's torture pit to relish the fact he could do it, perfectly alone. His solitude earns the survival of Zenin Maki, and thus the survival of her sister, Zenin Mai.

Second: he met someone who was curious about him there, also perfectly alone in one of the most inhospitable places alive, already similar by then, and earned his respect, unyielding for some reason not known to him, personally, but his ass is shown to me because I have tapped it and I liked the sound that came back.

This is because Kong Si-Woo and Toji have a 10-year history. Adults at that age, making new friends? Color me delighted.

Third: in earning the Korean man's respect: he earned the capacity for real trust, the kind that marks actual fucking solidarity. And then Toji, scorned Japanese man he is, lived for 10 more years with Kong Si-Woo near him and by him, because these are men who when they are killed will live on, until Toji fucked around and found out for the last time --- until, of course, the story itself brings him back to meet his grown son.

Now I ask you: will you be respected, or will you be interested in what real enabling, at all costs, looks like?

Will you give a shit the way Toji does, or will you give a shit the way Nanami does? Will you move the way Sugimoto does, or will you simmer quietly like a jar of moss like Qingming, pirated Dream of Eternity: Yin-Yang Master, softspoken and brutal in his withholding? Will you have fun like Satou Samuel Owen, or will you earn the respect and thus relieve the responsibility of Nagai Kei, at age 17, willingly took on because he saw clearly the danger Satou posed to every normal person alive on the planet, and decided for himself that he would end it now.

Will you be tortured like Suzaku Kururugi, so complete, fans of Code Geass feel it even today, or will you take decisive action to bring the very structures of the Jiujutsu Sorcerer's World down by disavowing the only child you've named for blessing, just so he could have a headstart?

Which character will you relieve of their responsibilities? Can you tell who needs it the most? Do you know what I'm saying here? I am saying I do not need Nanami Kento anymore and he has never needed me to speak. This is because he died in Shibuya, and I am glad he did, because Gege Akutami's writing abilities did too. I would not have survived the Nanami's badly written death the way I could survive Gojo's. Gege must have detected this in some way: he must, even with his franchise of an empire, because even a franchise of an empire comes from people: because he waited to kill Gojou, because he knew everybody seemed to like him, and genuinely love him, in many many lands, and what he did was brutal, and it was genuinely cruel: he bisected Gojou in half instead of blowing a hole through him despite putting him in Toji's killer fit.

It means Akutami was done with Gojou's character, and discarded his personhood, and gave his fans and comrades and enemies crumbs. Enemies of Gojo savored it: but they understood its brutality, and rejoiced because finally the biggest dick did win, as they foretold, because they foretold the death cult that is global racial capitalism, because they have survived, and they don't want anybody else to except for themselves, and their friends, of course. Even our enemies have friends. What does this tell you: it means everybody has the capacity to understand tragedy. Indeed, Everybody else simply understood it for tragedy. Now there's nothing wrong with a nice tragedy.

But is it interesting? Toni Morrison looked me in the eye in one of her interviews and I will tell you what she said to me: she said once you have gotten the jobs you have all trained so beautifully for, you must now go and free someone else. This is not a grab-bag candy game.

Now here's the thing about TRUE FICTION: AND TRANSFORMATIVE FICTION: we can bring him back.

I will do it. Anyway, I will do it. Why? Because I know what it takes to complete a story and respect a character's personhood. Because I learned how to draw first, and then I figured out how to write. Each time, it was a person who bestowed it to me. So now I read Dungeon Meshi and trill with delight. Now she gets it. So now I reread DOROHEDORO!!! Now this one too, she knows it. So now I follow Witch Hat Atelier, not only interested but believing in its conclusion: for Shimomura knows exactly how to dance to make her people dance, too. This is a skill.

Will you dance. You. Hey. Hello. I ask you now: Will you fucking dance? If so, come here. I protect all other/Othered animals. But first, you must show me your teeth, and then second, you must make it known to me you are not my enemy. For I am not god. I am merely a person born into dragon year. So now whenever I open my mouth, I do it knowing the whole world will listen. I stand alone, and I will go unheard, or I won't, and then: I will either be killed, or I will change people along the way. I don't mind either results. Because right now, Tosaki, the man with the mints and the cigarette and the crisp white suit, from Ajin: DEMI HUMAN is my favorite fucking character.

He dies at the end, by the way. He dies with no regrets. I seek to follow him.

Hey - Aaron Bushnell. I evoke your name to evoke your death, because your last words were Free Palestine and you chose to do it, standing there, perfectly alone, despite wanting to become a software engineer, transitioned out of active duty.

You have done your duty. I got it from here. I will not fail Hind Rajab again. Trust me, I won't. You plug it in right by me, or I will know. Now isn't that the real fun?

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drama--universe · 1 year ago

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Hello! I just want to ask. I see you know Miss the Dragon. Hey doesn't every episode feel like déjà vu? Yuchi does the same thing all the time; rescues Liu Ying. Of course the series has something in it, it's romantic and funny but sometimes I felt that Yuchi was just "pushing" Liu Ying and Liu Ying was overly clingy. Like how she kept yelling, ''Yichi gege!'' I apologize to Miss the Dragon fans but sometimes I found her behavior a bit childish. Otherwise the series was fine, for example Qing Qing and Xue Qianxun were great! And as for Immortal of Fate; I didn't expect him to be such a bastard in the end. I didn't expect such plots but I liked them. Ily bye!🩷

Hello! To answer your question, yes it was a bit déjà vu. I personally don't mind, because of the little changes and such, but I can definitely see how it gets annoying fast for some. As for the yichi gege, that's not abnormal for Chinese dramas nor is the more childish behavior and I've gotten used to it due to other drama's. You kind of to accept it, unfortunately, but in the drama it makes a bit of sense since the FL is still pretty young in every recarnation and thus a bit more childlike. Overall, it has a good story and cast, I'm afraid it was just a bit too long and thus falls in repeat pretty often.

