BANNARI AMMAN, SATHYAMANGALAM (TN)
#bharatmandir #navratri2021

Bannari Amman Kshetram is dedicated to Mariamman, known as the goddess of Rain. This divine place is located at the foothill of Thimbam.

Bannari Amman is a form of Shakti. The murti is swayambhu (self-manifested).

Some 300 years ago, cowherds used to leave cattle here for grazing. One day, a cow was seen shedding her milk under a Vengai tree. Next day also, the cow did the same thing. Villagers removed the thickly grown grass and found a sand-hill and Swayambu Linga near it.
@GunduHuDuGa
It is believed that a deity protected traders from Kerala who travelled through this place to Mysore. Fascinated by the beautiful surroundings, she decided to stay here to protect devotees. A shrine was built and Bannari Mariamman is worshipped here since then.
Bannari Amman is the only shrine that faces the south direction. Instead of Vibhuti and Kumkuma, Bannari soil is given to the devotees. The Kundam festival (fire walk on heated charcoal) is most famous here.

More from Jaya_Upadhyaya

More from All

https://t.co/6cRR2B3jBE
Viruses and other pathogens are often studied as stand-alone entities, despite that, in nature, they mostly live in multispecies associations called biofilms—both externally and within the host.

https://t.co/FBfXhUrH5d


Microorganisms in biofilms are enclosed by an extracellular matrix that confers protection and improves survival. Previous studies have shown that viruses can secondarily colonize preexisting biofilms, and viral biofilms have also been described.


...we raise the perspective that CoVs can persistently infect bats due to their association with biofilm structures. This phenomenon potentially provides an optimal environment for nonpathogenic & well-adapted viruses to interact with the host, as well as for viral recombination.


Biofilms can also enhance virion viability in extracellular environments, such as on fomites and in aquatic sediments, allowing viral persistence and dissemination.
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)

You May Also Like