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We consider continuous quantum measurement of a superconducting qubit in the circuit QED setup with a moderate bandwidth of the measurement resonator, i.e., when the “bad-cavity” limit is not applicable. The goal is a simple des...
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We consider continuous quantum measurement of a superconducting qubit in the circuit QED setup with a moderate bandwidth of the measurement resonator, i.e., when the “bad-cavity” limit is not applicable. The goal is a simple description of the quantum evolution due to measurement, i.e., the measurement backaction. Extending the quantum Bayesian approach previously developed for the bad-cavity regime, we show that the evolution equations remain the same, but now they should be applied to the entangled qubit-resonator state, instead of the qubit state alone. The derivation uses only elementary quantum mechanics and basic properties of coherent states, thus being accessible to nonexperts.
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Over the last 25 years, several studies have tested for a link between geomagnetic field intensity and reversal frequency. However, despite a large increase in the number of absolute paleointensity determinations, and improved met...
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Over the last 25 years, several studies have tested for a link between geomagnetic field intensity and reversal frequency. However, despite a large increase in the number of absolute paleointensity determinations, and improved methods for obtaining such data, two competing models have arisen. Here we employ a new tool for objectively analyzing paleomagnetic time series to investigate the possibility of a link between reversal frequency and paleointensity. Transdimensional Markov chain Monte Carlo techniques are applied to a quality-filtered version of the global paleointensity (PINT) database for the last 202 Myr to model long-term paleointensity behavior. A large ensemble of models is sampled, from which a final representative mean model is extracted. The resulting paleointensity model confirms published conclusions that the single-silicate crystal method gives significantly different results from more conventional whole rock paleointensity methods; this makes it difficult to jointly model the two data types in the same analysis. When the much larger whole rock data set is considered, a stable paleointensity of 5.46±0.28 × 10~(22) A/m~2 for the last 202 Myr is consistent with the 95% confidence interval of the paleointensity model. Statistical tests indicate no significant correlation between reversal frequency and field intensity at the 0.05 level. However, this result is likely due to the characteristics of the PINT database rather than being a genuine, physically representative conclusion. Given the paucity of data and general state of the global paleointensity database, concerted efforts to increase the number of high-quality, well-dated paleointensity data are required before conclusions about a link between geomagnetic field intensity and reversal frequency can be confidently drawn.
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When the dynamics of liquids and disordered systems at mesoscopic level is investigated by means of inelastic scattering (e.g., neutron or x ray), spectra are often characterized by a poor definition of the excitation lines and sp...
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When the dynamics of liquids and disordered systems at mesoscopic level is investigated by means of inelastic scattering (e.g., neutron or x ray), spectra are often characterized by a poor definition of the excitation lines and spectroscopic features in general and one important issue is to establish howmany of these lines need to be included in the modeling function and to estimate their parameters. Furthermore, when strongly damped excitations are present, commonly used and widespread fitting algorithms are particularly affected by the choice of initial values of the parameters. An inadequate choice may lead to an inefficient exploration of the parameter space, resulting in the algorithm getting stuck in a local minimum. In this paper, we present a Bayesian approach to the analysis of neutron Brillouin scattering data in which the number of excitation lines is treated as unknown and estimated along with the other model parameters. We propose a joint estimation procedure based on a reversible-jump Markov chain Monte Carlo algorithm, which efficiently explores the parameter space, producing a probabilistic measure to quantify the uncertainty on the number of excitation lines as well as reliable parameter estimates. The method proposed could turn out of great importance in extracting physical information from experimental data, especially when the detection of spectral features is complicated not only because of the properties of the sample, but also because of the limited instrumental resolution and count statistics. The approach is tested on generated data set and then applied to real experimental spectra of neutron Brillouin scattering from a liquid metal, previously analyzed in a more traditional way.
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Differentiating C_3 and C_4 grass pollen in the paleorecord is difficult because of their morphological similarity. Using a spooling wire microcombustion device interfaced with an isotope ratio mass spectrometer, Single Pollen Iso...
