If you don't have firsthand experience with how traditional financial institutions (pensions, corporate treasuries etc) administratively permission the moving of assets, it's hard to understand why it's still very difficult for an institution to own bitcoin. Here's why: /1

Statue of Equality (Ramanuja) Muchintal, Hyderabad, Telangana

The Statue of Equality is a monument in India dedicated to the 11th-century Vaishnavaite Sage Bhagavad Ramanuja, commemorating 1000 years since his birth. It was inaugurated by Prime Minister Narendra Modi ji in 2019.

In 2014, Sri Chinna Jeeyar came up with the idea to commemorate the 1000th anniversary of Sri Ramanuja's teachings by building the Statue of Equality. The statue is constructed on an estimated 34 acres in Hyderabad, India.

It consists of a 216 foot tall statue of Ramanuja and is surrounded by 108 Divyadesams (model temples) and includes an educational gallery. The project budget is 1000 crore rupees.The foundation stone for the Statue of Equality was laid by Chinna Jeeyar.

The statue was built by Aerosun Corporation in China before being shipped to India. It is made of panchaloha, a combination of gold, silver, copper, brass and titanium. The statue relies on donations to fund its construction.

The temple is located on the 27,870 square metre second floor with a 120 kg statue of Ramanuja, representing the years he lived, meant for daily worship. The 1,365 square metre third floor including a Vedic digital library and research center.

Lately there have been various rumours in the media about myself and Jumbo in relation to Williams. To clarify things, Jumbo is not and will not invest any money in my career. 1/2

Of course we’re collectively looking at career opportunities, however this is certainly not our philosophy and strategy. I hope to announce my future plans soon, be patient 😉 2/2

Ok, I'll hop on this Elizabeth Warren/Native American testing bandwagon (gently and non-politically).

I've been a @23andMe customer for a while, and have followed their ancestry updates closely.

All is more or less as expected....except for this bit about Native American.

The family is almost completely composed of Spanish peasants (from various regions) who emigrated, along with a massive wave in the late 19th-cent./early 20th-cent., to Cuba back when it was a booming economy (richer than Spain's) and worth emigrating to (Communism killed that).

Also, the native population of Cuba was annihilated early on---was the first place the Spanish colonized after all. Having a native background in Cuba would be like having the same in, say, Massachusetts, particularly if you're (say) mostly Irish. Just really, really unlikely.

(Note: the North African/Arab background is less mysterious. The Iberian peninsula was part of the Muslim world for centuries. It would be odd *not* to have some Arab/Middle Eastern background coming from Spain. Given the family is mostly from Northern Spain, it's small though.)

I have a Spanish passport, have been back to the ancestral villages in Spain, seen the church where my grandmother was baptized, my grandfather told me stories about growing up as the child of Spanish colonists in rural Cuba. The native bit just clashes with all the family lore.

Important paper from Google on large batch optimization. They do impressively careful experiments measuring # iterations needed to achieve target validation error at various batch sizes. The main "surprise" is the lack of surprises. [thread]

https://t.co/7QIx5CFdfJ

The paper is a good example of lots of elements of good experimental design. They validate their metric by showing lots of variants give consistent results. They tune hyperparamters separately for each condition, check that optimum isn't at the endpoints, and measure sensitivity.

They have separate experiments where the hold fixed # iterations and # epochs, which (as they explain) measure very different things. They avoid confounds, such as batch norm's artificial dependence between batch size and regularization strength.

When the experiments are done carefully enough, the results are remarkably consistent between different datasets and architectures. Qualitatively, MNIST behaves just like ImageNet.

Importantly, they don't find any evidence for a "sharp/flat optima" effect whereby better optimization leads to worse final results. They have a good discussion of experimental artifacts/confounds in past papers where such effects were reported.

(THREAD) The Whitaker scandal is deepening, as it becomes clear that Sessions' Russia recusal was a coordinated sham and his firing an act of obstruction—and that his replacement is a White House plant inside the Mueller probe. I hope you'll retweet and read on for more details.

1/ From 2014-17 Whitaker worked for World Patent Marketing—which during his tenure defrauded consumers out of $26 million and was successfully prosecuted by the feds. His involvement in the scam confirmed he had loose morals and that the feds would never want to employ him again.

2/ Despite the seeming impossibility of a man with Whitaker's background getting a job at Justice ever again—he'd been found to have used his former title as a US attorney to fraudulently threaten consumers with valid complaints with criminal penalties—Whitaker found an opening.

3/ Within 60 days of parachuting out of World Patent Marketing as it was being fined $26 million by the feds, Whitaker was working for CNN and telling a fellow attorney panelist that his purpose in working for CNN was to get noticed by one man—Donald Trump—and thereby get a job.

4/ Whitaker spent his time at CNN making ludicrous statements about the Mueller probe: there was no obstruction or collusion, he said; Mueller had no authority to look into any aspect of Trump's finances or to subpoena him, he said. All the while, he hoped Trump was watching him.