Land, family and Hawaiian resistance

Contents were included in anthropology course on Peoples of Hawai'i taught beginning in 1971"A Nation Rising: A review" added as an addendum. July 28, 2015.The book takes the form of an extended essay, quoting facts and figures, the words of individuals, and observations of behavior recorded for the most part during the 1970's and '80's. It contains what individual Hawaiians at particular times and in particular circumstances have shared with me, as understood by me. I emphasize that many Hawaiians would have views and experiences which differ from those cited herein. The connections I see are, like all such, arguable. I may have gotten it wrong. If so, someone hopefully will correct me. But in any case, it is my essay. I do not claim that it represents the views of all Hawaiians; only those quoted herein and subject to the limitations mentioned

Locating the most energetic electrons in Cassiopeia A

We present deep ($>$2.4 Ms) observations of the Cassiopeia A supernova remnant with {\it NuSTAR}, which operates in the 3--79 keV bandpass and is the first instrument capable of spatially resolving the remnant above 15 keV. We find that the emission is not entirely dominated by the forward shock nor by a smooth "bright ring" at the reverse shock. Instead we find that the $>$15 keV emission is dominated by knots near the center of the remnant and dimmer filaments near the remnant's outer rim. These regions are fit with unbroken power-laws in the 15--50 keV bandpass, though the central knots have a steeper ($\Gamma \sim -3.35$) spectrum than the outer filaments ($\Gamma \sim -3.06$). We argue this difference implies that the central knots are located in the 3-D interior of the remnant rather than at the outer rim of the remnant and seen in the center due to projection effects. The morphology of $>$15 keV emission does not follow that of the radio emission nor that of the low energy ($<$12 keV) X-rays, leaving the origin of the $>$15 keV emission as an open mystery. Even at the forward shock front we find less steepening of the spectrum than expected from an exponentially cut off electron distribution with a single cutoff energy. Finally, we find that the GeV emission is not associated with the bright features in the {\it NuSTAR} band while the TeV emission may be, suggesting that both hadronic and leptonic emission mechanisms may be at work.Comment: 12 pages, 11 figures, accepted for publication in Ap

Testing gravitational-wave searches with numerical relativity waveforms: Results from the first Numerical INJection Analysis (NINJA) project

The Numerical INJection Analysis (NINJA) project is a collaborative effort between members of the numerical relativity and gravitational-wave data analysis communities. The purpose of NINJA is to study the sensitivity of existing gravitational-wave search algorithms using numerically generated waveforms and to foster closer collaboration between the numerical relativity and data analysis communities. We describe the results of the first NINJA analysis which focused on gravitational waveforms from binary black hole coalescence. Ten numerical relativity groups contributed numerical data which were used to generate a set of gravitational-wave signals. These signals were injected into a simulated data set, designed to mimic the response of the Initial LIGO and Virgo gravitational-wave detectors. Nine groups analysed this data using search and parameter-estimation pipelines. Matched filter algorithms, un-modelled-burst searches and Bayesian parameter-estimation and model-selection algorithms were applied to the data. We report the efficiency of these search methods in detecting the numerical waveforms and measuring their parameters. We describe preliminary comparisons between the different search methods and suggest improvements for future NINJA analyses.Comment: 56 pages, 25 figures; various clarifications; accepted to CQ

Testing gravitational-wave searches with numerical relativity waveforms: Results from the first Numerical INJection Analysis (NINJA) project

The Numerical INJection Analysis (NINJA) project is a collaborative effort between members of the numerical relativity and gravitational-wave data analysis communities. The purpose of NINJA is to study the sensitivity of existing gravitational-wave search algorithms using numerically generated waveforms and to foster closer collaboration between the numerical relativity and data analysis communities. We describe the results of the first NINJA analysis which focused on gravitational waveforms from binary black hole coalescence. Ten numerical relativity groups contributed numerical data which were used to generate a set of gravitational-wave signals. These signals were injected into a simulated data set, designed to mimic the response of the initial LIGO and Virgo gravitational-wave detectors. Nine groups analysed this data using search and parameter-estimation pipelines. Matched filter algorithms, un-modelled-burst searches and Bayesian parameter estimation and model-selection algorithms were applied to the data. We report the efficiency of these search methods in detecting the numerical waveforms and measuring their parameters. We describe preliminary comparisons between the different search methods and suggest improvements for future NINJA analyses. © 2009 IOP Publishing Ltd

Status of NINJA: The Numerical INJection Analysis project

The 2008 NRDA conference introduced the Numerical INJection Analysis project (NINJA), a new collaborative effort between the numerical relativity community and the data analysis community. NINJA focuses on modeling and searching for gravitational wave signatures from the coalescence of binary system of compact objects. We review the scope of this collaboration and the components of the first NINJA project, where numerical relativity groups, shared waveforms and data analysis teams applied various techniques to detect them when embedded in colored Gaussian noise. © 2009 IOP Publishing Ltd

The Alaska Arctic Vegetation Archive (AVA-AK)

The Alaska Arctic Vegetation Archive (AVA-AK, GIVD-ID: NA-US-014) is a free, publically available database archive of vegetation-plot data from the Arctic tundra region of northern Alaska. The archive currently contains 24 datasets with 3,026 non-overlapping plots. Of these, 74% have geolocation data with 25-m or better precision. Species cover data and header data are stored in a Turboveg database. A standardized Pan Arctic Species List provides a consistent nomenclature for vascular plants, bryophytes, and lichens in the archive. A web-based online Alaska Arctic Geoecological Atlas (AGA-AK) allows viewing and downloading the species data in a variety of formats, and provides access to a wide variety of ancillary data. We conducted a preliminary cluster analysis of the first 16 datasets (1,613 plots) to examine how the spectrum of derived clusters is related to the suite of datasets, habitat types, and environmental gradients. Here, we present the contents of the archive, assess its strengths and weaknesses, and provide three supplementary files that include the data dictionary, a list of habitat types, an overview of the datasets, and details of the cluster analysis

Environmentalism, pre-environmentalism, and public policy

In the last decade, thousands of new grassroots groups have formed to oppose environmental pollution on the basis that it endangers their health. These groups have revitalized the environmental movement and enlarged its membership well beyond the middle class. Scientists, however, have been unable to corroborate these groups' claims that exposure to pollutants has caused their diseases. For policy analysts this situation appears to pose a choice between democracy and science. It needn't. Instead of evaluating the grassroots groups from the perspective of science, it is possible to evaluate science from the perspective of environmentalism. This paper argues that environmental epidemiology reflects ‘pre-environmentalist’ assumptions about nature and that new ideas about nature advanced by the environmental movement could change the way scientists collect and interpret data.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45449/1/11077_2005_Article_BF01006494.pd