Chuan Qin, a party member of CCP, Director of the Institute of Laboratory Animal Sciences, CAMS; 1st person on finding and establishing the 1st animal model of SARS infection & also awarded the Advanced Individual Award of the United Front Work System due to the finding in 2003
https://t.co/7VE46nGYf2
A suspected contractor of Chuan QIN team - Cyagen, a biotechnological CRO founded in Guangzhou and established several branches globally, including Santa Clara, California. Cyagen is the world's largest provider of custom-engineered mouse and rat models
Not easy to find evidence on Qin's involvement in the serial passage experiments. But quite easy to find sth from their contractor. Obviously this company contracts their program of humanised rats regarding to #SARS_CoV_2 and passage experiments on rats
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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)
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THREAD PART 1.
On Sunday 21st June, 14 year old Noah Donohoe left his home to meet his friends at Cave Hill Belfast to study for school. #RememberMyNoah💙
He was on his black Apollo mountain bike, fully dressed, wearing a helmet and carrying a backpack containing his laptop and 2 books with his name on them. He also had his mobile phone with him.
On the 27th of June. Noah's naked body was sadly discovered 950m inside a storm drain, between access points. This storm drain was accessible through an area completely unfamiliar to him, behind houses at Northwood Road. https://t.co/bpz3Rmc0wq
"Noah's body was found by specially trained police officers between two drain access points within a section of the tunnel running under the Translink access road," said Mr McCrisken."
Noah's bike was also found near a house, behind a car, in the same area. It had been there for more than 24 hours before a member of public who lived in the street said she read reports of a missing child and checked the bike and phoned the police.
On Sunday 21st June, 14 year old Noah Donohoe left his home to meet his friends at Cave Hill Belfast to study for school. #RememberMyNoah💙
He was on his black Apollo mountain bike, fully dressed, wearing a helmet and carrying a backpack containing his laptop and 2 books with his name on them. He also had his mobile phone with him.
On the 27th of June. Noah's naked body was sadly discovered 950m inside a storm drain, between access points. This storm drain was accessible through an area completely unfamiliar to him, behind houses at Northwood Road. https://t.co/bpz3Rmc0wq
"Noah's body was found by specially trained police officers between two drain access points within a section of the tunnel running under the Translink access road," said Mr McCrisken."
Noah's bike was also found near a house, behind a car, in the same area. It had been there for more than 24 hours before a member of public who lived in the street said she read reports of a missing child and checked the bike and phoned the police.