Correlation Effects in the Compton Profile of Silicon

Ab initio nonlocal pseudopotential variational quantum Monte Carlo techniques are used to compute the correlation effects on the valence momentum density and Compton profile of silicon. Our results for this case are in excellent agreement with the Lam-Platzman correction computed within the local density approximation. Within the approximations used, we rule out valence electron correlations as the dominant source of discrepancies between calculated and measured Compton profiles of silicon.

Immune activation in Retinal Aging: A Gene Expression Study.

PURPOSE
To investigate changes in gene expression during aging of the retina in the mouse.

METHODS
Total RNA was extracted from the neuroretina of young (3-month-old) and old (20-month-old) mice and processed for microarray analysis. Age-related, differentially expressed genes were assessed by the empiric Bayes shrinkagemoderated t-statistics method. Statistical significance was based on dual criteria of a ratio of change in gene expression >2 and a P < 0.01. Differential expression in 11 selected genes was further verified by real-time PCR. Functional pathways involved in retinal ageing were analyzed by an online software package (DAVID-2008) in differentially expressed gene lists. Age-related changes in differential expression in the identified retinal molecular pathways were further confirmed by immunohistochemical staining of retinal flat mounts and retinal cryosections.

RESULTS
With ageing of the retina, 298 genes were upregulated and 137 genes were downregulated. Functional annotation showed that genes linked to immune responses (Ir genes) and to tissue stress/injury responses (TS/I genes) were most likely to be modified by ageing. The Ir genes affected included those regulating leukocyte activation, chemotaxis, endocytosis, complement activation, phagocytosis, and myeloid cell differentiation, most of which were upregulated, with only a few downregulated. Increased microglial and complement activation in the aging retina was further confirmed by confocal microscopy of retinal tissues. The most strongly upregulated gene was the calcitonin receptor (Calcr; >40-fold in old versus young mice).

CONCLUSIONS
The results suggest that retinal ageing is accompanied by activation of gene sets, which are involved in local inflammatory responses. A modified form of low-grade chronic inflammation (para-inflammation) characterizes these aging changes and involves mainly the innate immune system. The marked upregulation of Calcr in ageing mice most likely reflects this chronic inflammatory/stress response, since calcitonin is a known systemic biomarker of inflammation/sepsis. © Association for Research in Vision and Ophthalmology.

Reducing Uncertainty in Aeroelastic Flutter Boundaries Using Experimental Data

Flutter prediction as currently practiced is usually deterministic, with a single structural model used to represent an aircraft. By using interval analysis to take into account structural variability, recent work has demonstrated that small changes in the structure can lead to very large changes in the altitude at which
utter occurs (Marques, Badcock, et al., J. Aircraft, 2010). In this follow-up work we examine the same phenomenon using probabilistic collocation (PC), an uncertainty quantification technique which can eficiently propagate multivariate stochastic input through a simulation code,
in this case an eigenvalue-based fluid-structure stability code. The resulting analysis predicts the consequences of an uncertain structure on incidence of
utter in probabilistic terms { information that could be useful in planning
flight-tests and assessing the risk of structural failure. The uncertainty in
utter altitude is confirmed to be substantial. Assuming that the structural uncertainty represents a epistemic uncertainty regarding the
structure, it may be reduced with the availability of additional information { for example aeroelastic response data from a flight-test. Such data is used to update the structural uncertainty using Bayes' theorem. The consequent
utter uncertainty is significantly reduced across the entire Mach number range.

Discrete Conditional Phase-type model (DC_Ph) for patient waiting time with a logistic regression component to predict patient admission to hospital

Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.

A multiconfigurational time-dependent Hartree-Fock method for excited electronic states. I. General formalism and application to open-shell states

The solution of the time-dependent Schrodinger equation for systems of interacting electrons is generally a prohibitive task, for which approximate methods are necessary. Popular approaches, such as the time-dependent Hartree-Fock (TDHF) approximation and time-dependent density functional theory (TDDFT), are essentially single-configurational schemes. TDHF is by construction incapable of fully accounting for the excited character of the electronic states involved in many physical processes of interest; TDDFT, although exact in principle, is limited by the currently available exchange-correlation functionals. On the other hand, multiconfigurational methods, such as the multiconfigurational time-dependent Hartree-Fock (MCTDHF) approach, provide an accurate description of the excited states and can be systematically improved. However, the computational cost becomes prohibitive as the number of degrees of freedom increases, and thus, at present, the MCTDHF method is only practical for few-electron systems. In this work, we propose an alternative approach which effectively establishes a compromise between efficiency and accuracy, by retaining the smallest possible number of configurations that catches the essential features of the electronic wavefunction. Based on a time-dependent variational principle, we derive the MCTDHF working equation for a multiconfigurational expansion with fixed coefficients and specialise to the case of general open-shell states, which are relevant for many physical processes of interest. (C) 2011 American Institute of Physics. [doi: 10.1063/1.3600397]

Quantum chemistry and in situ FTir spectroscopy studies on potential-dependent properties of CO adsorbed on Pt electrodes

