Knowledge Discovery from CVs: A Topic Modeling Procedure

With a huge number of CVs available online, recruiting via the web has become an integral part of human resource management for companies. Automated text mining methods can be used to analyze large databases containing CVs. We present a topic modeling procedure consisting of five steps with the aim of identifying competences in CVs in an automated manner. Both the procedure and its exemplary application to CVs from IT experts are described in detail. The specific characteristics of CVs are considered in each step for optimal results. The exemplary application suggests that clearly interpretable topics describing fine-grained competences (e.g., Java programming, web design) can be discovered. This information can be used to rapidly assess the contents of a CV, categorize CVs and identify candidates for job offers. Furthermore, a topic-based search technique is evaluated to provide helpful decision support

Concepts and Methods from Artificial Intelligence in Modern Information Systems – Contributions to Data-driven Decision-making and Business Processes

Today, organizations are facing a variety of challenging, technology-driven developments, three of the most notable ones being the surge in uncertain data, the emergence of unstructured data and a complex, dynamically changing environment. These developments require organizations to transform in order to stay competitive. Artificial Intelligence with its fields decision-making under uncertainty, natural language processing and planning offers valuable concepts and methods to address the developments. The dissertation at hand utilizes and furthers these contributions in three focal points to address research gaps in existing literature and to provide concrete concepts and methods for the support of organizations in the transformation and improvement of data-driven decision-making, business processes and business process management. In particular, the focal points are the assessment of data quality, the analysis of textual data and the automated planning of process models. In regard to data quality assessment, probability-based approaches for measuring consistency and identifying duplicates as well as requirements for data quality metrics are suggested. With respect to analysis of textual data, the dissertation proposes a topic modeling procedure to gain knowledge from CVs as well as a model based on sentiment analysis to explain ratings from customer reviews. Regarding automated planning of process models, concepts and algorithms for an automated construction of parallelizations in process models, an automated adaptation of process models and an automated construction of multi-actor process models are provided

Thermal noise of whispering gallery resonators

By direct application of the fluctuation-dissipation theorem, we numerically calculate the fundamental dimensional fluctuations of crystalline CaF2 whispering gallery resonators in the case of structural damping, and the limit that this noise imposes on the frequency stability of such resonators at both room and cryogenic temperatures. We analyze elasto-optic noise - the effect of Brownian dimensional fluctuation on frequency via the strain-dependence of the refractive index - a noise term that has so far not been considered for whispering-gallery resonators. We find that dimensional fluctuation sets a lower limit of 1E-16 to the Allan deviation for a 10-millimeter-radius sphere at 5 K, predominantly via induced fluctuation of the refractive index.Comment: 7 pages, 3 figure

Rat Monoclonal Antibodies Specific for LST1 Proteins

The LST1 gene is located in the human MHC class III region and encodes transmembrane and soluble isoforms that have been suggested to play a role in the regulation of the immune response and are associated with inflammatory diseases such as rheumatoid arthritis. Here we describe the generation and characterization of the first monoclonal antibodies against LST1. Two hybridoma lines secreting monoclonal antibodies designated 7E2 and 8D12 were established. The 7E2 antibody detects recombinant and endogenous LST1 by Western blot analysis while 8D12 reacts with recombinant and endogenous LST1 in immunoprecipitation and flow cytometry procedures. The newly established antibodies were used to survey LST1 protein expression in human cell lines, which was found to be tightly regulated, allowing the expression of transmembrane isoforms but suppressing soluble isoforms

Proteomic analysis of heart failure hospitalization among patients with chronic kidney disease: The Heart and Soul Study.

BACKGROUND:Patients with chronic kidney disease (CKD) are at increased risk for heart failure (HF). We aimed to investigate differences in proteins associated with HF hospitalizations among patients with and without CKD in the Heart and Soul Study. METHODS AND RESULTS:We measured 1068 unique plasma proteins from baseline samples of 974 participants in The Heart and Soul Study who were followed for HF hospitalization over a median of 7 years. We sequentially applied forest regression and Cox survival analyses to select prognostic proteins. Among participants with CKD, four proteins were associated with HF at Bonferroni-level significance (p<2.5x10(-4)): Angiopoietin-2 (HR[95%CI] 1.45[1.33, 1.59]), Spondin-1 (HR[95%CI] 1.13 [1.06, 1.20]), tartrate-resistant acid phosphatase type 5 (HR[95%CI] 0.65[0.53, 0.78]) and neurogenis locus notch homolog protein 1 (NOTCH1) (HR[95%CI] 0.67[0.55, 0.80]). These associations persisted at p<0.01 after adjustment for age, estimated glomerular filtration and history of HF. CKD was a significant interaction term in the associations of NOTCH1 and Spondin-1 with HF. Pathway analysis showed a trend for higher representation of the Cardiac Hypertrophy and Complement/Coagulation pathways among proteins prognostic of HF in the CKD sub-group. CONCLUSIONS:These results suggest that markers of heart failure differ between patients with and without CKD. Further research is needed to validate novel markers in cohorts of patients with CKD and adjudicated HF events

