33 research outputs found

    Systematics and Glacial Population History of the Alternifolium Group of the Flowering Plant Genus Chrysosplenium (Saxifragaceae)

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    The flowering plant genus Chrysosplenium comprises approximately 57 species of herbaceous perennials. These species are mainly distributed in the Northern Hemisphere where they occur in moist habitats. Though the center of diversity, and presumed location of origin, for the genus is east temperate Asia, more recently radiating taxa have invaded the arctic of North America and Europe. There are six species of Chrysosplenium in North America and four of them (i.e., C. iowense, C. tetrandrum, C. wrightii, and C. rosendahlii) belong to the section Alternifolia. Termed the Alternifolium group, this collection of species presents an excellent opportunity to study the evolution of variation in arctic and alpine environments. Similar to many arctic taxa, these species display very little morphologic or genetic variation, but they exhibit diversity in chromosome number, breeding system, geographic distribution, and ecology. Though the Alternifolium group has been the subject of numerous taxonomic studies, no thorough investigation of its evolutionary history has been conducted. This study used a combination of genetic and phenotypic data (e.g., DNA sequence, Inter-Simple Sequence Repeat, morphology) to determine the patterns of variation present within the Alternifolium group and then used these patterns to infer historical processes that might have contributed to them. Through the course of the study, however, it also became necessary to investigate the applicability of genetic estimates derived from different molecular markers and statistical methods. Appropriate comparisons among genetic estimates are critical to accurately interpret results and generate new predictions

    Unsupervised extraction and normalization of product attributes from web pages.

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    Xiong, Jiani."July 2010."Thesis (M.Phil.)--Chinese University of Hong Kong, 2010.Includes bibliographical references (p. 59-63).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Motivation --- p.4Chapter 1.3 --- Our Approach --- p.8Chapter 1.4 --- Potential Applications --- p.12Chapter 1.5 --- Research Contributions --- p.13Chapter 1.6 --- Thesis Organization --- p.15Chapter 2 --- Literature Survey --- p.16Chapter 2.1 --- Supervised Extraction Approaches --- p.16Chapter 2.2 --- Unsupervised Extraction Approaches --- p.19Chapter 2.3 --- Attribute Normalization --- p.21Chapter 2.4 --- Integrated Approaches --- p.22Chapter 3 --- Problem Definition and Preliminaries --- p.24Chapter 3.1 --- Problem Definition --- p.24Chapter 3.2 --- Preliminaries --- p.27Chapter 3.2.1 --- Web Pre-processing --- p.27Chapter 3.2.2 --- Overview of Our Framework --- p.31Chapter 3.2.3 --- Background of Graphical Models --- p.32Chapter 4 --- Our Proposed Framework --- p.36Chapter 4.1 --- Our Proposed Graphical Model --- p.36Chapter 4.2 --- Inference --- p.41Chapter 4.3 --- Product Attribute Information Determination --- p.47Chapter 5 --- Experiments and Results --- p.49Chapter 6 --- Conclusion --- p.57Bibliography --- p.59Chapter A --- Dirichlet Process --- p.64Chapter B --- Hidden Markov Models --- p.6

    Data Science: Measuring Uncertainties

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    With the increase in data processing and storage capacity, a large amount of data is available. Data without analysis does not have much value. Thus, the demand for data analysis is increasing daily, and the consequence is the appearance of a large number of jobs and published articles. Data science has emerged as a multidisciplinary field to support data-driven activities, integrating and developing ideas, methods, and processes to extract information from data. This includes methods built from different knowledge areas: Statistics, Computer Science, Mathematics, Physics, Information Science, and Engineering. This mixture of areas has given rise to what we call Data Science. New solutions to the new problems are reproducing rapidly to generate large volumes of data. Current and future challenges require greater care in creating new solutions that satisfy the rationality for each type of problem. Labels such as Big Data, Data Science, Machine Learning, Statistical Learning, and Artificial Intelligence are demanding more sophistication in the foundations and how they are being applied. This point highlights the importance of building the foundations of Data Science. This book is dedicated to solutions and discussions of measuring uncertainties in data analysis problems

