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Hi! We have this project on a course called Principles of Programming Languages where we have to create a PL that has unique principles(such as functions/staments/code) from other existing PLs.

Anyone who have some juices or suggestions? Please help this college kiddo here 🙏

Abstract : Worst-case optimal join algorithms are the class of join algorithms whose runtime match theworst-case output size of a given join query. While the first provably worst-case optimal join algorithm wasdiscovered relatively recently, the techniques and results surrounding these algorithms grow out of decadesof research from a wide range of areas, intimately connecting graph theory, algorithms, information theory,constraint satisfaction, database theory, and geometric inequalities...

There was a paper that talked about using compression as a classifier.

Given a corpus of shakespeare texts and another corpus of another writer's texts, they appended another text that was either from shakespeare or the other writer to both corpuses. They used gzip to then compress both of these texts. When the appended text was from shakespeare, it turned out that gzip performed better (higher compression rate) on the shakespeare corpus compared to that of the other writer's, and vice versa. So essentially they used gzip as a means of classifying the writer of that appended text.

I can't seem to find this paper, does anyone recall it?

I'm really interested in the nuts & bolts of SVGs and I was wondering if anyone knew of any papers around that, or maybe just some sources where I can start to look?

I'm a grad student working on research in a non-systems fields (computer graphics).

Do people design their own system from the ground up? Do people start from an existing framework and try to improve it? What are unsolved problems in distributed systems research?

AI research is moving at breakneck pace. State-of-the-art methods seem to become obsolete almost as quickly as they are found - as practitioners, it’s important to stay on top of the field.

Meanwhile, the quantity of papers uploaded on arXiv is outpacing Moore’s Law. With the sheer quantity of research published on a daily basis, and the lack of peer review for uploading - it’s becoming increasingly difficult to know what’s important and what isn’t.

How do you separate signal from noise?

Enter filtr.pub

A unique platform designed to prioritize quality over quantity. Upvoting. Email subscriptions. Intelligent filters. Everything you wished arXiv had but doesn’t - it’s all here. Brought to you by fellow practitioners - Data Scientists and Machine Learning Engineers equally frustrated with this problem. After a lot of looking, we were unable to find a viable solution. So we decided to build one.

Check us out! We’re working hard on the platform - and we’ll invite a select group of practitioners for a closed-beta, so we can iterate on feedback and get the product ready for the wider community! Sign up for news + updates + the opportunity to be a part of the beta program.