Uncorrelated Model Research Articles

On structure-based priors in Bayesian geophysical inversion

Abstract Bayesian methods are extensively used to analyse geophysical data sets. A critical and somewhat overlooked component of high-dimensional Bayesian inversion is the definition of the prior probability density function that describes the joint probability of model parameters before considering available data sets. If insufficient prior information is available about model parameter correlations, then it is tempting to assume that model parameters are uncorrelated. When working with a spatially gridded model representation, this overparametrization leads to posterior realizations with far too much variability to be deemed realistic from a geological perspective. In this study, we introduce a new approach for structure-based prior sampling with Markov chain Monte Carlo that is suitable when only limited prior information is available. We evaluate our method using model structure measures related to standard roughness and damping metrics for l1- and l2-norms. We show that our structure-based prior approach is able to adequately sample the chosen prior distribution of model structure. The usefulness and applicability of the methodology is demonstrated on synthetic and field-based crosshole ground penetrating radar data. We find that our method provides posterior model realizations and statistics that are significantly more satisfactory than those based on underlying assumptions of uncorrelated model parameters or on explicit penalties on model structure within an empirical Bayes framework.

Fossil record of stem groups employed in evaluating the chronogram of insects (Arthropoda: Hexapoda).

Insecta s. str. (=Ectognatha), comprise the largest and most diversified group of living organisms, accounting for roughly half of the biodiversity on Earth. Understanding insect relationships and the specific time intervals for their episodes of radiation and extinction are critical to any comprehensive perspective on evolutionary events. Although some deeper nodes have been resolved congruently, the complete evolution of insects has remained obscure due to the lack of direct fossil evidence. Besides, various evolutionary phases of insects and the corresponding driving forces of diversification remain to be recognized. In this study, a comprehensive sample of all insect orders was used to reconstruct their phylogenetic relationships and estimate deep divergences. The phylogenetic relationships of insect orders were congruently recovered by Bayesian inference and maximum likelihood analyses. A complete timescale of divergences based on an uncorrelated log-normal relaxed clock model was established among all lineages of winged insects. The inferred timescale for various nodes are congruent with major historical events including the increase of atmospheric oxygen in the Late Silurian and earliest Devonian, the radiation of vascular plants in the Devonian, and with the available fossil record of the stem groups to various insect lineages in the Devonian and Carboniferous.

Coherent Change Detection Using InSAR Temporal Decorrelation Model: A Case Study for Volcanic Ash Detection

Detection of changes caused by major events-such as earthquakes, volcanic eruptions, and floods-from interferometric synthetic aperture radar (SAR) data is challenging because of the coupled effects with temporal decorrelation caused by natural phenomena, including rain, snow, wind, and seasonal changes. The coupled effect of major events and natural phenomena sometimes leads to misinterpretation of interferometric coherence maps and often degrades the performance of change detection algorithms. To differentiate decorrelation sources caused by natural changes from those caused by an event of interest, we formulated a temporal decorrelation model that accounts for the random motion of canopy elements, temporally correlated dielectric changes, and temporally uncorrelated dielectric changes of canopy and ground. The model parameters are extracted from the interferometric pairs associated with natural changes in canopy and ground using the proposed temporal decorrelation model. In addition, the cumulative distribution functions of the temporally uncorrelated model parameters, which are associated with natural changes in canopy and ground, are estimated from interferometric pairs acquired before the event. Model parameters are also extracted from interferometric SAR data acquired across the event and compared with the cumulative probabilities of natural changes in order to calculate the probability of a major event. Subsequently, pixels with cumulative probabilities greater than 75% are marked as changed due to the event. A case study for detecting volcanic ash during the eruption of the Shinmoedake volcano in January 2011 was carried out using L-band Advanced Land Observation Satellite PALSAR data.

