We introduce the inner theory or, more verbosely, isotropy Lawvere theory functor, which generalizes the isotropy group/monoid by assigning a Lawvere theory of coherently extendable arrows to each object of a category with finite powers. Then, we characterize the inner theory for categories of models of an algebraic (or, more generally, quasi-equational) theory, and note its relationship with a notion of definability for morphisms. Finally, we explore a variety of examples.

Thesis (Ph. D.)--University of Oklahoma, 1993. / Includes vita. Includes bibliographical references.

A pivotal moment in machine learning research was the creation of an important new research area, known as Ensemble Learning. In this work, we argue that ensembles are a very general concept, and though they have been widely used, they can be applied in more situations than they have been to date. Rather than using them only to combine the output of an algorithm, we can apply them to decisions made inside the algorithm itself, during the learning step. We call this approach Inner Ensembles. The motivation to develop Inner Ensembles was the opportunity to produce models with the similar advantages as regular ensembles, accuracy and stability for example, plus additional advantages such as comprehensibility, simplicity, rapid classification and small memory footprint. The main contribution of this work is to demonstrate how broadly this idea can be applied, and highlight its potential impact on all types of algorithms. To support our claim, we first provide a general guideline for applying Inner Ensembles to different algorithms. Then, using this framework, we apply them to two categories of learning methods: supervised and un-supervised. For the former we chose Bayesian network, and for the latter K-Means clustering. Our results show that 1) the overall performance of Inner Ensembles is significantly better than the original methods, and 2) Inner Ensembles provide similar performance improvements as regular ensembles.

A pivotal moment in machine learning research was the creation of an important new research area, known as Ensemble Learning. In this work, we argue that ensembles are a very general concept, and though they have been widely used, they can be applied in more situations than they have been to date. Rather than using them only to combine the output of an algorithm, we can apply them to decisions made inside the algorithm itself, during the learning step. We call this approach Inner Ensembles. The motivation to develop Inner Ensembles was the opportunity to produce models with the similar advantages as regular ensembles, accuracy and stability for example, plus additional advantages such as comprehensibility, simplicity, rapid classification and small memory footprint. The main contribution of this work is to demonstrate how broadly this idea can be applied, and highlight its potential impact on all types of algorithms. To support our claim, we first provide a general guideline for applying Inner Ensembles to different algorithms. Then, using this framework, we apply them to two categories of learning methods: supervised and un-supervised. For the former we chose Bayesian network, and for the latter K-Means clustering. Our results show that 1) the overall performance of Inner Ensembles is significantly better than the original methods, and 2) Inner Ensembles provide similar performance improvements as regular ensembles.

No description available.

Abstract In the Cape Fynbos of South Africa, Psoralea pinnata (L.) plants occur naturally in both wetland and welldrained soils and yet effectively fix N2 under the two contrasting conditions. In this study, nodule structure and functioning in P. pinnata plants from the two habitats were evaluated using light and transmission electron microscopy (TEM), as well as the 15N natural abundance technique. The results showed that, structurally, fully developed P. pinnata nodules were spherical in shape with six components (namely, lenticels, periderm, outer cortex, middle cortex, inner cortex, and a central bacteria-infected medulla region). Morphometric analysis revealed 44 and 84 % increase in cell area and volume of wetland nodules compared to those from upland. The percentage area of nodules occupied by the middle cortex in wetland nodules was twice that of upland nodules. As a result, the size of the medulla region in wetland nodules was significantly reduced compared to upland nodules. Additionally, the average area of medulla occupied by intercellular air spaces in wetland nodules was about five times that of upland nodules (about 431 % increase in wetland over upland nodules). TEM data also showed more bacteroids in symbiosomes of upland nodules when compared to wetland nodules. However, isotopic analysis of above-ground plant parts revealed no differences in symbiotic parameters such as N concentration, ∂15N and %Ndfa between wetland and upland P. pinnata plants. These results suggest that, under limiting O2 conditions especially in wetlands, nodules make structural and functional adjustments to meet the O2 demands of N2-fixing bacteroids.

The Central Asian Orogenic Belt formed by accretion subsequent to the contraction of the Palaeo-Asian Ocean that ultimately disappeared along the Solonker Suture Zone in East Asia. Since typical regional collisional features are absent, the tectonic evolution of the suture remains speculative. Integrated sedimentary provenance analyses across the accretionary collision zone between the Mongolian Arcs and the North China Craton place new constraints on the events that led to final suturing. An investigation on the geochronological and geochemical variability in Permian strata along a southeast-northwest transect revealed distinct differences across the Solonker Suture Zone: northern basins carry a broad Mesoproterozoic to latest Precambrian age signature, and their provenance terranes are of mixed juvenile to crustal magmatic origin. In contrast, southern basins contain detritus from the North China Craton, and their sources are of dominantly crustal contaminated magmatic origin. Provenance analysis suggests, that in the Early Palaeozoic (ca. 429 Ma) the Palaeo-Asian Ocean was consumed along the Uliastai Arc and the North China Craton, initiating the formation of the Northern and Southern Accretionary Orogens, respectively. By the end of the Middle Carboniferous the Mongolian Arcs consolidated after accretion of the Uliastai Arc. In the Late Carboniferous (ca. 314 Ma) the Hegenshan back-arc basin opened, detaching the Northern Accretionary Orogen. While subduction continued there, it may have temporarily ceased along the Southern Accretionary Orogen after accretion of a microcontinent (ca. 300 Ma). During the Middle Permian back-arc basin closure led to the formation and obduction of the Hegenshan supra-subduction zone ophiolite. Eventually, the Palaeo-Asian Ocean closed after wedge-wedge collision, which would not involve continental deep subduction, thus, leading to cryptic suturing from the Late Permian to Early Triassic. Statistical analyses on the heterogeneity and similarity of the age probability density functions require a complex Permian palaeo-geographic setting, involving a variety of arc basins, which received sediments dependent on the contemporary arc geometry. Early stages of the sequence likely resembled a Pacific-type scenario, including Japan-type back-arc basin opening, whereas the late stages were similar to the archipelago-type setting of present-day Southeast Asia. / published_or_final_version / Earth Sciences / Doctoral / Doctor of Philosophy

No description available.

No description available.

The usual practice in any discussion of an inner-product space is to restrict the field over which the inner-product space is defined to the field of complex numbers. In defining the inner-product function, (x,y), a second function is needed; namely the conjugate function (x,y)* so that (x,y) ± (y,x)*. We will attempt to generalize this concept by investigating the existence of a conjugate function defined on fields other than the field of complex numbers and relate this function to an inner-product function defined on a linear space L over these fields.