Today Thirunakshatram Kulashekara Azhwar 🙏
He is the amsam of lords Kausthubha and was born in the month of masi in punarvasu.
He rendered a prabhandham called Perumal Thirumozhi -105 pasurams.

In thiruvanjikalam the capital city of chera Nadu,the king dhrudavradhan and his queen worshipped their favourite deity.
With the blessings of Narayana their prayers were answered and the queen delivered a child on the suklapaksha dwadesi,punarpoosa nakshathiram ,maasi month.

Realising the entire kingdom was blessed by the birth of this child ,the happy parents named him Kulasekaran.
Great love for Narayana Kulasekaran lost interest in ruling his kingdom. so he made his son Dridavratan the king of the land and went on pilgrimages ,Yatra to various Divya desams like srirangam, thirumalai,thirukannapuram,thiruchitrakoodam to have divine dharshan of perumal.
In the song he composed for Thirumalai Tiruvenkatamudaiyan....

ChediyAya valvinaigal thirukkum thirumaie
Nediyanae vengkatava !Nin Koyilin vachal
adiyarum vanavarum arambaiyarum kidanthiyanggum
Padiyayk kidanthu un pavala vayk kannbene
He says he wishes to be a stepping stone in front of the sannidhi.Just as he desired even today the stepping stone in the garbagriha is called Kulasekaran Padi.
After having visited, prayed and offered his services to various temples, Azhwar finally reached Mannarkoil in Tirunelveli District, Southern Tamilnnadu, which is near Ambasamuthram. In this temple, the deity is in three states, namely, Standing, Sitting and Inclining positions.
Azhwar had many rounds of wonderful dharsan and he was happy. He also reached the lotus feet of Paramathma from here. There is a separate shrine in this temple for Azhwar🙏🙏

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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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Recently, the @CNIL issued a decision regarding the GDPR compliance of an unknown French adtech company named "Vectaury". It may seem like small fry, but the decision has potential wide-ranging impacts for Google, the IAB framework, and today's adtech. It's thread time! 👇

It's all in French, but if you're up for it you can read:
• Their blog post (lacks the most interesting details):
https://t.co/PHkDcOT1hy
• Their high-level legal decision: https://t.co/hwpiEvjodt
• The full notification: https://t.co/QQB7rfynha

I've read it so you needn't!

Vectaury was collecting geolocation data in order to create profiles (eg. people who often go to this or that type of shop) so as to power ad targeting. They operate through embedded SDKs and ad bidding, making them invisible to users.

The @CNIL notes that profiling based off of geolocation presents particular risks since it reveals people's movements and habits. As risky, the processing requires consent — this will be the heart of their assessment.

Interesting point: they justify the decision in part because of how many people COULD be targeted in this way (rather than how many have — though they note that too). Because it's on a phone, and many have phones, it is considered large-scale processing no matter what.