Fast Update of Conditional Simulation Ensembles

Gaussian random field (GRF) conditional simulation is a key ingredient in many spatial statistics problems for computing Monte-Carlo estimators and quantifying uncertainties on non-linear functionals of GRFs conditional on data. Conditional simulations are known to often be computer intensive, especially when appealing to matrix decomposition approaches with a large number of simulation points. This work studies settings where conditioning observations are assimilated batch sequentially, with one point or a batch of points at each stage. Assuming that conditional simulations have been performed at a previous stage, the goal is to take advantage of already available sample paths and by-products to produce updated conditional simulations at mini- mal cost. Explicit formulae are provided, which allow updating an ensemble of sample paths conditioned on n ≥ 0 observations to an ensemble conditioned on n + q observations, for arbitrary q ≥ 1. Compared to direct approaches, the proposed formulae proveto substantially reduce computational complexity. Moreover, these formulae explicitly exhibit how the q new observations are updating the old sample paths. Detailed complexity calculations highlighting the benefits of this approach with respect to state-of-the-art algorithms are provided and are complemented by numerical experiments.

The calculation of Afρ and mass loss rate for comets

Ab initio calculations of Afρ are presented using Mie scattering theory and a Direct Simulation Monte Carlo (DSMC) dust outflow model in support of the Rosetta mission and its target 67P/Churyumov-Gerasimenko (CG). These calculations are performed for particle sizes ranging from 0.010 μm to 1.0 cm. The present status of our knowledge of various differential particle size distributions is reviewed and a variety of particle size distributions is used to explore their effect on Afρ , and the dust mass production View the MathML sourcem˙. A new simple two parameter particle size distribution that curtails the effect of particles below 1 μm is developed. The contributions of all particle sizes are summed to get a resulting overall Afρ. The resultant Afρ could not easily be predicted a priori and turned out to be considerably more constraining regarding the mass loss rate than expected. It is found that a proper calculation of Afρ combined with a good Afρ measurement can constrain the dust/gas ratio in the coma of comets as well as other methods presently available. Phase curves of Afρ versus scattering angle are calculated and produce good agreement with observational data. The major conclusions of our calculations are: – The original definition of A in Afρ is problematical and Afρ should be: qsca(n,λ)×p(g)×f×ρqsca(n,λ)×p(g)×f×ρ. Nevertheless, we keep the present nomenclature of Afρ as a measured quantity for an ensemble of coma particles.– The ratio between Afρ and the dust mass loss rate View the MathML sourcem˙ is dominated by the particle size distribution. – For most particle size distributions presently in use, small particles in the range from 0.10 to 1.0 μm contribute a large fraction to Afρ. – Simplifying the calculation of Afρ by considering only large particles and approximating qsca does not represent a realistic model. Mie scattering theory or if necessary, more complex scattering calculations must be used. – For the commonly used particle size distribution, dn/da ∼ a−3.5 to a−4, there is a natural cut off in Afρ contribution for both small and large particles. – The scattering phase function must be taken into account for each particle size; otherwise the contribution of large particles can be over-estimated by a factor of 10. – Using an imaginary index of refraction of i = 0.10 does not produce sufficient backscattering to match observational data. – A mixture of dark particles with i ⩾ 0.10 and brighter silicate particles with i ⩽ 0.04 matches the observed phase curves quite well. – Using current observational constraints, we find the dust/gas mass-production ratio of CG at 1.3 AU is confined to a range of 0.03–0.5 with a reasonably likely value around 0.1.

Late Pliocene age models of Agulhas Plateau sediment cores

An ensemble of new, high-resolution records of surface ocean hydrography from the Indian-Atlantic oceanic gateway, south of Africa, demonstrates recurrent and high-amplitude salinity oscillations in the Agulhas Leakage area during the penultimate glacial-interglacial cycle. A series of millennial-scale salinification events, indicating strengthened salt leakage into the South Atlantic, appear to correlate with abrupt changes in the North Atlantic climate and Atlantic Meridional Overturning Circulation (AMOC). This interhemispheric coupling, which plausibly involved changes in the Hadley Cell and midlatitude westerlies that impacted the interocean transport at the tip of Africa, suggests that the Agulhas Leakage acted as a source of negative buoyancy for the perturbed AMOC, possibly aiding its return to full strength. Our finding points to the Indian-to-Atlantic salt transport as a potentially important modulator of the AMOC during the abrupt climate changes of the Late Pleistocene.