Hope that this answer makes it clear 😊 Definitely isn't the worst I've seen with female/male lead's, Ahro from Hwarang makes my blood boil for example 😂

#anonymous asks #Asks

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bookofjin · 2 years ago

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Account of the Braided Bandits (SS095)

On one side of the River, Daxi Jin and Lord Black-Lance. On the other side at the Tiger's Pen, Mao Dezu.

The Braided Heads Bandits have the family name Tuoba. Their ancestors were descendants of the Han general Li Ling. Ling surrendered to the Xiongnu and had several hundreds and thousands of offspring, each one established fame and reputation. The Braided Heads likewise are one of them. At the beginning of Jin, the Braided Heads kind had their section groups of several ten thousand families in Yunzhong.

At the end of Emperor Hui's regin, the Inspector of Bing province, the Duke of Dongying, Sima Teng, was besieged by the Xiongnu at Jinyang. The Shanyu of the Braided Heads, Yichi, dispatched an army to help Teng.

Emperor Huai's 3rd Year of Yongjia [309 AD], Chi's younger brother Lu led the section groups from Yunzhong to enter Yanmen. He went to the Inspector of Bing province, Liu Kun, to press for Loufan etc., five counties. Kun was not able to hold authority, and moreover wished to depend on Lu for aid. He therefore sent up words:

Lu's older brother Chi had merit in saving Teng. Old achievements ought to be recorded. [I] request to move the people of the five counties to Xinxing, and use their land to settle him.

Kun also petitioned to ennoble Lu as Duke of Dai commandery.

At the beginning of Emperor Min's regin, he again advanced Lu to be King of Dai, and added revenue from Changshan commandery. Afterwards within Lu's state there was great chaos, and when Lu died, his son was also young and immature. The section groups divided and scattered.

Lu's grandson Shiyijian was brave and strong, the multitudes then adhered to him. He was titled Duke of Shangluo, to the north he had the Sand Desert, to the south he occupied Yin Mountain, his multitudes numbered several hundred thousands. Afterwards he was routed by Fu Jian, who took him back to Chang'an. Later he was allowed to return north.

[Shiyi]jian died, his son Kai, courtesy name Shegui, was installed in replacement. Before this, Murong Chui of the Xianbei usurped the title in Zhongshan. Xiaowu of Jin's 21st Year of Taiyuan [396 AD], Chui died. Kai led 100 000 cavalry to besiege Zhongshan. Next Year, 4th Month [13 May – 11 June 397], he overcame it. Thereupon he ruled the central provinces, declared himself as Wei, and titled the year Tianci [“Heaven's Bestowal”]. 1st Year, he set his seat at Pingcheng in Dai commandery's Sangqian county, established schools and officials, and set up boards of the Masters of Writing.

Kai was quite learned and informed, and comprehended astronomy. His customs was to sacrifice to Heaven in the 4th Month, and at the end of the 6th Month lead a great multitude to Yin Mountain. He spoke of it as turning back the frost. The distance between Yin Mountain and Pingcheng is 600 li. There is deep, far-reaching and rich forest, and frost and snow have never once melted away, perhaps he intended to use warm air to turn back the cold.

When he died, he was secretly buried, without a place for the grave mound. Reaching the seeing off of the burial, they had both emptily built an inner coffin and erected a barrow and outer coffin. All of the chariots, horses, and implements he had made use of while alive they burned to see off the perished.

Kai was violent, cruel and fond of killing, the people could not bear his instructions. Before this, there was a spirit magician who warned Kai he would have a violent misfortune, only by executing Qinghe and killing ten thousand people could it be avoided. Kai therefore wiped out Qinghe, one commandery. He often killed people with his own hand, wishing to make it number a full ten thousand. Sometimes, he would drive a small carriage, and with his own hand hold the sword [and?] strike the carriage rim at people's brain. When one person died, another person replaced them, all in one action. The dead were several tens. At night he constantly changed and altered the place where he slept, so that people did not know. Only a loved concubine named Wanren [“Ten thousand Persons”] knew about his location. Wanren had secret intercourse with Kai's son, the King of Qinghe. He worried the affair would become known and wished to kill Kai. He made Wanren his inner agent. At night they waited until Kai was alone at the place, and killed him. As Kai was approaching death, he said:

The talk about Qinghe and ten thousand people then were about you.

That year was Emperor An's 5th Year of Yixi [409 AD].

Kai's second son, the King of Qi, Si, courtesy name Mumo, apprehended the King of Qinghe, and responded to him with shouting and weeping, saying:

The weightiest in a person's life is the father. Why are you talking of making rebellion?

He pressured and made him kill himself. Si was installed in replacement. He posthumously titled Kai as the Guiding and Martial [daowu] August Emperor.

13th Year [417 AD], Gaozu went west to attack Chang'an. Si had previously taken as wife a daughter of Yao Xing, and therefore dispatched 100 cavalry to gather and join up north of the He to save him. They were greatly routed by Gaozu, the affair is in the biographies of Zhu Chaoshi and others. Hence he dispatched envoys to seek peace, and from then envoys and instructions passed through yearly. Gaozu dispatched the General Within the Halls, Shen Fan, Suo Jisun as responding envoys. They were already returning from instructing and had reached the He but not yet crossed, when Si heard the news of Gaozu's collapse. He pursued and apprehended Fan and others, and cut of peaceful relations. Only when Taizu was enthroned did he dispatch Fan and others back home.

3rd Year of Yongchu, 10th Month [31 October 422 – 29 November 422], Si himself led a multitude to arrive at Fangcheng. He dispatched the General of Zheng Troops and Inspector of Yang province, the Duke of Shanyang, Daxi Jin, the General of Wu Troops and Inspector of Guang province, the Duke of Cangwu, Gongsun Biao, and the Master of Writing Hua Ji, to lead more than 20 000 infantry and cavalry, cross south south-west of Huatai at Shiji [the “Stone Crossing”] on the border of Dongyan county, with the supply wagons, the weak and tired, accompanying himself.

