xtacy

Yep, that's right. Its cardinality is the same as rationals, since it's countable.

I think this is a very important area with applications to legal issues; in the computer systems domain, a close application I can think of is troubleshooting / root-cause analysis.

In legal situations and troubleshooting alike, one is often interested not in the "general" causes of why things happen (e.g., an increase in load will contribute an increase in latency), but causes that explain specific cases (e.g., the increase in latency on this incident was because of an increase in file system latency at the backend.)

These concepts are quite interesting; one linked article discusses many variations and refinements in detail: https://plato.stanford.edu/entries/causation-law/. I found these useful mental models to organize my thoughts when troubleshooting systems and doing root-cause analysis:

- But-for cause: We can say that A is the actual cause of B when the following holds: If not for A, not B. To avoid pathologies like "If not for big bang, this B wouldn't have happened", we have ...

- Proximal cause: In the causal chain of explanations: If not for A1, not A2; if not for A2, not A3, ..., the proximal cause of A(k) is the closest, i.e., A(k-1).

- Necessary element of a sufficient set (NESS criterion): Things get interesting here if we need a conjunction of many events to happen simultaneously for a desired effect. For simplicity, assume that events are boolean, and causal relationships can be defined using boolean formulae. Here, A is a cause of B in the NESS sense, when B = (A and C1) OR (C2 and C3) OR ... A here is "necessary" in the sufficient set {A, C1} for B to happen.

The distinction serves a real and practical purpose, as mentioned in the introduction: responsibility/blame assignment.

In legal situations, this blame assignment is critical as it helps identify who is actually responsible for some bad outcome.

Would you agree?

It's a funny coincidence that I was just reading this book (https://toc.cryptobook.us/), Figure 4.5 on book page 102, that talks about this exact issue.

I doubt the author realises that the system must be resistant against a class of attacks (in this case, I believe a chosen plaintext attack). From reading the thread, and comparing it to the example in the book, it seems like the non-uniform patterns in the output highlight a possibility of a CPA.

And of course, the author wants a fully implemented + concrete attack instead of pointing to what's an obvious flaw to the crypto community.

The analogy is a bit extreme, don't you think so? Isn't there a difference between developing a technology and actively taking part in selling it for a use-case that you disagree with?

There's an interesting podcast where Philip Goff and Sean Carroll discuss about various aspects of panpsychism. Sean plays a very good devil's advocate, so if you enjoy a nice friendly critic of the above ideas, I highly recommend this episode!

https://www.preposterousuniverse.com/podcast/2019/11/04/71-p...

How enormous are these pension fund liabilities? Looking at the balance sheet of GM, their last annual pension fund liability was put at $18B, roughly 10% of their total liabilities. It doesn't seem much and probably shouldn't factor that highly into comparisons with TSLA, right?

Yep, that's the underlying principle behind Mellor-Crummey Spinlock: https://lwn.net/Articles/590243/.

Aren't most modern spinlock implementations are ticket-based, which ensures fairness among waiters (FIFO-like)? The linux kernel implementations most definitely are.

I agree that the naive implementations are not.

Interesting. Wouldn't existing intrusion detection systems fire alerts on seeing random DNS queries from a host to `domain.tld`?

That points to the core of the issue. "Fairness" in ML algorithms can be hard to define and assess.

It's easy to say "omit gender from the model", but the real issue here has to do with the _causal_ pathways between your input variables and the output variable.

Since ML mostly works by exploiting correlations between the input and output variables, omitting gender doesn't mean gender's influence is removed. You'll have to omit all the causal pathways from gender -> the output, effectively "d-separating" [1] gender from the output. Whether that's practical or not depends on how well we understand the data generating process.

[1] http://bayes.cs.ucla.edu/BOOK-2K/d-sep.html

One of the best explanations I've read on this is here: https://www.reddit.com/r/Economics/comments/d6e8qv/the_fed_p....

Although, I must admit I don't understand the carry-trade dynamics that much. I hope someone else can throw some light on that!