Geochemical and isotopic composition of basalts from the axial Mid-Atlantic Ridge, South Atlantic

One of the essential problems of oceanic tectonics is estimation of the influence of plumes of the deep hot mantle on processes in the axial spreading zone. Areas of two giant (St. Helena and Tristan da Cunha) plumes in the Mid-Atlantic Ridge (MAR) rift zone (South Atlantic) are characterized by the effusion of basalts that differ from typical depleted riftogenic tholeiites by anomalously high contents of lithophile components and specific isotopic compositions. Moreover, the rift valley floor with basalt effusion is significantly uplifted above the adjacent sectors of the rift. The formation of the St. Helena Seamount located 400 km east of the MAR axis is related to magmatism that is active to this day. St. Helena Island is a member of the structural ensemble of large volcanic seamounts (Bonaparte, Bagration, and Kutuzov). Like St. Helena Island, each seamount incorporates a series of smaller rises of different morphologies and dimensions. Thus, a system of subparallel series of NE-trending (~45°) rises extend from the seamount ensemble to the African continent. According to the plate tectonics concept, the seamount series represent hotspots related to a deep mantle plume that can be projected onto the present-day St. Helena Island area (St. Helena plume). At the same time, the inferred topographic map based on satellite altimetry data shows that the seamount series also extend along the opposite southwestern direction (~225°) toward the axial MAR and even intersect the latter structure. This fact cannot be explained by the hotspot hypothesis, which suggests stationary positions of plumes relative to the mobile oceanic plate. In the course of Cruise 10 of the R/V Akademik Ioffe (2002), detailed geological and geophysical investigations were carried out at the junction of one structural series with the MAR rift zone located near the Martin Vaz Fracture Zone (Martin Vaz test area, 19°-20° S). The present communication is devoted to the study of lithology, geochemistry, and isotopy of basalts dredged at the test area.

Sampling sites, sampled birds and plants of young larch plantations in Tokachi district, eastern Hokkaido, northern Japan, 2011

Models and data used to describe species-area relationships confound sampling with ecological process as they fail to acknowledge that estimates of species richness arise due to sampling. This compromises our ability to make ecological inferences from and about species-area relationships. We develop and illustrate hierarchical community models of abundance and frequency to estimate species richness. The models we propose separate sampling from ecological processes by explicitly accounting for the fact that sampled patches are seldom completely covered by sampling plots and that individuals present in the sampling plots are imperfectly detected. We propose a multispecies abundance model in which community assembly is treated as the summation of an ensemble of species-level Poisson processes and estimate patch-level species richness as a derived parameter. We use sampling process models appropriate for specific survey methods. We propose a multispecies frequency model that treats the number of plots in which a species occurs as a binomial process. We illustrate these models using data collected in surveys of early-successional bird species and plants in young forest plantation patches. Results indicate that only mature forest plant species deviated from the constant density hypothesis, but the null model suggested that the deviations were too small to alter the form of species-area relationships. Nevertheless, results from simulations clearly show that the aggregate pattern of individual species density-area relationships and occurrence probability-area relationships can alter the form of species-area relationships. The plant community model estimated that only half of the species present in the regional species pool were encountered during the survey. The modeling framework we propose explicitly accounts for sampling processes so that ecological processes can be examined free of sampling artefacts. Our modeling approach is extensible and could be applied to a variety of study designs and allows the inclusion of additional environmental covariates.

A constitutive model for the in-plane mechanical behavior of nonwoven fabrics

A constitutive model is presented for the in-plane mechanical behavior of nonwoven fabrics. The model is developed within the context of the finite element method and provides the constitutive response for a mesodomain of the fabric corresponding to the area associated to a finite element. The model is built upon the ensemble of three blocks, namely fabric, fibers and damage. The continuum tensorial formulation of the fabric response rigorously takes into account the effect of fiber rotation for large strains and includes the nonlinear fiber behavior. In addition, the various damage mechanisms experimentally observed (bond and fiber fracture, interfiber friction and fiber pull-out) are included in a phenomenological way and the random nature of these materials is also taken into account by means of a Monte Carlo lottery to determine the damage thresholds. The model results are validated with recent experimental results on the tensile response of smooth and notched specimens of a polypropylene nonwoven fabric.

