Sequential sampling of junction trees for decomposable graphs

The junction-tree representation provides an attractive structural property for organizing a decomposable graph. In this study, we present a novel stochastic algorithm, which we call the junction-tree expander, for sequential sampling of junction trees for decomposable graphs. We show that recursive application of the junction-tree expander, expanding incrementally the underlying graph with one vertex at a time, has full support on the space of junction trees with any given number of underlying vertices. A direct application of our suggested algorithm is demonstrated in a sequential Monte Carlo setting designed for sampling from distributions on spaces of decomposable graphs, where the junction-tree expander can be effectively employed as proposal kernel; see the companion paper Olsson et al. 2019 [16]. A numerical study illustrates the utility of our approach by two examples: in the first one, how the junction-tree expander can be incorporated successfully into a particle Gibbs sampler for Bayesian structure learning in decomposable graphical models; in the second one, we provide an unbiased estimator of the number of decomposable graphs for a given number of vertices. All the methods proposed in the paper are implemented in the Python library trilearn.Comment: 31 pages, 7 figure

B747/JT9D flight loads and their effect on engine running clearances and performance deterioration; BCAC NAIL/P and WA JT9D engine diagnostics programs

Flight loads on the 747 propulsion system and resulting JT9D blade to outer airseal running clearances during representative acceptance flight and revenue flight sequences were measured. The resulting rub induced clearance changes, and engine performance changes were then analyzed to validate and refine the JT9D-7A short term performance deterioration model

Sensor Adaptation and Development in Robots by Entropy Maximization of Sensory Data

A method is presented for adapting the sensors of a robot to the statistical structure of its current environment. This enables the robot to compress incoming sensory information and to find informational relationships between sensors. The method is applied to creating sensoritopic maps of the informational relationships of the sensors of a developing robot, where the informational distance between sensors is computed using information theory and adaptive binning. The adaptive binning method constantly estimates the probability distribution of the latest inputs to maximize the entropy in each individual sensor, while conserving the correlations between different sensors. Results from simulations and robotic experiments with visual sensors show how adaptive binning of the sensory data helps the system to discover structure not found by ordinary binning. This enables the developing perceptual system of the robot to be more adapted to the particular embodiment of the robot and the environment

Discovering Motion Flow by Temporal-Informational Correlations in Sensors

A method is presented for adapting the sensors of a robot to its current environment and to learn motion flow detection by observing the informational relations between sensors and actuators. Examples are shown where the robot learns to detect motion flow from sensor data generated by its own movement

A case-control study of rheumatoid arthritis identifies an associated single nucleotide polymorphism in the NCF4 gene, supporting a role for the NADPH-oxidase complex in autoimmunity

Rheumatoid arthritis (RA) is a chronic inflammatory disease with a heritability of 60%. Genetic contributions to RA are made by multiple genes, but only a few gene associations have yet been confirmed. By studying animal models, reduced capacity of the NADPH-oxidase (NOX) complex, caused by a single nucleotide polymorphism (SNP) in one of its components (the NCF1 gene), has been found to increase severity of arthritis. To our knowledge, however, no studies investigating the potential role played by reduced reactive oxygen species production in human RA have yet been reported. In order to examine the role played by the NOX complex in RA, we investigated the association of 51 SNPs in five genes of the NOX complex (CYBB, CYBA, NCF4, NCF2, and RAC2) in a Swedish case-control cohort consisting of 1,842 RA cases and 1,038 control individuals. Several SNPs were found to be mildly associated in men in NCF4 (rs729749, P = 0.001), NCF2 (rs789181, P = 0.02) and RAC2 (rs1476002, P = 0.05). No associations were detected in CYBA or CYBB. By stratifying for autoantibody status, we identified a strong association for rs729749 (in NCF4) in autoantibody negative disease, with the strongest association detected in rheumatoid factor negative men (CT genotype versus CC genotype: odds ratio 0.34, 95% confidence interval 0.2 to 0.6; P = 0.0001). To our knowledge, this is the first genetic association identified between RA and the NOX complex, and it supports previous findings from animal models of the importance of reactive oxygen species production capacity to the development of arthritis

Designing transformative spaces for sustainability in social-ecological systems

Transformations toward sustainability have recently gained traction, triggered in part by a growing recognition of the dramatic socio-cultural, political, economic, and technological changes required to move societies toward more desirable futures in the Anthropocene. However, there is a dearth of literature that emphasizes the crucial aspects of sustainability transformations in the diverse contexts of the Global South. Contributors to this Special Feature aim to address this gap by weaving together a series of case studies that together form an important navigational tool on the “how to” as well as the “what” and the “where to” of sustainability transformations across diverse challenges, sectors, and geographies. They propose the term “transformative space” as a “safe-enough” collaborative process whereby actors invested in sustainability transformations can experiment with new mental models, ideas, and practices that can help shift social-ecological systems onto more desirable pathways. The authors also highlight the challenges posed to researchers as they become “transformative space-makers,” navigating the power dynamics inherent in these processes. Because researchers and practitioners alike are challenged to provide answers to complex and often ambiguous or incomplete questions around sustainability, the ideas, reflections and learning gathered in this Special Feature provide some guidance on new ways of engaging with the world

Long-wavelength spin- and spin-isospin correlations in nucleon matter

We analyse the long-wavelength response of a normal Fermi liquid using Landau theory. We consider contributions from intermediate states containing one additional quasiparticle-quasihole pair as well as those from states containing two or more additional quasiparticle-quasihole pairs. For the response of an operator corresponding to a conserved quantity, we show that the behavior of matrix elements to states with more than one additional quasiparticle-quasihole pair at low excitation energies $\omega$ varies as $1/\omega$. It is shown how rates of processes involving transitions to two quasiparticle-quasihole states may be calculated in terms of the collision integral in the Landau transport equation for quasiparticles.Comment: 10 pages, 3 figure