The notion of "stolen land" is bullshit bourgeois obfuscation: the current land owners are blaming the existing national collective—largely composed of workers—for supposed "crimes" committed centuries ago.
The land that came to be owned by the US & international bourgeoisie was never "of indigenous people" in some romantic egalitarian sense. It was owned by & fought over for by various tribal elites—whether agricultural or hunter gatherer.
Many Asian Americans are saying, "We belong here." But, let's not forget that "we" are on stolen land. Making a claim to belonging means being committed to indigenous people's rights. #StopAAPIHate
— Pawan Dhingra (@phdhingra1) April 1, 2021
The notion of "stolen land" is bullshit bourgeois obfuscation: the current land owners are blaming the existing national collective—largely composed of workers—for supposed "crimes" committed centuries ago.
In many cases the successors of the supposed aggrieved groups don't exist. If they do deference doesn't help them.
Historically, the state's attempts to offer indigenous people special dispensations has only hurt them, rendering them dependent...
While the conditions of workers in the US is grave, being one confers...
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Do Share the above tweet 👆
These are going to be very simple yet effective pure price action based scanners, no fancy indicators nothing - hope you liked it.
https://t.co/JU0MJIbpRV
52 Week High
One of the classic scanners very you will get strong stocks to Bet on.
https://t.co/V69th0jwBr
Hourly Breakout
This scanner will give you short term bet breakouts like hourly or 2Hr breakout
Volume shocker
Volume spurt in a stock with massive X times
1. Mini Thread on Conflicts of Interest involving the authors of the Nature Toilet Paper:
https://t.co/VUYbsKGncx
Kristian G. Andersen
Andrew Rambaut
Ian Lipkin
Edward C. Holmes
Robert F. Garry
2. Thanks to @newboxer007 for forwarding the link to the research by an Australian in Taiwan (not on
3. K.Andersen didn't mention "competing interests"
Only Garry listed Zalgen Labs, which we will look at later.
In acknowledgements, Michael Farzan, Wellcome Trust, NIH, ERC & ARC are mentioned.
Author affiliations listed as usual.
Note the 328 Citations!
https://t.co/nmOeohM89Q
4. Kristian Andersen (1)
Andersen worked with USAMRIID & Fort Detrick scientists on research, with Robert Garry, Jens Kuhn & Sina Bavari among
5. Kristian Andersen (2)
Works at Scripps Research Institute, which WAS in serious financial trouble, haemorrhaging 20 million $ a year.
But just when the first virus cases were emerging, they received great news.
They issued a press release dated November 27, 2019:
https://t.co/VUYbsKGncx
Kristian G. Andersen
Andrew Rambaut
Ian Lipkin
Edward C. Holmes
Robert F. Garry
2. Thanks to @newboxer007 for forwarding the link to the research by an Australian in Taiwan (not on
3. K.Andersen didn't mention "competing interests"
Only Garry listed Zalgen Labs, which we will look at later.
In acknowledgements, Michael Farzan, Wellcome Trust, NIH, ERC & ARC are mentioned.
Author affiliations listed as usual.
Note the 328 Citations!
https://t.co/nmOeohM89Q
4. Kristian Andersen (1)
Andersen worked with USAMRIID & Fort Detrick scientists on research, with Robert Garry, Jens Kuhn & Sina Bavari among
Our Hans Kristian Andersen working with Jens H. Kuhn, Sina Bavari, Robert F. Garry, Stuart T. Nichol,Gustavo Palacios, Sheli R. Radoshitzky from USAMRIID and Fort Detrick to tell more fairy tales? Full emails listed for queries...https://t.co/kLRoQTxiGD pic.twitter.com/uHNuGraPP2
— Billy Bostickson \U0001f3f4\U0001f441&\U0001f441 \U0001f193 (@BillyBostickson) August 26, 2020
5. Kristian Andersen (2)
Works at Scripps Research Institute, which WAS in serious financial trouble, haemorrhaging 20 million $ a year.
But just when the first virus cases were emerging, they received great news.
They issued a press release dated November 27, 2019:
How can we use language supervision to learn better visual representations for robotics?
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)