Contributions in Merlin

Medical products used in the developed world often fail to adequately serve resource-limited settings where electricity, transportation and health care workers are not readily available. We suggest that the problem is not only a lack of coordinated financial resources to purchase existing medical products, but also a lack of products that are specifically designed for resource-limited settings. While donor organisations with a focus on global health are increasingly willing to bear the additional financial risk for the research and development of such high-impact medical products, corporations are still reluctant to take their best scientists and engineers away from more commercially attractive projects. Universities, on the other hand, given their teaching and research missions, are well positioned to engage in such high-risk development projects. A group of biomedical, engineering, business and social science researchers at Northwestern University (NU) propose a creative model to address significant social and health needs. The team's initial product focus is a rapid test for diagnosing infants with HIV. The NU model aligns the incentives and expertise of industry, donors and academia to innovate medical products, such as the infant HIV diagnostic test, for resource-limited settings.

We develop a dynamic framework to identify aggregate market fears ahead of a major market crash through the skewness premium of European options. Our methodology is based on measuring the distribution of a skewness premium through a q-Gaussian density and a maximum entropy principle. Our findings indicate that the October 19th, 1987 crash was predictable from the study of the skewness premium of deepest out-of-the-money options about two months prior to the crash.

We consider the Top-k Approximate Subtree Matching (TASM) problem: finding the k best matches of a small query tree, e.g., a DBLP article with 15 nodes, in a large document tree, e.g., DBLP with 26M nodes, using the canonical tree edit distance as a similarity measure between subtrees. Evaluating the tree edit distance for large XML trees is difficult: the best known algorithms have cubic runtime and quadratic space complexity, and, thus, do not scale. Our solution is TASMpostorder, a memory-efficient and scalable TASM algorithm. We prove an upper-bound for the maximum subtree size for which the tree edit distance needs to be evaluated. The upper bound depends on the query and is independent of the document size and structure. A core problem is to efficiently prune subtrees that are above this size threshold. We develop an algorithm based on the prefix ring buffer that allows us to prune all subtrees above the threshold in a single postorder scan of the document. The size of the prefix ring buffer is linear in the threshold. As a result, the space complexity of TASM-postorder depends only on k and the query size, and the runtime of TASM-postorder is linear in the size of the document. Our experimental evaluation on large synthetic and real XML documents confirms our analytic results.

Der Schwerpunkt liegt 2010 auf dem Thema «Arbeitsflexibilität und Familie» in Schweizer Unternehmen – Faktoren, die gerade in Zeiten der Krise von Bedeutung sind. Im Zentrum stehen u. a. folgende Fragen: Welche Beschäftigten profitieren von Flexibilität und Familienangeboten in Firmen? Inwieweit werden die Bedürfnisse der einzelnen Beschäftigten befriedigt? Welche Folgen haben solche Angebote und deren Bewertung auf Einstellung und Verhalten? Neu gibt es in der fünften Ausgabe des Schweizer HR-Barometers erstmals eine Betrachtung allgemeiner Wirkungszusammenhänge im Bereich des Human Resource Managements. Auch wird auf relevante Massnahmen aufmerksam gemacht, die Arbeitseinstellung und -verhalten beeinflussen. Der Schweizer HR-Barometer wird von einem wissenschaftlichen Forschungsteam der ETH und der Universität Zürich im jährlichen Turnus herausgegeben.

To understand human behavior, it is important to know under what conditions people deviate from selfish rationality. This study explores the interaction of natural survival instincts and internalized social norms using data on the sinking of the Titanic and the Lusitania. We show that time pressure appears to be crucial when explaining behavior under extreme conditions of life and death. Even though the two vessels and the composition of their passengers were quite similar, the behavior of the individuals on board was dramatically different. On the Lusitania, selfish behavior dominated (which corresponds to the classical homo economicus); on the Titanic, social norms and social status (class) dominated, which contradicts standard economics. This difference could be attributed to the fact that the Lusitania sank in 18 min, creating a situation in which the short-run flight impulse dominated behavior. On the slowly sinking Titanic (2 h, 40 min), there was time for socially determined behavioral patterns to reemerge. Maritime disasters are traditionally not analyzed in a comparative manner with advanced statistical (econometric) techniques using individual data of the passengers and crew. Knowing human behavior under extreme conditions provides insight into how widely human behavior can vary, depending on differing external conditions.

Many processes in life sciences involve the handling and processing of large data sets and require hence large amounts of computing power. Peptide-spectrum assignments in Shotgun-Proteomics constitute such an example. A lot of academic institutions install thus large cluster systems, consisting of hundreds or even thousands of computers administered by Sun Grid Engine (or similar approaches) for solving such problems. However, these computing resources, that are usually very massive, are often used for a short period of time. As the data amounts of various measurement instruments increase, those local infrastructures are pushed to their limits because the compute capacity can not be extended ""on demand"". In this context, commercial cloud computing solutions like Amazon EC2 could provide an interesting, alternative solution for future compute power shortages. In this thesis, we investigate the appropriateness of such cloud computing solutions for parallel processing of real datasets in bioinformatics. In particular, the economical and technical aspects of porting the algorithms from the domain of ""Shotgun-Proteomics"" and ""Deep-Sequencing"" to cloud computing will be investigated. Furthermore, a concept that would allow the user to quickly find an answer to the question which computing system is more suitable in a particular situation depending on the application and the amount of input data will be developed. Consequently, this work will give information about availability, value for money (computing costs, input/output data transfer costs), scalability and portability both for existing solutions (UZH Matterhorn Cluster / FGCZ Cluster)) and for the cloud computing approach. Based on this data one can find out at what point commercial cloud computing is cheaper than ""local cluster computing"" by considering purchase costs, maintenance, staff costs, cooling costs and so on. To achieve this purpose, a lot of simulations on the cloud computing side need to be performed for jobs that are already processed in the local cluster. These simulations would allow to collect information about correctness, running time, job size and other interesting features that would enable to discern the advantages of one method or the other. The software prototype, developed in the context of this thesis will be able to communicate with the FGCZ data management system, where the data sets as well as their processing results reside.

We study competition among market designers who create new trading platforms, when boundedly rational traders learn to select among them. We ask whether ‘Walrasian’ platforms, leading to market‐clearing trading outcomes, will dominate the market in the long run. If several market designers compete, we find that traders learn to select non‐market clearing platforms with prices systematically above the market‐clearing level, provided at least one such platform is introduced by a market designer. This in turn leads market designers to introduce non‐market clearing platforms. Hence platform competition induces non‐competitive market outcomes.

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