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This paper deals with parameter estimation problem of large‐scale systems. A recursive distributed parameter estimation algorithm, based on the minimization of the prediction estimation error method, is developed. Specifically, the class of large‐scale systems that are composed of several interconnected sub‐systems is considered. Each interconnected sub‐system is modelled by a linear discrete‐time state space mathematical model with unknown parameters. The convergence analysis is then achieved using the Lyapunov approach. The theoretical analysis and simulation results prove the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Aiming at the low accuracy or inability to solve the problem when the traditional reliability optimization method deals with the practical engineering reliability analysis problem, this paper proposes a structural reliability analysis method using the improved wolf swarm algorithm (IWSA). Firstly, the reliability optimization mathematical model is established with the minimum reliability index as the objective function and the limit state equation as the equality constraint. Then the augmented multiplier method is used to transform the constrained optimization mathematical model into an unconstrained optimization mathematical model. Finally, IWSA is used to solve the problem. The proposed method is applied to a numerical example and an engineering example. The computation results of the proposed method in solving the numerical example are compared with the Monte Carlo simulation (MCS), which verifies that the proposed method in this paper has good calculation accuracy. The maximum deformation under the working condition is used as the target to establish a limit state function, and the reliability index is obtained by the proposed method combining with the finite element analysis software, which provides a certain theoretical guiding significance for the reliability analysis of engineering structures. [ABSTRACT FROM AUTHOR]

The article examines model-based research in managerial accounting. The authors express the belief that accounting research has improved in quality and proliferation and that this area of accounting research should become more active in terms of setting theories. Model-based managerial accounting research is defined as an "explicit model" that must be scrutinized "deductively." The article concludes with a discussion of whether or not such literature is useful in accounting research and that the point of this investigation is to develop accounting methods and to simulate accounting scenarios to understand the situation better.

This paper introduces a computational framework to analyze global identification of linearized DSGE models. A formal identification condition is established that relies on the restrictions linking the observationally equivalent state space representations and on the inherent constraints imposed by the model solution on the deep parameters. This condition is next used to develop an algorithm that checks global identification by searching for observationally equivalent model parametrizations. The algorithm is efficient as the identification conditions it employs shrink considerably the space of candidate deep parameter points and the model does not need to be solved at each of these points. The working of the algorithm is demonstrated with two examples. [ABSTRACT FROM AUTHOR]

The article discusses geometric mean criterion and stochastic dominance decision making models. The author derives the mathematical relationship between the geometric mean and the integrals of the probability density functions used in stochastic dominance testing from a general class of distributions. However, both the models can be justified by the expected utility hypothesis. The geometric mean criterion follows as a result of the assumption that the decision-maker has a logarithmic utility function. The stochastic dominance models require the assumption of the signs of the first few derivatives of the decision-maker's utility function. The author notes that stochastic dominance showed great promise in its early development as a method for constructing a theory of security price forecasting because of its limited set of assumptions regarding investor preferences.

This paper expresses the constant elasticity of variance option pricing formula in terms of the noncentral chi-square distribution. This allows the application of well-known approximation formulas and the derivation of a whole class of closed-form solutions. In addition, a simple and efficient algorithm for computing this distribution is presented. [ABSTRACT FROM AUTHOR]

Abstract: Classifier combination methods have proved to be an effective tool to increase the performance of classification techniques that can be used in any pattern recognition applications. Despite a significant number of publications describing successful classifier combination implementations, the theoretical basis is still not matured enough and achieved improvements are inconsistent. In this paper, we propose a novel statistical validation technique known as correlation‐based classifier combination technique for combining classifier in any pattern recognition problem. This validation has significant influence on the performance of combinations, and their utilization is necessary for complete theoretical understanding of combination algorithms. The analysis presented is statistical in nature but promises to lead to a class of algorithms for rank‐based decision combination. The potentials of the theoretical and practical issues in implementation are illustrated by applying it on 2 standard datasets in pattern recognition domain, namely, handwritten digit recognition and letter image recognition datasets taken from UCI Machine Learning Database Repository ( http://www.ics.uci.edu/_mlearn). An empirical evaluation using 8 well‐known distinct classifiers confirms the validity of our approach compared to some other combinations of multiple classifiers algorithms. Finally, we also suggest a methodology for determining the best mix of individual classifiers. [ABSTRACT FROM AUTHOR]

