We examine some of the methodologies implemented by the Centers for Disease Control and Prevention's (CDC) BioSense program. The program uses data from hospitals and public health departments to detect outbreaks using the Early Aberration Reporting System (EARS). The EARS method W2 allows one to monitor syndrome counts (W2count) from each source and the proportion of counts of a particular syndrome relative to the total number of visits (W2rate). We investigate the performance of the W2r method designed using an empiric recurrence interval (RI) in this dissertation research. An adaptive threshold monitoring method is introduced based on fitting sample data to the underlying distributions, then converting the current value to a Z-score through a p-value. We compare the upper thresholds on the Z-scores required to obtain given values of the recurrence interval for different sets of parameter values. We then simulate one-week outbreaks in our data and calculate the proportion of times these methods correctly signal an outbreak using Shewhart and exponentially weighted moving average (EWMA) charts. Our results indicate the adaptive threshold method gives more consistent statistical performance across different parameter sets and amounts of baseline historical data used for computing the statistics. For the power analysis, the EWMA chart is superior to its Shewhart counterpart in nearly all cases, and the adaptive threshold method tends to outperform the W2 rate method. Two modified W2r methods proposed in the dissertation also tend to outperform the W2r method in terms of the RI threshold functions and in the power analysis. / Ph. D.

This thesis consists of four chapters. Chapter 1 contains the preliminaries. We give the background, notation and some results needed for this work, and we describe our main results of this thesis. In Chapter 2 we will introduce a class of weighted Bergman spaces. We then will discuss some properties about the multiplication operator, Mz , on them. We also characterize the dual spaces of these weighted Bergman spaces. In Chapter 3 we will characterize the reducing subspaces of multiple weighted shifts. The reducing subspaces of the Bergman and the Dirichlet shift of multiplicity N are portrayed from this characterization. In Chapter 4 we will introduce the class of super-isometrically dilatable operators and describe their elementary properties. We then will discuss an equivalent description of the invariant subspace lattice for the Bergman shift. We will also discuss the interpolating sequences on the bidisk. Finally, we will examine a special class of super-isometrically dilatable operators. One corollary of this work is that we will prove that the compression of the Bergman shift on two compliments of two invariant subspaces are unitarily equivalent if and only if the two invariant subspaces are equal. / Ph. D.

Product reliability is an important characteristic for all manufacturers, engineers and consumers. Industrial statisticians have been planning experiments for years to improve product quality and reliability. However, rarely do experts in the field of reliability have expertise in design of experiments (DOE) and the implications that experimental protocol have on data analysis. Additionally, statisticians who focus on DOE rarely work with reliability data. As a result, analysis methods for lifetime data for experimental designs that are more complex than a completely randomized design are extremely limited. This dissertation provides two new analysis methods for reliability data from life tests. We focus on data from a sub-sampling experimental design. The new analysis methods are illustrated on a popular reliability data set, which contains sub-sampling. Monte Carlo simulation studies evaluate the capabilities of the new modeling methods. Additionally, Monte Carlo simulation studies highlight the principles of experimental design in a reliability context. The dissertation provides multiple methods for statistical inference for the new analysis methods. Finally, implications for the reliability field are discussed, especially in future applications of the new analysis methods. / Ph. D.

