A Variable Metric Probabilistic k-Nearest-Neighbours Classifier

Copyright © 2004 Springer Verlag. The final publication is available at link.springer.com5th International Conference, Exeter, UK. August 25-27, 2004. ProceedingsBook title: Intelligent Data Engineering and Automated Learning – IDEAL 2004k-nearest neighbour (k-nn) model is a simple, popular classifier. Probabilistic k-nn is a more powerful variant in which the model is cast in a Bayesian framework using (reversible jump) Markov chain Monte Carlo methods to average out the uncertainy over the model parameters.The k-nn classifier depends crucially on the metric used to determine distances between data points. However, scalings between features, and indeed whether some subset of features is redundant, are seldom known a priori. Here we introduce a variable metric extension to the probabilistic k-nn classifier, which permits averaging over all rotations and scalings of the data. In addition, the method permits automatic rejection of irrelevant features. Examples are provided on synthetic data, illustrating how the method can deform feature space and select salient features, and also on real-world data

A new parameterization of the Effective Temperature for L-band Radiometry.

An accurate value of the effective temperature is critical for soil emissivity retrieval, and hence soil moisture content retrieval, from passive microwave observations. Computation of the effective temperature needs fine profile measurements of soil temperature and soil moisture. The availability of a two year long data set of these surface variables from SMOSREX (Surface Monitoring Of the Soil Reservoir EXperiment) makes it possible to study the effective temperature at the seasonal to interannual scale. This study shows that present parameterizations do not adequately describe the seasonal variations in sensing depth. Therefore, a new parameterization is proposed that is stable at the seasonal to interannual scales while retaining simplicity. Copyright 2006 by the American Geophysical Union

Economic and Market Analysis of CO2 Utilization Technologies – Focus on CO2 derived from North Dakota lignite

AbstractBased on information obtained about the technical aspects of the technologies, several challenges are expected to be faced by any potential CO2 utilization technologies intended for North Dakota lignite plants. The weather, alkaline content of lignite fly ash, and space limitations in the immediate vicinity of existing power plants are challenging hurdles to overcome. Currently, no CO2 utilization option is ready for implementation or integration with North Dakota power plants. Mineralization technologies suffer from the lack of a well-defined product and insufficient alkalinity in lignite fly ash. Algae and microalgae technologies are not economically feasible and will have weather- related challenges

Cannabis use and risk of schizophrenia: a Mendelian randomization study.

Cannabis use is observationally associated with an increased risk of schizophrenia, but whether the relationship is causal is not known. Using a genetic approach, we took 10 independent genetic variants previously identified to associate with cannabis use in 32 330 individuals to determine the nature of the association between cannabis use and risk of schizophrenia. Genetic variants were employed as instruments to recapitulate a randomized controlled trial involving two groups (cannabis users vs nonusers) to estimate the causal effect of cannabis use on risk of schizophrenia in 34 241 cases and 45 604 controls from predominantly European descent. Genetically-derived estimates were compared with a meta-analysis of observational studies reporting ever use of cannabis and risk of schizophrenia or related disorders. Based on the genetic approach, use of cannabis was associated with increased risk of schizophrenia (odds ratio (OR) of schizophrenia for users vs nonusers of cannabis: 1.37; 95% confidence interval (CI), 1.09-1.67; P-value=0.007). The corresponding estimate from observational analysis was 1.43 (95% CI, 1.19-1.67; P-value for heterogeneity =0.76). The genetic markers did not show evidence of pleiotropic effects and accounting for tobacco exposure did not alter the association (OR of schizophrenia for users vs nonusers of cannabis, adjusted for ever vs never smoker: 1.41; 95% CI, 1.09-1.83). This adds to the substantial evidence base that has previously identified cannabis use to associate with increased risk of schizophrenia, by suggesting that the relationship is causal. Such robust evidence may inform public health messages about cannabis use, especially regarding its potential mental health consequences

Summer CO2 evasion from streams and rivers in the Kolyma River basin, north-east Siberia

Inland water systems are generally supersaturated in carbon dioxide (CO2) and are increasingly recognized as playing an important role in the global carbon cycle. The Arctic may be particularly important in this respect, given the abundance of inland waters and carbon contained in Arctic soils; however, a lack of trace gas measurements from small streams in the Arctic currently limits this understanding.We investigated the spatial variability of CO2 evasion during the summer low-flow period from streams and rivers in the northern portion of the Kolyma River basin in north-eastern Siberia. To this end, partial pressure of carbon dioxide (pCO2) and gas exchange velocities (k) were measured at a diverse set of streams and rivers to calculate CO2 evasion fluxes. We combined these CO2 evasion estimates with satellite remote sensing and geographic information system techniques to calculate total areal CO2 emissions. Our results show that small streams are substantial sources of atmospheric CO2 owing to high pCO2 and k, despite being a small portion of total inland water surface area. In contrast, large rivers were generally near equilibrium with atmospheric CO2. Extrapolating our findings across the Panteleikha-Ambolikha sub-watersheds demonstrated that small streams play a major role in CO2 evasion, accounting for 86% of the total summer CO2 emissions from inland waters within these two sub-watersheds. Further expansion of these regional CO2 emission estimates across time and space will be critical to accurately quantify and understand the role of Arctic streams and rivers in the global carbon budget

Universal behavior of localization of residue fluctuations in globular proteins

Localization properties of residue fluctuations in globular proteins are studied theoretically by using the Gaussian network model. Participation ratio for each residue fluctuation mode is calculated. It is found that the relationship between participation ratio and frequency is similar for all globular proteins, indicating a universal behavior in spite of their different size, shape, and architecture.Comment: 4 pages, 3 figures. To appear in Phys. Rev.

Parameter estimation in spatially extended systems: The Karhunen-Loeve and Galerkin multiple shooting approach

Parameter estimation for spatiotemporal dynamics for coupled map lattices and continuous time domain systems is shown using a combination of multiple shooting, Karhunen-Loeve decomposition and Galerkin's projection methodologies. The resulting advantages in estimating parameters have been studied and discussed for chaotic and turbulent dynamics using small amounts of data from subsystems, availability of only scalar and noisy time series data, effects of space-time parameter variations, and in the presence of multiple time-scales.Comment: 11 pages, 5 figures, 4 Tables Corresponding Author - V. Ravi Kumar, e-mail address: [email protected]