Investigation of the effect of correlated uncertain rate parameters on a model of hydrogen combustion using a generalized HDMR method

The High Dimensional Model Representation (HDMR) method has been applied in several previous studies to obtain global sensitivity indices of uncorrelated model parameters in combustion systems. However, the rate parameters of combustion models are intrinsically correlated and therefore uncertainty analysis methods are needed that can handle such parameters. A generalized HDMR method is presented here, which uses the Rosenblatt transformation on a correlated model parameter sample to obtain a sample of independent parameters. The method provides a full set of both correlated and marginal sensitivity indices. Ignition delay times predicted by an optimized hydrogen–air combustion model in stoichiometric mixtures near the three explosion limits are investigated with this new global sensitivity analysis tool. The sensitivity indices which account for all the correlated effects of the rate parameters are shown to dominate uncertainties in the model output. However, these correlated indices mask the individual influence of parameters. The final marginal uncorrelated sensitivity indices for individual parameters better indicate the change of importance of homogeneous gas phase and species wall-loss reactions as the pressure is increased from above the first explosion limit to above the third limit. However, these uncorrelated indices are small and whilst they provide insights into the dominant chemical and physical processes of the model over the range of conditions studied, the correlations between parameters have a very significant effect. The implications of this result on model tuning will be discussed.

Equine rhinitis B viruses in horse fecal samples from the Middle East.

BackgroundAmong all known picornaviruses, only two species, equine rhinitis A virus and equine rhinitis B virus (ERBV) are known to infect horses, causing respiratory infections. No reports have described the detection of ERBV in fecal samples of horses and no complete genome sequences of ERBV3 are available.MethodsWe performed a molecular epidemiology study to detect ERBVs in horses from Dubai and Hong Kong. Complete genome sequencing of the ERBVs as well as viral loads and genome, phylogenetic and evolutionary analysis were performed on the positive samples.ResultsERBV was detected in four (13.8 %) of the 29 fecal samples in horses from Dubai, with viral loads 8.28 × 103 to 5.83 × 104 copies per ml, but none of the 47 fecal samples in horses from Hong Kong by RT-PCR. Complete genome sequencing and phylogenetic analysis showed that three of the four strains were ERBV3 and one was ERBV2. The major difference between the genomes of ERBV3 and those of ERBV1 and ERBV2 lied in the amino acid sequences of their VP1 proteins. The Ka/Ks ratios of all the coding regions in the ERBV3 genomes were all <0.1, suggesting that ERBV3 were stably evolving in horses. Using the uncorrelated lognormal distributed relaxed clock model on VP1 gene, the date of the most recent common ancestor (MRCA) of ERBV3 was estimated to be 1785 (HPDs, 1176 to 1937) and the MRCA dates of ERBV1 and ERBV2 were estimated to be 1848 (HPDs, 1466 to 1949) respectively.ConclusionsBoth acid stable (ERBV3) and acid labile (ERBV2) ERBVs could be found in fecal samples of horses. Detection of ERBVs in fecal samples would have implications for their transmission and potential role in gastrointestinal diseases as well as fecal sampling as an alternative method of identifying infected horses.

Nonlinear Statistical Reconstruction for Flat-Panel Cone-Beam CT with Blur and Correlated Noise Models.

Flat-panel cone-beam CT (FP-CBCT) is a promising imaging modality, partly due to its potential for high spatial resolution reconstructions in relatively compact scanners. Despite this potential, FP-CBCT can face difficulty resolving important fine scale structures (e.g, trabecular details in dedicated extremities scanners and microcalcifications in dedicated CBCT mammography). Model-based methods offer one opportunity to improve high-resolution performance without any hardware changes. Previous work, based on a linearized forward model, demonstrated improved performance when both system blur and spatial correlations characteristics of FP-CBCT systems are modeled. Unfortunately, the linearized model relies on a staged processing approach that complicates tuning parameter selection and can limit the finest achievable spatial resolution. In this work, we present an alternative scheme that leverages a full nonlinear forward model with both system blur and spatially correlated noise. A likelihood-based objective function is derived from this forward model and we derive an iterative optimization algorithm for its solution. The proposed approach is evaluated in simulation studies using a digital extremities phantom and resolution-noise trade-offs are quantitatively evaluated. The correlated nonlinear model outperformed both the uncorrelated nonlinear model and the staged linearized technique with up to a 86% reduction in variance at matched spatial resolution. Additionally, the nonlinear models could achieve finer spatial resolution (correlated: 0.10 mm, uncorrelated: 0.11 mm) than the linear correlated model (0.15 mm), and traditional FDK (0.40 mm). This suggests the proposed nonlinear approach may be an important tool in improving performance for high-resolution clinical applications.

