String-averaging expectation-maximization for maximum likelihood estimation in emission tomography

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Landscape changes in Serra do Japi: legal protection or scientific expectation?

The creation of new legally protected areas brings many conflicts that distance the real landscape from the expected according to environmental law or conservation researchers. In this study, we mapped and compared the changes in Serra da Japi (Sao Paulo State, Brazil) throughout 40 years with scenarios of legal protection and scientific expectation on forest conservation, to evaluate the distance between them. This may allow us to infer the direction of historical changes and assist in the debate among decision makers. The results showed that most legal requirements on forest protection in the current landscape have been met. The 1960s was the period when the forest cover was closest to the desirable conservation stage. Although the Serra do Japi has maintained large areas of forests during the entire study period, human interference increased with the expansion of reforestation and urban areas, and access roads were identified as a primary potential driving forces of change. In addition, habitat loss was observed in the landscape, which can represent the first phase of a sequence of modifications detrimental to the environmental conservation of this protected area, including decision changes to land use. In conclusion, the changes evolved toward conservation expectations, but not toward the forest configuration of scientific expectation.

'Alternate-goal bias' in antisaccades and the influence of expectation

Saccadic performance depends on the requirements of the current trial, but also may be influenced by other trials in the same experiment. This effect of trial context has been investigated most for saccadic error rate and reaction time but seldom for the positional accuracy of saccadic landing points. We investigated whether the direction of saccades towards one goal is affected by the location of a second goal used in other trials in the same experimental block. In our first experiment, landing points ('endpoints') of antisaccades but not prosaccades were shifted towards the location of the alternate goal. This spatial bias decreased with increasing angular separation between the current and alternative goals. In a second experiment, we explored whether expectancy about the goal location was responsible for the biasing of the saccadic endpoint. For this, we used a condition where the saccadic goal randomly changed from one trial to the next between locations on, above or below the horizontal meridian. We modulated the prior probability of the alternate-goal location by showing cues prior to stimulus onset. The results showed that expectation about the possible positions of the saccadic goal is sufficient to bias saccadic endpoints and can account for at least part of this phenomenon of 'alternate-goal bias'.

Group Stereotypes and The Self: How Social Status and Interaction Expectation Affect Self-Stereotyping

This study examined how ingroup status affects the tendency for people to internalize ingroup stereotypes (i.e. self-stereotype) when expecting to interact with another individual who holds stereotypic views of them. Past research has demonstrated that people self-stereotype when they want to affiliate with another individual who holds stereotypic views of them. By self-stereotyping, individuals create a common bond or shared set of beliefs with the other individual. This line of research has not yet examinedif there are any moderators in the relationship between affiliation motivation and self-stereotyping. However, there is reason to believe that members of lower-status groups are more likely to feel the need to create this common bond through self-stereotyping because 1) they identify more closely with their social group, 2) their group identity is more salient 3) they are more aware of the expectations of others, 4) and they care more about the quality of an interaction with a member from a higher-status group. For this experiment, I recruited twenty-seven members of Alpha Chi Omega andtwenty-eight members of Delta Gamma, two sororities that are perceived to be middle-ranked (as determined by a pre-test survey). Upon arriving to the study, half the participants were informed that they would be interacting with a member of Kappa Kappa Gamma, a higher-ranked sorority (as determined by a pre-test survey) and half the participants were informed that they would be interacting with a member of a Chi Omega, a lower-ranked sorority (as determined by a pre-test survey). Participants were also informed that this partner held stereotypic views of their (i.e. the participant’s)sorority. After, participants were given the Self-Stereotyping Measure in which they rated how well sixteen characteristics described themselves. The results of the series of analyses performed on participants’ ratings on the Self-Stereotyping Measure indicated that when expecting to interact with another individual, members of low-status groups self-stereotype more than members of high-statusgroups and those who do not expect to interact. Furthermore, unexpectedly, among members of high-status groups, those who expected to interact with a member of a low-status group self-stereotyped less than those who did not expect to interact. Thus, this research provides support for the hypothesis that group status is a moderator in the relationship between self-stereotyping and affiliation motivation.

