Measuring Feedback in Damped Lyman Alpha Systems

We measure feedback (heating rates) in damped Lyman alpha systems from the cooling rate of the neutral gas. Since cooling occurs through [C II] 158 micron emission, we infer cooling from C II^{*} 1335.7 absorption lines detected with HIRES on the Keck I telescope. The inferred heating rates are about 30 times lower than for the Galaxy ISM. At z = 2.8, the implied star formation rate per unit area is 10^{-2.4+-0.3} solar masses per kpc^{2} per year, and the the star formation rate per unit comoving volume is 10^{-0.8+-0.2} solar masses per Mpc^{3} per year. This is the first measurement of star formation rates in objects likely to be the progenitors of current galaxies.Comment: 7 pages, 5 figures, Proceedings of the ESO/ECF/STScI Workshop on Deep Field

A Guide to Modeling Credit Term Structures

We give a comprehensive review of credit term structure modeling methodologies. The conventional approach to modeling credit term structure is summarized and shown to be equivalent to a particular type of the reduced form credit risk model, the fractional recovery of market value approach. We argue that the corporate practice and market observations do not support this approach. The more appropriate assumption is the fractional recovery of par, which explicitly violates the strippable cash flow valuation assumption that is necessary for the conventional credit term structure definitions to hold. We formulate the survival-based valuation methodology and give alternative specifications for various credit term structures that are consistent with market observations, and show how they can be empirically estimated from the observable prices. We rederive the credit triangle relationship by considering the replication of recovery swaps. We complete the exposition by presenting a consistent measure of CDS-Bond basis and demonstrate its relation to a static hedging strategy, which remains valid for non-par bonds and non-flat term structures of interest rates and credit risk.Comment: 54 pages, 13 figures (references fixed

How to improve business process performance using process mining

Due to the increased use of information systems by organizations to support process execution, detailed information on the implementation of business processes is being recorded. This fact enables using process mining projects as a powerful tool for improving performance. Process Mining is a relative young research discipline that sits between data science on the one hand and process modelling and analysis on the other hand. Process mining allows gaining knowledge of the organization’s actual business processes by extracting data from existing information systems mediums such as event logs, transaction logs etc. The purpose of this presentation is to demonstrate how a process for conducting process mining projects was designed, developed and applied in some organizational units. The process was implemented through nine research steps, inspired by the V-model, where elements on the right-hand side aim to answer questions presented in steps on the left-hand side. In the first two steps, the research problem and the research objectives were defined. A literature review was performed in step 3. In the fourth step, requirements for the process were identified and implemented. In step 5, a running example was carried out to test the process. Verification and validation of the process were performed in step 6 and step 7. Step 8 covered the discussion of results. The last step concludes the research, including checking if the research problem was solved. Organizations seeking for performance improvement can now benefit of a process that explicitly states which process mining tools, techniques and algorithms to be used in process mining projects

The Nature of Alpha

We suggest an empirical model of investment strategy returns which elucidates the importance of non-Gaussian features, such as time-varying volatility, asymmetry and fat tails, in explaining the level of expected returns. Estimating the model on the (former) Lehman Brothers Hedge Fund Index data, we demonstrate that the volatility compensation is a significant component of the expected returns for most strategy styles, suggesting that many of these strategies should be thought of as being `short vol'. We present some fundamental and technical reasons why this should indeed be the case, and suggest explanation for exception cases exhibiting `long vol' characteristics. We conclude by drawing some lessons for hedge fund portfolio construction.Comment: 22 pages, 5 figures, 3 table

Anisotropic Flow at the SPS and RHIC

The results on directed and elliptic flow for Pb + Pb at the full energy of the SPS (158 GeV/A) and from the first year of Au + Au at RHIC (sqrt{s_{_{NN}}=130 GeV) are reviewed. The different experiments agree well and a consistent picture has emerged indicating early time thermalization at RHIC.Comment: 4 pages. For the proceedings of the International Workshop on the Physics of the Quark-Gluon Plasma, Palaiseau, France, 4-7 Sept. 0

Sentiment Analysis for Words and Fiction Characters From The Perspective of Computational (Neuro-)Poetics

Two computational studies provide different sentiment analyses for text segments (e.g., ‘fearful’ passages) and figures (e.g., ‘Voldemort’) from the Harry Potter books (Rowling, 1997 - 2007) based on a novel simple tool called SentiArt. The tool uses vector space models together with theory-guided, empirically validated label lists to compute the valence of each word in a text by locating its position in a 2d emotion potential space spanned by the > 2 million words of the vector space model. After testing the tool’s accuracy with empirical data from a neurocognitive study, it was applied to compute emotional figure profiles and personality figure profiles (inspired by the so-called ‚big five’ personality theory) for main characters from the book series. The results of comparative analyses using different machine-learning classifiers (e.g., AdaBoost, Neural Net) show that SentiArt performs very well in predicting the emotion potential of text passages. It also produces plausible predictions regarding the emotional and personality profile of fiction characters which are correctly identified on the basis of eight character features, and it achieves a good cross-validation accuracy in classifying 100 figures into ‘good’ vs. ‘bad’ ones. The results are discussed with regard to potential applications of SentiArt in digital literary, applied reading and neurocognitive poetics studies such as the quantification of the hybrid hero potential of figures