A silicon single-crystal cryogenic optical resonator

We report on the demonstration and characterization of a silicon optical resonator for laser frequency stabilization, operating in the deep cryogenic regime at temperatures as low as 1.5 K. Robust operation was achieved, with absolute frequency drift less than 20 Hz over 1 hour. This stability allowed sensitive measurements of the resonator thermal expansion coefficient ($\alpha$). We found $\alpha=4.6\times10^{-13}$ ${\rm K^{-1}}$ at 1.6 K. At 16.8 K $\alpha$ vanishes, with a derivative equal to $-6\times10^{-10}$ ${\rm K}^{-2}$. The temperature of the resonator was stabilized to a level below 10 $\mu$K for averaging times longer than 20 s. The sensitivity of the resonator frequency to a variation of the laser power was also studied. The corresponding sensitivities and the expected Brownian noise indicate that this system should enable frequency stabilization of lasers at the low-$10^{-17}$ level.Comment: 5 pages, 5 figure

Implementierung einer Werkzeugkette zur Erstellung von Bewegungsprofilen für Kinetose-Untersuchungen im Simulator

Kinetose, auch als Reisekrankheit bezeichnet, ist ein Phänomen, welches sich durch vielfältige Symptome äußern kann. Bei der Auslegung neuer Flugzeugtypen und -kabinen ist es wichtig dieses Phänomen frühzeitig zu berücksichtigen, um die Entwicklung von Kinetose zu minimieren. Zur Untersuchung dieses Phänomens steht daher am Deutschen Zentrum für Luft- und Raumfahrt die Simulatorumgebung Air Vehicle Simulator (AVES) zusammen mit der Advanced Future Cabin (AFC) zur Verfügung. Der AVES ist ein Full Motion Flugsimulator mit wechselbarem Cockpitmodul. Eines dieser Module ist die Advanced Future Cabin, welche eine realistische Nachbildung einer Flugzeugkabine ist. Um Objektivität und Reliabilität für die Untersuchung von Kinetose auch in dieser Simulatorumgebung gewährleisten zu können, ist unter anderem eine möglichst hohe Standardisierung der Durchführungsbedingungen wichtig. Um diese zu erreichen, wird eine ReplayInfrastruktur verwendet. Diese ist in der Lage voraufgezeichnete Flugprofile wiederzugeben, welche dann vom Ton-, Sichtund Bewegungssystem des Air Vehicle Simulators umgesetzt werden. So können die Durchführungsbedingungen für gesamte Flugprofile gut reproduziert werden. Problematisch ist jedoch, dass die Aufzeichnungen bisher durch manuelles Erfliegen der Flugprofile erstellt wurden. Dies führt zu einem hohen manuellen Aufwand und zu Problemen bezüglich der Reproduzierbarkeit. Daher werden mit einer modularen Werkzeugkette neue Ansätze zur automatischen Erstellung von Flugprofilen untersucht, um Kinetose im Simulator zukünftig einfacher und präziser bewerten zu können

Event-Driven Duplicate Detection: A Probability-based Approach

The importance of probability-based approaches for duplicate detection has been recognized in both research and practice. However, existing approaches do not aim to consider the underlying real-world events resulting in duplicates (e.g., that a relocation may lead to the storage of two records for the same customer, once before and after the relocation). Duplicates resulting from real-world events exhibit specific characteristics. For instance, duplicates resulting from relocations tend to have significantly different attribute values for all address-related attributes. Hence, existing approaches focusing on high similarity with respect to attribute values are hardly able to identify possible duplicates resulting from such real-world events. To address this issue, we propose an approach for event-driven duplicate detection based on probability theory. Our approach assigns the probability of being a duplicate resulting from real-world events to each analysed pair of records while avoiding limiting assumptions (of existing approaches). We demonstrate the practical applicability and effectiveness of our approach in a real-world setting by analysing customer master data of a German insurer. The evaluation shows that the results provided by the approach are reliable and useful for decision support and can outperform well-known state-of-the-art approaches for duplicate detection