Why??
Using the 'Bay of Pigs' incident as a perfect smokescreen, India planned to take back Goa from Portugal.
UNKNOWN EVENTS OF INDIA
How Egypt helped Bharat to recapture back Goa from the Portuguese in 1961 and end the European colonial stronghold in Goa
Dec 1961 : When India blitzed Portuguese-held Goa, both USA & Portugal moved a ceasefire resolution in the United Nations (UN)
Why??
Using the 'Bay of Pigs' incident as a perfect smokescreen, India planned to take back Goa from Portugal.
Salazar then dispatched the Portuguese Navy towards India.
Salazar's plan was simple.
2) Article 5 of NATO states that an attack on one member of NATO is an attack on all of its members
This unprecedented move by Gamal Abdel Nasser shocked NATO and USA. Why did Nasser help India? The Suez Crisis of 1956.
The crisis arose when Nasser decided to nationalize the Suez Canal, which had been owned by the British.
British and French troops invaded Egypt alongwith Israel. Soon the Soviets threatened to get involved on Egypt’s side and American President, Dwight Eisenhower, ordered Britain and France to withdraw immediately.
The British were humiliated.
The Indian contingent (a first for India to send armed troops for peacekeeping) left for the Suez left on November 15, 1956 and was placed then in the Sinai Peninsula. Indian peacekeepers built a reputation for themselves
These entire episodes made NATO angry.
More from WORLD OF SANATAN DHARMA
It is generally believed that Sanskrit is a language like any other except that it is more complicated and dead for all purposes https://t.co/3sjsz8cikb
At best, people are willing to admit that it has a great literature and a cultural value at the other end, there are people who consider it as a mysterious combination of words to create a religious atmosphere through prayers, chanting, incantations, etc.
But Sanskrit is much more than that and possesses within itself many of the attributes of a great and useful language.
It is both a science and an art combined in one. As a language, Sanskrit has a degree of permanence which no other language has. In olden days, when teaching
was oral, there was a need to keep information in tact and pass them on from generation to generation accurately. This led, on one hand, to a strong emphasis towards versification, poetry and phonetics. On the other side, formulae were devised of inventing and converting words
in such a way that chances of distortion were kept at a minimum.
3. After Panini's grammer, Sanskrit language was so much standardized that further linguistic development was not possible. By a general consensus the world over, it is well recognized that Sanskrit literature,
The tidbits of wisdom acquired while reading Samhitas- an entirely different lesson learnt every single time!
— Dr Vishakha Moghe \U0001f1ee\U0001f1f3 (@drmoghes) June 5, 2021
A language can be just so full with consciousness and wisdom!!
Wonder how on earth they call #Sanskrit a dead language?
At best, people are willing to admit that it has a great literature and a cultural value at the other end, there are people who consider it as a mysterious combination of words to create a religious atmosphere through prayers, chanting, incantations, etc.
But Sanskrit is much more than that and possesses within itself many of the attributes of a great and useful language.
It is both a science and an art combined in one. As a language, Sanskrit has a degree of permanence which no other language has. In olden days, when teaching
was oral, there was a need to keep information in tact and pass them on from generation to generation accurately. This led, on one hand, to a strong emphasis towards versification, poetry and phonetics. On the other side, formulae were devised of inventing and converting words
in such a way that chances of distortion were kept at a minimum.
3. After Panini's grammer, Sanskrit language was so much standardized that further linguistic development was not possible. By a general consensus the world over, it is well recognized that Sanskrit literature,
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These are going to be very simple yet effective pure price action based scanners, no fancy indicators nothing - hope you liked it.
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One of the classic scanners very you will get strong stocks to Bet on.
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Hourly Breakout
This scanner will give you short term bet breakouts like hourly or 2Hr breakout
Volume shocker
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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)
