ClearView: An interactive context preserving hotspot visualization technique

Volume rendered imagery often includes a barrage of 3D information like shape, appearance and topology of complex structures, and it thus quickly overwhelms the user. In particular, when focusing on a specific region a user cannot observe the relationship between various structures unless he has a mental picture of the entire data. In this paper we present ClearView, a GPU-based, interactive framework for texture-based volume ray-casting that allows users which do not have the visualization skills for this mental exercise to quickly obtain a picture of the data in a very intuitive and user-friendly way. ClearView is designed to enable the user to focus on particular areas in the data while preserving context information without visual clutter. ClearView does not require additional feature volumes as it derives any features in the data from image information only. A simple point-and-click interface enables the user to interactively highlight structures in the data. ClearView provides an easy to use interface to complex volumetric data as it only uses transparency in combination with a few specific shaders to convey focus and context information.     

Reinforcement learning to adjust parametrized motor primitives to new situations

Humans manage to adapt learned movements very quickly to new situations by generalizing learned behaviors from similar situations. In contrast, robots currently often need to re-learn the complete movement. In this paper, we propose a method that learns to generalize parametrized motor plans by adapting a small set of global parameters, called meta-parameters. We employ reinforcement learning to learn the required meta-parameters to deal with the current situation, described by states. We introduce an appropriate reinforcement learning algorithm based on a kernelized version of the reward-weighted regression. To show its feasibility, we evaluate this algorithm on a toy example and compare it to several previous approaches. Subsequently, we apply the approach to three robot tasks, i.e., the generalization of throwing movements in darts, of hitting movements in table tennis, and of throwing balls where the tasks are learned on several different real physical robots, i.e., a Barrett WAM, a BioRob, the JST-ICORP/SARCOS CBi and a Kuka KR?6.     

Efficient data management in a large-scale epidemiology research project

This article describes the concept of a "Central Data Management" (CDM) and its implementation within the large-scale population-based medical research project "Personalized Medicine". The CDM can be summarized as a conjunction of data capturing, data integration, data storage, data refinement, and data transfer. A wide spectrum of reliable "Extract Transform Load" (ETL) software for automatic integration of data as well as "electronic Case Report Forms" (eCRFs) was developed, in order to integrate decentralized and heterogeneously captured data. Due to the high sensitivity of the captured data, high system resource availability, data privacy, data security and quality assurance are of utmost importance. A complex data model was developed and implemented using an Oracle database in high availability cluster mode in order to integrate different types of participant-related data. Intelligent data capturing and storage mechanisms are improving the quality of data. Data privacy is ensured by a multi-layered role/right system for access control and de-identification of identifying data. A well defined backup process prevents data loss. Over the period of one and a half year, the CDM has captured a wide variety of data in the magnitude of approximately 5terabytes without experiencing any critical incidents of system breakdown or loss of data. The aim of this article is to demonstrate one possible way of establishing a Central Data Management in large-scale medical and epidemiological studies.     

The effect of flooring on musculoskeletal symptoms in the lower extremities and low back among female nursing assistants

This study examines the effect of changing the floor from a 2-mm homogenous vinyl floor to a 4-mm heterogeneous vinyl floor (1.5-mm wear layer and 2.5-mm foam layer) on musculoskeletal symptoms in the lower extremities and low back among nursing assistants in a geriatric care centre. A pre-post design with a reference group consisting of nursing assistants from a similar geriatric care centre was used. Follow-up measurements were carried out 12 and 24 months after the intervention by means of questionnaires. At the 1-year follow-up, the pain intensity score in the feet of the intervention group had decreased compared with the baseline value and remained statistically significant at the 2-year follow-up. The decrease in pain intensity score of the feet in the intervention group was statistically significantly different from the reference group, both after 1 and 2 years. PRACTITIONER SUMMARY: The results show the importance of flooring in the workplace with regard to reducing musculoskeletal symptoms. Appropriate flooring is especially important in the female-dominated health care sector, where workers must stand or walk for long periods.     

Large-Scale Hydrogen Production

There is a growing need for hydrogen in processing heavier and dirtier fossil fuels and a future hydrogen economy is widely suggested as the next generation fuel/energy source once fossil fuels diminish in availability. Sustainable fuels are still regarded as too expensive given the large amounts of natural gas and a projected, ample supply of fossil fuels beyond the next twenty-plus years. Today, the steam reforming of hydrocarbons is the most favorable route to H₂. If CO₂ sequestration were ever to become widely practiced, fossil fuels would continue to play an important role in the future hydrogen economy.     

Generation of Asynchronous Component Adapters

Utilizing services of legacy and third party COTS components has become vital for the timely development and evolution of software systems. Component interfaces typically have to be adapted before they can be re-used in a system context. This paper presents an approach to generating component adapters. The approach focuses on mapping multiple events, and their associated parameters, generated from a source component to multiple calls in the target component. The novelty of our mechanism is that it allows developers to generate asynchronous as well as synchronous adapters. As a result we can generate adapters for components with incompatible event structure. A prototype called Asynchronous Component Adapter (ACA) was developed which demonstrates the idea with Java Beans, but the methodology can be applied to any component-based system that supports introspection like Microsoft .NET assemblies.     

Model-based cloud resource management with TOSCA and OCCI

Software and Systems Modeling - With the advent of cloud computing, different cloud providers with heterogeneous cloud services (compute, storage, network, applications, etc.) and their related...