No description available.

No description available.

No description available.

No description available.

No description available.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23798号 / 医博第4844号 / 新制||医||1058(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 黒田 知宏, 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

We define the affinization of an arbitrary monoidal category C, corresponding to the category of C-diagrams on the cylinder. We also give an alternative characterization in terms of adjoining dot generators to C. The affinization formalizes and unifies many constructions appearing in the literature. In particular, we describe a large number of examples coming from Hecke-type algebras, braids, tangles, and knot invariants. When C is rigid, its affinization is isomorphic to its horizontal trace, although the two definitions look quite different. In general, the affinization and the horizontal trace are not isomorphic. We then use the affinization to show our main result, which is an explicit isomorphism between the central charge k reduction of the universal central extension of the elliptic Hall algebra and the trace, or zeroth Hochschild homology, of the quantum Heisenberg category of central charge k. We use this isomorphism to construct large families of representations of the universal extension of the elliptic Hall algebra.

Nowadays, manufacturing companies compete as much with their products as with their supply-chain. Within the overarching stages of supply-chain, purchasing is a process that responds to the organizations needs of acquiring raw materials, components and semi-finished products in order to enable the execution of operations. Purchasing is an important aspect of an organization, having a big influence on its competitive positioning. In order to achieve better organizational performance, companies spendings are often separated into discrete groups of products and services, using a combination of supplier-relationship management, category-specific strategies and cross-functional teams. This segmentation is called purchasing category management, and is expected in many cases to improve the overall purchasing performance. Therefore, a case study was conducted with the purpose of investigating how PCM could help a luxury automotive manufacturer facing purchasing problems to improve its operational performance. This thesis presents a framework built on previous studies exposing what factors are linked to purchasing performance and explaining the concept of purchasing category management. The framework shows the importance of purchasing expertise, cross-functional work, clear organizational structures, strategic purchasing and monitoring of performance. The study identifies the problems the case company is experiencing: Lack of cooperation between departments; a troublesome purchasing structure leading to duplicated work and insufficient technical knowledge; a conflictual cross-functional cooperation between purchasers and engineers and lastly a lack of monitoring of the performance of purchasers and suppliers. At last, a discussion is held of how these problems could be mitigated by guidelines found in the literature and the use of category management. This study identifies a gap in research on the topic of purchasing category management. This gap was compensated through the use of complementary literature on the topics of purchasing and purchasing performance. / Numera konkurrerar tillverkningsföretag lika mycket med deras produkter som med deras leveranskedja. Inom de övergripande stadierna i leveranskedjan är inköp en process som svarar på organisationens behov av att skaffa råvaror, komponenter och halvfabrikat för att möjliggöra genomförandet av verksamhetens funktioner. Inköp är en viktig aspekt av en organisation och har stort inflytande på dess konkurrensposition. För att uppnå bättre organisatoriska resultat delas företagens utgifter ofta i diskreta grupper av produkter och tjänster, med en kombination av hantering av leverantörsrelationer, kategorispecifika strategier och tvärfunktionella team. Denna segmentering kallas för Purchasing Category Management (PCM) och förväntas i många fall förbättra den totala inköpsprestandan. Här genomförs en fallstudie med syftet att undersöka hur PCM kan kan hjälpa ett företag som står inför inköpsproblem för att förbättra den operativa prestationen. Denna avhandling presenterar ett ramverk som bygger på tidigare studier som visar vilka faktorer som är kopplade till inköpsprestanda och förklarar begreppet inköpskategorihantering. Ramverket visar vikten av inköpsexpertis, tvärfunktionellt arbete, tydliga organisationsstrukturer, strategisk inköp och prestationsövervakning. Undersökningen identifierar de problem som fallföretaget har: Brist på samarbete mellan avdelningar; en besvärlig inköpsstruktur som leder till duplicerat arbete och otillräcklig teknisk kunskap; ett konfliktövergripande tvärfunktionellt samarbete mellan inköpare och ingenjörer och slutligen brist på övervakning av inköparnas och leverantörernas resultat. Äntligen diskuteras hur dessa problem kan mildras genom riktlinjer som finns i litteraturen och användningen av kategorihantering. Denna studie identifierar ett gap i forskning om ämnet PCM. Denna klyfta kompenserades genom användning av kompletterande litteratur om ämnena inköp och inköpsprestanda.

