Simultaneous Representation of Proper and Unit Interval Graphs

In a confluence of combinatorics and geometry, simultaneous representations provide a way to realize combinatorial objects that share common structure. A standard case in the study of simultaneous representations is the sunflower case where all objects share the same common structure. While the recognition problem for general simultaneous interval graphs - the simultaneous version of arguably one of the most well-studied graph classes - is NP-complete, the complexity of the sunflower case for three or more simultaneous interval graphs is currently open. In this work we settle this question for proper interval graphs. We give an algorithm to recognize simultaneous proper interval graphs in linear time in the sunflower case where we allow any number of simultaneous graphs. Simultaneous unit interval graphs are much more "rigid" and therefore have less freedom in their representation. We show they can be recognized in time O(|V|*|E|) for any number of simultaneous graphs in the sunflower case where G=(V,E) is the union of the simultaneous graphs. We further show that both recognition problems are in general NP-complete if the number of simultaneous graphs is not fixed. The restriction to the sunflower case is in this sense necessary

Many-to-One Boundary Labeling with Backbones

In this paper we study \emph{many-to-one boundary labeling with backbone leaders}. In this new many-to-one model, a horizontal backbone reaches out of each label into the feature-enclosing rectangle. Feature points that need to be connected to this label are linked via vertical line segments to the backbone. We present dynamic programming algorithms for label number and total leader length minimization of crossing-free backbone labelings. When crossings are allowed, we aim to obtain solutions with the minimum number of crossings. This can be achieved efficiently in the case of fixed label order, however, in the case of flexible label order we show that minimizing the number of leader crossings is NP-hard.Comment: 23 pages, 10 figures, this is the full version of a paper that is about to appear in GD'1

On complexity and efficiency of mutual information estimation on static and dynamic data

Mutual Information (MI) is an established measure for the dependence of two variables and is often used as a generalization of correlation measures. Existing methods to estimate MI focus on static data. However, dynamic data is ubiquitous as well, and MI estimates on it are useful for stream mining and advanced monitoring tasks. In dynamic data, small changes (e.g., insertion or deletion of a value) may often invalidate the previous estimate. In this article, we study how to efficiently adjust an existing MI estimate when such a change occurs. As a first step, we focus on the well-known nearest-neighbor based estimators for static data and derive a tight lower bound for their computational complexity, which is unknown so far. We then propose two dynamic data structures that can update existing estimates asymptotically faster than any approach that computes the estimates independently, i.e., from scratch. Next, we infer a lower bound for the computational complexity of such updates, irrespective of the data structure and the algorithm, and present an algorithm that is only a logarithmic factor slower than this bound. In absolute numbers, these solutions offer fast and accurate estimates of MI on dynamic data as well.</p

Dissertatio inauguralis medica, de hepatis in morbis praecipue sympathia

http://tartu.ester.ee/record=b1898858~S1*es

Special Issue JOMS – Journal of Medical Systems, 2016 on Agent-Empowered HealthCare Systems

The Agent-Empowered HealthCare Systems Special Issue includes a selection of papers that provide discussion on the most recent and innovative work regarding the study and application of agent-based technology to healthcare.info:eu-repo/semantics/publishedVersio

A Multiagent System for Dynamic Data Aggregation in Medical Research

The collection of medical data for research purposes is a challenging and long-lasting process. In an effort to accelerate and facilitate this process we propose a new framework for dynamic aggregation of medical data from distributed sources. We use agent-based coordination between medical and research institutions. Our system employs principles of peer-to-peer network organization and coordination models to search over already constructed distributed databases and to identify the potential contributors when a new database has to be built. Our framework takes into account both the requirements of a research study and current data availability. This leads to better definition of database characteristics such as schema, content, and privacy parameters. We show that this approach enables a more efficient way to collect data for medical research