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mattheww

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mattheww
·letztes Jahr·discuss
Did my thesis research at Brookhaven National Lab, home of the Relativistic Heavy Ion Collider (RHIC), which is the predecessor of the heavy ion program at the LHC.

While there, one of the more senior scientists relayed an exchange from an ongoing review of the program. At the time, RHIC was colliding gold in the heavy ion program.

One of the reviewers asked if RHIC could save money by switching to a cheaper element, like lead. None of the RHIC representatives knew what to say. I don't remember the exact numbers, but RHIC used something like < 1 milligram of gold over the lifetime of the program.
mattheww
·vor 3 Jahren·discuss
You're right, the thing limiting the value is not demand - there are more than enough faculty and high-level staff that want to live there.

The thing limiting the value is the other restrictions that Stanford imposes on these houses - namely they essentially control the price the houses sell for because they all have to be financed through a Stanford-controlled lending program.
mattheww
·vor 4 Jahren·discuss
FYI, if you make a realistic calculation (including tax and multiple winners), the EV of a lottery ticket is still negative.

Depending on your tax bracket, the non-jackpot prizes have about $0.19 of EV.

In a state where you don't pay tax on lottery winnings, like CA, you cash option post-tax is currently estimated at $408,403,045.

My estimate of the multiple winner correction to the EV is 0.8, based on numbers of winners for prizes >$500M.

Your jackpot EV is therefore $1.08 and the total winnings EV is therefore $1.27, meaning the overall EV is -$0.73.

For there to be positive EV, that delta needs to all come from the jackpot, so the headline jackpot number would need to go from $1.1B to $1.8B to get it there.
mattheww
·vor 4 Jahren·discuss
There are four questions at the end of the post.

For sure, the second one is answered - it is possible to parallelize GNNs to the billion-scale, while still using message passing. It requires rethinking how message passing is implemented, modifying objective functions that work in parallel, and changing ML infrastructure. You're not going to get to large graphs with generic distributed Tensorflow.

I don't know if the third question is fully answered, but there are many approaches to preserving locality, either by changing architectures or changing objective functions.

Also, errata: PinSage was developed for Pinterest, not Etsy (hence, not EtsySage).