Smoothing splines estimators in functional linear regression with errors-in-variables

The total least squares method is generalized in the context of the functional linear model. A smoothing splines estimator of the functional coefficient of the model is first proposed without noise in the covariates and an asymptotic result for this estimator is obtained. Then, this estimator is adapted to the case where the covariates are noisy and an upper bound for the convergence speed is also derived. The estimation procedure is evaluated by means of simulations.     

Using regression splines to assess the impact of information technology investments on productivity in the health care industry

Abstract. This paper explores the impact of information technology (IT) investments on productivity using a new technique, multivariate adaptive regression splines (MARS). We believe that it provides additional insights on the nature of the impact of IT investments on productivity. The results from our study are compared with findings from a previous study that has also used the same data set. While the results of a previous study indicate that IT investments have a positive but uniform impact on productivity, our study suggests that the impact of IT on productivity is not uniform but is contingent on other complementary factors. Our findings describe that the complementary relationship exists between IT and non‐IT related investments. Thus, improved organizational productivity cannot be expected from investment in IT alone but only together with non‐IT investments. Our findings also point out that further investment may not necessarily bring on higher organizational productivity.     

高维数据挖掘中基于中位数回归的特征提取新方法

为降低噪声对数据特征提取(变量选择)效果的不利影响,基于中位数回归分析方法,利用变量选择降维技术(正则化估计),提出了一种稳健、有效的特征提取(变量选择)新方法,并具体给出了估计算法,该算法具有快速计算的特点.实验结果表明,新方法能够有效地对高维数据集进行估计和变量选择,且具有较高的准确性,即使数据中的信噪比很低时,该方法仍具有较好的效果.因此,该方法为高维数据挖掘特征提取提供了稳健且有效的方法.     

Functional k-means inverse regression

A new dimension reduction method is proposed for functional multivariate regression with a multivariate response and a functional predictor by extending the functional sliced inverse regression model. Naive application of existing dimension reduction techniques for univariate response will create too many hyper-rectangular slices. To avoid this curse of dimensionality, a new slicing method is proposed by clustering over the space of the multivariate response, which generates a much smaller set of slices of flexible shapes. The proposed method can be applied to any number of response variables and can be particularly useful for exploratory analysis. In addition, a new eigenvalue-based method for determining the dimensionality of the reduced space is developed. Real and simulation data examples are then presented to demonstrate the effectiveness of the proposed method.     

Functional PLS logit regression model

Functional logistic regression has been developed to forecast a binary response variable from a functional predictor. In order to fit this model, it is usual to assume that the functional observations and the parameter function of the model belong to a same finite space generated by a basis of functions. This consideration turns the functional model into a multiple logit model whose design matrix is the product of the matrix of sample paths basic coefficients and the matrix of the inner products between basic functions. The likelihood estimation of the parameter function of this model is very inaccurate due to the high dependence structure of the so obtained design matrix (multicollinearity). In order to solve this drawback several approaches have been proposed. These employ standard multivariate data analysis methods on the design matrix. This is the case of the functional principal component logistic regression model. As an alternative a functional partial least squares logit regression model is proposed, that has as covariates a set of partial least squares components of the design matrix of the multiple logit model associated to the functional one.     

A psychophysiological approach to the assessment of work underload

The findings of a laboratory investigation of the relationship between the subjective and physiological components of work underload are reported. The subjective component is described in terms of the subjective work underload checklist, mental effort, and cognitive arousal. The physiological component is defined in terms of heart rate and heart rate variability. Evidence for an increase in work underload with a decrease in heart rate is provided. The relevance of this research to the aerospace environment is discussed and the need to investigate the behavioural component of work underload emphasized.     

Effects of different physical workload parameters on mental workload and performance

The design and evaluation of an occupational task should include an assessment of mental workload, since excessive levels of mental workload can cause errors or delayed information processing. Physically demanding work that is performed concurrently with a cognitive task may impact mental workload by impairing mental processing or decreasing performance. The primary objective of this study was to determine whether there is a differential effect of various types of physical activity on both mental workload and cognitive performance. Objective and subjective assessment tools (heart rate variability and visual analog scale) were used as indicators of mental workload, while correct responses during an arithmetic task reflected levels of performance. Thirty participants (ages 18-24 years) performed a combination of tasks inducing both physical and mental workload. Type of physical effort, frequency of movement, and force exertion level were manipulated to alter the workload associated with the physical activity. Changes in subjective ratings generally corresponded to changes in both performance on the arithmetic task and objective mental workload assessment. Some discrepancies occurred at the highest physical force exertion level as participants perceived an increase in effort to maintain the same level of performance. Further research is needed to determine the force exertion threshold, beyond which the physical effort required interferes with mental workload and/or cognitive performance.

Relevance to industry

Technological advancements have increased the requirement for many workers to execute cognitive tasks concurrently with physical activity. When designing and evaluating such situations it is important to determine the interactive effects of these activities. A simple, uni-dimensional tool is suggested as a screening tool to identify situations requiring excessive or increased mental workload that many degrade performance or place additional stress on the individual.     