High pre-harvest rainfall in 2006 caused significant pre-harvest sprouting (PHS) and weathering throughout the mid-Atlantic soft red winter wheat (SRWW) (Triticum aestivum L.) growing region. Sprouting and weathering caused decreased flour quality due to lowered dough viscosity and decreased ability to withstand mixing and processing for baked goods. Due to its decreased quality, severely sprouted grain is sold for feed, at a lower price per bushel. Pre-harvest sprouting negatively affects the chain of production from the field to baking operations. The purpose of this research was to evaluate the inherent dormancy and PHS resistance, of current SRWW cultivars and to assess the relationship between falling number and flour quality after grain weathering. Employing a weighted germination index (WGI), a large range in dormancy was observed across SRWW cultivars and seed production. Artificial weathering tests confirmed the use of WGI as a tool for screening for dormancy of SRWW cultivars. The WGI consistently identified cultivars with significantly higher or lower inherent dormancy. "Coker 9553" was highly dormant and resistant to PHS. This cultivar maintained an average falling number of 300 seconds even after receiving an average of 215 mm of rainfall, while the mean falling number for all SRWW cultivars after this amount of weathering was 131 seconds. After only moderate weathering, nine of 15 SRWW cultivars in the study exhibited severe sprouting, demonstrating the need for increased PHS resistance in SRWW wheat. Pre-harvest sprouting resistance groupings, based on average 2008 cultivar falling number were accurately predicted by WGI at both 10 (R2=0.79) and 30°C (R2=0.72) No consistent relationship was observed between head angle, glume tenacity or awn length and PHS resistance. Water absorption, dough stability, farinograph arrival and departure times, peak, and 20-minute drop were measured from grain samples with varying degrees of weathering. All parameters were negatively affected by weathering in 2008. Flour quality parameters were more affected by genotype than falling number suggesting that falling number should not be used as the sole indicator of flour quality after grain weathering. It is clear that there are vast differences in dormancy levels and PHS resistance among SRWW cultivars and stronger dormancy and higher resistance to PHS does not automatically ensure higher quality flour. / Master of Science

K3 surfaces have garnered attention across various fields, from optics and dynamics to high energy physics, making them a subject of extensive study for many decades. Recent work by mathematicians, including Brandhorst [1], has focused on non-symplectic automorphisms, aiming to categorize K3 surfaces that admit such automorphisms. Brandhorst made a list of unique K3 surfaces with purely non-symplectic automorphisms and established specific criteria for a K3 surface to be isomorphic to one on his list. This thesis aims to provide an alternative representation of select K3 surfaces from Brandhorst's list. While Brandhorst predominantly characterizes these surfaces as elliptic K3 surfaces, we offer a description of these surfaces as hypersurfaces in weighted projective space. Our approach involves verifying the criteria established by Brandhorst, thereby establishing an isomorphism between the surfaces in question. Through this study, we contribute to the understanding of K3 surfaces and their automorphisms while also demonstrating the correspondence between different spaces and methodologies for analyzing K3 surfaces. This work lays the groundwork for further investigations into K3 surfaces with purely non-symplectic automorphisms, paving the way for deeper insights into their structural properties and geometric intricacies.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第24998号 / 医博第5032号 / 新制||医||1069(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 佐藤 俊哉, 教授 山本 洋介, 教授 永井 洋士 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Yes / Background: The Diagnostic and Statistical Manual of Mental Disorders (DSM-V) issued new diagnostic criteria for autism spectrum disorders (ASD) which resulted in missing the diagnosis of some cases of Asperger’s syndrome (AS). This negatively affected the support received by those affected. In this study, we explored if AS could be biologically stratified from the broader spectrum through a gene co-expression network preservation analysis. Methods: We analysed the GEO microarray data of 24 individuals with Asperger’s syndrome and 72 individuals with autism. Then, we used a weighted gene co-expression network (WGCNA) pipeline to construct gene co-expression networks. We explored whether these modules share the same co-expression patterns between autism and Asperger’s syndrome using network preservation analysis. Results: Our results showed that all co-expression modules of autism are preserved into the Asperger’s syndrome. However, three modules of Asperger’s syndrome out of 30 modules were not preserved in autism. Gene enrichment analysis revealed that these modules were involved in chromatin remodelling, immune and neuroinflammatory response, synaptic and neuronal development. Brain enrichment analysis showed significant downregulation of neurodevelopment genes in different brain regions associated with impaired social recognition in Asperger’s syndrome. Conclusions: The identified genetic and molecular profiles suggest that Asperger’s syndrome, despite sharing numerous similarities with autism, possesses a distinct genetic profile that makes it a distinct subtype of autism. This distinction could have significant implications for the management and treatment strategies tailored to individuals with Asperger’s syndrome.