Objective: Automated machine learning (autoML) platforms allow health care professionals to play an active role in the development of machine learning (ML) algorithms according to scientific or clinical needs. The aim of this study was to develop and evaluate such a model for automated detection and grading of distal hand osteoarthritis (OA). Methods: A total of 13,690 hand radiographs from 2,863 patients within the Swiss Cohort of Quality Management (SCQM) and an external control data set of 346 non‐SCQM patients were collected and scored for distal interphalangeal OA (DIP‐OA) using the modified Kellgren/Lawrence (K/L) score. Giotto (Learn to Forecast [L2F]) was used as an autoML platform for training two convolutional neural networks for DIP joint extraction and subsequent classification according to the K/L scores. A total of 48,892 DIP joints were extracted and then used to train the classification model. Heatmaps were generated independently of the platform. User experience of a web application as a provisional user interface was investigated by rheumatologists and radiologists. Results: The sensitivity and specificity of this model for detecting DIP‐OA were 79% and 86%, respectively. The accuracy for grading the correct K/L score was 75%, with a κ score of 0.76. The accuracy per DIP‐OA class differed, with 86% for no OA (defined as K/L scores 0 and 1), 71% for a K/L score of 2, 46% for a K/L score of 3, and 67% for a K/L score of 4. Similar values were obtained in an independent external test set. Qualitative and quantitative user experience testing of the web application revealed a moderate to high demand for automated DIP‐OA scoring among rheumatologists. Conversely, radiologists expressed a low demand, except for the use of heatmaps. Conclusion: AutoML platforms are an opportunity to develop clinical end‐to‐end ML algorithms. Here, automated radiographic DIP‐OA detection is both feasible and usable, whereas grading among individual K/L scores (eg, for clinical trials) remains challenging. [ABSTRACT FROM AUTHOR]

In web surfing, image retrieval algorithms play a vital role. Many algorithms are existing but they have their drawbacks and also low retrieval rates. Locality preserving projections is a novel method for image retrieval proposed in this study. As the method includes principal component analysis, nearest neighbour and eigen features in extracting the hyper parameters for classification, it has better retrieval rate. This novel approach is tested using the Yale facial image dataset and the performance of the algorithm is tested by using different classifiers like Decision Tree etc. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

System identification provides many convenient and useful methods for engineering modelling. This study targets the parameter identification problems for multivariable equation-error autoregressive moving average systems. To reduce the influence of the coloured noises on the parameter estimation, the data filtering technique is adopted to filter the input and output data, and to transform the original system into a filtered system with white noises. Then the filtered system is decomposed into several subsystems and a filtering-based partially-coupled generalised extended stochastic gradient algorithm is developed via the coupling concept. In contrast to the multivariable generalised extended stochastic gradient algorithm, the proposed algorithm can give more accurate parameter estimates. Finally, the effectiveness of the proposed algorithm is well demonstrated by simulation examples. [ABSTRACT FROM AUTHOR]

In this paper, we show how the construction of a trans-dimensional equivalent of the Gibbs sampler can be used to obtain a powerful suite of adaptive algorithms suitable for trans-dimensional MCMC samplers. These algorithms adapt at the local scale, optimizing performance at each iteration in contrast to the globally adaptive scheme proposed by others for the fixeddimensional problem. Our adaptive scheme ensures suitably high acceptance rates for MCMC and RJMCMC proposals without the need for (often prohibitively) time-consuming pilot-tuning exercises. We illustrate our methods using the problem of Bayesian model discrimination for the important class of autoregressive time series models and, through the use of a variety of prior and proposal structures, demonstrate their ability to provide powerful and effective adaptive sampling schemes. [ABSTRACT FROM AUTHOR]