The Defence Master of Huatai, General who Soothes the Distant and Grand Warden of Dong commandery, Wang Jingdu, hurried to report to the General of the Best of the Army and Inspector of Si province, Mao Dezu. He defended Hulao, and dispatched Marshal Zhai Guang to lead the Army Advisor Pang Zi, Grand Warden of Shangdang, Liu Tanzhi, and others with 3 000 infantry and cavalry to resist them.

The army stayed at Tulou in Juan county. The bandits moved camp to two li east of Huatai City. They constructed assault implements and went daily to threaten the city. Dezu, since the defenders of Huatai were few, made Zhai Guang recruit strong soldiers among the army, and dispatched the General who Soothes the Distant, Liu Fangzhi, to lead them, and help Jingdu with the defence. Fangzhi brought along more than 80 people, and broke through to enter the city.

Dezu also dispatched the General who Chastise the Bandits and Grand Warden of Hongnong, Dou Yingming, leading 500 people, and the General who Establishes the Martial, Dou Ba, leading 250 people. Both were to use water forces and succeed each other in issuing out, and would together be under the authority of Zhai Guang.

Earlier, the fugitive Sima Chuzhi and others would often hide and conceal themselves on the borders of Chenliu commandery. When the bandits had crossed south, they hurried to join up with them. They chased away and fomented in the border areas, and greatly became a worry for the people. Dezu dispatched the Prefect of Changshe, Wang Fazheng, to lead 500 people and occupy Shaoling, while general Liu Lian led 200 cavalry to reach Yongiu and defend it. Chuzhi assaulted Lian at Baima county, and was routed by Lian. By chance army supplies sent off from the palace arrived, and Lian went to welcome them, but a commoner from Suanzao, Wang Yu, knew that Lian was to the south, and hurried to report to the bandits. The bandit general Hua Ji led a thousand to drive a raid on Cangyuan, the troops and personnel fully went over the walls to scatter and flee. The Grand Warden of Chenliu, Yan Man, was captured by the bandits. The bandits immediately employed Wang Yu as Grand Warden of Chenliu, controlling the troops defending Cangyuan.

11th Month [30 November – 28 December 422], the bandits attacked the walls of Huatai with full strength. The north-eastern walls collapsed into ruin, and Wang Jingdu set out and ran. Jingdu's Marshal Yang Zan stood firm in defence, and did not move. [Though] the multitudes dispersed, he was unyielding and steadfast and did not surrender, and was killed by the bandits.

Dou Yingming struck the bandits' supply wagons at Shiji, and routed them. He killed more than 500 of the thieves, and beheaded their Defence Masters [lacuna]-lian Neitou, Zhang Suo'er and others. Yingming from Shiji proceeded to Huatai, heard the city was already lost, and thereupon advanced to station at Yinmao. Dou Ba hurried to go to Zhai Guang.

When the bandits had overcome Huatai, they combined their strength towards Guang and others. His strength was no match, he pulled back and withdrew, turned to fight and then went forward. For two days and one night, he cut down travel to ten or so li. The bandits' infantry armies continuously arrived. Guang and others' arrows were exhausted and their strength at an end, they were greatly defeated. Guang, Ba, Tanzhi, and others each dispersed on their own and turned back. The bandits exploited the victory to then arrive at Hulao. Dezu set out with infantry and cavalry intending to strike them. The bandits withdrew and stationed at Tulao, and again withdrew to turn back to Huatai.

The people of Chang'an, Weichang, and Lantian counties lived beside Hulao. Dezu in all cases made them enter the city. The bandits separately dispatched Lord Black-Lance to lead 3 000 people to Heyang, intending to cross south and capture Jinyong. Dezu dispatched the General who Rouses Power and Prefect of Heyin, Dou Huang, with 500 people to defend Xiaolei [lit. “small ramparts”], the Prefect of Goushi, Wang Yu, with 400 people to occupy Jiancang, the Prefect of Gong, Chen Chen, with 500 people to strengthen Xiaoping, and the Army Advisor Supervising Protector, Zhang Ji, to station at Niulan. He also dispatched the general and leader Ma Dui [?], together with the Prefect of Luoyang, Yang Yi, a combined 200 cavalry, to hem the banks of the He and follow the moment to go and link up.

12th Month [29 December 422 – 27 January 423], the bandits set up defences at the Luo Stream's small ramparts. Dezu dispatched Zhai Guang to hurriedly go and strike them. The bandits withdrew and fled. Guang calmly erected defensive dikes, repaired and organized the walls and fortifications, and then turned back to Hulao.

The Inspector of Yu province, Liu Cui, dispatched the [Assistant at] Headquarters Gao Daojin, to lead 500 infantry and cavalry to occupy Xiang. He also dispatched Marshal Xu Qiong to support him. The palace dispatched generals Fu Boqian, Yao Zhen, Du Tan, Liang Lingzai, and others with various naval and infantry forces to carry on the advance. The Inspector of Xu province, Wang Zhongde, led an army to stay at Hulu.

Lord Black-Lance dispatched his Senior Clerk to bring along 1 000 people to pressure Dou Huang and Yang Yi. Huang and others confronted, struck, and seized him, capturing alive 200 people. Afterwards, the General of Zheng Troops with 5 000 cavalry unexpectedly assaulted Huang and others. Black-Lance crossed and combined strength with him, and they attacked the ramparts on four sides. Huang and others' strength was little and their multitudes scattered. Huang and Yi both were heavily wounded.

The bandits' general, the Duke of Anping, E Qing, crossed south with two armies of 7 000 people, east below of Que'ao and arrived at Sidoukou, about 100 li from Yinmao. The Inspector of Yan province, Xu Yan, abandoned the army and garrisons, and fled. Hence Taishan and other commanderies equally neglected defences.

