Disinvestments : Sale of Partial stake also qualifies under disinvestment

Department of Disinvestment was brought under ministry of finance in May 2004 immediately after UPA Government took over.

Inflation adjusted value of disinvestment over the past 👇👇

2009-10 : ₹46,981 Cr
2010 -11 : ₹40,352 Cr
2011 -12 : ₹23,776 Cr
2012 -13: ₹36,876 Cr
2013 -14: ₹22,385 Cr

UPA 2: ₹1,70,370 Crores

2014-15 : ₹32,443 Cr
2015-16 : ₹23,997 Cr
2016-17 : ₹46,247 Cr
2017-18 : ₹1,00,057 Cr
2018-19 : ₹84,972 Cr

NDA 1: ₹2,87,716 Crores
2017-18 recorded the highest disinvestment. Of the ₹1,00,057 Cr disinvested, ONGC got ₹36,915 Cr of Government stake in HPCL.

Even in 2018-19, ₹15,000 Cr disinvestment happened by virtue of Power finance corporation buying out major stake in Rural electrification corporation
There are multiple such examples of one Public sector taking over another in the last 5 years and benefiting the government by virtue of it.

In NDA 2 below are the disinvestments
2019-20: ₹50,299 Cr
2020-21: ₹19,499 Cr

So to which Crony capitalists did UPA sell out to??

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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)

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Ivor Cummins has been wrong (or lying) almost entirely throughout this pandemic and got paid handsomly for it.

He has been wrong (or lying) so often that it will be nearly impossible for me to track every grift, lie, deceit, manipulation he has pulled. I will use...


... other sources who have been trying to shine on light on this grifter (as I have tried to do, time and again:


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19 to 80 is an over 300% difference.

Tweet: https://t.co/36FnYnsRT9


Example #2 - "Yes, I'm comparing the Noridcs / No, you cannot compare the Nordics."

I wonder why...

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Example #3 - "I'm only looking at what makes the data fit in my favour" a.k.a moving the goalposts.

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I think a plausible explanation is that whatever Corbyn says or does, his critics will denounce - no matter how much hypocrisy it necessitates.


Corbyn opposes the exploitation of foreign sweatshop-workers - Labour MPs complain he's like Nigel

He speaks up in defence of migrants - Labour MPs whinge that he's not listening to the public's very real concerns about immigration:

He's wrong to prioritise Labour Party members over the public:

He's wrong to prioritise the public over Labour Party