Title : Learning fractals by Expectation-Maximization

Presenter Peter Bloem
Abstract For decades, fractals have been put forward as the de-facto model for many natural and social complex phenomena: from the shape of a coast line, to the price fluctuations in the stock market. Given a fractal model it is a simple exercise to write an algorithm that draws it, or samples data from it. The reverse is not so simple: given a fractal image, or fractal data, how do we find the model that produced it? This is called the fractal inverse problem and the lack of workable solutions has been one of the main problems holding the field back. We present a new approach based on the classic Expectation-Maximization algorithm.

Title : Genuine Semantic Publishing

Presenter Tobias Kuhn
Abstract In the last years, many approaches and systems have been presented for what has been called semantic publishing. Closer inspection, however, reveals that these approaches are mostly not about publishing semantic representations, as the name seems to suggest. Rather, most approaches take the processes and outcomes of the current narrative-based publishing system for granted and only work with the already published papers. This includes semantic annotations, semantic interlinking, semantic integration, and semantic discovery, but with the semantics coming into play only after the publication of the original article. While these are interesting approaches, they fall short of providing a vision to transcend the current publishing paradigm. We argue for taking the term semantic publishing seriously and work towards a vision of genuine semantic publishing, where computational tools and algorithms can help us with dealing with the wealth of human knowledge by letting researchers capture their research results with formal semantics from the start. We argue that genuine semantic publications should come with formal semantics as an integral and primary component at the time of publication, that these representations should be complete in essence, in the sense that they cover the main results, that they should be authentic in the sense that they originate from the authors, and that they should be fine-grained and light-weight for optimized re-usability and minimized publication overhead.