Structural and dynamical properties of supercritical H2O–SiO2 fluids studied by ab initio molecular dynamics

In this study we report the structure of supercritical H2O–SiO2 fluid composed of 50mol% H2O and 50mol% SiO2 at 3000K and 2400K, investigated by means of ab initio molecular dynamics of models comprising 192 and 96 atoms. The density is set constant to 1.88g/cm3, which yields a pressure of 4.3GPa at 3000K and 3.6GPa at 2400K. Throughout the trajectories, water molecules are formed and dissociated via the network modifying reaction 2 SiOH=SiOSi+H2O. The calculation of the reaction constant K=[OH-]2/[H2O][O2-] is carried out on the basis of the experimentally relevant Qn species notation and agrees well with an extrapolation of experimental data to 3000K. After quench from 3000K to 2400K, the degree of polymerization of the silicate network in the 192-atom models increases noticeably within several tens of picoseconds, accompanied by release of molecular H2O. An unexpected opposite trend is observed in smaller 96-atom models, due to a finite size effect, as several uncorrelated models of 192 and 96 atoms indicate. The temperature-dependent slowing down of the H2O–silica interaction dynamics is described on the basis of the bond autocorrelation function.

Measuring mixing patterns in complex networks by Spearman rank correlation coefficient

In this paper, we utilize Spearman rank correlation coefficient to measure mixing patterns in complex networks. Compared with the widely used Pearson coefficient, Spearman coefficient is rank-based, nonparametric, and size-independent. Thus it is more effective to assess linking patterns of diverse networks, especially for large-size networks. We demonstrate this point by testing a variety of empirical and artificial networks. Moreover, we show that normalized Spearman ranks of stubs are subject to an interesting linear rule where the correlation coefficient is just the Spearman coefficient. This compelling linear relationship allows us to directly produce networks with any prescribed Spearman coefficient. Our method apparently has an edge over the well known uncorrelated configuration model.

Fluctuations in Electronic Energy Affecting Singlet Fission Dynamics and Mixing with Charge-Transfer State: Quantum Dynamics Study.

Singlet fission is a spin-allowed process by which a singlet excited state is converted to two triplet states. To understand mechanisms of the ultrafast fission via a charge transfer (CT) state, one has investigated the dynamics through quantum-dynamical calculations with the uncorrelated fluctuation model; however, the electronic states are expected to experience the same fluctuations induced by the surrounding molecules because the electronic structure of the triplet pair state is similar to that of the singlet state except for the spin configuration. Therefore, the fluctuations in the electronic energies could be correlated, and the 1D reaction coordinate model may adequately describe the fission dynamics. In this work we develop a model for describing the fission dynamics to explain the experimentally observed behaviors. We also explore impacts of fluctuations in the energy of the CT state on the fission dynamics and the mixing with the CT state. The overall behavior of the dynamics is insensitive to values of the reorganization energy associated with the transition from the singlet state to the CT state, although the coherent oscillation is affected by the fluctuations. This result indicates that the mixing with the CT state is rather robust under the fluctuations in the energy of the CT state as well as the high-lying CT state.