Zheng Troops, together with Gongsun Bao and the General of Song Troops and Inspector of Yan province, the Marquis of Jiaozhi, Pu Ji, with 15 000 cavalry then went towards Hulao. They formed camp 5 li south-east of the city, and divided off infantry and cavalry unfolding [?] from Chenggao towards the western gate in Hulao's outer walls. Dezu confronted and struck them, killing and wounding more than a hundred people. The bandits withdrew to protect the camp.

The General who Garrisons the North, Tan Daoji, led a navy north to rescue. The General of Chariots and Cavalry, the King of Luling, Yizhen, dispatched the Dragon-Prancing General, Shen Shuli, with 3 000 people to go to the Inspector of Yu province, Liu Cui, to measure the suitability of hurrying aid[?].

#chinese history #ss095

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hacialikara · 1 month ago

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Tesla’nın robotaksi hamlesine Çin’den tokat gibi yanıt!

Baidu, sürücüsüz taksi hizmeti Apollo Go ile otonom araç sektöründe önemli bir adım attı. “Çinli Google” olarak da bilinen Baidu, JMC ile geliştirdiği Yichi 06 (Apollo RT6) modelini 29 bin dolar gibi bir fiyatla piyasaya sürdü ve teslimatlara başladı. Bu hamle, Tesla’nın yeni tanıttığı Cybercab modeli için önemli bir rakip. Tesla Cybercab vs Baidu Yichi 06 (Apollo RT6) Yichi 06; minimalist…

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#teknoloji

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nawapon17 · 1 month ago

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telkomuniversityputi · 5 months ago

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Flying Qudit: Membuka Dimensi Baru Komunikasi Kuantum

Gambar 1. Foton sinyal, yang dimanipulasi oleh sirkuit fotonik terpadu, menciptakan qudit 4D yang direpresentasikan oleh sekumpulan bola oranye. Sementara itu, foton diam, yang direpresentasikan oleh bola biru, bertindak sebagai pengendali jarak jauh untuk foton sinyal.Kredit: Haoqi Zhao, Yichi Zhang, Zihe Gao, Jieun Yim, Shuang Wu, Natalia M. Litchinitser, Li Ge, dan Liang Feng, diedit Para…

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talesofpassingtime · 11 months ago

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'You won’t find any tea in Liquorland,’ Yu Yichi replied. ‘Liquor is our tea.'

— Mo Yan, The Republic of Wine

#booklr

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jcmarchi · 1 year ago

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Google at NeurIPS 2023

New Post has been published on https://thedigitalinsider.com/google-at-neurips-2023/

Google at NeurIPS 2023

This week the 37th annual Conference on Neural Information Processing Systems (NeurIPS 2023), the biggest machine learning conference of the year, kicks off in New Orleans, LA. Google is proud to be a Diamond Level sponsor of NeurIPS this year and will have a strong presence with >170 accepted papers, two keynote talks, and additional contributions to the broader research community through organizational support and involvement in >20 workshops and tutorials. Google is also proud to be a Platinum Sponsor for both the Women in Machine Learning and LatinX in AI workshops. We look forward to sharing some of our extensive ML research and expanding our partnership with the broader ML research community.

Attending for NeurIPS 2023 in person? Come visit the Google Research booth to learn more about the exciting work we’re doing to solve some of the field’s most interesting challenges. Visit the @GoogleAI X (Twitter) account to find out about Google booth activities (e.g., demos and Q&A sessions).

You can learn more about our latest cutting edge work being presented at the conference in the list below (Google affiliations highlighted in bold). And see Google DeepMind’s blog to learn more about their participation at NeurIPS 2023.

Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization Adel Javanmard, Vahab Mirrokni

Better Private Linear Regression Through Better Private Feature Selection Travis Dick, Jennifer Gillenwater*, Matthew Joseph

Binarized Neural Machine Translation Yichi Zhang, Ankush Garg, Yuan Cao, Łukasz Lew, Behrooz Ghorbani*, Zhiru Zhang, Orhan Firat

BoardgameQA: A Dataset for Natural Language Reasoning with Contradictory Information Mehran Kazemi, Quan Yuan, Deepti Bhatia, Najoung Kim, Xin Xu, Vaiva Imbrasaite, Deepak Ramachandran

Boosting with Tempered Exponential Measures Richard Nock, Ehsan Amid, Manfred Warmuth

Concept Algebra for (Score-Based) Text-Controlled Generative Models Zihao Wang, Lin Gui, Jeffrey Negrea, Victor Veitch

Deep Contract Design via Discontinuous Networks Tonghan Wang, Paul Dütting, Dmitry Ivanov, Inbal Talgam-Cohen, David C. Parkes

Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detection Cheng-Ju Ho, Chen-Hsuan Tai, Yen-Yu Lin, Ming-Hsuan Yang, Yi-Hsuan Tsai

Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback Han Shao, Lee Cohen, Avrim Blum, Yishay Mansour, Aadirupa Saha, Matthew Walter

Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy Anastasia Koloskova*, Ryan McKenna, Zachary Charles, J Keith Rush, Hugh Brendan McMahan

Hardness of Low Rank Approximation of Entrywise Transformed Matrix Products Tamas Sarlos, Xingyou Song, David P. Woodruff, Qiuyi (Richard) Zhang

Module-wise Adaptive Distillation for Multimodality Foundation Models

Chen Liang, Jiahui Yu, Ming-Hsuan Yang, Matthew Brown, Yin Cui, Tuo Zhao, Boqing Gong, Tianyi Zhou

Multi-Swap k-Means++ Lorenzo Beretta, Vincent Cohen-Addad, Silvio Lattanzi, Nikos Parotsidis

OpenMask3D: Open-Vocabulary 3D Instance Segmentation Ayça Takmaz, Elisabetta Fedele, Robert Sumner, Marc Pollefeys, Federico Tombari, Francis Engelmann

Order Matters in the Presence of Dataset Imbalance for Multilingual Learning Dami Choi*, Derrick Xin, Hamid Dadkhahi, Justin Gilmer, Ankush Garg, Orhan Firat, Chih-Kuan Yeh, Andrew M. Dai, Behrooz Ghorbani