Smoothing of PV power fluctuations by geographical dispersion

The quality and the reliability of the power generated by large grid-connected photovoltaic (PV) plants are negatively affected by the source characteristic variability. This paper deals with the smoothing of power fluctuations because of geographical dispersion of PV systems. The fluctuation frequency and the maximum fluctuation registered at a PV plant ensemble are analyzed to study these effects. We propose an empirical expression to compare the fluctuation attenuation because of both the size and the number of PV plants grouped. The convolution of single PV plants frequency distribution functions has turned out to be a successful tool to statistically describe the behavior of an ensemble of PV plants and determine their maximum output fluctuation. Our work is based on experimental 1-s data collected throughout 2009 from seven PV plants, 20 MWp in total, separated between 6 and 360 km.

Electrical Power Fluctuations in a Network of DC/AC inverters in a Large PV Plant: relationship between correlation, distance and time scale

This paper analyzes the correlation between the fluctuations of the electrical power generated by the ensemble of 70 DC/AC inverters from a 45.6 MW PV plant. The use of real electrical power time series from a large collection of photovoltaic inverters of a same plant is an impor- tant contribution in the context of models built upon simplified assumptions to overcome the absence of such data. This data set is divided into three different fluctuation categories with a clustering proce- dure which performs correctly with the clearness index and the wavelet variances. Afterwards, the time dependent correlation between the electrical power time series of the inverters is esti- mated with the wavelet transform. The wavelet correlation depends on the distance between the inverters, the wavelet time scales and the daily fluctuation level. Correlation values for time scales below one minute are low without dependence on the daily fluctuation level. For time scales above 20 minutes, positive high correlation values are obtained, and the decay rate with the distance depends on the daily fluctuation level. At intermediate time scales the correlation depends strongly on the daily fluctuation level. The proposed methods have been implemented using free software. Source code is available as supplementary material.

Collective Organization of Complex Networks: Emergent Scales, Vulnerability, and Targeting of Desired Dynamics

