His nephew, Salar Masud would go on to continue his dream of raiding and plundering India every single year.
ЁЯМ║Do you know the great King of Bahraich named Suheldev?
Here's his story.ЁЯМ║
The Ghznavid invasions had crippled India like no other invasion ever before. Mahmud of Ghzni was perhaps one of the first invaders to actually dent the strong Indian defences after alexander the great.
![](https://pbs.twimg.com/media/FFRp5vEVQAYx97W.jpg)
His nephew, Salar Masud would go on to continue his dream of raiding and plundering India every single year.
At a young age of 16 he led his army to capture various parts of northern India including Ajmer and Delhi by the help of the Ghaznavid Army.
![](https://pbs.twimg.com/media/FFRp6qtVIAMPjeC.jpg)
Throughout his journey, the bardic tales states that he plundered temples and monasteries, showing to the populace how weak their local rulers were.
![](https://pbs.twimg.com/media/FFRp692VgAYFXRC.jpg)
Suheldev was the valiant son of Mordhwaj and a very just ruler.
He was the one who united all the northern kingdoms under one rule to oppose the oncoming assault.
![](https://pbs.twimg.com/media/FFRp7_zUYAsTHQK.jpg)
The Invading army had up to thirty-two thousand veteran campaigners by their side.
The oncoming army has to be dealt with strength and strategy.
![](https://pbs.twimg.com/media/FFRqH-4VEAcX_gc.jpg)
The other kings who had capitulated under the invading horde either just surrendered in hopes of mercy or were destroyed due to their sheer lack of manpower.
He wasnтАЩt going to just let his men die so that he could satiate his thirst for glory. He was playing for the keeps this time.
![](https://pbs.twimg.com/media/FFRqY99VUAQ77yp.jpg)
It is said that the Ghaznavid army had hundreds of domestic animals which provided meat and milk for consumption.
Suheldev dispatched his soldiers for a surprise attack.
And his grand strategy will prove just right.
SalarтАЩs men woke up from their slumber and tried to ready the breakfast when to their shock, the entire cattle was freed while their sentries and guards lay dead of the ground.
![](https://pbs.twimg.com/media/FFRqrqSVQAkzUOk.jpg)
Before Ghazi could react to the news, the ground beneath him started to shake.
![](https://pbs.twimg.com/media/FFRqzJtUYAErxzk.jpg)
![](https://pbs.twimg.com/media/FFRq8foVUAMLzZh.jpg)
Enemy soldiers formed tight skirmishing parties and tried to tackle the defenders.
![](https://pbs.twimg.com/media/FFRrNnpVEAAvyS5.jpg)
According to the local legends, as a small ridge between the enemy troops opened, SuheldevтАЩs mount galloped forward trying to land a decisive blow to the enemy leader.
![](https://pbs.twimg.com/media/FFRrbnwVIAMbCO7.jpg)
Quickly Salar Masud shouted for help and his aide de camp responded.This was exactly what Suheldev was counting on.
![](https://pbs.twimg.com/media/FFRrrgMVgAcnjD9.jpg)
Suheldev made his escape in nick of time as he killed enemy force while getting out. According to some sources Suheldev also shot a retreating arrow which landed right on SalarтАЩs neck.
For his valour he was awarded the title of тАШProtector of DharmaтАЩ for the entire region.
B'coz he died during war,he was termed as a Ghazi and a shrine was built for him in admiration and respect.
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Paper: https://t.co/gIsRPtSjKz
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Evaluation: https://t.co/aOzQu95J8z
ЁЯз╡ЁЯСЗ(1 / 12)
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Starting with a masked autoencoder over frames from these video clips, make a choice:
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2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
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Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
ЁЯз╡ЁЯСЗ(1 / 12)
![](https://pbs.twimg.com/media/Fp_Pp79agAA36b8.jpg)
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).
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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.
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