PopSign ASL v1.0: An Isolated American Sign Language Dataset Collected via Smartphones Thad Starner, Sean Forbes, Matthew So, David Martin, Rohit Sridhar, Gururaj Deshpande, Sam Sepah, Sahir Shahryar, Khushi Bhardwaj, Tyler Kwok, Daksh Sehgal, Saad Hassan, Bill Neubauer, Sofia Vempala, Alec Tan, Jocelyn Heath, Unnathi Kumar, Priyanka Mosur, Tavenner Hall, Rajandeep Singh, Christopher Cui, Glenn Cameron, Sohier Dane, Garrett Tanzer

Semi-Implicit Denoising Diffusion Models (SIDDMs) Yanwu Xu*, Mingming Gong, Shaoan Xie, Wei Wei, Matthias Grundmann, Kayhan Batmanghelich, Tingbo Hou

State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding Devleena Das, Sonia Chernova, Been Kim

StoryBench: A Multifaceted Benchmark for Continuous Story Visualization Emanuele Bugliarello*, Hernan Moraldo, Ruben Villegas, Mohammad Babaeizadeh, Mohammad Taghi Saffar, Han Zhang, Dumitru Erhan, Vittorio Ferrari, Pieter-Jan Kindermans, Paul Voigtlaender

Subject-driven Text-to-Image Generation via Apprenticeship Learning Wenhu Chen, Hexiang Hu, Yandong Li, Nataniel Ruiz, Xuhui Jia, Ming-Wei Chang, William W. Cohen

TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Kaidi Cao*, Bahare Fatemi, Mike Burrows, Charith Mendis*, Bryan Perozzi

Training Chain-of-Thought via Latent-Variable Inference Du Phan, Matthew D. Hoffman, David Dohan*, Sholto Douglas, Tuan Anh Le, Aaron Parisi, Pavel Sountsov, Charles Sutton, Sharad Vikram, Rif A. Saurous

Unified Lower Bounds for Interactive High-dimensional Estimation under Information Constraints Jayadev Acharya, Clement L. Canonne, Ziteng Sun, Himanshu Tyagi

What You See is What You Read? Improving Text-Image Alignment Evaluation Michal Yarom, Yonatan Bitton, Soravit Changpinyo, Roee Aharoni, Jonathan Herzig, Oran Lang, Eran Ofek, Idan Szpektor

When Does Confidence-Based Cascade Deferral Suffice? Wittawat Jitkrittum, Neha Gupta, Aditya Krishna Menon, Harikrishna Narasimhan, Ankit Singh Rawat, Sanjiv Kumar

Accelerating Molecular Graph Neural Networks via Knowledge Distillation Filip Ekström Kelvinius, Dimitar Georgiev, Artur Petrov Toshev, Johannes Gasteiger

AVIS: Autonomous Visual Information Seeking with Large Language Model Agent Ziniu Hu*, Ahmet Iscen, Chen Sun, Kai-Wei Chang, Yizhou Sun, David Ross, Cordelia Schmid, Alireza Fathi

Beyond Invariance: Test-Time Label-Shift Adaptation for Addressing “Spurious” Correlations Qingyao Sun, Kevin Patrick Murphy, Sayna Ebrahimi, Alexander D’Amour

Collaborative Score Distillation for Consistent Visual Editing Subin Kim, Kyungmin Lee, June Suk Choi, Jongheon Jeong, Kihyuk Sohn, Jinwoo Shin

CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graphs Guangyao Zhai, Evin Pınar Örnek, Shun-Cheng Wu, Yan Di, Federico Tombari, Nassir Navab, Benjamin Busam

Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy Amit Daniely, Nathan Srebro, Gal Vardi

A Computationally Efficient Sparsified Online Newton Method Fnu Devvrit*, Sai Surya Duvvuri, Rohan Anil, Vineet Gupta, Cho-Jui Hsieh, Inderjit S Dhillon

DDF-HO: Hand-Held Object Reconstruction via Conditional Directed Distance Field Chenyangguang Zhang, Yan Di, Ruida Zhang, Guangyao Zhai, Fabian Manhardt, Federico Tombari, Xiangyang Ji

Double Auctions with Two-sided Bandit Feedback Soumya Basu, Abishek Sankararaman

Grammar Prompting for Domain-Specific Language Generation with Large Language Models Bailin Wang, Zi Wang, Xuezhi Wang, Yuan Cao, Rif A. Saurous, Yoon Kim

Inconsistency, Instability, and Generalization Gap of Deep Neural Network Training Rie Johnson, Tong Zhang*

Large Graph Property Prediction via Graph Segment Training Kaidi Cao*, Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Dustin Zelle, Yanqi Zhou, Charith Mendis*, Jure Leskovec, Bryan Perozzi

On Computing Pairwise Statistics with Local Differential Privacy Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon

On Student-teacher Deviations in Distillation: Does it Pay to Disobey? Vaishnavh Nagarajan, Aditya Krishna Menon, Srinadh Bhojanapalli, Hossein Mobahi, Sanjiv Kumar

Optimal Cross-learning for Contextual Bandits with Unknown Context Distributions Jon Schneider, Julian Zimmert

Near-Optimal k-Clustering in the Sliding Window Model David Woodruff, Peilin Zhong, Samson Zhou

Post Hoc Explanations of Language Models Can Improve Language Models Satyapriya Krishna, Jiaqi Ma, Dylan Z Slack, Asma Ghandeharioun, Sameer Singh, Himabindu Lakkaraju

Recommender Systems with Generative Retrieval Shashank Rajput*, Nikhil Mehta, Anima Singh, Raghunandan Hulikal Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Q. Tran, Jonah Samost, Maciej Kula, Ed H. Chi, Maheswaran Sathiamoorthy

Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models Ying Fan, Olivia Watkins, Yuqing Du, Hao Liu, Moonkyung Ryu, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh*, Kangwook Lee, Kimin Lee*