Model checking is a well-established method for automatic system verification. Besides the extensively studied qualitative case, there is also an increasing interest in the quantitative analysis of system properties. Many important quantities can be formalised as the accumulated values of weight functions. These measures include resource usage such as energy consumption, or performance metrics such as the cost-utility ratio or reliability guarantees. Different kinds of accumulation like summation, averaging and ratios are necessary to cover the diverse spectrum of quantities. This work provides a general framework for the formalisation and verification of system models and property specifications with accumulative values. On the modelling side, we rely on weighted extensions of well-known modelling formalisms. Besides weighted transition systems, we investigate weighted probabilistic models such as Markov chains and Markov decision processes (MDPs). The weights in this sense are functions, mapping each state or transition in the model to a value, e.g., a rational vector. For the specification side, we provide a language in the form of an extension of temporal logic with new modalities that impose restrictions on the accumulated weight along path fragments. These fragments are regular and can be characterised by finite automata, so called monitors. Specifically, we extend linear temporal logic (LTL) and (probabilistic) computation tree logic (CTL) with such constraints. The framework allows variation to weaker formalisms, like non-negative or integral weight functions and bounded accumulation. We study the border of decidability of the model-checking problem for different combinations of these restrictions and give complexity results and algorithms for the decidable fragment. An implementation of the model-checking algorithms on top of the popular probabilistic model checker PRISM is provided. We also investigate several optimization techniques that can be applied to a broad range of formula patterns. The practical behaviour of the implementation and its optimization methods is put to the test by a set of scaling experiments for each model type.:1. Introduction 1.1. Goal of the Thesis 1.2. Main Contributions 1.3. Related Work 1.4. Outline 1.5. Resources 2. Preliminaries 2.1. Modeling Formalisms 2.2. Finite Automata 2.3. Propositional Logic 2.4. Temporal Logics 2.4.1. Linear Temporal Logic 2.4.2. Computation Tree Logic 2.5. Model-Checking Problems 2.5.1. Markov Decision Processes 2.5.2. Markov Chains 2.5.3. Transition Systems 2.5.4. Calculate Probabilities 3. Specifications with Weight Accumulation 3.1. Weight Constraints 3.1.1. Syntax of Weight Constraints 3.1.2. Weighted Models 3.1.3. Interpretation of Weight Constraints 3.1.4. Properties of Weight Constraints 3.2. Monitor Automata 3.2.1. Automata Classes 3.2.2. Observing WMDP Paths 3.3. Variants 3.3.1. Weight Ratios 3.3.2. Other Linear Accumulation Operators 3.3.3. Other Weight Combinations 3.3.4. Filtered Semantics 4. Linear Temporal Logic with Accumulation 4.1. Syntax and Semantics of AccLTL 4.1.1. Syntax of AccLTL 4.1.2. Semantics of AccLTL 4.1.3. Past Variant 4.1.4. Transformation of Weight Functions 4.1.5. Examples for AccLTL Formulae 4.2. Decidability Results for Accumulation LTL 4.2.1. Encoding the Post Correspondence Problem 4.2.2. Reduction of the AccLTL Model-Checking Problem 4.3. Complexity Results for Bounded Accumulation LTL 4.3.1. Transformation to Unweighted MDP and LTL 4.3.2. Reduction to LTL Model-Checking Problems 4.3.3. Algorithm 4.4. Decidability Results for Conic Accumulation LTL and RMDPs 4.4.1. Transformation to Unweighted MDP and LTL 4.4.2. Simple Weight Constraints 4.4.3. 1-dimensional Weight Constraints 4.5. NP-hard and coNP-hard Formulae for WTS and WMCs 4.5.1. Formulae for WTS 4.5.2. Formulae for WMC 4.6. Efficiently Decidable Patterns 4.7. Summary 5. Computation Tree Logic with Accumulation 5.1. Syntax and Semantics 5.1.1. Syntax and Semantics of AccCTL 5.1.2. Syntax and Semantics of AccPCTL 5.2. Decidability Results for Accumulation (P)CTL 5.3. Complexity Results for Bounded Accumulation (P)CTL 5.3.1. Weighted Markov Decision Processes 5.3.2. Weighted Markov Chains 5.3.3. Weighted Transition Systems 5.4. Decidability Results for Conic Accumulation (P)CTL and RMDPs 5.5. Summary 6. Implementation and Experiments 6.1. Implementation Details 6.1.1. Formula Expression 6.1.2. Model Construction 6.2. Optimizations 6.2.1. Single Track Method 6.2.2. Rewriting Without Until 6.2.3. Monitor Filtering 6.2.4. Detection of Optimization Methods 6.3. Scaling Experiments 6.3.1. Scaling Dimensions 6.3.2. Setting 6.3.3. Model Description 6.3.4. Input Size 6.3.5. Optimization Effects 6.3.6. Filtering 7. Conclusions 7.1. Summary 7.2. Outlook and Future Work A. Bibliography B. Material for the experiments B.1. Environment for the Experiments B.1.1. Container Image B.1.2. Model Definitions