Best-case performance of quantum annealers on native spin-glass benchmarks: How chaos can affect success probabilities

Recent tests performed on the D-Wave Two quantum annealer have revealed no clear evidence of speedup over conventional silicon-based technologies. Here, we present results from classical parallel-tempering Monte Carlo simulations combined with isoenergetic cluster moves of the archetypal benchmark problem-an Ising spin glass-on the native chip topology. Using realistic uncorrelated noise models for the D-Wave Two quantum annealer, we study the best-case resilience, i.e., the probability that the ground-state configuration is not affected by random fields and random-bond fluctuations found on the chip. We thus compute classical upper-bound success probabilities for different types of disorder used in the benchmarks and predict that an increase in the number of qubits will require either error correction schemes or a drastic reduction of the intrinsic noise found in these devices. We outline strategies to develop robust, as well as hard benchmarks for quantum annealing devices, as well as any other computing paradigm affected by noise.

Estimating Divergence Times and Substitution Rates in Rhizobia.

Accurate estimation of divergence times of soil bacteria that form nitrogen-fixing associations with most leguminous plants is challenging because of a limited fossil record and complexities associated with molecular clocks and phylogenetic diversity of root nodule bacteria, collectively called rhizobia. To overcome the lack of fossil record in bacteria, divergence times of host legumes were used to calibrate molecular clocks and perform phylogenetic analyses in rhizobia. The 16S rRNA gene and intergenic spacer region remain among the favored molecular markers to reconstruct the timescale of rhizobia. We evaluate the performance of the random local clock model and the classical uncorrelated lognormal relaxed clock model, in combination with four tree models (coalescent constant size, birth–death, birth–death incomplete sampling, and Yule processes) on rhizobial divergence time estimates. Bayes factor tests based on the marginal likelihoods estimated from the stepping-stone sampling analyses strongly favored the random local clock model in combination with Yule process. Our results on the divergence time estimation from 16S rRNA gene and intergenic spacer region sequences are compatible with age estimates based on the conserved core genes but significantly older than those obtained from symbiotic genes, such as nodIJ genes. This difference may be due to the accelerated evolutionary rates of symbiotic genes compared to those of other genomic regions not directly implicated in nodulation processes.

Model-based iterative reconstruction for flat-panel cone-beam CT with focal spot blur, detector blur, and correlated noise

While model-based reconstruction methods have been successfully applied to flat-panel cone-beam CT (FP-CBCT) systems, typical implementations ignore both spatial correlations in the projection data as well as system blurs due to the detector and focal spot in the x-ray source. In this work, we develop a forward model for flat-panel-based systems that includes blur and noise correlation associated with finite focal spot size and an indirect detector (e.g. scintillator). This forward model is used to develop a staged reconstruction framework where projection data are deconvolved and log-transformed, followed by a generalized least-squares reconstruction that utilizes a non-diagonal statistical weighting to account for the correlation that arises from the acquisition and data processing chain. We investigate the performance of this novel reconstruction approach in both simulated data and in CBCT test-bench data. In comparison to traditional filtered backprojection and model-based methods that ignore noise correlation, the proposed approach yields a superior noise-resolution tradeoff. For example, for a system with 0.34 mm FWHM scintillator blur and 0.70 FWHM focal spot blur, using the correlated noise model instead of an uncorrelated noise model increased resolution by 42% (with variance matched at 6.9 × 10−8 mm−2). While this advantage holds across a wide range of systems with differing blur characteristics, the improvements are greatest for systems where source blur is larger than detector blur.

Optimality of Transmit Beamforming in Spatially Correlated MIMO Rician Fading Channels

This work exploits the algorithm for the "rate-optimum beamformer" in spatially correlated MIMO Rician fading channels and proposes a probabilistic analysis for the "optimality of transmit beamforming condition", (the necessary and sufficient condition when beamforming achieves ergodic capacity). The proposed analysis allows now for simulations to be performed in order to study the effects of the channel and system parameters on the optimality of transmit beamforming condition. Results demonstrate that the spatially correlated MIMO Rician fading channel model incorporates known characteristics from the corresponding spatially uncorrelated model, while they also reveal new and unique characteristics, not observed or discussed in the open literature previously: (a) the optimality condition is affected by the angle between the dominant eigenvector of the channel mean matrix and the dominant eigenvector of the channel covariance matrix, (b) the manner that the optimality condition is affected by the transmit antenna correlation varies with respect to the remaining channel distribution parameters.