ABSTRACT. Ibrahim (1990) used the EM-algorithm to obtain maximum likelihood estimates of the regression parameters in generalized linear models with partially missing covariates. The technique was termed EM by the method of weights. In this paper, we generalize this technique to Cox regression analysis with missing values in the covariates. We specify a full model letting the unobserved covariate values be random and then maximize the observed likelihood. The asymptotic covariance matrix is estimated by the inverse information matrix. The missing data are allowed to be missing at random but also the nonignorable non-response situation may in principle be considered. Simulation studies indicate that the proposed method is more efficient than the method suggested by Paik & Tsai (1997). We apply the procedure to a clinical trials example with six covariates with three of them having missing values. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Stability assessment of a lossy power system during a transient is challenging because of the stringent time limit for a conclusion to reliably support protection functions. A coverage-based stability assessment is pursued with focus on resolving computation and scalability issues. This approach involves online tracking of each generator's electromechanical state using a local quasi-steady-state sinusoidal measurement model, and determining whether the state is enclosed in an offline-computed post-fault region of attraction (RoA) at the time the RoA is established by a protection action. The RoA is numerically estimated offline with a scalable ellipsoidal expansion algorithm, and the need for the expansion is delineated. The approach to transient stability assessment is tested on a lossy 68-bus system subject to transmission faults. The study concludes that the coveragebased stability assessment can offer significant advantages in both reliability and swiftness over the existing assessment methods. [ABSTRACT FROM AUTHOR]

Recent works in domain-independent plan merging have shown that the optimal plan-merging problem is NP-hard, and heuristic algorithms have been proposed to generate near-optimal solutions. These works have shown heuristic algorithms which assume that the mergeability of two actions can be determined by considering only the actions themselves. In this paper, we show that it is often that case that the surrounding actions in the plan must also be considered when determining the mergeability of two or more actions; therefore, the context in which the actions are used affects their mergeability. To address this problem, we have developed a plan-merging methodology that merges partial-order plans based on the the notion of plan fragments, which encapsulate actions with the context in which they are being utilized. This methodology applies to a class of planning domains, called decomposable domains, which consist of actions that follow all or some portion of a type sequence. Merging is performed hierarchically by action type. We also investigate the previously unexplored notion of alternative actions in domain-independent plan merging, which can improve the quality of the resulting merged plan. A novel approach is presented for removing cyclic dependencies that may occur during the plan-merging process. A key part of the methodology is the computation of a minimum cost cover of plan fragments. We provide theoretical analyses of two optimal algorithms and a greedy-based algorithm for computing the minimum cost cover. We support the theoretical analysis of these algorithms with experimental data to show that the greedy approach is near-optimal and efficient. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to describe a new gradient method for maximizing general functions. After a brief discussion of various known gradient methods the mathematical foundation is laid for the new algorithm which rests on maximizing a quadratic approximation to the function on a suitably chosen spherical region. The method requires no assumptions about the concavity of the function to be maximized and automatically modifies the step size in the light of the success of the quadratic approximation to the function. The paper further discusses some practical problems of implementing the algorithm and presents recent computational experience with it. [ABSTRACT FROM AUTHOR]

To address the unsatisfactory performance of particle swarm optimization (PSO), a novel multi‐strategy self‐optimizing simulated annealing particle swarm optimization (SOSAPSO) method for permanent magnet synchronous motor (PMSM) parameter identification is proposed. The full‐rank mathematical model and the fitness function are developed. In SOSAPSO, the velocity term of the PSO is simplified and dynamic opposition‐based learning (DOBL) is introduced in the inertia weight update process to avoid population monotonicity. Moreover, A Cauchy–Gaussian hybrid variation strategy based on similarity and density is devised to achieve self‐learning in deep regions. Meanwhile, the simulated annealing (SA) with a memory and tempering mechanism is introduced into SOSAPSO, and the greedy optimization algorithm (GOA) is used to enhance local fine‐exploitation capabilities when SOSAPSO evolution is stalled. The test results indicate the proposed method can effectively avoid local convergence problems and has better robustness and convergence speed. [ABSTRACT FROM AUTHOR]

The article presents the discussion on competitiveness being a determining factor for the survival of businesses. Topics include sciences research to combine experimental approaches with theoretical modelling tools from systems theory, physics, engineering, computer science; and situation driven organizations on the search for simulation models.

