Buzz monitoring in word space

This paper discusses the task of tracking mentions of some topically interesting textual entity from a continuously and dynamically changing flow of text, such as a news feed, the output from an Internet crawler or a similar text source - a task sometimes referred to as buzz monitoring. Standard approaches from the field of information access for identifying salient textual entities are reviewed, and it is argued that the dynamics of buzz monitoring calls for more accomplished analysis mechanisms than the typical text analysis tools provide today. The notion of word space is introduced, and it is argued that word spaces can be used to select the most salient markers for topicality, find associations those observations engender, and that they constitute an attractive foundation for building a representation well suited for the tracking and monitoring of mentions of the entity under consideration

Semantics-directed implementation of method-call interception

We describe a form of method-call interception (MCI) that allows the programmer to superimpose extra functionality onto method calls at run-time. We provide a reference semantics and a reference implementation for corresponding language constructs. The setup applies to class-based, statically typed, compiled languages such as Java. The semantics of MCI is used to direct a language implementation with a number of valuable properties: simplicity of the implementational model and run-time adaptation capabilities and static type safety and separate compilation and reasonable performance. Our implementational development employs sourcecode instrumentation. We start from a naive implementational model, which is subsequently refined to optimise program execution. The implementation is assessed via benchmarks

Holographic optical trapping

Holographic optical tweezers use computer-generated holograms to create arbitrary three-dimensional configurations of single-beam optical traps useful for capturing, moving and transforming mesoscopic objects. Through a combination of beam-splitting, mode forming, and adaptive wavefront correction, holographic traps can exert precisely specified and characterized forces and torques on objects ranging in size from a few nanometers to hundreds of micrometers. With nanometer-scale spatial resolution and real-time reconfigurability, holographic optical traps offer extraordinary access to the microscopic world and already have found applications in fundamental research and industrial applications.Comment: 8 pages, 7 figures, invited contribution to Applied Optics focus issue on Digital Holograph

Geometrically nonlinear Cosserat elasticity in the plane: applications to chirality

Modelling two-dimensional chiral materials is a challenging problem in continuum mechanics because three-dimensional theories reduced to isotropic two-dimensional problems become non-chiral. Various approaches have been suggested to overcome this problem. We propose a new approach to this problem by formulating an intrinsically two-dimensional model which does not require references to a higher dimensional one. We are able to model planar chiral materials starting from a geometrically non-linear Cosserat type elasticity theory. Our results are in agreement with previously derived equations of motion but can contain additional terms due to our non-linear approach. Plane wave solutions are briefly discussed within this model.Comment: 22 pages, 1 figure; v2 updated versio

Agents Cut Emissions On how a Multi-Agent System Contributes to a more Sustainable Energy Consumption

AbstractThe Vehicle-to-Grid technology allows electric vehicles to not only procure electric energy, but also to feed energy back into the grid network. However, by using Vehicle-to-Grid, energy literally degenerates into an article of merchandise and becomes of interest to several stakeholders. In this paper, we describe a multi-agent system, which embraces this exact view and maximises the interest of several stakeholders in using Vehicle-to-Grid capable electric vehicles. In order to emphasise the applicability of our approach we performed a field test with real electric vehicles and charging stations. We describe both, implementational details as well as the results of our field test in this paper

Principal Patterns on Graphs: Discovering Coherent Structures in Datasets

Graphs are now ubiquitous in almost every field of research. Recently, new research areas devoted to the analysis of graphs and data associated to their vertices have emerged. Focusing on dynamical processes, we propose a fast, robust and scalable framework for retrieving and analyzing recurring patterns of activity on graphs. Our method relies on a novel type of multilayer graph that encodes the spreading or propagation of events between successive time steps. We demonstrate the versatility of our method by applying it on three different real-world examples. Firstly, we study how rumor spreads on a social network. Secondly, we reveal congestion patterns of pedestrians in a train station. Finally, we show how patterns of audio playlists can be used in a recommender system. In each example, relevant information previously hidden in the data is extracted in a very efficient manner, emphasizing the scalability of our method. With a parallel implementation scaling linearly with the size of the dataset, our framework easily handles millions of nodes on a single commodity server