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

In addition, she and her team applied a patent regarding to the animal model fo SARS infection.
From the beginning of the SARS-CoV-2 pandemic, Chuan Qin published papers in journals on the application of humanized mouse model experiments in SARS-CoV-2. https://t.co/h2tnuFfys8
https://t.co/7VE46nGYf2
Something more, she invented a recombinant vaccine-SARS vaccine and shared the patent with Alan Diamond AIDS Research Center of Columbia University.
Not surprisingly, she engaged in the CCP's 11th Five-Year Plan, the MCF's long-term strategic plan, in 2008, and worked on the study of Laboratory Animal Technology Platform, a subset of Prevention and Control of Major Infectious Diseases such as AIDS and Viral Hepatitis Project
Due to her passion on developing #UnrestrictedBioweapon for CCP, she received RMB 15.15 million of research funding and applied for 18 domestic and international patents from the State Council and the PLA.
FOLLOW THE MONEY💸💸💸(1)
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
FOLLOW THE MONEY💸💸💸(2)
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
-- The End --
Believe/not, developments of #UnrestrictedBioweapon have become a mature manufacturing chain since 2000s/earlier. MCF, 5-Year plans...these facts shows #CCP always has plans. This is not an accident or some individual's misbehaviour. Qin isn't the only 1
#LimengYan

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कुंडली में 12 भाव होते हैं। कैसे ज्योतिष द्वारा रोग के आंकलन करते समय कुंडली के विभिन्न भावों से गणना करते हैं आज इस पर चर्चा करेंगे।
कुण्डली को कालपुरुष की संज्ञा देकर इसमें शरीर के अंगों को स्थापित कर उनसे रोग, रोगेश, रोग को बढ़ाने घटाने वाले ग्रह


रोग की स्थिति में उत्प्रेरक का कार्य करने वाले ग्रह, आयुर्वेदिक/ऐलोपैथी/होमियोपैथी में से कौन कारगर होगा इसका आँकलन, रक्त विकार, रक्त और आपरेशन की स्थिति, कौन सा आंतरिक या बाहरी अंग प्रभावित होगा इत्यादि गणना करने में कुंडली का प्रयोग किया जाता है।


मेडिकल ज्योतिष में आज के समय में Dr. K. S. Charak का नाम निर्विवाद रूप से प्रथम स्थान रखता है। उनकी लिखी कई पुस्तकें आज इस क्षेत्र में नए ज्योतिषों का मार्गदर्शन कर रही हैं।
प्रथम भाव -
इस भाव से हम व्यक्ति की रोगप्रतिरोधक क्षमता, सिर, मष्तिस्क का विचार करते हैं।


द्वितीय भाव-
दाहिना नेत्र, मुख, वाणी, नाक, गर्दन व गले के ऊपरी भाग का विचार होता है।
तृतीय भाव-
अस्थि, गला,कान, हाथ, कंधे व छाती के आंतरिक अंगों का शुरुआती भाग इत्यादि।

चतुर्थ भाव- छाती व इसके आंतरिक अंग, जातक की मानसिक स्थिति/प्रकृति, स्तन आदि की गणना की जाती है


पंचम भाव-
जातक की बुद्धि व उसकी तीव्रता,पीठ, पसलियां,पेट, हृदय की स्थिति आंकलन में प्रयोग होता है।

षष्ठ भाव-
रोग भाव कहा जाता है। कुंडली मे इसके तत्कालिक भाव स्वामी, कालपुरुष कुंडली के स्वामी, दृष्टि संबंध, रोगेश की स्थिति, रोगेश के नक्षत्र औऱ रोगेश व भाव की डिग्री इत्यादि।
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)

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