This is what they do. They came this close =>||<= to doing it to America.
Texas isn't the first state Republiqans have destroyed. Does anyone remember what happened to Kansas? It wasn't a sudden disaster like this, it was a slow collapse, intentionally engineered.
This is what they do, one way or another. Any red state that hasn't collapsed yet, will.
This is what they do. They came this close =>||<= to doing it to America.
Texas wasn't a mistake. It was a trial run.
https://t.co/0N3u2M6U6R
Remember Flint? pic.twitter.com/s4jDJwoe41
— \U0001d5a6\U0001d5c2\U0001d5c7\U0001d5c0\U0001d5be\U0001d5cb \U0001d5ab\U0001d5d2\U0001d5c7 \U0001f514 (@GingerLyn_V2) February 19, 2021
Imagine Republiqans control the House, Senate and White House at that time, and have neither the competence nor the desire to help.
https://t.co/rviVz674kL
It sounds like a conspiracy theory, but research just how many repubs are funded by russia since the 90shttps://t.co/oBbFn3jbUk
— Fink (@ratfink6923) February 19, 2021
I have already heard rightist commentators who are suddenly shocked--shocked, I say!--by all the COVID deaths happening under Biden.
https://t.co/Xq8MkkWlk4
I live in KS. Brownback destroyed our state. The schools, our roads, did nothing about the unemployment enrollment issue. He put us in debt until the republican legislature stepped up.
— Kevin Bock (@kbock00) February 19, 2021
Now, they blame the governor for everything!
Red state, red legislation but, dem govnr is prob!
They blame Democrats for the enormous deficits Republiqans create.
More from All
https://t.co/6cRR2B3jBE
Viruses and other pathogens are often studied as stand-alone entities, despite that, in nature, they mostly live in multispecies associations called biofilms—both externally and within the host.
https://t.co/FBfXhUrH5d
Microorganisms in biofilms are enclosed by an extracellular matrix that confers protection and improves survival. Previous studies have shown that viruses can secondarily colonize preexisting biofilms, and viral biofilms have also been described.
...we raise the perspective that CoVs can persistently infect bats due to their association with biofilm structures. This phenomenon potentially provides an optimal environment for nonpathogenic & well-adapted viruses to interact with the host, as well as for viral recombination.
Biofilms can also enhance virion viability in extracellular environments, such as on fomites and in aquatic sediments, allowing viral persistence and dissemination.
Viruses and other pathogens are often studied as stand-alone entities, despite that, in nature, they mostly live in multispecies associations called biofilms—both externally and within the host.
https://t.co/FBfXhUrH5d
Microorganisms in biofilms are enclosed by an extracellular matrix that confers protection and improves survival. Previous studies have shown that viruses can secondarily colonize preexisting biofilms, and viral biofilms have also been described.
...we raise the perspective that CoVs can persistently infect bats due to their association with biofilm structures. This phenomenon potentially provides an optimal environment for nonpathogenic & well-adapted viruses to interact with the host, as well as for viral recombination.
Biofilms can also enhance virion viability in extracellular environments, such as on fomites and in aquatic sediments, allowing viral persistence and dissemination.
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)
