Application of Naïve Bayesian sequential analysis to primary care optometry

The objective of this study was to investigate the effects of circularity, comorbidity, prevalence and presentation variation on the accuracy of differential diagnoses made in optometric primary care using a modified form of naïve Bayesian sequential analysis. No such investigation has ever been reported before. Data were collected for 1422 cases seen over one year. Positive test outcomes were recorded for case history (ethnicity, age, symptoms and ocular and medical history) and clinical signs in relation to each diagnosis. For this reason only positive likelihood ratios were used for this modified form of Bayesian analysis that was carried out with Laplacian correction and Chi-square filtration. Accuracy was expressed as the percentage of cases for which the diagnoses made by the clinician appeared at the top of a list generated by Bayesian analysis. Preliminary analyses were carried out on 10 diagnoses and 15 test outcomes. Accuracy of 100% was achieved in the absence of presentation variation but dropped by 6% when variation existed. Circularity artificially elevated accuracy by 0.5%. Surprisingly, removal of Chi-square filtering increased accuracy by 0.4%. Decision tree analysis showed that accuracy was influenced primarily by prevalence followed by presentation variation and comorbidity. Analysis of 35 diagnoses and 105 test outcomes followed. This explored the use of positive likelihood ratios, derived from the case history, to recommend signs to look for. Accuracy of 72% was achieved when all clinical signs were entered. The drop in accuracy, compared to the preliminary analysis, was attributed to the fact that some diagnoses lacked strong diagnostic signs; the accuracy increased by 1% when only recommended signs were entered. Chi-square filtering improved recommended test selection. Decision tree analysis showed that accuracy again influenced primarily by prevalence, followed by comorbidity and presentation variation. Future work will explore the use of likelihood ratios based on positive and negative test findings prior to considering naïve Bayesian analysis as a form of artificial intelligence in optometric practice.

Distributed sequential task allocation in foraging swarms

When designing a practical swarm robotics system, self-organized task allocation is key to make best use of resources. Current research in this area focuses on task allocation which is either distributed (tasks must be performed at different locations) or sequential (tasks are complex and must be split into simpler sub-tasks and processed in order). In practice, however, swarms will need to deal with tasks which are both distributed and sequential. In this paper, a classic foraging problem is extended to incorporate both distributed and sequential tasks. The problem is analysed theoretically, absolute limits on performance are derived, and a set of conditions for a successful algorithm are established. It is shown empirically that an algorithm which meets these conditions, by causing emergent cooperation between robots can achieve consistently high performance under a wide range of settings without the need for communication. © 2013 IEEE.

P-Sequential Spaces and Cleavability

∗ Supported by the Serbian Scientific Foundation, grant No 04M01

Dimension Reduction of the Explanatory Variables in Multiple Linear Regression

2002 Mathematics Subject Classification: 62J05, 62G35.

An Algorithm to Mine Normalized Weighted Sequential Patterns Using a Prefix-projected Database

Sequential pattern mining is an important subject in data mining with broad applications in many different areas. However, previous sequential mining algorithms mostly aimed to calculate the number of occurrences (the support) without regard to the degree of importance of different data items. In this paper, we propose to explore the search space of subsequences with normalized weights. We are not only interested in the number of occurrences of the sequences (supports of sequences), but also concerned about importance of sequences (weights). When generating subsequence candidates we use both the support and the weight of the candidates while maintaining the downward closure property of these patterns which allows to accelerate the process of candidate generation.

Projected sequential Gaussian processes:a C++ tool for interpolation of large datasets with heterogeneous noise

Heterogeneous datasets arise naturally in most applications due to the use of a variety of sensors and measuring platforms. Such datasets can be heterogeneous in terms of the error characteristics and sensor models. Treating such data is most naturally accomplished using a Bayesian or model-based geostatistical approach; however, such methods generally scale rather badly with the size of dataset, and require computationally expensive Monte Carlo based inference. Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential Bayesian framework for inference in such projected processes is presented. The observations are considered one at a time which avoids the need for high dimensional integrals typically required in a Bayesian approach. A C++ library, gptk, which is part of the INTAMAP web service, is introduced which implements projected, sequential estimation and adds several novel features. In particular the library includes the ability to use a generic observation operator, or sensor model, to permit data fusion. It is also possible to cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the covariance parameters is explored, including the impact of the projected process approximation on likelihood profiles. We illustrate the projected sequential method in application to synthetic and real datasets. Limitations and extensions are discussed. © 2010 Elsevier Ltd.