Effects of expectation and caffeine on arousal, well-being, and reaction time

The objective of this study is to determine the impact of expectation associated with placebo and caffeine ingestion. We used a three-armed, randomized, double-blind design. Two three-armed experiments varying instruction (true, false, control) investigated the role of expectations of changes in arousal (blood pressure, heart rate), subjective well-being, and reaction time (RT). In Experiment 1 (N = 45), decaffeinated coffee was administered, and expectations were produced in one group by making them believe they had ingested caffeinated coffee. In Experiment 2 (N = 45), caffeinated orange juice was given in both experimental groups, but only one was informed about the true content. In Experiment 1, a significant effect for subjective alertness was found in the placebo treatment compared to the control group. However, for RT and well-being no significant effects were found. In Experiment 2, no significant expectancy effects were found. Caffeine produced large effects for blood pressure in both treatments compared to the control group, but the effects were larger for the false information group. For subjective well-being (alertness, calmness), considerable but nonsignificant changes were found for correctly informed participants, indicating possible additivity of pharmacologic effect and expectations. The results tentatively indicate that placebo and expectancy effects primarily show through introspection.

Estimating and quantifying uncertainties on level sets using the Vorob'ev expectation and deviation with Gaussian process models

Several methods based on Kriging have recently been proposed for calculating a probability of failure involving costly-to-evaluate functions. A closely related problem is to estimate the set of inputs leading to a response exceeding a given threshold. Now, estimating such a level set—and not solely its volume—and quantifying uncertainties on it are not straightforward. Here we use notions from random set theory to obtain an estimate of the level set, together with a quantification of estimation uncertainty. We give explicit formulae in the Gaussian process set-up and provide a consistency result. We then illustrate how space-filling versus adaptive design strategies may sequentially reduce level set estimation uncertainty.

Expectation Conditional Maximization-Based Deformable Shape Registration

This paper addresses the issue of matching statistical and non-rigid shapes, and introduces an Expectation Conditional Maximization-based deformable shape registration (ECM-DSR) algorithm. Similar to previous works, we cast the statistical and non-rigid shape registration problem into a missing data framework and handle the unknown correspondences with Gaussian Mixture Models (GMM). The registration problem is then solved by fitting the GMM centroids to the data. But unlike previous works where equal isotropic covariances are used, our new algorithm uses heteroscedastic covariances whose values are iteratively estimated from the data. A previously introduced virtual observation concept is adopted here to simplify the estimation of the registration parameters. Based on this concept, we derive closed-form solutions to estimate parameters for statistical or non-rigid shape registrations in each iteration. Our experiments conducted on synthesized and real data demonstrate that the ECM-DSR algorithm has various advantages over existing algorithms.

Expectation-based types of volunteers in Swiss sports clubs

Volunteer research in sports clubs has paid hardly any attention to the individual expectations even though matching conditions to the specific volunteer’s expectations represents a major management challenge. This article presents a person-oriented approach to the expectation profiles of volunteers that delivers the basis for identifying different volunteer segments. The approach assumes explicitly that volunteers in sports clubs develop specific expectations regarding their working conditions. These expectations were determined in a sample of 441 members of 45 selected sports clubs. Proximately, a cluster analysis revealed that volunteers vary in their expectations regarding voluntary work. Four different types of volunteers could be identified: (1) recognition seekers, (2) material incentive seekers, (3) participation and communication seekers, and (4) support seekers. These “expectation-based volunteer types” could also be characterized in socioeconomic, membershiprelated, and volunteer-work-related terms. These types could serve as a basis for designing specific voluntary work conditions in sports clubs.

Disentangling mental imagery and perceptual expectation

Mental imagery and perception are thought to rely on similar neural circuits, and many recent behavioral studies have attempted to demonstrate interactions between actual physical stimulation and sensory imagery in the corresponding sensory modality. However, there has been a lack of theoretical understanding of the nature of these interactions, and both interferential and facilitatory effects have been found. Facilitatory effects appear strikingly similar to those that arise due to experimental manipulations of expectation. Using a self-motion discrimination task, we try to disentangle the effects of mental imagery from those of expectation by using a hierarchical drift diffusion model to investigate both choice data and response times. Manipulations of expectation are reasonably well understood in terms of their selective influence on parameters of the drift diffusion model, and in this study, we make the first attempt to similarly characterize the effects of mental imagery. We investigate mental imagery within the computational framework of control theory and state estimation. • Mental imagery and perception are thought to rely on similar neural circuits; however, on more theoretical grounds, imagery seems to be closely related to the output of forward models (sensory predictions). • We reanalyzed data from a study of imagined self-motion. • Bayesian modeling of response times may allow us to disentangle the effects of mental imagery on behavior from other cognitive (top-down) effects, such as expectation.

