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Ask HN: What was the best CS paper you read in 2017?

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Re: Ask HN: What was the best CS paper you read in 2017?

#71

Post by catnaroek »

sulam wrote: post #69
catnaroek wrote: post #65

Earlier quoted context omitted.

> There are immediate applications to data warehousing. I am not too worried about data warehouses. Data warehouses can be fed by batch processes, which you can run, say, once every day or once every week. This leads to the design of algorithms that optimize aggregate measures of performance (e.g. amortized complexity, average complexity, throughput) rather than microscopic measures of performance (e.g. worst-case co…

So, would you take a bet that this won't have been used in any self-evident way within the next 5 years? Because I would take the other side of that bet. :)

I'm not in the business of predicting the future, since that is beyond my control. What other people do with learned indices is up to them.

I'm just stating my concerns.

Re: Ask HN: What was the best CS paper you read in 2017?

#72

Post by sktrdie »

Statecharts: a visual formalism for complex systems: http://www.inf.ed.ac.uk/teaching/courses/seoc/2005_2006/reso...

Even though it's from 1986 it's enlightening how useful it is to think about reactive systems using statecharts - especially as a UI developer this seems to make total sense and was a ah-ha moment when I first read it a couple months ago.

Re: Ask HN: What was the best CS paper you read in 2017?

#73

Post by rozim »

I probably enjoyed 'Mastering the game of Go with deep neural networks and tree search' the most and especially appreciated the 'Methods' section where they go into a good amount of detail about their implementation.

https://storage.googleapis.com/deepmind-media/alphago/AlphaG...

Re: Ask HN: What was the best CS paper you read in 2017?

#75

Post by mcguire »

godelmachine wrote: post #55
CalChris wrote: post #18

seL4: Formal Verification of an OS Kernel (from 2009). https://www.sigops.org/sosp/sosp09/papers/klein-sosp09.pdf seL4 is about 9000 LOC. So this gives a good indication of what formal verification (Isabelle/HOL) is currently capable of. seL4 is also quite fast as a result of removing unnecessary checks. https://sel4.systems/ seL4 is smaller than L4Ka::Pistachio and it's also capability based which L4 isn't. They cou…

I want to learn formal verification on my own. Do you know of any resources? I got Rolf Dreshler's book on circuit verification, but I would like to master both hardware and software. Would you kindly provide with some pointers?

Two schools:

* Coq/dependent types. Check out Software Foundations by Benjamin Pierce, et. al. Everything's online. Also see Idris, which has a good book from Manning.

* SMT-solver-based verification of existing languages. See SPARK/Ada (there's a good book but I can't remember the name presently) and the GNAT website. Also, Frama-C, although the documentation is more spotty. Then there's Rustan Leino's work on Dafny and Boogie. Oh, and Why3. And some work on verifying Java code that I haven't played with yet.

Re: Ask HN: What was the best CS paper you read in 2017?

#76

Post by toomim »

azhenley wrote: post #30
toomim wrote: post #26

Earlier quoted context omitted.

Ah, if only CHI and UIST had good papers these days. :/

Hey, I had a CHI paper this year! I think it publishes many great papers each year.

Welcome to the echo chamber.

In my experience, the only people who think CHI publishes great papers are the people publishing at CHI.

(Disclosure: I've published at CHI a few times myself. I'm done with it.)

Re: Ask HN: What was the best CS paper you read in 2017?

#77

Post by catnaroek »

oh-kumudo wrote: post #63
catnaroek wrote: post #39

Earlier quoted context omitted.

I've read that paper, and I wasn't particularly impressed. Is there any concrete evidence that learned indices scale to databases with complex schemata and a large transactional volume? Could learned indices be profitably used, say, in the database backend of an ERP system?

Data warehousing is one obvious application: data is immutable; have a sort key. So NN as a builtin once index structure is perfect for such case. It is only a matter of time for commercial analytic DB to pick up this idea, I would presume.

You're right, data warehousing seems like a compelling use case for learned indices. I'm primarily worried about OLTP systems, though.

Re: Ask HN: What was the best CS paper you read in 2017?

#78

Post by mcguire »

mcguire wrote: post #75
godelmachine wrote: post #55

Earlier quoted context omitted.

I want to learn formal verification on my own. Do you know of any resources? I got Rolf Dreshler's book on circuit verification, but I would like to master both hardware and software. Would you kindly provide with some pointers?

Two schools: * Coq/dependent types. Check out Software Foundations by Benjamin Pierce, et. al. Everything's online. Also see Idris, which has a good book from Manning. * SMT-solver-based verification of existing languages. See SPARK/Ada (there's a good book but I can't remember the name presently) and the GNAT website. Also, Frama-C, although the documentation is more spotty. Then there's Rustan Leino's work on Dafny…

Three, three schools:

* Model checking/high level formal specs. Lamport's TLA+, Alloy, possibly Z. There are some good TLA+ things online, I just couldn't get into it. (I feel bad.)

Re: Ask HN: What was the best CS paper you read in 2017?

#79

Post by sulam »

yazr wrote: post #43
chubot wrote: post #27

Earlier quoted context omitted.

I'm gonna throw some cold water on this and say this is not a new paradigm by any means. https://www.sigarch.org/the-unreasonable-ineffectiveness-of-... However, it's certainly possible that the time for this idea has come. Google is probably in the best position to apply it. I will say that after having worked at Google for over a decade, some of it on data center performance, there are plenty of inefficiencies that…

Can you please give some examples of resource-mis-allocations ?! AFAIK storage is not the system bottle it used to be. We always want more, but network and cores are relatively plentiful. If we could magically (and safely) modify the software stack, which areas could give x2 or x3 improvements ?

Network latency right now is the biggest issue we have. If we could magically (using your term here!) get computational resources and data dramatically closer to end users, it would easily give 2 or 3x improvement. Doing this safely of course means consistently in this case, and being able to solve things like safe replication of large data sets. I dunno how to do it, but you asked and that's the biggest thing I can think of.
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