POV: you need a break from your vacation so you attend a seminar about a cool new paper at your old university. 🤓

paper: https://dl.acm.org/doi/10.1145/3776643

preprint: https://arxiv.org/abs/2505.05162

#AcademicChatter #AcademicMastodon #ProfessorLife #LatourOnTour #ComputationalComplexity #Concurrency #ComputerScience

[Show GN: ManiSurve – 10,000개 노드 NP 문제를 0.09초 만에 해결하는 다항 시간 엔진

ManiSurve v1.5는 NP-Complete 문제를 다항 시간(P) 내에 해결하는 새로운 엔진으로, 10,000개 노드와 50,000개 간선을 가진 그래프 컬러링 문제를 0.09초 만에 해결했습니다. 이산적 충돌을 리만 다양체 위의 연속적인 곡률로 해석하여 지수 시간의 벽을 깨는 로직을 사용했습니다. 개발자는 초보 연구자로서 커뮤니티의 피드백을 요청하며, GitHub에 코드를 공개했습니다.

https://news.hada.io/topic?id=27084

#npcomplete #algorithm #graphcoloring #manisurve #computationalcomplexity

Well played.

https://link.springer.com/chapter/10.1007/978-3-642-13122-6_15

#uno #CardGames #ComputationalComplexity #FunWithAlgorithms #Algorithms #Algorithmics #Games #AI #ComputerScience #ProfessorLife #AcademicChatter #AcademicMastodon

On Monday, November 17, at 3:30pm ET, I get to give the next VCGT talk on the computational complexity of the game #BattleSheep: https://sites.google.com/view/virtual-cgt/seminar

Abstract: Battle Sheep is a board game published by Blue Orange Games where players take turns moving stacks of sheep tokens around a hexagonal board, always leaving at least one sheep behind. In this talk we'll learn the basics of the game, play once, and finally show that determining the winnability of the game is PSPACE-complete. This talk assumes no prior knowledge of computational complexity.

#CombinatorialGames #ComputationalComplexity #PSPACE

Computational Complexity of Neural Networks

https://lunalux.io/introduction-to-neural-networks/computational-complexity-of-neural-networks/

#HackerNews #ComputationalComplexity #NeuralNetworks #AIResearch #MachineLearning #TechTrends

Real coin flips are ~49-51 not 50-50 https://www.scientificamerican.com/article/scientists-destroy-illusion-that-coin-toss-flips-are-50-50/

But you can guarantee equal probability with a simple trick! Flip 2x in a row starting with the same side up.

HT->call it H
TH->call it T
HH,TT->try again

(due to von Neumann https://en.wikipedia.org/wiki/Randomness_extractor#Von_Neumann_extractor)

This leads to randomness extractors: from a given random process, what's the biggest uniform distribution you can get efficiently?

Randomness extractors give another interpretation of #entropy:

avg # bits needed to *describe* the outcome
=
# uniformly random bits you can *extract* from the outcome

#math #probability #ComputationalComplexity #TCS #InformationTheory