Robust M-Estimation Based Bayesian Cluster Enumeration for Real Elliptically Symmetric Distributions

Robustly determining the optimal number of clusters in a data set is an essential factor in a wide range of applications. Cluster enumeration becomes challenging when the true underlying structure in the observed data is corrupted by heavy-tailed noise and outliers. Recently, Bayesian cluster enumeration criteria have been derived by formulating cluster enumeration as maximization of the posterior probability of candidate models. This article generalizes robust Bayesian cluster enumeration so that it can be used with any arbitrary Real Elliptically Symmetric (RES) distributed mixture model. Our framework also covers the case of M-estimators that allow for mixture models, which are decoupled from a specific probability distribution. Examples of Huber's and Tukey's M-estimators are discussed. We derive a robust criterion for data sets with finite sample size, and also provide an asymptotic approximation to reduce the computational cost at large sample sizes. The algorithms are applied to simulated and real-world data sets, including radar-based person identification, and show a significant robustness improvement in comparison to existing methods

A Multi Antenna Receiver for Galileo SoL Applications

One of the main features of the Galileo Satellite Navigation System is integrity. To ensure a reliable and robust navigation for Safety of Life applications, like CAT III aircraft landings, new receiver technologies are indispensable. Therefore, the German Aerospace Centre originated the development of a complete safety-of-life Galileo receiver to demonstrate the capabilities of new digital beam-forming and signal-processing algorithms for the detection and mitigation of interference. To take full advantage of those algorithms a carefully designed analogue signal processing is needed. The development addresses several challenging questions in the field of antenna design, frontend development and digital signal processing. The paper will give an insight in the activity and will present latest results

Real Elliptically Skewed Distributions and Their Application to Robust Cluster Analysis

This article proposes a new class of Real Elliptically Skewed (RESK) distributions and associated clustering algorithms that allow for integrating robustness and skewness into a single unified cluster analysis framework. Non-symmetrically distributed and heavy-tailed data clusters have been reported in a variety of real-world applications. Robustness is essential because a few outlying observations can severely obscure the cluster structure. The RESK distributions are a generalization of the Real Elliptically Symmetric (RES) distributions. To estimate the cluster parameters and memberships, we derive an expectation maximization (EM) algorithm for arbitrary RESK distributions. Special attention is given to a new robust skew-Huber M-estimator, which is also the maximum likelihood estimator (MLE) for the skew-Huber distribution that belongs to the RESK class. Numerical experiments on simulated and real-world data confirm the usefulness of the proposed methods for skewed and heavy-tailed data sets

Robust and Efficient Aggregation for Distributed Learning

Distributed learning paradigms, such as federated and decentralized learning, allow for the coordination of models across a collection of agents, and without the need to exchange raw data. Instead, agents compute model updates locally based on their available data, and subsequently share the update model with a parameter server or their peers. This is followed by an aggregation step, which traditionally takes the form of a (weighted) average. Distributed learning schemes based on averaging are known to be susceptible to outliers. A single malicious agent is able to drive an averaging-based distributed learning algorithm to an arbitrarily poor model. This has motivated the development of robust aggregation schemes, which are based on variations of the median and trimmed mean. While such procedures ensure robustness to outliers and malicious behavior, they come at the cost of significantly reduced sample efficiency. This means that current robust aggregation schemes require significantly higher agent participation rates to achieve a given level of performance than their mean-based counterparts in non-contaminated settings. In this work we remedy this drawback by developing statistically efficient and robust aggregation schemes for distributed learning

Age-Related Influence of Contingencies on a Saccade Task

Background: Adolescence is characterized by increased risk-taking and sensation seeking, presumably brought about by developmental changes within reward-mediating brain circuits. A better understanding of the neural mechanisms underlying reward-seeking during adolescence can have critical implications for the development of strategies to enhance adolescent performance in potentially dangerous situations. Yet little research has investigated the influence of age on the modulation of behavior by incentives with neuroscience-based methods Methods: A monetary reward antisaccade task (the RST) was used with 23 healthy adolescents and 30 healthy adults. Performance accuracy, latency and peak velocity of saccade responses (prosaccades and antisaccades) were analyzed. Results: Performance accuracy across all groups was improved by incentives (obtain reward, avoid punishment) for both, prosaccades and antisaccades. However, modulation of antisaccade errors (direction errors) by incentives differed between groups: adolescents modulated saccade latency and peak velocity depending on contingencies, with incentives aligning their performance to that of adults; adults did not show a modulation by incentives. Conclusions: These findings suggest that incentives modulate a global measure of performance (percent direction errors) in adults and adolescents, and exert a more powerful influence on the control of incorrect motor responses in adolescents than in adults. These findings suggest that this task can be used in neuroimaging studies as a probe of the influence of incentives on cognitive control from a developmental perspective as well as in health and disease

Autoselection of Cytoplasmic Yeast Virus Like Elements Encoding Toxin/Antitoxin Systems Involves a Nuclear Barrier for Immunity Gene Expression

Cytoplasmic virus like elements (VLEs) from Kluyveromyces lactis (Kl), Pichia acaciae (Pa) and Debaryomyces robertsiae (Dr) are extremely A/T-rich (>75%) and encode toxic anticodon nucleases (ACNases) along with specific immunity proteins. Here we show that nuclear, not cytoplasmic expression of either immunity gene (PaORF4, KlORF3 or DrORF5) results in transcript fragmentation and is insufficient to establish immunity to the cognate ACNase. Since rapid amplification of 3' ends (RACE) as well as linker ligation of immunity transcripts expressed in the nucleus revealed polyadenylation to occur along with fragmentation, ORF-internal poly(A) site cleavage due to the high A/T content is likely to prevent functional expression of the immunity genes. Consistently, lowering the A/T content of PaORF4 to 55% and KlORF3 to 46% by gene synthesis entirely prevented transcript cleavage and permitted functional nuclear expression leading to full immunity against the respective ACNase toxin. Consistent with a specific adaptation of the immunity proteins to the cognate ACNases, cross-immunity to non-cognate ACNases is neither conferred by PaOrf4 nor KlOrf3. Thus, the high A/T content of cytoplasmic VLEs minimizes the potential of functional nuclear recruitment of VLE encoded genes, in particular those involved in autoselection of the VLEs via a toxin/antitoxin principle