Replicable Clustering Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni, Grigoris Velegkas, Felix Zhou

Replicability in Reinforcement Learning Amin Karbasi, Grigoris Velegkas, Lin Yang, Felix Zhou

Riemannian Projection-free Online Learning Zihao Hu, Guanghui Wang, Jacob Abernethy

Sharpness-Aware Minimization Leads to Low-Rank Features Maksym Andriushchenko, Dara Bahri, Hossein Mobahi, Nicolas Flammarion

What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models Khashayar Gatmiry, Zhiyuan Li, Ching-Yao Chuang, Sashank Reddi, Tengyu Ma, Stefanie Jegelka

Block Low-Rank Preconditioner with Shared Basis for Stochastic Optimization Jui-Nan Yen, Sai Surya Duvvuri, Inderjit S Dhillon, Cho-Jui Hsieh

Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain

Boundary Guided Learning-Free Semantic Control with Diffusion Models Ye Zhu, Yu Wu, Zhiwei Deng, Olga Russakovsky, Yan Yan

Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference Tao Lei, Junwen Bai, Siddhartha Brahma, Joshua Ainslie, Kenton Lee, Yanqi Zhou, Nan Du*, Vincent Y. Zhao, Yuexin Wu, Bo Li, Yu Zhang, Ming-Wei Chang

Conformal Prediction for Time Series with Modern Hopfield Networks Andreas Auer, Martin Gauch, Daniel Klotz, Sepp Hochreiter

Does Visual Pretraining Help End-to-End Reasoning? Chen Sun, Calvin Luo, Xingyi Zhou, Anurag Arnab, Cordelia Schmid

Effective Robustness Against Natural Distribution Shifts for Models with Different Training Data Zhouxing Shi*, Nicholas Carlini, Ananth Balashankar, Ludwig Schmidt, Cho-Jui Hsieh, Alex Beutel*, Yao Qin

Improving Neural Network Representations Using Human Similarity Judgments Lukas Muttenthaler*, Lorenz Linhardt, Jonas Dippel, Robert A. Vandermeulen, Katherine Hermann, Andrew K. Lampinen, Simon Kornblith

Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency Xiyang Liu, Prateek Jain, Weihao Kong, Sewoong Oh, Arun Sai Suggala

Mnemosyne: Learning to Train Transformers with Transformers Deepali Jain, Krzysztof Choromanski, Avinava Dubey, Sumeet Singh, Vikas Sindhwani, Tingnan Zhang, Jie Tan

Nash Regret Guarantees for Linear Bandits Ayush Sawarni, Soumyabrata Pal, Siddharth Barman

A Near-Linear Time Algorithm for the Chamfer Distance Ainesh Bakshi, Piotr Indyk, Rajesh Jayaram, Sandeep Silwal, Erik Waingarten.

On Differentially Private Sampling from Gaussian and Product Distributions Badih Ghazi, Xiao Hu*, Ravi Kumar, Pasin Manurangsi

On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes Jia Lin Hau, Erick Delage, Mohammad Ghavamzadeh*, Marek Petrik

ResMem: Learn What You Can and Memorize the Rest Zitong Yang, Michal Lukasik, Vaishnavh Nagarajan, Zonglin Li, Ankit Singh Rawat, Manzil Zaheer, Aditya Krishna Menon, Sanjiv Kumar

Responsible AI (RAI) Games and Ensembles Yash Gupta, Runtian Zhai, Arun Suggala, Pradeep Ravikumar

RoboCLIP: One Demonstration Is Enough to Learn Robot Policies Sumedh A Sontakke, Jesse Zhang, Sébastien M. R. Arnold, Karl Pertsch, Erdem Biyik, Dorsa Sadigh, Chelsea Finn, Laurent Itti

Robust Concept Erasure via Kernelized Rate-Distortion Maximization Somnath Basu Roy Chowdhury, Nicholas Monath, Kumar Avinava Dubey, Amr Ahmed, Snigdha Chaturvedi

Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms Alexander Bukharin, Yan Li, Yue Yu, Qingru Zhang, Zhehui Chen, Simiao Zuo, Chao Zhang, Songan Zhang, Tuo Zhao

Simplicity Bias in 1-Hidden Layer Neural Networks Depen Morwani*, Jatin Batra, Prateek Jain, Praneeth Netrapalli

SLaM: Student-Label Mixing for Distillation with Unlabeled Examples Vasilis Kontonis, Fotis Iliopoulos, Khoa Trinh, Cenk Baykal, Gaurav Menghani, Erik Vee

SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding Paul-Edouard Sarlin*, Eduard Trulls, Marc Pollefeys, Jan Hosang, Simon Lynen

SOAR: Improved Indexing for Approximate Nearest Neighbor Search Philip Sun, David Simcha, Dave Dopson, Ruiqi Guo, Sanjiv Kumar

StyleDrop: Text-to-Image Synthesis of Any Style Kihyuk Sohn, Lu Jiang, Jarred Barber, Kimin Lee*, Nataniel Ruiz, Dilip Krishnan, Huiwen Chang*, Yuanzhen Li, Irfan Essa, Michael Rubinstein, Yuan Hao, Glenn Entis, Irina Blok, Daniel Castro Chin

Three Towers: Flexible Contrastive Learning with Pretrained Image Models Jannik Kossen*, Mark Collier, Basil Mustafa, Xiao Wang, Xiaohua Zhai, Lucas Beyer, Andreas Steiner, Jesse Berent, Rodolphe Jenatton, Efi Kokiopoulou

Two-Stage Learning to Defer with Multiple Experts Anqi Mao, Christopher Mohri, Mehryar Mohri, Yutao Zhong

AdANNS: A Framework for Adaptive Semantic Search Aniket Rege, Aditya Kusupati, Sharan Ranjit S, Alan Fan, Qingqing Cao, Sham Kakade, Prateek Jain, Ali Farhadi

Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small Scorer Bowen Tan*, Yun Zhu, Lijuan Liu, Eric Xing, Zhiting Hu, Jindong Chen

Causal-structure Driven Augmentations for Text OOD Generalization Amir Feder, Yoav Wald, Claudia Shi, Suchi Saria, David Blei

Dense-Exponential Random Features: Sharp Positive Estimators of the Gaussian Kernel Valerii Likhosherstov, Krzysztof Choromanski, Avinava Dubey, Frederick Liu, Tamas Sarlos, Adrian Weller

Diffusion Hyperfeatures: Searching Through Time and Space for Semantic Correspondence Grace Luo, Lisa Dunlap, Dong Huk Park, Aleksander Holynski, Trevor Darrell

Diffusion Self-Guidance for Controllable Image Generation Dave Epstein, Allan Jabri, Ben Poole, Alexei A Efros, Aleksander Holynski

Fully Dynamic k-Clustering in Õ(k) Update Time Sayan Bhattacharya, Martin Nicolas Costa, Silvio Lattanzi, Nikos Parotsidis

Improving CLIP Training with Language Rewrites Lijie Fan, Dilip Krishnan, Phillip Isola, Dina Katabi, Yonglong Tian

<!–k-Means Clustering with Distance-Based Privacy Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong

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LayoutGPT: Compositional Visual Planning and Generation with Large Language Models Weixi Feng, Wanrong Zhu, Tsu-Jui Fu, Varun Jampani, Arjun Reddy Akula, Xuehai He, Sugato Basu, Xin Eric Wang, William Yang Wang

Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management Dhawal Gupta*, Yinlam Chow, Azamat Tulepbergenov, Mohammad Ghavamzadeh*, Craig Boutilier

Optimal Unbiased Randomizers for Regression with Label Differential Privacy Ashwinkumar Badanidiyuru, Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Jacob Leeman, Pasin Manurangsi, Avinash V Varadarajan, Chiyuan Zhang

Paraphrasing Evades Detectors of AI-generated Text, but Retrieval Is an Effective Defense Kalpesh Krishna, Yixiao Song, Marzena Karpinska, John Wieting, Mohit Iyyer

ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation Shuyang Sun*, Weijun Wang, Qihang Yu*, Andrew Howard, Philip Torr, Liang-Chieh Chen*

Robust and Actively Secure Serverless Collaborative Learning Nicholas Franzese, Adam Dziedzic, Christopher A. Choquette-Choo, Mark R. Thomas, Muhammad Ahmad Kaleem, Stephan Rabanser, Congyu Fang, Somesh Jha, Nicolas Papernot, Xiao Wang

SpecTr: Fast Speculative Decoding via Optimal Transport Ziteng Sun, Ananda Theertha Suresh, Jae Hun Ro, Ahmad Beirami, Himanshu Jain, Felix Yu

Structured Prediction with Stronger Consistency Guarantees Anqi Mao, Mehryar Mohri, Yutao Zhong

Affinity-Aware Graph Networks Ameya Velingker, Ali Kemal Sinop, Ira Ktena, Petar Veličković, Sreenivas Gollapudi

ARTIC3D: Learning Robust Articulated 3D Shapes from Noisy Web Image Collections Chun-Han Yao*, Amit Raj, Wei-Chih Hung, Yuanzhen Li, Michael Rubinstein, Ming-Hsuan Yang, Varun Jampani

Black-Box Differential Privacy for Interactive ML Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer

Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits Haolin Liu, Chen-Yu Wei, Julian Zimmert

DaTaSeg: Taming a Universal Multi-Dataset Multi-Task Segmentation Model

Xiuye Gu, Yin Cui*, Jonathan Huang, Abdullah Rashwan, Xuan Yang, Xingyi Zhou, Golnaz Ghiasi, Weicheng Kuo, Huizhong Chen, Liang-Chieh Chen*, David Ross

Easy Learning from Label Proportions Robert Busa-Fekete, Heejin Choi*, Travis Dick, Claudio Gentile, Andres Munoz Medina

Efficient Data Subset Selection to Generalize Training Across Models: Transductive and Inductive Networks Eeshaan Jain, Tushar Nandy, Gaurav Aggarwal, Ashish Tendulkar, Rishabh Iyer, Abir De

Faster Differentially Private Convex Optimization via Second-Order Methods Arun Ganesh, Mahdi Haghifam*, Thomas Steinke, Abhradeep Guha Thakurta

Finding Safe Zones of Markov Decision Processes Policies Lee Cohen, Yishay Mansour, Michal Moshkovitz

Focused Transformer: Contrastive Training for Context Scaling Szymon Tworkowski, Konrad Staniszewski, Mikołaj Pacek, Yuhuai Wu*, Henryk Michalewski, Piotr Miłoś

Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge Abhin Shah, Karthikeyan Shanmugam, Murat Kocaoglu

H-Consistency Bounds: Characterization and Extensions Anqi Mao, Mehryar Mohri, Yutao Zhong

Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation David Brandfonbrener, Ofir Nachum, Joan Bruna

Most Neural Networks Are Almost Learnable Amit Daniely, Nathan Srebro, Gal Vardi

Multiclass Boosting: Simple and Intuitive Weak Learning Criteria Nataly Brukhim, Amit Daniely, Yishay Mansour, Shay Moran

NeRF Revisited: Fixing Quadrature Instability in Volume Rendering Mikaela Angelina Uy, Kiyohiro Nakayama, Guandao Yang, Rahul Krishna Thomas, Leonidas Guibas, Ke Li

Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation Wei-Ning Chen, Dan Song, Ayfer Ozgur, Peter Kairouz

Private Federated Frequency Estimation: Adapting to the Hardness of the Instance Jingfeng Wu*, Wennan Zhu, Peter Kairouz, Vladimir Braverman

RETVec: Resilient and Efficient Text Vectorizer Elie Bursztein, Marina Zhang, Owen Skipper Vallis, Xinyu Jia, Alexey Kurakin