The ability to remember spatial locations is critical to human functioning, both in an evolutionary and an everyday sense. And yet, spatial memories and judgments often show systematic errors. Explanations for such errors have ranged from assumptions that memories are nonmetric, to the use of imperfect inferences, to the optimal combination of multiple sources of information. More recently, bias has been explained through the Category Adjustment Model - a Bayesian model in which fine-grained and categorical information are optimally combined (Huttenlocher, Hedges, & Duncan, 1991). However, experiments testing this model have largely used locations contained in simple geometric shapes. Use of this paradigm raises the issue of whether the results generalize to location memory in the complex natural world, as it should if it is to provide an over-arching framework for thinking about spatial memory. Here, this issue is addressed using a novel extension of the location memory paradigm that allows for testing of location memory in an everyday, 3D environment. The results support two predictions of the Category Adjustment Model - that memory for locations is biased toward central values, and that the magnitude of error increases with the retention interval. Future directions for testing the model in an increasingly ecologically valid manner are discussed. / Psychology

The missing heritability in genome-wide association studies (GWAS) is an intriguing open scientific problem which has attracted great recent interest. The interaction effects among risk factors, both genetic and environmental, are hypothesized to be one of the main missing heritability sources. Moreover, detection of multilocus interaction effect may also have great implications for revealing disease/biological mechanisms, for accurate risk prediction, personalized clinical management, and targeted drug design. However, current analysis of GWAS largely ignores interaction effects, partly due to the lack of tools that meet the statistical and computational challenges posed by taking into account interaction effects. Here, we propose a novel statistically-based framework (Significant Conditional Association) for systematically exploring, assessing significance, and detecting interaction effect. Further, our SCA work has also revealed new theoretical results and insights on interaction detection, as well as theoretical performance bounds. Using in silico data, we show that the new approach has detection power significantly better than that of peer methods, while controlling the running time within a permissible range. More importantly, we applied our methods on several real data sets, confirming well-validated interactions with more convincing evidence (generating smaller p-values and requiring fewer samples) than those obtained through conventional methods, eliminating inconsistent results in the original reports, and observing novel discoveries that are otherwise undetectable. The proposed methods provide a useful tool to mine new knowledge from existing GWAS and generate new hypotheses for further research. Microarray gene expression studies provide new opportunities for the molecular characterization of heterogeneous diseases. Multiclass gene selection is an imperative task for identifying phenotype-associated mechanistic genes and achieving accurate diagnostic classification. Most existing multiclass gene selection methods heavily rely on the direct extension of two-class gene selection methods. However, simple extensions of binary discriminant analysis to multiclass gene selection are suboptimal and not well-matched to the unique characteristics of the multi-category classification problem. We report a simpler and yet more accurate strategy than previous works for multicategory classification of heterogeneous diseases. Our method selects the union of one-versus-everyone phenotypic up-regulated genes (OVEPUGs) and matches this gene selection with a one-versus-rest support vector machine. Our approach provides even-handed gene resources for discriminating both neighboring and well-separated classes, and intends to assure the statistical reproducibility and biological plausibility of the selected genes. We evaluated the fold changes of OVEPUGs and found that only a small number of high-ranked genes were required to achieve superior accuracy for multicategory classification. We tested the proposed OVEPUG method on six real microarray gene expression data sets (five public benchmarks and one in-house data set) and two simulation data sets, observing significantly improved performance with lower error rates, fewer marker genes, and higher performance sustainability, as compared to several widely-adopted gene selection and classification methods. / Ph. D.