A Note on The Weak Sequential Core of Dynamic TU Games

This paper addresses a problem with an argument in Kranich, Perea, and Peters (2005) supporting their definition of the Weak Sequential Core and their characterization result. We also provide the remedy, a modification of the definition, to rescue the characterization.

Early career special education teachers' perceptions of school site induction support

Research highlights teacher attrition as one of the biggest challenges facing public schools and their attempts to provide a quality teacher for every student (Ingersoll & Smith, 2003). The teacher shortage is particularly daunting in special education where teachers are over twice as likely to leave the field. The first few years of teaching are the most critical in determining whether or not a beginning teacher will stay in the teaching profession (Whitaker, 2000). ^ A mixed-methods sequential explanatory design was utilized to examine research questions focused on the components of induction support that early career teachers received at their school site, including what they considered most valuable to their long-term retention in the classroom and their development as a quality teacher. Eighty seven early career special education teachers were surveyed during the first phase of the study and six participants were interviewed during the second phase. ^ Data analysis of the Likert-scale survey used in the study revealed that the majority of the respondents received at least 21 of the 25 listed induction components. Moreover, early career special education teachers indicated that they valued all 25 induction components. In addition, findings revealed that over two thirds of the respondents indicated a desire to remain a special education teacher. Overall, early career special education teachers felt confident in their abilities to teach students with disabilities; however, nearly half of the respondents did not feel satisfied with the induction they received. Independent t-tests showed a statistically significant difference between teachers who indicated a desire to remain in special education and those that did not on the level of satisfaction with their induction experience. ^ The six interviews provided elaboration and clarification of the survey responses. The participants expressed their passion for the art of teaching, their dedication to students with disabilities, and their frustration with being a beginning teacher. Furthermore, it was reported that the overall school culture was not very supportive. Participants offered relevant ideas for additional or alternate induction components that would be more effective.^

Sequential Logistic Regression: A Method to Reveal Subtlety in Self-Efficacy

This paper uses self-efficacy to predict the success of women in introductory physics. We show how sequential logistic regression demonstrates the predictive ability of self-efficacy, and reveals variations with type of physics course. Also discussed are the sources of self-efficacy that have the largest impact on predictive ability.

Investigating the Effects of Cognitive Apprenticeship-Based Instructional Coaching on Science Teaching Efficacy Beliefs

The overall purpose of this collected papers dissertation was to examine the utility of a cognitive apprenticeship-based instructional coaching (CAIC) model for improving the science teaching efficacy beliefs (STEB) of preservice and inservice elementary teachers. Many of these teachers perceive science as a difficult subject and feel inadequately prepared to teach it. However, teacher efficacy beliefs have been noted as the strongest indicator of teacher quality, the variable most highly correlated with student achievement outcomes. The literature is scarce on strong, evidence-based theoretical models for improving STEB. This dissertation is comprised of two studies. STUDY #1 was a sequential explanatory mixed-methods study investigating the impact of a reformed CAIC elementary science methods course on the STEB of 26 preservice teachers. Data were collected using the Science Teaching Efficacy Belief Instrument (STEBI-B) and from six post-course interviews. A statistically significant increase in STEB was observed in the quantitative strand. The qualitative data suggested that the preservice teachers perceived all of the CAIC methods as influential, but the significance of each method depended on their unique needs and abilities. STUDY #2 was a participatory action research case study exploring the utility of a CAIC professional development program for improving the STEB of five Bahamian inservice teachers and their competency in implementing an inquiry-based curriculum. Data were collected from pre- and post-interviews and two focus group interviews. Overall, the inservice teachers perceived the intervention as highly effective. The scaffolding and coaching were the CAIC methods portrayed as most influential in developing their STEB, highlighting the importance of interpersonal relationship aspects in successful instructional coaching programs. The teachers also described the CAIC approach as integral in supporting their learning to implement the new inquiry-based curriculum. The overall findings hold important implications for science education reform, including its potential to influence how preservice teacher training and inservice teacher professional development in science are perceived and implemented. Additionally, given the noteworthy results obtained over the relatively short durations, CAIC interventions may also provide an effective means of achieving improvements in preservice and inservice teachers’ STEB more expeditiously than traditional approaches.

