University of California, Irvine

About: University of California, Irvine is a education organization based out in Irvine, California, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 47031 authors who have published 113602 publications receiving 5521832 citations. The organization is also known as: UC Irvine & UCI.

Oceanic methane biogeochemistry.

Abstract: This review shows that thermodynamic and kinetic constraints largely prevent large-scale methanogenesis in the open ocean water column. One example of open-ocean methanogenesis involves anoxic digestive tracts and fecal pellet microenvironments; methane released during fecal pellet disaggregation results in the mixed-layer methane maximum. However, the bulk of the methane in the ocean is added by coastal runoff, seeps, hydrothermal vents, decomposing hydrates, and mud volcanoes. Since methane is present in the open ocean at nanomolar concentrations, and since the flux to the atmosphere is small, the ultimate fate of ocean methane additions must be oxidation within the ocean. As indicated in the Introduction and highlighted in Table 3, sources of methane to the ocean water column are poorly quantified. There are only a small number of direct water column methane oxidation rates, so sinks are also poorly quantified. We know that methane oxidation rates are sensitive to ambient methane concentrations, but we have no information on reaction kinetics and only one report of the effect of pressure on methane oxidation. Our perspective on methane sources and the extent of methane oxidation has been changed dramatically by new techniques involving gene probes, determination of isotopically depleted biomarkers, and recent 14C-CH4 measurements showing that methane geochemistry in anoxic basins is dominated by seeps providing fossil methane. The role of anaerobic oxidation of methane has changed from a controversial curiosity to a major sink in anoxic basins and sediments. © 2007 American Chemical Society.

Articulated pose estimation with flexible mixtures-of-parts

Abstract: We describe a method for human pose estimation in static images based on a novel representation of part models. Notably, we do not use articulated limb parts, but rather capture orientation with a mixture of templates for each part. We describe a general, flexible mixture model for capturing contextual co-occurrence relations between parts, augmenting standard spring models that encode spatial relations. We show that such relations can capture notions of local rigidity. When co-occurrence and spatial relations are tree-structured, our model can be efficiently optimized with dynamic programming. We present experimental results on standard benchmarks for pose estimation that indicate our approach is the state-of-the-art system for pose estimation, outperforming past work by 50% while being orders of magnitude faster.

An online algorithm for segmenting time series

Abstract: In recent years, there has been an explosion of interest in mining time-series databases. As with most computer science problems, representation of the data is the key to efficient and effective solutions. One of the most commonly used representations is piecewise linear approximation. This representation has been used by various researchers to support clustering, classification, indexing and association rule mining of time-series data. A variety of algorithms have been proposed to obtain this representation, with several algorithms having been independently rediscovered several times. In this paper, we undertake the first extensive review and empirical comparison of all proposed techniques. We show that all these algorithms have fatal flaws from a data-mining perspective. We introduce a novel algorithm that we empirically show to be superior to all others in the literature.

Bayesian Cognitive Modeling: A Practical Course

Abstract: Part I. Getting Started: 1. The basics of Bayesian analysis 2. Getting started with WinBUGS Part II. Parameter Estimation: 3. Inferences with binomials 4. Inferences with Gaussians 5. Some examples of data analysis 6. Latent mixture models Part III. Model Selection: 7. Bayesian model comparison 8. Comparing Gaussian means 9. Comparing binomial rates Part IV. Case Studies: 10. Memory retention 11. Signal detection theory 12. Psychophysical functions 13. Extrasensory perception 14. Multinomial processing trees 15. The SIMPLE model of memory 16. The BART model of risk taking 17. The GCM model of categorization 18. Heuristic decision-making 19. Number concept development.