Symbolic Discovery of Optimization Algorithms Xiangning Chen*, Chen Liang, Da Huang, Esteban Real, Kaiyuan Wang, Hieu Pham, Xuanyi Dong, Thang Luong, Cho-Jui Hsieh, Yifeng Lu, Quoc V. Le

A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence Junyi Zhang, Charles Herrmann, Junhwa Hur, Luisa F. Polania, Varun Jampani, Deqing Sun, Ming-Hsuan Yang

A Trichotomy for Transductive Online Learning Steve Hanneke, Shay Moran, Jonathan Shafer

A Unified Fast Gradient Clipping Framework for DP-SGD William Kong, Andres Munoz Medina

Unleashing the Power of Randomization in Auditing Differentially Private ML Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh

(Amplified) Banded Matrix Factorization: A unified approach to private training Christopher A Choquette-Choo, Arun Ganesh, Ryan McKenna, H Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta, Zheng Xu

Adversarial Resilience in Sequential Prediction via Abstention Surbhi Goel, Steve Hanneke, Shay Moran, Abhishek Shetty

Alternating Gradient Descent and Mixture-of-Experts for Integrated Multimodal Perception Hassan Akbari, Dan Kondratyuk, Yin Cui, Rachel Hornung, Huisheng Wang, Hartwig Adam

Android in the Wild: A Large-Scale Dataset for Android Device Control Christopher Rawles, Alice Li, Daniel Rodriguez, Oriana Riva, Timothy Lillicrap

Benchmarking Robustness to Adversarial Image Obfuscations Florian Stimberg, Ayan Chakrabarti, Chun-Ta Lu, Hussein Hazimeh, Otilia Stretcu, Wei Qiao, Yintao Liu, Merve Kaya, Cyrus Rashtchian, Ariel Fuxman, Mehmet Tek, Sven Gowal

Building Socio-culturally Inclusive Stereotype Resources with Community Engagement Sunipa Dev, Jaya Goyal, Dinesh Tewari, Shachi Dave, Vinodkumar Prabhakaran

Consensus and Subjectivity of Skin Tone Annotation for ML Fairness Candice Schumann, Gbolahan O Olanubi, Auriel Wright, Ellis Monk Jr*, Courtney Heldreth, Susanna Ricco

Counting Distinct Elements Under Person-Level Differential Privacy Alexander Knop, Thomas Steinke

DICES Dataset: Diversity in Conversational AI Evaluation for Safety Lora Aroyo, Alex S. Taylor, Mark Diaz, Christopher M. Homan, Alicia Parrish, Greg Serapio-García, Vinodkumar Prabhakaran, Ding Wang

Does Progress on ImageNet Transfer to Real-world Datasets? Alex Fang, Simon Kornblith, Ludwig Schmidt

Estimating Generic 3D Room Structures from 2D Annotations Denys Rozumnyi*, Stefan Popov, Kevis-kokitsi Maninis, Matthias Nießner, Vittorio Ferrari

Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias Yue Yu, Yuchen Zhuang, Jieyu Zhang, Yu Meng, Alexander Ratner, Ranjay Krishna, Jiaming Shen, Chao Zhang

MADLAD-400: A Multilingual And Document-Level Large Audited Dataset Sneha Kudugunta, Isaac Caswell, Biao Zhang, Xavier Garcia, Derrick Xin, Aditya Kusupati, Romi Stella, Ankur Bapna, Orhan Firat

Mechanic: A Learning Rate Tuner Ashok Cutkosky, Aaron Defazio, Harsh Mehta

NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotations Varun Jampani, Kevis-kokitsi Maninis, Andreas Engelhardt, Arjun Karpur, Karen Truong, Kyle Sargent, Stefan Popov, Andre Araujo, Ricardo Martin Brualla, Kaushal Patel, Daniel Vlasic, Vittorio Ferrari, Ameesh Makadia, Ce Liu*, Yuanzhen Li, Howard Zhou

Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations Anudhyan Boral, Zhong Yi Wan, Leonardo Zepeda-Nunez, James Lottes, Qing Wang, Yi-Fan Chen, John Roberts Anderson, Fei Sha

Restart Sampling for Improving Generative Processes Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi Jaakkola

Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial? Fan Yao, Chuanhao Li, Karthik Abinav Sankararaman, Yiming Liao, Yan Zhu, Qifan Wang, Hongning Wang, Haifeng Xu

Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union Zifu Wang, Maxim Berman, Amal Rannen-Triki, Philip Torr, Devis Tuia, Tinne Tuytelaars, Luc Van Gool, Jiaqian Yu, Matthew B. Blaschko

RoboHive: A Unified Framework for Robot Learning Vikash Kumar, Rutav Shah, Gaoyue Zhou, Vincent Moens, Vittorio Caggiano, Abhishek Gupta, Aravind Rajeswaran

SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data Mélisande Teng, Amna Elmustafa, Benjamin Akera, Yoshua Bengio, Hager Radi, Hugo Larochelle, David Rolnick

Sparsity-Preserving Differentially Private Training of Large Embedding Models Badih Ghazi, Yangsibo Huang*, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang

StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners Yonglong Tian, Lijie Fan, Phillip Isola, Huiwen Chang, Dilip Krishnan

Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning Zachary Charles, Nicole Mitchell, Krishna Pillutla, Michael Reneer, Zachary Garrett

Universality and Limitations of Prompt Tuning Yihan Wang, Jatin Chauhan, Wei Wang, Cho-Jui Hsieh

Unsupervised Semantic Correspondence Using Stable Diffusion Eric Hedlin, Gopal Sharma, Shweta Mahajan, Hossam Isack, Abhishek Kar, Andrea Tagliasacchi, Kwang Moo Yi

YouTube-ASL: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpus Dave Uthus, Garrett Tanzer, Manfred Georg

The Noise Level in Linear Regression with Dependent Data Ingvar Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni

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