Contributions in Merlin

The image compositing stages in cluster-parallel rendering for gathering and combining partial rendering results into a final display frame are fundamentally limited by node-to-node image throughput. Therefore, efficient image coding, compression and transmission must be considered to minimize that bottleneck. This paper studies the different performance limiting factors such as image representation, region-of-interest detection and fast image compression. Additionally, we show improved compositing performance using lossy YUV subsampling and we propose a novel fast region-of-interest detection algorithm that can improve in particular sort-last parallel rendering.

In this paper, we introduce a novel out-of-core parallel and scalable technique for rendering massive terrain datasets. The parallel rendering task decomposition is implemented on top of an existing terrain renderer using an open source framework for cluster-parallel rendering. Our approach achieves parallel rendering by division of the rendering task either in sort-last (database) or sort-first (screen domain) manner and presents an optimal method for implicit load balancing in the former mode. The efficiency of our approach is validated using massive elevation models.

The feature list of modern IDEs is growing steadily and mastering these tools becomes more and more demanding, especially for novice programmers. Despite their remarkable capabilities, IDEs often still cannot directly answer the questions that arise during program comprehension tasks. Instead developers have to map their questions to multiple concrete queries that can be answered only by combining several tools and examining the output of each of them manually to distill an appropriate answer. Existing approaches have in common that they are either limited to a set of predefined, hardcoded questions, or that they require to learn a specific query language only suitable for that limited purpose. We present a framework to query for information about a software system using guided-input natural language resembling plain English. For that, we model data extracted by classical software analysis tools with an OWL ontology and use knowledge processing technologies from the Semantic Web to query it. We also present a case study that demonstrates how our framework can be used to answer queries about static source code information for program comprehension purposes.

null

The state-of-the-art in mining software repositories mirrors software artifacts from various sources into monolithic relational databases. This puts a lot of querying power in the hands of the software miners, however it comes at the cost of enclosing the data and hamper cross-application reuse. In this paper we discuss four problem scenarios to illustrate that Semantic Web technology is able to overcome these limitations. However, it requires that the software engineering research community agrees on two prerequisites: (a) a common vocabulary to talk about software repositories -- an ontology; (b) a strategy for generating unique and stable references to all software artifacts inside such a repository - a Universal Resource Identifier (URI).

We analyze the attractiveness of investment strategies over a variety of investment horizons from the viewpoint of an investor with preferences described by Cumulative Prospect Theory (CPT), currently the most prominent descriptive theory for decision making under uncertainty. A bootstrap technique is applied using historical return data of 1926–2008. To allow for variety in investors’ preferences, we conduct several sensitivity analyses and further provide robustness checks for the results. In addition, we analyze the attractiveness of the investment strategies based on a set of experimentally elicited preference parameters. Our study reveals that strategy attractiveness substantially depends on the investment horizon. While for almost every preference parameter combination a bond strategy is preferred for the short run, stocks show an outperformance for longer horizons. Portfolio insurance turns out to be attractive for almost every investment horizon. Interestingly, we find probability weighting to be a driving factor for insurance strategies’ attractiveness.

Publication and citation rankings have become major indicators of the scientific worth of universities and determine to a large extent the career of individual scholars. Such rankings do not effectively measure research quality, which should be the essence of any evaluation. These quantity rankings are not objective; two citation rankings, based on different samples, produce entirely different results. For that reason, an alternative ranking is developed as a quality indicator, based on membership on academic editorial boards of professional journals. It turns out that the ranking of individual scholars based on that measure is far from objective. Furthermore, the results differ markedly, depending on whether research quantity or quality is considered. Thus, career decisions based on rankings are dominated by chance and do not reflect research quality. We suggest that evaluations should rely on multiple criteria. Public management should return to approved methods such as engaging independent experts who in turn provide measurements of research quality for their research communities.

Functional neuroimaging techniques are used widely in cognitive neuroscience to investigate aspects of functional specialization and functional integration in the human brain. Functional integration can be characterized in two ways, functional connectivity and effective connectivity. While functional connectivity describes statistical dependencies between data, effective connectivity rests on a mechanistic model of the causal effects that generated the data. This review addresses the conceptual and methodological basis of established techniques for characterizing effective connectivity using functional magnetic resonance imaging (fMRI) data. In particular, we focus on dynamic causal modeling (DCM) of fMRI data and emphasize the importance of model selection procedures and nonlinear mechanisms for context-dependent changes in connection strengths.

The advent of social markup languages and lightweight public data access methods has created an opportunity to share the social, documentary and system information locked in most servers as a mashup. Whereas solutions already exists for creating and managing mashups from network sources, we propose here a mashup framework whose primary information sources are the applications and user files of a server. This enables us to use server legacy data sources that are already maintained as part of basic administration to semantically link user documents and accounts using social web constructs.