One of my professors used to say -- Writing is to thinking as alum is to murky water. Indeed, writing makes you crystallise your thoughts.

I think you meant to say -- Type 1 is a false positive, and type 2 is a false negative.

Reminds me of this article from Thasos Group that analysed smart phone signals at a Tesla factory to infer overtime work: http://thasosgroup.com/blog/thasos-data-tesla-wsj/

Great summary. I also love Prof. V. Balakrishnan's exposition on this topic. The video is long, but he motivates the ideas very clearly and in context: https://www.youtube.com/watch?v=GOkZs2RZMQY.

Yes, the claims are pretty much in the same spirit. Although the first (Weierstrass's) theorem [1] was stated for real-valued functions in a 1-D closed interval [a, b], Stone-Weirstrass is a generalisation of the above theorem [2] that's applicable in more general scenarios. Here is the formal statement:

- [1] http://mathworld.wolfram.com/WeierstrassApproximationTheorem...

- [2] http://mathworld.wolfram.com/Stone-WeierstrassTheorem.html

Neural Networks use a different "basis" (sigmoid, ReLU, etc.), but the underlying idea shares the same spirit.

This series is well written. The speed up is expected due because you're incorporating second-order information during the optimisation. For more insight into second order optimisation methods, take a look at Newton's method: https://en.wikipedia.org/wiki/Newton%27s_method. The intuition, derivation, and proof of correctness and convergence speed are quite illuminating.

No, that's not true. The insert function (hash, rbtree, etc.) takes a hash node as an argument. So, in the kernel implementation the caller is expected to hash.

EDIT: check out some examples to see for yourself: https://elixir.bootlin.com/linux/latest/ident/hlist_add_head

FWIW, this magic is how the Linux kernel implements generic linked lists, hash tables, and red-black trees:

https://stackoverflow.com/questions/15832301/understanding-c...

Yes, the authors mention it in the introduction as a "well accepted manifold assumption."

Not all "paradoxes" are similar to Simpson's paradox. See this PDF for more similar "paradoxes":

https://ftp.cs.ucla.edu/pub/stat_ser/r409-corrected-reprint....

(I quote the word paradox, because they aren't really paradoxes once we have an understanding of what's going on.)

/r/MachineLearning comments on the same paper from April 2018:

https://www.reddit.com/r/MachineLearning/comments/89yp8g/r_t...

Interesting, thanks for sharing your insights. I thought that huge institutions prefer to trade on exchanges directly (for anonymity), but it looks like there are other incentives in play.

Yeah, they still have to fill you at the NBBO, and this trade routing is no different from what other brokers (Schwab/E-Trade/etc.) do when you place a trade.

I wonder whether the fact that Robinhood doesn't internalise its trades (i.e., capitalise on the bid/ask spread by matching buyers/sellers from their own customer base) is the reason why market makers pay more to Robinhood?

Interesting article, but I would have loved it if they touched upon how to effectively follow these principles in a world where there's competition. For instance, getting feedback, thoroughly vetting ideas, etc., is a good practice. But if you are competing against those who exploit the limited attention of investors/peers to quickly get ahead (in the short term), they _could_ get an enormous first-mover advantage. How does one deal with that?

It might be worth getting a PhD first in the US and then migrating to Canada!

OLAP systems tend to be read only (for analytics) so the question of transactions and consistency isn't really applicable.

Nice writeup, but given the title, I was hoping you would also have a constructive test case that shows that these systems fail to meet their guarantees (like Jepsen did). :)

I wonder if any of the aforementioned systems (Calvin/Spanner/YugaByte) that can opportunistically commit transactions and detect issues and roll back + retry all within the scope of the RPC so it can still conform to linearisability requirement?

That's right. The situation is quite complex with foreign debts that are hard to pay back. The situation gets worse with tariffs against Turkey hurting their ability to trade, which creates caution among investors. This in turn causes less demand for their currency, which makes things worse.

Econofact has some background and history that I found quite interesting and well explained:

https://econofact.org/the-financial-and-economic-crisis-in-t...