1) OBSERVATION: The frequencies are changing. I would have to assess stats on the electromagnetic field to see its fluctuations. All I know is, I am highly sensitive to energy/frequencies, and they are changing, because…

Some 1300 years ago in Karnataka there lived a genius who wrote "Gaṇitasārasan̄graha" (which is earliest text devoted entirely to mathematics)

The man who's work later borrowed by Arabs & Europeans

Thread on "Mahāvīracharya" astounding but forgotten legacy

Mahāvirachārya was patronised by rashtrakuta king amoghavarasha

His book"Gaṇita-sāra-saṅgraha"is earliest text devoted entirely to Mathematics

He has described the currently used method of calculating(LCM)of given numbers.The same method was used in Europe later in1500CE

Mahavira wrote "Gaṇita-sāra-saṅgraha" (GSS) in 8th century CE which contains some 1100 slokās & contains elementary topics in arithmetic , algebra , geometry , measurements , logic , number theory , dynamical systems etc

A sheer mathematical genius

He was devout jain
His magnum opus "Ganit sara sangraha " start with a sloka bowing to the glory of the jinas

"संख्याज्ञानप्रदिपेन जैनेंद्रन महत्वेशा,
प्रकाशितम जगतसर्व येन तम प्रणमाम्यहम"

He also praised his predecessor aryabhata , brahmagupta & other greats in his works

The importance & popularity of "Gaṇita-sāra-saṅgraha"was so huge that it enjoyed statues of most important textbook of mathematics in South Indian for over 3 centuries

A large no of manuscript copies of
"Gaṇita-sāra-saṅgraha"whr discovered in Kerala shows it's popularity

https://t.co/odzm9VTdwZ

Thread for today. /0

It is fashionable for people to create sympathy to the unseen / unknown organisms. The poor things that can't stand up and fight for their own existence.

It makes for a brilliant narrative. It melts your hearts. And you get to play saviour of the oppressed by outraging. /1

But, for most part, such things are also false.

Amazon are the lungs of the world. Never mind that most of the world's oxygen production happens in open oceans. But these are harmless ones. Of course, we all love to maintain forests.

However, some things are sheer scare mongering.

For instance - take this: coral reefs will be permanently gone if mean / median temperatures raise by 1°C ..

But, really - how? Then you get a lecture on how CO2 will affect calcification.

Dig a bit deeper from another angle+

+ and you find that the earth was much much warmer during the age of Dinosaurs. So much so, that we had cold blooded lizards growing to sizes of buildings. Surely, there must have been tons of CO2 then!

And the ocean temps were quite high too.

Light travels at about 1 foot per nanosecond.

Hold your hand up 12 inches from your face: you’re seeing your hand as it was a nanosecond ago. Everything you look at is, to one degree or another, in the past. The farther away in space, the more ancient in time.

You can’t see the Sun as it is now, but you can see it as it was about 8 minutes ago. You can’t see Alpha Centauri now, but you can see it 4.4 years ago. You can see the Andromeda Galaxy as it was 2.5 million years in the past. And so on.

With powerful telescopes, we can see galaxies whose light has been traveling to us for more than 13 billion years. We see them shining in a universe that’s still young, where gravity has just begun to pull matter together into stars and galaxies.

We can see something even more distant, and more ancient, than the first galaxies. If we peer out far enough, in between the galaxies, we can see parts of the Universe that are so far away, it has taken the light from that distance almost the entire age of the cosmos to reach us.

Next up at #enigma2021, Sanghyun Hong will be speaking about "A SOUND MIND IN A VULNERABLE BODY: PRACTICAL HARDWARE ATTACKS ON DEEP LEARNING"

(Hint: speaker is on the

In recent years ML models have worked from research labs to production, which makes ML security important. Adversarial ML research studies how to mess with ML

For example by messing with the training data (c.f. Tay which became super-racist super-fast) or by foiling ML models by changing inputs in ways humans can't see.

Prior work considers ML models in a standalone, mathematical way
* looks at the robustness in an isolated manner
* doesn't look at the whole ecosystem and how the model is used -- ML models are running in real hardware with real software which has real vulns!

This talk focuses on hardware-level vulnerabilities. This is particularly interesting because these can break cryptographic guarantees (because those are outside of their threat models)
e.g. fault injection attacks, side-channel attacks