Biosynthesis of neocarazostatin A reveals the sequential carbazole prenylation and hydroxylation in the tailoring steps

Acknowledgments Y.Y. acknowledges the financial support from “973” Program (2012CB721006) and National Natural Science Foundation of China (31570033). R.E., K.K., H.D., and M.J. acknowledge the financial support of the Leverhulme Trust-Royal Society Africa Award (AA090088).

Model updating using genetic algorithms with sequential niche technique

The authors would like to express their gratitude to organizations and people that supported this research. Piotr Omenzetter’s work within the Lloyd’s Register Foundation Centre for Safety and Reliability Engineering at the University of Aberdeen is supported by Lloyd’s Register Foundation. The Foundation helps to protect life and property by supporting engineering-related education, public engagement and the application of research. Ben Ryder of Aurecon and Graeme Cummings of HEB Construction assisted in obtaining access to the bridge and information for modelling. Luke Williams and Graham Bougen, undergraduate research students, assisted with testing.

Bayesian Emulation for Sequential Modeling, Inference and Decision Analysis

The advances in three related areas of state-space modeling, sequential Bayesian learning, and decision analysis are addressed, with the statistical challenges of scalability and associated dynamic sparsity. The key theme that ties the three areas is Bayesian model emulation: solving challenging analysis/computational problems using creative model emulators. This idea defines theoretical and applied advances in non-linear, non-Gaussian state-space modeling, dynamic sparsity, decision analysis and statistical computation, across linked contexts of multivariate time series and dynamic networks studies. Examples and applications in financial time series and portfolio analysis, macroeconomics and internet studies from computational advertising demonstrate the utility of the core methodological innovations.

Chapter 1 summarizes the three areas/problems and the key idea of emulating in those areas. Chapter 2 discusses the sequential analysis of latent threshold models with use of emulating models that allows for analytical filtering to enhance the efficiency of posterior sampling. Chapter 3 examines the emulator model in decision analysis, or the synthetic model, that is equivalent to the loss function in the original minimization problem, and shows its performance in the context of sequential portfolio optimization. Chapter 4 describes the method for modeling the steaming data of counts observed on a large network that relies on emulating the whole, dependent network model by independent, conjugate sub-models customized to each set of flow. Chapter 5 reviews those advances and makes the concluding remarks.

(Figure 1 and 2) Phosphorus pools in the investigated sediments quantified by SEDEX sequential extraction and distribution of recovered 33P spike between sedimentary P pools after incubation

Phosphorus is an essential nutrient for life. In the ocean, phosphorus burial regulates marine primary production**1, 2. Phosphorus is removed from the ocean by sedimentation of organic matter, and the subsequent conversion of organic phosphorus to phosphate minerals such as apatite, and ultimately phosphorite deposits**3, 4. Bacteria are thought to mediate these processes**5, but the mechanism of sequestration has remained unclear. Here, we present results from laboratory incubations in which we labelled organic-rich sediments from the Benguela upwelling system, Namibia, with a 33P-radiotracer, and tracked the fate of the phosphorus. We show that under both anoxic and oxic conditions, large sulphide-oxidizing bacteria accumulate 33P in their cells, and catalyse the nearly instantaneous conversion of phosphate to apatite. Apatite formation was greatest under anoxic conditions. Nutrient analyses of Namibian upwelling waters and sediments suggest that the rate of phosphate-to-apatite conversion beneath anoxic bottom waters exceeds the rate of phosphorus release during organic matter mineralization in the upper sediment layers. We suggest that bacterial apatite formation is a significant phosphorus sink under anoxic bottom-water conditions. Expanding oxygen minimum zones are projected in simulations of future climate change**6, potentially increasing sequestration of marine phosphate, and restricting marine productivity.