    Intelligent Biosignal Analysis Methods

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    This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others

    Bayesian Model Selection for the Solution of Spatial Inverse Problems with Geophysical, Geotechnical and Thermodynamical Applications

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    Bayesian inference is based on three evidence components: experimental observations, model predictions and expert’s beliefs. Integrating experimental evidence into the calibration or selection of a model, either empirically of physically based, is of great significance in almost every area of science and engineering because it maps the response of the process of interest into a set of parameters, which aim at explaining the process’ governing characteristics. This work introduces the use of the Bayesian paradigm to construct full probabilistic description of parameters of spatial processes. The influence of uncertainty is first discussed on the calibration of an empirical relationship between remolded undrained shear strength Su−r and liquidity index IL, as a potential predictor of the soil strength. Two site-specific datasets are considered in the analysis. The key emphasis of the study is to construct a unified regression model reflecting the characteristics of the both contributing data sets, while the site dependency of the data is properly accounted for. We question the regular Bayesian updating process, since a test of statistics proves that the two data sets belong to different populations. Application of “Disjunction” probability operator is proposed as an alternative to arrive at a more conclusive Su−r−IL model. Next, the study is extended to a functional inverse problem where the object of inference constructs a spatial random field. We introduce a methodology to infer the spatial variation of the elastic characteristics of a heterogeneous earth model via Bayesian approach, given the probed medium’s response to interrogating waves measured at the surface. A reduced dimension, self regularized treatment of the inverse problem using partition modeling is introduced, where the velocity field is discretized by a variable number of disjoint regions defined by Voronoi tessellations. The number of partitions, their geometry and weights dynamically vary during the inversion, in order to recover the subsurface image. The idea of treating the number of tessellation (number of parameters) as a parameter itself is closely associated with probabilistic model selection. A reversible jump Markov chain Monte Carlo (RJMCMC) scheme is applied to sample the posterior distribution of varying dimension. Lastly, direct treatment of a Bayesian model selection through the definition of the Bayes factor (BF) is developed for linear models, where it is employed to define the most likely order of the virial Equation of State (EOS). Virial equation of state is a constitutive model describing the thermodynamic behavior of low-density fluids in terms of the molar density, pressure and temperature. Bayesian model selection has successfully determined the best EOS that describes four sets of isotherms, where approximate (BIC) method either failed to select a model or fevered an overly-flexible model, which specifically perform poorly in terms of prediction

    The Second Conference on Lunar Bases and Space Activities of the 21st Century, volume 1

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    These papers comprise a peer-review selection of presentations by authors from NASA, LPI industry, and academia at the Second Conference (April 1988) on Lunar Bases and Space Activities of the 21st Century, sponsored by the NASA Office of Exploration and the Lunar Planetary Institute. These papers go into more technical depth than did those published from the first NASA-sponsored symposium on the topic, held in 1984. Session topics covered by this volume include (1) design and operation of transportation systems to, in orbit around, and on the Moon, (2) lunar base site selection, (3) design, architecture, construction, and operation of lunar bases and human habitats, and (4) lunar-based scientific research and experimentation in astronomy, exobiology, and lunar geology

    Composite Load Spectra for Select Space Propulsion Structural Components

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    Generic load models are described with multiple levels of progressive sophistication to simulate the composite (combined) load spectra (CLS) that are induced in space propulsion system components, representative of Space Shuttle Main Engines (SSME), such as transfer ducts, turbine blades and liquid oxygen (LOX) posts. These generic (coupled) models combine the deterministic models for composite load dynamic, acoustic, high-pressure and high rotational speed, etc., load simulation using statistically varying coefficients. These coefficients are then determined using advanced probabilistic simulation methods with and without strategically selected experimental data. The entire simulation process is included in a CLS computer code. Applications of the computer code to various components in conjunction with the PSAM (Probabilistic Structural Analysis Method) to perform probabilistic load evaluation and life prediction evaluations are also described to illustrate the effectiveness of the coupled model approach
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