The electronic and vibrational properties of CO adsorbed on Pt electrodes at different potentials have been studied, by using methods of self-consistent-charge discrete variational Xa (SCC-DV-Xa) cluster calculations and in situ FTir spectroscopy. Two new models have been developed and verified to be successful: (1) using a "metallic state cluster" to imitate a metal (electrode) surface; and (2) charging the cluster and shifting its Fermi level (e{lunate}) to simulate, according to the relation of -d e{lunate}e dE, quantitatively the variation of the electrode potential (E). It is shown that the binding of PtCO is dominated by the electric charge transfer of dp ? 2p, while that of s ? Pt is less important in this binding. The electron occupancy of the 2p orbital of CO weakens the CO bond and decreases the v. Variation of E mainly influences the charge transfer process of dp ? 2p, but hardly influences that of s ? Pt. A linear potential-dependence of v has been shown and the calculated dv/dE = 35.0 cm V. All results of calculations coincide with the ir experimental data. © 1993.

Quantum chemistry studies on electronic properties of CN adsorbed on silver electrodes

The electronic properties of CN adsorbed on Ag electrodes at different potentials have been studied by using the method of self-consistent-charge discrete variational Xa (SCC-DV-Xa) cluster calculations. It is shown that the binding of NCAg is dominated by both electrostatic and polarization effects derived from the charge of CN, and that the transfer of s charge from CN to silver is the largest donation contribution. The electrode potential influences this charge transfer process and the interaction between CN adsorbate and silver electrode. The results of quantum chemistry calculations fit well with the experimental data of in situ spectroscopic studies on the CN/Ag electrode systems. © 1991.

Positron scattering and annihilation in hydrogenlike ions

Diagrammatic many-body theory is used to calculate the scattering phase shifts, normalized annihilation rates Zeff, and annihilation ? spectra for positron collisions with the hydrogenlike ions He+, Li2+, B4+, and F8+. Short-range electron-positron correlations and longer-range positron-ion correlations are accounted for by evaluating nonlocal corrections to the annihilation vertex and the exact positron self-energy. The numerical calculation of the many-body theory diagrams is performed using B-spline basis sets. To elucidate the role of the positron-ion repulsion, the annihilation rate is also estimated analytically in the Coulomb-Born approximation. It is found that the energy dependence and magnitude of Zeff are governed by the Gamow factor that characterizes the suppression of the positron wave function near the ion. For all of the H-like ions, the correlation enhancement of the annihilation rate is found to be predominantly due to corrections to the annihilation vertex, while the corrections to the positron wave function play only a minor role. Results of the calculations for s-, p-, and d-wave incident positrons of energies up to the positronium-formation threshold are presented. Where comparison is possible, our values are in excellent agreement with the results obtained using other, e.g., variational, methods. The annihilation-vertex enhancement factors obtained in the present calculations are found to scale approximately as 1+(1.6+0.46l)/Zi, where Zi is the net charge of the ion and l is the positron orbital angular momentum. Our results for positron annihilation in H-like ions provide insights into the problem of positron annihilation with core electrons in atoms and condensed matter systems, which have similar binding energies.

Quantum annealing of an ising spin-glass by Green's function Monte Carlo

We present an implementation of quantum annealing (QA) via lattice Green's function Monte Carlo (GFMC), focusing on its application to the Ising spin glass in transverse field. In particular, we study whether or not such a method is more effective than the path-integral Monte Carlo- (PIMC) based QA, as well as classical simulated annealing (CA), previously tested on the same optimization problem. We identify the issue of importance sampling, i.e., the necessity of possessing reasonably good (variational) trial wave functions, as the key point of the algorithm. We performed GFMC-QA runs using such a Boltzmann-type trial wave function, finding results for the residual energies that are qualitatively similar to those of CA (but at a much larger computational cost), and definitely worse than PIMC-QA. We conclude that, at present, without a serious effort in constructing reliable importance sampling variational wave functions for a quantum glass, GFMC-QA is not a true competitor of PIMC-QA.

Efficient Marginal Likelihood Computation for Gaussian Process Regression

In a Bayesian learning setting, the posterior distribution of a predictive model arises from a trade-off between its prior distribution and the conditional likelihood of observed data. Such distribution functions usually rely on additional hyperparameters which need to be tuned in order to achieve optimum predictive performance; this operation can be efficiently performed in an Empirical Bayes fashion by maximizing the posterior marginal likelihood of the observed data. Since the score function of this optimization problem is in general characterized by the presence of local optima, it is necessary to resort to global optimization strategies, which require a large number of function evaluations. Given that the evaluation is usually computationally intensive and badly scaled with respect to the dataset size, the maximum number of observations that can be treated simultaneously is quite limited. In this paper, we consider the case of hyperparameter tuning in Gaussian process regression. A straightforward implementation of the posterior log-likelihood for this model requires O(N^3) operations for every iteration of the optimization procedure, where N is the number of examples in the input dataset. We derive a novel set of identities that allow, after an initial overhead of O(N^3), the evaluation of the score function, as well as the Jacobian and Hessian matrices, in O(N) operations. We prove how the proposed identities, that follow from the eigendecomposition of the kernel matrix, yield a reduction of several orders of magnitude in the computation time for the hyperparameter optimization problem. Notably, the proposed solution provides computational advantages even with respect to state of the art approximations that rely on sparse kernel matrices.