Cuando una colectividad de sistemas dinámicos acoplados mediante una estructura irregular de interacciones evoluciona, se observan dinámicas de gran complejidad y fenómenos emergentes imposibles de predecir a partir de las propiedades de los sistemas individuales. El objetivo principal de esta tesis es precisamente avanzar en nuestra comprensión de la relación existente entre la topología de interacciones y las dinámicas colectivas que una red compleja es capaz de mantener. Siendo este un tema amplio que se puede abordar desde distintos puntos de vista, en esta tesis se han estudiado tres problemas importantes dentro del mismo que están relacionados entre sí. Por un lado, en numerosos sistemas naturales y artificiales que se pueden describir mediante una red compleja la topología no es estática, sino que depende de la dinámica que se desarrolla en la red: un ejemplo son las redes de neuronas del cerebro. En estas redes adaptativas la propia topología emerge como consecuencia de una autoorganización del sistema. Para conocer mejor cómo pueden emerger espontáneamente las propiedades comúnmente observadas en redes reales, hemos estudiado el comportamiento de sistemas que evolucionan según reglas adaptativas locales con base empírica. Nuestros resultados numéricos y analíticos muestran que la autoorganización del sistema da lugar a dos de las propiedades más universales de las redes complejas: a escala mesoscópica, la aparición de una estructura de comunidades, y, a escala macroscópica, la existencia de una ley de potencias en la distribución de las interacciones en la red. El hecho de que estas propiedades aparecen en dos modelos con leyes de evolución cuantitativamente distintas que siguen unos mismos principios adaptativos sugiere que estamos ante un fenómeno que puede ser muy general, y estar en el origen de estas propiedades en sistemas reales. En segundo lugar, proponemos una medida que permite clasificar los elementos de una red compleja en función de su relevancia para el mantenimiento de dinámicas colectivas. En concreto, estudiamos la vulnerabilidad de los distintos elementos de una red frente a perturbaciones o grandes fluctuaciones, entendida como una medida del impacto que estos acontecimientos externos tienen en la interrupción de una dinámica colectiva. Los resultados que se obtienen indican que la vulnerabilidad dinámica es sobre todo dependiente de propiedades locales, por tanto nuestras conclusiones abarcan diferentes topologías, y muestran la existencia de una dependencia no trivial entre la vulnerabilidad y la conectividad de los elementos de una red. Finalmente, proponemos una estrategia de imposición de una dinámica objetivo genérica en una red dada e investigamos su validez en redes con diversas topologías que mantienen regímenes dinámicos turbulentos. Se obtiene como resultado que las redes heterogéneas (y la amplia mayora de las redes reales estudiadas lo son) son las más adecuadas para nuestra estrategia de targeting de dinámicas deseadas, siendo la estrategia muy efectiva incluso en caso de disponer de un conocimiento muy imperfecto de la topología de la red. Aparte de la relevancia teórica para la comprensión de fenómenos colectivos en sistemas complejos, los métodos y resultados propuestos podrán dar lugar a aplicaciones en sistemas experimentales y tecnológicos, como por ejemplo los sistemas neuronales in vitro, el sistema nervioso central (en el estudio de actividades síncronas de carácter patológico), las redes eléctricas o los sistemas de comunicaciones. ABSTRACT The time evolution of an ensemble of dynamical systems coupled through an irregular interaction scheme gives rise to dynamics of great of complexity and emergent phenomena that cannot be predicted from the properties of the individual systems. The main objective of this thesis is precisely to increase our understanding of the interplay between the interaction topology and the collective dynamics that a complex network can support. This is a very broad subject, so in this thesis we will limit ourselves to the study of three relevant problems that have strong connections among them. First, it is a well-known fact that in many natural and manmade systems that can be represented as complex networks the topology is not static; rather, it depends on the dynamics taking place on the network (as it happens, for instance, in the neuronal networks in the brain). In these adaptive networks the topology itself emerges from the self-organization in the system. To better understand how the properties that are commonly observed in real networks spontaneously emerge, we have studied the behavior of systems that evolve according to local adaptive rules that are empirically motivated. Our numerical and analytical results show that self-organization brings about two of the most universally found properties in complex networks: at the mesoscopic scale, the appearance of a community structure, and, at the macroscopic scale, the existence of a power law in the weight distribution of the network interactions. The fact that these properties show up in two models with quantitatively different mechanisms that follow the same general adaptive principles suggests that our results may be generalized to other systems as well, and they may be behind the origin of these properties in some real systems. We also propose a new measure that provides a ranking of the elements in a network in terms of their relevance for the maintenance of collective dynamics. Specifically, we study the vulnerability of the elements under perturbations or large fluctuations, interpreted as a measure of the impact these external events have on the disruption of collective motion. Our results suggest that the dynamic vulnerability measure depends largely on local properties (our conclusions thus being valid for different topologies) and they show a non-trivial dependence of the vulnerability on the connectivity of the network elements. Finally, we propose a strategy for the imposition of generic goal dynamics on a given network, and we explore its performance in networks with different topologies that support turbulent dynamical regimes. It turns out that heterogeneous networks (and most real networks that have been studied belong in this category) are the most suitable for our strategy for the targeting of desired dynamics, the strategy being very effective even when the knowledge on the network topology is far from accurate. Aside from their theoretical relevance for the understanding of collective phenomena in complex systems, the methods and results here discussed might lead to applications in experimental and technological systems, such as in vitro neuronal systems, the central nervous system (where pathological synchronous activity sometimes occurs), communication systems or power grids.

A study of feature combination for vehicle detection based on image processing

Video analytics play a critical role in most recent traffic monitoring and driver assistance systems. In this context, the correct detection and classification of surrounding vehicles through image analysis has been the focus of extensive research in the last years. Most of the pieces of work reported for image-based vehicle verification make use of supervised classification approaches and resort to techniques, such as histograms of oriented gradients (HOG), principal component analysis (PCA), and Gabor filters, among others. Unfortunately, existing approaches are lacking in two respects: first, comparison between methods using a common body of work has not been addressed; second, no study of the combination potentiality of popular features for vehicle classification has been reported. In this study the performance of the different techniques is first reviewed and compared using a common public database. Then, the combination capabilities of these techniques are explored and a methodology is presented for the fusion of classifiers built upon them, taking into account also the vehicle pose. The study unveils the limitations of single-feature based classification and makes clear that fusion of classifiers is highly beneficial for vehicle verification.

Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces

This study explored the utility of the impact response surface (IRS) approach for investigating model ensemble crop yield responses under a large range of changes in climate. IRSs of spring and winter wheat Triticum aestivum yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect. The sensitivity of modelled yield to systematic increments of changes in temperature (-2 to +9°C) and precipitation (-50 to +50%) was tested by modifying values of baseline (1981 to 2010) daily weather, with CO2 concentration fixed at 360 ppm. The IRS approach offers an effective method of portraying model behaviour under changing climate as well as advantages for analysing, comparing and presenting results from multi-model ensemble simulations. Though individual model behaviour occasionally departed markedly from the average, ensemble median responses across sites and crop varieties indicated that yields decline with higher temperatures and decreased precipitation and increase with higher precipitation. Across the uncertainty ranges defined for the IRSs, yields were more sensitive to temperature than precipitation changes at the Finnish site while sensitivities were mixed at the German and Spanish sites. Precipitation effects diminished under higher temperature changes. While the bivariate and multi-model characteristics of the analysis impose some limits to interpretation, the IRS approach nonetheless provides additional insights into sensitivities to inter-model and inter-annual variability. Taken together, these sensitivities may help to pinpoint processes such as heat stress, vernalisation or drought effects requiring refinement in future model development.