Hepatitis B Vaccination for Patients with Chronic Renal Failure

Background Chronic renal failure patients are at particular risk of hepatitis B virus infection. Early studies have demonstrated that renal failure patients benefit from vaccination; however, not all studies have consistently shown benefit. Objectives To determine the beneficial and harmful effects of hepatitis B vaccine and of a reinforced vaccination series in chronic renal failure patients. Search methods We searched The Cochrane Hepato-Biliary Group Controlled Trials Register, The Cochrane Renal Group Controlled Trials Register, The Cochrane Controlled Trials Register on The Cochrane Library (Issue 1, 2002), PubMed/MEDLINE (1966 to July 2003), EMBASE (1985 toNovember 2003), Current Clinical Practice Guidelines (Canadian Immunization Guide and Vaccine Preventable Diseases Surveillance Manual), and Science Citation Index as well as journals, published abstracts, and reference lists of articles. Selection criteria Randomised clinical trials comparing plasma vaccine with placebo, recombinant vaccine with placebo, recombinant vaccine with plasma vaccine, and a reinforced vaccination series (ie, more than three inoculations) with three inoculations of vaccine in chronic renal failure patients. Data collection and analysis Primary outcome measures included incidence of patients developing hepatitis B virus antibodies and infections while secondary outcomes included adverse events, liver-related morbidity, and mortality. Random effects models were used and reported relative risks and 95% confidence intervals (RR and 95% CI). Main results We included seven randomised clinical trials. None of them had high quality. Plasma vaccine was significantly more effective than placebo in achieving hepatitis B antibodies (RR 23.0, 95% CI 14.39 to 36.76, 3 trials). We found no statistically significant difference between plasma vaccine or placebo regarding hepatitis B virus infections (RR 0.50, 95% CI 0.20 to 1.24). We found no statistically significant differences between recombinant vaccine and plasma vaccine in achieving hepatitis B antibodies (RR 0.65, 95% CI 0.28 to 1.53, 2 trials). Heterogeneity was significant and appeared to be attributable to the dose of vaccine. Two trials examined a reinforced recombinant vaccine strategy, which was not statistically more effective than three inoculations of recombinant vaccine regarding development of hepatitis B antibodies (RR 1.36, 95% CI 0.85 to 2.16). Authors’ conclusions Plasma derived vaccines are more effective than placebo in achieving hepatitis B antibodies, while no statistically significant difference was found between recombinant and plasma vaccines. No statistically significant difference of effectiveness was observed between a reinforced vaccination series versus routine vaccinations of three inoculations of recombinant vaccine

Vascular Dynamics of Cerebral Gliomas Investigated with Selective Catheter Angiography, Perfusion CT and MRI

Purpose:: To assess if intratumoral blood circulation parameters from dynamic susceptibility contrast (DSC) MRI and dynamic CT deliver comparable results and to compare tumor-related changes in regional cerebral blood flow (rCBF) and regional cerebral blood volume (rCBV) with arterial, intratumoral, and venous transition detected with digital subtraction angiography (DSA). Patients and Methods:: Ten patients with cerebral gliomas were prospectively studied with DSC-MRI, dynamic CT and DSA. Tumor areas were segmented and perfusion maps for rCBF, rCBV, and mean transit time (MTT) were computed from DSC-MRI and dynamic CT. Arterial circulation time (ACT), intermediate circulation time (ICT), and venous appearance time (VAT) were measured with DSA. Asymmetry indices (AIs) were calculated for MRI- and CT-based perfusion values, for ICT and VAT and compared among each other. Results:: DSC-MRI and dynamic CT yielded comparable AI values for rCBF (MRI: 39.5 ± 20.4, CT: 36.0 ± 17.9, Pearson's correlation r2 = 0.91) and rCBV (MRI: 44.6 ± 20.9 vs. CT: 40.9 ± 16.3, r2 = 0.84). The MTT AI (MRI: -4.7 ± 11.2 vs. CT: -0.5 ± 10.4, r2 = 0.47) showed only a weak correlation. ICT correlated with rCBV (ICT: 38.4 ± 14.7, r2 = 0.59, and dynamic CT: r2 = 0.81) and VAT with rCBF (VAT: 31.7 ± 17.6, r2 = 0.73, and dynamic CT: r2 = 0.87), but not with MTT. Conclusion:: CT and MRI methods provide consistent information about tumor vascularity of cerebral gliomas in accordance with DS

Employment Protection and Domestic Violence: Addressing Abuse in the Labor Grievance Process

The effects of domestic violence are not limited to the home environment. Its effects are felt in employment when abused employees are absent from work and when violent incidents erupt in the workplace. For example, a bruised employee might be too injured and embarrassed to attend work, or an estranged spouse might stalk and harass a victim on the job. Another issue arises in that employers often discipline victims of domestic violence for absenteeism and incidents of violence that occur in the workplace. Discipline of union members is governed by collective bargaining agreements and subject to the labor grievance process. These grievances often end in arbitration, where the union represents the battered employee. Because of this occurrence, employers, unions, and arbitrators must be educated about domestic violence to ensure victims of abuse receive adequate job protection