A New Perspective and Extension of the Gaussian Filter

The Gaussian Filter (GF) is one of the most widely used filtering algorithms; instances are the Extended Kalman Filter, the Unscented Kalman Filter and the Divided Difference Filter. GFs represent the belief of the current state by a Gaussian with the mean being an affine function of the measurement. We show that this representation can be too restrictive to accurately capture the dependences in systems with nonlinear observation models, and we investigate how the GF can be generalized to alleviate this problem. To this end, we view the GF from a variational-inference perspective. We analyse how restrictions on the form of the belief can be relaxed while maintaining simplicity and efficiency. This analysis provides a basis for generalizations of the GF. We propose one such generalization which coincides with a GF using a virtual measurement, obtained by applying a nonlinear function to the actual measurement. Numerical experiments show that the proposed Feature Gaussian Filter (FGF) can have a substantial performance advantage over the standard GF for systems with nonlinear observation models.Comment: Will appear in Robotics: Science and Systems (R:SS) 201

The Coordinate Particle Filter - A novel Particle Filter for High Dimensional Systems

Parametric filters, such as the Extended Kalman Filter and the Unscented Kalman Filter, typically scale well with the dimensionality of the problem, but they are known to fail if the posterior state distribution cannot be closely approximated by a density of the assumed parametric form. For nonparametric filters, such as the Particle Filter, the converse holds. Such methods are able to approximate any posterior, but the computational requirements scale exponentially with the number of dimensions of the state space. In this paper, we present the Coordinate Particle Filter which alleviates this problem. We propose to compute the particle weights recursively, dimension by dimension. This allows us to explore one dimension at a time, and resample after each dimension if necessary. Experimental results on simulated as well as real data confirm that the proposed method has a substantial performance advantage over the Particle Filter in high-dimensional systems where not all dimensions are highly correlated. We demonstrate the benefits of the proposed method for the problem of multi-object and robotic manipulator tracking

Taylor Approximations for Model Uncertainty within the Tweedie Exponential Dispersion Family

The use of generalized linear models (GLM) to estimate claims reserves has become a standard method in insurance. Most frequently, the exponential dispersion family (EDF) is used; see e.g. England, Verrall. We study the so-called Tweedie EDF and test the sensitivity of the claims reserves and their mean square error of predictions (MSEP) over this family. Furthermore, we develop second order Taylor approximations for the claims reserves and the MSEPs for members of the Tweedie family that are difficult to obtain in practice, but are close enough to models for which claims reserves and MSEP estimations are easy to determine. As a result of multiple case studies, we find that claims reserves estimation is relatively insensitive to which distribution is chosen amongst the Tweedie family, in contrast to the MSEP, which varies widel

Proteome remodelling during development from blood to insect-form Trypanosoma brucei quantified by SILAC and mass spectrometry

Trypanosoma brucei is the causative agent of human African sleeping sickness and Nagana in cattle. In addition to being an important pathogen T. brucei has developed into a model system in cell biology

Nurses' and physicians' reported difficulties and enablers to recognising and reporting child abuse in Swiss paediatric emergency and paediatric surgery departments - an observational study.

BACKGROUND Under-detection and under-reporting of child abuse remains a considerable challenge in paediatric care, with a high number of cases missed each year in Switzerland and abroad. Published data regarding the obstacles and facilitators of detecting and reporting child maltreatment among paediatric nursing and medical staff in the paediatric emergency department (PED) are scarce. Despite the existence of international guidelines, the measures taken to counteract the incomplete detection of harm done to children in paediatric care are insufficient. AIM We sought to examine up-to-date obstacles and enablers for detecting and reporting child abuse among nursing and medical staff in PED and paediatric surgery departments in Switzerland. METHODS We surveyed 421 nurses and physicians working in PEDs and on paediatric surgical wards in six large Swiss paediatric hospitals using an online questionnaire between February 1, 2017, and August 31, 2017. RESULTS The survey was returned by 261/421 (62.0%) respondents (complete n = 200, 76.6%; incomplete n = 61, 23.3%) with a preponderance of nurses (n = 150/261; 57.5%), 106/261 (40.6%) physicians, and 1/261 (0.4%) psychologists (n = 4/261; 1.5% missing profession). The stated obstacles to reporting child abuse were uncertainty about the diagnosis (n = 58/80; 72.5%), feeling unaccountable for notification (n = 28/80; 35%), uncertainty of whether reporting has any consequences (n = 5/80; 6.25%), lack of time (n = 4/80; 5%), forgetting to report (n = 2/80; 2.5%), and parental protection (n = 2/80; 2.5%) (unspecific answer, n = 4/80; 5%, multiple answers were possible, therefore items don not sum up to 100%). Even though most (n = 249/261 95.4%) respondents had previously been confronted with child abuse at/outside work, only 185/245 (75.5%) reported cases; significantly fewer nursing (n = 100/143, 69.9%) than medical staff (n = 83/99, 83.8%) (p = 0.013). Furthermore, significantly more nursing (n = 27/33; 81.8%) than medical staff (n = 6/33; 18.2%) (p = 0.005) reported a discrepancy between the number of suspected and reported cases (total 33/245 (13.5%). An overwhelming amount of participants were strongly interested in mandatory child abuse training (n= 226/242, 93.4%) and in the availability of standardised patient questionnaires and documentation forms (n = 185/243, 76.1%). CONCLUSION In line with previous studies, insufficient knowledge about and lack of confidence in detecting the signs and symptoms of child abuse were the principal obstacles to reporting maltreatment. To finally address this unacceptable gap in child abuse detection, we recommend the implementation of mandatory child protection education in all countries where no such education has been implemented in addition to the introduction of cognitive aid tools and validated screening tools to increase child abuse detection rates and ultimately prevent further harm to children

Representation with Incomplete Votes

Platforms for online civic participation rely heavily on methods for condensing thousands of comments into a relevant handful, based on whether participants agree or disagree with them. These methods should guarantee fair representation of the participants, as their outcomes may affect the health of the conversation and inform impactful downstream decisions. To that end, we draw on the literature on approval-based committee elections. Our setting is novel in that the approval votes are incomplete since participants will typically not vote on all comments. We prove that this complication renders non-adaptive algorithms impractical in terms of the amount of information they must gather. Therefore, we develop an adaptive algorithm that uses information more efficiently by presenting incoming participants with statements that appear promising based on votes by previous participants. We prove that this method satisfies commonly used notions of fair representation, even when participants only vote on a small fraction of comments. Finally, an empirical evaluation using real data shows that the proposed algorithm provides representative outcomes in practice

Draft Genome Sequences of Enterococcus mundtii Strains Isolated from Beef Slaughterhouses in Kenya

We present here draft genome sequences of Enterococcus mundtii strains K7-EM, P2-EM, C11-EM, and H18-EM, which were isolated from slaughterhouse equipment, carcasses, and personnel of small- and medium-sized beef slaughterhouses in Kenya

Prototyping a Tool for Processing Genetic Meta-Data in Microbiological Laboratories

Next generation sequencing (NGS) technologies allow improved understanding of pathogens. In the upstream processing of generating genomic data, there is still a lack of process-oriented tools for managing corresponding meta data. In this paper, we provide a description of how a process-oriented software prototype was developed that allowed the capture and collation of metadata involved when doing NGS. Our question was: How to develop an interactive web application that supports the process-oriented management of genetic data independent of any sequencing technique