The quantitative measure and statistical distribution of fame

Fame and celebrity play an ever-increasing role in our culture. However, despite the cultural and economic importance of fame and its gradations, there exists no consensus method for quantifying the fame of an individual, or of comparing that of two individuals. We argue that, even if fame is difficult to measure with precision, one may develop useful metrics for fame that correlate well with intuition and that remain reasonably stable over time. Using datasets of recently deceased individuals who were highly renowned, we have evaluated several internet-based methods for quantifying fame. We find that some widely-used internet-derived metrics, such as search engine results, correlate poorly with human subject judgments of fame. However other metrics exist that agree well with human judgments and appear to offer workable, easily accessible measures of fame. Using such a metric we perform a preliminary investigation of the statistical distribution of fame, which has some of the power law character seen in other natural and social phenomena such as landslides and market crashes. In order to demonstrate how such findings can generate quantitative insight into celebrity culture, we assess some folk ideas regarding the frequency distribution and apparent clustering of celebrity deaths.Comment: 17 pages, 6 figure

LINKING MULTIVARIATE OBSERVATIONS OF THE LAND SURFACE TO VEGETATION PROPERTIES AND ECOSYSTEM PROCESSES

Remotely sensed images from satellites and aircrafts, as well as regional networks and monitoring stations such as eddy flux towers, are collecting large volumes of multivariate data that contain information about the land surface and ecosystem processes. To derive from these systems information and knowledge relevant to how the Earth system functions and how it is changing, we need tools that to filter and mine the large data streams currently being acquired at different spatial and temporal scales. A challenge for Earth System Science lies in accurately identifying and portraying the relationships between the measurements at the sensor and quantity o f interest (i.e. ecosystem process or land surface property)

Beyond peak reservoir storage? A global estimate of declining water storage capacity in large reservoirs

Water storage is an important way to cope with temporal variation in water supply and demand. The storage capacity and the lifetime of water storage reservoirs can be significantly reduced by the inflow of sediments. A global, spatially explicit assessment of reservoir storage loss in conjunction with vulnerability to storage loss has not been done. We estimated the loss in reservoir capacity for a global data set of large reservoirs from 1901 to 2010, using modeled sediment flux data. We use spatially explicit population data sets as a proxy for storage demand and calculate storage capacity for all river basins globally. Simulations suggest that the net reservoir capacity is declining as a result of sedimentation (5% compared to the installed capacity). Combined with increasing need for storage, these losses challenge the sustainable management of reservoir operation and water resources management in many regions. River basins that are most vulnerable include those with a strong seasonal flow pattern and high population growth rates such as the major river basins in India and China. Decreasing storage capacity globally suggests that the role of reservoir water storage in offsetting sea-level rise is likely weakening and may be changing sign

The stories of Andrei Bitov, 1958-1966: a search for individual perception

The thesis traces and analyses Andrei Bitov's development from literary impressionist and short-story writer of the late fifties to philosopher and novelist of the mid-sixties. The writer's search for vision and idea is revealed through a detailed chronological study of each major work and cycle of short stories. The progression of Bitov's solitary hero through successive stages of childhood, adolescence and adulthood and his interaction with everyday problems follow a particular pattern towards self-perception. Although not a moralist, the writer guides the reader on an inward search for self-knowledge through his characters' experiences including both religious and mystical revelations. In addition to the treatment of the common themes of life, death and growing-up, Bitov gives literary expression to Zen Buddhist notions of Koan and Satori and reinterprets the nineteenth century concept of poshlost in the new idea of poluson. The usual classification of Bitov as a "psychological" writer of molodaya proza is viewed as too narrow a definition despite the outward appearance of "confessionalism" and storylines concerning the alienated young man. The year I966 is taken as the end of Bitov's early phase with the completion of the novel Dni cheloveka and the beginnings of Pushkinsky dom. The mid-sixties mark a transition in Bitov's search from one of idea to one of form and style. The thesis seeks to throw new light on Andrei Bitov's contribution tithe Soviet short story of the sixties with a reappraisal of both the nature and progression of his writing, and the inclusion of original unpublished material from Bitov himself

Statistical uncertainty of eddy flux–based estimates of gross ecosystem carbon exchange at Howland Forest, Maine