Learning to rank by using multivariate adaptive regression splines and conic multivariate adaptive regression splines

Learning to rank is a supervised learning problem that aims to construct a ranking model for the given data. The most common application of learning to rank is to rank a set of documents against a query. In this work, we focus on point‐wise learning to rank, where the model learns the ranking values. Multivariate adaptive regression splines (MARS) and conic multivariate adaptive regression splines (CMARS) are supervised learning techniques that have been proven to provide successful results on various prediction problems. In this article, we investigate the effectiveness of MARS and CMARS for point‐wise learning to rank problem. The prediction performance is analyzed in comparison to three well‐known supervised learning methods, artificial neural network (ANN), support vector machine, and random forest for two datasets under a variety of metrics including accuracy, stability, and robustness. The experimental results show that MARS and ANN are effective methods for learning to rank problem and provide promising results.     

Classifying densities using functional regression trees: Applications in oceanology

The problem of building a regression tree is considered when the response variable is a probability density function. Splitting criteria which are well adapted to measure the dissimilarity between densities are proposed using the Csiszár's f-divergence. The comparison between performances of trees constructed with various criteria is tackled through numerical simulations. Afterwards, a tree is constructed to predict the size distribution of a zooplankton community using a set of explanatory environmental variables. Functional PCA is used in order to interpret the main modes of variation of the size spectra around the predicted density in each terminal node. Finally, a bagging procedure is used to increase the accuracy of the tree-based model.     

Diagnostics for functional regression via residual processes

Methods of regression diagnostics for functional regression models are developed which relate a functional response to predictor variables that can be multivariate vectors or random functions. For this purpose, a residual process is defined by subtracting the predicted from the observed response functions. This residual process is expanded into functional principal components (FPC), and the corresponding FPC scores are used as natural proxies for the residuals in functional regression models. For the case of a univariate covariate, a randomization test is proposed based on these scores to examine if the residual process depends on the covariate. If this is the case, it indicates lack of fit of the model. Graphical methods based on the FPC scores of observed and fitted functions can be used to complement more formal tests. The methods are illustrated with data from a recent study of Drosophila fruit flies regarding life-cycle gene expression trajectories as well as functional data from a dose-response experiment for Mediterranean fruit flies (Ceratitis capitata).     

IDSSs opportunities and problems: Steps to development of an IDSS

IDSSs should contribute to the enhancement of human performance, but their effectiveness can be guaranteed only in the case of certain decision types. The issues explored in this paper show that they can help to overcome some human limitations, especially in complex data and information processes, in uncertainty management, and in coherent reasoning. Integrating human and machine expertise is clearly beneficial, nevertheless with the aim of building intelligent solutions we should not ignore the role of human factors and the problems deriving from the integration of knowledge of multiple experts. The risk is that the systems will become clumsy and vulnerable to embarrassing failures. The paper explores the opportunities for exploitation of IDSSs to provide intelligent advice, intelligent analysis and intelligent evaluation. Some suggestions for research have been proposed looking at the ideas put forward in a recent research project dealing with the development of a system supporting local government authorities on environmental impact assessment procedure.     

Prediction of anterior scoliotic spinal curve from trunk surface using support vector regression

This paper proposes a framework for the training of learning systems for regression when (i) the number of examples is small and contains interdependencies, and (ii) each sample consists of large quantities of discrete data that are functional in nature. The objective is to achieve robust yet nonlinear relations between inputs and outputs. In this study, laser scans of the trunk surface and reconstructions of spinal data from X-rays from scoliosis patients were functionally represented as surfaces and curves. Leading functional principal component coefficients thereof constituted comprehensive features, and achieved sufficient dimensionality reduction for the prediction of spine from trunk. As a learning method, support vector regression (SVR) was chosen for its strong generalizability capability that stems from penalizing model complexity. A first robust prediction in this research application was obtained, with coefficients of determination for leading outputs of 0.70 and 0.82, respectively, in the test set. Those translated to a spinal curve prediction L₂-error of 3.61 mm, comparable to measurement error in data.     

Alternative solutions to multi-variate control performance assessment problems

Performance assessment of multi-variate control with minimum variance control as the benchmark requires an interactor matrix to filter the closed-loop output. This is to transfer the coordinate of the original variables into a new one in order to identify the control invariant disturbance dynamics from the first few terms of the closed-loop output Markov parameters. There has been a great deal of interest to simplify this approach, in particular, to find methods that do not need the interactor matrix. With this motivation, this paper explores alternative solutions to multi-variate control performance assessment problems. In particular, we will consider two practical scenarios: (1) known time delays between each pair of inputs and outputs, (2) no a priori knowledge about the process model or time delays at all. Solutions to these two scenarios are proposed. Two data-driven algorithms based on subspace approach are derived for the calculation of performance measures. Several examples illustrate the feasibility of the proposed approaches.     