THE RELATIONSHIP OF PRESCRIPTION RECEIPT AND EXPECTATION OF SICK-ROLE LEGITIMATION BY AMBULATORY VETERANS

This study explored the relationship of attitudes, needs, and health services utilization patterns of elderly veterans who were identified and categorized by their expectation for and receipt of sick-role legitimation. Three prescription types (new, change, renewal) were defined as the operational variables. A population of 676 ambulatory, chronically ill (average age 60 years) veterans were sent a questionnaire (74% response rate). In addition, retrospective medical and prescription record review was performed for a 45% sample of respondents. The results were analyzed using discriminant function and regression analysis. Fewer than 20% of the veterans responding expected to receive more prescriptions than were presently prescribed, whereas over 80% expected refill authorizations. Distinct attitudinal, need, and utilization patterns were identified. ^

An approach to operational modal analysis using the expectation maximization algorithm

This paper presents the Expectation Maximization algorithm (EM) applied to operational modal analysis of structures. The EM algorithm is a general-purpose method for maximum likelihood estimation (MLE) that in this work is used to estimate state space models. As it is well known, the MLE enjoys some optimal properties from a statistical point of view, which make it very attractive in practice. However, the EM algorithm has two main drawbacks: its slow convergence and the dependence of the solution on the initial values used. This paper proposes two different strategies to choose initial values for the EM algorithm when used for operational modal analysis: to begin with the parameters estimated by Stochastic Subspace Identification method (SSI) and to start using random points. The effectiveness of the proposed identification method has been evaluated through numerical simulation and measured vibration data in the context of a benchmark problem. Modal parameters (natural frequencies, damping ratios and mode shapes) of the benchmark structure have been estimated using SSI and the EM algorithm. On the whole, the results show that the application of the EM algorithm starting from the solution given by SSI is very useful to identify the vibration modes of a structure, discarding the spurious modes that appear in high order models and discovering other hidden modes. Similar results are obtained using random starting values, although this strategy allows us to analyze the solution of several starting points what overcome the dependence on the initial values used.

Maximum Likelihood Estimation of Modal Parameters in Structures Using the Expectation Maximization Algorithm

This paper presents a time-domain stochastic system identification method based on maximum likelihood estimation (MLE) with the expectation maximization (EM) algorithm. The effectiveness of this structural identification method is evaluated through numerical simulation in the context of the ASCE benchmark problem on structural health monitoring. The benchmark structure is a four-story, two-bay by two-bay steel-frame scale model structure built in the Earthquake Engineering Research Laboratory at the University of British Columbia, Canada. This paper focuses on Phase I of the analytical benchmark studies. A MATLAB-based finite element analysis code obtained from the IASC-ASCE SHM Task Group web site is used to calculate the dynamic response of the prototype structure. A number of 100 simulations have been made using this MATLAB-based finite element analysis code in order to evaluate the proposed identification method. There are several techniques to realize system identification. In this work, stochastic subspace identification (SSI)method has been used for comparison. SSI identification method is a well known method and computes accurate estimates of the modal parameters. The principles of the SSI identification method has been introduced in the paper and next the proposed MLE with EM algorithm has been explained in detail. The advantages of the proposed structural identification method can be summarized as follows: (i) the method is based on maximum likelihood, that implies minimum variance estimates; (ii) EM is a computational simpler estimation procedure than other optimization algorithms; (iii) estimate more parameters than SSI, and these estimates are accurate. On the contrary, the main disadvantages of the method are: (i) EM algorithm is an iterative procedure and it consumes time until convergence is reached; and (ii) this method needs starting values for the parameters. Modal parameters (eigenfrequencies, damping ratios and mode shapes) of the benchmark structure have been estimated using both the SSI method and the proposed MLE + EM method. The numerical results show that the proposed method identifies eigenfrequencies, damping ratios and mode shapes reasonably well even in the presence of 10% measurement noises. These modal parameters are more accurate than the SSI estimated modal parameters.

Operational Modal Analysis using Expectation Maximization Algorithm

This paper presents a time-domain stochastic system identification method based on Maximum Likelihood Estimation and the Expectation Maximization algorithm. The effectiveness of this structural identification method is evaluated through numerical simulation in the context of the ASCE benchmark problem on structural health monitoring. Modal parameters (eigenfrequencies, damping ratios and mode shapes) of the benchmark structure have been estimated applying the proposed identification method to a set of 100 simulated cases. The numerical results show that the proposed method estimates all the modal parameters reasonably well in the presence of 30% measurement noise even. Finally, advantages and disadvantages of the method have been discussed.