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Differentiating C_3 and C_4 grass pollen in the paleorecord is difficult because of their morphological similarity. Using a spooling wire microcombustion device interfaced with an isotope ratio mass spectrometer, Single Pollen Isotope Ratio AnaLysis (SPIRAL) enables classification of grass pollen as C_3 or C_4 based upon δ~(13)C values. To address several limitations of this novel technique, we expanded an existing SPIRAL training dataset of pollen δ~(13)C data from 8 to 31 grass species. For field validation, we analyzed δ~(13)C of individual grains of grass pollen from the surface sediments of 15 lakes in Africa and Australia, added these results to a prior dataset of 10 lakes from North America, and compared C_4-pollen abundance in surface sediments with C_4-grass abundance on the surrounding landscape. We also developed and tested a hierarchical Bayesian model to estimate the relative abundance of C_3- and C_4-grass pollen in unknown samples, including an estimation of the likelihood that either pollen type is present in a sample. The mean (±SD) δ~(13)C values for the C_3 and C_4 grasses in the training dataset were -29.6±9.5‰ and -13.8±9.5‰, respectively. Across a range of % C_4 in samples of known composition, the average bias of the Bayesian model was <3% for C_4 in samples of at least 50 grains, indicating that the model accurately predicted the relative abundance of C_4 grass pollen. The hierarchical framework of the model resulted in less bias than a previous threshold-based C_3/C_4 classification method, especially near the high or low extremes of C_4 abundance. In addition, the percent of C_4 grass pollen in surface-sediment samples estimated using the model was strongly related to the abundance of C_4 grasses on the landscape (n=24, p<0.001, r~2=0.65). These results improve δ~(13)C-based quantitative reconstructions of grass community composition in the paleorecord and demonstrate the utility of the Bayesian framework to aid the interpretation of stable isotope data.
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The populations are usually compared with respect to their means to establish superiority of one population over the other or to check if the two populations are equivalent. In this paper we shall compare two normal populations wi...
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The populations are usually compared with respect to their means to establish superiority of one population over the other or to check if the two populations are equivalent. In this paper we shall compare two normal populations with respect to their variances. The two populations might be two different lots of ammunition and of might be the (population) target dispersion of the ith lot, or the two populations might be two different measuring instruments and σ_i~2 might be the (population) variance of measurement of the rth instrument. This variance, which characterizes the reproducibility of repeated measurements of the same quantity, can be used as an index of the precision of the measuring instrument. There has been only one paper on this subject (Bechhofer and Sobel, 1954). In this paper we introduce a new approach for choosing between two normal population variances. When a choice has to be made in favor of one of two populations, the cost of sampling (experimenting) in order to obtain information on which to base the decision must be balanced against the cost of making the wrong choice. The cost of sampling is assumed to be the cost of an incorrect choice for half the sample (which is divided between the two populations). Fixed sample size and sequential experiments are considered. Bayesian approaches are used for determining the optimal size of a fixed sample experiment and the optimal position of the boundaries of a sequential experiment.
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In 2013, one of Sweden's largest archaeological excavations started in association with the building of the European Spallation Source (ESS) multidisciplinary research center in Lund. The 160 radiocarbon dates that were produced f...
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In 2013, one of Sweden's largest archaeological excavations started in association with the building of the European Spallation Source (ESS) multidisciplinary research center in Lund. The 160 radiocarbon dates that were produced for the project represent the most exhaustive dating program for a Scandinavian site so far and provide evidence for the human impact and activities on the site from the Mesolithic to the Iron Age. This article presents the results within a Bayesian statistical framework for the 70 C-14 dates from the Early Neolithic settlement (object 1) and a burial site with dolmens and wooden faades. For the first time, a highly precise chronology provides deeper insight into the Neolithization processes and the early settlement strategies in southern Scandinavia from similar to 3800 cal BC onwards.
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Sixteen Korean feline panleukopenia virus (FPLV) strains were compared with 48 non-Korean strains and two vaccine strains to conduct phylogenetic analysis of the FPLVs currently circulating among cats in Korea. Most of the residue...
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Sixteen Korean feline panleukopenia virus (FPLV) strains were compared with 48 non-Korean strains and two vaccine strains to conduct phylogenetic analysis of the FPLVs currently circulating among cats in Korea. Most of the residues that discriminate between FPLVs and canine parvoviruses (CPV-2, -2a, -2b, and -2c), including 80-Lys, 93-Lys, 103-Val, 323-Asp, 564-Asn, and 568-Ala, were conserved in the Korean FLPVs; however, exceptions were observed in two strains, namely K50/08 (80-Gln) and V142 (323-Asn). Phylogenetic analysis using the Bayesian inference and Neighbor-joining method showed that FPLVs were not segregated on a clear temporal or geographical basis. Three clusters (G1, G2, and G3) were formed by the VP2 nucleotide sequences analysed and Korean strains belonged to the Cl (n = 13) and G2 (n = 3) clusters. The ratio of non-synonymous to synonymous substitutions (dN/dS) revealed that purifying selection acts on the VP2 gene of Korean FPLVs
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Single-particle cryogenic electron microscopy (cryo-EM) has emerged as a powerful technique to visualize the structural landscape sampled by a protein complex. However, algorithmic and computational bottlenecks in analyzing hetero...
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Single-particle cryogenic electron microscopy (cryo-EM) has emerged as a powerful technique to visualize the structural landscape sampled by a protein complex. However, algorithmic and computational bottlenecks in analyzing heterogeneous cryo-EM datasets have prevented the full realization of this potential. CryoDRGN is a machine learning system for heterogeneous cryo-EM reconstruction of proteins and protein complexes from single-particle cryo-EM data. Central to this approach is a deep generative model for heterogeneous cryo-EM density maps, which we empirically find is effective in modeling both discrete and continuous forms of structural variability. Once trained, cryoDRGN is capable of generating an arbitrary number of 3D density maps, and thus interpreting the resulting ensemble is a challenge. Here, we showcase interactive and automated processing approaches for analyzing cryoDRGN results. Specifically, we detail a step-by-step protocol for the analysis of an existing assembling 50S ribosome dataset, including preparation of inputs, network training and visualization of the resulting ensemble of density maps. Additionally, we describe and implement methods to comprehensively analyze and interpret the distribution of volumes with the assistance of an associated atomic model. This protocol is appropriate for structural biologists familiar with processing single-particle cryo-EM datasets and with moderate experience navigating Python and Jupyter notebooks. It requires 3-4 days to complete. CryoDRGN is open source software that is freely available.
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Airborne volcanic ash can pose a hazard to aviation, agriculture, and both human and animal health. It is therefore important that ash clouds are monitored both day and night, even when they travel far from their source. Infrared ...
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Airborne volcanic ash can pose a hazard to aviation, agriculture, and both human and animal health. It is therefore important that ash clouds are monitored both day and night, even when they travel far from their source. Infrared satellite data provide perhaps the only means of doing this, and since the hugely expensive ash crisis that followed the 2010 Eyjafjallj?kull eruption, much research has been carried out into techniques for discriminating ash in such data and for deriving key properties. Such techniques are generally specific to data from particular sensors, and most approaches result in a binary classification of pixels into “ash” and “ash free” classes with no indication of the classification certainty for individual pixels. Furthermore, almost all operational methods rely on expert-set thresholds to determine what constitutes “ash” and can therefore be criticized for being subjective and dependent on expertise that may not remain with an institution. Very few existing methods exploit available contemporaneous atmospheric data to inform the detection, despite the sensitivity of most techniques to atmospheric parameters. The Bayesian method proposed here does exploit such data and gives a probabilistic, physically based classification. We provide an example of the method’s implementation for a scene containing both land and sea observations, and a large area of desert dust (often misidentified as ash by other methods). The technique has already been successfully applied to other detection problems in remote sensing, and this work shows that it will be a useful and effective tool for ash detection.
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In this paper, we present an efficient unsupervised Bayesian approach and a prior distribution adapted to piecewise regular images. This approach is based on a hierarchical prior distribution promoting sparsity on image gradients....
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In this paper, we present an efficient unsupervised Bayesian approach and a prior distribution adapted to piecewise regular images. This approach is based on a hierarchical prior distribution promoting sparsity on image gradients. It is fully automatic since hyperparameters are estimated jointly with the image of interest. The estimation of all unknowns is performed efficiently thanks to a fast variational Bayesian approximation method. We highlight the good performance of the proposed approach through comparisons with state of the art approaches on an application to a diffraction tomographic problem. (C) 2019 Elsevier B.V. All rights reserved.
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