Root Cause Analysis by a Combined Sparse Classification and Monte Carlo Approach

Classification methods with embedded feature selection capability are very appealing for the analysis of complex processes since they allow the analysis of root causes even when the number of input variables is high. In this work, we investigate the performance of three techniques for classification within a Monte Carlo strategy with the aim of root cause analysis. We consider the naive bayes classifier and the logistic regression model with two different implementations for controlling model complexity, namely, a LASSO-like implementation with a L1 norm regularization and a fully Bayesian implementation of the logistic model, the so called relevance vector machine. Several challenges can arise when estimating such models mainly linked to the characteristics of the data: a large number of input variables, high correlation among subsets of variables, the situation where the number of variables is higher than the number of available data points and the case of unbalanced datasets. Using an ecological and a semiconductor manufacturing dataset, we show advantages and drawbacks of each method, highlighting the superior performance in term of classification accuracy for the relevance vector machine with respect to the other classifiers. Moreover, we show how the combination of the proposed techniques and the Monte Carlo approach can be used to get more robust insights into the problem under analysis when faced with challenging modelling conditions.

Positron scattering and annihilation on noble-gas atoms

Positron scattering and annihilation on noble-gas atoms is studied ab initio using many-body theory methods for positron energies below the positronium formation threshold. We show that in this energy range, the many-body theory yields accurate numerical results and provides a near-complete understanding of the positron–noble-gas atom system. It accounts for positron-atom and electron-positron correlations, including the polarization of the atom by the positron and the nonperturbative effect of virtual positronium formation. These correlations have a large influence on the scattering dynamics and result in a strong enhancement of the annihilation rates compared to the independent-particle mean-field description. Computed elastic scattering cross sections are found to be in good agreement with recent experimental results and Kohn variational and convergent close-coupling calculations. The calculated values of the annihilation rate parameter Zeff (effective number of electrons participating in annihilation) rise steeply along the sequence of noble-gas atoms due to the increasing strength of the correlation effects, and agree well with experimental data.

Kinetics of levoglucosan and formaldehyde formation during cellulose pyrolysis process

The mechanisms and kinetics studies of the formation of levoglucosan and formaldehyde from anhydroglucose radical have been carried out theoretically in this paper. The geometries and frequencies of all the stationary points are calculated at the B3LYP/6-31+G(D,P) level based on quantum mechanics, Six elementary reactions are found, and three global reactions are involved. The variational transition-state rate constants for the elementary reactions are calculated within 450-1500 K. The global rate constants for every pathway are evaluated from the sum of the individual elementary reaction rate constants. The first-order Arrhenius expressions for these six elementary reactions and the three pathways are suggested. By comparing with the experimental data, computational methods without tunneling correction give good description for Path1 (the formation of levoglucosan); while methods with tunneling correction (zero-curvature tunneling and small-curvature tunneling correction) give good results for Path2 (the first possibility for the formation of formaldehyde), all the test methods give similar results for Path3 (the second possibility for the formation of formaldehyde), all the modeling results for Path3 are in good agreement with the experimental data, verifying that it is the most possible way for the formation of formaldehyde during cellulose pyrolysis. © 2012 Elsevier Ltd. All rights reserved.

Kinetics study on thermal dissociation of levoglucosan during cellulose pyrolysis

(Chemical Equation Presented) The mechanisms and kinetics studies of the levoglucosan (LG) primary decomposition during cellulose pyrolysis have been carried out theoretically in this paper. Three decomposition mechanisms (C-O bond scission, C-C bond scission, and LG dehydration) including nine pathways and 16 elementary reactions were studied at the B3LYP/6-31 + G(D,P) level based on quantum mechanics. The variational transi-tion- state rate constants for every elementary reaction and every pathway were calculated within 298-1550 K. The first-order Arrhenius expressions for these 16 elementary reactions and nine pathways were suggested. It was concluded that computational method using transition state theory (TST) without tunneling correction gives good description for LG decomposition by comparing with the experimental result. With the temperature range of 667-1327 K, one dehydration pathway, with one water molecule composed of a hydrogen atom from C3 and a hydroxyl group from C2, is a preferred LG decomposition pathway by fitting well with the experimental results. The calculated Arrhenius plot of C-O bond scission mechanism is better agreed with the experimental Arrhenius plot than that of C-C bond scission. This C-O bond scission mechanism starts with breaking of C1-O5 and C6-O1 bonds with formation of CO molecule (C1-O1) simultaneously. C-C bond scission mechanism is the highest energetic barrier pathway for LG decomposition. © 2013 Elsevier Ltd. All rights reserved.

Extended Tree Augmented Naive Classifier

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds’ algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). A range of experiments show that we obtain models with better accuracy than TAN and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator.