The planet is experiencing a dramatic loss of species. The majority of species are unknown to science, and it is usually infeasible to conduct a census of a region to acquire a complete inventory of all life forms. Therefore, it is important to estimate and conduct statistical inference on the total number of species in a region based on samples obtained from field observations. Such estimates may suggest the number of species new to science and at potential risk of extinction. In this thesis, we develop novel methodology to conduct statistical inference, based on abundance-based data collected from multiple sampling locations, on the number of species within a taxonomic group residing in a region. The primary contribution of this work is the formulation of novel statistical methodology for analysis in this setting, where abundances of species are recorded at multiple sampling units across a region. This particular area has received relatively little attention in the literature. In the first chapter, the problem of estimating the number of species is formulated in a broad context, one that occurs in several seemingly unrelated fields of study. Estimators are commonly developed from statistical sampling models. Depending on the organisms or objects under study, different sampling techniques are used, and consequently, a variety of statistical models have been developed for this problem. A review of existing estimation methods, categorized by the associated sampling model, is presented in the second chapter. The third chapter develops a new negative binomial mixture model. The negative binomial model is employed to account for the common tendency of individuals of a particular species to occur in clusters. An exponential mixing distribution permits inference on the number of species that exist in the region, but were in fact absent from the sampling units. Adopting a classical approach for statistical inference, we develop the maximum likelihood estimator, and a corresponding profile-log-likelihood interval estimate of species richness. In addition, a Gaussian-based confidence interval based on large-sample theory is presented. The fourth chapter further extends the hierarchical model developed in Chapter 3 into a Bayesian framework. The motivation for the Bayesian paradigm is explained, and a hierarchical model based on random effects and discrete latent variables is presented. Computing the posterior distribution in this case is not straight-forward. A data augmentation technique that indirectly places priors on species richness is employed to compute the model using a Metropolis-Hastings algorithm. The fifth chapter examines the performance of our new methodology. Simulation studies are used to examine the mean-squared error of our proposed estimators. Comparisons to several commonly-used non-parametric estimators are made. Several conclusions emerge, and settings where our approaches can yield superior performance are clarified. In the sixth chapter, we present a case study. The methodology is applied to a real data set of oribatid mites (a taxonomic order of micro-arthropods) collected from multiple sites in a tropical rainforest in Panama. We adjust our statistical sampling models to account for the varying masses of material sampled from the sites. The resulting estimates of species richness for the oribatid mites are useful, and contribute to a wider investigation, currently underway, examining the species richness of all arthropods in the rainforest. Our approaches are the only existing methods that can make full use of the abundance-based data from multiple sampling units located in a single region. The seventh and final chapter concludes the thesis with a discussion of key considerations related to implementation and modeling assumptions, and describes potential avenues for further investigation. / Graduate

Master of Science / Department of Psychological Sciences / Richard J. Harris / The revised hierarchical model assumes a strong lexical link from L2 to L1 and a strong conceptual link from L1 to L2, with both links being contingent on L2 fluency. The bilingual memory literature has discussed the role of L2 fluency in bilingual lexical and semantic retrieval; however, little is known on how priming for a target language (L1 or L2) may affect lexical and semantic access or how it is affected by L2 proficiency. The present study utilized the revised hierarchical model to examine how language priming and intermediate levels of L2 fluency affects bilingual lexical and semantic retrieval in a yes/no image/word task. 181 participants read four paragraphs of discourse to prime for a specific target language (English or Spanish) and performed a modified picture-word interference task (MPWI), in which they had to determine if image/word pairs were congruent (matched) or incongruent (did not match). The main dependent variables were accuracy and RT on the MPWI task. Additional DVs were accuracy and RT on comprehension questions over the content of the priming discourse and question type (explicit, factual, and pragmatic). Across intermediate levels of L2 fluency, those more fluent performed faster and were more accurate on the MPWI task than those less fluent. No differences were observed when the image/word pairs were congruent for English or Spanish, yet there was a language difference when incongruent for Spanish. Readers had highest percent correct for explicit questions and lowest for pragmatic questions, took longer on factual than pragmatic question, and took longer to respond when priming discourse and questions were in Spanish than when in English. The results are interpreted and discussed in terms of the revised hierarchical model, in that fluency, at least at the intermediate level, affects processing time more than accuracy. Limitations of the study, future directions, and implications for L2 educators are also discussed.

This work deals with communication in multiagent systems. The protocols for negotiation and argumentation are shown, and model example of their usage is described. We describe hierarchical model of trust in contexts, that is used for representation of agent's believes. The argumentation protocol for those agents is designed, and is used for solving conflicts.

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Internationella studier har visat att poliser drivs av såväl inre som yttre motivation och att de vidtar varierande copingstrategier för att hantera yrkets baksidor. Syftet med undersökningen var att utforska vad som påverkar arbetsmotivationen för svenska poliser i yttre tjänst samt vilka copingstrategier de använder sig av för att hantera yrkets psykologiska påfrestningar. Kvalitativa djupintervjuer genomfördes med 10 poliser i yttre tjänst. En tematisk analysmetod resulterade i sex teman under samtliga frågeställningar. Resultatet visade att polisers motivation är beroende av faktorer från individ till samhälle. Faktumet att polisyrket i hög grad tillfredsställer psykologiska behov tycktes delvis kompensera för yrkets psykologiska påfrestningar. Copingstrategier förekom i form av individuell mental självhjälp såväl som att söka hjälp utifrån. Organisatoriska faktorer och pågående attityd- och normförskjutningar i samhället visade sig ha en kraftig påverkan på polisernas motivation. Studien synliggjorde hur dessa pågående processer färgar polisers vardagliga arbete, vilket utgjorde studiens främsta bidrag.

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Intrinsic connectivity networks (ICNs) refer to brain functional networks that are consistently found under various conditions, during tasks or at rest. Some studies demonstrated that while some stimuli do not impact intrinsic connectivity, other stimuli actually activate intrinsic connectivity through suppression, excitation, moderation or modi cation. Most analyses of functional magnetic resonance imaging (fMRI) data use ad-hoc methods to estimate the latent structure of ICNs. Modeling the effects on ICNs has also not been fully investigated. Bayesian Intrinsic Connectivity Network (BICNet) captures the ICN structure with We propose a BICNet model, an extended Bayesian dynamic sparse latent factor model, to identify the ICNs and quantify task-related effects on the ICNs. BICNet has the following advantages: (1) It simultaneously identifies the individual and group-level ICNs; (2) It robustly identifies ICNs by jointly modeling resting-state fMRI (rfMRI) and task-related fMRI (tfMRI); (3) Compared to independent component analysis (ICA)-based methods, it can quantify the difference of ICNs amplitudes across different states; (4) The sparsity of ICNs automatically performs feature selection, instead of ad-hoc thresholding. We apply BICNet to the rfMRI and language tfMRI data from the Human Connectome Project (HCP) and identify several ICNs related to distinct language processing functions.

This thesis describes a robust and efficient design of Student-t based Robust Spatio-Temporal Prediction, namely, St-RSTP, to provide estimation based on observations over spatio-temporal neighbors. It is crucial to many applications in geographical information systems, medical imaging, urban planning, economy study, and climate forecasting. The proposed St-RSTP is more resilient to outliers or other small departures from model assumptions than its ancestor, the Spatio-Temporal Random Effects (STRE) model. STRE is a statistical model with linear order complexity for processing large scale spatiotemporal data. However, STRE has been shown sensitive to outliers or anomaly observations. In our design, the St-RSTP model assumes that the measurement error follows Student's t-distribution, instead of a traditional Gaussian distribution. To handle the analytical intractable inference of Student's t model, we propose an approximate inference algorithm in the framework of Expectation Propagation (EP). Extensive experimental evaluations, based on both simulation and real-life data sets, demonstrated the robustness and the efficiency of our Student-t prediction model compared with the STRE model. / Master of Science

京都大学 / 0048 / 新制・課程博士 / 博士(社会健康医学) / 甲第20289号 / 社医博第78号 / 社新制||医||9(附属図書館) / 京都大学大学院医学研究科社会健康医学系専攻 / (主査)教授 山田 亮, 教授 中山 健夫, 教授 古川 壽亮 / 学位規則第4条第1項該当 / Doctor of Public Health / Kyoto University / DFAM

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