Phylogenetic Analysis of HIV-1 CRF65_CPX Reveals Yunnan Province Is Still a Source Contributing to the Spread of HIV-1 in China

Phylogenetic Analysis of HIV-1 CRF65_CPX Reveals Yunnan Province Is Still a Source Contributing to the Spread of HIV-1 in China

A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity.

BackgroundSolar archival tags (henceforth called geolocators) are tracking devices deployed on animals to reconstruct their long-distance movements on the basis of locations inferred post hoc with reference to the geographical and seasonal variations in the timing and speeds of sunrise and sunset. The increased use of geolocators has created a need for analytical tools to produce accurate and objective estimates of migration routes that are explicit in their uncertainty about the position estimates.ResultsWe developed a hidden Markov chain model for the analysis of geolocator data. This model estimates tracks for animals with complex migratory behaviour by combining: (1) a shading-insensitive, template-fit physical model, (2) an uncorrelated random walk movement model that includes migratory and sedentary behavioural states, and (3) spatially explicit behavioural masks.The model is implemented in a specially developed open source R package FLightR. We used the particle filter (PF) algorithm to provide relatively fast model posterior computation. We illustrate our modelling approach with analysis of simulated data for stationary tags and of real tracks of both a tree swallow Tachycineta bicolor migrating along the east and a golden-crowned sparrow Zonotrichia atricapilla migrating along the west coast of North America.ConclusionsWe provide a model that increases accuracy in analyses of noisy data and movements of animals with complicated migration behaviour. It provides posterior distributions for the positions of animals, their behavioural states (e.g., migrating or sedentary), and distance and direction of movement.Our approach allows biologists to estimate locations of animals with complex migratory behaviour based on raw light data. This model advances the current methods for estimating migration tracks from solar geolocation, and will benefit a fast-growing number of tracking studies with this technology.

Geographies of an Online Social Network.

How is online social media activity structured in the geographical space? Recent studies have shown that in spite of earlier visions about the “death of distance”, physical proximity is still a major factor in social tie formation and maintenance in virtual social networks. Yet, it is unclear, what are the characteristics of the distance dependence in online social networks. In order to explore this issue the complete network of the former major Hungarian online social network is analyzed. We find that the distance dependence is weaker for the online social network ties than what was found earlier for phone communication networks. For a further analysis we introduced a coarser granularity: We identified the settlements with the nodes of a network and assigned two kinds of weights to the links between them. When the weights are proportional to the number of contacts we observed weakly formed, but spatially based modules resemble to the borders of macro-regions, the highest level of regional administration in the country. If the weights are defined relative to an uncorrelated null model, the next level of administrative regions, counties are reflected.

Convergence, Admissibility, and Fit of Alternative Confirmatory Factor Analysis Models for MTMM Data.

We compared six different analytic models for multitrait-multimethod (MTMM) data in terms of convergence, admissibility, and model fit to 258 samples of previously reported data. Two well-known models, the correlated trait-correlated method (CTCM) and the correlated trait-correlated uniqueness (CTCU) models, were fit for reference purposes in comparison to four other under- or unstudied models, including (a) Rindskopf's reparameterization of the CTCM (CTCM-R) model, (b) a correlated trait-constrained uncorrelated method model and two of its more general cases, (c) a correlated trait-constrained correlated method model, and (d) a correlated trait-uncorrelated method model. Results show that (a) the CTCM-R model often solved convergence and admissibility problems with the CTCM model at rates equivalent to the CTCU model and (b) constrained models often provided convergent and admissible solutions but significantly worse model fit, indicating that they are often not plausible when analyzing real data. A follow-up simulation study showed that the CTCM-R model also provided the most accurate estimates of the full range of parameters relevant to a confirmatory factor analytic model of MTMM data.

Characterisation of the relationship between Spirometra erinaceieuropaei and Diphyllobothrium species using complete cytb and cox1 genes

Spirometra erinaceieuropaei is an important diphyllobothriid cestode whose larval plerocercoid (sparganum) can parasitise humans, causing sparganosis. In China, sparganosis is an important foodborne parasitic zoonosis and poses a serious threat to human health. However, our knowledge of the taxonomic classification of S. erinaceieuropaei and its relationship with other diphyllobothriid cestodes, such as Diphyllobothrium, remains fragmentary. In this study, we determined the complete sequences of cytochrome b (cytb) and cytochrome c oxidase subunit I (cox1) genes from 61 isolates of S. erinaceieuropaei collected from 11 geographical locations in eastern China and compared them to the sequences of Diphyllobothrium species from the GenBank database. Then, we evaluated the genetic variation of S. erinaceieuropaei and the phylogenetic relationship between S. erinaceieuropaei and Diphyllobothrium. Both negative Tajima’s D and Fu’s FS values of neutrality tests and unimodal curve of mismatch distribution analyses supported demographic population expansion. Bayesian skyline plot (BSP) analyses indicated that a population expansion occurred in the Pleistocene. Phylogenetic analysis based on cytb, cox1, and cytb+cox1 under all three methods tested (neighbour-joining, maximum parsimony, and maximum likelihood) suggested that the S. erinaceieuropaei isolates from eastern China and the genus Diphyllobothrium are separate monophyletic groups. Furthermore, the uncorrelated log-normal relaxed molecular-clock model suggested that divergence between Spirometra and Diphyllobothrium began in the late Miocene.

Molecular phylogeny of the ‘Cape snow’ genus Syncarpha (Asteraceae: Gnaphalieae) reveals a need for generic re-delimitation

A phylogenetic hypothesis is presented for the charismatic but taxonomically poorly-known Cape daisy genus Syncarpha, based on near-complete ingroup sampling and good coverage of outgroup taxa. A combination of nuclear ribosomal and chloroplast spacer DNA sequence data gives a well-resolved phyogenetic hypothesis, the robustness of which is assessed via both parsimony bootstrap and Bayesian posterior probabilities based on the uncorrelated lognormal relaxed clock model. Syncarpha species fall into two well-supported and distantly-related clades that last shared a common ancestor in the mid-Miocene. The larger Syncarpha 1 grouping contains the type species and corresponds to African ‘Helipterum’; this clade is sister to Edmondia and belongs in a larger clade which also includes the Australian Gnaphalieae. The Syncarpha 2 clade contains the taxa associated with Syncarpha paniculata (formerly Helichrysum paniculatum) and is more closely related to Plecostachys and some species of Gnaphalium. Formal assessment of monophyly lays the groundwork for future revisionary taxonomic work.

Pre-asymptotic Transport Upscaling in Inertial and Unsteady Flows Through Porous Media

In most classical formulations of flow and transport through porous media Reynolds numbers are assumed to be small ( $$\mathrm{Re}<1$$ ), meaning that the role of inertia is considered negligible. However, many examples of practical relevance exist where this is not the case and inertial effects can be important leading to changes in flow structure and even giving rise to unsteady and turbulent flows as Reynolds numbers become larger. This change in flow structure can have a profound impact on how solutes are transported through the porous medium, influencing how effective large-scale transport should be modeled. Here we simulate, using high-resolution numerical models, flow and transport through an idealized porous medium for flow conditions over a range of Reynolds numbers, including steady and unsteady flows. For all these conditions we propose and test three upscaled models for transport—an advection dispersion equation, an uncorrelated spatial model (USM) and a spatial Markov model (SMM). The USM and SMM fall into the wider and more general family of continuous time random walk models. We test these models by their ability to reproduce pre-asymptotic and asymptotic plume second centered moments and breakthrough curves. We demonstrate that for steady flows where inertial effects are strong, the spatial Markov model outperforms the other two, faithfully capturing many of the non-Fickian features of transport, while for unsteady flows the uncorrelated spatial model performs best, due to the fact that unsteadiness in the flow field dampens the role of correlation on large scale transport. We conclude that correlation must be accounted for to properly upscale transport in steady flows, while it can be neglected in unsteady flows.