Structural kinetics of transcription activation at the malT promoter of Escherichia coli by UV laser footprinting

We have studied the kinetics of transcriptional initiation and activation at the malT and malTp1 promoters of Escherichia coli using UV laser footprinting. Contrary to previous studies and because of the very rapid signal acquisition by this technique, we can obtain structural information about true reaction intermediates of transcription initiation. The consequences of adding a transcriptional activator, the cAMP receptor protein/cAMP complex (CRP), are monitored in real time, permitting us to assign specific interactions to the activation of discrete steps in transcription initiation. Direct protein–protein contacts between CRP and the RNA polymerase appeared very rapidly, followed by DNA melting around the −10 hexamer. CRP slightly increased the rate of this isomerization reaction but, more importantly, favored the establishment of additional contacts between the DNA upstream of the CRP binding site and RNA polymerase subsequent to open complex formation. These contacts make a major contribution to transcriptional activation by stabilizing open forms of the promoter complex, thereby indirectly accelerating promoter escape. The ensemble of the kinetic, structural signals demonstrated directly that CRP exerts most of its activating effects on the late stages of transcriptional initiation at the malT promoter.

A transient expansion of the native state precedes aggregation of recombinant human interferon-γ

Aggregation of proteins, even under conditions favoring the native state, is a ubiquitous problem in biotechnology and biomedical engineering. Providing a mechanistic basis for the pathways that lead to aggregation should allow development of rational approaches for its prevention. We have chosen recombinant human interferon-γ (rhIFN-γ) as a model protein for a mechanistic study of aggregation. In the presence of 0.9 M guanidinium hydrochloride, rhIFN-γ aggregates with first order kinetics, a process that is inhibited by addition of sucrose. We describe a pathway that accounts for both the observed first-order aggregation of rhIFN-γ and the effect of sucrose. In this pathway, aggregation proceeds through a transient expansion of the native state. Sucrose shifts the equilibrium within the ensemble of rhIFN-γ native conformations to favor the most compact native species over more expanded ones, thus stabilizing rhIFN-γ against aggregation. This phenomenon is attributed to the preferential exclusion of sucrose from the protein surface. In addition, kinetic analysis combined with solution thermodynamics shows that only a small (9%) expansion surface area is needed to form the transient native state that precedes aggregation. The approaches used here link thermodynamics and aggregation kinetics to provide a powerful tool for understanding both the pathway of protein aggregation and the rational use of excipients to inhibit the process.

The structure and precision of retinal spike trains

Assessing the reliability of neuronal spike trains is fundamental to an understanding of the neural code. We measured the reproducibility of retinal responses to repeated visual stimuli. In both tiger salamander and rabbit, the retinal ganglion cells responded to random flicker with discrete, brief periods of firing. For any given cell, these firing events covered only a small fraction of the total stimulus time, often less than 5%. Firing events were very reproducible from trial to trial: the timing jitter of individual spikes was as low as 1 msec, and the standard deviation in spike count was often less than 0.5 spikes. Comparing the precision of spike timing to that of the spike count showed that the timing of a firing event conveyed several times more visual information than its spike count. This sparseness and precision were general characteristics of ganglion cell responses, maintained over the broad ensemble of stimulus waveforms produced by random flicker, and over a range of contrasts. Thus, the responses of retinal ganglion cells are not properly described by a firing probability that varies continuously with the stimulus. Instead, these neurons elicit discrete firing events that may be the fundamental coding symbols in retinal spike trains.

Observation of strange kinetics in protein folding

Highly nonexponential folding kinetics in aqueous solution have been observed during temperature jump-induced refolding of two proteins, yeast phosphoglycerate kinase and a ubiquitin mutant. The observations are most easily interpreted in terms of downhill folding, which posits a heterogeneous ensemble of structures en route to the folded state. The data are also reconciled with exponential kinetics measured under different experimental conditions and with titration experiments indicating cooperative folding.