若干电解质浓溶液活度系数估算的支持向量回归算法

应用支持向量回归算法筛选出与电解质浓溶液活度系数相关的离子特性参数集：阴阳离子半径比(R．／Rm)、阴离子半径Rm、阳离子半径R．和阴阳离子电荷数比(Zx／Zm)。并以此为自变量集，用支持向量回归算法或PLS算法总结活度系数的经验规律，进而提出利用一批浓电解质溶液已知的活度系数数据“转推”其他电解质溶液的活度系数的算法。用留一法考察了这种“转推”算法的准确程度。并引用离子系的对应态理论对支持向量回归求得的经验关系的物理意义作了讨论和解释。     

A new existence theory for positive periodic solutions to functional differential equations with impulse effects

The principle of this paper is to deal with a new existence theory for positive periodic solutions to a kind of nonautonomous functional differential equations with impulse actions at fixed moments. Easily verifiable sufficient criteria are established. The approach is based on the fixed-point theorem in cones. The paper extends some previous results and obtains some new results.     

Methods of searching for guaranteeing and optimistic solutions to integer optimization problems under uncertainty

The paper studies complex integer optimization problems with inexact coefficients of the linear objective function and convex quadratic constraint functions. Exact and approximate decomposition methods are developed and proved to search for guaranteeing and optimistic solutions to such problems. The methods are based on approximation of initial problems by problems of a simpler structure. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 1, pp. 103–114, January–February 2007.     

Heart rate response to industrial work at different outdoor temperatures with or without temperature control system at the plant

Different outdoor temperatures, the association between indoor temperature control at the workplace and working heart rates of industrial employees were evaluated. The subjects, 6,016 male and female employees in 21 industrial plants in Israel, were screened for cardiovascular risk factors between 1985–87 (The CORDIS Study). The data collected included resting heart rate, working heart rate (based on one hour ambulatory ECG), outdoor temperatures, temperature control (TC) status of the plant, workload, age and health-related habits. At outdoor temperatures below or above 22–28°C, subjects working in plants with TC had lower mean working heart rate HR (- 2 bpm) than those working in plants without TC (p < 0·0004 after adjustment for confounders). No statistically significant differences in mean working HR were found between subjects working with TC (at all the outdoor temperatures) and those without TC within the outdoor temperature range 22–28°C. Based on working heart rate, indoor temperature control in industrial plants appears to moderate the cardiovascular strain in working subjects during both cold and hot days.     

Salivary cortisol and cardiovascular reactivity to a public speaking task in a virtual and real-life environment

Public speaking is a well-known psychosocial stress to occur in social-evaluative situations. This study examined self-reported, autonomic and endocrine stress responses to a 5-min public speaking task. Participants were asked to present either in front of i) a real audience, or ii) a virtual audience or iii) an empty virtual lecture hall. Thus, the main objective of this study was to examine the influence of real or virtual social stimuli on stress reactivity. Additionally, possible sex differences in stress responses were evaluated. Sixty-six women and men (20–33 years) underwent a multidimensional assessment of stress including self-reported state anxiety, heart rate, heart rate variability and saliva cortisol secretion. Results showed comparable increases in all stress responses in both the real and the virtual public speaking group. These findings indicate that the Self Preservation Theory is not limited to physically present social entities, but may also be extended to virtual social stimuli; as such this observation is also in line with the so called Media Equation Concept. Implications of the current results for therapy and research are subsequently discussed.     

Who benefits from simulator training: Personality and heart rate variability in relation to situation awareness during navigation training

Objective

This study tested the relationship between individual differences and Situation Awareness (SA) during training in a navigation simulator.

Background

Simulators have become an important tool in the training and education of maritime personnel with a view to improving decisions and performance. There are a few, inconclusive empirical studies that have focused on mapping personality characteristics and their link to the ability to generate and maintain SA during simulator training.

Method

Thirty-six first-year students from the Royal Norwegian Navy Officer Candidate School participated in the study. SA was measured using both subjective and observer ratings.

Results

The results indicated that low scores on Neuroticism and high scores on Extraversion and Conscientiousness (resilient personality type) predicted both subjective and observer-rated SA. Furthermore, participants with high SA were able to modulate their Heart Rate Variability (HRV), with suppression of HRV during navigation training as well showing recovery of HRV.

Application

The potential applications of this research include the assessment of personality differences as a tool in selecting navigators, and the use of HRV as an objective index of adaptability to environmental demands.     

Sample size calculations for a functional human motion analysis: Application to vehicle ingress discomfort prediction

The ease of entering a vehicle, known as ingress, is one of the important ergonomic factors that car manufacturers consider during the process of vehicle design. Manufacturers frequently conduct human subject tests to assess ingress discomfort for different vehicle designs. Using subject tests, manufacturers are able to estimate the proportion of participants that report that they are discomfortable entering a vehicle, referred to in this paper as fraction disaccommodated (FD). Manufacturers then conduct statistical tests in order to determine if the FD of two vehicle designs are significantly different, and to determine the required sample size in testing the FD difference between two vehicle designs under pre-specified testing power. Since conducting human subject tests is often expensive and time consuming, another alternative is to estimate the FD using simulated human motion data. Determining the number of simulations that is required is an important statistical question that is dependent on the prediction performance of the simulation analysis. In this paper, a dual bootstrap approach is proposed to obtain the standard deviation of the estimated FD based on functional predictors. This standard deviation is then used to calculate the power in testing the difference between two estimated FDs.