We present an uncertainty analysis of gross ecosystem carbon exchange (GEE) estimates derived from 7 years of continuous eddy covariance measurements of forest-atmosphere CO2fluxes at Howland Forest, Maine, USA. These data, which have high temporal resolution, can be used to validate process modeling analyses, remote sensing assessments, and field surveys. However, separation of tower-based net ecosystem exchange (NEE) into its components (respiration losses and photosynthetic uptake) requires at least one application of a model, which is usually a regression model fitted to nighttime data and extrapolated for all daytime intervals. In addition, the existence of a significant amount of missing data in eddy flux time series requires a model for daytime NEE as well. Statistical approaches for analytically specifying prediction intervals associated with a regression require, among other things, constant variance of the data, normally distributed residuals, and linearizable regression models. Because the NEE data do not conform to these criteria, we used a Monte Carlo approach (bootstrapping) to quantify the statistical uncertainty of GEE estimates and present this uncertainty in the form of 90% prediction limits. We explore two examples of regression models for modeling respiration and daytime NEE: (1) a simple, physiologically based model from the literature and (2) a nonlinear regression model based on an artificial neural network. We find that uncertainty at the half-hourly timescale is generally on the order of the observations themselves (i.e., ∼100%) but is much less at annual timescales (∼10%). On the other hand, this small absolute uncertainty is commensurate with the interannual variability in estimated GEE. The largest uncertainty is associated with choice of model type, which raises basic questions about the relative roles of models and data

Evaluating multiple causes of persistent low microwave backscatter from Amazon forests after the 2005 drought

Amazonia has experienced large-scale regional droughts that affect forest productivity and biomass stocks. Space-borne remote sensing provides basin-wide data on impacts of meteorological anomalies, an important complement to relatively limited ground observations across the Amazon’s vast and remote humid tropical forests. Morning overpass QuikScat Ku-band microwave backscatter from the forest canopy was anomalously low during the 2005 drought, relative to the full instrument record of 1999–2009, and low morning backscatter persisted for 2006–2009, after which the instrument failed. The persistent low backscatter has been suggested to be indicative of increased forest vulnerability to future drought. To better ascribe the cause of the low post-drought backscatter, we analyzed multiyear, gridded remote sensing data sets of precipitation, land surface temperature, forest cover and forest cover loss, and microwave backscatter over the 2005 drought region in the southwestern Amazon Basin (4°-12°S, 66°-76°W) and in adjacent 8°x10° regions to the north and east. We found moderate to weak correlations with the spatial distribution of persistent low backscatter for variables related to three groups of forest impacts: the 2005 drought itself, loss of forest cover, and warmer and drier dry seasons in the post-drought vs. the pre-drought years. However, these variables explained only about one quarter of the variability in depressed backscatter across the southwestern drought region. Our findings indicate that drought impact is a complex phenomenon and that better understanding can only come from more extensive ground data and/or analysis of frequent, spatially-comprehensive, high-resolution data or imagery before and after droughts

Adjusting Lidar-Derived Digital Terrain Models in Coastal Marshes Based on Estimated Aboveground Biomass Density

Digital elevation models (DEMs) derived from airborne lidar are traditionally unreliable in coastal salt marshes due to the inability of the laser to penetrate the dense grasses and reach the underlying soil. To that end, we present a novel processing methodology that uses ASTER Band 2 (visible red), an interferometric SAR (IfSAR) digital surface model, and lidar-derived canopy height to classify biomass density using both a three- class scheme (high, medium and low) and a two-class scheme (high and low). Elevation adjustments associated with these classes using both median and quartile approaches were applied to adjust lidar-derived elevation values closer to true bare earth elevation. The performance of the method was tested on 229 elevation points in the lower Apalachicola River Marsh. The two-class quartile-based adjusted DEM produced the best results, reducing the RMS error in elevation from 0.65 m to 0.40 m, a 38% improvement. The raw mean errors for the lidar DEM and the adjusted DEM were 0.61 +/- 0.24 m and 0.32 +/- 0.24 m, respectively, thereby reducing the high bias by approximately 49%

High-angle-of-attack pneumatic lag and upwash corrections for a hemispherical flow direction sensor

As part of the NASA F-14 high angle of attack flight test program, a nose mounted hemispherical flow direction sensor was calibrated against a fuselage mounted movable vane flow angle sensor. Significant discrepancies were found to exist in the angle of attack measurements. A two fold approach taken to resolve these discrepancies during subsonic flight is described. First, the sensing integrity of the isolated hemispherical sensor is established by wind tunnel data extending to an angle of attack of 60 deg. Second, two probable causes for the discrepancies, pneumatic lag and upwash, are examined. Methods of identifying and compensating for lag and upwash are presented. The wind tunnel data verify that the isolated hemispherical sensor is sufficiently accurate for static conditions with angles of attack up to 60 deg and angles of sideslip up to 30 deg. Analysis of flight data for two high angle of attack maneuvers establishes that pneumatic lag and upwash are highly correlated with the discrepancies between the hemispherical and vane type sensor measurements

Surface Roughness Parameterization Using Land Use / Land Cover Enhanced by Lidar Point Cloud Data

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv