Maximum margin Bayesian network classifiers

We present a maximum margin parameter learning algorithm for Bayesian network classifiers using a conjugate gradient (CG) method for optimization. In contrast to previous approaches, we maintain the normalization constraints on the parameters of the Bayesian network during optimization, i.e., the probabilistic interpretation of the model is not lost. This enables us to handle missing features in discriminatively optimized Bayesian networks. In experiments, we compare the classification performance of maximum margin parameter learning to conditional likelihood and maximum likelihood learning approaches. Discriminative parameter learning significantly outperforms generative maximum likelihood estimation for naive Bayes and tree augmented naive Bayes structures on all considered data sets. Furthermore, maximizing the margin dominates the conditional likelihood approach in terms of classification performance in most cases. We provide results for a recently proposed maximum margin optimization approach based on convex relaxation. While the classification results are highly similar, our CG-based optimization is computationally up to orders of magnitude faster. Margin-optimized Bayesian network classifiers achieve classification performance comparable to support vector machines (SVMs) using fewer parameters. Moreover, we show that unanticipated missing feature values during classification can be easily processed by discriminatively optimized Bayesian network classifiers, a case where discriminative classifiers usually require mechanisms to complete unknown feature values in the data first.     

A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots

This paper addresses the problem of building large-scale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximum-likelihood estimation problem. It then devises a practical algorithm for generating the most likely map from data, along with the most likely path taken by the robot. Experimental results in cyclic environments of size up to 80 by 25 meter illustrate the appropriateness of the approach.     

Regional Process Modeling for Design for Environment

This research is an effort towards providing higher level Design for Environment (DFE) tools for a broad industrial region. Issues ranging from the levels immediately above existing design tools, to the envisioned highest level for a broad geographical region are discussed. A tool for the regional planning of the DFE activities is proposed, considering a model construction based on material flows across the industry. The Multi-Lifecycle approach is supported by organizing the input/output flows for industries, potentially utilizing waste material, side products and recycling. Capitalizing on the conceptual integration of the design and process activities, an Abstract Design Environment is used for the design of the basically process oriented material flow tool. Within the context, the relations among design, process and flow-modeling concepts are discussed.     

Bayesian Landmark Learning for Mobile Robot Localization

To operate successfully in indoor environments, mobile robots must be able to localize themselves. Most current localization algorithms lack flexibility, autonomy, and often optimality, since they rely on a human to determine what aspects of the sensor data to use in localization (e.g., what landmarks to use). This paper describes a learning algorithm, called BaLL, that enables mobile robots to learn what features/landmarks are best suited for localization, and also to train artificial neural networks for extracting them from the sensor data. A rigorous Bayesian analysis of probabilistic localization is presented, which produces a rational argument for evaluating features, for selecting them optimally, and for training the networks that approximate the optimal solution. In a systematic experimental study, BaLL outperforms two other recent approaches to mobile robot localization.     

Mapping of continuous floristic gradients in grasslands using hyperspectral imagery

Transitions between plant species assemblages are often continuous with the form of the transition dependent on the ‘slope’ of environmental gradients and on the style of self-organization in vegetation. Image segmentation can present misleading or even erroneous results if applied to continuous spatial changes in vegetation. Even methods that allow for multiple-class memberships of pixels presuppose the existence of ideal types of species assemblages that constitute mixtures—an assumption that does not fit the case of continua where any section of a gradient is as ‘pure’ as any other section like in modulations of grassland species composition.Thus, we attempted to spatially model floristic gradients in Bavarian meadows by extrapolating axes of an unconstrained ordination of species data. The models were based on high-resolution hyperspectral airborne imagery. We further modelled the distribution of plant functional response types (Ellenberg indicator values) and the cover values of selected species. The models were made with partial least squares (PLS) regression analyses. The realistic utility of the regression models was evaluated by full leave-one-out cross-validation.The modelled floristic gradients showed a considerable agreement with ground-based observations of floristic gradients (R²=0.71 and 0.66 for the first two axes of ordination). Apart from mapping the most important continuous floristic differences, we mapped gradients in the appearance of plant functional response groups as represented by averaged Ellenberg indicator values for soil pH (R²=0.76), water supply (R²=0.66) and nutrient supply (R²=0.75), while models for the cover of single species were weak.Compared to many other vegetation attributes, plant species composition is difficult to detect with remote sensing techniques. This is partly caused by a lack of compatibility between methods of vegetation ecology and remote sensing. We believe that the present study has the potential to increase compatibility as neither spectral nor vegetation information gets lost by a classifying step.     

Axiomatizing the Monodic Fragment of

As we can show that the two-variable monadic fragment of thefull first-order extension of the propositional branching time logic is not recursively enumerable, we show in thisarticle that the monodic fragment of can be axiomatized.     

JavAdaptor—Flexible runtime updates of Java applications

Software is changed frequently during its life cycle. New requirements come, and bugs must be fixed. To update an application, it usually must be stopped, patched, and restarted. This causes time periods of unavailability, which is always a problem for highly available applications. Even for the development of complex applications, restarts to test new program parts can be time consuming and annoying. Thus, we aim at dynamic software updates to update programs at runtime. There is a large body of research on dynamic software updates, but so far, existing approaches have shortcomings either in terms of flexibility or performance. In addition, some of them depend on specific runtime environments and dictate the program's architecture. We present JavAdaptor , the first runtime update approach based on Java that (a) offers flexible dynamic software updates, (b) is platform independent, (c) introduces only minimal performance overhead, and (d) does not dictate the program architecture. JavAdaptor combines schema changing class replacements by class renaming and caller updates with Java HotSwap using containers and proxies. It runs on top of all major standard Java virtual machines. We evaluate our approach's applicability and performance in non‐trivial case studies and compare it with existing dynamic software update approaches. Copyright © 2012 John Wiley & Sons, Ltd.     

Hyperinsulinemic Hypoglycemia Associated with a CaV1.2 Variant with Mixed Gain- and Loss-of-Function Effects

The voltage-dependent L-type calcium channel isoform Ca_V1.2 is critically involved in many physiological processes, e.g., in cardiac action potential formation, electromechanical coupling and regulation of insulin secretion by beta cells. Gain-of-function mutations in the calcium voltage-gated channel subunit alpha 1 C (CACNA1C) gene, encoding the Ca_V1.2 α₁-subunit, cause Timothy syndrome (TS), a multisystemic disorder that includes autism spectrum disorders and long QT (LQT) syndrome. Strikingly, TS patients frequently suffer from hypoglycemia of yet unproven origin. Using next-generation sequencing, we identified a novel heterozygous CACNA1C mutation in a patient with congenital hyperinsulinism (CHI) and associated hypoglycemic episodes. We characterized the electrophysiological phenotype of the mutated channel using voltage-clamp recordings and in silico action potential modeling experiments. The identified Ca_V1.2^L566P mutation causes a mixed electrophysiological phenotype of gain- and loss-of-function effects. In silico action potential modeling supports that this mixed electrophysiological phenotype leads to a tissue-specific impact on beta cells compared to cardiomyocytes. Thus, CACNA1C variants may be associated with non-syndromic hyperinsulinemic hypoglycemia without long-QT syndrome, explained by very specific electrophysiological properties of the mutated channel. We discuss different biochemical characteristics and clinical impacts of hypoglycemia in the context of CACNA1C variants and show that these may be associated with significant morbidity for Timothy Syndrome patients. Our findings underline that the potential of hypoglycemia warrants careful attention in patients with CACNA1C variants, and such variants should be included in the differential diagnosis of non-syndromic congenital hyperinsulinism.     

Topical Capsaicin in Poly(lactic-co-glycolic)acid (PLGA) Nanoparticles Decreases Acute Itch and Heat Pain

Background: Capsaicin, the hot pepper agent, produces burning followed by desensitization. To treat localized itch or pain with minimal burning, low capsaicin concentrations can be repeatedly applied. We hypothesized that alternatively controlled release of capsaicin from poly(lactic-co-glycolic acid) (PLGA) nanoparticles desensitizes superficially terminating nociceptors, reducing burning. Methods: Capsaicin-loaded PLGA nanoparticles were prepared (single-emulsion solvent evaporation) and characterized (size, morphology, capsaicin loading, encapsulation efficiency, in vitro release profile). Capsaicin-PLGA nanoparticles were applied to murine skin and evaluated in healthy human participants (n = 21) for 4 days under blinded conditions, and itch and nociceptive sensations evoked by mechanical, heat stimuli and pruritogens cowhage, β-alanine, BAM8-22 and histamine were evaluated. Results: Nanoparticles (loading: 58 µg capsaicin/mg) released in vitro 23% capsaicin within the first hour and had complete release at 72 h. In mice, 24 h post-application Capsaicin-PLGA nanoparticles penetrated the dermis and led to decreased nociceptive behavioral responses to heat and mechanical stimulation (desensitization). Application in humans produced a weak to moderate burning, dissipating after 3 h. A loss of heat pain up to 2 weeks was observed. After capsaicin nanoparticles, itch and nociceptive sensations were reduced in response to pruritogens cowhage, β-alanine or BAM8-22, but were normal to histamine. Conclusions: Capsaicin nanoparticles could be useful in reducing pain and itch associated with pruritic diseases that are histamine-independent.