A number of different gene selection approaches based on gene expression profiles (GEP) have been developed for tumour classification. A gene selection approach selects the most informative genes from the whole gene space, which is an important process for tumour classification using GEP. This study presents an improved swarm intelligent optimisation algorithm to select genes for maintaining the diversity of the population. The most essential characteristic of the proposed approach is that it can automatically determine the number of the selected genes. On the basis of the gene selection, the authors construct a variety of the tumour classifiers, including the ensemble classifiers. Four gene datasets are used to evaluate the performance of the proposed approach. The experimental results confirm that the proposed classifiers for tumour classification are indeed effective. [ABSTRACT FROM AUTHOR]

In the single-image dehazing problem, it is critical that the transmission is accurately estimated. However, the extracted transmission in the dark channel model cannot effectively deal with the edge and the sky area because of the poor applicability of the dark channel prior to these areas. This study aims to solve that problem by proposing a novel variational model (VM) to optimise the transmission. This VM introduces a smoothness term and a gradient-preserving term to mitigate the false edge and the distorted sky area in the recovered image. Further, a fast algorithm to solve the VM is proposed on the basis of the additional operator splitting algorithm. This algorithm is an effective linear time algorithm and has excellent performance on optimising the transmission. The average running time of the algorithm shows an improvement of over 20 times that of the guided image filtering in these experiments. Experimental results also show that the proposed algorithm is both effective and efficient for optimising the transmission. [ABSTRACT FROM AUTHOR]

The problems of the existence of a competitive equilibrium in models with hidden knowledge and self-selection are well-known (Rothschild and Stiglitz (1976), Wilson (1977), Riley (1979)). Recent analyses of such models argue for a particular outcome-the Pareto- dominant separating, zero-profit one (Cho and Kreps (1986), Riley (1985)). We prove the existence of such an outcome under very general conditions; and, generalizing the reactive equilibrium concept introduced by Riley (1979), we prove this outcome is the unique reactive equilibrium. This paper also introduces the strong equilibrium-the largest set of offers each of which at least breaks even on all who accept-which provides a direct and general proof of the existence of a unique reactive equilibrium. [ABSTRACT FROM AUTHOR]

This paper presents the necessary and sufficient conditions for determining the signs of the solution variables of a system of linear equations based only upon a knowledge of the signs of the coefficient matrix and the signs of the "right hand side" variables. This problem was initially formulated in economics due to the idea that the signs of an equation's derivatives might have a stronger empirical basis than that of a particular functional form. A new interest in qualitative problems has arisen in connection with the need to develop analytic measures in order to better manage the understanding and use of large, computer-based mathematical systems. The conditions for the qualitative determinancy of nonhomogeneous systems are developed in terms of a small number of necessary conditions which are jointly sufficient. Algorithmic approaches are given for testing a given system for qualitative determinancy. For nonhomogeneous systems algorithms are given for constructing all possible qualitatively determinate systems of a given size. For the homogeneous case conditions are also given for the qualitative invertibility of the (irreducible) coefficient matrix. These conditions are then related to the problem of partially qualitatively determinate systems and the signs in the qualitative inverse of a matrix. [ABSTRACT FROM AUTHOR]

In this paper we outline the computation of general equilibrium in a pure exchange economy via a fixed point decomposition procedure. For general equilibrium models of the required structure, a full equilibrium may be computed through the solution of a sequence of smaller scale 'sub-equilibrium' problems. The text contains a presentation of the methods involved along with a discussion of initial computational experience for some numerical examples. [ABSTRACT FROM AUTHOR]

This paper presents a numerical algorithm for computing full information maximum likelihood (FIML) and nonlinear three-stage least squares (3SLS) coefficient estimates for large nonlinear macroeconometric models. The new algorithm, which is demonstrated by actually computing FIML and 3SLS coefficient estimates for two versions of the 97 equation Fair model, is substantially more effective than other algorithms on large FIML and 3SLS estimation problems. [ABSTRACT FROM AUTHOR]

The article demonstrates the connection between switching regressions and logistic discrimination. It provides an alternative, intuitive, motivation for the Behboodiah-Hartley algorithm for numerically solving the likelihood equations. The argument gives an interpretation of the maximum likelihood estimator of the switching regression model as a weighted regression estimator with logit probabilities as weights. The results of the paper suggest that the classification of the observations could be done on the basis of the estimated logistic probabilities, conditioning on the estimated value of the parameters.

The article demonstrates how the Dantzig-Gale method of computing equilibrium prices in the context of the nonsubstitution theorem can be extended to the case where the underlying production functions are neoclassical rather than of the fixed coefficients, activity analysis variety. The Wolfe-Dantzig, Van Slyke variable coefficients simplex algorithm is used. It was observed that the subproblems are trivial to solve, if the duality between cost and production functions is used. The paper indicates that the algorithm can be extended in another direction.

