Log in to MyKarger to check if you already have access to this content. Buy a Karger Article Bundle (KAB) and profit from a discount! If you would like to redeem your KAB credit, please log in. Background: Artificial intelligence systems recently demonstrated an increase in polyp- and adenoma detection rate. Over the daytime the adenoma detection rate decreases as tiredness leads to a lack of attention.

The minimum graph cut and minimum $s$-$t$-cut problems are important primitives in the modeling of combinatorial problems in computer science, including in computer vision and machine learning. Some of the most efficient algorithms for finding global minimum cuts are randomized algorithms based on Karger's groundbreaking contraction algorithm. Here, we study whether Karger's algorithm can be successfully generalized to other cut problems. We first prove that a wide class of natural generalizations of Karger's algorithm cannot efficiently solve the $s$-$t$-mincut or the normalized cut problem to optimality. However, we then present a simple new algorithm for seeded segmentation / graph-based semi-supervised learning that is closely based on Karger's original algorithm, showing that for these problems, extensions of Karger's algorithm can be useful. The new algorithm has linear asymptotic runtime and yields a potential that can be interpreted as the posterior probability of a sample belonging to a given seed / class. We clarify its relation to the random walker algorithm / harmonic energy minimization in terms of distributions over spanning forests. On classical problems from seeded image segmentation and graph-based semi-supervised learning on image data, the method performs at least as well as the random walker / harmonic energy minimization / Gaussian processes.

The average person receives upwards of 150 emails a day, and it often seems like no amount of tagging or filtering can close the floodgates. One major source of stress is the never-ending conversation threads made possible by group emails. Mailing lists can be a fantastic medium for substantive discussions, but often they deliver too much of what we don't want and not enough of what we do. Believe it or not, such tools have barely changed since the pre-Internet days of Arpanet 40 years ago: You either opt in or opt out, you get dozens of irrelevant emails, and the views of a few loudmouths usually end up drowning out the rest. In an age of Facebook and Reddit, users expect a sense of control over how they consume their content, and yet that control and personalization often doesn't extend to their own inboxes.

Big data techniques such as collecting browsing habits for marketers could soon be adapted and made available to individuals. It wouldn't be through traditional mass collection and analysis, but by a tool that will allow individuals to choose what they want to share with others. A kind of Big Data goes miniature data. The scheme is being developed at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). The idea is that individuals should be able to share things they do online with others of their choosing in order to democratize the data that's being collected anyway, the scientists think. In other words, it shouldn't be just Google et al who knows what everyone is doing on the Internet.

We propose novel algorithms for the problem of crowdsourcing binary labels. Such binary labeling tasks are very common in crowdsourcing platforms, for instance, to judge the appropriateness of web content or to flag vandalism. We propose two unsupervised algorithms: one simple to implement albeit derived heuristically, and one based on iterated bayesian parameter estimation of user reputation models. We provide mathematical insight into the benefits of the proposed algorithms over existing approaches, and we confirm these insights by showing that both algorithms offer improved performance on many occasions across both synthetic and real-world datasets obtained via Amazon Mechanical Turk.