Adaptive Clustering: Better Representatives with Reinforcement Learning

Adaptive clustering uses reinforcement learning to learn the reward values of successive data clusterings. Adaptive clustering applies when external feedback exists for a clustering task. It supports the reuse of clusterings by memorizing what worked well in a previous context. It explores multiple paths in a reinforcement learning environment when the goal is to find better cluster representatives based on arbitrary environmental feedback. Our experiments apply adaptive clustering to instance-based learning relying on a distance function modification approach. The results show that adaptive clustering can find better representatives, if compared with traditional instance-based learning, such as k-nearest neighbor classifiers. Moreover, we introduce as a by-product a new instance-based learning technique that classifies examples by solely using cluster representatives; the technique shows high promise in our experimental evaluation

Piece-Wise Model Fitting Using Local Data Patterns”, to appear

Abstract. In this paper we propose a novel classification algorithm that fits models of different complexity on separate regions of the input space. The goal is to achieve a balance between global and local learning strategies by decomposing the classification task into simpler subproblems; each task narrows the learning problem to a local region of high example density over the input space. Specifically, our proposed approach is to apply a clustering algorithm to every set of training examples that belong to the same class; each cluster becomes an intermediate concept that is learned by selecting a model with an (estimated) optimal degree of complexity. Experimental results on real-world domains show consistent good performance in predictive accuracy with our piece-wise model fitting strategy.

OHB initiatives in development of additive manufacturing technology for opto-mechanical and mechatronic space systems

Additive Manufacturing (AM) technology has shown impressive new opportunities and convincing results over the last years, mainly in terrestrial applications. Today, it has proven to represent a completely new approach to shape complex mechanical parts, with enormous potential for optimization of dedicated parameters. Numerous possibilities shine up for the aerospace industry, among others, and let engineers dream of mechanical parts which only some years ago looked like science fiction. OHB System has been involved in the development of Additive Manufacturing since more than five years via several ESA studies, DLR-funded projects and by significant internal R&D activities. These projects and studies have convinced us of the potential of AM for future satellite platforms, instruments and payloads. A new dimension of freedom in generating shapes and geometries is opened, offering more flexibility for optimizing the parts and components according to functional and performance requirements. On the other hand, the efforts of qualifying an AM part to flight worthiness are significantly higher than for conventional manufacturing technologies, taking into account all the required aspects of material and production process control, inspection and testing. A concise trade-off has to be performed for each potential use case to find out whether these high efforts and resulting costs are justified by the benefits of the new technology in terms of e.g. light weighting, ease of integration and performance improvement. The paper will introduce the OHB AM roadmap, which has been developed jointly by OHB experts from both sites in Bremen and Oberpfaffenhofen, following in-depth analysis of the potential impact of the technology on space systems. It will furthermore provide an overview of applications where AM is expected to offer extraordinary opportunities. Among these 'high-potential' applications are the two following topics: • opto-mechanical assemblies (isostatic structures, optical mounts) and • mechatronic systems (compliant mechanisms or integrated smart structures). The paper will report on the objectives and work logic of ongoing studies in these specific topics and provide intermediate results

Management of coronary disease in patients with advanced kidney disease

BACKGROUND Clinical trials that have assessed the effect of revascularization in patients with stable coronary disease have routinely excluded those with advanced chronic kidney disease. METHODS We randomly assigned 777 patients with advanced kidney disease and moderate or severe ischemia on stress testing to be treated with an initial invasive strategy consisting of coronary angiography and revascularization (if appropriate) added to medical therapy or an initial conservative strategy consisting of medical therapy alone and angiography reserved for those in whom medical therapy had failed. The primary outcome was a composite of death or nonfatal myocardial infarction. A key secondary outcome was a composite of death, nonfatal myocardial infarction, or hospitalization for unstable angina, heart failure, or resuscitated cardiac arrest. RESULTS At a median follow-up of 2.2 years, a primary outcome event had occurred in 123 patients in the invasive-strategy group and in 129 patients in the conservative-strategy group (estimated 3-year event rate, 36.4% vs. 36.7%; adjusted hazard ratio, 1.01; 95% confidence interval [CI], 0.79 to 1.29; P=0.95). Results for the key secondary outcome were similar (38.5% vs. 39.7%; hazard ratio, 1.01; 95% CI, 0.79 to 1.29). The invasive strategy was associated with a higher incidence of stroke than the conservative strategy (hazard ratio, 3.76; 95% CI, 1.52 to 9.32; P=0.004) and with a higher incidence of death or initiation of dialysis (hazard ratio, 1.48; 95% CI, 1.04 to 2.11; P=0.03). CONCLUSIONS Among patients with stable coronary disease, advanced chronic kidney disease, and moderate or severe ischemia, we did not find evidence that an initial invasive strategy, as compared with an initial conservative strategy, reduced the risk of death or nonfatal myocardial infarction