Resonance structure in the Li^- photodetachment cross section

We report on the first observation of resonance structure in the total cross section for the photodetachment of Li^-. The structure arises from the autodetaching decay of doubly excited ^1P states of Li^- that are bound with respect to the 3p state of the Li atom. Calculations have been performed for both Li^- and H^- to assist in the identification of these resonances. The lowest lying resonance is a symmetrically excited intrashell resonance. Higher lying asymmetrically excited intershell states are observed which converge on the Li(3p) limit.Comment: 4 pages, 2 figure, 19 references, RevTeX, figures in ep

Electron affinity of Li: A state-selective measurement

We have investigated the threshold of photodetachment of Li^- leading to the formation of the residual Li atom in the $2p ^2P$ state. The excited residual atom was selectively photoionized via an intermediate Rydberg state and the resulting Li^+ ion was detected. A collinear laser-ion beam geometry enabled both high resolution and sensitivity to be attained. We have demonstrated the potential of this state selective photodetachment spectroscopic method by improving the accuracy of Li electron affinity measurements an order of magnitude. From a fit to the Wigner law in the threshold region, we obtained a Li electron affinity of 0.618 049(20) eV.Comment: 5 pages,6 figures,22 reference

Photodetachment study of the 1s3s4s ^4S resonance in He^-

A Feshbach resonance associated with the 1s3s4s ^{4}S state of He^{-} has been observed in the He(1s2s ^{3}S) + e^- (\epsilon s) partial photodetachment cross section. The residual He(1s2s ^{3}S) atoms were resonantly ionized and the resulting He^+ ions were detected in the presence of a small background. A collinear laser-ion beam apparatus was used to attain both high resolution and sensitivity. We measured a resonance energy E_r = 2.959 255(7) eV and a width \Gamma = 0.19(3) meV, in agreement with a recent calculation.Comment: LaTeX article, 4 pages, 3 figures, 21 reference

Crowd-Based Mining of Reusable Process Model Patterns

Process mining is a domain where computers undoubtedly outperform humans. It is a mathematically complex and computationally demanding problem, and event logs are at too low a level of abstraction to be intelligible in large scale to humans. We demonstrate that if instead the data to mine from are models (not logs), datasets are small (in the order of dozens rather than thousands or millions), and the knowledge to be discovered is complex (reusable model patterns), humans outperform computers. We design, implement, run, and test a crowd-based pattern mining approach and demonstrate its viability compared to automated mining. We specifically mine mashup model patterns (we use them to provide interactive recommendations inside a mashup tool) and explain the analogies with mining business process models. The problem is relevant in that reusable model patterns encode valuable modeling and domain knowledge, such as best practices or organizational conventions, from which modelers can learn and benefit when designing own models. © 2014 Springer International Publishing Switzerland

Isotope shift in the electron affinity of chlorine

The specific mass shift in the electron affinity between ^{35}Cl and ^{37}Cl has been determined by tunable laser photodetachment spectroscopy to be -0.51(14) GHz. The isotope shift was observed as a difference in the onset of the photodetachment process for the two isotopes. In addition, the electron affinity of Cl was found to be 29138.59(22) cm^{-1}, giving a factor of 2 improvement in the accuracy over earlier measurements. Many-body calculations including lowest-order correlation effects demonstrates the sensitivity of the specific mass shift and show that the inclusion of higher-order correlation effects would be necessary for a quantitative description.Comment: 16 pages, 6 figures, LaTeX2e, amsmat

Probabilistic evaluation of process model matching techniques

Process model matching refers to the automatic identification of corresponding activities between two process models. It represents the basis for many advanced process model analysis techniques such as the identification of similar process parts or process model search. A central problem is how to evaluate the performance of process model matching techniques. Often, not even humans can agree on a set of correct correspondences. Current evaluation methods, however, require a binary gold standard, which clearly defines which correspondences are correct. The disadvantage of this evaluation method is that it does not take the true complexity of the matching problem into account and does not fairly assess the capabilities of a matching technique. In this paper, we propose a novel evaluation method for process model matching techniques. In particular, we build on the assessment of multiple annotators to define probabilistic notions of precision and recall. We use the dataset and the results of the Process Model Matching Contest 2015 to assess and compare our evaluation method. We found that our probabilistic evaluation method assigns different ranks to the matching techniques from the contest and allows to gain more detailed insights into their performance

Activity matching with human intelligence

Effective matching of activities is the first step toward successful process model matching and search. The problem is nontrivial and has led to a variety of computational similarity metrics and matching approaches, however all still with low performance in terms of precision and recall. In this paper, instead, we study how to leverage on human intelligence to identify matches among activities and show that the problem is not as straightforward as most computational approaches assume. We access human intelligence (i) by crowdsourcing the activity matching problem to generic workers and (ii) by eliciting ground truth matches from experts. The precision and recall we achieve and the qualitative analysis of the results testify huge potential for a human-based activity matching that contemplates disagreement and interpretation.17 page(s