An incomplete data approach to the analysis of covariance structures

In this paper, linear structural equation models with latent variables are considered. It is shown how many common models arise from incomplete observation of a relatively simple system. Subclasses of models with conditional independence interpretations are also discussed. Using an incomplete data point of view, the relationships between the incomplete and complete data likelihoods, assuming normality, are highlighted. For computing maximum likelihood estimates, the EM algorithm and alternatives are surveyed. For the alternative algorithms, simplified expressions for computing function values and derivatives are given. Likelihood ratio tests based on complete and incomplete data are related, and an example on using their relationship to improve the fit of a model is given.This research forms part of the author's doctoral thesis and was supported by a Commonwealth Postgraduate Research Award. The author also wishes to acknowledge the support of CSIRO during the preparation of this paper and the referees' comments which led to substantial improvements.     

The role of auditory context in action-effect-related motor adaptation

Previous research indicates that adding auditory effects to a (silent) action can lead to substantial efficiency gains in the performance of the action, while compromising the connection between the motor and the auditory event (e.g., by removing, or by delaying the auditory effects), leads the agent to compensate for the loss of auditory feedback by executing actions in a way which increases the probability of success or enhances feedback in other modalities, thus departing from the optimal action performance. The current study explored how this motor adaptation was affected when the quality of auditory feedback was reduced by contextual factors, while keeping the physical link between the action and auditory effect intact. In two experiments, participants elicited pure tones by pinching a force sensitive resistor (FSR). In some of the conditions action-effect contingency was reduced by intermixing externally initiated tones with the self-induced ones. Pinch-force measurements indicated that action optimization was affected by contextual factors. The influence of auditory context was the most pronounced when the discrimination of self-induced and external tones was made difficult by the similarity and temporal proximity of the self-induced and external tones. In these conditions, tone eliciting actions were more forceful in comparison to conditions in which no external tones were presented, and in comparison to conditions in which the external tones were easily distinguishable from self-induced ones. This suggests that contextual factors can induce similar motor adjustments as manipulating the physical connection between the action and its sensory consequences.     

研究者伦理培训与多角度调查分析对培训优化的思考

通过对新入职的医务人员（医生、护士、药师）进行科研伦理培训和问卷调查研究,分析临床研究实践中暴露的科研伦理问题,并根据培训后考试结果,了解参加临床试验的研究者在进行培训后对伦理审查认知的提升度。同时,分析设置的培训课程所存在的问题,认为常规模式化的培训并不能满足临床研究者的需求。根据培训所应侧重的内容及方式,对培训内容、培训频率、培训人员角色、培训方式、培训师资、培训管理进行优化,使参加临床试验研究者的伦理认知态度得到显著提升,为落实临床研究者伦理素养的培训的培育需求,提供参考。

     

Using spotted hyena optimizer for training feedforward neural networks

Spotted hyena optimizer (SHO) is a novel metaheuristic optimization algorithm based on the behavior of spotted hyena and their collaborative behavior in nature. In this paper, we design a spotted hyena optimizer for training feedforward neural network (FNN), which is regarded as a challenging task since it is easy to fall into local optima. Our objective is to apply metaheuristic optimization algorithm to tackle this problem better than the mathematical and deterministic methods. In order to confirm that using SHO to train FNN is more effective, five classification datasets and three function-approximations are applied to benchmark the performance of the proposed method. The experimental results show that the proposed SHO algorithm for optimization FNN has the best comprehensive performance and has more outstanding performance than other the state-of-the-art metaheuristic algorithms in terms of the performance measures.     

A New VNS Metaheuristic Using MADS as a Local Optimizer

We propose a new hybrid metaheuristic VNSMADS that is an implementation of the Variable Neighbourhood Search (VNS) algorithm with the Mesh Adaptive Direct Search (MADS) as the local search. Because MADS is a Derivative‐Free Optimization method, the targeted problems may be non‐smooth on black box functions possessing several local optima. Constraints other than bounds on the variables are not considered. VNSMADS is applied on a real‐world problem from Finance: the portfolio selection problem using the investor preferences. Numerical results show the efficiency of our approach. Copyright © 2012 John Wiley & Sons, Ltd.     

Multiple-objective weighting factor auxiliary optimization problems

In this paper we consider the generation of efficient solutions using weighting factor, qth-power approaches for some non-convex auxiliary function optimization forms. Algorithms are given for finding e-optimal solutions for these optimization problems.     

Choice functions in multistage optimization

This paper is devoted to the multistage optimization problem with a vector-valued gain function. The notion of optimality is determined here by the choice function. The conditions for the choice function that guarantee the applicability and efficiency of dynamic programming are found.     

Approximately Efficient Solutions in Vector Optimization

An approach to approximating solutions in vector optimization is developed for vector optimization problems with arbitrary ordering cones. This paper presents a study of approximately efficient points of a given set with respect to a convex cone in an ordered Banach space. Existence results for such approximately efficient points are obtained. A domination property related to these existence results is observed and then it is proved that each element of a given set is approximated by the sum of a point in a convex cone inducing the ordering and a point in a finite set consisting of such approximately efficient points of the set.     

A monotonically convergent algorithm for orthogonal congruence rotation

Brokken has proposed a method for orthogonal rotation of one matrix such that its columns have a maximal sum of congruences with the columns of a target matrix. This method employs an algorithm for which convergence from every starting point is not guaranteed. In the present paper, an iterative majorization algorithm is proposed which is guaranteed to converge from every starting point. Specifically, it is proven that the function value converges monotonically, and that the difference between subsequent iterates converges to zero. In addition to the better convergence properties, another advantage of the present algorithm over Brokken's one is that it is easier to program. The algorithms are compared on 80 simulated data sets, and it turned out that the new algorithm performed well in all cases, whereas Brokken's algorithm failed in almost half the cases. The derivation of the algorithm is given in full detail because it involves a series of inequalities that can be of use to derive similar algorithms in different contexts.This research has been made possible by a fellowship from the Royal Netherlands Academy of Arts and Sciences to the first author. The authors are obliged to Willem J. Heiser and Jos M. F. ten Berge for useful comments on an earlier version of this paper.