[pt] Esta dissertação estuda métodos e algoritmos para critérios de seleções dos retângulos envolventes focando em arquiteturas de detecção de objetos baseada redes neurais convolucionais para tempo real, que processam mais de 30fps, que também possibilitam a expansão para outras arquiteturas. O objetivo desta dissertação é melhorar as métricas Recall e Precision, proporcionando mais assertividade nos resultados destas arquiteturas sem a necessidade de recriá-las ou retreiná-las, diminuindo, assim, os recursos para manutenções. As arquiteturas que trabalham em tempo real normalmente não apresentam melhores resultados, pois são desenvolvidas visando a redução do tempo de execução. Para resolver estes problemas, serão testados outros métodos de critérios de seleção de retângulos envolventes em estado da arte, são eles: Nonmaximum Suppression (NMS), Soft-NMS, Non-Maximum Weighted (NMW) e Weighted Boxes Fusion (WBF). Os resultados obtidos foram comparados aos originais das arquiteturas, utilizando as métricas mAP, Recall e Precision. Através desta comparação foi possível comprovar que os novos critérios apresentaram bons resultados. O tempo de execução dos novos critérios também foi analisado com execuções de imagens em lotes, contornando alguns overheads dos critérios mais pesados. As arquiteturas utilizadas como base nos experimentos foram baseadas nos sistemas YOLOv3-Tiny e YOLOv4-Tiny, utilizando o dataset QMUL-OpenLogo público e especializado em logotipos e baseado em fotos reais. / [en] This dissertation studies methods and algorithms for bounding box selection criteria focusing on object detection architectures based on convolutional neural networks for real-time, processing over 30fps, which also allow expansion to other architectures. The goal of this study is to improve the Recall and Precision metrics, providing more assertiveness in the results of these architectures without the need to recreate or retrain them, thus reducing the resources for maintenance. Architectures that work in real-time usually do not present good results, because they are developed aiming to reduce execution time. To solve these problems, other state-of-the-art bounding box selection criteria methods will be tested: Non-maximum Suppression (NMS), Soft-NMS, Non-Maximum Weighted (NMW) and Weighted Boxes Fusion (WBF). The results obtained were compared to the original architectures, using the mAP, Recall and Precision metrics. Through this comparison it was possible to prove that the new criteria presented satisfactory results. The execution time of the new criteria was also analyzed with batch image executions, bypassing some overheads of the heavier criteria. The architectures used as basis for the experiments were based on the YOLOv3-Tiny and YOLOv4-Tiny systems, using the public dataset QMUL-OpenLogo specialized in logos and based on real photos.

In this thesis, three methods are investigated to express context-free tree languages by regular tree grammars. The first method is a characterization. We show restrictions to context-free tree grammars such that, for each restricted context-free tree grammar, a regular tree grammar can be constructed that induces the same tree language. The other two methods are approximations. An arbitrary context-free tree language can be approximated by a regular tree grammar with a restricted pushdown storage. Furthermore, we approximate weighted context-free tree languages, induced by weighted linear nondeleting context-free tree grammars, by showing how to approximate optimal weights for weighted regular tree grammars.