Policy PointsDespite 30 years of attention to eliminating population health inequity, it remains entrenched, calling for new approaches.Targeted universalism, wellness‐based local development, and Jedi Public Health approaches that are community informed, evidence based, and focused on improving everyday settings and diverse lived experiences are important policy directions.State and federal revenue transfers are necessary to mitigate the harms of austerity and assure greater equity in fiscal and population health in places like Detroit, Michigan. Context: US population health inequity remains entrenched, despite mandates to eliminate it. To promote a public health approach of consequence in this domain, stakeholders call for moving from risk‐factor epidemiology toward consideration of dynamic local variations in the physiological impacts of structured lived experience. Methods: Using a community‐based, participatory research approach, we collected and analyzed a unique data set of 239 black, white, and Mexican adults from a stratified, multistage probability sample of three Detroit, Michigan, neighborhoods. We drew venous blood, collected saliva, took anthropometric measurements, and assayed specimens to measure allostatic load (AL), an indicator of stress‐mediated biological dysregulation, linking participants' AL scores and survey responses. In a series of nested Poisson models, we regressed AL on socioeconomic, psychosocial, neighborhood, and behavioral stressors to test the hypothesis that race/ethnicity and poverty‐to‐income ratio (PIR) are conceptually fluctuating variables whose impacts on AL are sensitive to structured lived experience. Findings: White and Mexican Detroit participants with PIR < 1 have higher AL than counterparts nationally; black participants in Detroit and nationwide had comparable AL. Within Detroit, disparities by PIR were higher in whites than blacks, with no significant difference by PIR in Mexicans. The size of estimated effects of having PIR < 1 for whites is 58 percentage points greater than that of Mexicans and twice that of blacks. Conclusions: Structurally rooted unobserved heterogeneity bias threatens the validity of independent main effects interpretations of associations between race/ethnicity, socioeconomic characteristics, or place and health. One‐size‐fits‐all analytic or policy models developed from the perspective of the dominant social group insufficiently address the experiences of diverse populations in specific settings and historical moments; nor do they recognize culturally mediated protective resources residents may have developed against material and psychosocial hardship. [ABSTRACT FROM AUTHOR]

In this study, an attempt is made to find an effective solution on the small direct current (DC) motor's parameters identification problem by employing two recently introduced stochastic nature-inspired techniques. The first one is called 'salp swarm algorithm' (SSA) while the second one is called 'ant-lion optimiser' (ALO). These algorithms have been inspired by the interaction strategy between the ocean salps and the ants and ant-lions, respectively, in nature. To appraise the effectiveness of these algorithms, a DC motor model has been appropriately implemented and its performance is evaluated by using speed step responses, while all model's parameters are considered unknown and therefore search variables. Integral of the square of the error (ISE), integral absolute error and integral in time of absolute error have been adopted as objective functions for the algorithms' evaluation. In order to judge the acceptability of these algorithms, the simulation results are compared with those of another one similar technique, namely the 'grey wolf optimiser' (GWO). The obtained results reveal very satisfactory performance and confirm that the examined SSA, ALO and GWO algorithms can identify accurately the DC motor parameters and can be applied effectively to the specific problem. Another finding is that SSA combined with ISE criterion seems to be the most appropriate technique among the three algorithms. [ABSTRACT FROM AUTHOR]

For most capacitive power transfer applications, a reliable signal transfer link between the primary side and the secondary side plays an essential role. The parameters design of the system has a key effect on the operating performance, however, the existing parameter design method can only get an acceptable solution, instead of a globally optimal solution. This study has proposed a general and global optimal parameter design method based on the constraint multi-objective algorithm. The mathematical models of the power and signal channels as long as the power noise is established and all the objective functions are given according to the models. Besides, the constraints considering the reasonable region of the parameter values and the limitation of the voltage and current on the elements are given. Then the multi-objective optimisation problem with the objective functions and constraints are solved by the non-dominated sorting genetic algorithm II, and the proper global optimal parameters are selected from the Pareto set. Finally, a simulation model and an experimental setup with 180 W power and 200 kbps signal transfer capability are constructed, and the results verified the effectiveness and correctness of this method. [ABSTRACT FROM AUTHOR]

