Rational Pension Reform

This paper is motivated by the idea to create, wherever possible, rational mechanisms that adapt pension systems automatically to a changed economic and demographic environment, rather than to leave such adaptations to discretionary high-profile pension reforms which all too often stir political opposition. The paper delineates the theory behind such rational mechanisms, shows the advantages and limits of „self-stabilizing“ pension systems, and compares the Swedish and the German approaches to rule-bound pension policy

OPPORTUNITY: Towards opportunistic activity and context recognition systems

Opportunistic sensing allows to efficiently collect information about the physical world and the persons behaving in it. This may mainstream human context and activity recognition in wearable and pervasive computing by removing requirements for a specific deployed infrastructure. In this paper we introduce the newly started European research project OPPORTUNITY within which we develop mobile opportunistic activity and context recognition systems. We outline the project’s objective, the approach we follow along opportunistic sensing, data processing and interpretation, and autonomous adaptation and evolution to environmental and user changes, and we outline preliminary results

The Electron Capture in 163^{163} Ho Experiment - a Short Update

The definition of the absolute neutrino mass scale is one of the main goals of the Particle Physics today. The study of the end-point regions of the β- and electron capture (EC) spectrum offers a possibility to determine the effective electron (anti-)neutrino mass in a completely model independent way, as it only relies on the energy and momentum conservation. The ECHo (Electron Capture in 163Ho) experiment has been designed in the attempt to measure the effective mass of the electron neutrino by performing high statistics and high energy resolution measurements of the 163 Ho electron capture spectrum. To achieve this goal, large arrays of low temperature metallic magnetic calorimeters (MMCs) implanted with with 163Ho are used. Here we report on the structure and the status of the experiment

Collecting complex activity data sets in highly rich networked sensor environments

We deployed 72 sensors of 10 modalities in 15 wireless and wired networked sensor systems in the environment, in objects, and on the body to create a sensor-rich environment for the machine recognition of human activities. We acquired data from 12 subjects performing morning activities, yielding over 25 hours of sensor data. We report the number of activity occurrences observed during post-processing, and estimate that over 13000 and 14000 object and environment interactions occurred. We describe the networked sensor setup and the methodology for data acquisition, synchronization and curation. We report on the challenges and outline lessons learned and best practice for similar large scale deployments of heterogeneous networked sensor systems. We evaluate data acquisition quality for on-body and object integrated wireless sensors; there is less than 2.5% packet loss after tuning. We outline our use of the dataset to develop new sensor network self-organization principles and machine learning techniques for activity recognition in opportunistic sensor configurations. Eventually this dataset will be made public

A.: Towards collective spatial awareness using binary relations

In this paper, we present an architecture for recognizing spatial relationships between real-world objects with integrated computing capabilities. The aim is to develop mechanisms for providing collectives of such artifacts with an awareness about spatial relations to other artifacts of the same collective. Therefore, an approach for recognizing binary relationships by a pairwise comparison of the spatial contexts of artifacts, as well as a novel algorithm which takes advantage of the relation properties in order to make all artifacts of the collective spatially-aware, are presented. The scope is on orientation- and distance-relationships, which are represented in a qualitative way. A general focus of this work is on the efficiency of the solutions with regard to their application in embedded systems. We expect the presented approach to be valuable for the interaction among artifacts and humans, as well as for acquiring high-level context information about artifact collectives. 1