The ArgoNeuT Detector in the NuMI Low-Energy beam line at Fermilab

The ArgoNeuT liquid argon time projection chamber has collected thousands of neutrino and antineutrino events during an extended run period in the NuMI beam-line at Fermilab. This paper focuses on the main aspects of the detector layout and related technical features, including the cryogenic equipment, time projection chamber, read-out electronics, and off-line data treatment. The detector commissioning phase, physics run, and first neutrino event displays are also reported. The characterization of the main working parameters of the detector during data-taking, the ionization electron drift velocity and lifetime in liquid argon, as obtained from through-going muon data complete the present report.Comment: 43 pages, 27 figures, 5 tables - update referenc

Representative Clustering of Uncertain Data

This paper targets the problem of computing meaningful clusterings from uncertain data sets. Existing methods for clustering uncertain data compute a single clustering without any indication of its quality and reliability; thus, decisions based on their results are questionable. In this paper, we describe a framework, based on possible-worlds semantics; when applied on an uncertain dataset, it computes a set of representative clusterings, each of which has a probabilistic guarantee not to exceed some maximum distance to the ground truth clustering, i.e., the clustering of the actual (but unknown) data. Our framework can be combined with any existing clustering algorithm and it is the first to provide quality guarantees about its result. In addition, our experimental evaluation shows that our representative clusterings have a much smaller deviation from the ground truth clustering than existing approaches, thus reducing the effect of uncertainty. 1

BIOWEPRO. Biomolekulare Wechselwirkung von Proteinen

Schomburg D, Jakob U, Meyer M, et al. BIOWEPRO. Biomolekulare Wechselwirkung von Proteinen. In: Wolf G, Schmidt R, van der Meer M, eds. Statusseminar des BMBF. Bioinformatik. 1995: 125-153

Fine-grained document clustering via ranking and its application to social media analytics

Extracting valuable insights from a large volume of unstructured data such as texts through clustering analysis is paramount to many big data applications. However, document clustering is challenged by the computational complexity of the underlying methods and the high dimensionality of data, especially when the number of required clusters is large. A fine-grained clustering solution is required to understand a data set that represents heterogeneous topics such as social media data. This paper presents the Fine-Grained document Clustering via Ranking (FGCR) approach which leverages the search engine capability of handling big data efficiently. Ranking scores from a search engine are used to calculate dynamic clusters’ representations called loci in an unsupervised learning setting. Clustering decisions are efficiently made based on an optimal selection from a small subset of loci instead of the entire cluster set as in the conventional centroid-based clustering. A comprehensive empirical study on several social media data sets shows that FGCR is able to produce insightful and accurate fine-grained solution. Moreover, it is magnitudes faster and requires less computational resources compared to other state-of-the-art document clustering approaches