I propose to estimate structural impulse responses from macroeconomic time series by doing Bayesian inference on the Structural Vector Moving Average representation of the data. This approach has two advantages over Structural Vector Autoregressions. First, it imposes prior information directly on the impulse responses in a flexible and transparent manner. Second, it can handle noninvertible impulse response functions, which are often encountered in applications. Rapid simulation of the posterior distribution of the impulse responses is possible using an algorithm that exploits the Whittle likelihood. The impulse responses are partially identified, and I derive the frequentist asymptotics of the Bayesian procedure to show which features of the prior information are updated by the data. The procedure is used to estimate the effects of technological news shocks on the U.S. business cycle. [ABSTRACT FROM AUTHOR]

Abstract: This work considers scalable incremental extreme learning machine (I‐ELM) algorithms, which could be suitable for big data regression. During the training of I‐ELMs, the hidden neurons are presented one by one, and the weights are based solely on simple direct summations, which can be most efficiently mapped on parallel environments. Existing incremental versions of ELMs are the I‐ELM, enhanced incremental ELM (EI‐ELM), and convex incremental ELM (CI‐ELM). We study the enhanced and convex incremental ELM (ECI‐ELM) algorithm, which is a combination of the last 2 versions. The main findings are that ECI‐ELM is fast, accurate, and fully scalable when it operates in a parallel system of distributed memory workstations. Experimental simulations on several benchmark data sets demonstrate that the ECI‐ELM is the most accurate among the existing I‐ELM, EI‐ELM, and CI‐ELM algorithms. We also analyze the convergence as a function of the hidden neurons and demonstrate that ECI‐ELM has the lowest error rate curve and converges much faster than the other algorithms in all of the data sets. The parallel simulations also reveal that the data parallel training of the ECI‐ELM can guarantee simplicity and straightforward mappings and can deliver speedups and scale‐ups very close to linear. [ABSTRACT FROM AUTHOR]

One of the main objectives of empirical analysis of experiments and quasi‐experiments is to inform policy decisions that determine the allocation of treatments to individuals with different observable covariates. We study the properties and implementation of the Empirical Welfare Maximization (EWM) method, which estimates a treatment assignment policy by maximizing the sample analog of average social welfare over a class of candidate treatment policies. The EWM approach is attractive in terms of both statistical performance and practical implementation in realistic settings of policy design. Common features of these settings include: (i) feasible treatment assignment rules are constrained exogenously for ethical, legislative, or political reasons, (ii) a policy maker wants a simple treatment assignment rule based on one or more eligibility scores in order to reduce the dimensionality of individual observable characteristics, and/or (iii) the proportion of individuals who can receive the treatment is a priori limited due to a budget or a capacity constraint. We show that when the propensity score is known, the average social welfare attained by EWM rules converges at least at n−1/2 rate to the maximum obtainable welfare uniformly over a minimally constrained class of data distributions, and this uniform convergence rate is minimax optimal. We examine how the uniform convergence rate depends on the richness of the class of candidate decision rules, the distribution of conditional treatment effects, and the lack of knowledge of the propensity score. We offer easily implementable algorithms for computing the EWM rule and an application using experimental data from the National JTPA Study. [ABSTRACT FROM AUTHOR]

An object-based evaluation method using a pattern recognition algorithm (i.e., classification trees) is applied to the simulated orographic precipitation for idealized experimental setups using the National Center of Atmospheric Research (NCAR) Community Atmosphere Model (CAM) with the finite volume (FV) and the Eulerian spectral transform dynamical cores with varying resolutions. Daily simulations were analyzed and three different types of precipitation features were identified by the classification tree algorithm. The statistical characteristics of these features (i.e., maximum value, mean value, and variance) were calculated to quantify the difference between the dynamical cores and changing resolutions. Even with the simple and smooth topography in the idealized setups, complexity in the precipitation fields simulated by the models develops quickly. The classification tree algorithm using objective thresholding successfully detected different types of precipitation features even as the complexity of the precipitation field increased. The results show that the complexity and the bias introduced in small-scale phenomena due to the spectral transform method of CAM Eulerian spectral dynamical core is prominent, and is an important reason for its dissimilarity from the FV dynamical core. The resolvable scales, both in horizontal and vertical dimensions, have significant effect on the simulation of precipitation. The results of this study also suggest that an efficient and informative study about the biases produced by GCMs should involve daily (or even hourly) output (rather than monthly mean) analysis over local scales. [ABSTRACT FROM AUTHOR]

Occlusion is one of the most challenging problems in many video processing applications such as surveillance, gait recognition, activity recognition and so on. Attempts have been made to develop algorithms for handling occlusion and evaluate their performance on various datasets. However, these studies are subjective in nature and the datasets are hardly characterised in terms of the level of occlusion, thereby precluding any form of quantitative comparison of performance. This shows a compelling need to design an explicit, unambiguous and quantitative model, which should be able to objectively represent occlusion in a video. This study proposes an occlusion model based on the position and pose uncertainties of the moving subjects in a video. The proposed occlusion model is able to characterise the level of occlusion present in a video. It is also employed to synthetically generate occlusion for walking sequences, thus providing a direction for controlled dataset generation against which human identification algorithms can be tested. Given an input video with a subject moving without any occlusion, a particle swarm optimisation-based parameter estimation methodology is presented that generates the desired level of occlusion. The proposed approaches have been tested on the TUM-IITKGP and PETS2010 datasets. Finally, as an application, the occlusion model has been used to generate an occluded gait datasets and the performances of different gait recognition algorithms have been compared under varying levels of occlusion. [ABSTRACT FROM AUTHOR]

In this study, a novel high-order finite-difference time-domain (HO-FDTD) method, Runge-Kutta (RK)-HO-FDTD, is proposed. The RK-HO-FDTD method employs the strong stability preserving RK algorithm to substitute time derivates and the Taylor series to replace spatial derivates. The characteristics of the stability, dispersion and convergence are studied. The proposed new method presents a better numerical dispersion and a faster convergence rate both in time and space domain. Compared with the HO-FDTD method, if the mesh size is fixed, it is found that the computational memory of the RK-HO-FDTD method is more than two times of the HO-FDTD method for the same mesh size; but if keeping the same accuracy, the computational cost of the RK-HO-FDTD is the controllable times that of the HO-FDTD method. And compared with the HO-FDTD and RK-MRTD methods, the new scheme presents more accuracy and great potential in electromagnetic problems. [ABSTRACT FROM AUTHOR]

Ecologists and paleontologists often rely on higher taxon surrogates instead of complete inventories of biological diversity. Despite their intrinsic appeal, the performance of these surrogates has been markedly inconsistent across empirical studies, to the extent that there is no consensus on appropriate taxonomic resolution (i.e., whether genus- or family-level categories are more appropriate) or their overall usefulness. A framework linking the reliability of higher taxon surrogates to biogeographic setting would allow for the interpretation of previously published work and provide some needed guidance regarding the actual application of these surrogates in biodiversity assessments, conservation planning, and the interpretation of the fossil record. We developed a mathematical model to show how taxonomic diversity, community structure, and sampling effort together affect three measures of higher taxon performance: the correlation between species and higher taxon richness, the relative shapes and asymptotes of species and higher taxon accumulation curves, and the efficiency of higher taxa in a complementarity-based reserve-selection algorithm. In our model, higher taxon surrogates performed well in communities in which a few common species were most abundant, and less well in communities with many equally abundant species. Furthermore, higher taxon surrogates performed well when there was a small mean and variance in the number of species per higher taxa. We also show that empirically measured species-higher-taxon correlations can be partly spurious (i.e., a mathematical artifact), except when the species accumulation curve has reached an asymptote. This particular result is of considerable practical interest given the widespread use of rapid survey methods in biodiversity assessment and the application of higher taxon methods to taxa in which species accumulation curves rarely reach an asymptote, e.g., insects. [ABSTRACT FROM AUTHOR]

In this article, we introduce a new method to approximate Markov perfect equilibrium in large-scale Ericson and Pakes (1995)-style dynamic oligopoly models that are not amenable to exact solution due to the curse of dimensionality. The method is based on an algorithm that iterates an approximate best response operator using an approximate dynamic programming approach. The method, based on mathematical programming, approximates the value function with a linear combination of basis functions. We provide results that lend theoretical support to our approach. We introduce a rich yet tractable set of basis functions, and test our method on important classes of models. Our results suggest that the approach we propose significantly expands the set of dynamic oligopoly models that can be analyzed computationally. [ABSTRACT FROM AUTHOR]

. Deterministic Bayesian inference for latent Gaussian models has recently become available using integrated nested Laplace approximations (INLA). Applying the INLA-methodology, marginal estimates for elements of the latent field can be computed efficiently, providing relevant summary statistics like posterior means, variances and pointwise credible intervals. In this article, we extend the use of INLA to joint inference and present an algorithm to derive analytical simultaneous credible bands for subsets of the latent field. The algorithm is based on approximating the joint distribution of the subsets by multivariate Gaussian mixtures. Additionally, we present a saddlepoint approximation to compute Bayesian contour probabilities, representing the posterior support of fixed parameter vectors of interest. We perform a simulation study and apply the given methods to two real examples. [ABSTRACT FROM AUTHOR]

We propose an ℓ-penalized estimation procedure for high-dimensional linear mixed-effects models. The models are useful whenever there is a grouping structure among high-dimensional observations, that is, for clustered data. We prove a consistency and an oracle optimality result and we develop an algorithm with provable numerical convergence. Furthermore, we demonstrate the performance of the method on simulated and a real high-dimensional data set. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Chemical Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Journal of Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background and Purpose:The antimalarial compounds quinine, chloroquine and mefloquine affect the electrophysiological properties of Cys-loop receptors and have structural similarities to 5-HT3 receptor antagonists. They may therefore act at 5-HT3 receptors.Experimental Approach:The effects of quinine, chloroquine and mefloquine on electrophysiological and ligand binding properties of 5-HT3A receptors expressed in HEK 293 cells and Xenopus oocytes were examined. The compounds were also docked into models of the binding site.Key Results:5-HT3 responses were blocked with IC 50 values of 13.4 μM, 11.8 μM and 9.36 μM for quinine, chloroquine and mefloquine. Schild plots indicated quinine and chloroquine behaved competitively with pA 2 values of 4.92 (K B=12.0 μM) and 4.97 (K B=16.4 μM). Mefloquine displayed weakly voltage-dependent, non-competitive inhibition consistent with channel block. On and off rates for quinine and chloroquine indicated a simple bimolecular reaction scheme. Quinine, chloroquine and mefloquine displaced [3H]granisetron with K i values of 15.0, 24.2 and 35.7 μM. Docking of quinine into a homology model of the 5-HT3 receptor binding site located the tertiary ammonium between W183 and Y234, and the quinoline ring towards the membrane, stabilised by a hydrogen bond with E129. For chloroquine, the quinoline ring was positioned between W183 and Y234 and the tertiary ammonium stabilised by interactions with F226.Conclusions and Implications:This study shows that quinine and chloroquine competitively inhibit 5-HT3 receptors, while mefloquine inhibits predominantly non-competitively. Both quinine and chloroquine can be docked into a receptor binding site model, consistent with their structural homology to 5-HT3 receptor antagonists.British Journal of Pharmacology (2007) 151, 666–677; doi:10.1038/sj.bjp.0707238; published online 14 May 2007 [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Artificial neural networks are bio-inspired mathematical models that have been widely used to solve complex problems. The training of a neural network is an important issue to deal with, since traditional gradient-based algorithms become easily trapped in local optimal solutions, therefore increasing the time taken in the experimental step. This problem is greater in recurrent neural networks, where the gradient propagation across the recurrence makes the training difficult for long-term dependences. On the other hand, evolutionary algorithms are search and optimization techniques which have been proved to solve many problems effectively. In the case of recurrent neural networks, the training using evolutionary algorithms has provided promising results. In this work, we propose two hybrid evolutionary algorithms as an alternative to improve the training of dynamic recurrent neural networks. The experimental section makes a comparative study of the algorithms proposed, to train Elman recurrent neural networks in time-series prediction problems. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Chemical Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Examines the non-parametric maximum likelihood estimator for doubly censored current status data. Application of doubly censored current status data on AIDS partner studies; Distribution of interest in the current status model; Use of iterative weighted pool-adjacent-violator-algorithm to compute the estimator.

Copyright of Canadian Journal of Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Presents an alternative iterative method for determining the approximate internal rate of return in capital budgeting. Algorithm for approximating the internal rate of return; Comparison of the iteration method, Newton's method and the bisection method.