Drift estimation in sparse sequential dynamic imaging, with application to nanoscale fluorescence microscopy.

A major challenge in many modern superresolution fluorescence microscopy techniques at the nanoscale lies in the correct alignment of long sequences of sparse but spatially and temporally highly resolved images. This is caused by the temporal drift of the protein structure, e.g. due to temporal thermal inhomogeneity of the object of interest or its supporting area during the observation process. We develop a simple semiparametric model for drift correction in single-marker switching microscopy. Then we propose an M-estimator for the drift and show its asymptotic normality. This is used to correct the final image and it is shown that this purely statistical method is competitive with state of the art calibration techniques which require the incorporation of fiducial markers in the specimen. Moreover, a simple bootstrap algorithm allows us to quantify the precision of the drift estimate and its effect on the final image estimation. We argue that purely statistical drift correction is even more robust than fiducial tracking, rendering the latter superfluous in many applications. The practicability of our method is demonstrated by a simulation study and by a single-marker switching application. This serves as a prototype for many other typical imaging techniques where sparse observations with high temporal resolution are blurred by motion of the object to be reconstructed

Determinants of self-reporting under the European corporate leniency program

We empirically investigate the determinants of self-reporting under the European corporate leniency program. Applying a data set consisting of 442 firm groups that participated in 76 cartels decided by the European Commission between 2000 and 2011, we find that the probability of a firm becoming the chief witness increases with its character as repeat offender, the size of the expected basic fine, the number of countries active in one group as well as the size of the firm’s share in the cartelized market. Our results have important implications for an effective prosecution of anti-cartel law infringers

Studying the Effects of Code Inspection and Structural Testing on Software Quality

The most common techniques for detecting defects in software artifacts are inspection and testing. Since both techniques are effort consuming, they are often presented as being counterparts or even rivals rather than as being complementary. Hence, few controlled empirical studies investigate the effects of inspection and testing on software quality when applied in sequence. This paper contributes a controlled experiment to shed light on this issue. Twenty subjects performed sequentially code inspection and structural testing using different coverage values as test criteria on a C-code module. We adopted this sequence because it is recommended for use in industry. The results of this experiment show that inspection significantly outperforms structural testing with respect to (cost-)effectiveness for defect detection. Furthermore, the experimental results indicate little evidence to support the hypothesis that structural testing detects defects of a particular class that were missed b y inspection and vice versa. These findings lead us to the conclusion that inspection and structural testing do not complement each other well. In fact, prior inspection seems to hinder the (cost-)effectiveness of structural testing. Since inspection out-performs structural testing and since 39 percent (on average) of the defects were not detected at all, it might be more valuable to apply inspection together with other testing techniques, such as boundary value analysis, to achieve a better defect coverage. We are aware that a single experiment does not provide conclusive evidence. Hence, we consider it only one step in the determination of the optimal mix of defect detection techniques. Additional research as well as replication of this experiment are required to make further progress into this direction

Evaluating Capture-Recapture Models with Two Inspectors

NRC publication: Ye

Investigating the impact of reading techniques on the accuracy of different defect content estimation techniques

Software inspections have established an impressive track record for early defect detection and correction. To increase their benefits, recent research efforts have focused on two different areas: systematic reading techniques and defect content estimation techniques. While reading techniques are to provide guidance for inspection participants on how to scrutinize a software artifact in a systematic manner, defect content estimation techniques aim at controlling and evaluating the inspection process by providing an estimate of the total number of defects in an inspected document. Although several empirical studies have been conducted to evaluate the accuracy of defect content estimation techniques, only few consider the reading approach as an influential factor. In this paper we examine the impact of two specific reading techniques - a scenario-based reading technique and checklist-based reading - on the accuracy of different defect content estimation techniques. The examination is based on data that were collected in a large experiment with students of the Vienna University of Technology. The results suggest that the choice of the reading technique has little impact on the accuracy of defect content estimation techniques. Although more empirical work is necessary to corroborate this finding, it implies that practitioners can use defect content estimation techniques without any consideration of their current reading technique

An Internally Replicated Quasi-Experimental Comparison of Checklist and Perspective-based Reading of Code Documents

The basic premise of software inspections is that they detect and remove defects before they propagate to subsequent development phases where their detection and correction cost escalates. To exploit their full potential, software inspections must call for a close and strict examination of the inspected artefact. For this, reading techniques for defect detection may be helpful since these techniques tell inspection participants what to look for and, more importantly, how to scrutinise a software artefact in a systematic manner. Recent research efforts investigated the benefits of scenario-based reading techniques. A major finding has been that these techniques help inspection teams find more defects than existing state-of-the-practice approaches, such as, ad-hoc or checklist-based reading (CBR). In this paper we experimentally compare one scenario-based reading technique, namely perspective-based reading (PBR), for defect detection in code documents with the more traditional CBR approa ch. The comparison was performed in a series of three studies, as a quasi-experiment and two internal replications, with a total of 60 professional software developers at Bosch Telecom GmbH. Meta-analytic techniques were applied to analyse the data. Our results indicate that PBR is more effective than CBR (i.e., it resulted in inspection teams detecting more unique defects than CBR), and that the cost of defect detection using PBR is significantly lower than CBR. This study therefore provides evidence demonstrating the efficacy of PBR scenarios for code documents in an industrial setting