What was the last paper within the realm of computing you read and loved? What did it inspire you to build or tinker with? Come share the ideas in an awesome academic/research paper with fellow engineers, programmers, and paper-readers. Lead a session and show off code that you wrote that implements these ideas or just give us the lowdown about the paper (because of HARD MATH!). Otherwise, just come, listen, and discuss.
As a Papers We Love chapter we welcome everyone from the programming community for an evening of ideas, vibrant discussions and hanging out with your fellow travellers.
The Barcelona Chapter meets monthly at different locations throughout the city. Keep an eye on our Meetup.com page to find out the latest address.
Please make sure to RSVP before attending to make it easier for us to organize all correctly. You can find our schedule and RSVP here on Meetup.com.
Papers We Love has a Code of Conduct. Please contact one of the Meetup's organizers if anyone is not following it. Be good to each other and to the PWL community!
Sign-up: Please RSVP for meetings via Meetup.com
Contact: jordi.montes AT upc DOT acm DOT com
Sponsors and Organizers
More info: https://upc.acm.org
Expander graphs and applications
Expanding graphs grew up in the context of communication networks. The notion of expansion is related to the growth of neighborhoods of sets in graphs and it is so natural that it found its place in a wide area of applications, some of them unexpected, in computer science, statistics and mathematics. The aim of the talk is to describe some of these applications in computation, random walks and in error correcting codes. A probabilistic argument shows that expander graphs are abundant, but explicit constructions turn out to be connected to sophisticated mathematical tools, which will be only summarized in the talk.
Nowadays there is a rich literature on the topic. The talk is based on the excellent…
• What we'll do
Argimiro Arratia will peresent the paper Matroids and the greedy algorithm J. Edmonds (1971).
Argimiro Arratia is currently a professor of UPC and obtained his Ph.D in Mathematics (Wisconsin)
You can find more information about him following the next link: http://www.cs.upc.edu/~argimiro/
A greedy algorithm is one of the simplest strategies to solve an optimization problem. It is based on a step-by-step selection of the best candidate (local) solution, in the hope that in the end of this process a global optimal solution is obtained. Greedy algorithms do not always output global optimal solutions, but for many optimization problems they do, in which case one says that the greedy algorithm works. Is there some criterion to know when it will work?
A result that we should love, and I will present in this talk, due to Jack Edmonds and published in 1971, state…
• What we'll do
Papers we love Barcelona is back and we already have a list of amazing talks for the next months. The events will alternate between noon at the University and evenings at some company to be sure that everyone can attend.
Searching with Dices: a survey on randomized data structures by Conrado Martinez (https://www.cs.upc.edu/~conrado/).
The usefulness of randomization in the design of algorithms has been known for a long time (Rabin's primality test or Karger's algorithm). Randomized algorithms usually yield algorithms which are simple, elegant, with guaranteed expected performance and practical.
Not many people know about why they work (theoretical basis) or how to analyze these algorithms. In this talk Conrado Martinez will explain two well-know data structures: Skip-lists (https://en.wikipedia.org/wiki/Skip_li…
Data Streams as Random Permutations (Paper) (https://hal.inria.fr/hal-01197221/document)
Presented by Jordi Montes, Research Engineer
Cardinality estimation has a wide range of applications from databases to network systems. The problem has been studied since the 80's and many algorithms have been proposed: Adaptive Sampling, HyperLogLog or Recorinality to say some of them.
In this talk we will discuss why cardinality estimation is an important problem, how it has been solved before and why looking at data streams as random permutations can be useful (Hint: This simple observation allows a wealth of classical and recent results from combinatorics to be recycled, with minimal effort, as estimators for various statistics over data streams.).