THREAD:

1/
A former judge of the #SupremeCourt exposed yesterday's Radical Comments made by SC Judges.

Former judge Dhingra said that the comments made by the #SupremeCourtOfIndia on #NupurSharmaControversy are not only Childish but also Absurd.⚡️🔥

ReTweet & Read👇

2/

Former judge said that the matter which went to the #SupremeCourtOfIndia , was only to transfer all the FIRs to Delhi & not to prove the guilt on #Nupur. By what authority did the court make such comments?

If Judges had the guts, They should've wrote them in Written Order!
3/

Why did the Supreme Court wrote only "Petition Dismissed" in the written order?

Why not the entire comments were put down in the Written Order, so that people can ask the #SupremeCourt , on the basis of which hearing you made all these comments?
4/

What right does #SupremeCourt have, because of which it found #Nupur guilty without Trial?

Court itself accused #Nupur, itself declared her guilty while pronouncing the Judgement orally. As per Justice Dhingra, This sends a wrong message to the whole country.
5/
Justice Dhingra asked that Honorable #SupremeCourt said one more thing that why didn't #Nupur go to the Lower Court.

Whereas, the Supreme Court itself not only listens to the petition of the Rich Businessmen directly, but opens the Court till 12 Midnight too!
6/

Former judge said that It's work of the Lower Court to decide, where the FIR will be registered & Who's Guilty.

Lower Court will hear the remarks made by Nupur Sharma. After Trial, the court will say whether Statement was wrong or was it based on Facts. SC must stay Silent!

More from All

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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I'll begin with the ancient history ... and it goes way back. Because modern humans - and before that, the ancestors of humans - almost certainly originated in Ethiopia. 🇪🇹 (sub-thread):


The first likely historical reference to Ethiopia is ancient Egyptian records of trade expeditions to the "Land of Punt" in search of gold, ebony, ivory, incense, and wild animals, starting in c 2500 BC 🇪🇹


Ethiopians themselves believe that the Queen of Sheba, who visited Israel's King Solomon in the Bible (c 950 BC), came from Ethiopia (not Yemen, as others believe). Here she is meeting Solomon in a stain-glassed window in Addis Ababa's Holy Trinity Church. 🇪🇹


References to the Queen of Sheba are everywhere in Ethiopia. The national airline's frequent flier miles are even called "ShebaMiles". 🇪🇹