How Social Reputation Networks Interact with Competition in Anonymous Online Trading: An Experimental Study

Many Internet markets rely on ‘feedback systems’, essentially social networks of reputation, to facilitate trust and trustworthiness in anonymous transactions. Market competition creates incentives that arguably may enhance or curb the effectiveness of these systems. We investigate how different forms of market competition and social reputation networks interact in a series of laboratory online markets, where sellers face a moral hazard. We find that competition in strangers networks (where market encounters are one-shot) most frequently enhances trust and trustworthiness, and always increases total gains-from-trade. One reason is that information about reputation trumps pricing in the sense that traders usually do not conduct business with someone having a bad reputation not even for a substantial price discount. We also find that a reliable reputation network can largely reduce the advantage of partners networks (where a buyer and a seller can maintain repeated exchange with each other) in promoting trust and trustworthiness if the market is sufficiently competitive. We conclude that, overall, competitive online markets have more effective social reputation networks.reputation systems, e-commerce, internet markets, trust

Noncanonical Links in Generalized Linear Models - When is the Effort Justified?

Generalized linear models (GLM) allow for a wide range of statistical models for regression data. In particular, the logistic model is usually applied for binomial observations. Canonical links for GLM's such as the logit link in the binomial case, are often used because in this case sufficient statistics for the regression parameter exist which allow for simple interpretation of the results. However, in some applications, the overall fit as measured by the p-values of goodness of fit statistics (as the residual deviance) can be improved significantly by the use of a noncanonical link. In this case, the interpretation of the influence of the covariables is more complicated compared to GLM's with canonical link functions. It will be illustrated through simulation that the p-value associated with the common goodness of link tests is not appropriate to quantify the changes to mean response estimates and other quantities of interest when switching to a noncanonical link. In particular, the rate of misspecifications becomes considerably large, when the inverse information value associated with the underlying parametric link model increases. This shows that the classical tests are often too sensitive, in particular, when the number of observations is large. The consideration of a generalized p-value function is proposed instead, which allows the exact quantification of a suitable distance to the canonical model at a controlled error rate. Corresponding tests for validating or discriminating the canonical model can easily performed by means of this function

Fully-Automatic Multiresolution Idealization for Filtered Ion Channel Recordings: Flickering Event Detection

We propose a new model-free segmentation method, JULES, which combines recent statistical multiresolution techniques with local deconvolution for idealization of ion channel recordings. The multiresolution criterion takes into account scales down to the sampling rate enabling the detection of flickering events, i.e., events on small temporal scales, even below the filter frequency. For such small scales the deconvolution step allows for a precise determination of dwell times and, in particular, of amplitude levels, a task which is not possible with common thresholding methods. This is confirmed theoretically and in a comprehensive simulation study. In addition, JULES can be applied as a preprocessing method for a refined hidden Markov analysis. Our new methodolodgy allows us to show that gramicidin A flickering events have the same amplitude as the slow gating events. JULES is available as an R function jules in the package clampSeg

Multiscale scanning with nuisance parameters

We investigate the problem to find anomalies in a $d$-dimensional random field via multiscale scanning in the presence of nuisance parameters. This covers the common situation that either the baseline-level or additional parameters such as the variance are unknown and have to be estimated from the data. We argue that state of the art approaches to determine asymptotically correct critical values for the multiscale scanning statistic will in general fail when naively such parameters are replaced by plug-in estimators. Opposed to this, we suggest to estimate the nuisance parameters on the largest scale and to use the remaining scales for multiscale scanning. We prove a uniform invariance principle for the resulting adjusted multiscale statistic (AMS), which is widely applicable and provides a computationally feasible way to simulate asymptotically correct critical values. We illustrate the implications of our theoretical results in a simulation study and in a real data example from super-resolution STED microscopy. This allows us to identify interesting regions inside a specimen in a pre-scan with controlled family-wise error rate

Sensitivity and Generalization of a Neural Network for Estimating Left Atrial Fibrotic Volume Fractions from the 12-lead ECG

Features extracted from P waves of the 12-lead electrocardiogram (ECG) have proven valuable for non-invasively estimating the left atrial fibrotic volume fraction associated with the arrhythmogenesis of atrial fibrillation. However, feature extraction in the clinical context is prone to errors and oftentimes yields unreliable results in the presence of noise. This leads to inaccurate input values provided to machine learning algorithms tailored at estimating the amount of atrial fibrosis with clinical ECGs.Another important aspect for clinical translation is the network’s generalization ability regarding newECGs.To quantify a network’s sensitivity to inaccurately extracted P wave features, we added Gaussian noise to the features extracted from 540,000 simulated ECGs consisting of P wave duration, dispersion, terminal force in lead V1, peak-to-peak amplitudes, and additionallythoracic and atrial volumes. For assessing generalization, we evaluated the network performance for train-validation-test splits divided such that ECGs simulated with the same atria or torso geometry only belongedto either the trainingand validationor the test set. The root mean squared error (RMSE) of the network increased the most in case of noisy torso volumes and P wave durations. Large generalization errors witha RMSEdifference between training and test set of more than 2% fibrotic volume fraction only occurred ifveryhigh or low atria and torso volumes were left out during training.Our results suggest that P wave duration and thoracic volume are features that have to be measured accurately if employed for estimating atrial fibrosis with a neural network. Furthermore, our method is capable of generalizing wellto ECGs simulated with anatomical models excluded during training and thus meets an important requirement for clinical translation

Isolation and characterisation of antifungal compounds from lactic acid bacteria and their application in wheat and gluten-free bread

As part of the “free-from” trend, biopreservation for bread products has increasingly become important to prevent spoilage since artificial preservatives are more and more rejected by consumers. A literature review conducted as part of this thesis revealed that the evaluation of more suitable antifungal strains of lactic acid bacteria (LAB) is important. Moreover, increasing the knowledge about the origin of the antifungal effect is fundamental for further enhancement of biopreservation. This thesis addresses the investigation of Lactobacillus amylovorus DSM19280, Lb. brevis R2: and Lb. reuteri R29 for biopreservation using in vitro trials and in situ sourdough fermentations of quinoa, rice and wheat flours as biopreservatives in breads. Their contribution to quality and shelf life extension on bread was compared and related to their metabolic activity and substrate features. Moreover, the quantity of antifungal carboxylic acids produced during sourdough fermentation was analysed. Overall a specific profile of antifungal compounds was found in the sourdough samples which were strain and substrate dependently different. The best preservative effect in quinoa sourdough and wheat sourdough bread was achieved when Lb. amylovorus DSM19280 fermented sourdough was used. However, the concentration of the antifungal compounds found in these biopreservatives were much lower when compared with Lb. reuteri R29 as the highest producer. Nevertheless, the artificial application of the highest concentration of these antifungal compounds in chemically acidified wheat sourdough bread succeeded in a longer shelf life than achieved only by acidifying the dough. This evidences their partial contribution to the antifungal activity and their synergy. Additionally, a HRGC/MS method for the identification and quantification of the antifungal active compounds cyclo(Leu-Pro), cyclo(Pro-Pro), cyclo(Met-Pro) and cyclo(Phe-Pro) was successfully developed by using stable isotope dilutions assays with the deuterated counterparts. It was observed that the concentrations of cyclo(Leu-Pro), cyclo(Pro-Pro), and cyclo(Phe-Pro) increased only moderately in MRS-broth and wort fermentation by the activity of the selected microorganism, whereas the concentration of cyclo(Met-Pro) stayed unchanged